THAPBI PICT-a fast, cautious, and accurate metabarcoding analysis pipeline.

Autor:	Cock PJA; Information and Computational Sciences, The James Hutton Institute, Dundee, United Kingdom., Cooke DEL; Cell and Molecular Sciences, The James Hutton Institute, Dundee, United Kingdom., Thorpe P; Cell and Molecular Sciences, The James Hutton Institute, Dundee, United Kingdom.; The Data Analysis Group, School of Life Sciences, The University of Dundee, Dundee, United Kingdom., Pritchard L; Information and Computational Sciences, The James Hutton Institute, Dundee, United Kingdom.; Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, United Kingdom.
Jazyk:	angličtina
Zdroj:	PeerJ [PeerJ] 2023 Aug 18; Vol. 11, pp. e15648. Date of Electronic Publication: 2023 Aug 18 (Print Publication: 2023).
DOI:	10.7717/peerj.15648
Abstrakt:	THAPBI PICT is an open source software pipeline for metabarcoding analysis of Illumina paired-end reads, including cases of multiplexing where more than one amplicon is amplified per DNA sample. Initially a Phytophthora ITS1 Classification Tool (PICT), we demonstrate using worked examples with our own and public data sets how, with appropriate primer settings and a custom database, it can be applied to other amplicons and organisms, and used for reanalysis of existing datasets. The core dataflow of the implementation is (i) data reduction to unique marker sequences, often called amplicon sequence variants (ASVs), (ii) dynamic thresholds for discarding low abundance sequences to remove noise and artifacts (rather than error correction by default), before (iii) classification using a curated reference database. The default classifier assigns a label to each query sequence based on a database match that is either perfect, or a single base pair edit away (substitution, deletion or insertion). Abundance thresholds for inclusion can be set by the user or automatically using per-batch negative or synthetic control samples. Output is designed for practical interpretation by non-specialists and includes a read report (ASVs with classification and counts per sample), sample report (samples with counts per species classification), and a topological graph of ASVs as nodes with short edit distances as edges. Source code available from https://github.com/peterjc/thapbi-pict/ with documentation including installation instructions. Competing Interests: The authors declare that they have no competing interests. (© 2023 Cock et al.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu