• Je něco špatně v tomto záznamu ?

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

. 2020 ; 9 (3) : . [pub] 20200301

Jazyk angličtina Země Spojené státy americké

Typ dokumentu časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/bmc21012827

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.

Citace poskytuje Crossref.org

000      
00000naa a2200000 a 4500
001      
bmc21012827
003      
CZ-PrNML
005      
20210507102323.0
007      
ta
008      
210420s2020 xxu f 000 0|eng||
009      
AR
024    7_
$a 10.1093/gigascience/giaa022 $2 doi
035    __
$a (PubMed)32161947
040    __
$a ABA008 $b cze $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a xxu
100    1_
$a Zafeiropoulos, Haris $u Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes P.O. Box 2214, 71003, Heraklion, Crete, Greece
245    10
$a PEMA: a flexible Pipeline for Environmental DNA Metabarcoding Analysis of the 16S/18S ribosomal RNA, ITS, and COI marker genes / $c H. Zafeiropoulos, HQ. Viet, K. Vasileiadou, A. Potirakis, C. Arvanitidis, P. Topalis, C. Pavloudi, E. Pafilis
520    9_
$a 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.
650    _2
$a zvířata $7 D000818
650    _2
$a Archaea $7 D001105
650    _2
$a Bacteria $7 D001419
650    _2
$a taxonomické DNA čárové kódování $x metody $x normy $7 D058893
650    _2
$a respirační komplex IV $x genetika $7 D003576
650    _2
$a environmentální DNA $x chemie $x genetika $7 D000080309
650    _2
$a houby $7 D005658
650    _2
$a metagenomika $x metody $x normy $7 D056186
650    _2
$a rostliny $7 D010944
650    _2
$a RNA ribozomální 16S $x genetika $7 D012336
650    _2
$a RNA ribozomální 18S $x genetika $7 D012337
650    _2
$a referenční standardy $7 D012015
650    _2
$a senzitivita a specificita $7 D012680
650    _2
$a software $7 D012984
655    _2
$a časopisecké články $7 D016428
655    _2
$a práce podpořená grantem $7 D013485
700    1_
$a Viet, Ha Quoc $u Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes P.O. Box 2214, 71003, Heraklion, Crete, Greece
700    1_
$a Vasileiadou, Katerina $u Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes P.O. Box 2214, 71003, Heraklion, Crete, Greece $u Charles University, Department of Ecology, Faculty of Science, Viničná 7, CZ-12844, Prague, Czech Republic
700    1_
$a Potirakis, Antonis $u Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes P.O. Box 2214, 71003, Heraklion, Crete, Greece
700    1_
$a Arvanitidis, Christos $u Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes P.O. Box 2214, 71003, Heraklion, Crete, Greece $u LifeWatch ERIC, Plaza España SN, SECTOR II-III 41013, Seville,Spain
700    1_
$a Topalis, Pantelis $u Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology (FORTH), Foundation for Research and Technology - Hellas, N. Plastira 100, GR-70013, Heraklion, Crete, Greece
700    1_
$a Pavloudi, Christina $u Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes P.O. Box 2214, 71003, Heraklion, Crete, Greece
700    1_
$a Pafilis, Evangelos $u Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes P.O. Box 2214, 71003, Heraklion, Crete, Greece
773    0_
$w MED00186214 $t GigaScience $x 2047-217X $g Roč. 9, č. 3 (2020)
856    41
$u https://pubmed.ncbi.nlm.nih.gov/32161947 $y Pubmed
910    __
$a ABA008 $b sig $c sign $y p $z 0
990    __
$a 20210420 $b ABA008
991    __
$a 20210507102323 $b ABA008
999    __
$a ok $b bmc $g 1651071 $s 1133206
BAS    __
$a 3
BAS    __
$a PreBMC
BMC    __
$a 2020 $b 9 $c 3 $e 20200301 $i 2047-217X $m GigaScience $n Gigascience $x MED00186214
LZP    __
$a Pubmed-20210420

Najít záznam

Citační ukazatele

Nahrávání dat ...

Možnosti archivace

Nahrávání dat ...