Low-cost and highly efficient generation of near-complete bacterial pathogen genomes by TELL-Seq

Low-cost and highly efficient generation of near-complete bacterial pathogen genomes by TELL-Seq | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Low-cost and highly efficient generation of near-complete bacterial pathogen genomes by TELL-Seq View ORCID Profile Zhuoying Wu , View ORCID Profile Ting Zhang , Sheng Li , Lingzhu Shen , View ORCID Profile Zhangnv Yang , Yunfeng Yang , Xuanheng Chen , Bolin Li , Shicong Zhou , Xintian Zhou , View ORCID Profile Beibei Wu , Jianmin Jiang , View ORCID Profile Xiao Li doi: https://doi.org/10.1101/2024.08.17.608388 Zhuoying Wu 1 Zhejiang Provincial Center for Disease Control and Prevention , Hangzhou, Zhejiang Province, 310051, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zhuoying Wu Ting Zhang 2 The X Laboratory and AIIT-Juno Center for Mendelian Genomics, Advanced Institute of Information Technology, Peking University , Xiaoshan District, Hangzhou, Zhejiang Province, China . 311215 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ting Zhang Sheng Li 2 The X Laboratory and AIIT-Juno Center for Mendelian Genomics, Advanced Institute of Information Technology, Peking University , Xiaoshan District, Hangzhou, Zhejiang Province, China . 311215 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lingzhu Shen 2 The X Laboratory and AIIT-Juno Center for Mendelian Genomics, Advanced Institute of Information Technology, Peking University , Xiaoshan District, Hangzhou, Zhejiang Province, China . 311215 3 Whiting School of Engineering, Johns Hopkins University , Baltimore, MD. USA . 21218 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zhangnv Yang 1 Zhejiang Provincial Center for Disease Control and Prevention , Hangzhou, Zhejiang Province, 310051, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zhangnv Yang Yunfeng Yang 2 The X Laboratory and AIIT-Juno Center for Mendelian Genomics, Advanced Institute of Information Technology, Peking University , Xiaoshan District, Hangzhou, Zhejiang Province, China . 311215 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xuanheng Chen 2 The X Laboratory and AIIT-Juno Center for Mendelian Genomics, Advanced Institute of Information Technology, Peking University , Xiaoshan District, Hangzhou, Zhejiang Province, China . 311215 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bolin Li 2 The X Laboratory and AIIT-Juno Center for Mendelian Genomics, Advanced Institute of Information Technology, Peking University , Xiaoshan District, Hangzhou, Zhejiang Province, China . 311215 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shicong Zhou 2 The X Laboratory and AIIT-Juno Center for Mendelian Genomics, Advanced Institute of Information Technology, Peking University , Xiaoshan District, Hangzhou, Zhejiang Province, China . 311215 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xintian Zhou 2 The X Laboratory and AIIT-Juno Center for Mendelian Genomics, Advanced Institute of Information Technology, Peking University , Xiaoshan District, Hangzhou, Zhejiang Province, China . 311215 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Beibei Wu 1 Zhejiang Provincial Center for Disease Control and Prevention , Hangzhou, Zhejiang Province, 310051, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Beibei Wu For correspondence: bbwu{at}cdc.zj.cn jmjiang{at}cdc.zj.cn xiaoli{at}bjmu.edu.cn Jianmin Jiang 4 Key Lab of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention , Hangzhou 310051, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: bbwu{at}cdc.zj.cn jmjiang{at}cdc.zj.cn xiaoli{at}bjmu.edu.cn Xiao Li 2 The X Laboratory and AIIT-Juno Center for Mendelian Genomics, Advanced Institute of Information Technology, Peking University , Xiaoshan District, Hangzhou, Zhejiang Province, China . 311215 5 Department of Epidemiology and Biostatistics, School of Public Health , Peking University, Haidian District, Beijing, China .100083 6 Medical and Population Genetics Program, Broad Institute of MIT and Harvard , Cambridge, MA, USA . 