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PEMA: a flexible Pipeline for Environmental DNA Metabarcoding Analysis of the 16S/18S ribosomal RNA, ITS, and COI marker genes
H. Zafeiropoulos, HQ. Viet, K. Vasileiadou, A. Potirakis, C. Arvanitidis, P. Topalis, C. Pavloudi, E. Pafilis
Jazyk angličtina Země Spojené státy americké
Typ dokumentu časopisecké články, práce podpořená grantem
NLK
BioMedCentral Open Access
od 2012
Directory of Open Access Journals
od 2012
Free Medical Journals
od 2012
PubMed Central
od 2012
Europe PubMed Central
od 2012
Open Access Digital Library
od 2011-01-01
Open Access Digital Library
od 2012-01-01
Open Access Digital Library
od 2012-01-01
Oxford Journals Open Access Collection
od 2011
ROAD: Directory of Open Access Scholarly Resources
od 2012
- MeSH
- Archaea MeSH
- Bacteria MeSH
- environmentální DNA chemie genetika MeSH
- houby MeSH
- metagenomika metody normy MeSH
- referenční standardy MeSH
- respirační komplex IV genetika MeSH
- RNA ribozomální 16S genetika MeSH
- RNA ribozomální 18S genetika MeSH
- rostliny MeSH
- senzitivita a specificita MeSH
- software MeSH
- taxonomické DNA čárové kódování metody normy MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
BACKGROUND: Environmental DNA and metabarcoding allow the identification of a mixture of species and launch a new era in bio- and eco-assessment. Many steps are required to obtain taxonomically assigned matrices from raw data. For most of these, a plethora of tools are available; each tool's execution parameters need to be tailored to reflect each experiment's idiosyncrasy. Adding to this complexity, the computation capacity of high-performance computing systems is frequently required for such analyses. To address the difficulties, bioinformatic pipelines need to combine state-of-the art technologies and algorithms with an easy to get-set-use framework, allowing researchers to tune each study. Software containerization technologies ease the sharing and running of software packages across operating systems; thus, they strongly facilitate pipeline development and usage. Likewise programming languages specialized for big data pipelines incorporate features like roll-back checkpoints and on-demand partial pipeline execution. FINDINGS: PEMA is a containerized assembly of key metabarcoding analysis tools that requires low effort in setting up, running, and customizing to researchers' needs. Based on third-party tools, PEMA performs read pre-processing, (molecular) operational taxonomic unit clustering, amplicon sequence variant inference, and taxonomy assignment for 16S and 18S ribosomal RNA, as well as ITS and COI marker gene data. Owing to its simplified parameterization and checkpoint support, PEMA allows users to explore alternative algorithms for specific steps of the pipeline without the need of a complete re-execution. PEMA was evaluated against both mock communities and previously published datasets and achieved results of comparable quality. CONCLUSIONS: A high-performance computing-based approach was used to develop PEMA; however, it can be used in personal computers as well. PEMA's time-efficient performance and good results will allow it to be used for accurate environmental DNA metabarcoding analysis, thus enhancing the applicability of next-generation biodiversity assessment studies.
Charles University Department of Ecology Faculty of Science Viničná 7 CZ 12844 Prague Czech Republic
LifeWatch ERIC Plaza España SN SECTOR 2 3 41013 Seville Spain
Citace poskytuje Crossref.org
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