Pepid: a Highly Modifiable, Bioinformatics-Oriented Peptide Search Engine
Bibliographic record
Abstract
Abstract Motivation Current peptide search engines are optimized for wet-lab workflows, i.e. they operate in an “end-to-end” manner to achieve good identification results, not to be modified or provide algorithmic insight. This makes developing new software methods to solve problems in peptide identification methods difficult, often requiring a full engine rewrite. Recently, many deep learning methods were proposed as solutions to various parts of the peptide identification task, but virtually none of those methods have been implemented in any actual peptide search process. We believe that the lack of a reliable bioinformatics research platform for peptide identification that enables such integrations is slowing down proteomics research as a whole. Results We present pepid, a bioinformatics research-oriented peptide search engine. Unlike other search engines, pepid is specifically designed with ease of computational research in mind. Our design is highly flexible and allows easy modifications with little required software development expertise, allowing researchers to focus on analysing and improving peptide identification methods.It also takes recent computational trends into account, such as the recent slew of deep learning publications in proteomics, and features a multi-phased batched operations design that is more appropriate than the spectrum batch “end-to-end” designs of existing search engines for those approaches. We show that pepid is competitive with common engines in terms of both identification rates and runtime, forming a minimum required baseline to enable further identification research. Availability and Implementation Pepid is available as open source software under the MIT license at https://github.com/lemieux-lab/pepid . Other data referenced in the text is 3rd party. The selected yeast proteome can be found on SwissProt with accession ID UP000002311 while the human proteome’s accession ID is UP0000005640. The ProteomeTools spectra can be found in the PRIDE archive under accession D PXD004732 and the One Hour Yeast Proteome can be found at the ChorusProject at https://chorusproject.org/anonymous/download/experiment/-8823069691100997209 and https://chorusproject.org/anonymous/download/experiment/449795368199176159 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".