Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Roary: rapid large-scale prokaryote pan genome analysis.

ABSTRACT:

Unlabelled

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@sanger.ac.uk

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Page AJ

PROVIDER: S-EPMC4817141 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Roary: rapid large-scale prokaryote pan genome analysis.

Page Andrew J AJ Cummins Carla A CA Hunt Martin M Wong Vanessa K VK Reuter Sandra S Holden Matthew T G MT Fookes Maria M Falush Daniel D Keane Jacqueline A JA Parkhill Julian J

Bioinformatics (Oxford, England) 20150720 22

<h4>Unlabelled</h4>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 resul ...[more]

PMID: 26198102

Similar Datasets

Piggy: a rapid, large-scale pan-genome analysis tool for intergenic regions in bacteria.

Project description:BackgroundThe concept of the "pan-genome," which refers to the total complement of genes within a given sample or species, is well established in bacterial genomics. Rapid and scalable pipelines are available for managing and interpreting pan-genomes from large batches of annotated assemblies. However, despite overwhelming evidence that variation in intergenic regions in bacteria can directly influence phenotypes, most current approaches for analyzing pan-genomes focus exclusively on protein-coding sequences.FindingsTo address this we present Piggy, a novel pipeline that emulates Roary except that it is based only on intergenic regions. A key utility provided by Piggy is the detection of highly divergent ("switched") intergenic regions (IGRs) upstream of genes. We demonstrate the use of Piggy on large datasets of clinically important lineages of Staphylococcus aureus and Escherichia coli.ConclusionsFor S. aureus, we show that highly divergent (switched) IGRs are associated with differences in gene expression and we establish a multilocus reference database of IGR alleles (igMLST; implemented in BIGSdb).

| S-EPMC5890482 | biostudies-literature

Novel large-scale chromosomal transfer in Bacteroides fragilis contributes to its pan-genome and rapid environmental adaptation.

Project description:Bacteroides fragilis, an important component of the human gastrointestinal microbiota, can cause lethal extra-intestinal infection upon escape from the gastrointestinal tract. We demonstrated transfer and recombination of large chromosomal segments from B. fragilis HMW615, a multidrug resistant clinical isolate, to B. fragilis 638R. In one example, the transfer of a segment of ~435 Kb/356 genes replaced ~413 Kb/326 genes of the B. fragilis 638R chromosome. In addition to transfer of antibiotic resistance genes, these transfers (1) replaced complete divergent polysaccharide biosynthesis loci; (2) replaced DNA inversion-controlled intergenic shufflons (that control expression of genes encoding starch utilization system outer membrane proteins) with more complex, divergent shufflons; and (3) introduced additional intergenic shufflons encoding divergent Type 1 restriction/modification systems. Conjugative transposon-like genes within a transferred segment and within a putative integrative conjugative element (ICE5) ~45 kb downstream from the transferred segment both encode proteins that may be involved in the observed transfer. These data indicate that chromosomal transfer is a driver of antigenic diversity and nutrient adaptation in Bacteroides that (1) contributes to the dissemination of the extensive B. fragilis pan-genome, (2) allows rapid adaptation to a changing environment and (3) can confer pathogenic characteristics to host symbionts.

| S-EPMC5729914 | biostudies-literature

Large-Scale CLL Genome Analysis

Project description:Large-Scale CLL Genome Analysis

| PRJNA80303 | ENA

A novel method of consensus pan-chromosome assembly and large-scale comparative analysis reveal the highly flexible pan-genome of Acinetobacter baumannii.