02142 7 Center for Genomic Medicine and Analytical and Translational Genetic Unit, Massachusetts General Hospital , Boston, MA, USA . 02114 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Xiao Li For correspondence: bbwu{at}cdc.zj.cn jmjiang{at}cdc.zj.cn xiaoli{at}bjmu.edu.cn Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Recent evidences suggest that de novo genome assembly can provide additional insights into genomic variation landscape beyond short-read NGS analyses. Despite the advent of longread sequencing technologies, generating high-quality bacterial genome assemblies remains expensive and requires high-quality and large quantities of DNA. TELL-Seq, an emerging linked-read technology, has been reported as a low-cost alternative for producing nearcomplete genome assembly in certain bacterial species. However, a systematic assessment of its performance and characteristics in de novo bacterial genome assembly has not yet been conducted. To address this, we benchmarked TELL-Seq using a set of clinical and standard bacterial pathogens with a wide range of genome size (2.0 6.4 Mbp), GC-content (32% - 68%), genome complexity (mappability from 0.15 to 0.60) and Gram types. Our findings indicate that, with 1/6 cost and 1/15,000 of DNA compared to PacBio HiFi, TELL-Seq could generate as high quality near-complete bacterial genome assemblies. The optimal depth for assembly is around 200×, as increased depth improves contiguity and completeness, though quality plateaus at 250×. In general, genome complexity was significantly correlated with assembly quality, but GC-content was not. Nevertheless, genomes with extreme GC-content may require higher depths for accurate assembly. Our study suggests that TELL-Seq could be a scalable method for large-scale bacterial genomic surveys. The data generated from this study could serve as a benchmark dataset for further algorithm development. Impact Statement An increasing body of literature have shown that de novo genome assembles could provide additional insights into genomic variation beyond standard short-read technologies. It’s particularly useful in clinical and epidemiological studies of bacteria, due to high variable nature of their genomes. Although great advances have been made in long-read sequencing technology, it’s still expensive with high requirement over sample DNA’s quality and quantity, limiting its adoption in large-scale studies or surveys. Linked-read technology is an inexpensive alternative which used single molecule barcoding to mark origin of reads for better scaffolding of contigs assembled from normal short reads. TELL-Seq, as one of these promising technologies, was reported to be able to inexpensively and efficiently generate near-complete bacterial de novo genomic assemblies with low DNA inputs. There’s not yet a systematic benchmarking of this technology in bacterial pathogens. In this study, a set of clinically important bacterial pathogens with a wide spectrum of GC-content, genome size, genome complexity, and Gram Type were used to investigate the performance of TELL-Seq technology in de novo genome assembly. We found that, with about 1/6 of cost and 1/15000 of DNA quantity, it could generate comparable de novo assemblies compared to standard reference genomes or PacBio HiFi assemblies, while beating conventional short-read assemblies. We found several key factors, such as genome complexity, and sequencing depth, which would impact the quality of the assembly. Our study suggests that a sequencing depth of 200× is sufficient to achieve satisfactory results. Our study has revealed characteristics of this technology in bacterial genome assembly and paved the way for its large-scale application in clinical or epidemiological surveys. Data Summary Raw sequencing data for all isolates have been deposited to SRA, accessible through these NCBI BioProject PRJNA1133244. A full list of SRA BioSample accession numbers are available in Tab S5. Assemblies and analyses companion this paper is available at: https://github.com/x-lab/tellseq_bacteria Introduction The advent of long-read sequencing technology has underscored the importance of de novo assemblies in