JASPAR

JASPAR: an open‐access database for eukaryotic transcription factor binding profiles. The analysis of regulatory regions in genome sequences is strongly based on the detection of potential transcription factor binding sites. The preferred models for representation of transcription factor binding specificity have been termed position‐specific scoring matrices. JASPAR is an open‐access database of annotated, high‐quality, matrix‐based transcription factor binding site profiles for multicellular eukaryotes. The profiles were derived exclusively from sets of nucleotide sequences experimentally demonstrated to bind transcription factors. The database is complemented by a web interface for browsing, searching and subset selection, an online sequence analysis utility and a suite of programming tools for genome‐wide and comparative genomic analysis of regulatory regions. JASPAR is available at http://jaspar. cgb.ki.se.


References in zbMATH (referenced in 42 articles )

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  1. Akalin, Altuna: Computational genomics with R. With the assistance of Verdan Franke, Bora Uyar and Jonathan Ronen (2021)
  2. Murugan, Rajamanickam: Theory on the looping mediated directional-dependent propulsion of transcription factors along DNA (2020)
  3. Radoszewski, Jakub; Starikovskaya, Tatiana: Streaming (k)-mismatch with error correcting and applications (2020)
  4. Eggeling, Ralf; Grosse, Ivo; Koivisto, Mikko: Algorithms for learning parsimonious context trees (2019)
  5. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  6. Toivonen, Jarkko; Taipale, Jussi; Ukkonen, Esko: Seed-driven learning of position probability matrices from large sequence sets (2017)
  7. Barton, Carl; Liu, Chang; Pissis, Solon P.: On-line pattern matching on uncertain sequences and applications (2016)
  8. Pesch, Robert: Cross-species network and transcript transfer (2016)
  9. Weishaupt, Holger; Johansson, Patrik; Engström, Christopher; Nelander, Sven; Silvestrov, Sergei; Swartling, Fredrik J.: Graph centrality based prediction of cancer genes (2016)
  10. Young, William Chad; Raftery, Adrian E.; Yeung, Ka Yee: A posterior probability approach for gene regulatory network inference in genetic perturbation data (2016)
  11. Sohn, Insuk; Shim, Jooyong; Hwang, Changha; Kim, Sujong; Lee, Jae Won: Transcription factor-binding site identification and gene classification via fusion of the supervised-weighted discrete kernel clustering and support vector machine (2014)
  12. Panigrahi, Priya P.; Singh, Tiratha Raj: Computational studies on Alzheimer’s disease associated pathways and regulatory patterns using microarray gene expression and network data: revealed association with aging and other diseases (2013)
  13. Mahdevar, Ghasem; Sadeghi, Mehdi; Nowzari-Dalini, Abbas: Transcription factor binding sites detection by using alignment-based approach (2012)
  14. Wang, Dianhui; Do, Hai Thanh: Computational localization of transcription factor binding sites using extreme learning machines (2012) ioport
  15. Giraud, Mathieu; Varré, Jean-Stéphane: Parallel position weight matrices algorithms (2011) ioport
  16. Marschall, Tobias: Construction of minimal deterministic finite automata from biological motifs (2011)
  17. Alamanova, Denitsa; Stegmaier, Philip; Kel, Alexander E.: Creating pwms of transcription factors using 3D structure-based computation of protein-DNA free binding energies (2010) ioport
  18. Drews, Frank; Lichtenberg, Jens; Welch, Lonnie: Scalable parallel word search in multicore/multiprocessor systems (2010) ioport
  19. Georgi, Benjamin; Costa, Ivan Gesteira; Schliep, Alexander: Pymix - the python mixture package - a tool for clustering of heterogeneous biological data (2010) ioport
  20. Liu, Li-Fang; Jiao, Li-Cheng: Detection of over-represented motifs corresponding to known TFBSs via motif clustering and matching (2010)

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