PEAKS DB: De Novo Sequencing Assisted Database Search for Sensitive and Accurate Peptide Identification

By Jing Zhang, Lei Xin, Baozhen Shan, Weiwu Chen, Mingjie Xie, Denis Yuen, Weiming Zhang, Zefeng Zhang, Gilles A. Lajoie, B. Ma. Molecular & Cellular Proteomics, 2011

985 citations recorded by the source corpus.

Many software tools have been developed for the automated identification of peptides from tandem mass spectra. The accuracy and sensitivity of the identification software via database search are critical for successful proteomics experiments. A new database search tool, PEAKS DB, has been developed by incorporating the de novo sequencing results into the database search. PEAKS DB achieves significantly improved accuracy and sensitivity over two other commonly used software packages. Additionally, a new result validation method, decoy fusion, has been introduced to solve the issue of overconfidence that exists in the conventional target decoy method for certain types of peptide identification software.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/2ed64ddd3338a40680f0eebfd2340a98575116e1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Mathias Wilhelm
2. B. Kuster
3. Hannes Hahne
4. Marcus Bantscheff
5. David M. Rocke
6. Mikhail M. Savitski
7. Alexey I. Nesvizhskii
8. John Quackenbush

In Spectroscopy

1. Mathias Wilhelm
2. B. Kuster
3. Hannes Hahne
4. Marcus Bantscheff
5. Mikhail M. Savitski
6. Alexey I. Nesvizhskii

Citations in the corpus

• Sex estimation using sexually dimorphic amelogenin protein fragments in human enamel (2019, methodology): “AMELX_HUMAN and AMELY_HUMAN protein were quantified using label free quantitation by combining the peak ion intensity measurements (PEAKs™ software, version 8.5) of all peptides (> 1% FDR) specific to either protein and normalized for enamel mass (CI/mg) (Zhang et al., 2012).”
• A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets (2015, methodology): “This idea was in part inspired by the decoy fusion approach used for peptides (38), and in part by the established practice in the field of using a concatenated target and decoy database in order to select only for those PSMs that have the best score in either the target or the decoy space, rather t”