JASPAR 2024: 20th Anniversary of the Open-access Database of Transcription Factor Binding Profiles

Overview

Journal Nucleic Acids Res

Publisher Oxford University Press

Specialty Biochemistry

Date 2023 Nov 14

PMID 37962376

Authors

Ieva Rauluseviciute

Rafael Riudavets-Puig

Romain Blanc-Mathieu

Jaime A Castro-Mondragon

Katalin Ferenc

Vipin Kumar

Roza Berhanu Lemma

Jeremy Lucas

Jeanne Cheneby

Damir Baranasic

Aziz Khan

Oriol Fornes

Sveinung Gundersen

Morten Johansen

Eivind Hovig

Boris Lenhard

Albin Sandelin

Wyeth W Wasserman

Francois Parcy

Anthony Mathelier

Affiliations

Soon will be listed here.

Abstract

JASPAR (https://jaspar.elixir.no/) is a widely-used open-access database presenting manually curated high-quality and non-redundant DNA-binding profiles for transcription factors (TFs) across taxa. In this 10th release and 20th-anniversary update, the CORE collection has expanded with 329 new profiles. We updated three existing profiles and provided orthogonal support for 72 profiles from the previous release's UNVALIDATED collection. Altogether, the JASPAR 2024 update provides a 20% increase in CORE profiles from the previous release. A trimming algorithm enhanced profiles by removing low information content flanking base pairs, which were likely uninformative (within the capacity of the PFM models) for TFBS predictions and modelling TF-DNA interactions. This release includes enhanced metadata, featuring a refined classification for plant TFs' structural DNA-binding domains. The new JASPAR collections prompt updates to the genomic tracks of predicted TF binding sites (TFBSs) in 8 organisms, with human and mouse tracks available as native tracks in the UCSC Genome browser. All data are available through the JASPAR web interface and programmatically through its API and the updated Bioconductor and pyJASPAR packages. Finally, a new TFBS extraction tool enables users to retrieve predicted JASPAR TFBSs intersecting their genomic regions of interest.

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References

Isakova A, Groux R, Imbeault M, Rainer P, Alpern D, Dainese R . SMiLE-seq identifies binding motifs of single and dimeric transcription factors. Nat Methods. 2017; 14(3):316-322. DOI: 10.1038/nmeth.4143. View

Mathelier A, Zhao X, Zhang A, Parcy F, Worsley-Hunt R, Arenillas D . JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles. Nucleic Acids Res. 2013; 42(Database issue):D142-7. PMC: 3965086. DOI: 10.1093/nar/gkt997. View

Quang D, Xie X . DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res. 2016; 44(11):e107. PMC: 4914104. DOI: 10.1093/nar/gkw226. View

Gramates L, Agapite J, Attrill H, Calvi B, Crosby M, Dos Santos G . FlyBase: a guided tour of highlighted features. Genetics. 2022; 220(4). PMC: 8982030. DOI: 10.1093/genetics/iyac035. View

Johnson D, Mortazavi A, Myers R, Wold B . Genome-wide mapping of in vivo protein-DNA interactions. Science. 2007; 316(5830):1497-502. DOI: 10.1126/science.1141319. View

Reid J, Evans K, Dyer N, Wernisch L, Ott S . Variable structure motifs for transcription factor binding sites. BMC Genomics. 2010; 11:30. PMC: 2824720. DOI: 10.1186/1471-2164-11-30. View

Ullah F, Ben-Hur A . A self-attention model for inferring cooperativity between regulatory features. Nucleic Acids Res. 2021; 49(13):e77. PMC: 8287919. DOI: 10.1093/nar/gkab349. View

Reuter J, Spacek D, Snyder M . High-throughput sequencing technologies. Mol Cell. 2015; 58(4):586-97. PMC: 4494749. DOI: 10.1016/j.molcel.2015.05.004. View

Tian F, Yang D, Meng Y, Jin J, Gao G . PlantRegMap: charting functional regulatory maps in plants. Nucleic Acids Res. 2019; 48(D1):D1104-D1113. PMC: 7145545. DOI: 10.1093/nar/gkz1020. View

10.

Sapoval N, Aghazadeh A, Nute M, Antunes D, Balaji A, Baraniuk R . Current progress and open challenges for applying deep learning across the biosciences. Nat Commun. 2022; 13(1):1728. PMC: 8976012. DOI: 10.1038/s41467-022-29268-7. View

11.

Berger M, Bulyk M . Universal protein-binding microarrays for the comprehensive characterization of the DNA-binding specificities of transcription factors. Nat Protoc. 2009; 4(3):393-411. PMC: 2908410. DOI: 10.1038/nprot.2008.195. View

12.

Khan A, Mathelier A . JASPAR RESTful API: accessing JASPAR data from any programming language. Bioinformatics. 2017; 34(9):1612-1614. DOI: 10.1093/bioinformatics/btx804. View

13.

Wu Y, Huang Y, Li J, You Z, Hu P, Hu L . Knowledge graph embedding for profiling the interaction between transcription factors and their target genes. PLoS Comput Biol. 2023; 19(6):e1011207. PMC: 10313080. DOI: 10.1371/journal.pcbi.1011207. View

14.

Fornes O, Castro-Mondragon J, Khan A, van der Lee R, Zhang X, Richmond P . JASPAR 2020: update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 2019; 48(D1):D87-D92. PMC: 7145627. DOI: 10.1093/nar/gkz1001. View

15.

Bartlett A, OMalley R, Huang S, Galli M, Nery J, Gallavotti A . Mapping genome-wide transcription-factor binding sites using DAP-seq. Nat Protoc. 2017; 12(8):1659-1672. PMC: 5576341. DOI: 10.1038/nprot.2017.055. View

16.

Novakovsky G, Dexter N, Libbrecht M, Wasserman W, Mostafavi S . Obtaining genetics insights from deep learning via explainable artificial intelligence. Nat Rev Genet. 2022; 24(2):125-137. DOI: 10.1038/s41576-022-00532-2. View

17.

Roulet E, Busso S, Camargo A, Simpson A, Mermod N, Bucher P . High-throughput SELEX SAGE method for quantitative modeling of transcription-factor binding sites. Nat Biotechnol. 2002; 20(8):831-5. DOI: 10.1038/nbt718. View

18.

Sheffield N, Bock C . LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor. Bioinformatics. 2015; 32(4):587-9. PMC: 4743627. DOI: 10.1093/bioinformatics/btv612. View

19.

Kolmykov S, Yevshin I, Kulyashov M, Sharipov R, Kondrakhin Y, Makeev V . GTRD: an integrated view of transcription regulation. Nucleic Acids Res. 2020; 49(D1):D104-D111. PMC: 7778956. DOI: 10.1093/nar/gkaa1057. View

20.

Wilkinson M, Dumontier M, Aalbersberg I, Appleton G, Axton M, Baak A . The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016; 3:160018. PMC: 4792175. DOI: 10.1038/sdata.2016.18. View