Dataset Information

Pangloss: A Tool for Pan-Genome Analysis of Microbial Eukaryotes.

ABSTRACT: Although the pan-genome concept originated in prokaryote genomics, an increasing number of eukaryote species pan-genomes have also been analysed. However, there is a relative lack of software intended for eukaryote pan-genome analysis compared to that available for prokaryotes. In a previous study, we analysed the pan-genomes of four model fungi with a computational pipeline that constructed pan-genomes using the synteny-dependent Pan-genome Ortholog Clustering Tool (PanOCT) approach. Here, we present a modified and improved version of that pipeline which we have called Pangloss. Pangloss can perform gene prediction for a set of genomes from a given species that the user provides, constructs and optionally refines a species pan-genome from that set using PanOCT, and can perform various functional characterisation and visualisation analyses of species pan-genome data. To demonstrate Pangloss's capabilities, we constructed and analysed a species pan-genome for the oleaginous yeast Yarrowia lipolytica and also reconstructed a previously-published species pan-genome for the opportunistic respiratory pathogen Aspergillus fumigatus. Pangloss is implemented in Python, Perl and R and is freely available under an open source GPLv3 licence via GitHub.

SUBMITTER: McCarthy CGP

PROVIDER: S-EPMC6678930 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundMicroarray and DNA-sequencing based technologies continue to produce enormous amounts of data on gene expression. This data has great potential to illuminate our understanding of biology and medicine, but the data alone is of limited value without computational tools to allow human investigators to visualize and interpret it in the context of their problem of interest.ResultsWe created a web server called SHOE that provides an interactive, visual presentation of the available evidence of transcriptional regulation and gene co-expression to facilitate its exploration and interpretation. SHOE predicts the likely transcription factor binding sites in orthologous promoters of humans, mice, and rats using the combined information of 1) transcription factor binding preferences (position-specific scoring matrix (PSSM) libraries such as Transfac32, Jaspar, HOCOMOCO, ChIP-seq, SELEX, PBM, and iPS-reprogramming factor), 2) evolutionary conservation of putative binding sites in orthologous promoters, and 3) co-expression tendencies of gene pairs based on 1,714 normal human cells selected from the Gene Expression Omnibus Database.ConclusionSHOE enables users to explore potential interactions between transcription factors and target genes via multiple data views, discover transcription factor binding motifs on top of gene co-expression, and visualize genes as a network of gene and transcription factors on its native gadget GeneViz, the CellDesigner pathway analyzer, and the Reactome database to search the pathways involved. As we demonstrate here when using the CREB1 and Nf-?B datasets, SHOE can reliably identify experimentally verified interactions and predict plausible novel ones, yielding new biological insights into the gene regulatory mechanisms involved. SHOE comes with a manual describing how to run it on a local PC or via the Garuda platform ( www.garuda-alliance.org ), where it joins other popular gadgets such as the CellDesigner pathway analyzer and the Reactome database, as part of analysis workflows to meet the growing needs of molecular biologists and medical researchers. SHOE is available from the following URL http://ec2-54-150-223-65.ap-northeast-1.compute.amazonaws.com A video demonstration of SHOE can be found here: https://www.youtube.com/watch?v=qARinNb9NtE.

Project description:BACKGROUND: The pan-genome of a bacterial species consists of a core and an accessory gene pool. The accessory genome is thought to be an important source of genetic variability in bacterial populations and is gained through lateral gene transfer, allowing subpopulations of bacteria to better adapt to specific niches. Low-cost and high-throughput sequencing platforms have created an exponential increase in genome sequence data and an opportunity to study the pan-genomes of many bacterial species. In this study, we describe a new online pan-genome sequence analysis program, Panseq. RESULTS: Panseq was used to identify Escherichia coli O157:H7 and E. coli K-12 genomic islands. Within a population of 60 E. coli O157:H7 strains, the existence of 65 accessory genomic regions identified by Panseq analysis was confirmed by PCR. The accessory genome and binary presence/absence data, and core genome and single nucleotide polymorphisms (SNPs) of six L. monocytogenes strains were extracted with Panseq and hierarchically clustered and visualized. The nucleotide core and binary accessory data were also used to construct maximum parsimony (MP) trees, which were compared to the MP tree generated by multi-locus sequence typing (MLST). The topology of the accessory and core trees was identical but differed from the tree produced using seven MLST loci. The Loci Selector module found the most variable and discriminatory combinations of four loci within a 100 loci set among 10 strains in 1 s, compared to the 449 s required to exhaustively search for all possible combinations; it also found the most discriminatory 20 loci from a 96 loci E. coli O157:H7 SNP dataset. CONCLUSION: Panseq determines the core and accessory regions among a collection of genomic sequences based on user-defined parameters. It readily extracts regions unique to a genome or group of genomes, identifies SNPs within shared core genomic regions, constructs files for use in phylogeny programs based on both the presence/absence of accessory regions and SNPs within core regions and produces a graphical overview of the output. Panseq also includes a loci selector that calculates the most variable and discriminatory loci among sets of accessory loci or core gene SNPs. AVAILABILITY: Panseq is freely available online at http://76.70.11.198/panseq. Panseq is written in Perl.

Dataset Information

Pangloss: A Tool for Pan-Genome Analysis of Microbial Eukaryotes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets