Background: Graph-based representation of genome assemblies has been recently used in different contexts - from improved reconstruction of plasmid sequences and refined analysis of metagenomic data to read error correction and reference-free haplotype reconstruction. While many of these applications heavily utilize the alignment of long nucleotide sequences to assembly graphs, first general-purpose software tools for finding such alignments have been released only recently and their deficiencies and limitations are yet to be discovered. Moreover, existing tools can not perform alignment of amino acid sequences, which could prove useful in various contexts - in particular the analysis of metagenomic sequencing data. Results: In this work we present a novel SPAligner (Saint-Petersburg Aligner) tool for aligning long diverged nucleotide and amino acid sequences to assembly graphs. We demonstrate that SPAligner is an efficient solution for mapping third generation sequencing reads onto assembly graphs of various complexity and also show how it can facilitate the identification of known genes in complex metagenomic datasets. Conclusions: Our work will facilitate accelerating the development of graph-based approaches in solving sequence to genome assembly alignment problem. SPAligner is implemented as a part of SPAdes tools library and is available on Github.

Original languageEnglish
Article number306
JournalBMC Bioinformatics
Volume21
Issue numberSuppl 12
DOIs
StatePublished - 24 Jul 2020
Event3rd International Conference on Bioinformatics - From Algorithms to Applications (BiATA) - Saint Petersburg, Russian Federation
Duration: 20 Jun 201922 Jun 2019

    Research areas

  • Assembly graph, Graph alignment, Molecular sequences alignment, Genetic Variation, Sequence Alignment, Algorithms, Base Sequence, Humans, Software, Statistics as Topic, Haplotypes/genetics, beta-Lactamases/chemistry

    Scopus subject areas

  • Applied Mathematics
  • Molecular Biology
  • Structural Biology
  • Biochemistry
  • Computer Science Applications

ID: 49272157