ArticlePloS one2013
SeqNLS: nuclear localization signal prediction based on frequent pattern mining and linear motif scoring.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers.
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76 citing papers in PubMed, 144 citations in OpenAlex.
- SPSignal: a web tool for structure-assisted prediction of nuclear localization and nuclear export signals in proteins.Nucleic acids research · 2026Article
- FuNGI: Fungal Nucleolar Genomic Inventory-a comprehensive database of fungal proteins with predicted nucleolar localization signals.Database : the journal of biological databases and curation · 2026Article
- Evolutionary History of the DD41D Family of Tc1/Mariner Transposons in Two Mayetiola Species.Biochemical genetics · 2025Article
- Discovering the nuclear localization signal universe through a deep learning model with interpretable attention units.Patterns (New York, N.Y.) · 2025Article
- New insights into nuclear import and nucleolar localization of yeast RNA exosome subunits.Molecular biology of the cell · 2025Article
- Deep learning prioritizes cancer mutations that alter protein nucleocytoplasmic shuttling to drive tumorigenesis.Nature communications · 2025Article
- Identification of a Nuclear Localization Signal (NLS) in Human Transcription Elongation Factor ELL2.Cell biochemistry and function · 2024Article
- De Novo Transcriptome Profiling of Mustard Aphid (Insects · 2024Article
- ADBP-1 regulates ADR-2 nuclear localization to control editing substrate selection.Nucleic acids research · 2024Article
- Molecular characterization of canine circovirus based on the Capsid gene in Thailand.BMC veterinary research · 2024Article
- Nuclear AGO2 promotes myocardial remodeling by activating ANKRD1 transcription in failing hearts.Molecular therapy : the journal of the American Society of Gene Therapy · 2024Article
- Genome-wide identification of WRKY transcription factor family members in Miscanthus sinensis (Miscanthus sinensis Anderss).Scientific reports · 2024Article
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- The Abl-interactor Abi suppresses the function of the BRAG2 GEF family member Schizo.Biology open · 2024Article
- DTX2 promotes glioma development via regulation of HLTF.Biology direct · 2024Article
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- Examining the functional space of gut microbiome-derived peptides.MicrobiologyOpen · 2023Article
- Mutational analysis of the functional motifs of the DEAD-box RNA helicase Me31B/DDX6 in Drosophila germline development.FEBS letters · 2023Article
- Changing course: Glucose starvation drives nuclear accumulation of Hexokinase 2 in S. cerevisiae.PLoS genetics · 2023Article
- The Terminal Extensions of Dbp7 Influence Growth and 60S Ribosomal Subunit Biogenesis inInternational journal of molecular sciences · 2023Article
16 more citing papers are in PubMed but not listed here.
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2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Nuclear localization signals (NLSs) are stretches of residues in proteins mediating their importing into the nucleus. NLSs are known to have diverse patterns, of which only a limited number are covered by currently known NLS motifs. Here we propose a sequential pattern mining algorithm SeqNLS to effectively identify potential NLS patterns without being constrained by the limitation of current knowledge of NLSs. The extracted frequent sequential patterns are used to predict NLS candidates which are then filtered by a linear motif-scoring scheme based on predicted sequence disorder and by the relatively local conservation (IRLC) based masking. The experiment results on the newly curated Yeast and Hybrid datasets show that SeqNLS is effective in detecting potential NLSs. The performance comparison between SeqNLS with and without the linear motif scoring shows that linear motif features are highly complementary to sequence features in discerning NLSs. For the two independent datasets, our SeqNLS not only can consistently find over 50% of NLSs with prediction precision of at least 0.7, but also outperforms other state-of-the-art NLS prediction methods in terms of F1 score or prediction precision with similar or higher recall rates. The web server of the SeqNLS algorithm is available at http://mleg.cse.sc.edu/seqNLS.
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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 OpenQuestion graph.