Quantitative evaluation of microbiome sequencing resolution under varying experimental conditions using defined mock communities

Quantitative evaluation of microbiome sequencing resolution under varying experimental conditions using defined mock communities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quantitative evaluation of microbiome sequencing resolution under varying experimental conditions using defined mock communities Songhee Lee, Hyeonah Lee, Jung Wook Kim, Hyeon-Jin Kim, Kwang Jun Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7005374/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Objective evaluation of sequencing resolution is crucial for comparing technologies and ensuring reproducibility in microbiome analysis. Specifically, a systematic approach is necessary to quantitatively assess the effect of various platforms and experimental conditions on species-level resolution. Therefore, this study quantitatively evaluated multiple strategies, including 16S V3–V4 (16P), full-length 16S rRNA gene (16F), and whole metagenome shotgun sequencing (WMS), using a commercial DNA-based mock community (MC) and a domestically developed whole-cell MC (Korea MC [KMC]). The WMS strategy included 12 combinations of input DNA concentrations and sequencing output levels. A total of 64 WMS libraries were constructed for KMC samples, and 112 sequencing datasets were analysed. Taxonomic resolution was assessed using an adjusted F1-score integrating detection sensitivity and abundance-level reproducibility. Results Qualitatively examining the detected species against the expected species across platforms, WMS showed a true positive abundance ratio of over 90%, 16F was observed to have an average of 60%, and 16P was observed to have an average of less than 10%. The combination of 10 ng input and 10 gigabases output consistently yielded the highest species-level resolution. However, reduced performance was observed in some MCs under 1 ng or 100 ng DNA input conditions. Detection sensitivity varied by taxon and condition. Specifically, Streptococcus pneumoniae and Cryptococcus neoformans were detected only under high-input or -output conditions, whereas Escherichia coli exhibited optimal accuracy at intermediate inputs. Acinetobacter species demonstrated reduced resolution as input DNA increased. KMC samples showed species- and format-specific variability in DNA extraction efficiency. Conclusions This study establishes a quantitative framework for assessing species-level resolution across sequencing conditions and taxa using defined MCs. The findings provide practical guidance for selecting sequencing strategies aligned with analytical objectives and resource constraints. Mock community Sequencing strategy Metagenomics 16S rRNA gene Taxonomic resolution Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. BACKGROUND As microbiome analysis technologies evolve, methodological discussions have increasingly focused on enabling high-resolution profiling of microbial communities and ensuring the reliability of analytical outcomes [ 1 – 3 ]. Microbial community profiling is a foundational aspect of microbiome research and is typically conducted using either 16S ribosomal RNA gene sequencing (16S rRNA sequencing) or whole metagenome shotgun sequencing (WMS) [ 4 , 5 ]. Amplicon sequencing is a cost-effective and relatively uncomplicated experimental procedure, but is typically limited to taxonomic resolution at the genus level [ 6 , 7 ]. WMS enables accurate taxonomic and functional profiling at the species or even strain level [ 8 ]. High-resolution taxonomic profiling is crucial in contexts such as disease diagnosis, personalized medicine, and environmental monitoring, where distinguishing between closely related species or strains can impact. however, it involves higher costs and more complex analytical workflows than amplicon sequencing [ 9 ]. Moreover, WMS-based analyses are highly sensitive to technical variability introduced during sample preparation and sequencing [ 10 , 11 ]. Even among studies analysing similar sample types, considerable differences in microbial composition estimates have been observed, stemming from heterogeneous protocols and inconsistent analytical pipelines [ 12 – 14 ]. The variability poses a notable challenge to inter-study comparability and reproducibility, highlighting the critical need for technical standardisation and systematic validation throughout the microbiome analysis workflow [ 15 , 16 ]. Species-level resolution in microbiome analysis is influenced by both the sequencing platform and experimental variables such as input DNA concentration and sequencing output yield [ 17 – 19 ]. For example, sequencing output yield, which is often limited by the high cost of sequencing, can reduce read depth, thereby lowering the sensitivity for detecting low-abundance taxa and ultimately constraining species-level resolution [ 18 , 20 ]. Although these variables are known to affect microbial profiling, few studies have systematically evaluated their effects at the species level [ 21 , 22 ]. Even when such assessments exist, they are often limited to dichotomous metrics such as recall or precision, which do not properly reflect changes in accuracy or resolution at the level of abundance [ 23 , 24 ]. To rigorously examine these effects, sequencing performance must be tested against reference materials with known composition and abundance [ 25 , 26 ]. Mock communities (MCs), defined as artificial mixtures with known microbial compositions, serve as essential reference controls for evaluating methodological accuracy, reproducibility, and potential bias in microbial profiling workflows [ 13 , 27 , 28 ]. Widely used commercial MC standards, such as the HMP-ATCC and ZymoBIOMICS, are designed based on balanced microbial compositions and serve as benchmarks for evaluating analytical performance [ 29 , 30 ]. However, the However, the However, the However, the However, the HMP-ATCC mock community consists of a fixed set of strains derived from Western populations, which may limit its relevance for studies targeting different cohorts or microbiome contexts [ 31 – 33 ]. As an alternative, researcher-constructed MCs allow customised designs, such as incorporating region-specific strains or adjusting abundance ratios, which may better suit specific research needs [ 28 , 34 , 35 ]. When prepared in whole-cell format, these MCs also enable assessment of the full experimental workflow, including DNA extraction, providing a more comprehensive evaluation than DNA-based MCs [ 13 , 36 ]. Therefore, this study aimed to quantitatively evaluate the effect of various experimental conditions on species-level resolution and abundance accuracy using two types of MC formats (whole-cell and DNA-based), different sequencing platforms, input DNA concentrations, and sequencing output yields. This study sought to contribute to the establishment of reproducible and reliable microbial profiling guidelines. 2. METHODS 2.1. Experimental framework and sequencing conditions This study was designed to evaluate sequencing performance using MCs with different formats and microbial compositions. Two distinct MC formats were used: a commercially available DNA-based MC and a domestically developed whole-cell MC, referred to as the Korea MC (KMC). Each MC format included four body site-specific mock communities representing the gut, oral, skin, and vaginal environments (Fig. 1 A). Sequencing was performed using three platforms: short-read amplicon sequencing targeting the 16S rRNA V3–V4 region (16P), long-read full-length 16S rRNA gene sequencing (16F), and short-read WMS sequencing (Fig. 1 B). For WMS, 12 experimental conditions were constructed by combining 3 predefined input DNA concentrations with 4 levels of sequencing output yield (Fig. 1 C). Each MC underwent sequencing under a single 16P condition, a single 16F condition, and 12 WMS conditions, resulting in a total of 14 datasets per mock community. Applied across 4 body sites and 2 MC formats, a total of 112 sequencing datasets were generated and analyzed (Fig. 1 D). 2.2. Overview of the MC samples The DNA-based MCs were labeled MCG, MCO, MCS, and MCV, corresponding to the gut, oral, skin, and vaginal sites, respectively. They were derived from commercially available ATCC Bacterial Mix standards (MSA-2006™, MSA-2007™, MSA-2008™, and MSA-2009™, respectively). These four types of microbial whole cell mock communities were developed by lyophilizing standardized bacterial suspension as a part of the research and development program for clinical metagenomics funded by Korea National Institute of Health. This format facilitates room-temperature storage and enables the standardized assessment of DNA extraction efficiency across sample types. The species included in each MC are listed in Additional File 1: Table S1 and S2 and served as the ground-truth reference set for taxonomic accuracy assessment. 2.3. DNA extraction and quality control Genomic DNA from the KMC was extracted using the DNeasy PowerSoil Kit (QIAGEN, Hilden, Germany) according to the manufacturer's instructions. DNA integrity was evaluated via 1% agarose gel electrophoresis. The concentration and purity of the extracted microbial DNA were assessed using both a Nanodrop 2000 UV-Vis spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and a Qubit 3.0 fluorometer (Thermo Fisher Scientific). Only samples that passed quality control thresholds were used for downstream library preparation. 2.4. Library preparation 2.4.1. 16S rRNA V3–V4 region amplicon sequencing Amplicon sequencing targeting the V3–V4 region of the 16S rRNA gene (16P) was performed using the primer pair 341F (5′-CCTACGGGNGGCWGCAG-3′) and 805R (5′-GACTACHVGGGTATCTAATCC-3′). PCR reactions were prepared according to the manufacturer's instructions using a Herculase II polymerase-based master mix (Agilent Technologies, Santa Clara, CA, USA) and V3–V4-specific primers. PCR amplification was performed on an ABI GeneAmp® 9700 thermocycler (Applied Biosystems, Foster City, CA, USA) using the following cycling conditions: an initial denaturation at 95°C for 3 min; 25 cycles of denaturation at 95°C for 30 s, annealing at 55°C for 30 s, and extension at 72°C for 30 s; followed by a final extension at 72°C for 5 min and a hold at 4°C. A separate index PCR was performed with 10 additional cycles under the same conditions. After amplification, the PCR products were purified using AMPure XP Beads (Beckman Coulter, Brea, CA, USA). Equimolar pooling was performed based on fluorescence-based quantification. Indexing was achieved using the Nextera XT Index Primer Set (Illumina Inc., San Diego, CA, USA) in a separate PCR reaction using the same enzyme and buffer system. The purified and pooled libraries were sequenced as 2 × 300 bp paired-end reads on the Illumina MiSeq platform (Illumina Inc.) per the manufacturer's instructions. 2.4.2. 16S rRNA full-length amplicon sequencing Amplicon sequencing targeting the full-length 16S rRNA gene (16F) was performed using the universal primer pair 27F (5′-AGRGTTYGATYMTGGCTCAG-3′) and 1492R (5′-RGYTACCTTGTTACGACTT-3′). Sample-specific barcode sequences provided by the manufacturer were added to both primers to allow multiplexed sequencing. PCR reactions were prepared according to the manufacturer's instructions using the KAPA HiFi HotStart ReadyMix (Roche, Basel, Switzerland). PCR amplification was performed using a Veriti™ Thermal Cycler (Applied Biosystems) with the following cycling conditions: an initial denaturation at 95°C for 3 min; 27 cycles of denaturation at 95°C for 30 s, annealing at 57°C for 30 s, and extension at 72°C for 60 s; followed by a final extension at 72°C for 5 min and a hold at 4°C. Amplicons were evaluated for quality using a TapeStation™ system (Agilent Technologies), and equimolar pooling of all samples was performed before library construction. Libraries were constructed using the SMRTbell Express Template Prep Kit 2.0 (Pacific Biosciences, Menlo Park, CA, USA) according to the manufacturer's instructions for full-length 16S library preparation. Library size and concentration were assessed using the Qubit Fluorometer with the Qubit™ 1X dsDNA HS Assay Kit (Invitrogen, Carlsbad, CA, USA) and the TapeStation system. Primer annealing and polymerase binding were performed using the Sequel II Binding Kit 2.1 and DNA Internal Control Complex 1.0 (Pacific Biosciences). Sequencing was conducted on a Sequel II system (Pacific Biosciences) using the Sequel II Sequencing Kit 2.0. 