2007 |
Bellora, Nicolás, Farré, Domènec, Mar Albà, M PEAKS: identification of regulatory motifs by their position in DNA sequences. (Article) Bioinformatics (Oxford, England), 23 (2), pp. 243–4, 2007, ISSN: 1367-4811. (Abstract | Links | BibTeX | Tags: Algorithms, Automated, Automated: methods, Base Sequence, Chromosome Mapping, Chromosome Mapping: methods, DNA, DNA: genetics, DNA: methods, Molecular Sequence Data, Nucleic Acid, Nucleic Acid: genetics, Pattern Recognition, Regulatory Sequences, Sequence Alignment, Sequence Alignment: methods, Sequence Analysis, Software, Transcriptional Activation, Transcriptional Activation: genetics) @article{Bellora2007a, title = {PEAKS: identification of regulatory motifs by their position in DNA sequences.}, author = {Bellora, Nicolás and Farré, Domènec and Mar Albà, M}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17098773}, issn = {1367-4811}, year = {2007}, date = {2007-01-01}, journal = {Bioinformatics (Oxford, England)}, volume = {23}, number = {2}, pages = {243--4}, abstract = {Many DNA functional motifs tend to accumulate or cluster at specific gene locations. These locations can be detected, in a group of gene sequences, as high frequency 'peaks' with respect to a reference position, such as the transcription start site (TSS). We have developed a web tool for the identification of regions containing significant motif peaks. We show, by using different yeast gene datasets, that peak regions are strongly enriched in experimentally-validated motifs and contain potentially important novel motifs. AVAILABILITY: http://genomics.imim.es/peaks}, keywords = {Algorithms, Automated, Automated: methods, Base Sequence, Chromosome Mapping, Chromosome Mapping: methods, DNA, DNA: genetics, DNA: methods, Molecular Sequence Data, Nucleic Acid, Nucleic Acid: genetics, Pattern Recognition, Regulatory Sequences, Sequence Alignment, Sequence Alignment: methods, Sequence Analysis, Software, Transcriptional Activation, Transcriptional Activation: genetics} } Many DNA functional motifs tend to accumulate or cluster at specific gene locations. These locations can be detected, in a group of gene sequences, as high frequency 'peaks' with respect to a reference position, such as the transcription start site (TSS). We have developed a web tool for the identification of regions containing significant motif peaks. We show, by using different yeast gene datasets, that peak regions are strongly enriched in experimentally-validated motifs and contain potentially important novel motifs. AVAILABILITY: http://genomics.imim.es/peaks |
2002 |
Albà, M Mar, Laskowski, Roman A, Hancock, John M Detecting cryptically simple protein sequences using the SIMPLE algorithm. (Article) Bioinformatics (Oxford, England), 18 (5), pp. 672–8, 2002, ISSN: 1367-4803. (Abstract | Links | BibTeX | Tags: Algorithms, Amino Acid, Amino Acid Sequence, Amino Acid: genetics, Databases, Genetic, Genetic Variation, Internet, Minisatellite Repeats, Minisatellite Repeats: genetics, Models, Molecular Sequence Data, Protein, Protein: methods, Proteins, Proteins: chemistry, Repetitive Sequences, Saccharomyces cerevisiae, Saccharomyces cerevisiae: genetics, Sensitivity and Specificity, Sequence Analysis, Sequence Homology, Software, Statistical) @article{Alba2002, title = {Detecting cryptically simple protein sequences using the SIMPLE algorithm.}, author = {Albà, M Mar and Laskowski, Roman A and Hancock, John M}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12050063}, issn = {1367-4803}, year = {2002}, date = {2002-01-01}, journal = {Bioinformatics (Oxford, England)}, volume = {18}, number = {5}, pages = {672--8}, abstract = {Low-complexity or cryptically simple sequences are widespread in protein sequences but their evolution and function are poorly understood. To date methods for the detection of low complexity in proteins have been directed towards the filtering of such regions prior to sequence homology searches but not to the analysis of the regions per se. However, many of these regions are encoded by non-repetitive DNA sequences and may therefore result from selection acting on protein structure and/or function.}, keywords = {Algorithms, Amino Acid, Amino Acid Sequence, Amino Acid: genetics, Databases, Genetic, Genetic Variation, Internet, Minisatellite Repeats, Minisatellite Repeats: genetics, Models, Molecular Sequence Data, Protein, Protein: methods, Proteins, Proteins: chemistry, Repetitive Sequences, Saccharomyces cerevisiae, Saccharomyces cerevisiae: genetics, Sensitivity and Specificity, Sequence Analysis, Sequence Homology, Software, Statistical} } Low-complexity or cryptically simple sequences are widespread in protein sequences but their evolution and function are poorly understood. To date methods for the detection of low complexity in proteins have been directed towards the filtering of such regions prior to sequence homology searches but not to the analysis of the regions per se. However, many of these regions are encoded by non-repetitive DNA sequences and may therefore result from selection acting on protein structure and/or function. |
Publication List
Amino Acid Animals Computational Biology Databases de novo gene DNA Evolution Genetic Genome human Humans Mice Molecular Molecular Sequence Data Proteins Proteins: chemistry Proteins: genetics Repetitive Sequences ribosome profiling RNA-Seq Selection Sequence Analysis Sequence Homology transcriptomics yeast
2007 |
PEAKS: identification of regulatory motifs by their position in DNA sequences. (Article) Bioinformatics (Oxford, England), 23 (2), pp. 243–4, 2007, ISSN: 1367-4811. |
2002 |
Detecting cryptically simple protein sequences using the SIMPLE algorithm. (Article) Bioinformatics (Oxford, England), 18 (5), pp. 672–8, 2002, ISSN: 1367-4803. |