centriflaken: an automated data analysis pipeline for assembly and in silico analyses of foodborne pathogens from metagenomic samples

centriflaken: an automated data analysis pipeline for assembly and in silico analyses of foodborne pathogens from metagenomic samples | 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 centriflaken: an automated data analysis pipeline for assembly and in silico analyses of foodborne pathogens from metagenomic samples Kranti Konganti , Julie Kase , View ORCID Profile Narjol Gonzalez-Escalona doi: https://doi.org/10.1101/2025.07.18.665485 Kranti Konganti 1 Scientific Computing Support Find this author on Google Scholar Find this author on PubMed Search for this author on this site Julie Kase 2 Microbial Methods Development Branch, Division of Food and Environmental Safety Find this author on Google Scholar Find this author on PubMed Search for this author on this site Narjol Gonzalez-Escalona 3 Genomics Development and Applications Branch, Division of Food Safety Genomics , Office of Applied Microbiology and Technology (OAMT), Office of Laboratory Operations and Applied Science (OLOAS) , Human Foods Program, Food & Drug Administration Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Narjol Gonzalez-Escalona For correspondence: narjol.gonzalez-escalona{at}fda.hhs.gov Abstract Full Text Info/History Metrics Preview PDF Abstract Rapid and comprehensive analysis of metagenomic data from samples associated with foodborne outbreaks is of critical importance in food safety. Equally important is the need for automated analysis pipelines that allow the rapid and effective construction of metagenomic assembled genomes (MAGs) to enable bacterial source-tracking from metagenomic data. Here, we present centriflaken, an automated precision metagenomics pipeline for detecting and characterizing Shiga toxin-producing Escherichia coli (STEC) from metagenomic data. centriflaken streamlines the process of generating metagenome-assembled genomes (MAGs) and conducting in silico analyses of STECs, significantly reducing the time and manual effort required for comprehensive pathogen profiling. centriflaken was validated using Oxford Nanopore long-read sequencing data from agricultural water enrichments, successfully reproducing results from our previous study that involved multiple manual bioinformatics steps (Maguire et al., 2021). The tool’s efficacy was further demonstrated through its application to ZymoBIOMICS microbial community standards and 21 additional irrigation water samples, completing STEC precision metagenomics analyses in less than 7 hours per sample. centriflaken’s versatility allows for the analysis of user-defined taxa beyond STEC, including other foodborne pathogens like Listeria monocytogenes or Salmonella . The pipeline generates comprehensive summary plots and tables, accessible through a MultiQC HTML report. Designed for portability, centriflaken packages all software dependencies within containers and virtual environments. This open-source tool is available on GitHub under the MIT license ( https://github.com/CFSAN-Biostatistics/centriflaken ), offering a powerful resource for rapid, automated pathogen detection and characterization in food safety applications. Author summary Metagenomic sequencing, particularly using nanopore technology, generates vast amounts of data that are challenging to analyze, especially when searching for specific pathogens like Shiga toxin-producing Escherichia coli (STEC). This challenge is compounded when processing multiple samples simultaneously. The pipeline “centriflaken” was developed to streamline this complex process, automating the extraction of E. coli reads from metagenomic data and performing in-silico characterization of potential STEC present in the samples. centriflaken builds on the concept of precision metagenomics, employing a suite of automated data analysis workflows powered by Nextflow. The pipeline processes metagenomic data, generates metagenome-assembled genomes (MAGs), and conducts in-silico analyses as described in Maguire et al. (2021). By running steps in parallel, centriflaken enables users without extensive bioinformatics skills or STEC genomic knowledge to efficiently analyze complex metagenomic data. Key features of centriflaken include: 1) Automated workflow for STEC detection and characterization, 2) Parallel processing for improved efficiency, 3) User-friendly interface for non-specialists, 4) Scalability for analyzing multiple samples simultaneously, and 5) Compatibility with high-performance computing (HPC) clusters and cloud environments. This freely available software democratizes complex metagenomic analysis, making it accessible to a broader range of researchers and food safety professionals. INTRODUCTION As farm-to-table food trends increase in popularity, we have seen an increase in produce related outbreaks of foodborne illness. Shiga toxin-producing E. coli (STEC) are a major source of foodborne illness [ 1 – 4 ]. In fact, O157:H7 accounts for 70% of E. coli related foodborne outbreaks [ 5 – 10 ]. Recent outbreaks underscore the ongoing threat of STEC contamination. For instance, in October 2024, a multi-state outbreak caused by a O121:H19 STEC strain linked to organic carrots resulted in 48 illnesses, 20 hospitalizations, and 1 death, highlighting the critical need for rapid and accurate STEC detection and characterization methods ( https://www.cdc.gov/ecoli/outbreaks/e-coli-o121.html ). Due to the ubiquitous nature and the varying degrees of pathogenicity of E. coli , its mere presence does not equate to concerns to public health, unlike with bacteria such as Salmonella or Listeria monocytogenes . Therefore, the detection of E. coli presence alone is insufficient to assess health risk. Moreover, there are over 400 described STEC serotypes that express one or more of stx 1 genes and stx 2 genes [ 11 , 12 ] and among those, around 100 serotypes have been described as causing illness in humans, with hemolytic uremic syndrome (HUS) being deadly [ 13 – 18 ]. However, serotype and stx gene presence and variant type are not enough to predict STEC risk for humans. STEC must also have other virulence genes linked to attachment and colonization of the intestine such as intimin ( eae gene), AggR ( aggR gene), auto-agglutinating adhesin Saa ( saa gene), chromosome located T3SS effectors encoded by genes such as tir, espA, espB, espK, among others, and other virulence genes located in plasmids ( toxB, hexA, etpD, katP ) [ 19 – 23 ]. In our previous study, we established the use of nanopore sequencing to determine the presence and full characterization of STECs in enrichments of agricultural water [ 24 ]. We determined that the limit for obtaining a complete, fragmented assembly for STECs by nanopore sequencing of DNA extracted from enriched agricultural water was 10 5 CFU/ml [ 24 ]. There are many advantages of using nanopore sequencing for STEC identification and characterization: affordability, potential to multiplex, portability, less manual labor required from personnel, use of low DNA concentration, longer reads than other available technologies (>100 kb) and the analysis could also be done in real-time (using their metagenomics workflow available at EPI2ME labs). A previous pilot study produced preliminary evidence that MinION sequencing of agricultural water using the ligation kit has the potential to be used for rapid microbiome determination in the field with optimal results for water quality surveillance [ 25 ]. Agricultural water has been implicated as a source of contamination for produce resulting in foodborne illness and outbreaks [ 26 – 31 ]. Current FDA protocols for the detection and isolation of STECs require multiple rounds of selective plating and whole genome sequencing (WGS) of a single isolate. On-site field testing is increasingly becoming a priority to decrease the time for detection of pathogenic microbes and for the fast implementation of corrective measures. While the most abundant species in the microbiome is likely to fluctuate seasonally, they may be an indicator to changing populations and importantly may serve to monitor deviations in microbial water quality. However, any of these studies or experiments will generate a large amount of data. Analyzing nanopore data or any other high-throughput data is a very complex procedure and usually requires many steps to generate the final reportable results creating a bottleneck. Metagenomic workflows are usually compute intense and need to be analyzed on High-Performance Compute Clusters (HPCs). In the case of data generated from metagenomic samples, tools are needed to detect the presence of STEC and comprehensively assess their virulence potential bysubtyping the stx genes, determining serotype, identifying attachment genes such as eae and saa and detecting other virulence-related genes. A precision metagenomics approach is not meant to replace the cultural method [ 32 ], but it is a way to detect and identify a STEC strain earlier and evaluate its potential risk to the public. Improving STEC genome characterization methods has significant implications for public health and food safety. Rapid, accurate identification and genome characterization of STECs can lead to faster response times in outbreak situations, more targeted recalls, and ultimately, fewer cases of foodborne illness. Moreover, enhanced detection capabilities could inform more precise and effective preventive controls in food production and processing, aligning with FDA’s risk-based approach to food safety. The existing protocol or pipeline in our FDA laboratory uses many sequential manual steps, described in Maguire et al. (2021) [ 24 ]. Briefly, the steps are divided into: 1) classify reads using WIMP workflow available