Project description:Infections by pan-drug resistant Acinetobacter baumannii plague military and civilian healthcare systems. Previous A. baumannii pan-genomic studies used modest sample sizes of low diversity and comparisons to a single reference genome, limiting our understanding of gene order and content. A consensus representation of multiple genomes will provide a better framework for comparison. A large-scale comparative study will identify genomic determinants associated with their diversity and adaptation as a successful pathogen.We determine draft-level genomic sequence of 50 diverse military isolates and conduct the largest bacterial pan-genome analysis of 249 genomes. The pan-genome of A. baumannii is open when the input genomes are normalized for diversity with 1867 core proteins and a paralog-collapsed pan-genome size of 11,694 proteins. We developed a novel graph-based algorithm and use it to assemble the first consensus pan-chromosome, identifying both the order and orientation of core genes and flexible genomic regions. Comparative genome analyses demonstrate the existence of novel resistance islands and isolates with increased numbers of resistance island insertions over time, from single insertions in the 1950s to triple insertions in 2011. Gene clusters responsible for carbon utilization, siderophore production, and pilus assembly demonstrate frequent gain or loss among isolates.The highly variable and dynamic nature of the A. baumannii genome may be the result of its success in rapidly adapting to both abiotic and biotic environments through the gain and loss of gene clusters controlling fitness. Importantly, some archaic adaptation mechanisms appear to have reemerged among recent isolates.

| S-EPMC4507327 | biostudies-literature

Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data.

Project description:The emergence of microbial antibiotic resistance is a global health threat. In clinical settings, the key to controlling spread of resistant strains is accurate and rapid detection. As traditional culture-based methods are time consuming, genetic approaches have recently been developed for this task. The detection of antibiotic resistance is typically made by measuring a few known determinants previously identified from genome sequencing, and thus requires the prior knowledge of its biological mechanisms. To overcome this limitation, we employed machine learning models to predict resistance to 11 compounds across four classes of antibiotics from existing and novel whole genome sequences of 1936 E. coli strains. We considered a range of methods, and examined population structure, isolation year, gene content, and polymorphism information as predictors. Gradient boosted decision trees consistently outperformed alternative models with an average accuracy of 0.91 on held-out data (range 0.81-0.97). While the best models most frequently employed gene content, an average accuracy score of 0.79 could be obtained using population structure information alone. Single nucleotide variation data were less useful, and significantly improved prediction only for two antibiotics, including ciprofloxacin. These results demonstrate that antibiotic resistance in E. coli can be accurately predicted from whole genome sequences without a priori knowledge of mechanisms, and that both genomic and epidemiological data can be informative. This paves way to integrating machine learning approaches into diagnostic tools in the clinic.

| S-EPMC6310291 | biostudies-literature

Robust and rapid algorithms facilitate large-scale whole genome sequencing downstream analysis in an integrative framework.

Project description:Whole genome sequencing (WGS) is a promising strategy to unravel variants or genes responsible for human diseases and traits. However, there is a lack of robust platforms for a comprehensive downstream analysis. In the present study, we first proposed three novel algorithms, sequence gap-filled gene feature annotation, bit-block encoded genotypes and sectional fast access to text lines to address three fundamental problems. The three algorithms then formed the infrastructure of a robust parallel computing framework, KGGSeq, for integrating downstream analysis functions for whole genome sequencing data. KGGSeq has been equipped with a comprehensive set of analysis functions for quality control, filtration, annotation, pathogenic prediction and statistical tests. In the tests with whole genome sequencing data from 1000 Genomes Project, KGGSeq annotated several thousand more reliable non-synonymous variants than other widely used tools (e.g. ANNOVAR and SNPEff). It took only around half an hour on a small server with 10 CPUs to access genotypes of ∼60 million variants of 2504 subjects, while a popular alternative tool required around one day. KGGSeq's bit-block genotype format used 1.5% or less space to flexibly represent phased or unphased genotypes with multiple alleles and achieved a speed of over 1000 times faster to calculate genotypic correlation.

| S-EPMC5435951 | biostudies-literature

An interactive viral genome evolution network analysis system enabling rapid large-scale molecular tracing of SARS-CoV-2.

Project description: Not available

| S-EPMC8743795 | biostudies-literature

Galaxy: a platform for interactive large-scale genome analysis.