2.4.3. WMS sequencing Shotgun metagenomic libraries were constructed using the TruSeq DNA Nano Library Prep Kit (Illumina Inc.) according to the manufacturer's instructions. Input DNA (100 ng) was enzymatically fragmented to an average insert size of 200–400 bp using the Covaris LE220 system (Covaris, Woburn, MA, USA). Fragmented DNA was end-repaired, A-tailed, and ligated with Illumina adapter indices following the standard workflow. After ligation, libraries were amplified using the Enhanced PCR Mix provided in the kit with eight PCR cycles under the following conditions: 95°C for 3 min; eight cycles of 98°C for 20 s, 60°C for 15 s, and 72°C for 30 s; followed by 72°C for 5 min and a final hold at 4°C. PCR products were purified using AMPure XP Beads (Beckman Coulter) and eluted in 30 µL of resuspension buffer. Library size distribution was evaluated using the TapeStation™ system (Agilent Technologies), and library concentrations were determined using qPCR. The pooled libraries were sequenced on an Illumina NovaSeq 6000 platform (Illumina Inc.) to generate 2 × 150 bp paired-end reads. To evaluate the effect of input DNA quantity and sequencing depth, three predefined input DNA concentrations and four target sequencing output levels were used, resulting in 12 shotgun library conditions. Six libraries were physically prepared and sequenced, whereas the remaining six datasets were generated via in silico subsampling of sequencing reads. Eight libraries were generated for each MC: one from 16P sequencing, one from 16F sequencing, and six from WMS sequencing. The six WMS libraries were designed using three predefined combinations of input DNA concentrations and two sequencing output yield levels. Thus, eight libraries were constructed per MC, resulting in a total of 64 libraries across all conditions. 2.5. Data processing and taxonomic profiling 2.5.1. 16s rRNA v3–v4 region amplicon sequencing Raw reads were quality-checked using FastQC (Babraham Institute, Cambridge, UK), and primer sequences were trimmed with Cutadapt (v4.0, Germany). Denoising, dereplication, and chimaera removal were performed using the DADA2 plugin in QIIME2 (v2023.2, USA). Reads with an average quality score < Q20 and length < 250 bp were excluded before amplicon sequence variant (ASV) inference. ASVs were generated, and taxonomic classification was conducted using a naïve Bayes classifier trained on the SILVA 138.1 database trimmed to the V3–V4 region. 2.5.2. 16S rRNA full-length amplicon sequencing Full-length 16S rRNA reads generated using the Oxford Nanopore Technologies platform were adapter-trimmed using Cutadapt (v4.0, Dortmund, Germany) and quality-filtered to retain reads with a minimum length of 1,200 bp and an average quality score of ≥ Q10. Taxonomic classification was performed using Kraken2 (v2.1.2, USA) with a custom database established using full-length 16S rRNA gene sequences of the SILVA 138.1 reference set. The resulting taxonomic profiles were used for downstream microbial composition and diversity analyses. 2.5.3. WMS sequencing Sequencing data underwent a systematic bioinformatics pipeline for quality control, trimming, host genome removal, and microbiome profiling. Initially, raw sequencing reads were subjected to quality control using FastQC (version 0.11.9) to evaluate sequence quality and adapter contamination [ 37 ]. Adapter trimming and quality filtering were conducted with Trim Galore (version 0.6.7) Reads shorter than 50 bp after trimming or those with average quality scores below 20 were excluded from further analyses [ 38 ]. Subsequently, trimmed reads were aligned against the human reference genome hg38 using BWA-MEM (version 0.7.17) to remove host-derived sequences. Reads not mapped to the human genome were extracted, converted to BAM format, sorted by name, and then converted into FASTQ format using SAM tools (version 1.11) for downstream metagenomic analysis [ 39 , 40 ]. Taxonomic profiling was performed using MetaPhlAn (v4.0), with its default marker gene database and settings. Four output yield conditions (1, 5, 10, and 20 Gb) were analysed to evaluate the effect of sequencing depth. Libraries corresponding to 1 Gb and 20 Gb were physically constructed and sequenced, whereas 5 Gb and 10 Gb datasets were computationally simulated by subsampling reads from the 20 Gb libraries at predefined proportions. Taxonomic abundance profiles derived from this read-based approach were used for all subsequent analyses (Additional File 1: Table S3). 2.6. Statistical analyses 2.6.1. Quantitative accuracy assessment An adjusted F1-score was used to evaluate both the quantitative accuracy of relative abundance estimates and their agreement with the expected composition of each MC. For each taxon, the observed relative abundance (Y) was compared to the predefined expected value (M). A tolerance threshold (T), defined as 10% of M, was applied to identify the range within which deviations were not penalised [ 2 , 5 , 13 , 41 , 42 ]. Deviations exceeding this threshold were penalised using a power function of the form (excess) 𝑝 , where the excess was defined as the difference between the deviation and the threshold. The exponent 𝑝 = 0.5 was selected to implement a sublinear power function, which increases at a slower rate than a linear function with respect to the excess deviation, thereby limiting the rate of penalty escalation for moderate errors. True positives (TP), false positives (FP), and false negatives (FN) were computed based on the following definitions: \(\:\) Equation (1). Adjusted F1-score incorporating tolerance-based penalisation of abundance deviation The final score was computed as the harmonic mean of precision and recall, where precision was defined as TP divided by TP plus FP, and recall as TP divided by TP plus FN. 2.6.2. Data visualization All visualisations were conducted using R version 4.3.1. Bar graphs were generated using the ggplot2 package to compare relative abundances across sequencing methods and conditions. Heatmaps were constructed using ComplexHeatmap, gplots, and pheatmap to visualise taxonomic distributions and sample clustering. The ComplexHeatmap package was primarily used to incorporate multiple metadata layers and customise layout configurations. Spider graphs were produced using the fmsb package to present multivariate comparisons across sequencing strategies. 3. RESULTS 3.1. Qualitative species-level detection based on presence or absence across sequencing platforms To compare the species-level qualitative detection capability across sequencing platforms, we compared the expected taxa present in each MC with the taxa detected in the analysis results (Additional File 1: Table S1 and S2). The presence/absence data was used to construct a confusion matrix for each sample, enabling a visual comparison of detection performance (Fig. 2 ). In KMCG, none of the 12 expected taxa were detected in 16P. Eleven were matched in 16F and 12 were matched in WMS (Fig. 2 A). In KMCO, 1 out of 10 species were detected using 16P, 8 species using 16F, and all 10 species using WMS (Fig. 2 B). In KMCS, 3 out of 9 species was detected using 16P, 8 species using 16F, and all 9 species using WMS (Fig. 2 C). In KMCV, 1 out of 11 species were detected using 16P, 9 species using 16F, and all 11 species using WMS (Fig. 2 D). In MCG, 3 out of 12 species were detected using 16P, 11 species using 16F, and all 12 species using WMS (Additional File 2: Figure S1 ). In MCO, 2 out of 6 species were detected using 16P, 6 species using 16F, and all 6 species using WMS. In MCS, 1 out of 6 species was detected using 16P, 6 species using 16F, and all 6 species using WMS. In MCV, 3 out of 6 species were detected using 16P, 4 species using 16F, and all 6 species using WMS (Additional File 2: Figure S1 ). Overall, WMS detected all expected species in all KMC and MC samples, with 16F showing somewhat lower matching and 16P showing the lowest detection rate across all KMC and MCs. 3.2. Quantitative species-level detection based on abundance across sequencing platforms We evaluated the reproducibility of abundance across sequencing platforms by MC type. The total correct abundance was calculated by adding the relative abundance of items corresponding to the predefined constituent species among the species observed in each KMC. Since there was no difference between the expected and detected species, WMS was 100% for all, and 16F was somewhat lower, showing that 16F had almost no correct species (Fig. 3 A). When observing the number of species, WMS detected no species other than the expected species, so the number was the same as the expected species. 16F was observed more than the expected species, and 16P was observed almost none (Fig. 3 B). The true positive abundance ratio for the detected species was also higher for the expected species than for the detected species in the order of WMS, 16F, and 16P (Fig. 3 C). Finally, the false negative abundance ratio was higher in the order of 16P, 16F, and WMS, contrary to the true positive abundance (Fig. 3 D). The results in MC were also observed in the same manner (Additional File 2: Figure S2). In particular, WMS showed 100% matching of detected species to expected species in qualitative observation, but KMC showed an average of 86% and KM showed an average of 93%. 3.3. Impact of WMS sequencing conditions on quantitative performance To identify the variation in quantitative detection values of WMS, we compared species-level abundance reproducibility under various WMS sequencing conditions, focusing on the variation in input DNA concentration and sequencing output yield of WMS (Fig. 4 ). We created 12 different WMS conditions by setting the input DNA concentration to 1, 10, and 100 ng and the sequencing output to 1, 5, and 20 Gb. In the case of KMCS, more detected species were observed than expected species when DNA input was 1 ng, so the total correct abundance ratio and true positive abundance ratio were also observed to be the lowest (Fig. 4 A and B). In addition, an upward trend was observed for 10 ng, including KMCO with the highest true positive abundance ratio (Fig. 4 C). This showed that the input DNA volume affected the results. On the other hand, when comparing the sequencing output, all four KMCs showed consistent total correct positives and species numbers, with no significant difference between the outputs (Figs. 4 E and 4 G). The number of true positives and false negatives also remained constant regardless of the output, confirming that the sequencing depth had a limited effect on the reproducibility under the tested conditions. In the case of DNA-based MC, it was observed to be relatively stable without any effect according to the WMS sequencing condition compared to cell-based KMC (Additional file 2: Figure S3). 3.4. Species-level abundance reproducibility across WMS sequencing conditions The resolution was evaluated at the species level for each KMC by comparing the reproducibility of quantitative detection using the adjusted F1-score (Figs. 5 and 6 ). This metric was used to assess condition-specific variability in the recovery of individual reference species across WMS sequencing parameters. Overall, WMS sequencing achieved high reproducibility for most species, although notable variations in resolution were observed across sequencing conditions. Species-specific sensitivity differences were evident, indicating that optimal detection may vary by taxon and experimental setup (Additional File 2: Figure S4 and S5). In KMCG, most species showed high reproducibility, but Lacticaseibacillus paracasei consistently showed low scores with an average score of 77 points (Fig. 5 ). Clostridium perfringens showed no deviation according to the degree of output yield with an average score of 96 points in the conditions of 1 ng and 10 ng DNA input, but it was lowered to 86 points in all output conditions when the input was 100 ng. When observed by output, differences in input were observed in all output conditions, so C. perfringens seemed to be affected by the amount of DNA input. In KMCO, Streptococcus pyogenes , Escherichia coli , Klebsiella pneumoniae , and Streptococcus pneumoniae showed low reproducibility, and in particular, S. pneumoniae was close to 0. S. pyogenes , E. coli , and K. pneumoniae showed improved reproducibility only in the 10 ng condition. In particular, E. coli showed high resolution only when the input was 10 ng, regardless of the output conditions. S. pneumoniae showed that the resolution improved as the input level increased and the output level increased, and the highest resolution was shown in the combination of input 10 ng and output 20 Gb. KMCS displayed the lowest overall reproducibility among the four KMCs, with no consistent improvement by output level. At a DNA input of 1 ng, Enterococcus faecium , Corynebacterium striatum , Staphylococcus epidermidis , and S. aureus exhibited poor reproducibility, which improved at input levels ≥ 10 ng. In contrast, Acinetobacter bereziniae and A. ursingii demonstrated high reproducibility at the 1 ng DNA input condition; however, their reproducibility declined as input DNA concentration increased. In KMCV, most species exhibited high reproducibility, except for Cryptococcus neoformans. The reproducibility of C. neoformans was shown to be affected by both input DNA level and output level, and improved as input DNA level and output level increased. In particular, the combination of 100 ng of input DNA and 20 Gb of output showed the most improved resolution. When analyzing DNA-based MC, relatively little variability was observed compared to cell-based KMC. Acinetobacter johnsonii showed high resolution at an input level of 10 ng (Additional File 2: Figures S4 and S5). 