MegaGTA: a sensitive and accurate metagenomic gene-targeted assembler using iterative de Bruijn graphs

BMC Bioinformatics
Dinghua LiTak-Wah Lam

Abstract

The recent release of the gene-targeted metagenomics assembler Xander has demonstrated that using the trained Hidden Markov Model (HMM) to guide the traversal of de Bruijn graph gives obvious advantage over other assembly methods. Xander, as a pilot study, indeed has a lot of room for improvement. Apart from its slow speed, Xander uses only 1 k-mer size for graph construction and whatever choice of k will compromise either sensitivity or accuracy. Xander uses a Bloom-filter representation of de Bruijn graph to achieve a lower memory footprint. Bloom filters bring in false positives, and it is not clear how this would impact the quality of assembly. Xander does not keep track of the multiplicity of k-mers, which would have been an effective way to differentiate between erroneous k-mers and correct k-mers. In this paper, we present a new gene-targeted assembler MegaGTA, which attempts to improve Xander in different aspects. Quality-wise, it utilizes iterative de Bruijn graphs to take full advantage of multiple k-mer sizes to make the best of both sensitivity and accuracy. Computation-wise, it employs succinct de Bruijn graphs (SdBG) to achieve low memory footprint and high speed (the latter is benefited from a highly efficient pa...Continue Reading

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Citations

Mar 1, 2019·Briefings in Bioinformatics·Martin AylingRichard M Leggett
Mar 25, 2021·Briefings in Bioinformatics·Masood Ur Rehman KayaniLei Chen

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Datasets Mentioned

BETA
SRR172903

Software Mentioned

metaQUAST
MegaGTA Xander
JAVA
REAGO
MegaGTA
MEGAHIT
UCHIME
Framebot
SAT
FragGeneScan

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