Journal
BIOINFORMATICS
Volume 32, Issue 9, Pages 1417-1419Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btv756
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Funding
- Center for Integrated Protein Sciences Munich (CIPSM)
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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.
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