Promoter Sequence Analysis through No Gap Multiple Sequence Alignment of Motif Pairs
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Summary
A new multiple sequence alignment method for analyzing the similarity/homology existing in the promoter sequences by extracting the motif pair feature from the binarized position specific motif matrix (PSMM) of each promoter pair or sets.
- Type
- article
- Published
- 2015-01-01
- Cited by
- 6
- References
- 15
- Access
- Open access
- OpenAlex
- https://openalex.org/W1727089480
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:60870653
Keywords
Sequence logo, Motif (music), Sequence motif, Alignment-free sequence analysis, Computer science
References
- The promoter connection
- Using MUMmer to Identify Similar Regions in Large Sequence Sets
- Regulatory Modules Shared within Gene Classes as Well as Across Gene Classes Can Be Detected by the Same in Silico Approach
- Functional promoter modules can be detected by formal models independent of overall nucleotide sequence similarity
- A survey of sequence alignment algorithms for next-generation sequencing
- CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice.
- Recent Evolutions of Multiple Sequence Alignment Algorithms
- A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction
- MUSCLE: multiple sequence alignment with high accuracy and high throughput.
- Replacing suffix trees with enhanced suffix arrays
- M-Coffee: combining multiple sequence alignment methods with T-Coffee
- T-Coffee: A novel method for fast and accurate multiple sequence alignment.
- Fast and accurate short read alignment with Burrows–Wheeler transform
- Theoretical and practical advances in genome halving
- Alignment Free Frequency Based Distance Measures for Promoter Sequence Comparison
- DIALIGN : multiple DNA and protein sequence alignment at BiBiServ
Cited by
- Alignment free promoter sequence analysis using feature reduced cumulative distribution of motifs
- Entropy Based Feature Selection for Lacunarity Analysis of Position Specific Motif Matrices of Promoter Sequences
- Effective Feature Selection for Classification of Promoter Sequences
- Statistical and Alignment Based Methods for Comparison of Non-Coding DNA Sequences
- Feature Reduced Weighted Fuzzy Binarization for Histogram Comparison of Promoter Sequences
- Two-Step Verifications for Multi-instance Features Selection: A Machine Learning Approach
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