annoSnake: a Snakemake workflow for taxonomic and functional annotation of metagenomes and metagenome-assembled genomes (MAGs)

annoSnake: a Snakemake workflow for taxonomic and functional annotation of metagenomes and metagenome-assembled genomes (MAGs) | 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 annoSnake: a Snakemake workflow for taxonomic and functional annotation of metagenomes and metagenome-assembled genomes (MAGs) View ORCID Profile Bastian Heimburger , View ORCID Profile Rebecca Clement , View ORCID Profile Tamara R. Hartke doi: https://doi.org/10.1101/2025.11.03.686227 Bastian Heimburger 1 Johann-Friedrich-Blumenbach Institute of Zoology and Anthropology, University of Goettingen , Untere Karspüle 2, Goettingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bastian Heimburger For correspondence: bheimbu{at}gwdg.de Rebecca Clement 2 Biological Sciences, The George Washington University , Washington DC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rebecca Clement Tamara R. Hartke 1 Johann-Friedrich-Blumenbach Institute of Zoology and Anthropology, University of Goettingen , Untere Karspüle 2, Goettingen, Germany 3 Museum Koenig Bonn, Leibniz Institute for the Analysis of Biodiversity Change (LIB) , Adenauerallee 127, Bonn, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tamara R. Hartke Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Omic technologies revolutionised research in microbiomics and helped decipher the complex community structure of gut microbiota, each comprising hundreds of species of bacteria, archaea, and protists. Even as sequencing costs decrease, understanding these communities remains a challenge. Here, we introduce annoSnake, a Snakemake workflow that facilitates taxonomic and functional annotation of metagenomes and metagenomic-assembled genomes (MAGs). This user-friendly and highly flexible workflow integrates state-of-the-art software packages, taking raw sequencing reads, assembling them into contigs, assigning taxonomies and annotating them using various popular protein databases. The workflow handles paired-end as well as interleaved read data, and provides publication-ready figures and tables for downstream analysis. annoSnake, as all Snakemake workflows, is highly scalable, reproducible, and portable, enabling execution across diverse high-performance computing environments with remarkable efficiency. Introduction Metagenomics is increasingly popular due to cost-efficient high-throughput sequencing technologies ( Lema et al. 2023 , Nam et al. 2023 ). However, data analysis remains tedious and time-consuming ( New and Brito 2020 , Setubal 2021 , Zhou et al. 2022 ). Typically, standalone programs such as MEGAHIT ( Li et al. 2015 ), Prokka (Seeman 2014) and Diamond ( Buchfink et al. 2021 ) are used to perform a single task (e.g. genome assembly, taxonomic or functional annotation) en route to fully annotated metagenomic contigs, making researchers navigate multiple data formats and database setups. Although some (semi)automatic pipelines exist ( Kultima et al. 2016 , Dong and Strous 2018), no existing pipeline covers assembly and annotation of metagenomes and MAGs simultaneously. Here, we introduce annoSnake, a Snakemake workflow that streamlines taxonomic and functional annotation of metagenomes and MAGs, from raw sequencing reads to fully annotated metagenomes and MAGs. Users select input read format (paired-end or interleaved) and annotation databases (eg. Pfam, KEGG, CAZy), which are automatically downloaded and set up. Resulting CSV tables and figures (PDF and HTML formats) are ready for downstream analysis and publication. Snakemake workflows, like annoSnake, ensure reproducibility (dependencies are explicitly defined), scalability (designed to run on cluster environments), and testability (debugging is easy: single tasks are encapsulated into separate rules), allowing researchers to concentrate on interpreting results rather than bioinformatic problems. We demonstrate annoSnake here with a termite gut metagenomics project. Description To start, users install Snakemake ( Mölder et al. 2021 ; https://snakemake.readthedocs.io/en/stable/getting_started/installation.html ), clone the annoSnake GitHub repository, and configure the . /profile/params . yaml file (see below). As the workflow is optimised for High Performance Computing (HPC) clusters using the Slurm workload manager ( Yoo et al. 2003 ), users modify the . /profile/config . yaml file according to their cluster environment specifications. Users provide either paired-end or interleaved sequencing data (in gzipped format), conforming to the naming scheme (i) $SAMPLE_R1/_R2.fastq.gz or (ii) $SAMPLE.fastq.gz. There is no need to trim or filter reads in advance. First, annoSnake creates all necessary Conda environments and sets up protein