3.5. Comparative resolution evaluation across KMC types The final species-level resolution scores for each WMS sequencing condition are summarised in Fig. 7 . In KMCG, all condition combinations yielded consistently high F1-scores, with values exceeding 95, regardless of the input and output settings (Fig. 7 A). In KMCO, 10 ng input DNA generally resulted in scores > 90 across output levels (Fig. 7 B). In KMCO, high-resolution scores were mainly related to output yields ≥ 10 Gb, regardless of input DNA concentration. In KMCS, conditions with ≥ 10 ng of DNA input showed high resolution scores overall at all output levels, while conditions with ≤ 10 ng showed relatively low scores (Fig. 7 C). In KMCV, high resolution was observed when the input level was ≥ 10 ng, and high resolution was observed when the output level was ≥ 10 Gb (Fig. 7 D). Overall, the combination of 10 ng of input DNA and 10 Gb or more of output DNA consistently showed the highest resolution scores in most KMCs. MCG showed a resolution of 100 points in all conditions (Additional File 2: Figure S6). Next, MCV showed 99 score, and MCO and MCS also showed high scores of 97 scores on average in all conditions. 4. DISCUSSION Overall, WMS consistently yielded the highest reproducibility across all mock communities, while the amplicon-based strategies showed lower performance. Among the tested WMS conditions, the combination of 10 ng input DNA and ≥ 10 Gb sequencing output produced the most robust and consistent results. However, certain mock communities, such as KMCS, exhibited persistently low detection accuracy for specific taxa or under specific conditions. These findings demonstrate that both platform type and sequencing configuration critically affect species-level sensitivity, warranting further evaluation of the underlying biases using more refined metrics. These findings demonstrate that both platform type and sequencing configuration critically affect species-level sensitivity, warranting further evaluation of the underlying biases using more refined metrics. Assessing taxonomic resolution based solely on species presence or richness is insufficient to capture quantitative detection accuracy. Although traditional metrics like precision or recall partially reflect presence or recovery, they fail to account for deviations in abundance, such as species-specific over- or under-detection. To address this limitation, we employed an adjusted F1-score that incorporates quantitative imbalances among true positives, false positives, and false negatives. The tolerance threshold (T) was set to 10% for the expected value (M) in the adjusted F1 score. There is still no consensus on the acceptable level of deviation in microbial community studies [ 1 , 2 ]. The tolerance threshold (T) for strict quantitative comparison in the diagnostic field is set to < 5% [ 41 ]. In technical reproducibility tests for library prep, sequencing repeats, etc., it is set to ± 10–20% [ 1 , 42 ]. In studies related to taxonomic resolution, it was set to around 10% and compared and analyzed [ 43 ]. This metric assigns weighted penalties based on the numeric difference between observed and expected abundances, enabling an integrated assessment of both detection sensitivity and abundance fidelity. For example, KMCS displayed full species recovery in a qualitative presence/absence comparison (Fig. 2 C); however, under the 1 ng input condition, false negative abundance was notably high (Fig. 4 A). This approach enables quantitative comparisons of subtle differences in reproducibility, species-specific biases, and condition-driven sensitivity shifts that are not captured by qualitative analysis. It is particularly useful for evaluating the influence of library preparation and sequencing conditions on the effective resolution for each taxon. The systematic comparison of input and output combinations revealed that the 10 ng DNA input with the 10 Gb output yield produced the most consistent and accurate results. Notably, both lower (1 ng) and higher (100 ng) inputs were associated with reduced resolution. At the 1 ng DNA input, the limited starting material may introduce PCR bias and lead to reduced library complexity. In contrast, excessive input (100 ng) could result in nonspecific amplification or enzyme saturation, thereby decreasing quantification accuracy. These findings underscore that increased DNA input does not necessarily yield better resolution, highlighting the importance of optimizing concentration based on the analytical objective. A similar plateau effect was observed for sequencing output, with little improvement noted beyond 10 Gb. Excessive sequencing depth beyond this threshold can lead to redundancy, decreased efficiency, and no further gain in taxonomic resolution. In some cases, resolution scores at a sequencing depth of 20 Gb were even lower than those observed at 10 Gb. Moreover, some species were detected only under specific input-output combinations, indicating that sequencing conditions directly influence species-specific detection sensitivity. Notably, DNA-based mock communities often exhibited higher resolution consistency compared to whole-cell mocks. This may be attributed to the absence of DNA extraction variability, as DNA mocks provide purified, high-quality input DNA, ensuring uniform library preparation efficiency [ 13 , 26 ]. In contrast, whole-cell mocks are more susceptible to taxon-specific extraction efficiency and matrix effects, leading to greater variability in quantitative detection. Resolution patterns also varied across MCs and among taxa under the same sequencing conditions. For example, S. pneumoniae in KMCO achieved detectable resolution only under the 100 ng-20 Gb combination, whereas the signal was nearly absent under all other conditions. In contrast, E. coli in the same community exhibited optimal performance at the 10 ng DNA input, with sharp declines at other concentrations. Similarly, C. neoformans in KMCV demonstrated stable resolution only under long-read platforms and high-output levels. This suggests that the fungal cell structure and large genome size of C. neoformans may affect DNA extraction and library preparation efficiency. In KMCS, A. bereziniae and A. ursingii exhibited the highest resolution at the 1 ng DNA input, with declining accuracy at increasing concentrations. In contrast, the resolution of other taxa in KMCS improved with increasing input. Collectively, our results emphasize the difficulty of applying a single resolution criterion across all taxa. Depending on the analysis goal or target species, sequencing and library conditions should be flexibly optimized. Overall, accurate performance evaluation requires comprehensive consideration of mock matrix composition, genome properties, extraction efficiency, and interactions with sequencing platforms. C. perfringens of KMCG showed a decrease in resolution at 100 ng input (Fig. 5 ). Since C. perfringens has a genome structure with a high proportion of accessory genes, excessively high DNA amount can lead to PCR enzyme saturation or overamplification of specific sequences [ 44 , 45 ]. This can result in strain-specific genes or low-frequency genes being relatively underamplified and not detected, which can result in a decrease in species-level abundance reproducibility (F1-score). E. coli of KMCO showed high resolution only at 10 ng input regardless of output conditions, and in particular, S. pneumoniae showed a reproducibility close to 0 in most conditions except this. This is because the starting amount of DNA has a significant impact on the uniformity of PCR amplification, and coverage bias occurs when it is too low or too high, so that PCR efficiency and library complexity are balanced at this concentration and various fragments are amplified without bias [ 44 , 46 ]. A. bereziniae and A. ursingii of KMCS were more sensitive to this bias, suggesting that nonspecific amplification may have excessively generated short fragments when high-concentration DNA input was used [ 47 , 48 ]. In contrast, C. neoformans of KMCV showed low reproducibility overall, which supports previous studies that it has a thick cell wall and a capsule composed of polysaccharides, making DNA extraction difficult regardless of the DNA extraction kit, which may lead to a decrease in sequencing resolution [ 49 ]. In the results of this study, the resolution of C. neoformans increased as the input and output levels increased, indicating that high-concentration DNA input and high sequencing output are necessary to improve the resolution of C. neoformans [ 48 ]. Finally, L. paracasei of KMCG consistently showed low resolution scores. The GC content of the 16S rRNA gene of L. paracasei is approximately 46.2–46.6%, which may affect the binding efficiency of primers during PCR amplification. When the GC content is high, the stability of double-stranded DNA increases, making denaturation difficult, which may reduce the efficiency of PCR amplification [ 50 ]. It also suggests that the quantification of L. paracasei may be difficult due to the presence of other microorganisms in various sample matrices and chemical substances in the sample [ 51 ]. Collectively, these findings underscore the inherent difficulty of applying a uniform resolution criterion across diverse microbial taxa. Optimal sequencing and library preparation parameters appear to be species-dependent and must be tailored to the specific analytical goals. Consequently, rigorous performance evaluation requires the integrative consideration of multiple experimental and biological variables, including mock matrix composition, genome complexity, DNA extraction efficiency, and platform-specific biases. Previous studies using MCs have primarily focused on qualitative assessments of sequencing platforms or analytical tools, often evaluating taxonomic resolution based on the presence or absence of a species or detection capability [ 43 , 52 ]. However, few studies have quantitatively evaluated accuracy at the species level or systematically measured detection deviations [ 53 , 54 ]. Instead, most investigations have relied on simple detection rates or descriptive differences in relative abundance, without incorporating metrics that capture abundance-level biases [ 55 , 56 ]. Although prior research has reported platform-specific biases in short-read (16P; 16S V3–V4) and long-read (16F; full-length 16S) amplicon sequencing, the combined influence of sequencing input/output conditions and library construction parameters has been largely overlooked [ 28 , 57 , 58 ]. To address this gap, we incorporated both amplicon- and WMS-based approaches and systematically evaluated 64 distinct input/output combinations across 112 datasets. This allowed for a comprehensive analysis of taxonomic resolution beyond platform-level comparison, enabling quantification of the influence of various experimental parameters on performance at the species level. A distinguishing feature of our study is the use of both commercially available DNA-based MCs (ATCC) and domestically developed whole-cell bead-based MCs (KMCs). The KMCs were formulated to mimic the matrix-bound state of microbes in clinical samples, allowing for assessment of DNA extraction efficiency and variation. These whole-cell mocks also offer practical advantages, such as room-temperature stability and long-term preservation [ 59 ]. In addition, the microbial composition was designed to reflect Korean-specific body site microbiota, increasing the biological relevance for population-specific applications [ 60 , 61 ]. This dual mock system enhances the interpretability of resolution outcomes, distinguishing our study from prior work. Notably, by integrating a multi-layered experimental design, our study provides objective evidence for selecting optimal sequencing configurations and contributes practical guidance for reproducible and accurate microbial analysis under variable conditions. This study has several limitations. Although defined MCs provide a controlled framework for evaluating taxonomic resolution under controlled conditions, they are inherently less complex than real clinical or environmental samples and lack host-derived components or interspecies microbial interactions. Consequently, resolution performance under mock conditions may differ from that observed in actual biological samples. Moreover, in some species, we noted discrepancies between input concentration and detection, likely owing to matrix effects and sensitivity in DNA extraction. Nevertheless, our results suggest that experimental limitations such as low input DNA or limited sequencing depth do not necessarily prevent meaningful analysis. Rather than discarding such samples, researchers may consider retaining them by aligning analytical strategies with specific biological goals and taxon characteristics, thereby enabling more informed and context-sensitive decision-making. For example, in several MCs, reproducible performance was maintained at 10 Gb sequencing depth without requiring higher input, highlighting the feasibility of cost-effective analysis. Moreover, the use of whole-cell MCs provided valuable insights into the influence of DNA extraction and matrix structure, serving as a benchmark for optimising real-world workflows. The evaluation metrics and analytical framework proposed in this study can be applied to clinical or environmental samples to validate reproducibility under more complex conditions in future studies. Moreover, further investigations could integrate genome assembly accuracy, metagenome-assembled genome recovery, and functional gene resolution to advance efforts in developing practical and robust guidelines for sequencing strategy selection across diverse microbiome research objectives. 