at EPI2me, 2) a python script to extract or bin reads that matched the requested taxa (i.e. E. coli ), 3) Assemble the extracted reads using flye assembler [ 33 ], 4) Classify the contigs with Kraken 2 [ 34 ] and extract contigs matching requested taxa, 5) perform all in silico analysis on those contigs (MLST, AMR, serotyping, and virulotyping), and 6) aggregate all data into a final report and conduct a risk assessment for the sample. Several other workflows for metagenomic analyzes have been published and reviewed by Van Damme et al. 2021 [ 35 ]. These authors described the creation of a very useful metagenomic tool that allows for the analyses of metagenomic data using both short and long reads in a hybrid approach which allows for the reconstruction of more complete and more accurate metagenome-assembled genomes (MAGs) [ 35 ]. While these approaches have strengths, none of them allows for a precision metagenomic analysis (taxa specific) and in silico virulotyping, serotype, and AMR prediction for STECs in a single workflow. Here we present centriflaken to address this gap. centriflaken is an integrated suite of automated data analysis workflows enabled by Nextflow [ 36 ]. This pipeline processes metagenomic data to generate metagenome-assembled genomes (MAGs) and performs comprehensive in silico analysis for Shiga toxin-producing Escherichia coli (STEC), following the approach described by Maguire et al. (2021). centriflaken streamlines the entire process, from raw sequencing data to final results, and provides user-friendly output in the form of summary plots and tables, accessible through a MultiQC HTML report generated automatically by the pipeline. The development of centriflaken aligns closely with FDA’s New Era of Smarter Food Safety initiative, which emphasizes the use of new technologies and approaches to create a safer, more digital, and traceable food system. By enabling faster, more comprehensive analysis of metagenomic data, centriflaken supports FDA’s goals of strengthening root cause analyses, improving predictive analytics, and responding more rapidly to outbreaks. MATERIALS AND METHODS Centriflaken pipeline A high-level overview of centriflaken workflow is presented in Figure 1 . The pipeline can be run on any UNIX based machine. The main analysis starts by taking UNIX input paths to the fastq_pass reads folder generated by Oxford Nanopore. It then filters out any reads less than 4000 bp and then performs taxonomic classification and extracts all E. coli only reads using centrifuge [ 37 ] followed by taxon specific assembly using flye assembler v2.9 [ 33 ]. The assembled contigs are reclassified to identify the E. coli taxon using kraken2 [ 34 ] and extract the E. coli contigs to produce a final Metagenomically assembled Genome (MAG). The final MAG is interrogated (in the case of STEC) against several specific databases. Download figure Open in new tab Figure 1. A brief overview of the centriflaken pipeline. The pipeline starts by taking as input a UNIX path to FASTQ files after which multiple read files belonging to the same sample are merged followed by excluding reads whose length is less than 4000 bp. Then, reads are extracted belonging to a taxon of interest using Centrifuge following which the target assembly is performed using flye. Kraken2 is now used to extract contigs binned as the taxa of interest and these contigs are used for further downstream analysis such as subtyping, virulotyping, AMR gene finding, etc. The outputs from all these downstream processes are used to generate a summary report using MultiQC. Serotypefinder [ 14 ] is used to serotype the MAGs, ABRicate ( https://github.com/tseemann/abricate ) is run to identify known AMR genes using various databases (AMRfinder plus [ 38 ], MEGARes 2.0 [ 39 ], RESFINDER [ 40 ], ARG-ANNOT [ 41 ]) and their results are shown in a final MultiQC report. All the analysis steps are automated using Nextflow and the pipeline runs in parallel for all samples. The pipeline took approximately 6 hours to finish processing the data produced in Maguire et al. (2021) [ 24 ], which previously took many days to run all steps for all samples manually or through automated bash scripts. The Centriflaken GitHub repository provides guidance on acquiring the necessary Centrifuge and Kraken databases. For Centrifuge, links to pre-built indices are available, while for Kraken, step-by-step instructions for database construction are provided. Detailed information can be found in the repository’s README file at: https://github.com/CFSAN-Biostatistics/centriflaken/blob/main/README.md#databases . It is recommended to run centriflaken using conda software virtual environments for users who do not have access to HPC cluster with singularity. The pipeline can also be run using docker or singularity containers by switch the -profile flag when invoking the pipeline. Detailed instructions on setup and running the pipeline on data from Maguire et al. 2021 [ 24 ] can be found at https://github.com/CFSAN-Biostatistics/centriflaken/blob/main/readme/centriflaken.md . centriflaken is also made available via Galaxy user interface via the FDA HFP GalaxyTrakr instance ( https://galaxytrakr.org ). The previously published protocol ( https://www.protocols.io/view/centriflaken-an-automated-data-analysis-pipeline-f-kxygxzdbwv8j/v5 ) can be used to run analysis on GalaxyTrakr. While Centriflaken is designed to work with both Illumina short reads and Oxford Nanopore long reads, this publication specifically focuses on its application to long-read sequencing data generated by Oxford Nanopore technologies. Validation of centriflaken and accuracy assessment Centriflaken was initially tested and validated using data from Maguire et. al. 2021 [ 24 ]. The presence of the complete genome and synteny of the completely closed genomes on the final assemblies was checked using the whole genome alignment tool available in the CLC Genomics workbench software v24.0 (QIAGEN, Redwood City, USA). Further testing was conducted with data generated from two ZymoBIOMICS Microbial Community Standards (ZymoBIOMICS Microbial Community Standard II - log distribution - catalog number D6311, and ZymoBIOMICS Microbial Community DNA Standard - catalog number D6306) (ZymoBIOMICS, Irvine, CA), and can be found on GenBank using the accession numbers in supplementary Table 1. The centriflaken final testing was done using 21 overnight enriched samples for STEC collected in two different seasons during 2020 and 2021. Overnight enrichments of agricultural waters Water samples were collected and prepared per this protocol: dx.doi.org/10.17504/protocols.io.j8nlk8nydl5r/v1 . Briefly, water samples (225 ml) from the Southwestern US were enriched by adding an equal volume of 2× modified Buffered Peptone water with pyruvate (mBPWp; NEOGEN, Lansing, MI, USA) as described in USFDA Bacteriological Analytical Manual (BAM) Chapter 4A [ 32 ], with 6.0 X mL of acriflavin-cefsulodin-vancomycin (ACV) supplement added after five hours of static incubation at 37°C ± 1°C. After the addition of the ACV supplement, enrichments were incubated at 42°C ± 1°C for an additional 16 ±3 hours. DNA extraction Genomic DNA from 1ml of each of the 21 enriched agricultural water sample was extracted using the Maxwell RSC Cultured Cell DNA Kit with a Maxwell RSC Instrument (Promega Corporation, Madison, WI, USA) according to the manufacturer’s instructions DNA concentration was determined by a Qubit 4 Fluorometer (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. STEC qPCR detection The presence of STEC was determined by qPCR as described in Chapter 4A of the FDA BAM detecting stx1 , stx2 , and wzy [ 32 ]. Briefly, the DNA retrieved from the 21 samples were diluted 1:10 in nuclease-free water and 2µl was added to 28µl master mix containing 0.25µM stx1 and stx2 primers, 0.3µM wzy primers, 0.2µM stx1 probe, 0.15µM stx2 and wzy probes, 1X Internal Positive Control Mix (Cat: 4308323, Applied Biosystems), 1X Express qPCR Supermix Universal Taq (Cat: 11785200, Invitrogen), and ROX passive dye. All primers and probes (Supplementary Table 2) employed in this study were purchased from IDT (Coralville, IA, USA). Nanopore sequencing DNA recovered from the 21 enriched AW samples was sequenced using a GridION nanopore sequencer (Oxford Nanopore Technologies, Oxford, UK). The sequencing libraries were prepared using the Genomic DNA by Ligation kit (SQK-LSK109) and run in FLO-MIN106 (R9.4.1) flow cells, according to the manufacturer’s instructions for 72 hours (Oxford Nanopore Technologies). The runs were live base called using either Guppy v3.2.10 or 4.2.3 included in the MinKNOW v19.12.6 and v20.10.6 software (Oxford Nanopore Technologies, Oxford, UK), respectively, with the fast-calling model. The two ZymoBIOMICS Microbial Community Standards (ZymoBIOMICS Microbial Community Standard II - log distribution - catalog number D6311, and ZymoBIOMICS Microbial Community DNA Standard - catalog number D6306) (ZymoBIOMICS, Irvine, CA), were also sequenced as described above using 1 ug of DNA as the initial amount for preparing the library. Data availability The raw ONT data for the 7 samples used for the initial validation can be found under Bioproject PRJNA639799. The raw ONT data generated for each individual sample in this study (ZymoBIOMICS samples and the 21 AW) were deposited in GenBank under BioProject accession number PRJNA1222809. Each sample SRA number matching its corresponding sample can be found in Supplementary Table 1. Results Software Pipeline Overview The data analysis steps in centriflaken are automated using the Nextflow workflow manager [ 36 ], which provides advantages such as process parallelization, sample provenance, retry on failure, tool dependency solutions using containers and conda software virtual environments among many others. Successful execution of the workflow produces output for each process in its own output folder named after the process name. The workflow design also ensures reproducibility of results, and it is easy to share HTML brief reports for further assessment. Due to the inherent advantages of using Nextflow, centriflaken can be run on-premises HPC clusters or in the cloud. Initial Validation with Known samples The entire centriflaken workflow was initially executed and validated on 7 samples from the Bioproject PRJNA639799 (available at NCBI) using conda software virtual environments. This metagenomic study provides nanopore-based data for each water enriched sample spiked with different STEC levels to determine detection and classification of STECs using nanopore sequencing [ 24 ]. These nanopore read dataset provided differential coverage for the spiked STEC strain, and the workflow runtime for all seven samples was around 20 hours. The entire process when performed manually took almost 24 hours for a single sample [ 24 ]. centriflaken reduced the analysis time from 24 hours per sample to approximately 3 hours per sample, representing an 87.5% reduction in processing time. centriflaken recovered STEC genomes with genome completeness of at least 85% when the concentration of spiked STEC was above 10 6 CFU/ml ( Table 1 and Figure 2 ). For comparison, the number of total contigs per STEC spiked water sample when performing the step by step analysis nanopore data workflow [ 24 ] was a little bit higher than when using centriflaken, but the same number of contigs containing the STEC spiked genome was recovered, confirming the equivalency of both analysis workflows ( Table 1 ). However, centriflaken has a step that extracts the contigs matching the requested taxa and therefore the analysis is only performed in those contigs, accelerating and making the analysis more precise (see workflow on Figure 1 ). The pipeline analysis also showed the same virulence profile for each sample to what was reported previously in Maguire et al 2021 ( Table 2 , supplementary MultiQC HTML report 1), demonstrating the accuracy and reproducibility of the centriflaken workflow. This high level of genome completeness at concentrations above 10 6 CFU/ml demonstrates centriflaken’s sensitivity and accuracy in detecting and characterizing STEC in complex metagenomic samples, a crucial capability for food safety applications Download figure Open in new tab Figure 2. Comparative analysis of EDL933 genome assemblies obtained by centriflaken during the initial validation with known samples. This figure compares the EDL933 genome of the strain used in Maguire et al. (2021) with assemblies obtained using centriflaken for the same samples at various EDL933 enrichment spiking levels. The analysis demonstrates the recovery of the E. coli O157:H7 MAG (Metagenome-Assembled Genome) in either completely closed or fragmented forms (see Table 1 ). Each horizontal track represents EDL933-matching contigs extracted from a sample. Homologous segments across genomes are indicated by the same color and connected. Sequence coordinates in base pairs are shown on respective scales. The reference genome, labeled as EDL933_2 and indicated with a star (*), serves as the basis for comparison. View this table: View inline View popup Download powerpoint Table 1. In silico virulence factor analysis of metagenomic assembled genomes (MAGs) obtained using centriflaken for the Maguire et al. (2021) dataset. Showing the agreement of the results obtained by both workflows (manually vs. centriflaken) View this table: View inline View popup Download powerpoint Table 2. Comparative results of the virulotyping of the same ONT data generated (Maguire et al 2021) [ 24 ] by both workflows (manually vs. centriflaken) Performance with ZymoBIOMICS Microbial Communities The centriflaken workflow was further tested on samples from a previous study using the ZymoBIOMICS microbial community DNA Bioproject PRJNA751542, Biosample SAMN20520875 [ 25 ] and two additional samples (ZymoBIOMICS Microbial Community DNA Standard and ZymoBIOMICS Microbial Community Standard II - log distribution) (NCBI accession provided here). Both communities consist of a mixture of 10 microbes belonging to different genera and species but at different proportions. ZymoBIOMICS microbial community composition is at a 12% concentration each bacterium and 2% for the two yeasts, while ZymoBIOMICS Microbial Community Standard II composition is quite different. ZymoBIOMICS Microbial Community Standard II composition is as follows: of Listeria monocytogenes - 89.1%, Pseudomonas aeruginosa - 8.9%, Bacillus subtilis - 0.89%, Saccharomyces cerevisiae - 0.89%, Escherichia coli - 0.089%, Salmonella enterica - 0.089%, Lactobacillus fermentum - 0.0089%, Enterococcus faecalis - 0.00089%, Cryptococcus neoformans - 0.00089%, and Staphylococcus aureus - 0.000089%. Only the ZymoBIOMICS microbial community DNA samples contained enough E. coli (10 8 CFU per 1 ug) to be within the limits of genome assembly (>10 6 CFU/ 1 ug) as establish previously for ONT metagenomic samples using the LSK-109 library preparation kit [ 24 ] (supplementary MultiQC HTML report 2). centriflaken identified the E. coli presence in the sample belonging to serotype O33:H32 and carried 4 known AMR genes using the NCBI AMRfinder plus tool ( blaEC, mdtM, emrD, and acrF ) [ 38 ]. The ZymoBIOMICS Microbial Community Standard II - log distribution contained ∼ 10 5 E. coli CFU per 1 ug, well below the assembly limit for the technique. That sample still returned a signal for E. coli (159 unique taxa matching reads) but no E. coli MAGs were recovered and therefore the correct identity as well as the virulence or serotype characteristics of the E. coli strain could not be determined (supplementary MultiQC HTML report 2). These results highlight centriflaken’s ability to accurately identify and characterize E. coli strains even in diverse microbial communities, while also demonstrating its limitations at lower bacterial concentrations. This information is vital for understanding the tool’s applicability in various real-world scenarios. Analysis of Unknown Agricultural Water After validating the centriflaken workflow with known samples, the next step was to assess its performance with ONT data obtained after sequencing unknown overnight enriched agricultural water samples suspected of containing STECs. For that purpose, we tested the performance on 21 samples obtained after overnight enrichment of agricultural water. The entire precision metagenomics STEC analysis of the 21 samples was completed in less than 7 hours using centriflaken (supplementary MultiQC HTML report 3). The total reads per experiment ranged from 1.3 to 2.8 million reads, of which 11 to 40% matched E. coli , with median read sizes between 4,252 to 6,177 bp ( Table 3 ). The number of total E. coli contigs per sample ranged from 178 to 1,069. View this table: View inline View popup Table 3. Sequencing statistics, serotypes, stx genes and eae genes, identified per sample Every sample contained more than one different E. coli strain, with varying degrees of diversity. If the predicted serotypes identified among the many MAGs obtained per sample is used as a measure of E. coli diversity, the number of E. coli strains per sample ranged from eight to forty-eight ( Table 3 ). The identification of multiple E. coli strains in each sample showcases centriflaken’s capacity to detect and differentiate various strains in complex environmental samples, a key advantage for comprehensive food safety assessments. There were only 3 samples that were identified as positive for the stx genes but one of them (FAL11384) lacked any adhesion genes used in our search ( Table 3 and 4 ). Sample FAL01198 was positive for subA and FAQ33923 detected the eae gene allele epsilon-7. On the other hand, 8 samples returned a signal for the eae gene, and one sample contained saa . The virulotyping showed the presence of several relevant virulence genes such as T3SS ( eae , espA, espB, espD, espF, espI, espJ, and tir ) and other plasmid borne virulence relevant genes ( espP and exhA ) ( Table 4 ), however a fully closed STEC genome was not recovered from any sample. While a fully closed STEC genome was not recovered, the detection of numerous virulence genes demonstrates centriflaken’s potential for comprehensive pathogen profiling, providing valuable insights for risk assessment in food safety contexts. AMR profiling showed the presence of a very diverse set of antimicrobial resistance genes per sample, from 2 to up to 20 genes, representing resistance to 10 antibiotic classes and multidrug efflux genes, underscores the potential of centriflaken for comprehensive pathogen characterization beyond just virulence factors (Supplementary Table 3). View this table: View inline View popup Download powerpoint Table 4. SSTEC virulotyping for 21 enriched agricultural waters by centriflaken. Discussion Importance of rapid and automated metagenomic analysis for food safety Given the critical role of irrigation water in food production, rapid and accurate detection of STECs in these waters is essential but especially during outbreak investigations. Traditional detection methods, such as qPCR followed by extensive selective plating and whole genome sequencing (WGS), are labor-intensive and may take up to two weeks to confirm the correct identification of an isolate. Fast metagenomic analysis is crucial for accelerating this timeline. By integrating qPCR with long-read metagenomic sequencing of the pre-enrichment step, definitive detection and virulence characterization of STECs can be achieved within 3 to 4 days. While microbiological confirmation is still