databases (i.e., download of databases and configuration, if necessary). Then, rules are processed consecutively to produce desired output files from input files ( Fig. 1 ). Download figure Open in new tab Figure 1. Full directed acyclic graph of jobs for an annoSnake run using paired-end sequencing reads. The workflow includes download and setup of databases; metagenome and MAG assembly; taxonomic, structural (protein-coding sequences, tRNAs, mRNAs), and functional (KEGG, CAZymes, Pfam) annotation of metagenomes and MAGs; and creation of tables and figures. In the worked example, annoSnake detects enzymes associated with lignocellulose digestion. However, annoSnake can facilitate searches for any KEGG pathway (e.g. digestive enzymes, immune response proteins). Users simply adjust the files (i) keggid_to_gene_name . csv and (ii) keggid_to_genes_pathway . csv within ./workflow/rules/scripts/ to include relevant KEGG IDs and gene names to query. Configuration files . /profile/params . yaml is the main configuration file. Users specify input and output directories and library type (‘paired-end’ or ‘interleaved’). Other options include (i) MAG assembly, (ii) databases for functional protein annotation, and (iii) corresponding cut-off E-values. . /profile/config . yaml handles Slurm job submission. Users specify the partition (or queue) on which jobs are executed. Note: for large data sets, it may be necessary to adjust the amount of memory per node and maximum wall-clock time. annoSnake workflow: step by step Metagenome assembly Raw reads are assembled with MEGAHIT v1.2.9 ( Li et al. 2015 ), optimised for metagenome assemblies. Users specify in . /profile/params . yaml the minimum length of contigs (default: 1500 bp) to retain for downstream analyses. To account for the complexity of metagenomes, ‘--presets meta-sensitive’ is used as default. Fasta headers of assembled metagenome contigs are modified prior to downstream analyses to include the $SAMPLE name. Quality of metagenome assemblies is assessed with metaQuast ( Mikheenko et al. 2016 ). Taxonomic annotation Taxonomic assignment of metagenome contigs is performed as follows: (a) Prokka 1.14.6 (in ‘--metagenome’ mode; Seeman 2014) identifies protein-coding sequences (CDS), rRNAs, and tRNAs used later in the workflow (see Fig. 1 ). (b) fetchMG v.1.2 ( https://github.com/motu-tool/fetchMGs ) extracts 40 single-copy marker genes present in most living organisms ( Sunagawa et al. 2013 ). (c) Marker gene protein sequences are taxonomically assigned with DIAMOND ( Buchfink et al. 2021 ) in ‘blastp’ mode. (d) Other predicted protein-coding sequences (in nucleotide format) are taxonomically assigned with DIAMOND in ‘blastx’ mode. Both annotations use GTDB database ver 202 ( Parks et al. 2022 ) as default reference. To retrieve taxonomic classifications of predicted protein-coding sequences, annoSnake implements a custom R script ‘gtdb_diamondlca.R’ ( Arora et al. 2022 ). Functional annotation Metagenomic contigs assigned as bacteria or archaea by ‘blastx’ are annotated. Users choose the databases for functional annotation: (a) default for CDS with carbohydrate metabolising properties is Hidden Markov models (HMM) of CAZy domains deposited in the dbCAN database release 11 ( Yin et al. 2012 ). (b) For hydrogenases, HMM searches against the Pfam database version 35 (Mistry et al. 2020). Results can be used to further classify catalytic subunits with the HydDB webtool ( Søndergaard et al. 2016 ; not implemented in annoSnake). (c) KofamScan v1.3.0 ( https://github.com/takaram/kofam_scan ) reconstructs prokaryotic metabolic pathways (i.e. genes related to methanogenesis, reductive against the KEGG database; Kanehisa and Goto 2000 , Kanehisa et al. 2023 ). Cut-off E-values (minimum significant hit) are specified in . /profile/params . yaml . Abundance of gene families Abundance of CDSs is quantified with Salmon v1.10.2 ( Patro et al. 2017 ) using specific settings for metagenomic analysis which align raw sequencing reads to assembled contigs annotated as bacteria or archaea. Salmon adjusts for biases such as GC-content and gene length, producing CDS abundances as Transcripts per Million (TPM; Patro et al. 2017 ). In addition, Salmon quantifies relative abundance of bacterial and archaeal lineages using the 40 single-copy marker genes (see section ‘Taxonomic annotation’). TPM values >1 (to exclude potentially unreliable data) are log-transformed for visualisation. Normalisation of TPM counts is performed via centered log-ratio (clr) transformation (Calle et al. 2019) to account for the compositional nature of metagenomic data and allow comparisons between genes and bacterial species ( Xia and Sun 2021 ). The transformation is executed using R package propr ( Quinn et al. 2017 ) with a pseudo count of 1 to handle zero values