5. CONCLUSIONS We quantitatively evaluated taxonomic resolution at the species level across various sequencing conditions using defined MCs, providing a basis for selecting optimal configurations tailored to specific analytical goals. By applying an adjusted F1-score, we quantified accuracy differences across input/output combinations and sequencing platforms. We identified the 10 ng DNA input and 10 Gb output condition as the most consistently reliable. We observed species-specific differences in detection patterns, with certain taxa detectable only under specific conditions, highlighting the need for strategic selection based on target species and study objectives. These findings offer practical guidance for maximizing sequencing efficiency under realistic constraints. Abbreviations MC - Mock community KMC - Korea domestically developed whole-cell-based mock community 16S rRNA sequencing - 16S ribosomal RNA gene sequencing 16F sequencing - full-length 16S rRNA gene sequencing 16P sequencing - 16S rRNA V3–V4 region sequencing WMS sequencing - Whole metagenome shotgun sequencing ASV - Amplicon sequence variant KMCG - Whole-cell mock community from the gut KMCO - Whole-cell mock community from the oral KMCS - Whole-cell mock community from the skin KMCV- Whole-cell mock community from the vaginal MCG - DNA-based mock community from the gut MCO - DNA-based mock community from the oral MCS - DNA-based mock community from the skin MCV - DNA-based mock community from the vaginal Gb – Gigabases TP - True positives FP - False positives FN - False negatives Declarations Ethics approval and consent to participate Not applicable Availability of data and materials The sequence data generated and analysed in this study are available under NCBI BioProject ID PRJNA1288716, https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1288716. Consent to participate Not applicable. Competing interests The authors declare that they have no competing interests Funding This research was supported by the National Institute of Health (NIH) research project (project No. 2023-NI-020-01). Authors' contributions S.H.L and H.A.L contributed to the conceptualisation of the study, and were responsible for writing the original draft and performing the formal analysis. They also revised the manuscript. H.J.K and J.W.K was involved in performing the experimental work and participated in reviewing and refining the manuscript. K.J.L. contributed to the acquisition of funding and the supervision of the study. All authors have read and approved the final manuscript. Acknowledgements The authors would like to thank Prof. Do-Kyun Kim of Gangnam Severance Hospital for providing the customized mock community material. We also extend our gratitude to all staff members involved in this study. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7005374","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":488850538,"identity":"e2a9c22c-f678-4888-b5b6-be2783f70aef","order_by":0,"name":"Songhee Lee","email":"","orcid":"","institution":"National Institute of Health","correspondingAuthor":false,"prefix":"","firstName":"Songhee","middleName":"","lastName":"Lee","suffix":""},{"id":488850539,"identity":"65586566-cab9-4a64-b7c6-0217ae8366de","order_by":1,"name":"Hyeonah Lee","email":"","orcid":"","institution":"National Institute of Health","correspondingAuthor":false,"prefix":"","firstName":"Hyeonah","middleName":"","lastName":"Lee","suffix":""},{"id":488850541,"identity":"f5810c55-c088-4bda-94ca-f636cd9375cc","order_by":2,"name":"Jung Wook Kim","email":"","orcid":"","institution":"National Institute of Health","correspondingAuthor":false,"prefix":"","firstName":"Jung","middleName":"Wook","lastName":"Kim","suffix":""},{"id":488850543,"identity":"d8bef671-8988-4af1-bac1-5b3afadd81b8","order_by":3,"name":"Hyeon-Jin Kim","email":"","orcid":"","institution":"National Institute of Health","correspondingAuthor":false,"prefix":"","firstName":"Hyeon-Jin","middleName":"","lastName":"Kim","suffix":""},{"id":488850545,"identity":"d4170164-0ebc-4790-968d-2ecefa868983","order_by":4,"name":"Kwang Jun Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACCTDJw8DAzwMRMCBei2QPaVpASs8Qq0VyRvKzh19kDucZnzl87MOHGgZj8wYCWqQl0syNZXgOF5udbUueOeMYg5nMAQJa5KQTzKQleA4nbjvPY8zMw8ZgI0FAB1BL+jewls39QC1//hGhRVo6x0zyA1DLBt4eY2bGNgYzglok578pk2bgSU+cceZYMmNvn4QxQS0SZ45vk/zZY53Y35N8mOHHNxvDGYS0gAAzbw/CCGI0MDAw/vhBnMJRMApGwSgYoQAAW8Y5BcszuCIAAAAASUVORK5CYII=","orcid":"","institution":"National Institute of Health","correspondingAuthor":true,"prefix":"","firstName":"Kwang","middleName":"Jun","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2025-06-30 01:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7005374/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7005374/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87423532,"identity":"ba9b8859-dcb0-4c98-b771-950434bf0b68","added_by":"auto","created_at":"2025-07-23 16:00:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":125007,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of study design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis figure illustrates the experimental workflow designed to evaluate species-level resolution using different types of mock communities and combinations of sequencing strategies and conditions.\u003c/p\u003e\n\u003cp\u003e(A) Composition of mock communities (MCs). Commercial DNA-based MCs and domestically developed whole-cell MCs (Korean MCs [KMCs]) were designed to represent four human body sites: gut, oral, skin and vaginal (KMCG, KMCO, KMCS, and KMCV, respectively). (B) Sequencing strategies applied included short-read amplicon (16S V3–V4; 16P) sequencing, long-read full-length 16S rRNA amplicon (16F) sequencing and short-read whole metagenome shotgun (WMS) sequencing. (C) WMS sequencing conditions consisted of three predefined input DNA concentrations (1, 10, and 100 ng) and four sequencing output yields (1, 5, 10, and 20 gigabases [Gb]), leading to 12 combinations. (D) A total of 112 datasets were generated: 16P (\u003cem\u003en = \u003c/em\u003e8), 16F (\u003cem\u003en = \u003c/em\u003e8) and WMS (\u003cem\u003en = \u003c/em\u003e96) covering all mock types and conditions.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7005374/v1/27c4d383c56fdf26ca349dcd.png"},{"id":87423529,"identity":"eda462de-2041-455e-ad7d-d51364bf5fe4","added_by":"auto","created_at":"2025-07-23 16:00:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":113125,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMatching heatmaps of qualitative taxon detection across sequencing platforms based on KMC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePresence or absence-based heatmaps visualise the agreement between expected and observed taxa across three sequencing platforms for each MC. Panels represent (A) KMCG, (B) KMCO, (C) KMCS, and (D) KMCV, corresponding to gut, oral, skin, and vaginal MCs, respectively. Each panel includes results from 16P, 16F, and WMS sequencing. These heatmaps represent the qualitative matching between the expected community composition and the observed taxa. Green indicates taxa that were expected and correctly observed (true positive), whereas red indicates expected taxa that were not observed (false negative).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7005374/v1/784901771ad7c9d7e5dfe26b.png"},{"id":87424470,"identity":"092071d7-41d0-4695-8043-0e1e09f5bce9","added_by":"auto","created_at":"2025-07-23 16:08:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46389,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuantitative evaluation of taxon detection performance across sequencing platforms using KMCs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBarplots show quantitative metrics used to assess detection performance across different sequencing strategies, based on the known MC composition. (A) Total relative abundance assigned to expected taxa in the MC. (B) Number of species observed per platform. The dashed line indicates the total number of species present in the MC. (C) Total relative abundance of true positive taxa, defined as taxa that were included in the MC and correctly observed. (D) Total relative abundance of false negative taxa, defined as expected taxa that were not observed.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7005374/v1/5571b0795af374a6535ddb1a.png"},{"id":87424878,"identity":"fffa48e1-0b32-46cd-965d-c8b8e51ec6c5","added_by":"auto","created_at":"2025-07-23 16:16:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":63163,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison bar graph of WMS sequencing conditions using KMCs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpecies-level abundance reconstruction was compared across 12 WMS conditions defined by combinations of input DNA concentration (1, 10, and 100 ng) and output yield (1, 5, 10, and 20 Gb). (A-D) Comparison of quantitative evaluations for all MCs according to input DNA volume. (E-H) Comparison of quantitative evaluations for all MCs according to output yield.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7005374/v1/12cb7efc1163f8cfe8ff3957.png"},{"id":87423533,"identity":"ca56f9e3-db11-4f67-adf7-6e15986448e6","added_by":"auto","created_at":"2025-07-23 16:00:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":154617,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpider chart of species-level resolution by input DNA concentration across four KMCs in WMS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdjusted F1 scores for each species are shown across four KMC types. This figure compares species-level resolution performance under different input DNA concentrations in WMS conditions.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7005374/v1/9b26e7e9258cb9efeb6d78a9.png"},{"id":87423535,"identity":"ae03dacd-5623-4dad-80a6-be8c81708943","added_by":"auto","created_at":"2025-07-23 16:00:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":134387,"visible":true,"origin":"","legend":"\u003cp\u003eSpider chart of species-level resolution by sequencing output yield across four KMCs in WMS\u003c/p\u003e\n\u003cp\u003eAdjusted F1 scores for each species are shown across four KMC types. This figure compares species-level resolution performance under different sequencing output yields in WMS conditions.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7005374/v1/031b85316825c0c3bf79ef0b.png"},{"id":87423546,"identity":"f7b13f84-92f9-4972-9ca0-2695d69fcdf2","added_by":"auto","created_at":"2025-07-23 16:00:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":57592,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmaps summarising species-level resolution scores across WMS conditions using KMCs\u003c/p\u003e\n\u003cp\u003eHeatmaps present the final resolution scores calculated for each WMS condition, based on MCs and stratified according to MC type. Panels show results for (A) KMCG, (B) KMCO, (C) KMCS, and (D) KMCV. Each cell represents the resolution score derived from a specific combination of input DNA concentration and sequencing output yield.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7005374/v1/ca638d09d6fdb2bdd64f6087.png"},{"id":91654474,"identity":"65d7c5ed-c4ef-485e-ade9-a7b56108dbd4","added_by":"auto","created_at":"2025-09-18 17:46:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1915767,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7005374/v1/5b0e34f2-1b06-4d07-9061-18f61f50c654.pdf"},{"id":87423537,"identity":"a9d3deb4-c4a1-4e93-889b-a7e83804bab5","added_by":"auto","created_at":"2025-07-23 16:00:54","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":696281,"visible":true,"origin":"","legend":"","description":"","filename":"Supplymentarymaterial.zip","url":"https://assets-eu.researchsquare.com/files/rs-7005374/v1/fdaa6013380057adee1438f6.