Please use this identifier to cite or link to this item: doi:10.22028/D291-41694
Title: PanPA: generation and alignment of panproteome graphs
Author(s): Dabbaghie, Fawaz
Srikakulam, Sanjay K.
Marschall, Tobias
Kalinina, Olga V.
Language: English
Title: Bioinformatics Advances
Volume: 3
Issue: 1
Publisher/Platform: Oxford University Press
Year of Publication: 2023
DDC notations: 610 Medicine and health
Publikation type: Journal 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 are still challenging due to this high diversity. In contrast, amino acid sequences have higher similarity due to evolutionary constraints, whereby a single amino acid may be encoded by several synonymous codons. Coding regions cover the majority of the genome in prokaryotes. Thus, panproteomes present an attractive alternative leveraging the higher 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 of protein sequences, indexes them, and builds a graph for each multiple sequence alignment. 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 1350 Escherichia coli. To demonstrate that panproteomes allow comparisons at longer phylogenetic distances, we compare DNA and protein alignments from 1073 Salmonella enterica assemblies against E. coli reference genome, pangenome, and panproteome using BWA, GraphAligner, and PanPA, respectively; with PanPA aligning around 22% more sequences. We also aligned a DNA short-reads whole genome sequencing (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 68% of the reads compared to 5% with BWA.
DOI of the first publication: 10.1093/bioadv/vbad167
URL of the first publication:
Link to this record: urn:nbn:de:bsz:291--ds-416940
ISSN: 2635-0041
Date of registration: 1-Mar-2024
Description of the related object: Supplementary data
Related object:
Faculty: M - Medizinische Fakultät
MI - Fakultät für Mathematik und Informatik
Department: M - Medizinische Biometrie, Epidemiologie und medizinische Informatik
MI - Informatik
Professorship: M - Prof. Dr. Olga Kalinina
MI - Prof. Dr. Tobias Marschall
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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