required, this approach potentially enables faster implementation of corrective actions since overall turnaround time is reduced by almost one week. centriflaken: addressing bioinformatic bottlenecks However, the bioinformatic analysis of sequencing data [ 24 ] remains complex, involving many tools and steps to generate a complete STEC analysis. This process often requires up to 24 hours per sample -even when those samples analyses are run in parallel. To address this bottleneck, a workflow named centriflaken was developed to automate the entire process and to reduce the analysis time. centriflaken enables taxa-specific precision metagenomic analysis, in silico virulotyping, serotyping, and AMR prediction for STECs in a single, reproducible workflow. centriflaken consolidates the different steps of a precision metagenomic approach into a scalable, highly reproducible, and easy-to-install workflow using Nextflow [ 36 ]. It outputs final reports, summary plots and tables through a MultiQC HTML report for user friendly review as part of the pipeline. By automating and streamlining the analysis process, centriflaken not only saves time but also reduces the potential for human error, enhancing the reliability and reproducibility of STEC detection in complex environmental samples. centriflaken uses many tools to provide a comprehensive analysis of ONT metagenomics samples. The associated tools were chosen based on the steps used for the analysis of ONT samples previously published [ 24 ] and by benchmark performance, e.g. , taxonomic classification and binning [ 37 ], E. coli reads extraction [ 24 ], assembly [ 33 ], taxonomic classification [ 34 ], and final in silico profiling [ 14 , 38 ]. Built with a modular Nextflow DSL2 design, centriflaken is easily updated to include improved tools in future revisions or new ONT chemistries,such as the recent shift from R9.4.1 to R10.4.1. Performance benchmarking centriflaken was first evaluated using previous analyzed data under Bioproject PRJNA639799 [ 24 ], and two ZymoBIOMICS microbial community datasets. For STEC-spiked samples, centriflaken achieved 100% genome completeness at concentrations >10⁷ CFU/ml and 85% at 10⁶ CFU/ml—mirroring the results from manual, stepwise analyses [ 24 ]. Serotyping, virulence gene detection, and AMR profiling were also consistent with previously reported outputs. In the ZymoBIOMICS microbial community dataset, centriflaken accurately identified the E. coli strain present in the community as serotype O33:H32 and detected four AMR-related genes, including blaEC , mdtM , emrD , and acrF [ 42 ]. In contrast, the E. coli genome was not recovered from the ZymoBIOMICS Microbial Community Standard II (Log Distribution), as its concentration was below the ∼10⁶ CFU threshold necessary for effective assembly [ 24 ]. In spiked agricultural water samples, centriflaken recovered STEC genomes with ≥85% completeness at ≥10⁶ CFU/ml ( Table 1 and Figure 2 ). Although manual analysis produced slightly more contigs overall [ 24 ], the number of contigs corresponding to the STEC genome was equivalent, confirming the equivalency of both analysis workflows ( Table 1 ). centriflaken also includes a step that extracts only the contigs belonging to the target taxa, streamlining the process and improving accuracy ( Figure 1 ). The virulence profiles identified were consistent with prior results from Maguire et al. (2021) [ 24 ], confirming the accuracy and reproducibility of the workflow. ( Table 2 , Supplementary MultiQC Report 1), Real-world application and limitations The performance of the centriflaken workflow was further evaluated using 21 real-life enriched agricultural water samples. The results showed that each sample contained multiple E. coli strains—ranging from 2 to 13—based on predicted serotype profiles. The presence of so many strains per sample, combined with their high concentrations as measured by qPCR (∼10⁶ CFU/ml), made it difficult to generate fully closed MAGs. This finding highlights a key limitation of the methodology: recovering and characterizing STEC genomes in silico is challenging when samples are highly complex and contain multiple E. coli strains, especially when these strains outcompete the STEC target. Despite this challenge, three samples were identified as containing stx genes and adhesion genes such as saa or eae . An additional eight samples produced E. coli MAGs that harbored eae genes. The eae subtypes identified included: gamma-2 (in two samples), epsilon-7/Xi (in four samples), and beta-1 (in one sample). Regarding AMR prevalence in those 21 samples, between 2 and 20 genes were detected per sample. These genes potentially confer resistance to 10 antibiotic classes, including multidrug efflux systems. While these limitations highlight areas for future improvement, they also underscore the complexity of real-world samples and the importance of centriflaken’s