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantitative evaluation of microbiome sequencing resolution under varying experimental conditions using defined mock communities","fulltext":[{"header":"1. BACKGROUND","content":"\u003cp\u003eAs microbiome analysis technologies evolve, methodological discussions have increasingly focused on enabling high-resolution profiling of microbial communities and ensuring the reliability of analytical outcomes [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Microbial community profiling is a foundational aspect of microbiome research and is typically conducted using either 16S ribosomal RNA gene sequencing (16S rRNA sequencing) or whole metagenome shotgun sequencing (WMS) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Amplicon sequencing is a cost-effective and relatively uncomplicated experimental procedure, but is typically limited to taxonomic resolution at the genus level [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. WMS enables accurate taxonomic and functional profiling at the species or even strain level [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. High-resolution taxonomic profiling is crucial in contexts such as disease diagnosis, personalized medicine, and environmental monitoring, where distinguishing between closely related species or strains can impact. however, it involves higher costs and more complex analytical workflows than amplicon sequencing [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Moreover, WMS-based analyses are highly sensitive to technical variability introduced during sample preparation and sequencing [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Even among studies analysing similar sample types, considerable differences in microbial composition estimates have been observed, stemming from heterogeneous protocols and inconsistent analytical pipelines [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The variability poses a notable challenge to inter-study comparability and reproducibility, highlighting the critical need for technical standardisation and systematic validation throughout the microbiome analysis workflow [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSpecies-level resolution in microbiome analysis is influenced by both the sequencing platform and experimental variables such as input DNA concentration and sequencing output yield [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For example, sequencing output yield, which is often limited by the high cost of sequencing, can reduce read depth, thereby lowering the sensitivity for detecting low-abundance taxa and ultimately constraining species-level resolution [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Although these variables are known to affect microbial profiling, few studies have systematically evaluated their effects at the species level [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Even when such assessments exist, they are often limited to dichotomous metrics such as recall or precision, which do not properly reflect changes in accuracy or resolution at the level of abundance [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. To rigorously examine these effects, sequencing performance must be tested against reference materials with known composition and abundance [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMock communities (MCs), defined as artificial mixtures with known microbial compositions, serve as essential reference controls for evaluating methodological accuracy, reproducibility, and potential bias in microbial profiling workflows [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Widely used commercial MC standards, such as the HMP-ATCC and ZymoBIOMICS, are designed based on balanced microbial compositions and serve as benchmarks for evaluating analytical performance [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, the However, the However, the However, the However, the HMP-ATCC mock community consists of a fixed set of strains derived from Western populations, which may limit its relevance for studies targeting different cohorts or microbiome contexts [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. As an alternative, researcher-constructed MCs allow customised designs, such as incorporating region-specific strains or adjusting abundance ratios, which may better suit specific research needs [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. When prepared in whole-cell format, these MCs also enable assessment of the full experimental workflow, including DNA extraction, providing a more comprehensive evaluation than DNA-based MCs [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTherefore, this study aimed to quantitatively evaluate the effect of various experimental conditions on species-level resolution and abundance accuracy using two types of MC formats (whole-cell and DNA-based), different sequencing platforms, input DNA concentrations, and sequencing output yields. This study sought to contribute to the establishment of reproducible and reliable microbial profiling guidelines.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Experimental framework and sequencing conditions\u003c/h2\u003e\u003cp\u003eThis study was designed to evaluate sequencing performance using MCs with different formats and microbial compositions. Two distinct MC formats were used: a commercially available DNA-based MC and a domestically developed whole-cell MC, referred to as the Korea MC (KMC). Each MC format included four body site-specific mock communities representing the gut, oral, skin, and vaginal environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eSequencing was performed using three platforms: short-read amplicon sequencing targeting the 16S rRNA V3\u0026ndash;V4 region (16P), long-read full-length 16S rRNA gene sequencing (16F), and short-read WMS sequencing (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). For WMS, 12 experimental conditions were constructed by combining 3 predefined input DNA concentrations with 4 levels of sequencing output yield (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Each MC underwent sequencing under a single 16P condition, a single 16F condition, and 12 WMS conditions, resulting in a total of 14 datasets per mock community. Applied across 4 body sites and 2 MC formats, a total of 112 sequencing datasets were generated and analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Overview of the MC samples\u003c/h2\u003e\u003cp\u003eThe DNA-based MCs were labeled MCG, MCO, MCS, and MCV, corresponding to the gut, oral, skin, and vaginal sites, respectively. They were derived from commercially available ATCC Bacterial Mix standards (MSA-2006\u0026trade;, MSA-2007\u0026trade;, MSA-2008\u0026trade;, and MSA-2009\u0026trade;, respectively). These four types of microbial whole cell mock communities were developed by lyophilizing standardized bacterial suspension as a part of the research and development program for clinical metagenomics funded by Korea National Institute of Health. This format facilitates room-temperature storage and enables the standardized assessment of DNA extraction efficiency across sample types. The species included in each MC are listed in Additional File 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2 and served as the ground-truth reference set for taxonomic accuracy assessment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. DNA extraction and quality control\u003c/h2\u003e\u003cp\u003eGenomic DNA from the KMC was extracted using the DNeasy PowerSoil Kit (QIAGEN, Hilden, Germany) according to the manufacturer's instructions. DNA integrity was evaluated via 1% agarose gel electrophoresis. The concentration and purity of the extracted microbial DNA were assessed using both a Nanodrop 2000 UV-Vis spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and a Qubit 3.0 fluorometer (Thermo Fisher Scientific). Only samples that passed quality control thresholds were used for downstream library preparation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Library preparation\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1. 16S rRNA V3\u0026ndash;V4 region amplicon sequencing\u003c/h2\u003e\u003cp\u003eAmplicon sequencing targeting the V3\u0026ndash;V4 region of the 16S rRNA gene (16P) was performed using the primer pair 341F (5\u0026prime;-CCTACGGGNGGCWGCAG-3\u0026prime;) and 805R (5\u0026prime;-GACTACHVGGGTATCTAATCC-3\u0026prime;). PCR reactions were prepared according to the manufacturer's instructions using a \u003cem\u003eHerculase\u003c/em\u003e II polymerase-based master mix (Agilent Technologies, Santa Clara, CA, USA) and V3\u0026ndash;V4-specific primers. PCR amplification was performed on an ABI GeneAmp\u0026reg; 9700 thermocycler (Applied Biosystems, Foster City, CA, USA) using the following cycling conditions: an initial denaturation at 95\u0026deg;C for 3 min; 25 cycles of denaturation at 95\u0026deg;C for 30 s, annealing at 55\u0026deg;C for 30 s, and extension at 72\u0026deg;C for 30 s; followed by a final extension at 72\u0026deg;C for 5 min and a hold at 4\u0026deg;C. A separate index PCR was performed with 10 additional cycles under the same conditions.\u003c/p\u003e\u003cp\u003eAfter amplification, the PCR products were purified using AMPure XP Beads (Beckman Coulter, Brea, CA, USA). Equimolar pooling was performed based on fluorescence-based quantification. Indexing was achieved using the Nextera XT Index Primer Set (Illumina Inc., San Diego, CA, USA) in a separate PCR reaction using the same enzyme and buffer system. The purified and pooled libraries were sequenced as 2 \u0026times; 300 bp paired-end reads on the Illumina MiSeq platform (Illumina Inc.) per the manufacturer's instructions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2. 16S rRNA full-length amplicon sequencing\u003c/h2\u003e\u003cp\u003eAmplicon sequencing targeting the full-length 16S rRNA gene (16F) was performed using the universal primer pair 27F (5\u0026prime;-AGRGTTYGATYMTGGCTCAG-3\u0026prime;) and 1492R (5\u0026prime;-RGYTACCTTGTTACGACTT-3\u0026prime;). Sample-specific barcode sequences provided by the manufacturer were added to both primers to allow multiplexed sequencing. PCR reactions were prepared according to the manufacturer's instructions using the KAPA HiFi HotStart ReadyMix (Roche, Basel, Switzerland). PCR amplification was performed using a Veriti\u0026trade; Thermal Cycler (Applied Biosystems) with the following cycling conditions: an initial denaturation at 95\u0026deg;C for 3 min; 27 cycles of denaturation at 95\u0026deg;C for 30 s, annealing at 57\u0026deg;C for 30 s, and extension at 72\u0026deg;C for 60 s; followed by a final extension at 72\u0026deg;C for 5 min and a hold at 4\u0026deg;C. Amplicons were evaluated for quality using a TapeStation\u0026trade; system (Agilent Technologies), and equimolar pooling of all samples was performed before library construction.\u003c/p\u003e\u003cp\u003eLibraries were constructed using the SMRTbell Express Template Prep Kit 2.0 (Pacific Biosciences, Menlo Park, CA, USA) according to the manufacturer's instructions for full-length 16S library preparation. Library size and concentration were assessed using the Qubit Fluorometer with the Qubit\u0026trade; 1X dsDNA HS Assay Kit (Invitrogen, Carlsbad, CA, USA) and the TapeStation system. Primer annealing and polymerase binding were performed using the Sequel II Binding Kit 2.1 and DNA Internal Control Complex 1.0 (Pacific Biosciences). Sequencing was conducted on a Sequel II system (Pacific Biosciences) using the Sequel II Sequencing Kit 2.0.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.4.3. WMS sequencing\u003c/h2\u003e\u003cp\u003eShotgun metagenomic libraries were constructed using the TruSeq DNA Nano Library Prep Kit (Illumina Inc.) according to the manufacturer's instructions. Input DNA (100 ng) was enzymatically fragmented to an average insert size of 200\u0026ndash;400 bp using the Covaris LE220 system (Covaris, Woburn, MA, USA). Fragmented DNA was end-repaired, A-tailed, and ligated with Illumina adapter indices following the standard workflow.\u003c/p\u003e\u003cp\u003eAfter ligation, libraries were amplified using the Enhanced PCR Mix provided in the kit with eight PCR cycles under the following conditions: 95\u0026deg;C for 3 min; eight cycles of 98\u0026deg;C for 20 s, 60\u0026deg;C for 15 s, and 72\u0026deg;C for 30 s; followed by 72\u0026deg;C for 5 min and a final hold at 4\u0026deg;C.\u003c/p\u003e\u003cp\u003ePCR products were purified using AMPure XP Beads (Beckman Coulter) and eluted in 30 \u0026micro;L of resuspension buffer. Library size distribution was evaluated using the TapeStation\u0026trade; system (Agilent Technologies), and library concentrations were determined using qPCR. The pooled libraries were sequenced on an Illumina NovaSeq 6000 platform (Illumina Inc.) to generate 2 \u0026times; 150 bp paired-end reads.