JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles

By Anthony Mathelier, Oriol Fornes, David J. Arenillas, Chih-Yu Chen, Grégoire Denay, Jessica J. Y. Lee, Wenqiang Shi, C. Shyr, Ge Tan, R. Worsley-Hunt and 5 more. Nucleic Acids Res., 2015

1,115 citations recorded by the source corpus.

JASPAR (http://jaspar.genereg.net) is an open-access database storing curated, non-redundant transcription factor (TF) binding profiles representing transcription factor binding preferences as position frequency matrices for multiple species in six taxonomic groups. For this 2016 release, we expanded the JASPAR CORE collection with 494 new TF binding profiles (315 in vertebrates, 11 in nematodes, 3 in insects, 1 in fungi and 164 in plants) and updated 59 profiles (58 in vertebrates and 1 in fungi). The introduced profiles represent an 83% expansion and 10% update when compared to the previous release. We updated the structural annotation of the TF DNA binding domains (DBDs) following a published hierarchical structural classification. In addition, we introduced 130 transcription factor flexible models trained on ChIP-seq data for vertebrates, which capture dinucleotide dependencies within TF binding sites. This new JASPAR release is accompanied by a new web tool to infer JASPAR TF binding profiles recognized by a given TF protein sequence. Moreover, we provide the users with a Ruby module complementing the JASPAR API to ease programmatic access and use of the JASPAR collection of profiles. Finally, we provide the JASPAR2016 R/Bioconductor data package with the data of this release.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a31dfd22f90da898aef113185bc35268d98f794b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework (2017, methodology): “The 2016 release of the JASPAR CORE collection stored 1082 non-redundant and manually curated TFbinding profiles as PFMs for TFs from six different taxonomic groups (vertebrates, plants, insects, nematodes, fungi and urochordata) (8).”
PlantTFDB 4.0: toward a central hub for transcription factors and regulatory interactions in plants (2016, methodology): “We firstly collected 1790 binding motifs derived from experiments from multiple databases (including PlantCistromeDB (20), CIS-BP (21), JASPAR (32), UniPROBE (33), TRANSFAC (public 7.0) (34)), literature (22) and motifs discovered from ChIPseq peaks using MEME-ChIP (35).”
JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework (2017, methodology): “In this report, we describe the seventh release of JASPAR (8,20–24), which comes with a major expansion and update of the CORE collection of TF-binding profiles as PFMs and TFFMs.”
ReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP-seq experiments (2017, positive): “2018), providing a 9% increase from JASPAR 2016 (26) by solely relying on the ReMap 2018 catalog.”
HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis (2017, methodology): “Specifically, the compulsory curation of the discovered models is dependent on external data, e.g. UniProt (37), TF Encyclopedia (38), and the JAS-PAR database (39,40), which we consulted at the model curation stage.”
hTFtarget: A Comprehensive Database for Regulations of Human Transcription Factors and Their Targets (2019, methodology): “The HOCOMOCO [22], JASPAR [23], TRANSFAC v2018 [24], and TFBSbank [31] databases focus on the collection and discovery of the motif profiles for TFBSs, while TRRUST elucidates TF–target interactions using text mining [13]. hTFtarget is the only currently available database integrating various resour”
Predicting enhancer-promoter interaction from genomic sequence with deep neural networks (2016, methodology): “As suggested by [25], we inject some prior knowledge by initializing about half (525 of 1024) of the convolutional kernels with known motifs from the JASPAR database [23] Specifically, recall that each kernel is a 4⇥40 weight matrix.”
Sequential regulatory activity prediction across chromosomes with convolutional neural networks (2017, methodology): “rs78461372 overlaps an ETS factor motif adjacent to a RUNX factor motif, where the G allele confers a stronger hit to the JASPAR database PWM for ETS, discovered using the motif search tool Tomtom (Gupta et al. 2007; Mathelier et al. 2016) (Figure 6).”
METTL14 Inhibits Hematopoietic Stem/Progenitor Differentiation and Promotes Leukemogenesis via mRNA m6A Modification (2017, neutral): “2.3 (Subramanian et al., 2005) http://software.broadinstitute.org/gsea/ index.jsp jaspar (Mathelier et al., 2016) http://jaspar.genereg.net/ ChIPBase v2.”
The Human Transcription Factors. (2018, positive): “Nonetheless, motifs for many well-studied proteins were initially obtained from very few sequences (e.g., dozens of Sanger reads) and used in thousands of subsequent studies (Mathelier et al., 2016; Matys et al., 2006), illustrating the utility of even approximate descriptions of binding ability.”