genomic studies [ 1 ], allowing for the discovery of genomic variations that conventional NGS technology often misses. In eukaryotes, many studies have successfully assembled telomere-to-telomere genomes, which have facilitated the construction of pan-genome models for variant discovery [ 2 - 5 ]. While the concept of pan-genomes originated from characterizing bacterial core and accessory gene sets, it has expanded to include all genome sequences, represented by graph data structures [ 6 ]. In bacterial genomics, there is growing interest in studying nucleotide-resolution pan-genomes [ 7 ]. Despite the rapid advancement of long-read sequencing technologies, their high cost and demanding DNA quality and quantity requirements limit their application. Technologies capture long-range information using short sequencing reads have been developed in the past decade to overcome the weakness of long-read and short-read sequencing technologies. These include synthetic long reads (SLR) sequencing, such as Illumina TrueSeq [ 8 ], and linked-read technologies, like MGI’s stLFR [ 9 ], Element’s LoopSeq [ 10 ], Universal Sequencing Technology’s TELL-Seq [ 11 ], and 10× Genomics’s linked-reads technology [ 12 ]. Linked-read technologies enhance short-read NGS performance by linking all sequencing reads that come from the same long DNA fragment with unique barcode. This scaffolding information improves de novo assembly results of short NGS reads. Linked-read sequencing performs comparably or even better than long-read sequencing in bacteria, which have simpler genomes compared to eukaryotes [ 13 ]. Near-complete bacterial de novo genome assembly has been achieved using TELL-Seq in some bacteria species [ 11 , 13 - 14 ]. As infectious diseases caused by bacterial pathogens remain a significant public health threat, there are growing advocacy for using pathogen genomics to ensure consistent and timely monitoring and control. Timely and cost-effective near-complete bacterial genomes enhance study accuracy, and linked-read technology shows great potential for this purpose. However, systematic evaluations and insights into key factors influencing de novo assembly quality are lacking. To address this, we selected a diverse range of standard and clinical bacterial pathogens, varying in GC-content, genome size, genome complexity, and Gram type, to benchmark the performance of TELL-Seq. TELL-Seq effectively assembled near-complete genomes in most cases, but caution is advised for highly complex genomes. We also provide recommendations on suitable sequencing depths and highlight factors influencing assembly quality. Our dataset can support further algorithmic development of linked-reads assembly. Results Selection of Diverse Bacterial Pathogens for Systematic Benchmarking To systematically assess the performance of TELL-Seq in de novo assembly of bacterial pathogen genomes, we selected 12 clinically relevant bacterial species, ensuring a broad range of GC-content (32% - 68%), genome sizes (2.0 Mbp - 6.4 Mbp), genome complexity (mean mappability from 0.15 to 0.60), and Gram types (50% Gram-positive) (Tab S1). For a comprehensive evaluation applicable to both research and clinical settings, we included one standard ATCC isolate and one corresponding clinical isolate for each species. One clinical sample did not meet quality control standards and was excluded from further analyses. We evaluated the quality of TELL-Seq-based de novo assemblies by comparing them against reference genomes for the standard isolates and PacBio HiFi-based assemblies for the clinical isolates. TELL-Seq produces near-complete genome assemblies at 1/6 cost of PacBio To evaluate the quality and cost-effectiveness of TELL-Seq for studying bacterial pathogens, we compared de novo assemblies of clinical pathogens using Illumina, TELL-Seq, and PacBio technologies. Our analysis revealed that TELL-Seq produced near-complete assemblies comparable to those achieved by PacBio, with an average genome coverage of 97.4%. The average fragment length for TELL-Seq was 10,128 bp, closely approaching PacBio’s average read length of 13,601 bp, and the longest contig covered 95% of the reference genome. Notably, TELL-Seq achieved these results at only one-sixth of the cost ($107 vs. $682) and required just 1/15,000th of the DNA input ( Tab 1 ). While Illumina short-read sequencing was the most cost-effective at $62 per sample, its shorter read length led to more fragmented assemblies, compromising assembly contiguity ( Tab 1 ). View this table: View inline View popup Download powerpoint Table 1: Sequencing and assembly quality for bacterial pathogens using different technologies Genome complexity, genome size and sequencing depth are the key factors influencing TELL-Seq genome assembly quality We further went on to identify factors that are crucial for the quality of TELL-Seq de novo assemblies. To begin with, we accessed the impact of sequencing depth, one of the most fundamental and cost relevant factors, to assembly quality. Through a down-sampling experiment, we noticed that there was a clear positive correlation between sequencing depth and assembly quality ( Fig.1b ), indicating that sufficient depth of coverage is critical to de novo assembly. However, this trend was non-linear, with a clear plateau effect around 200×. At 250× depth, a slight decline in assembly quality was observed. This might be due to limited ability of the Tell-Link assembly algorithm in handling over-deeply sequenced reads. We also noticed that at same depth of coverage, some genomes, such as N. meningitidis , had inferior assembly results compared to other species ( Fig. 1a ). We hypothesized that other factors, such as genome complexity, genome size and GC-content, might also influence the genome assembly quality. To further look into this, we asked if there were significant correlation between genomic characteristics (i.e. mappability, GC-content and genome size) and assemble quality (contig number, genome fraction, and N50/genome length). Minimum mappability displayed a significantly high correlation with assemble quality at almost all depth, with absolute correlation coefficient >0.5 and p-value < 0.05. As the sequencing depth increased, the assembly quality became less dependent on the genome complexity, with an inflection point observed at 200× for both contig number and the N50/genome length ratio ( Fig. 1b , Fig. S3 , Tab S4). This finding suggests that while an increase in sequencing depth may generally enhance assembly quality, in most cases, a depth of 200× was sufficient to achieve optimal correlation with the assessed assembly metrics. Some extent of correlation was found between genome size and assembly quality (Fig.1b), ascertained with Gram-type ( Fig.S3 ). We did not observe any significant correlation between GC-content and assemble quality ( Fig. 1b , Fig. S2 , Tab S4). Download figure Open in new tab Fig 1: TELL-Seq assemble quality and correlation with GC-content, min mappability and genome size (a) Distribution of TELL-Seq assembly quality indicators under different sequencing depth for clinical and standard strains. (b) Correlation between assemble quality indicators and GC-content, min mappability and genome size. Discussion In our study, we for the first time showed that TELL-Seq produces near-complete genome assemblies across a wide range of bacterial pathogens, comparable to PacBio but with only 1/6 of cost and 1/15000 of DNA quantity. With the Tell-Link de novo assembly workflow, 200× depth of coverage guaranteed a robust assembly performance, regardless of genome complexity. For most bacterial genomes of moderate complexity, a depth of 100× can be sufficient if cost is a concern. However, the assembly quality started to decrease when coverage was beyond 200×, possibly due to the limited ability of the assembly algorithm in handling too large of a dataset. Genome complexity, indicated by mappability, is crucial for assembly quality, not GC-content. Our conclusions were limited as only the official Tell-Link de novo assembly workflow was used in the experiments. A more comprehensive benchmarking would be beneficial to access differences amongst assembly algorithms. Our dataset would help develop better algorithms across different bacterial species. In conclusion, TELL-Seq is a cost effective and efficient approach for bacteria genomics, opening up unprecedented possibilities for large-scale de novo assembly analysis. Methods Sampling, DNA Extraction and Mappability assessment 12 Standard bacterial species with differing GC-content were obtained from the American Type Culture Collection (ATCC), while 12 corresponding clinical strains were obtained from Center for the Preservation of Microbial and Viral Strains of Zhejiang Provincial Center for Disease Control and Prevention. Genomic DNA was extracted using a genomic DNA extraction kit (QIAmp DNA Kit, QIAGEN, Germany), according to the manufacturer’s instructions. Mappability, a measure of how unique or repetitive regions in the genome are[ 15 ], was used as a proxy for assessing genome complexity. It was calculated for each reference using GenMap (v1.3.0) [ 15 ] with option -E 2 -K 30. Min and mean mappability were calculated across the whole genome. TELL-Seq library preparation and sequencing TELL-seq libraries were constructed using a TELL-Seq Microbial Library Prep Kit (Universal Sequencing Technology). 0.1 ng of genomic DNA from each isolate were used for library construction. The TELL-seq libraries were quantified by Qubit dsDNA BR Assay kit (Thermo Fisher Scientific) and pooled for sequencing on Illumina NovaSeq 6000 with 2 × 150 paired-end reads, 18-cycle Index 1 reads and 8-cycle Index 2 reads based on the manufacturer’s protocols. TELL-Seq sequenced raw data were first processed by the Tell-Read v1.1.1u2 for barcode correction and filtering before proceeding to downstream analysis ( https://universalsequencing.com/en-cn/pages/tell-seq-software ). PacBio library preparation and HiFi sequencing High-quality DNA samples were selected with major band >30kb, and randomly fragmented into 15-18kb length using a Covaris ultrasonic disruptor. The large fragment DNA was enriched and purified using magnetic beads. Damage repair and end repair were performed on the fragmented DNA. Stem-loop sequencing adapters at both ends of the DNA fragments were connected, and the failed fragments were remove using exonuclease. The constructed library was sequenced using the PacBio Sequel II/PacBio Sequel IIe platform. PacBio original sequencing reads are dumbbell-shaped sequences with both ends attached to the adapter, known as Polymerase reads. The raw data is broken down from the adapter and filtered out to obtain subreads, which are filtered according to the standard Filtering subreads by minimum length = 50. Based on subreads, ccs software is used to generate high-precision HiFi reads. Here, we set the criteria as min-passes=3, min-rq=0.99, which means that all read quality values are above Q20. This part of data is an effective input for downstream analysis. De novo assembly and quality assessment Tell-Link v1.1.1u2 ( https://universalsequencing.com/en-cn/pages/tell-seq-software ) utilizes the linked-read data derived from the Tell-Read output to construct a barcode-aware assembly graph, assemble contigs, and perform scaffolding. When TELL-Seq reads were considered as short reads, adapters were trimmed and quality checked using fastp (v.0.23.4) [ 16 ] with default option. The clean reads were then assembled using the Unicycler (v0.5.0) [ 17 ] pipeline with default option. PacBio HiFi reads were first trimmed with Filtlong (v0.2.1) ( https://github.com/rrwick/Filtlong ) with options --min_length 1000 --keep_percent 95. And then assembled with Flye (v2.9.3-b1797) with option --meta --pacbio-hifi. To assess assembly quality, Illumina short read assembly and TELL-Seq assembly were benchmarked against PacBio assembly for clinical strains and against reference for standard strians. QUAST (v5.0.2) [ 18 ] was employed to calculate evaluation metrics, while MUMmer (v3.1) [ 19 ] was utilized for the analysis of collinearities plots. Downsampling analysis for TELL-Seq genome assembly Raw TELL-Seq reads were downsampled to 50×, 100×, 150×, 200×, and 250× depths with seqtk ( https://github.com/lh3/seqtk ). Same assessment strategies were undertaken