lra: A long read aligner for sequences and contigs

It is computationally challenging to detect variation by aligning single-molecule sequencing (SMS) reads, or contigs from SMS assemblies. One approach to efficiently align SMS reads is sparse dynamic programming (SDP), where optimal chains of exact matches are found between the sequence and the geno...

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Veröffentlicht in:PLoS computational biology Jg. 17; H. 6; S. e1009078
Hauptverfasser: Ren, Jingwen, Chaisson, Mark J. P.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 01.06.2021
Public Library of Science (PLoS)
Schlagworte:
Accuracy
Algorithms
Assembly
Biological variation
Biology and Life Sciences
Chain dynamics
Cluster Analysis
Computational Biology
Computer Simulation
Contig Mapping - statistics & numerical data
Databases, Nucleic Acid - statistics & numerical data
Datasets
Dynamic programming
Fines & penalties
Genetic Variation
Genome, Human
Genomes
High-Throughput Nucleotide Sequencing
Humans
Linear functions
Nucleotide sequence
Physical Sciences
Programming, Linear
Research and Analysis Methods
Sequence Alignment - statistics & numerical data
Sequence Analysis, DNA
Software
Variation
United States
ISSN:1553-7358, 1553-734X, 1553-7358
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Zusammenfassung:It is computationally challenging to detect variation by aligning single-molecule sequencing (SMS) reads, or contigs from SMS assemblies. One approach to efficiently align SMS reads is sparse dynamic programming (SDP), where optimal chains of exact matches are found between the sequence and the genome. While straightforward implementations of SDP penalize gaps with a cost that is a linear function of gap length, biological variation is more accurately represented when gap cost is a concave function of gap length. We have developed a method, lra, that uses SDP with a concave-cost gap penalty, and used lra to align long-read sequences from PacBio and Oxford Nanopore (ONT) instruments as well as de novo assembly contigs. This alignment approach increases sensitivity and specificity for SV discovery, particularly for variants above 1kb and when discovering variation from ONT reads, while having runtime that are comparable (1.05-3.76×) to current methods. When applied to calling variation from de novo assembly contigs, there is a 3.2% increase in Truvari F1 score compared to minimap2+htsbox. lra is available in bioconda ( https://anaconda.org/bioconda/lra ) and github ( https://github.com/ChaissonLab/LRA ).
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The authors have declared that no competing interests exist.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1009078