\u003c/p\u003e\u003cp\u003eTo evaluate the effect of input DNA quantity and sequencing depth, three predefined input DNA concentrations and four target sequencing output levels were used, resulting in 12 shotgun library conditions. Six libraries were physically prepared and sequenced, whereas the remaining six datasets were generated via in silico subsampling of sequencing reads. Eight libraries were generated for each MC: one from 16P sequencing, one from 16F sequencing, and six from WMS sequencing. The six WMS libraries were designed using three predefined combinations of input DNA concentrations and two sequencing output yield levels. Thus, eight libraries were constructed per MC, resulting in a total of 64 libraries across all conditions.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Data processing and taxonomic profiling\u003c/h2\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.5.1. 16s rRNA v3\u0026ndash;v4 region amplicon sequencing\u003c/h2\u003e\u003cp\u003eRaw reads were quality-checked using FastQC (Babraham Institute, Cambridge, UK), and primer sequences were trimmed with Cutadapt (v4.0, Germany). Denoising, dereplication, and chimaera removal were performed using the DADA2 plugin in QIIME2 (v2023.2, USA). Reads with an average quality score\u0026thinsp;\u0026lt;\u0026thinsp;Q20 and length\u0026thinsp;\u0026lt;\u0026thinsp;250 bp were excluded before amplicon sequence variant (ASV) inference. ASVs were generated, and taxonomic classification was conducted using a na\u0026iuml;ve Bayes classifier trained on the SILVA 138.1 database trimmed to the V3\u0026ndash;V4 region.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.5.2. 16S rRNA full-length amplicon sequencing\u003c/h2\u003e\u003cp\u003eFull-length 16S rRNA reads generated using the Oxford Nanopore Technologies platform were adapter-trimmed using Cutadapt (v4.0, Dortmund, Germany) and quality-filtered to retain reads with a minimum length of 1,200 bp and an average quality score of \u0026ge;\u0026thinsp;Q10.\u003c/p\u003e\u003cp\u003eTaxonomic classification was performed using Kraken2 (v2.1.2, USA) with a custom database established using full-length 16S rRNA gene sequences of the SILVA 138.1 reference set. The resulting taxonomic profiles were used for downstream microbial composition and diversity analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e\u003cem\u003e2.5.3. WMS sequencing\u003c/em\u003e\u003c/h2\u003e\u003cp\u003eSequencing data underwent a systematic bioinformatics pipeline for quality control, trimming, host genome removal, and microbiome profiling. Initially, raw sequencing reads were subjected to quality control using FastQC (version 0.11.9) to evaluate sequence quality and adapter contamination [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Adapter trimming and quality filtering were conducted with Trim Galore (version 0.6.7) Reads shorter than 50 bp after trimming or those with average quality scores below 20 were excluded from further analyses [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Subsequently, trimmed reads were aligned against the human reference genome hg38 using BWA-MEM (version 0.7.17) to remove host-derived sequences. Reads not mapped to the human genome were extracted, converted to BAM format, sorted by name, and then converted into FASTQ format using SAM tools (version 1.11) for downstream metagenomic analysis [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Taxonomic profiling was performed using MetaPhlAn (v4.0), with its default marker gene database and settings. Four output yield conditions (1, 5, 10, and 20 Gb) were analysed to evaluate the effect of sequencing depth. Libraries corresponding to 1 Gb and 20 Gb were physically constructed and sequenced, whereas 5 Gb and 10 Gb datasets were computationally simulated by subsampling reads from the 20 Gb libraries at predefined proportions. Taxonomic abundance profiles derived from this read-based approach were used for all subsequent analyses (Additional File 1: Table S3).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Statistical analyses\u003c/h2\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e2.6.1. Quantitative accuracy assessment\u003c/h2\u003e\u003cp\u003eAn adjusted F1-score was used to evaluate both the quantitative accuracy of relative abundance estimates and their agreement with the expected composition of each MC. For each taxon, the observed relative abundance (Y) was compared to the predefined expected value (M). A tolerance threshold (T), defined as 10% of M, was applied to identify the range within which deviations were not penalised [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Deviations exceeding this threshold were penalised using a power function of the form (excess)\u003csup\u003e\u0026#119901;\u003c/sup\u003e, where the excess was defined as the difference between the deviation and the threshold. The exponent \u003cem\u003e\u0026#119901;\u003c/em\u003e = 0.5 was selected to implement a sublinear power function, which increases at a slower rate than a linear function with respect to the excess deviation, thereby limiting the rate of penalty escalation for moderate errors. True positives (TP), false positives (FP), and false negatives (FN) were computed based on the following definitions:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"496\" height=\"320\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEquation (1). Adjusted F1-score incorporating tolerance-based penalisation of abundance deviation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe final score was computed as the harmonic mean of precision and recall, where precision was defined as TP divided by TP plus FP, and recall as TP divided by TP plus FN.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e2.6.2. Data visualization\u003c/h2\u003e\u003cp\u003eAll visualisations were conducted using R version 4.3.1. Bar graphs were generated using the ggplot2 package to compare relative abundances across sequencing methods and conditions. Heatmaps were constructed using ComplexHeatmap, gplots, and pheatmap to visualise taxonomic distributions and sample clustering. The ComplexHeatmap package was primarily used to incorporate multiple metadata layers and customise layout configurations. Spider graphs were produced using the \u003cem\u003efmsb\u003c/em\u003e package to present multivariate comparisons across sequencing strategies.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Qualitative species-level detection based on presence or absence across sequencing platforms\u003c/h2\u003e\n \u003cp\u003eTo compare the species-level qualitative detection capability across sequencing platforms, we compared the expected taxa present in each MC with the taxa detected in the analysis results (Additional File 1: Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2). The presence/absence data was used to construct a confusion matrix for each sample, enabling a visual comparison of detection performance (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn KMCG, none of the 12 expected taxa were detected in 16P. Eleven were matched in 16F and 12 were matched in WMS (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). In KMCO, 1 out of 10 species were detected using 16P, 8 species using 16F, and all 10 species using WMS (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). In KMCS, 3 out of 9 species was detected using 16P, 8 species using 16F, and all 9 species using WMS (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). In KMCV, 1 out of 11 species were detected using 16P, 9 species using 16F, and all 11 species using WMS (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\n \u003cp\u003eIn MCG, 3 out of 12 species were detected using 16P, 11 species using 16F, and all 12 species using WMS (Additional File 2: Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). In MCO, 2 out of 6 species were detected using 16P, 6 species using 16F, and all 6 species using WMS. In MCS, 1 out of 6 species was detected using 16P, 6 species using 16F, and all 6 species using WMS. In MCV, 3 out of 6 species were detected using 16P, 4 species using 16F, and all 6 species using WMS (Additional File 2: Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Overall, WMS detected all expected species in all KMC and MC samples, with 16F showing somewhat lower matching and 16P showing the lowest detection rate across all KMC and MCs.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Quantitative species-level detection based on abundance across sequencing platforms\u003c/h2\u003e\n \u003cp\u003eWe evaluated the reproducibility of abundance across sequencing platforms by MC type. The total correct abundance was calculated by adding the relative abundance of items corresponding to the predefined constituent species among the species observed in each KMC. Since there was no difference between the expected and detected species, WMS was 100% for all, and 16F was somewhat lower, showing that 16F had almost no correct species (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). When observing the number of species, WMS detected no species other than the expected species, so the number was the same as the expected species. 16F was observed more than the expected species, and 16P was observed almost none (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). The true positive abundance ratio for the detected species was also higher for the expected species than for the detected species in the order of WMS, 16F, and 16P (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). Finally, the false negative abundance ratio was higher in the order of 16P, 16F, and WMS, contrary to the true positive abundance (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD). The results in MC were also observed in the same manner (Additional File 2: Figure S2). In particular, WMS showed 100% matching of detected species to expected species in qualitative observation, but KMC showed an average of 86% and KM showed an average of 93%.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Impact of WMS sequencing conditions on quantitative performance\u003c/h2\u003e\n \u003cp\u003eTo identify the variation in quantitative detection values of WMS, we compared species-level abundance reproducibility under various WMS sequencing conditions, focusing on the variation in input DNA concentration and sequencing output yield of WMS (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eWe created 12 different WMS conditions by setting the input DNA concentration to 1, 10, and 100 ng and the sequencing output to 1, 5, and 20 Gb. In the case of KMCS, more detected species were observed than expected species when DNA input was 1 ng, so the total correct abundance ratio and true positive abundance ratio were also observed to be the lowest (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA and B). In addition, an upward trend was observed for 10 ng, including KMCO with the highest true positive abundance ratio (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). This showed that the input DNA volume affected the results. On the other hand, when comparing the sequencing output, all four KMCs showed consistent total correct positives and species numbers, with no significant difference between the outputs (Figs. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eG). The number of true positives and false negatives also remained constant regardless of the output, confirming that the sequencing depth had a limited effect on the reproducibility under the tested conditions. In the case of DNA-based MC, it was observed to be relatively stable without any effect according to the WMS sequencing condition compared to cell-based KMC (Additional file 2: Figure S3).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Species-level abundance reproducibility across WMS sequencing conditions\u003c/h2\u003e\n \u003cp\u003eThe resolution was evaluated at the species level for each KMC by comparing the reproducibility of quantitative detection using the adjusted F1-score (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). This metric was used to assess condition-specific variability in the recovery of individual reference species across WMS sequencing parameters. Overall, WMS sequencing achieved high reproducibility for most species, although notable variations in resolution were observed across sequencing conditions. Species-specific sensitivity differences were evident, indicating that optimal detection may vary by taxon and experimental setup (Additional File 2: Figure S4 and S5).