ArticleBMC bioinformatics2017
Quantiprot - a Python package for quantitative analysis of protein sequences.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 15 citations in OpenAlex.
- Machine learning: Python tools for studying biomolecules and drug design.Molecular diversity · 2025Review
- AI-based Prediction of Protein Corona Composition on DNA Nanostructures.bioRxiv : the preprint server for biology · 2024Article
- PDAUG: a Galaxy based toolset for peptide library analysis, visualization, and machine learning modeling.BMC bioinformatics · 2022Article
- Probing ion channel functional architecture and domain recombination compatibility by massively parallel domain insertion profiling.Nature communications · 2021Article
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Authors and funding
3 authors at 1 institution in 1 country.
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Abstract
backgroundThe field of protein sequence analysis is dominated by tools rooted in substitution matrices and alignments. A complementary approach is provided by methods of quantitative characterization. A major advantage of the approach is that quantitative properties defines a multidimensional solution space, where sequences can be related to each other and differences can be meaningfully interpreted.
resultsQuantiprot is a software package in Python, which provides a simple and consistent interface to multiple methods for quantitative characterization of protein sequences. The package can be used to calculate dozens of characteristics directly from sequences or using physico-chemical properties of amino acids. Besides basic measures, Quantiprot performs quantitative analysis of recurrence and determinism in the sequence, calculates distribution of n-grams and computes the Zipf's law coefficient.
conclusionsWe propose three main fields of application of the Quantiprot package. First, quantitative characteristics can be used in alignment-free similarity searches, and in clustering of large and/or divergent sequence sets. Second, a feature space defined by quantitative properties can be used in comparative studies of protein families and organisms. Third, the feature space can be used for evaluating generative models, where large number of sequences generated by the model can be compared to actually observed sequences.
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