Yesterday I spoke at a workshop for JAMS TOAST (Sydney’s Joint Academic Microbiology Seminars – bioinformatics workshop) I was asked to cover tools for comparative genomics, so I put together a list of the tried and tested programs that I find most useful for this kind of analysis. So here is the list. First, a few caveats These are mostly tools with a graphical user interface (mostly Java based) this means they should be pretty accessible to most users, however if you want to do analyses that are a bit more custom or niche, you will have to get your hands dirty and use the commandline (which you should learn to do anyway!!) These tools are useful for small-ish scale genomic comparisons, in the order of 2-20 genomes. Most of these tools are for assembled data, hence we start with how to assemble your data this will become less of an issue as we move to long read sequencing with PacBio and MinION etc, but for the moment most of the data I work with is from large scale sequencing projects with Illumina (100s-1000s) so we use mapping-based approaches for a lot of tasks so I have included a few comments about this at the end. Beginner’s guide with walk-through tutorial Some of these tools, particularly the visualisation of whole genome comparisons (using Artemis & ACT, Mauve, and BRIG) are covered the in the tutorial from our 2013 ““. So if you want a walk-through, that’s a good place to start.
Note that we have updated the tutorial (as of July 2017) to version 2,. First things first – Are my reads good quality? – Generate graphical reports of read quality from the fastq files.
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Assembly – de Bruijn graph assembly, incorporating multiple kmers and read pairing information in the building of the graph. Think of this as a more sophisticated version of Velvet in my experience, it nearly always provides better assemblies than Velvet, except on the rare occasion (1-5% of read sets) where it fails to get a good assembly at all. In which case, try Velvet! – The first and most widely used de Bruijn graph assembler built to tackle the problem of short reads. Graphs are built using a single kmer value, and read pairing information used for scaffolding only (unlike SPAdes, where multiple kmers are incorporated into a single graph and read pairing is also used directly in building the graph).
How do you know what kmer to use? Hate the command line?
Try, a GUI wrapper for Velvet. How do I judge if I have a good assembly?Try What other assemblers are there?What’s best for what task? Take a look at.
How can I view my assembly graphs? Try – freshly released from Ryan Wick, a MSc (Bioinformatics) student in my lab. Bandage allows you to view and manipulate de Druijn graphs output by Velvet or SPAdes lots of super cool features and useful applications, see the for examples. Working with assembled data Now you have a nice set of assembled contigs – where are all the genes?
Whole genome annotation – Web tool (upload contigs), uses the subsystems in the SEED database and provides detailed annotation and pathway analysis. Takes several hours per genome but I think this is the best way to get a high quality annotation (if you have only a few genomes to annotate). – Standalone command line tool, takes just a few minutes per genome. This is the best way to get good quality annotation in a flash, which is particularly useful if you have loads of genomes or need to annotate a pangenome or metagenome.
Note however that the quality of functional information is not as good as RAST, and you will need several extra steps if you want to do functional profiling and pathway analysis of your genome(s) which is in-built in RAST. Annotating specific types of features Resistance genes. – best combination of easy interface + pretty good database. – best quality database (in my experience, focusing on Enterobacteriaceae). – easy interface, database needs ongoing development Virulence genes.
– for certain bugs only, but has good online tools for genome comparisons. – broader range of species, but varying levels of comprehensiveness and you need to do more of the work yourself.
Insertion sequences. – Upload your genome and have IS saga find all the transposes in your genome using their IS finder database Phage. – Upload your genome and this will identify likely prophage regions, summarising these at the level of whole phage and also individual genes. Viewing your genome – There are zillions of genome browsers out there, but I still love Artemis and not just because I’m from the Sanger Institute. Unlike most genome browsers, Artemis was custom-built for bacterial genomes, which let’s face it are really quite different from humans and other eukaryotes. The default view shows you your sequence and annotation, with 6 frame translation and allows you to easily edit or create features in the annotation, graph sequence-based functions like GC content and GC skew, and do all manner of other useful things.
It’s been around for a zillion years (well, at least 10 or so) and is very well developed and supported. Artemis has lots of cool features built in, including the ‘BamView’ feature that allows you to view BAM files that show the alignment of reads mapped to your genome, zoomed in to the base level or zoomed out to look at coverage and SNP distributions this is also super handy for viewing RNAseq data, as you can easily see the stacks of reads derived from coding regions. Artemis also has DNA Plotter built in, which you can use to generate those pretty circular figures of your genome sequences and their features.