Project description:Accessing and analyzing the exponentially expanding genomic sequence and functional data pose a challenge for biomedical researchers. Here we describe an interactive system, Galaxy, that combines the power of existing genome annotation databases with a simple Web portal to enable users to search remote resources, combine data from independent queries, and visualize the results. The heart of Galaxy is a flexible history system that stores the queries from each user; performs operations such as intersections, unions, and subtractions; and links to other computational tools. Galaxy can be accessed at http://g2.bx.psu.edu.

| S-EPMC1240089 | biostudies-literature

CuPCA: a web server for pan-cancer association analysis of large-scale cuproptosis-related genes.

Project description:Copper-induced cell death is a novel mechanism of cell death, which is defined as cuproptosis. The increasing level of copper can produce toxicity in cells and may cause the occurrence of cell death. Several previous studies have proved that cuproptosis has a tight association with various cancers. Thus, the discovery of relationships between cuproptosis-related genes (CRGs) and human cancers is of great importance. Pan-cancer analysis can efficiently help researchers find out the relationship between multiple cancers and target genes precisely and make various prognostic analyses on cancers and cancer patients. Pan-cancer web servers can provide researchers with direct results of pan-cancer prognostic analyses, which can greatly improve the efficiency of their work. However, to date, no web server provides pan-cancer analysis about CRGs. Therefore, we introduce the cuproptosis pan-cancer analysis database (CuPCA), the first database for various analysis results of CRGs through 33 cancer types. CuPCA is a user-friendly resource for cancer researchers to gain various prognostic analyses between cuproptosis and cancers. It provides single CRG pan-cancer analysis, multi-CRGs pan-cancer analysis, multi-CRlncRNA pan-cancer analysis, and mRNA-circRNA-lncRNA conjoint analysis. These analysis results can not only indicate the relationship between cancers and cuproptosis at both gene level and protein level, but also predict the conditions of different cancer patients, which include their clinical condition, survival condition, and their immunological condition. CuPCA procures the delivery of analyzed data to end users, which improves the efficiency of wide research as well as releases the value of data resources. Database URL: http://cupca.cn/.

| S-EPMC11373563 | biostudies-literature

Phylogenetic distribution of large-scale genome patchiness.

Project description:BACKGROUND: The phylogenetic distribution of large-scale genome structure (i.e. mosaic compositional patchiness) has been explored mainly by analytical ultracentrifugation of bulk DNA. However, with the availability of large, good-quality chromosome sequences, and the recently developed computational methods to directly analyze patchiness on the genome sequence, an evolutionary comparative analysis can be carried out at the sequence level. RESULTS: The local variations in the scaling exponent of the Detrended Fluctuation Analysis are used here to analyze large-scale genome structure and directly uncover the characteristic scales present in genome sequences. Furthermore, through shuffling experiments of selected genome regions, computationally-identified, isochore-like regions were identified as the biological source for the uncovered large-scale genome structure. The phylogenetic distribution of short- and large-scale patchiness was determined in the best-sequenced genome assemblies from eleven eukaryotic genomes: mammals (Homo sapiens, Pan troglodytes, Mus musculus, Rattus norvegicus, and Canis familiaris), birds (Gallus gallus), fishes (Danio rerio), invertebrates (Drosophila melanogaster and Caenorhabditis elegans), plants (Arabidopsis thaliana) and yeasts (Saccharomyces cerevisiae). We found large-scale patchiness of genome structure, associated with in silico determined, isochore-like regions, throughout this wide phylogenetic range. CONCLUSION: Large-scale genome structure is detected by directly analyzing DNA sequences in a wide range of eukaryotic chromosome sequences, from human to yeast. In all these genomes, large-scale patchiness can be associated with the isochore-like regions, as directly detected in silico at the sequence level.

| S-EPMC2397391 | biostudies-other

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data