ReviewScientifica2012
Position weight matrix, gibbs sampler, and the associated significance tests in motif characterization and prediction.
Review in Scientifica, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed.
- Pervasive noise in human pre-mRNA splice site selection.Molecular cell · 2026Article
- Transcription start sites experience a high influx of heritable variants fueled by early development.Nature communications · 2025Article
- Structural and mechanistic diversity in p53-mediated regulation of organismal longevity across taxonomical orders.PLoS computational biology · 2025Article
- Databases and computational methods for the identification of piRNA-related molecules: A survey.Computational and structural biotechnology journal · 2024Review
- Position Weight Matrix or Acyclic Probabilistic Finite Automaton: Which model to use? A decision rule inferred for the prediction of transcription factor binding sites.Genetics and molecular biology · 2024Article
- Automated next-generation profiling of genomic alterations in human cancers.Nature communications · 2022Article
- PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences.Biology · 2022Article
- Post-Alignment Adjustment and Its Automation.Genes · 2021Article
- A survey on deep learning in DNA/RNA motif mining.Briefings in bioinformatics · 2021Article
- Detailed Dissection and Critical Evaluation of the Pfizer/BioNTech and Moderna mRNA Vaccines.Vaccines · 2021Article
- TS-m6A-DL: Tissue-specific identification of N6-methyladenosine sites using a universal deep learning model.Computational and structural biotechnology journal · 2021Article
- Review
- Major Revisions in Arthropod Phylogeny Through Improved Supermatrix, With Support for Two Possible Waves of Land Invasion by Chelicerates.Evolutionary bioinformatics online · 2020Article
- iterb-PPse: Identification of transcriptional terminators in bacterial by incorporating nucleotide properties into PseKNC.PloS one · 2020Article
- Divergence of VRN-B3 alleles during the evolution of domesticated wheat.Molecular genetics and genomics : MGG · 2019Article
- Discovery and disentanglement of aligned residue associations from aligned pattern clusters to reveal subgroup characteristics.BMC medical genomics · 2018Article
- Revealing Subtle Functional Subgroups in Class A Scavenger Receptors by Pattern Discovery and Disentanglement of Aligned Pattern Clusters.Proteomes · 2018Article
- Review
- DAMBE6: New Tools for Microbial Genomics, Phylogenetics, and Molecular Evolution.The Journal of heredity · 2017Article
- PSSMHCpan: a novel PSSM-based software for predicting class I peptide-HLA binding affinity.GigaScience · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Position weight matrix (PWM) is not only one of the most widely used bioinformatic methods, but also a key component in more advanced computational algorithms (e.g., Gibbs sampler) for characterizing and discovering motifs in nucleotide or amino acid sequences. However, few generally applicable statistical tests are available for evaluating the significance of site patterns, PWM, and PWM scores (PWMS) of putative motifs. Statistical significance tests of the PWM output, that is, site-specific frequencies, PWM itself, and PWMS, are in disparate sources and have never been collected in a single paper, with the consequence that many implementations of PWM do not include any significance test. Here I review PWM-based methods used in motif characterization and prediction (including a detailed illustration of the Gibbs sampler for de novo motif discovery), present statistical and probabilistic rationales behind statistical significance tests relevant to PWM, and illustrate their application with real data. The multiple comparison problem associated with the test of site-specific frequencies is best handled by false discovery rate methods. The test of PWM, due to the use of pseudocounts, is best done by resampling methods. The test of individual PWMS for each sequence segment should be based on the extreme value distribution.
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.