bioRxivMicrobiology
Coinfinder: Detecting Significant Associations and Dissociations in Pangenomes
Abstract
2.The accessory genes of prokaryote and eukaryote pangenomes accumulate by horizontal gene transfer, differential gene loss, and the effects of selection and drift. We have developed Coinfinder, a software program that assesses whether sets of homologous genes (gene families) in pangenomes associate or dissociate with each other (i.e. are "coincident") more often than would be expected by chance. Coinfinder employs a user-supplied phylogenetic tree in order to assess the lineage-dependence (i.e. the phylogenetic distribution) of each accessory gene, allowing Coinfinder to focus on coincident gene pairs whose joint presence is not simply because they happened to appear in the same clade, but rather that they tend to appear together more often than expected across the phylogeny. Coinfinder is implemented in C++, Python3, and R and is freely available under the GPU license from https://github.com/fwhelan/coinfinder. 3. Impact statementCoinfinder identifies genes that co-occur (associate) or avoid (dissociate) with each other across the accessory genomes of a pangenome of interest. Genes that associate or dissociate more often than expected by chance, suggests that those genes have a connection (attraction or repulsion) that is interesting to explore. Identification of these groups of genes will further the fields understanding of the importance of accessory genes. Coinfinder is a freely available, open-source software which can identify gene patterns locally on a personal computer in a matter of hours. 4. Data summaryO_LICoinfinder is freely available at https://github.com/fwhelan/coinfinder. C_LIO_LIA list of the Identifiers of the genomes used within as well as all input/output files are available at https://github.com/fwhelan/coinfinder-manuscript. C_LI The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.
- assay: proteomics
- assay: metagenomics
- research focus: gene regulatory network inference
- research focus: clustering
- research focus: differential expression
- approach: statistical model
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