as above for assembly quality. Statsmodels and scipy libraries (v1.13.1) in Python (v3.9) was used to calculate the Spearman correlation coefficients, the corresponding p-values and adjusted p-values between assembly quality statistics and quality control indicators or genomic characteristics (mappability, genome size or GC-content). Funding Information This work was funded by the Key R&D Program of Zhejiang Province (2024C03216) to ZJCDC and the Ministry of Science and Technology, China (2020YFC2008801) to AIIT, PKU. Contribution BW, JJ and XL conceived and designed the experiments; ZW, ZY did the experiment; TZ, LS, SL and XL performed the data analysis; TZ, SL, LS, YY, XC, BL, SZ and XL developed the data analysis workflows; ZW, TZ, SL, LS, BW, JJ and XL wrote the manuscript; XZ helped managing and coordinating the research project. All authors contributed to the revision and review of the manuscript. Conflict of Interests No conflict of interests. Supplemental Material Download figure Open in new tab Fig S1 Scatter Plot of Minimum Mappability vs . Assembly Quality Across Different Sequencing Depths Scatter plot showing the relationship between minimum mappability and assembly quality across different sequencing depths. Download figure Open in new tab Fig S2 Scatter Plot of GC-content vs . Assembly Quality Across Different Sequencing Depths Scatter plot showing the relationship between GC-content and assembly quality across different sequencing depths. Download figure Open in new tab Fig S3 Wilcoxon Sum Rank test of assembly quality statistics between Gram positive and negative across different sequencing depths Tab S1: Overview of sequenced bacteria genomes Tab S2: Summary of PacBio raw sequencing data quality of clinical samples Tab S3: Summary of PacBio assembly quality of clinical samples Tab S4: Summary of TELL-Seq data quality Tab S5: BioSample Objects Acknowledgement We would like to thank Mr. Longfei Xie for his assistance with coordinating and conducting this study. References 1. ↵ Sereika M , Kirkegaard RH , Karst SM , Michaelsen TY , Sørensen EA , Wollenberg RD , et al. Oxford Nanopore R10.4 long-read sequencing enables the generation of near-finished bacterial genomes from pure cultures and metagenomes without short-read or reference polishing . Nat Methods . 2022 Jul ; 19 ( 7 ): 823 – 6 . OpenUrl CrossRef 2. ↵ O’Donnell S , Yue J-X , Saada OA , Agier N , Caradec C , Cokelaer T , et al. Telomere-to-telomere assemblies of 142 strains characterize the genome structural landscape in Saccharomyces cerevisiae . Nat Genet . 2023 Aug ; 55 ( 8 ): 1390 – 9 . OpenUrl CrossRef 3. Mao Y , Zhang G. A complete, telomere-to-telomere human genome sequence presents new opportunities for evolutionary genomics . Nat Methods . 2022 Jun ; 19 ( 6 ): 635 – 8 . OpenUrl 4. Nurk S , Koren S , Rhie A , Rautiainen M , Bzikadze AV , Mikheenko A , et al. The complete sequence of a human genome . Science . 2022 Apr ; 376 ( 6588 ): 44 – 53 . OpenUrl CrossRef PubMed 5. ↵ Bi G , Zhao S , Yao J , Wang H , Zhao M , Sun Y , et al. Near telomere-to-telomere genome of the model plant Physcomitrium patens . Nat Plants . 2024 Feb ; 10 ( 2 ): 327 – 43 . OpenUrl 6. ↵ Paten B , Novak AM , Eizenga JM , Garrison E. Genome graphs and the evolution of genome inference . Genome Res . 2017 ay 1; 27 ( 5 ): 665 – 76 . OpenUrl Abstract / FREE Full Text 7. ↵ Colquhoun RM , Hall MB , Lima L , Roberts LW , Malone KM , Hunt M , et al. Pandora: nucleotide-resolution bacterial pan-genomics with reference graphs . Genome Biol . 2021 Sep 14; 22 ( 1 ): 267 . OpenUrl CrossRef 8. ↵ Bankevich A , Pevzner PA . TruSPAdes: barcode assembly of TruSeq synthetic long reads . Nat Methods . 2016 Mar ; 13 ( 3 ): 248 – 50 . PMID: 26828418 OpenUrl CrossRef PubMed 9. ↵ Wang O , Chin R , Cheng X , Wu MKY , Mao Q , Tang J , et al. Efficient and unique cobarcoding of second-generation sequencing reads from long DNA molecules enabling cost-effective and accurate sequencing, haplotyping, and de novo assembly . Genome Res . 