JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles

Mathelier, Anthony; Zhao, Xiaobei; Zhang, Allen W.; Parcy, François; Worsley-Hunt, Rebecca; Arenillas, David J.; Buchman, Sorana; Chen, Chih-Yu; Chou, Alice; Ienasescu, Hans; Lim, Jonathan; Shyr, Casper; Tan, Ge; Zhou, Mi; Lenhard, Boris; Sandelin, Albin; Wasserman, Wyeth W.

doi:10.1093/nar/gkt997

Cited by 932 publications

(906 citation statements)

References 32 publications

Supporting

Mentioning

890

Contrasting

Order By: Relevance

“…As shown in Figure 1a, some of these binding sites were located at histone acetylated and DNAse-sensitive regions in the LAMP2 gene, both factors being typical of active enhancers. We then used a Python-based bioinformatics analysis to scan these binding regions for the consensus ARE as established in the JASPAR database [27]. We detected 8 putative AREs in the LAMP2 gene (Table S1) and 3 of them showed a relative score higher than 85%, a commonly used threshold for transcription factor binding-site analysis [28,29].…”

Section: Resultsmentioning

confidence: 99%

“…The putative MAFK, MAFF and BACH1 binding regions were localized in 200–400 base-pair-long DNase-sensitive and H3K27Ac-rich regions. In addition, a frequency matrix of the consensus ARE sequence based on the JASPAR database [27] was converted to a position-specific scoring matrix and a script was generated with the Python 3.4 program to scan the promoter sequences with candidate AREs as previously described [22]. …”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Transcription factor NFE2L2/NRF2 modulates chaperone-mediated autophagy through the regulation of LAMP2A

et al. 2018

View full text Add to dashboard Cite

Chaperone-mediated autophagy (CMA) is a selective degradative process for cytosolic proteins that contributes to the maintenance of proteostasis. The signaling mechanisms that control CMA are not fully understood but might involve response to stress conditions including oxidative stress. Considering the role of CMA in redoxtasis and proteostasis, we sought to determine if the transcription factor NFE2L2/NRF2 (nuclear factor, erythroid derived 2, like 2) has an impact on CMA modulation. In this work, we identified and validated 2 NFE2L2 binding sequences in the LAMP2 gene and demonstrated in several human and mouse cell types that NFE2L2 deficiency and overexpression was linked to reduced and increased LAMP2A levels, respectively. Accordingly, lysosomal LAMP2A levels were drastically reduced in nfe2l2-knockout hepatocytes, which also displayed a marked decrease in CMA activity. Oxidant challenge with paraquat or hydrogen peroxide, or pharmacological activation of NFE2L2 with sulforaphane or dimethyl fumarate also increased LAMP2A levels and CMA activity. Overall, our study identifies for the first time basal and inducible regulation of LAMP2A, and consequently CMA activity, by NFE2L2.Abbreviations: ACTB: actin, beta, ARE: antioxidant response element; ATG5: autophagy related 5; BACH1: BTB domain and CNC homolog 1; ChIP: chromatin immunoprecipitation; CMA: chaperone-mediated autophagy; DHE: dihydroethidium; DMF: dimethyl fumarate; ENCODE: Encyclopedia of DNA elements at the University of California, Santa Cruz; GAPDH: glyceraldehyde-3-phosphate dehydrogenase; GBA: glucosylceramidase beta; GFP: green fluorescent protein; HMOX1: heme oxygenase 1; H2O2: hydrogen peroxide; HSPA8/HSC70: heat shock protein family A (Hsp70) member 8; KEAP1: kelch like ECH associated protein 1; LAMP2A: lysosomal associated membrane protein 2A; LAMP2B: lysosomal associated membrane protein 2B; LAMP2C: lysosomal associated membrane protein 2C; LAMP1: lysosomal associated membrane protein 1; MAFF: MAF bZIP transcription factor F; MAFK: MAF bZIP transcription factor K; NFE2L2/NRF2: nuclear factor, erythroid derived 2, like 2; NQO1: NAD(P)H quinone dehydrogenase 1; PQ: paraquat; PI: protease inhibitors; qRT-PCR: quantitative real-time polymerase chain reaction; RNASE: ribonuclease A family member; SFN: sulforaphane; SQSTM1/p62: sequestosome 1; TBP: TATA-box binding protein

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Transcription factor NFE2L2/NRF2 modulates chaperone-mediated autophagy through the regulation of LAMP2A

