title: Homology-driven assembly of NOn-redundant protEin sequence sets (NOmESS) for mass spectrometry.
abstract: 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.
  
  
  
  NOmESS is written in C#. The source code as well as the executables can be downloaded from http://www.biochem.mpg.de/cox Execution of NOmESS requires BLASTp and cd-hit in addition.
  
  
  
  cox@biochem.mpg.de
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Computational Systems Biochemistry and Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried 82152, Germany.
authors.forename: Tikira
authors.initials: T
authors.lastname: Temu
authors.affiliation: Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried 82152, Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: Mann
authors.affiliation: Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried 82152, Germany.
authors.forename: Markus
authors.initials: M
authors.lastname: Räschle
authors.affiliation: Computational Systems Biochemistry and.
authors.forename: Jürgen
authors.initials: J
authors.lastname: Cox
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D001483:Base Sequence; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D013058:Mass Spectrometry; D040901:Proteomics