Bioinformatics for Protein Identification
Published:
Issued by: American Society for Mass Spectrometry (ASMS)
Instructors: Nuno Bandeira (University of California, San Diego), David Tabb (Stellenbosch University)
Completed on: June 5th, 2022
Course Overview:
This course provides a deep dive into the bioinformatics tools and algorithms that are essential for identifying proteins from shotgun LC-MS/MS datasets. It is designed for proteomics researchers to move beyond using software as a “black box” and gain a solid understanding of its inner workings. The curriculum focuses on the key steps in protein identification, from peptide-spectrum matching and error rate estimation to protein assembly, and also explores advanced techniques like spectral library searching and de novo sequencing.
Key Topics & Skills Acquired:
- Protein Identification Fundamentals: Peptide-spectrum matching, error rate estimation (target/decoy, distributional analysis), and protein assembly/parsimony.
- Database Search Algorithms: Understanding the structure, configuration, and implementation of various search engines.
- Advanced Identification Techniques: Spectral library searching, de novo sequence inference, and analysis of post-translational modifications (PTMs).
- Hands-On Software Experience: Gained practical skills with major proteomics software pipelines through live demonstrations:
- Trans-Proteomic Pipeline (TPP) from Seattle Proteome Center (ISB)
- ProteoSAFe from the Center for Computational Mass Spectrometry (UCSD)
- BumberDash / IDPicker from the Tabb Laboratory
Curriculum: The course was structured over two days:
- Day 1: Focused on database search algorithms, error rate estimation, protein parsimony, and a hands-on session with the Trans-Proteomic Pipeline.
- Day 2: Covered spectral library searching, post-translational modifications, and hands-on sessions with BumberDash/IDPicker and ProteoSAFe.
This course provided a robust theoretical and practical foundation in the bioinformatics of protein identification, enabling more reliable and informed analysis of proteomics data.
You can view the certificate here.