Inside the Pan-genome - Methods and Software Overview
Luís Carlos Guimarães, Leandro Benevides de Jesus, Marcus Vinícius Canário Viana, Artur Silva, Rommel Thiago Jucá Ramos, Siomar de Castro Soares, Vasco Azevedo
Abstract
Open-access reader
Luís Carlos Guimarães, Leandro Benevides de Jesus, Marcus Vinícius Canário Viana, Artur Silva, Rommel Thiago Jucá Ramos, Siomar de Castro Soares, Vasco Azevedo
Abstract
Open-access reader
The number of genomes that have been deposited in databases has increased exponentially after the advent of Next-Generation Sequencing (NGS), which produces high-throughput sequence data; this circumstance has demanded the development of new bioinformatics software and the creation of new areas, such as comparative genomics. In comparative genomics, the genetic content of an organism is compared against other organisms, which helps in the prediction of gene function and coding region sequences, identification of evolutionary events and determination of phylogenetic relationships. However, expanding comparative genomics to a large number of related bacteria, we can infer their lifestyles, gene repertoires and minimal genome size. In this context, a powerful approach called Pan-genome has been initiated and developed. This approach involves the genomic comparison of different strains of the same species, or even genus. Its main goal is to establish the total number of non-redundant genes that are present in a determined dataset. Pan-genome consists of three parts: core genome; accessory or dispensable genome; and species-specific or strain-specific genes. Furthermore, pan-genome is considered to be "open" as long as new genes are added significantly to the total repertoire for each new additional genome and "closed" when the newly added genomes cannot be inferred to significantly increase the total repertoire of the genes. To perform all of the required calculations, a substantial amount of software has been developed, based on orthologous and paralogous gene identification.
OpenAlex reports 117 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
The number of genomes that have been deposited in databases has increased exponentially after the advent of Next-Generation Sequencing (NGS), which produces high-throughput sequence data; this circumstance has demanded the development of new bioinformatics software and the creation of new areas, such as comparative genomics. In comparative genomics, the genetic content of an organism is compared against other organisms, which helps in the prediction of gene function and coding region sequences, identification of evolutionary events and determination of phylogenetic relationships. However, expanding comparative genomics to a large number of related bacteria, we can infer their lifestyles, gene repertoires and minimal genome size. In this context, a powerful approach called Pan-genome has been initiated and developed. This approach involves the genomic comparison of different strains of the same species, or even genus. Its main goal is to establish the total number of non-redundant genes that are present in a determined dataset. Pan-genome consists of three parts: core genome; accessory or dispensable genome; and species-specific or strain-specific genes. Furthermore, pan-genome is considered to be "open" as long as new genes are added significantly to the total repertoire for each new additional genome and "closed" when the newly added genomes cannot be inferred to significantly increase the total repertoire of the genes. To perform all of the required calculations, a substantial amount of software has been developed, based on orthologous and paralogous gene identification.
Key concepts: Genome, Comparative genomics, Biology, Genomics, Gene, Whole genome sequencing, Phylogenetic tree, Computational biology