ability to detect multiple strains simultaneously. Future directions and potential impact centriflaken continues to evolve. The modular structure of the workflow setup of centriflaken using Nextflow allows further refinements as soon as future developments in ONT sequencing or metagenomics arise, as observed recently with the introduction of newer chemistries (R10.4.1) [ 43 – 51 ]. Nextflow-based parallelization enhances computational efficiency. With R10.4.1’s improved basecalling accuracy [ 43 – 48 ], there is growing potential to recover complete STEC genomes directly from complex samples— enabling phylogenetic comparisons to public genomes via the NCBI Pathogen Detection database. ( https://www.ncbi.nlm.nih.gov/pathogens/ ). ONT long reads further facilitate genome closure by resolving repetitive regions, as previously demonstrated with STEC EDL933 O157:H7 [ 24 ]. This is fully leveraged in centriflaken’s reassembly step, which focuses analysis on user-defined taxa to reduce computational load and maximize accuracy by only using the addressed taxa through a precision metagenomic approach as previously described [ 24 ]. centriflaken is publicly available at: https://github.com/CFSAN-Biostatstics/centriflaken . centriflaken supports any user-defined taxon, including Listeria monocytogenes and Salmonella , expanding its utility for foodborne pathogen surveillance. All software dependencies are containerized for portability and reproducibility. The development of centriflaken as a useful tool in combination with metagenomic analysis is an important step in evaluation of a variety of samples associated with fresh produce. As a result, metagenomic analysis can now be completed within hours rather than weeks—providing precise detection and characterization of STEC and other pathogens in tested samples. While initially focused on STEC, centriflaken’s flexible design allows for adaptation to other foodborne pathogens. This versatility positions it as a valuable tool for comprehensive food safety monitoring, potentially revolutionizing how we approach pathogen detection and outbreak investigation. Looking to the future centriflaken is currently available to Human Foods Program users via the HFP HPC cluster and GalaxyTrakr: https://www.protocols.io/view/centriflaken-an-automated-data-analysis-pipeline-f-kxygxzdbwv8j/v5 . As ONT long-read metagenomics continues to mature, tools like centriflaken could play a transformative role in culture-independent pathogen detection and source tracking. Its portability makes it suitable for point-of-care testing and field deployment in food and water testing scenarios. Future studies should focus on optimizing centriflaken for even lower bacterial concentrations, integrating it with other emerging technologies like machine learning for predictive analytics, and validating its performance across a wider range of food and environmental matrices. Conclusion centriflaken significantly accelerates and streamlines STEC detection in environmental samples. It provides a scalable, automated alternative to labor-intensive traditional workflows, delivering fast, reproducible, and accurate results. With continued enhancements and integration of newer ONT chemistries (e.g. R10.4.1), centriflaken may support real-time surveillance of foodborne pathogens directly from complex samples and shorten response times in outbreak scenarios. Furthermore, ongoing improvements and adaptations will potentially enhance its utility for food safety monitoring. In conclusion, centriflaken represents a substantial advancement in metagenomic analysis for food safety applications. 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Share centriflaken: an automated data analysis pipeline for assembly and in silico analyses of foodborne pathogens from metagenomic samples Kranti Konganti , Julie Kase , Narjol Gonzalez-Escalona bioRxiv 2025.07.18.665485; doi: https://doi.org/10.1101/2025.07.18.665485 Share This Article: Copy Citation Tools centriflaken: an automated data analysis pipeline for assembly and in silico analyses of foodborne pathogens from metagenomic samples Kranti Konganti , Julie Kase , Narjol Gonzalez-Escalona bioRxiv 2025.07.18.665485; doi: https://doi.org/10.1101/2025.07.18.665485 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 (7642) Biochemistry (17715) Bioengineering (13907) Bioinformatics (42003) Biophysics (21470) Cancer Biology (18624) Cell Biology (25533) Clinical Trials (138) Developmental Biology (13390) Ecology (19935) Epidemiology (2067) Evolutionary Biology (24356) Genetics (15617) Genomics (22529) Immunology (17753) Microbiology (40432) Molecular Biology (17200) Neuroscience (88681) Paleontology (667) Pathology (2840) Pharmacology and Toxicology (4828) Physiology (7653) Plant Biology (15161) Scientific Communication and Education (2046) Synthetic Biology (4304) Systems Biology (9826) Zoology (2271)