DNMSO; an ontology for representing de novo sequencing results from Tandem-MS data

TAKAN S., Allmer J.

PEERJ, cilt.8, 2020 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 8
  • Basım Tarihi: 2020
  • Doi Numarası: 10.7717/peerj.10216
  • Dergi Adı: PEERJ
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, CAB Abstracts, EMBASE, Veterinary Science Database, Directory of Open Access Journals
  • Anahtar Kelimeler: Mass spectrometry, De novo sequencing, Ontology, DNMSO, DNML, Format, DATA STANDARD, PEPTIDE
  • Ankara Üniversitesi Adresli: Hayır

Özet

For the identification and sequencing of proteins, mass spectrometry (MS) has become the tool of choice and, as such, drives proteomics. MS/MS spectra need to be assigned a peptide sequence for which two strategies exist. Either database search or de novo sequencing can be employed to establish peptide spectrum matches. For database search, mzIdentML is the current community standard for data representation. There is no community standard for representing de novo sequencing results, but we previously proposed the de novo markup language (DNML). At the moment, each de novo sequencing solution uses different data representation, complicating downstream data integration, which is crucial since ensemble predictions may be more useful than predictions of a single tool. We here propose the de novo MS Ontology (DNMSO), which can, for example, provide many-to-many mappings between spectra and peptide predictions. Additionally, an application programming interface (API) that supports any file operation necessary for de novo sequencing from spectra input to reading, writing, creating, of the DNMSO format, as well as conversion from many other file formats, has been implemented. This API removes all overhead from the production of de novo sequencing tools and allows developers to concentrate on algorithm development completely. We make the API and formal descriptions of the format freely available at https://github.com/savastakan/dnmso.