Plus, when you’ve got used to using Artemis to get to know your shiny new genome, you can move on to viewing comparisons against other genomes using ACT – the Artemis Comparison Tool. Comparing whole genome assemblies NOTE: Walk-throughs of these tools, using examples from the 2011 E. Coli outbreak in Germany, are covered in the ““. – Visualises BLAST (or similar) comparisons of genomes.
This is most useful for comparisons of two or a few genomes, and makes it easy to spot and zoom in to regions of difference. – Whole genome alignment and viewer that can output SNPs, regions of difference, homologous blocks, etc.
It can also be used to assess assembly quality against a reference, using Mauve Contig Metrics. – Gives a global view of whole genome comparisons by visualising BLAST comparisons via pretty circular figures. This is suitable for comparing lots of genomes, although because you have to enter each one through the GUI, it’s tricky to do more than a dozen or so. Whole genome SNP-based phylogenies (from assembled data) You can’t go past Adam Phiippy’s Parsnp – Compare genomes to a reference (using MUMmer) to identify core genome SNPs and build a phylogeny Gingr – View the phylogeny and associated SNP calls (VCF format) also useful for visualising tree + VCF that you have created in other ways, e.g.
From mapping. Detecting recombination in whole genome comparisons – A new implementation of the approach first used in. Command-line driven and runs pretty fast ( BAM). For processing of BAMs we use: SAMtools and BAMtools for variant calling, and BAMstats and BEDtools for summarising coverage and other information from the alignments. Pipelines for specific tasks There are loads of pipelines around the place that use the basic tools above to do specific tasks. A few of ours are:.
– MLST, resistance genes, virulence genes. – IS (insertion sequence / tranposase) insertions. – Whole genome SNP-based phylogenies. UPDATE: Version 2 of the tutorial (posted July 2017) is. Originally posted by Kat on her, April 2013 This is a shameless plug for an article and accompanying tutorial I’ve just published together with David Edwards, my excellent MSc Bioinformatics student from the University of Melbourne. The accompanying tutorial is available. The idea for this came from discussions at last year’s ASM (Australian Society of Microbiology) meeting, where it was highlighted that there was a lack of courses and tutorials available for biologists to learn the basics of genomic analysis so that they can make use of next gen sequencing.
Michael Wise, a founding editor of BMC Microbial Informatics and Experimentation based at UWA in Perth, suggested the new journal would be an ideal home for such a tutorial so here we are: High throughput sequencing is now fast and cheap enough to be considered part of the toolbox for investigating bacteria, and there are thousands of bacterial genome sequences available for comparison in the public domain. Bacterial genome analysis is increasingly being performed by diverse groups in research, clinical and public health labs alike, who are interested in a wide array of topics related to bacterial genetics and evolution. Examples include outbreak analysis and the study of pathogenicity and antimicrobial resistance. In this beginner’s guide, we aim to provide an entry point for individuals with a biology background who want to perform their own bioinformatics analysis of bacterial genome data, to enable them to answer their own research questions. We assume readers will be familiar with genetics and the basic nature of sequence data, but do not assume any computer programming skills.
The main topics covered are assembly, ordering of contigs, annotation, genome comparison and extracting common typing information. Each section includes worked examples using publicly available E. Coli data and free software tools, all which can be performed on a desktop computer. Four great tools In the paper and tutorial, we introduce the four tools which we rely on most for basic analysis of bacterial genome assemblies: Velvet, ACT, Mauve and BRIG. All except ACT were developed as part of a PhD project, and have endured well beyond the original PhD to become well-known bioinformatics tools.
New students take note! In the paper, each tool is highlighted in its own figure, which includes some basic instructions. This is reproduced below, but is covered in much more detail in the tutorial that comes with the paper (link at the bottom). Velvet for genome assembly Possibly the most popular and widely used short read assembler, developed by the amazing Dan Zerbino during his PhD at EBI in Cambridge. Quite a PhD project!
Reads are assembled into contigs using Velvet and VelvetOptimiser in two steps, (1) velveth converts reads to k-mers using a hash table, and (2) velvetg assembles overlapping k-mers into contigs via a de Bruijn graph. VelvetOptimiser can be used to automate the optimisation of parameters for velveth and velvetg and generate an optimal assembly. To generate an assembly of E. Coli O104:H4 using the command-line tool Velvet:.
Download Velvet 23 (we used version 1.2.08 on Mac OS X, compiled with a maximum k-mer length of 101 bp). Download the paired-end Illumina reads for E.