appropriately. Metagenome-assembled genomes (MAGs) Metagenome contigs are binned into MAGs with three different binning algorithms (in default mode): (i) MetaBAT version 2.10.2 ( Kang et al. 2019 ); (ii) MetaCoAG v1.1.1 ( Mallawaarachchi and Lin 2022 ); and (iii) MaxBin 2.2.7 ( Wu et al. 2016 ). To increase contiguity and completeness of bins, annoSnake implements metaWRAP’s (v1.3; Uritskiy et al. 2018 ) ‘bin_refinement’ module to produce a consolidated bin set from the three algorithms. Users specify in ./profile/params.yaml minimum completeness and maximum contamination of MAGs to use for downstream analyses. Defaults are ≥50% completeness with ≤10% contamination, following Bowers et al. (2017). MAGs passing quality control (CheckM 1.2.2; Parks et al. 2015 ) are taxonomically classified with GTDB-Tk v2.3.2 ( Chaumeil et al. 2022 ) using GTDB database ver 214 as a reference ( Parks et al. 2020 ). Gene prediction is performed by Prokka ( Seemann 2014 ), using the ‘--metagenome’ option, accounting for highly fragmented metagenomes. Predicted protein sequences are annotated with MicrobeAnnotator ( Ruiz-Perez et al. 2021 ) using DIAMOND sequence aligner and KofamScan. Pathway completeness is assessed based on presence/absence. Databases Databases are automatically downloaded and set up. Users can choose to use their own protein databases, which must be stored in the correct format. All databases consume approx. 100 Gb of memory. Output annoSnake outputs tables (in CSV format) and figures (PDF and HTML formats), depending on protein annotations and whether MAGs are assembled or not. Tables and figures are ready for downstream (statistical) analysis and publication. Figures are visualised with ggplot2 3.4.3 ( Wickham 2016 ) and plotly 4.10.4 ( Sievert, 2020 ) in R version 4.2.0 (R Core Team 2024). Empirical data We highlight the functionality of annoSnake using low-coverage WGS data from the Australian Amitermes group (AAG). These derived termites exhibit diverse feeding habits and nesting behaviour ( Krishna et al. 2013 ). Although major decomposers of dead plant material in Australia ( Coventry et al. 1988 , Noble et al. 2009 , Evans et al. 2011 ), their microbiomes are virtually unknown (but see Platell 2019 ). The worked example uses paired-end Illumina NextSeq data from 31 colonies representing nine Amitermes and Drepanotermes species (for details see Tab. S1 and Supplementary Information); the output of the worked example can be found under . Results and Discussion Although low-coverage WGS provided a limited number of reads (∼212x10 6 total, average ∼7x10 6 reads/sample), annoSnake efficiently identified major bacterial lineages ( Fig. 2 ) and a large of set of metabolic-pathway genes (KEGG) and CAZYmes ( Fig. 3 and 4 ) related to lignocellulose digestion. For this small dataset, we estimate annoSnake saved tens of hours of bioinformatics work compared to the same tools in a non-automated workflow. It started downstream analyses without human intervention as the necessary data passed quality controls, reducing server time while avoiding errors and frustration associated with manually wrangling files and changing formats. Download figure Open in new tab Figure 2. annoSnake output of relative abundance of bacterial lineages in termite gut metagenomes. Colour scale represents log transcripts per million (TPM), with lighter colours indicating higher relative abundances and darker colours indicating lower relative abundances. Download figure Open in new tab Figure 3. annoSnake output for relative abundance of metabolic pathway genes (KEGG) involved in lignocellulose digestion. Colour scale represents log transcripts per million (TPM), with lighter colours indicating higher relative abundances and darker colours indicating lower relative abundances. Download figure Open in new tab Figure 4. annoSnake representation of relative abundance of 50 most abundant CAZymes in termite guts. CAZYme annotation was performed by HMM searches against the dbCAN database release 11. Colour scale represents log transcripts per million (TPM), with lighter colours indicating higher relative abundances and darker colours indicating lower relative abundances. Typically, grass- and wood-feeding termites have spirochete-dominated gut communities, while humus- and soil-feeders are rich in clostridia (Ottesen and Leadbetter 2011, Mikaelyan et al. 2015, 2016). annoSnake taxonomic annotation ( Fig. 2 ) indicated that spirochetes indeed dominate most gut communities of the leaf litter and grass harvesting Drepanotermes ( Watson and Perry 1981 ). Surprisingly, gut communities of D. tamminensis are heavily dominated by clostridia, with virtually no spirochetes or Fibrobacterota, as were communities of some D. gayi colonies. In the case of A. heterognathus , workers may have fed on dung in addition