ArticlePLoS computational biology2023
Amino acid sequence assignment from single molecule peptide sequencing data using a two-stage classifier.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Principles ofbioRxiv : the preprint server for biology · 2026Article
- Protein abundance inference via expectation-maximization in fluorosequencing.Bioinformatics advances · 2026Article
- A generalized protein identification method for novel and diverse sequencing technologies.NAR genomics and bioinformatics · 2024Article
- Estimating error rates for single molecule protein sequencing experiments.PLoS computational biology · 2024Article
- Beam search decoder for enhancing sequence decoding speed in single-molecule peptide sequencing data.PLoS computational biology · 2023Article
- Robust and scalable single-molecule protein sequencing with fluorosequencing.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
We present a machine learning-based interpretive framework (whatprot) for analyzing single molecule protein sequencing data produced by fluorosequencing, a recently developed proteomics technology that determines sparse amino acid sequences for many individual peptide molecules in a highly parallelized fashion. Whatprot uses Hidden Markov Models (HMMs) to represent the states of each peptide undergoing the various chemical processes during fluorosequencing, and applies these in a Bayesian classifier, in combination with pre-filtering by a k-Nearest Neighbors (kNN) classifier trained on large volumes of simulated fluorosequencing data. We have found that by combining the HMM based Bayesian classifier with the kNN pre-filter, we are able to retain the benefits of both, achieving both tractable runtimes and acceptable precision and recall for identifying peptides and their parent proteins from complex mixtures, outperforming the capabilities of either classifier on its own. Whatprot's hybrid kNN-HMM approach enables the efficient interpretation of fluorosequencing data using a full proteome reference database and should now also enable improved sequencing error rate estimates.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.