Mauve Mauve-users Mauve For Macy's
Coli O104:H4 strain TY-2482 (ENA accession SRR292770). Convert the reads to k-mers using this command: velveth outdata35 35 -fastq.gz -shortPaired -separate SRR2927701.fastq.gz SRR2927702.fastq.gz. Then, assemble overlapping k-mers into contigs using this command: velvetg outdata35 -clean yes -expcov 21 -covcutoff 2.81 -mincontiglgth 200 This will produce a set of contigs in multifasta format for further analysis. See Additional file 1: Tutorial for further details, including help with downloading reads and using VelvetOptimiser. ACT for pairwise genome comparison Part of the Sanger Institute’s Artemis suite of tools. Also look at Artemis (single genome viewer), DNA Plotter (which can draw circular diagrams of your genomes) and BAMView (which can display mapped reads overlaid on a reference genome), they are all available. Artemis and ACT are free, interactive genome browsers (we used ACT 11.0.0 on Mac OS X).
Open the assembled E. Coli O104:H4 contigs in Artemis and write out a single, concatenated sequence using File - Write - All Bases - FASTA Format. Generate a comparison file between the concatenated contigs and 2 alternative reference genomes using the website. Launch ACT and load in the reference sequences, contigs and comparison files, to get a 3-way comparison like the one shown here. Coli O104:H4 contigs are in the middle row, the enteroaggregative E. Coli strain Ec55989 is on top and the enterohaemorrhagic E.
Coli strain EDL933 is below. Details of the comparison can be viewed by zooming in, to the level of genes or DNA bases. Mauve for contig ordering and multiple genome comparison Developed by the wonderful Aaron Darling during his PhD, he is now Associate Professor at University of Technology Sydney. Also see, an optional plugin for assessing assembly quality which was developed for the Assemblathon. Mauve is a free alignment tool with an interactive browser for visualising results (we used Mauve 2.3.1 on Mac OS X). Launch Mauve and select File - Align with progressiveMauve.
Click ‘Add Sequence’ to add your genome assembly (e.g. Coli O104:H4 contigs) and other reference genomes for comparison.
Specify a file for output, then click ‘Align’. When the alignment is finished, a visualization of the genome blocks and their homology will be displayed, as shown here. Coli O104:H4 is on the top, red lines indicate contig boundaries within the assembly.
Sequences outside coloured blocks do not have homologs in the other genomes. BRIG (BLAST Ring Image Generator) for multiple genome comparison From Nabil-Fareed Alikhan at the University of Queensland, also as part of a graduate project, which I believe is still in progress BRIG is a free tool that requires a local installation of BLAST (we used BRIG 0.95 on Mac OS X). The output is a static image.
Launch BRIG and set the reference sequence (EHEC EDL933 chromosome) and the location of other E. Coli sequences for comparison. If you include reference sequences for the Stx2 phage and LEE pathogenicity island, it will be easy to see where these sequences are located. Click ‘Next’ and specify the sequence data and colour for each ring to be displayed in comparison to the reference. Click ‘Next’ and specify a title for the centre of the image and an output file, then click ‘Submit’ to run BRIG. BRIG will create an output file containing a circular image like the one shown here. It is easy to see that the Stx2 phage is present in the EHEC chromosomes (purple) and the outbreak genome (black), but not the EAEC or EPEC chromosomes.
Tutorial The tutorial accompanying the article is available. To give you an idea of what’s covered, here is the table of contents: 1. Genome assembly and annotation 2 1.1 Downloading E. Coli sequences for assembly. 2 1.2 Examining quality of reads (FastQC) 2 1.3 Velvet – assembling reads into contigs. 4 1.3.1 Using VelvetOptimiser to optimise de novo assembly with Velvet. 6 1.4 Ordering contigs against a reference using Mauve.
7 1.4.1 Viewing the ordered contigs (Mauve) 10 1.4.2 Viewing the ordered contigs (ACT). 13 1.5 Mauve Assembly Metrics – Statistical View of the Contigs 15 1.6 Annotation with RAST.
15 1.6.1 Alternatives to RAST. Comparative genome analysis. 20 2.1 Downloading E. Coli genome sequences for comparative analysis. 20 2.2 Mauve – for multiple genome alignment.
21 2.3 ACT – for detailed pairwise genome comparisons 24 2.3.1 Generating comparison files for ACT. 24 2.3.2 Viewing genome comparisons in ACT. 27 2.4 BRIG – Visualizing reference-based comparisons of multiple sequences 29 3. Typing and specialist tools.
34 3.1 PHAST – for identification of phage sequences. 34 3.2 ResFinder – for identification of resistance gene sequences 34 3.3 Multilocus sequence typing. 34 3.4 PATRIC – online genome comparison tool 34.