JASPAR, the open access database of transcription factor-binding profiles: new content and tools in the 2008 update

By J. Bryne, Eivind Valen, Man-Hung Eric Tang, T. Marstrand, O. Winther, I. Piedade, Anders Krogh, Boris Lenhard, Albin Sandelin. Nucleic Acids Res., 2007

783 citations recorded by the source corpus.

JASPAR is a popular open-access database for matrix models describing DNA-binding preferences for transcription factors and other DNA patterns. With its third major release, JASPAR has been expanded and equipped with additional functions aimed at both casual and power users. The heart of the JASPAR database—the JASPAR CORE sub-database—has increased by 12% in size, and three new specialized sub-databases have been added. New functions include clustering of matrix models by similarity, generation of random matrices by sampling from selected sets of existing models and a language-independent Web Service applications programming interface for matrix retrieval. JASPAR is available at http://jaspar.genereg.net.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/49578a040f3346f81759ac40cc174cd12cb40045

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In Molecular Biology

1. Wyeth W. Wasserman
2. Martha L. Bulyk
3. Brian G. Pierce
4. William Stafford Noble
5. Albin Sandelin
6. A. Bird
7. Dirk Schübeler
8. David J. Arenillas

Citations in the corpus

• Annotation of functional variation in personal genomes using RegulomeDB (2012, methodology): “Secondly, we scanned the genome at a reasonable threshold for added positional weight matrices (PWMs) (Berger et al. 2006, 2008; Matys et al. 2006; Bryne et al. 2008; Badis et al. 2009; Scharer et al. 2009; Wei et al. 2010), which were further used to intersect with other functional data (see…”
• UniPROBE: an online database of protein binding microarray data on protein–DNA interactions (2008, methodology): “The first section (Figure 2A) provides additional annotations for the factor of interest, including unique gene or protein accession numbers (if available as provided by the species-specific database), gene synonyms, DNA-binding domain amino acid sequence (if available) and links to databases such a”
• DNA-binding factors shape the mouse methylome at distal regulatory regions (2011, methodology): “By using only the limited set of 126 DNA binding matrices from the core Jaspar databas”
• Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features (2014, methodology): “This CTCF logo was directly obtained from the JASPAR database [38] (available at http://http://jaspar.”
• Facilitators and Impediments of the Pluripotency Reprogramming Factors’ Initial Engagement with the Genome (2012, positive): “(B) De novo sequence motifs overrepresented in the O, S, K, and M peaks compared to their canonical motifs found in the JASPAR database (Bryne et al., 2008).”
• RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse (2015, methodology): “All interactions are derived from literature curation or direct user submissions Mouse http://www.ebi.ac.uk/ intact/ (26) 16 October 2012 JASPAR An open-access database of annotated, matrix-based transcription factor binding site (TFBS) profiles for multicellular eukaryotes Human and mouse http://ja”
• Gateways to the FANTOM5 promoter level mammalian expression atlas (2015, methodology): “Finally, we used the same genome segmentation annotation pipeline to annotate CAGE peaks with respect to CpG island proximal region (retrieved from the UCSC table browser), TATA box proximal region (based on a genome-wide scanning of the JASPAR TATA-binding protein position weight matrix [74]), repe”
• Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors (2012, background): “TFs tend to bind gene-rich regions of the genome due to their role in regulating target gene expression (Carroll et al. 2006).”
• Genome-wide analysis of ETS-family DNA-binding in vitro and in vivo (2010, positive): “Activity of ETS proteins can also be modulated directly by phosphorylation; members of the ETS and ELK subgroups of ETS factors mediate transcriptional responses to Ras/MAPK signalling pathways in species ranging from Caenorhabditis elegans to humans (Brunner et al, 1994; O’Neill et al, 1994;…”
• CpG islands and the regulation of transcription. (2011, positive): “sites obtained from the JASPAR database (Bryne et al. 2008).”
• DEEP: a general computational framework for predicting enhancers (2014, methodology): “Usage of DHSs combined with TF binding site (TFBS) motifs extracted from well-known databases (Transfac (17), Jaspar (18), HOCOMOCO (19)) is promising, but has several limitations.”
• Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities. (2010, methodology): “first analyzed them by comparing them computationally with existing profiles from the Jaspar2 database (Bryne et al. 2008) and”
• Genome-wide prediction of transcription factor binding sites using an integrated model (2010, methodology): “Methods such as Comet [18], Cluster-Buster [19] and ModuleMiner [20] use motifs documented in databases - for example, JASPAR [21] and TRANSFAC [22] - or predicted by de novo motif finding algorithms, and search for clusters of TFBSs. Methods like Stubb [23] and EEL [24] also include motif conservat”