PARTIE: A partition engine to separate metagenomic and amplicon projects in the Sequence Read Archive

Pedro J. Torres; Robert A. Edwards; Katelyn A. McNair

doi:10.1093/bioinformatics/btx184

PARTIE: A partition engine to separate metagenomic and amplicon projects in the Sequence Read Archive

Pedro J. Torres, Robert A. Edwards, Katelyn A. McNair

Research output: Contribution to journal › Article › peer-review

20 Citations (Scopus)

57 Downloads (Pure)

Abstract

Motivation: The Sequence Read Archive (SRA) contains raw data from many different types of sequence projects. As of 2017, the SRA contained approximately ten petabases of DNA sequence (10¹⁶ bp). Annotations of the data are provided by the submitter, and mining the data in the SRA is complicated by both the amount of data and the detail within those annotations. Here, we introduce PARTIE, a partition engine optimized to differentiate sequence read data into metagenomic (random) and amplicon (targeted) sequence data sets. Results: PARTIE subsamples reads from the sequencing file and calculates four different statistics: k-mer frequency, 16S abundance, prokaryotic-and viral-read abundance. These metrics are used to create a RandomForest decision tree to classify the sequencing data, and PARTIE provides mechanisms for both supervised and unsupervised classification. We demonstrate the accuracy of PARTIE for classifying SRA data, discuss the probable error rates in the SRA annotations and introduce a resource assessing SRA data.

Original language	English
Pages (from-to)	2389-2391
Number of pages	3
Journal	Bioinformatics
Volume	33
Issue number	15
DOIs	https://doi.org/10.1093/bioinformatics/btx184
Publication status	Published - 1 Aug 2017
Externally published	Yes

Bibliographical note

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Keywords

Sequence Read Archive
PARTIE
bioinformatics

Access to Document

10.1093/bioinformatics/btx184Licence: CC BY

Torres_PARTIE_P2017Final published version, 191 KBLicence: CC BY

Cite this

@article{69435500359c4b768ca43fc38f47d267,

title = "PARTIE: A partition engine to separate metagenomic and amplicon projects in the Sequence Read Archive",

abstract = "Motivation: The Sequence Read Archive (SRA) contains raw data from many different types of sequence projects. As of 2017, the SRA contained approximately ten petabases of DNA sequence (1016 bp). Annotations of the data are provided by the submitter, and mining the data in the SRA is complicated by both the amount of data and the detail within those annotations. Here, we introduce PARTIE, a partition engine optimized to differentiate sequence read data into metagenomic (random) and amplicon (targeted) sequence data sets. Results: PARTIE subsamples reads from the sequencing file and calculates four different statistics: k-mer frequency, 16S abundance, prokaryotic-and viral-read abundance. These metrics are used to create a RandomForest decision tree to classify the sequencing data, and PARTIE provides mechanisms for both supervised and unsupervised classification. We demonstrate the accuracy of PARTIE for classifying SRA data, discuss the probable error rates in the SRA annotations and introduce a resource assessing SRA data.",

keywords = "Sequence Read Archive, PARTIE, bioinformatics",

author = "Torres, {Pedro J.} and Edwards, {Robert A.} and McNair, {Katelyn A.}",

note = "This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.",

year = "2017",

month = aug,

day = "1",

doi = "10.1093/bioinformatics/btx184",

language = "English",

volume = "33",

pages = "2389--2391",

journal = "Bioinformatics",

issn = "1367-4803",

publisher = "Oxford University Press",

number = "15",

}

TY - JOUR

T1 - PARTIE

T2 - A partition engine to separate metagenomic and amplicon projects in the Sequence Read Archive

AU - Torres, Pedro J.

AU - Edwards, Robert A.

AU - McNair, Katelyn A.

N1 - This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

PY - 2017/8/1

Y1 - 2017/8/1

N2 - Motivation: The Sequence Read Archive (SRA) contains raw data from many different types of sequence projects. As of 2017, the SRA contained approximately ten petabases of DNA sequence (1016 bp). Annotations of the data are provided by the submitter, and mining the data in the SRA is complicated by both the amount of data and the detail within those annotations. Here, we introduce PARTIE, a partition engine optimized to differentiate sequence read data into metagenomic (random) and amplicon (targeted) sequence data sets. Results: PARTIE subsamples reads from the sequencing file and calculates four different statistics: k-mer frequency, 16S abundance, prokaryotic-and viral-read abundance. These metrics are used to create a RandomForest decision tree to classify the sequencing data, and PARTIE provides mechanisms for both supervised and unsupervised classification. We demonstrate the accuracy of PARTIE for classifying SRA data, discuss the probable error rates in the SRA annotations and introduce a resource assessing SRA data.

AB - Motivation: The Sequence Read Archive (SRA) contains raw data from many different types of sequence projects. As of 2017, the SRA contained approximately ten petabases of DNA sequence (1016 bp). Annotations of the data are provided by the submitter, and mining the data in the SRA is complicated by both the amount of data and the detail within those annotations. Here, we introduce PARTIE, a partition engine optimized to differentiate sequence read data into metagenomic (random) and amplicon (targeted) sequence data sets. Results: PARTIE subsamples reads from the sequencing file and calculates four different statistics: k-mer frequency, 16S abundance, prokaryotic-and viral-read abundance. These metrics are used to create a RandomForest decision tree to classify the sequencing data, and PARTIE provides mechanisms for both supervised and unsupervised classification. We demonstrate the accuracy of PARTIE for classifying SRA data, discuss the probable error rates in the SRA annotations and introduce a resource assessing SRA data.

KW - Sequence Read Archive

KW - PARTIE

KW - bioinformatics

UR - http://www.scopus.com/inward/record.url?scp=85026411294&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/btx184

DO - 10.1093/bioinformatics/btx184

M3 - Article

C2 - 28369246

AN - SCOPUS:85026411294

SN - 1367-4803

VL - 33

SP - 2389

EP - 2391

JO - Bioinformatics

JF - Bioinformatics

IS - 15

ER -

PARTIE: A partition engine to separate metagenomic and amplicon projects in the Sequence Read Archive

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this