ArticleScientific reports2019
SPECTRUM - A MATLAB Toolbox for Proteoform Identification from Top-Down Proteomics Data.
Article in Scientific reports, 2019. 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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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Advances in Mass Spectrometry Instrumentation and Methodology for Analysis of Alternative Protein Isoforms.Journal of mass spectrometry : JMS · 2026Review
- The Solvation of theInternational journal of molecular sciences · 2022Article
- PERCEPTRON: an open-source GPU-accelerated proteoform identification pipeline for top-down proteomics.Nucleic acids research · 2021Article
- Proteoform Identification by Combining RNA-Seq and Top-Down Mass Spectrometry.Journal of proteome research · 2021Article
- GeenaR: A Web Tool for Reproducible MALDI-TOF Analysis.Frontiers in genetics · 2021Article
- EnvCNN: A Convolutional Neural Network Model for Evaluating Isotopic Envelopes in Top-Down Mass-Spectral Deconvolution.Analytical chemistry · 2020Article
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Authors and funding
19 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Top-Down Proteomics (TDP) is an emerging proteomics protocol that involves identification, characterization, and quantitation of intact proteins using high-resolution mass spectrometry. TDP has an edge over other proteomics protocols in that it allows for: (i) accurate measurement of intact protein mass, (ii) high sequence coverage, and (iii) enhanced identification of post-translational modifications (PTMs). However, the complexity of TDP spectra poses a significant impediment to protein search and PTM characterization. Furthermore, limited software support is currently available in the form of search algorithms and pipelines. To address this need, we propose 'SPECTRUM', an open-architecture and open-source toolbox for TDP data analysis. Its salient features include: (i) MS2-based intact protein mass tuning, (ii) de novo peptide sequence tag analysis, (iii) propensity-driven PTM characterization, (iv) blind PTM search, (v) spectral comparison, (vi) identification of truncated proteins, (vii) multifactorial coefficient-weighted scoring, and (viii) intuitive graphical user interfaces to access the aforementioned functionalities and visualization of results. We have validated SPECTRUM using published datasets and benchmarked it against salient TDP tools. SPECTRUM provides significantly enhanced protein identification rates (91% to 177%) over its contemporaries. SPECTRUM has been implemented in MATLAB, and is freely available along with its source code and documentation at https://github.com/BIRL/SPECTRUM/.
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