ngsLCA — A toolkit for fast and flexible lowest common ancestor inference and taxonomic profiling of metagenomic data
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ngsLCA — A toolkit for fast and flexible lowest common ancestor inference and taxonomic profiling of metagenomic data. / Wang, Yucheng; Korneliussen, Thorfinn Sand; Holman, Luke E.; Manica, Andrea; Pedersen, Mikkel Winther.
In: Methods in Ecology and Evolution, Vol. 13, No. 12, 2022, p. 2699-2708.Research output: Contribution to journal › Journal article › peer-review
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TY - JOUR
T1 - ngsLCA — A toolkit for fast and flexible lowest common ancestor inference and taxonomic profiling of metagenomic data
AU - Wang, Yucheng
AU - Korneliussen, Thorfinn Sand
AU - Holman, Luke E.
AU - Manica, Andrea
AU - Pedersen, Mikkel Winther
N1 - Publisher Copyright: © 2022 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.
PY - 2022
Y1 - 2022
N2 - Metagenomic data generated from environmental samples is increasingly common in the analysis of modern and ancient biological communities. To obtain taxonomic profiles from this type of data, DNA sequences are aligned against large genomic reference databases and the lowest common ancestor (LCA) needs to be inferred for each sequence with multiple alignments. To date, efforts have mainly focused on improving the speed, sensitivity and specificity of alignment tools, and little effort has been applied to the LCA algorithm that generates the taxonomic profiles from alignments. We present ngsLCA, a command-line toolkit with two separate modules: the main program (in C/C++) performing LCA inference, and an R package for generating tables and visualisations of the taxonomic profiles. ngsLCA processed large datasets in BAM/SAM alignment format 4–11 times faster and used less memory compared to other available programs. It is compatible with the NCBI taxonomy and has flexible parameter settings. Furthermore, the toolkit offers functions for filtering, contamination removal, taxonomic clustering, and multiple ways of visualising the generated taxonomic profiles. ngsLCA bridges a gap in current metagenomic analyses by supplying a computationally light, easy-to-use, accurate, fast and flexible LCA algorithm with R functions for processing and illustrating the taxonomic profiles.
AB - Metagenomic data generated from environmental samples is increasingly common in the analysis of modern and ancient biological communities. To obtain taxonomic profiles from this type of data, DNA sequences are aligned against large genomic reference databases and the lowest common ancestor (LCA) needs to be inferred for each sequence with multiple alignments. To date, efforts have mainly focused on improving the speed, sensitivity and specificity of alignment tools, and little effort has been applied to the LCA algorithm that generates the taxonomic profiles from alignments. We present ngsLCA, a command-line toolkit with two separate modules: the main program (in C/C++) performing LCA inference, and an R package for generating tables and visualisations of the taxonomic profiles. ngsLCA processed large datasets in BAM/SAM alignment format 4–11 times faster and used less memory compared to other available programs. It is compatible with the NCBI taxonomy and has flexible parameter settings. Furthermore, the toolkit offers functions for filtering, contamination removal, taxonomic clustering, and multiple ways of visualising the generated taxonomic profiles. ngsLCA bridges a gap in current metagenomic analyses by supplying a computationally light, easy-to-use, accurate, fast and flexible LCA algorithm with R functions for processing and illustrating the taxonomic profiles.
KW - environmental DNA (eDNA)
KW - lowest common ancestor (LCA)
KW - metagenomics
KW - next-generation sequencing
KW - sedimentary ancient DNA (sedaDNA)
KW - shotgun sequencing
KW - taxonomic profiling
KW - toolkit
U2 - 10.1111/2041-210X.14006
DO - 10.1111/2041-210X.14006
M3 - Journal article
AN - SCOPUS:85139980564
VL - 13
SP - 2699
EP - 2708
JO - Methods in Ecology and Evolution
JF - Methods in Ecology and Evolution
SN - 2041-210X
IS - 12
ER -
ID: 323855167