to their usual wood (Gay 1967, Ferrar and Watson 1970 ), resulting in a clostridia-rich community similar to facultative dung-feeder A. wheeleri (He et al. 2013). Unlike Arora et al. (2022) , no archaeal lineages were found in any samples. It is unclear whether these termites genuinely lack archaea or if this reflects low sequencing depth recovering only abundant community members. annoSnake functional annotation summarised potential metabolic pathways of interest ( Fig. 3 ). In all samples, annoSnake recovered several genes related to the dissimilatory sulfate reduction pathway: aprA, aprB , and dsrAB . Sulfate concentration and abundance of sulfate-reducing bacteria are generally low in termite guts (Brune and Okhuma 2011, Arora et al. 2022 ). Few genes related to methanogenesis, such as mcrABG , were found. This is expected, as methanogenesis is largely constrained to anaerobic methanogenic archaea ( Thauer et al. 2008 , Brune 2010 ), which were not detected here ( Fig. 1 ). To target acetogens in the data, we specified genes corresponding to seven enzymes of the Wood-Ljungdahl pathway: fdhF, fhs, folD, metF, acsABCDE, pta , and ack . Although fhs and folD were not found in our metagenomes, the presence of fdhF and acsABCDE is predictive of reductive acetogenesis ( Fig. 3 ), known from other termites ( Arora et al. 2022 ) and millipedes ( Nweze et al. 2024 ). This is supported by annoSnake assignment of 15 and 6 MAGs to clostridia and spirochetes, respectively, taxa which include potential acetogens (Okhuma et al. 2005, Mikaelyan et al. 2016). annoSnake recovered a large set of CAZymes ( Fig. 4 ). D. tamminensis gut metagenomes again suggest dietary flexibility, with numerous and abundant glucoside hydrolysases (GHs) in some colonies but not others. Although D. tamminensis harvests foodstuffs from wood to grass ( Watson and Perry 1981 ), the observed pattern of GHs in some colonies was consistent with humus/soil-feeding. We found many GHs in the spirochete-dominated D. gayi metagenome (D_gayi_BEC329; Fig. 2 and 4 ), as expected for a leaf-litter harvesting species, and low numbers of GHs in clostridia-dominated D. gayi colonies. Metagenomic binning of the 31 termite gut metagenomes yielded 30 MAGs in total with low (>30% completion and 90% completion and <5% contamination) ( Fig. 5 ), and average relative abundance of 3%. This included 15 MAGs of Bacillota, 1 MAG of Desulfobacterota, 7 MAGs of Fibrobacterota, 1 MAG of Pseudomonadota, and 6 MAGs of Spirochaetota. Interestingly, MAG BEC335_bin.2 from A. obeuntis was assigned to genus Azovibrio (Pseudomonadota), a diazotrophic endophyte of grasses (Reinhold-Hurek and Hurek 2006). A. obeuntis feeds mainly on dead tree roots ( Abensperg-Traun 1993 ), suggesting Azovibrio bacteria may be transient, entering the gut as termites moved through soil accessing their woody diet. Download figure Open in new tab Figure 5. annoSnake representation of lignocellulose digestion metabolic pathway gene presence/absence in MAGs (metagenome-assembled genomes) of 31 termite gut metagenomes (main panels); purple squares indicate absence of genes and yellow squares indicate presence of genes. Leftmost columns: checkM completeness and contamination scores; lighter colours indicate higher completeness and less contamination, respectively. Altogether, our results show that annoSnake is a powerful tool to facilitate metagenomic analysis. It is easy to implement and highly flexible, allowing the workflow to be easily adapted to different research questions and increasing efficiency in generating novel biological insights from metagenomic sequence data. Acknowledgements The authors hereby declare no conflict of interest. We acknowledge the traditional custodians of Country throughout Australia and pay our respects to Elders past and present. Samples were collected with permission of relevant state authorities. Footnotes Data availability statement annoSnake can be downloaded from https://github.com/bheimbu/annoSnake . Raw sequencing data used in the workflow demonstration is stored under https://figshare.com/s/59c0bbaacf2f8e573bf2 . References ↵ Abensperg-Traun , M. ( 1993 ). A comparison of two methods for sampling assemblages of subterranean, wood-eating termites (Isoptera) . Austral Ecology , 18 ( 3 ), 317 – 324 . doi: 10.1111/j.1442-9993.1993.tb00459.x OpenUrl CrossRef ↵ Arora , J. , Kinjo , Y. , Šobotník , J. , Buček , A. , Clitheroe , C. , Stiblik , P. , Roisin , Y. , Žifčáková , L. , Park , Y. C. , Kim , K. Y. , Sillam-Dussès , D. , Hervé , V. , Lo , N. , Tokuda , G. , Brune , A. , & Bourguignon , T. ( 2022 ). The functional evolution of termite gut microbiota . Microbiome , 10 ( 1 ), 78 . doi: 10.1186/s40168-022-01258-3 OpenUrl CrossRef PubMed ↵ K. N. Timmis Brune , A. ( 2010 ). 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