\u003c/p\u003e\n \u003cp\u003eIn KMCG, most species showed high reproducibility, but \u003cem\u003eLacticaseibacillus paracasei\u003c/em\u003e consistently showed low scores with an average score of 77 points (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). \u003cem\u003eClostridium perfringens\u003c/em\u003e showed no deviation according to the degree of output yield with an average score of 96 points in the conditions of 1 ng and 10 ng DNA input, but it was lowered to 86 points in all output conditions when the input was 100 ng. When observed by output, differences in input were observed in all output conditions, so \u003cem\u003eC. perfringens\u003c/em\u003e seemed to be affected by the amount of DNA input. In KMCO, \u003cem\u003eStreptococcus pyogenes\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, and \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e showed low reproducibility, and in particular, \u003cem\u003eS. pneumoniae\u003c/em\u003e was close to 0. \u003cem\u003eS. pyogenes\u003c/em\u003e, \u003cem\u003eE. coli\u003c/em\u003e, and \u003cem\u003eK. pneumoniae\u003c/em\u003e showed improved reproducibility only in the 10 ng condition. In particular, \u003cem\u003eE. coli\u003c/em\u003e showed high resolution only when the input was 10 ng, regardless of the output conditions. \u003cem\u003eS. pneumoniae\u003c/em\u003e showed that the resolution improved as the input level increased and the output level increased, and the highest resolution was shown in the combination of input 10 ng and output 20 Gb. KMCS displayed the lowest overall reproducibility among the four KMCs, with no consistent improvement by output level. At a DNA input of 1 ng, \u003cem\u003eEnterococcus faecium\u003c/em\u003e, \u003cem\u003eCorynebacterium striatum\u003c/em\u003e, \u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e, and S. aureus exhibited poor reproducibility, which improved at input levels\u0026thinsp;\u0026ge;\u0026thinsp;10 ng. In contrast, \u003cem\u003eAcinetobacter bereziniae\u003c/em\u003e and \u003cem\u003eA. ursingii\u003c/em\u003e demonstrated high reproducibility at the 1 ng DNA input condition; however, their reproducibility declined as input DNA concentration increased. In KMCV, most species exhibited high reproducibility, except for Cryptococcus neoformans. The reproducibility of \u003cem\u003eC. neoformans\u003c/em\u003e was shown to be affected by both input DNA level and output level, and improved as input DNA level and output level increased. In particular, the combination of 100 ng of input DNA and 20 Gb of output showed the most improved resolution.\u003c/p\u003e\n \u003cp\u003eWhen analyzing DNA-based MC, relatively little variability was observed compared to cell-based KMC. \u003cem\u003eAcinetobacter johnsonii\u003c/em\u003e showed high resolution at an input level of 10 ng (Additional File 2: Figures S4 and S5).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Comparative resolution evaluation across KMC types\u003c/h2\u003e\n \u003cp\u003eThe final species-level resolution scores for each WMS sequencing condition are summarised in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. In KMCG, all condition combinations yielded consistently high F1-scores, with values exceeding 95, regardless of the input and output settings (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA).\u003c/p\u003e\n \u003cp\u003eIn KMCO, 10 ng input DNA generally resulted in scores\u0026thinsp;\u0026gt;\u0026thinsp;90 across output levels (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB). In KMCO, high-resolution scores were mainly related to output yields\u0026thinsp;\u0026ge;\u0026thinsp;10 Gb, regardless of input DNA concentration. In KMCS, conditions with \u0026ge;\u0026thinsp;10 ng of DNA input showed high resolution scores overall at all output levels, while conditions with \u0026le;\u0026thinsp;10 ng showed relatively low scores (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC). In KMCV, high resolution was observed when the input level was \u0026ge;\u0026thinsp;10 ng, and high resolution was observed when the output level was \u0026ge;\u0026thinsp;10 Gb (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eD). Overall, the combination of 10 ng of input DNA and 10 Gb or more of output DNA consistently showed the highest resolution scores in most KMCs. MCG showed a resolution of 100 points in all conditions (Additional File 2: Figure S6). Next, MCV showed 99 score, and MCO and MCS also showed high scores of 97 scores on average in all conditions.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eOverall, WMS consistently yielded the highest reproducibility across all mock communities, while the amplicon-based strategies showed lower performance. Among the tested WMS conditions, the combination of 10 ng input DNA and \u0026ge;\u0026thinsp;10 Gb sequencing output produced the most robust and consistent results. However, certain mock communities, such as KMCS, exhibited persistently low detection accuracy for specific taxa or under specific conditions. These findings demonstrate that both platform type and sequencing configuration critically affect species-level sensitivity, warranting further evaluation of the underlying biases using more refined metrics. These findings demonstrate that both platform type and sequencing configuration critically affect species-level sensitivity, warranting further evaluation of the underlying biases using more refined metrics.\u003c/p\u003e\u003cp\u003eAssessing taxonomic resolution based solely on species presence or richness is insufficient to capture quantitative detection accuracy. Although traditional metrics like precision or recall partially reflect presence or recovery, they fail to account for deviations in abundance, such as species-specific over- or under-detection. To address this limitation, we employed an adjusted F1-score that incorporates quantitative imbalances among true positives, false positives, and false negatives. The tolerance threshold (T) was set to 10% for the expected value (M) in the adjusted F1 score. There is still no consensus on the acceptable level of deviation in microbial community studies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The tolerance threshold (T) for strict quantitative comparison in the diagnostic field is set to \u0026lt;\u0026thinsp;5% [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In technical reproducibility tests for library prep, sequencing repeats, etc., it is set to \u0026plusmn;\u0026thinsp;10\u0026ndash;20% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In studies related to taxonomic resolution, it was set to around 10% and compared and analyzed [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This metric assigns weighted penalties based on the numeric difference between observed and expected abundances, enabling an integrated assessment of both detection sensitivity and abundance fidelity. For example, KMCS displayed full species recovery in a qualitative presence/absence comparison (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC); however, under the 1 ng input condition, false negative abundance was notably high (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). This approach enables quantitative comparisons of subtle differences in reproducibility, species-specific biases, and condition-driven sensitivity shifts that are not captured by qualitative analysis. It is particularly useful for evaluating the influence of library preparation and sequencing conditions on the effective resolution for each taxon.\u003c/p\u003e\u003cp\u003eThe systematic comparison of input and output combinations revealed that the 10 ng DNA input with the 10 Gb output yield produced the most consistent and accurate results. Notably, both lower (1 ng) and higher (100 ng) inputs were associated with reduced resolution. At the 1 ng DNA input, the limited starting material may introduce PCR bias and lead to reduced library complexity. In contrast, excessive input (100 ng) could result in nonspecific amplification or enzyme saturation, thereby decreasing quantification accuracy. These findings underscore that increased DNA input does not necessarily yield better resolution, highlighting the importance of optimizing concentration based on the analytical objective.\u003c/p\u003e\u003cp\u003eA similar plateau effect was observed for sequencing output, with little improvement noted beyond 10 Gb. Excessive sequencing depth beyond this threshold can lead to redundancy, decreased efficiency, and no further gain in taxonomic resolution. In some cases, resolution scores at a sequencing depth of 20 Gb were even lower than those observed at 10 Gb. Moreover, some species were detected only under specific input-output combinations, indicating that sequencing conditions directly influence species-specific detection sensitivity. Notably, DNA-based mock communities often exhibited higher resolution consistency compared to whole-cell mocks. This may be attributed to the absence of DNA extraction variability, as DNA mocks provide purified, high-quality input DNA, ensuring uniform library preparation efficiency [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In contrast, whole-cell mocks are more susceptible to taxon-specific extraction efficiency and matrix effects, leading to greater variability in quantitative detection.\u003c/p\u003e\u003cp\u003eResolution patterns also varied across MCs and among taxa under the same sequencing conditions. For example, \u003cem\u003eS. pneumoniae\u003c/em\u003e in KMCO achieved detectable resolution only under the 100 ng-20 Gb combination, whereas the signal was nearly absent under all other conditions. In contrast, \u003cem\u003eE. coli\u003c/em\u003e in the same community exhibited optimal performance at the 10 ng DNA input, with sharp declines at other concentrations. Similarly, \u003cem\u003eC. neoformans\u003c/em\u003e in KMCV demonstrated stable resolution only under long-read platforms and high-output levels. This suggests that the fungal cell structure and large genome size of \u003cem\u003eC. neoformans\u003c/em\u003e may affect DNA extraction and library preparation efficiency. In KMCS, \u003cem\u003eA. bereziniae\u003c/em\u003e and \u003cem\u003eA. ursingii\u003c/em\u003e exhibited the highest resolution at the 1 ng DNA input, with declining accuracy at increasing concentrations. In contrast, the resolution of other taxa in KMCS improved with increasing input. Collectively, our results emphasize the difficulty of applying a single resolution criterion across all taxa. Depending on the analysis goal or target species, sequencing and library conditions should be flexibly optimized. Overall, accurate performance evaluation requires comprehensive consideration of mock matrix composition, genome properties, extraction efficiency, and interactions with sequencing platforms.\u003c/p\u003e\u003cp\u003e\u003cem\u003eC. perfringens\u003c/em\u003e of KMCG showed a decrease in resolution at 100 ng input (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Since \u003cem\u003eC. perfringens\u003c/em\u003e has a genome structure with a high proportion of accessory genes, excessively high DNA amount can lead to PCR enzyme saturation or overamplification of specific sequences [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This can result in strain-specific genes or low-frequency genes being relatively underamplified and not detected, which can result in a decrease in species-level abundance reproducibility (F1-score). \u003cem\u003eE. coli\u003c/em\u003e of KMCO showed high resolution only at 10 ng input regardless of output conditions, and in particular, \u003cem\u003eS. pneumoniae\u003c/em\u003e showed a reproducibility close to 0 in most conditions except this. This is because the starting amount of DNA has a significant impact on the uniformity of PCR amplification, and coverage bias occurs when it is too low or too high, so that PCR efficiency and library complexity are balanced at this concentration and various fragments are amplified without bias [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. \u003cem\u003eA. bereziniae\u003c/em\u003e and \u003cem\u003eA. ursingii\u003c/em\u003e of KMCS were more sensitive to this bias, suggesting that nonspecific amplification may have excessively generated short fragments when high-concentration DNA input was used [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In contrast, \u003cem\u003eC. neoformans\u003c/em\u003e of KMCV showed low reproducibility overall, which supports previous studies that it has a thick cell wall and a capsule composed of polysaccharides, making DNA extraction difficult regardless of the DNA extraction kit, which may lead to a decrease in sequencing resolution [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In the results of this study, the resolution of \u003cem\u003eC. neoformans\u003c/em\u003e increased as the input and output levels increased, indicating that high-concentration DNA input and high sequencing output are necessary to improve the resolution of \u003cem\u003eC. neoformans\u003c/em\u003e [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Finally, \u003cem\u003eL. paracasei\u003c/em\u003e of KMCG consistently showed low resolution scores. The GC content of the 16S rRNA gene of \u003cem\u003eL. paracasei\u003c/em\u003e is approximately 46.2\u0026ndash;46.6%, which may affect the binding efficiency of primers during PCR amplification. When the GC content is high, the stability of double-stranded DNA increases, making denaturation difficult, which may reduce the efficiency of PCR amplification [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. It also suggests that the quantification of \u003cem\u003eL. paracasei\u003c/em\u003e may be difficult due to the presence of other microorganisms in various sample matrices and chemical substances in the sample [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Collectively, these findings underscore the inherent difficulty of applying a uniform resolution criterion across diverse microbial taxa. Optimal sequencing and library preparation parameters appear to be species-dependent and must be tailored to the specific analytical goals. Consequently, rigorous performance evaluation requires the integrative consideration of multiple experimental and biological variables, including mock matrix composition, genome complexity, DNA extraction efficiency, and platform-specific biases.