Year: 2023
Type: article
Abstract: Motivation Compared to eukaryotes, prokaryote genomes are more diverse through different mechanisms, including a higher mutation rate and horizontal gene transfer. Therefore, using a linear representative reference can cause a reference bias. Graph-based pangenome methods have been developed to tackle this problem. However, comparisons in DNA space is still challenging due to this high diversity. In contrast, amino acids have higher similarity due to evolutionary constraints, resulting in conserved amino acids that, however, may be encoded by several synonymous codons. Coding regions cover the majority of the genome in prokaryotes. Thus, building panproteomes leverages the high sequence similarity while not losing much of the genome in non-coding regions. Results We present PanPA , a method that takes a set of multiple sequence alignments (MSAs) of proteins or protein clusters, indexes them, and builds a graph for each MSA. In the querying step, it can align DNA or amino acid sequences back to these graphs. We first showcase that PanPA generates correct alignments on a panproteome from 1,350 E. coli . To demonstrate that panproteomes allow longer phylogenetic distance comparison, we compare DNA and protein alignments from 1,073 S. enterica assemblies against E. coli reference genome, pangenome, and panproteome using BWA , GraphAligner , and PanPA respectively, where PanPA was able to produce around 22% more alignments. We also aligned DNA short-reads WGS sample from S. enterica against the E. coli reference with BWA and the panproteome with PanPA , where PanPA was able to find alignment for 69% of the reads compared to 5% with BWA Availability PanPA is available at https://github.com/fawaz-dabbaghieh/PanPA Contact [email protected] , [email protected] Supplementary information Supplementary data are available at Bioinformatics online.
Source: Bioinformatics Advances
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,460
OpenAlex ID: W4389010703