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      Roary: rapid large-scale prokaryote pan genome analysis

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          Abstract

          Summary: A typical prokaryote population sequencing study can now consist of hundreds or thousands of isolates. Interrogating these datasets can provide detailed insights into the genetic structure of prokaryotic genomes. We introduce Roary, a tool that rapidly builds large-scale pan genomes, identifying the core and accessory genes. Roary makes construction of the pan genome of thousands of prokaryote samples possible on a standard desktop without compromising on the accuracy of results. Using a single CPU Roary can produce a pan genome consisting of 1000 isolates in 4.5 hours using 13 GB of RAM, with further speedups possible using multiple processors.

          Availability and implementation: Roary is implemented in Perl and is freely available under an open source GPLv3 license from http://sanger-pathogens.github.io/Roary

          Contact: roary@ 123456sanger.ac.uk

          Supplementary information: Supplementary data are available at Bioinformatics online.

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          The microbial pan-genome.

          Duccio Medini, Claudio Donati, Hervé Tettelin … (2005)
          A decade after the beginning of the genomic era, the question of how genomics can describe a bacterial species has not been fully addressed. Experimental data have shown that in some species new genes are discovered even after sequencing the genomes of several strains. Mathematical modeling predicts that new genes will be discovered even after sequencing hundreds of genomes per species. Therefore, a bacterial species can be described by its pan-genome, which is composed of a "core genome" containing genes present in all strains, and a "dispensable genome" containing genes present in two or more strains and genes unique to single strains. Given that the number of unique genes is vast, the pan-genome of a bacterial species might be orders of magnitude larger than any single genome.
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            PGAP: pan-genomes analysis pipeline

            Yongbing Zhao, Jiayan Wu, Junhui Yang … (2011)
            Summary: With the rapid development of DNA sequencing technology, increasing bacteria genome data enable the biologists to dig the evolutionary and genetic information of prokaryotic species from pan-genome sight. Therefore, the high-efficiency pipelines for pan-genome analysis are mostly needed. We have developed a new pan-genome analysis pipeline (PGAP), which can perform five analytic functions with only one command, including cluster analysis of functional genes, pan-genome profile analysis, genetic variation analysis of functional genes, species evolution analysis and function enrichment analysis of gene clusters. PGAP's performance has been evaluated on 11 Streptococcus pyogenes strains. Availability:PGAP is developed with Perl script on the Linux Platform and the package is freely available from http://pgap.sf.net. Contact: junyu@big.ac.cn; xiaojingfa@big.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.
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              The large-scale blast score ratio (LS-BSR) pipeline: a method to rapidly compare genetic content between bacterial genomes

              Jason Sahl, J. Gregory Caporaso, David Rasko … (2014)
              Background. As whole genome sequence data from bacterial isolates becomes cheaper to generate, computational methods are needed to correlate sequence data with biological observations. Here we present the large-scale BLAST score ratio (LS-BSR) pipeline, which rapidly compares the genetic content of hundreds to thousands of bacterial genomes, and returns a matrix that describes the relatedness of all coding sequences (CDSs) in all genomes surveyed. This matrix can be easily parsed in order to identify genetic relationships between bacterial genomes. Although pipelines have been published that group peptides by sequence similarity, no other software performs the rapid, large-scale, full-genome comparative analyses carried out by LS-BSR. Results. To demonstrate the utility of the method, the LS-BSR pipeline was tested on 96 Escherichia coli and Shigella genomes; the pipeline ran in 163 min using 16 processors, which is a greater than 7-fold speedup compared to using a single processor. The BSR values for each CDS, which indicate a relative level of relatedness, were then mapped to each genome on an independent core genome single nucleotide polymorphism (SNP) based phylogeny. Comparisons were then used to identify clade specific CDS markers and validate the LS-BSR pipeline based on molecular markers that delineate between classical E. coli pathogenic variant (pathovar) designations. Scalability tests demonstrated that the LS-BSR pipeline can process 1,000 E. coli genomes in 27–57 h, depending upon the alignment method, using 16 processors. Conclusions. LS-BSR is an open-source, parallel implementation of the BSR algorithm, enabling rapid comparison of the genetic content of large numbers of genomes. The results of the pipeline can be used to identify specific markers between user-defined phylogenetic groups, and to identify the loss and/or acquisition of genetic information between bacterial isolates. Taxa-specific genetic markers can then be translated into clinical diagnostics, or can be used to identify broadly conserved putative therapeutic candidates.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Journal ID (hwp): bioinfo
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 15 November 2015
                Publication date (Electronic): 20 July 2015
                Publication date PMC-release: 20 July 2015
                Volume: 31
                Issue: 22
                Pages: 3691-3693
                Affiliations
                1Pathogen Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge,
                2Department of Medicine, University of Cambridge, Cambridge,
                3School of Medicine, University of St. Andrews, North Haugh, St Andrews and
                4College of Medicine, Swansea University, Swansea, UK
                Author notes
                *To whom correspondence should be addressed.

                Associate Editor: John Hancock

                Article
                Publisher ID: btv421
                DOI: 10.1093/bioinformatics/btv421
                PMC ID: 4817141
                PubMed ID: 26198102
                SO-VID: 17d89a6c-5f2a-49a1-a02d-c5da139d14c2
                Copyright © © The Author 2015. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 14 May 2015
                Date revision received : 26 June 2015
                Date accepted : 14 July 2015
                Page count
                Pages: 3
                Categories
                Subject: Applications Notes
                Subject: Sequence Analysis

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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