Amino Acid Animals Computational Biology Databases de novo gene Evolution Genetic Genome Humans lncRNA Mice Molecular Molecular Sequence Data Nucleic Acid Proteins Proteins: chemistry Proteins: genetics Repetitive Sequences ribosome profiling RNA-Seq Selection Sequence Analysis Sequence Homology transcriptomics yeast
2006 |
Blanco, Enrique, Farré, Domènec, Albà, M Mar, Messeguer, Xavier, Guigó, Roderic ABS: a database of Annotated regulatory Binding Sites from orthologous promoters. (Article) Nucleic acids research, 34 (Database issue), pp. D63–7, 2006, ISSN: 1362-4962. (Abstract | Links | BibTeX | Tags: Animals, Binding Sites, Chickens, Chickens: genetics, Databases, Genetic, Genomics, Humans, Internet, Mice, Nucleic Acid, Promoter Regions, Rats, Transcription Factors, Transcription Factors: metabolism, User-Computer Interface) @article{Blanco2006, title = {ABS: a database of Annotated regulatory Binding Sites from orthologous promoters.}, author = {Blanco, Enrique and Farré, Domènec and Albà, M Mar and Messeguer, Xavier and Guigó, Roderic}, url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1347478&tool=pmcentrez&rendertype=abstract}, issn = {1362-4962}, year = {2006}, date = {2006-01-01}, journal = {Nucleic acids research}, volume = {34}, number = {Database issue}, pages = {D63--7}, abstract = {Information about the genomic coordinates and the sequence of experimentally identified transcription factor binding sites is found scattered under a variety of diverse formats. The availability of standard collections of such high-quality data is important to design, evaluate and improve novel computational approaches to identify binding motifs on promoter sequences from related genes. ABS (http://genome.imim.es/datasets/abs2005/index.html) is a public database of known binding sites identified in promoters of orthologous vertebrate genes that have been manually curated from bibliography. We have annotated 650 experimental binding sites from 68 transcription factors and 100 orthologous target genes in human, mouse, rat or chicken genome sequences. Computational predictions and promoter alignment information are also provided for each entry. A simple and easy-to-use web interface facilitates data retrieval allowing different views of the information. In addition, the release 1.0 of ABS includes a customizable generator of artificial datasets based on the known sites contained in the collection and an evaluation tool to aid during the training and the assessment of motif-finding programs.}, keywords = {Animals, Binding Sites, Chickens, Chickens: genetics, Databases, Genetic, Genomics, Humans, Internet, Mice, Nucleic Acid, Promoter Regions, Rats, Transcription Factors, Transcription Factors: metabolism, User-Computer Interface} } Information about the genomic coordinates and the sequence of experimentally identified transcription factor binding sites is found scattered under a variety of diverse formats. The availability of standard collections of such high-quality data is important to design, evaluate and improve novel computational approaches to identify binding motifs on promoter sequences from related genes. ABS (http://genome.imim.es/datasets/abs2005/index.html) is a public database of known binding sites identified in promoters of orthologous vertebrate genes that have been manually curated from bibliography. We have annotated 650 experimental binding sites from 68 transcription factors and 100 orthologous target genes in human, mouse, rat or chicken genome sequences. Computational predictions and promoter alignment information are also provided for each entry. A simple and easy-to-use web interface facilitates data retrieval allowing different views of the information. In addition, the release 1.0 of ABS includes a customizable generator of artificial datasets based on the known sites contained in the collection and an evaluation tool to aid during the training and the assessment of motif-finding programs. |
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. |
