Homology-driven assembly of NOn-redundant protEin sequence sets (NOmESS) for mass spectrometry

Tikira Temu; Matthias Mann; Markus Räschle; Jürgen Cox

doi:10.1093/bioinformatics/btv756

Homology-driven assembly of NOn-redundant protEin sequence sets (NOmESS) for mass spectrometry

Tikira Temu, Matthias Mann, Markus Räschle, Jürgen Cox

3 Citations (Scopus)

Abstract

Summary: To enable mass spectrometry (MS)-based proteomic studies with poorly characterized organisms, we developed a computational workflow for the homology-driven assembly of a non-redundant reference sequence dataset. In the automated pipeline, translated DNA sequences (e.g. ESTs, RNA deep-sequencing data) are aligned to those of a closely related and fully sequenced organism. Representative sequences are derived from each cluster and joined, resulting in a non-redundant reference set representing the maximal available amino acid sequence information for each protein. We here applied NOmESS to assemble a reference database for the widely used model organism Xenopus laevis and demonstrate its use in proteomic applications.

Original language	English
Journal	Bioinformatics (Online)
Volume	32
Issue number	9
Pages (from-to)	1417-9
Number of pages	3
ISSN	1367-4811
DOIs	https://doi.org/10.1093/bioinformatics/btv756
Publication status	Published - 1 May 2016
Externally published	Yes

Keywords

Amino Acid Sequence
Animals
Base Sequence
High-Throughput Nucleotide Sequencing
Humans
Mass Spectrometry
Proteomics
Journal Article

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10.1093/bioinformatics/btv756

btv756.pdfFinal published version, 149 KBLicence: CC BY

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title = "Homology-driven assembly of NOn-redundant protEin sequence sets (NOmESS) for mass spectrometry",

abstract = "Summary: To enable mass spectrometry (MS)-based proteomic studies with poorly characterized organisms, we developed a computational workflow for the homology-driven assembly of a non-redundant reference sequence dataset. In the automated pipeline, translated DNA sequences (e.g. ESTs, RNA deep-sequencing data) are aligned to those of a closely related and fully sequenced organism. Representative sequences are derived from each cluster and joined, resulting in a non-redundant reference set representing the maximal available amino acid sequence information for each protein. We here applied NOmESS to assemble a reference database for the widely used model organism Xenopus laevis and demonstrate its use in proteomic applications.",

keywords = "Amino Acid Sequence, Animals, Base Sequence, High-Throughput Nucleotide Sequencing, Humans, Mass Spectrometry, Proteomics, Journal Article",

author = "Tikira Temu and Matthias Mann and Markus R{\"a}schle and J{\"u}rgen Cox",

note = "{\textcopyright} The Author 2016. Published by Oxford University Press.",

year = "2016",

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doi = "10.1093/bioinformatics/btv756",

language = "English",

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journal = "Bioinformatics (Online)",

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T1 - Homology-driven assembly of NOn-redundant protEin sequence sets (NOmESS) for mass spectrometry

AU - Temu, Tikira

AU - Mann, Matthias

AU - Räschle, Markus

AU - Cox, Jürgen

N1 - © The Author 2016. Published by Oxford University Press.

PY - 2016/5/1

Y1 - 2016/5/1

N2 - Summary: To enable mass spectrometry (MS)-based proteomic studies with poorly characterized organisms, we developed a computational workflow for the homology-driven assembly of a non-redundant reference sequence dataset. In the automated pipeline, translated DNA sequences (e.g. ESTs, RNA deep-sequencing data) are aligned to those of a closely related and fully sequenced organism. Representative sequences are derived from each cluster and joined, resulting in a non-redundant reference set representing the maximal available amino acid sequence information for each protein. We here applied NOmESS to assemble a reference database for the widely used model organism Xenopus laevis and demonstrate its use in proteomic applications.

AB - Summary: To enable mass spectrometry (MS)-based proteomic studies with poorly characterized organisms, we developed a computational workflow for the homology-driven assembly of a non-redundant reference sequence dataset. In the automated pipeline, translated DNA sequences (e.g. ESTs, RNA deep-sequencing data) are aligned to those of a closely related and fully sequenced organism. Representative sequences are derived from each cluster and joined, resulting in a non-redundant reference set representing the maximal available amino acid sequence information for each protein. We here applied NOmESS to assemble a reference database for the widely used model organism Xenopus laevis and demonstrate its use in proteomic applications.

KW - Amino Acid Sequence

KW - Animals

KW - Base Sequence

KW - High-Throughput Nucleotide Sequencing

KW - Humans

KW - Mass Spectrometry

KW - Proteomics

KW - Journal Article

U2 - 10.1093/bioinformatics/btv756

DO - 10.1093/bioinformatics/btv756

M3 - Journal article

C2 - 26743511

SN - 1367-4811

VL - 32

SP - 1417

EP - 1419

JO - Bioinformatics (Online)

JF - Bioinformatics (Online)

IS - 9

ER -

Homology-driven assembly of NOn-redundant protEin sequence sets (NOmESS) for mass spectrometry

Abstract

Keywords

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