De Novo Sequencing and Homology Searching
peer-reviewed · Molecular & Cellular Proteomics · 2012
peer-reviewed · Molecular & Cellular Proteomics · 2012. Bin Ma et al. In proteomics, de novo sequencing is the process of deriving peptide sequences from tandem mass spectra…
| Date | 2012-02-01 |
| Type | peer-reviewed |
| Venue | Molecular & Cellular Proteomics |
| Publisher | Elsevier BV |
| Contribution | review |
| DOI | 10.1074/mcp.O111.014902 |
| Citations (OpenAlex) | 184 |
| Venue 2-year citedness | 4.69 |
Abstract
In proteomics, de novo sequencing is the process of deriving peptide sequences from tandem mass spectra without the assistance of a sequence database. Such analyses have traditionally been performed manually by human experts, and more recently by computer programs that have been developed because of the need for higher throughput. Although powerful, de novo sequencing often can only determine partially correct sequence tags because of imperfect tandem mass spectra. However, these sequence tags can then be searched in a sequence database to identify the exact or a homologous peptide. Homology searches are particularly useful for the study of organisms whose genomes have not been sequenced. This tutorial will present background important to understanding de novo sequencing, suggestions on how to do this manually, plus descriptions of computer algorithms used to automate this process and to subsequently carryout homology-based database searches. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 1).
Methods and tools
- De novo sequencing and homology searching: Molecular & Cellular Proteomics review/tutorial on de novo sequencing and homology searching.
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Cited by (46)
- Compositional profiling of protein hydrolysates by high resolution liquid chromatography–mass spectrometry and chemometric analysis (2025) both
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- Metaproteomics Beyond Databases: Addressing the Challenges and Potentials of De Novo Sequencing (2025) crossref
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- HyPep: An Open-Source Software for Identification and Discovery of Neuropeptides Using Sequence Homology Search (2023) crossref
- Deep Learning Approaches for the Protein Scaffold Filling Problem (2022) crossref
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- Current state, existing challenges, and promising progress for de novo sequencing and assembly of monoclonal antibodies (2022) both
- Uncovering Hidden Members and Functions of the Soil Microbiome Using De Novo Metaproteomics (2022) semanticscholar
- Metaproteomic Characterization of Forensic Samples (2022) crossref
- Protein cycling in the eastern tropical North Pacific oxygen‐deficient zone: A de novo‐discovery peptidomic approach (2022) crossref
- Database-independent de novo metaproteomics of complex microbial communities (2021) crossref
- Uncovering hidden members and functions of the soil microbiome using de novo metaproteomics (2020) semanticscholar
- De novo sequencing of proteins by mass spectrometry (2020) both
- Flying blind, or just flying under the radar? The underappreciated power of de novo methods of mass spectrometric peptide identification (2020) crossref
- Quantitative profiling of microbial communities by de novo metaproteomics (2020) both
- Assessing Protein Sequence Database Suitability Using De Novo Sequencing (2020) both
- Uncovering Thousands of New Peptides with Sequence-Mask-Search Hybrid De Novo Peptide Sequencing Framework (2019) both
- Precision De Novo Peptide Sequencing Using Mirror Proteases of Ac-LysargiNase and Trypsin for Large-scale Proteomics (2019) both
- Estimating the Contribution of Proteasomal Spliced Peptides to the HLA-I Ligandome* (2018) crossref
- A potential golden age to come—current tools, recent use cases, and future avenues for de novo sequencing in proteomics (2018) crossref
- Identification, purification, biochemical and mass spectrometric characterization of novel phycobiliproteins from a marine red alga, Centroceras clavulatum (2018) crossref
- Estimating the Contribution of Proteasomal Spliced Peptides to the HLA-I Ligandome (2018) both
- Disentangling thermal stress responses in a reef-calcifier and its photosymbionts by shotgun proteomics (2018) both
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Cited in patents (2)
- US11573239B2 Methods and systems for de novo peptide sequencing using deep learning ↗ (Bioinformatics Solutions Inc., priority 2017-07-17, active)
- US11694769B2 Systems and methods for de novo peptide sequencing from data-independent acquisition using deep learning ↗ (Bioinformatics Solutions Inc., priority 2017-07-17, active)
Patents that list this paper among the literature they cite, whether the applicant or the examiner added it.