2019 May ; 29 ( 5 ): 798 – 808 . PMID: 30940689 OpenUrl Abstract / FREE Full Text 10. ↵ Jeong J , Yun K , Mun S , Chung W-H , Choi S-Y , Nam Y , et al. The effect of taxonomic classification by full-length 16S rRNA sequencing with a synthetic long-read technology . Sci Rep . 2021 Jan 18; 11 ( 1 ): 1727 . OpenUrl 11. ↵ Chen Z , Pham L , Wu T-C , Mo G , Xia Y , Chang PL , et al. Ultralow-input single-tube linked-read library method enables short-read second-generation sequencing systems to routinely generate highly accurate and economical long-range sequencing information . Genome Res . 2020 Jun 1; 30 ( 6 ): 898 – 909 . OpenUrl Abstract / FREE Full Text 12. ↵ Zheng GXY , Lau BT , Schnall-Levin M , Jarosz M , Bell JM , Hindson CM , et al. Haplotyping germline and cancer genomes using high-throughput linked-read sequencing . Nat Biotechnol . 2016 Mar ; 34 ( 3 ): 303 – 11 . PMID: 26829319 OpenUrl CrossRef PubMed 13. ↵ Anderson FM , Visser ND , Amses KR , Hodgins-Davis A , Weber AM , Metzner KM , et al. Candida albicans selection for human commensalism results in substantial within-host diversity without decreasing fitness for invasive disease . PLOS Biol . 2023 May 19; 21 ( 5 ): e3001822 . OpenUrl CrossRef 14. ↵ Ultra-long range phasing with linked-read sequencing technology [Internet] . [cited 2024 Jul 23 ]. Available from: https://www.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/ilmn-ust-phasing-app-note-m-gl-00581/ilmn-ust-phasing-app-note--gl-00581.pdf 15. ↵ Pockrandt C , Alzamel M , Iliopoulos CS , Reinert K. GenMap: ultra-fast computation of genome mappability . Bioinforma Oxf Engl . 2020 Jun 1; 36 ( 12 ): 3687 – 92 . PMID: 32246826 OpenUrl PubMed 16. ↵ Chen S , Zhou Y , Chen Y , Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor . Bioinforma Oxf Engl . 2018 Sep 1; 34 ( 17 ): i884 – 90 . PMID: 30423086 OpenUrl PubMed 17. ↵ Wick RR , Judd LM , Gorrie CL , Holt KE . Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads . PLoS Comput Biol . 2017 Jun 8; 13 ( 6 ): e1005595 . PMID: 28594827 OpenUrl CrossRef PubMed 18. ↵ Gurevich A , Saveliev V , Vyahhi N , Tesler G. QUAST: quality assessment tool for genome assemblies . Bioinforma Oxf Engl . 2013 Apr 15; 29 ( 8 ): 1072 – 5 . PMID: 23422339 OpenUrl CrossRef PubMed Web of Science 19. ↵ Kurtz S , Phillippy A , Delcher AL , Smoot M , Shumway M , Antonescu C , et al. Versatile and open software for comparing large genomes . Genome Biol . 2004 ; 5 ( 2 ): R12 . PMID: 14759262 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted August 19, 2024. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Low-cost and highly efficient generation of near-complete bacterial pathogen genomes by TELL-Seq Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. 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Share Low-cost and highly efficient generation of near-complete bacterial pathogen genomes by TELL-Seq Zhuoying Wu , Ting Zhang , Sheng Li , Lingzhu Shen , Zhangnv Yang , Yunfeng Yang , Xuanheng Chen , Bolin Li , Shicong Zhou , Xintian Zhou , Beibei Wu , Jianmin Jiang , Xiao Li bioRxiv 2024.08.17.608388; doi: https://doi.org/10.1101/2024.08.17.608388 Share This Article: Copy Citation Tools Low-cost and highly efficient generation of near-complete bacterial pathogen genomes by TELL-Seq Zhuoying Wu , Ting Zhang , Sheng Li , Lingzhu Shen , Zhangnv Yang , Yunfeng Yang , Xuanheng Chen , Bolin Li , Shicong Zhou , Xintian Zhou , Beibei Wu , Jianmin Jiang , Xiao Li bioRxiv 2024.08.17.608388; doi: https://doi.org/10.1101/2024.08.17.608388 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Bioinformatics Subject Areas All Articles Animal Behavior and Cognition (7644) Biochemistry (17728) Bioengineering (13917) Bioinformatics (42038) Biophysics (21489) Cancer Biology (18637) Cell Biology (25553) Clinical Trials (138) Developmental Biology (13401) Ecology (19941) Epidemiology (2067) Evolutionary Biology (24367) Genetics (15622) Genomics (22547) Immunology (17764) Microbiology (40475) Molecular Biology (17208) Neuroscience (88749) Paleontology (667) Pathology (2842) Pharmacology and Toxicology (4834) Physiology (7659) Plant Biology (15175) Scientific Communication and Education (2047) Synthetic Biology (4304) Systems Biology (9835) Zoology (2272)