Identification of consensus patterns in unaligned DNA sequences known to be functionally related
Abstract
We have developed a method for identifying consensus patterns in a set of unaligned DNA sequences known to bind a common protein or to have some other common biochemical function. The method is based on a matrix representation of binding site patterns. Each row of the matrix represents one of the four possible bases, each column represents one of the positions of the binding site and each element is determined by the frequency the indicated base occurs at the indicated position. The goal of the method is to find the most significant matrix--i.e. the one with the lowest probability of occurring by chance--out of all the matrices that can be formed from the set of related sequences. The reliability of the method improves with the number of sequences, while the time required increases only linearly with the number of sequences. To test this method, we analysed 11 DNA sequences containing promoters regulated by the Escherichia coli LexA protein. The matrices we found were consistent with the known consensus sequence, and could distinguish the generally accepted LexA binding sites from other DNA sequences.
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The position weight matrix of site-specific nucleotide frequencies flanking the start codon AUG for all protein-coding genes from human Chr01 shows a pattern consistent with the GCCRCCAUGG consensus.
“The associated position weight matrix [23,24,47,48], using intron nucleotide frequencies (0.26398, 0.21777, 0.22622 and 0.29203 for A, C, G, and T, respectively) as background frequencies, shows a site-specific pattern consistent with the GCCRCCAUGG consensus.”
From Detailed Dissection and Critical Evaluation of the Pfizer/BioNTech and Moderna mRNA Vaccines (2021) · cites this paper as [47]
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iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections
Janky, R., Verfaillie, A., Imrichová, H., Van de Sande, B., Standaert, L., Christiaens, V., Hulselmans, G., Herten, K., Naval Sanchez, M., Potier, D., et al. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003731 · Cited by 900 · see citing works
“…weight matrices (PWMs) as a predictive model for TF binding sites, which can be graphically represented as a motif logo [89], and use optimization algorithms (Expectation-Maximization [90], greedy algorithm [91,92] or Gibbs sampling [93–95]) to find the most common motifs to all input sequences.”
MethodDetailed Dissection and Critical Evaluation of the Pfizer/BioNTech and Moderna mRNA Vaccines
Xia, X. (2021) · Vaccines · doi:10.3390/vaccines9070734 · Cited by 201 · see citing works
- Authors
- Hertz, G. Z., Hartzell, G. W., & Stormo, G. D.
- Affiliations
- University of Colorado Boulder (US)
- Corresponding author
- Stormo, Gary D.
- ORCID
- Stormo, Gary D.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 1990
- Volume
- 6
- Issue
- 2
- Pages
- 81-92
- ISSN
- 0266-7061 (ISSN-L)
- 1367-4803
- 1460-2059
- OpenAlex
- W2110455321
- Cited by
- 349
- References
- 0
- Citations by year
- 2024: 3 · 2023: 2 · 2022: 4 · 2021: 7 · 2020: 7 · 2019: 4 · 2018: 15 · 2017: 5 · 2016: 13 · 2015: 10 · 2014: 10 · 2013: 12 · 2012: 17
- FWCI
- 2.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer applications in the biosciences (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Institute of General Medical Sciences (GM 28755, R01GM028755, GM-41766)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
- Keywords
- protein-DNA binding sites, Escherichia coli promoters, motif discovery, protein-DNA interactions
- MeSH terms
- LexA Repressor Protein, Algorithms, Bacterial Proteins, Base Sequence, Binding Sites, DNA, DNA, Bacterial, Escherichia coli, Genes, Bacterial, Molecular Sequence Data, Pattern Recognition, Automated, Serine Endopeptidases, Software
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