\u003c/p\u003e\u003cp\u003ePrevious studies using MCs have primarily focused on qualitative assessments of sequencing platforms or analytical tools, often evaluating taxonomic resolution based on the presence or absence of a species or detection capability [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. However, few studies have quantitatively evaluated accuracy at the species level or systematically measured detection deviations [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Instead, most investigations have relied on simple detection rates or descriptive differences in relative abundance, without incorporating metrics that capture abundance-level biases [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Although prior research has reported platform-specific biases in short-read (16P; 16S V3\u0026ndash;V4) and long-read (16F; full-length 16S) amplicon sequencing, the combined influence of sequencing input/output conditions and library construction parameters has been largely overlooked [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. To address this gap, we incorporated both amplicon- and WMS-based approaches and systematically evaluated 64 distinct input/output combinations across 112 datasets. This allowed for a comprehensive analysis of taxonomic resolution beyond platform-level comparison, enabling quantification of the influence of various experimental parameters on performance at the species level. A distinguishing feature of our study is the use of both commercially available DNA-based MCs (ATCC) and domestically developed whole-cell bead-based MCs (KMCs). The KMCs were formulated to mimic the matrix-bound state of microbes in clinical samples, allowing for assessment of DNA extraction efficiency and variation. These whole-cell mocks also offer practical advantages, such as room-temperature stability and long-term preservation [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. In addition, the microbial composition was designed to reflect Korean-specific body site microbiota, increasing the biological relevance for population-specific applications [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. This dual mock system enhances the interpretability of resolution outcomes, distinguishing our study from prior work. Notably, by integrating a multi-layered experimental design, our study provides objective evidence for selecting optimal sequencing configurations and contributes practical guidance for reproducible and accurate microbial analysis under variable conditions.\u003c/p\u003e\u003cp\u003eThis study has several limitations. Although defined MCs provide a controlled framework for evaluating taxonomic resolution under controlled conditions, they are inherently less complex than real clinical or environmental samples and lack host-derived components or interspecies microbial interactions. Consequently, resolution performance under mock conditions may differ from that observed in actual biological samples. Moreover, in some species, we noted discrepancies between input concentration and detection, likely owing to matrix effects and sensitivity in DNA extraction. Nevertheless, our results suggest that experimental limitations such as low input DNA or limited sequencing depth do not necessarily prevent meaningful analysis. Rather than discarding such samples, researchers may consider retaining them by aligning analytical strategies with specific biological goals and taxon characteristics, thereby enabling more informed and context-sensitive decision-making.\u003c/p\u003e\u003cp\u003eFor example, in several MCs, reproducible performance was maintained at 10 Gb sequencing depth without requiring higher input, highlighting the feasibility of cost-effective analysis. Moreover, the use of whole-cell MCs provided valuable insights into the influence of DNA extraction and matrix structure, serving as a benchmark for optimising real-world workflows. The evaluation metrics and analytical framework proposed in this study can be applied to clinical or environmental samples to validate reproducibility under more complex conditions in future studies. Moreover, further investigations could integrate genome assembly accuracy, metagenome-assembled genome recovery, and functional gene resolution to advance efforts in developing practical and robust guidelines for sequencing strategy selection across diverse microbiome research objectives.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eWe quantitatively evaluated taxonomic resolution at the species level across various sequencing conditions using defined MCs, providing a basis for selecting optimal configurations tailored to specific analytical goals. By applying an adjusted F1-score, we quantified accuracy differences across input/output combinations and sequencing platforms. We identified the 10 ng DNA input and 10 Gb output condition as the most consistently reliable. We observed species-specific differences in detection patterns, with certain taxa detectable only under specific conditions, highlighting the need for strategic selection based on target species and study objectives. These findings offer practical guidance for maximizing sequencing efficiency under realistic constraints.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMC - Mock community\u003c/p\u003e\n\u003cp\u003eKMC - Korea domestically developed whole-cell-based mock community\u003c/p\u003e\n\u003cp\u003e16S rRNA sequencing - 16S ribosomal RNA gene sequencing\u003c/p\u003e\n\u003cp\u003e16F sequencing - full-length 16S rRNA gene sequencing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e16P sequencing - 16S rRNA V3\u0026ndash;V4 region sequencing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWMS sequencing - Whole metagenome shotgun sequencing\u003c/p\u003e\n\u003cp\u003eASV - Amplicon sequence variant\u003c/p\u003e\n\u003cp\u003eKMCG - Whole-cell mock community from the gut\u003c/p\u003e\n\u003cp\u003eKMCO - Whole-cell mock community from the oral\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKMCS - Whole-cell mock community from the skin\u003c/p\u003e\n\u003cp\u003eKMCV- Whole-cell mock community from the vaginal\u003c/p\u003e\n\u003cp\u003eMCG - DNA-based mock community from the gut\u003c/p\u003e\n\u003cp\u003eMCO - DNA-based mock community from the oral\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMCS - DNA-based mock community from the skin\u003c/p\u003e\n\u003cp\u003eMCV - DNA-based mock community from the vaginal\u003c/p\u003e\n\u003cp\u003eGb \u0026ndash; Gigabases\u003c/p\u003e\n\u003cp\u003eTP - True positives\u003c/p\u003e\n\u003cp\u003eFP - False positives\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFN - False negatives\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sequence data generated and analysed in this study are available under NCBI BioProject ID PRJNA1288716, https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1288716.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Institute of Health (NIH) research project (project No. 2023-NI-020-01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.H.L and H.A.L contributed to the conceptualisation of the study, and were responsible for writing the original draft and performing the formal analysis. They also revised the manuscript. H.J.K and J.W.K was involved in performing the experimental work and participated in reviewing and refining the manuscript. K.J.L. contributed to the acquisition of funding and the supervision of the study. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Prof. Do-Kyun Kim of Gangnam Severance Hospital for providing the customized mock community material. We also extend our gratitude to all staff members involved in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDivision of Zoonotic and Vector-Borne Diseases Research, Center for Infectious Diseases Research, National Institute of Health\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePollock J, Glendinning L, Wisedchanwet T, Watson M. The madness of microbiome: attempting to find consensus best practice for 16S microbiome studies. Appl Environ Microbiol. 2018;84(7):e02627\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcLaren MR, Willis AD, Callahan BJ. Consistent and correctable bias in metagenomic sequencing experiments. elife. 2019;8:e46923.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKnight R, Vrbanac A, Taylor BC, Aksenov A, Callewaert C, Debelius J, et al. 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PLoS ONE. 2011;6(7):e22109.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mock community, Sequencing strategy, Metagenomics, 16S rRNA gene, Taxonomic resolution","lastPublishedDoi":"10.21203/rs.3.rs-7005374/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7005374/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eObjective evaluation of sequencing resolution is crucial for comparing technologies and ensuring reproducibility in microbiome analysis. Specifically, a systematic approach is necessary to quantitatively assess the effect of various platforms and experimental conditions on species-level resolution. Therefore, this study quantitatively evaluated multiple strategies, including 16S V3\u0026ndash;V4 (16P), full-length 16S rRNA gene (16F), and whole metagenome shotgun sequencing (WMS), using a commercial DNA-based mock community (MC) and a domestically developed whole-cell MC (Korea MC [KMC]). The WMS strategy included 12 combinations of input DNA concentrations and sequencing output levels. A total of 64 WMS libraries were constructed for KMC samples, and 112 sequencing datasets were analysed. Taxonomic resolution was assessed using an adjusted F1-score integrating detection sensitivity and abundance-level reproducibility.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eQualitatively examining the detected species against the expected species across platforms, WMS showed a true positive abundance ratio of over 90%, 16F was observed to have an average of 60%, and 16P was observed to have an average of less than 10%. The combination of 10 ng input and 10 gigabases output consistently yielded the highest species-level resolution. However, reduced performance was observed in some MCs under 1 ng or 100 ng DNA input conditions. Detection sensitivity varied by taxon and condition. Specifically, \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e and \u003cem\u003eCryptococcus neoformans\u003c/em\u003e were detected only under high-input or -output conditions, whereas \u003cem\u003eEscherichia coli\u003c/em\u003e exhibited optimal accuracy at intermediate inputs. \u003cem\u003eAcinetobacter\u003c/em\u003e species demonstrated reduced resolution as input DNA increased. KMC samples showed species- and format-specific variability in DNA extraction efficiency.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study establishes a quantitative framework for assessing species-level resolution across sequencing conditions and taxa using defined MCs. The findings provide practical guidance for selecting sequencing strategies aligned with analytical objectives and resource constraints.\u003c/p\u003e","manuscriptTitle":"Quantitative evaluation of microbiome sequencing resolution under varying experimental conditions using defined mock communities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 16:00:49","doi":"10.21203/rs.3.rs-7005374/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4ac03967-c57d-493e-9cbc-2515bd96e2d1","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-18T17:38:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-23 16:00:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7005374","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7005374","identity":"rs-7005374","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}