Dataset Information
Versatile genome assembly evaluation with QUAST-LG.
Ontology highlight
ABSTRACT: Motivation
The emergence of high-throughput sequencing technologies revolutionized genomics in early 2000s. The next revolution came with the era of long-read sequencing. These technological advances along with novel computational approaches became the next step towards the automatic pipelines capable to assemble nearly complete mammalian-size genomes.Results
In this manuscript, we demonstrate performance of the state-of-the-art genome assembly software on six eukaryotic datasets sequenced using different technologies. To evaluate the results, we developed QUAST-LG-a tool that compares large genomic de novo assemblies against reference sequences and computes relevant quality metrics. Since genomes generally cannot be reconstructed completely due to complex repeat patterns and low coverage regions, we introduce a concept of upper bound assembly for a given genome and set of reads, and compute theoretical limits on assembly correctness and completeness. Using QUAST-LG, we show how close the assemblies are to the theoretical optimum, and how far this optimum is from the finished reference.Availability and implementation
http://cab.spbu.ru/software/quast-lg.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Mikheenko A
PROVIDER: S-EPMC6022658 | biostudies-literature | 2018 Jul
REPOSITORIES: biostudies-literature
Publications
Versatile genome assembly evaluation with QUAST-LG.
Bioinformatics (Oxford, England) 20180701 13
<h4>Motivation</h4>The emergence of high-throughput sequencing technologies revolutionized genomics in early 2000s. The next revolution came with the era of long-read sequencing. These technological advances along with novel computational approaches became the next step towards the automatic pipelines capable to assemble nearly complete mammalian-size genomes.<h4>Results</h4>In this manuscript, we demonstrate performance of the state-of-the-art genome assembly software on six eukaryotic datasets ...[more]