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Importantly, our results show that DeepBind models trained in vitro work well at scoring in vivo data, suggesting an ability to capture genuine properties of nucleic acid binding Figure 6 Comparison of motifs learned by DeepBind with known motifs. Example motif detectors learned by DeepBind models, along with known motifs from CISBP-RNA 22 (for RBPs) and JASPAR 30 (for transcription factors). A protein's motifs can collectively suggest putative RNA-and DNA-binding properties, as outlined 51 , such as variable-width gaps (HNRNPA1, Tp53), position interdependence (CTCF, NR4A2), and secondary motifs (PTBP1, Sox10, Pou2f2).…”

Section: Discussionmentioning

confidence: 99%

“…3e and Supplementary Table 5). We additionally tested the DeepBind FoxA2 ChIP-seq model using 64 'electrophoretic mobility shift assay' (EMSA)-measured binding affinities 29 and found that it achieves the highest Spearman correlation among published methods, including JASPAR PWMs 30 , TRANSFAC PWMs 31 , and other models trained on ChIP-seq data (Supplementary Notes, sec. 6.3 and Supplementary Fig.…”

Section: A N a Ly S I Smentioning

confidence: 99%

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

et al. 2015

View full text Add to dashboard Cite

Knowing the sequence specificities of DNA-and RNA-binding proteins is essential for developing models of the regulatory processes in biological systems and for identifying causal disease variants. Here we show that sequence specificities can be ascertained from experimental data with 'deep learning' techniques, which offer a scalable, flexible and unified computational approach for pattern discovery. Using a diverse array of experimental data and evaluation metrics, we find that deep learning outperforms other state-of-the-art methods, even when training on in vitro data and testing on in vivo data. We call this approach DeepBind and have built a stand-alone software tool that is fully automatic and handles millions of sequences per experiment. Specificities determined by DeepBind are readily visualized as a weighted ensemble of position weight matrices or as a 'mutation map' that indicates how variations affect binding within a specific sequence.DNA-and RNA-binding proteins play a central role in gene regulation, including transcription and alternative splicing. The sequence specificities of a protein are most commonly characterized using position weight matrices 1 (PWMs), which are easy to interpret and can be scanned over a genomic sequence to detect potential binding sites. However, growing evidence indicates that sequence specificities can be more accurately captured by more complex techniques 2-5 . Recently, 'deep learning' has achieved record-breaking performance in a variety of information technology applications 6,7 . We adapted deep learning methods to the task of predicting sequence specificities and found that they compete favorably with the state of the art. Our approach, called DeepBind, is based on deep convolutional neural networks and can discover new patterns even when the locations of patterns within sequences are unknown-a task for which traditional neural networks require an exorbitant amount of training data.There are several challenging aspects in learning models of sequence specificity using modern high-throughput technologies. First, the data come in qualitatively different forms. Protein binding microarrays (PBMs) 8 and RNAcompete assays 9 provide a specificity coefficient for each probe sequence, whereas chromatin immunoprecipitation (ChIP)-seq 10 provides a ranked list of putatively bound sequences of varying length, and HT-SELEX 11 generates a set of very high affinity sequences. Second, the quantity of data is large. A typical high-throughput experiment measures between 10,000 and 100,000 sequences, and it is computationally demanding to incorporate them all. Third, each data acquisition technology has its own artifacts, biases and limitations, and we must discover the pertinent specificities despite these unwanted effects. For example, ChIP-seq reads often localize to "hyper-ChIPable" regions of the genome near highly expressed genes 12 .DeepBind (Fig. 1) addresses the above challenges. (i) It can be applied to both microarray and sequencing data; (ii) it can learn from millions of...

show abstract

“…Custom P erl scripts were used to scan the diatom NET alignment to identify conserved intergenic blocks (window, 20 bp; step, 10 bp) which do not overlap gene/expressed sequence tags in the species conserved. Searches for transcription factor binding sites were performed using J aspar 2014 (Mathelier et al ., 2014). …”

Section: Methodsmentioning

confidence: 99%

Finding a partner in the ocean: molecular and evolutionary bases of the response to sexual cues in a planktonic diatom

et al. 2017

View full text Add to dashboard Cite

Summary Microalgae play a major role as primary producers in aquatic ecosystems. Cell signalling regulates their interactions with the environment and other organisms, yet this process in phytoplankton is poorly defined. Using the marine planktonic diatom Pseudo‐nitzschia multistriata, we investigated the cell response to cues released during sexual reproduction, an event that demands strong regulatory mechanisms and impacts on population dynamics.We sequenced the genome of P. multistriata and performed phylogenomic and transcriptomic analyses, which allowed the definition of gene gains and losses, horizontal gene transfers, conservation and evolutionary rate of sex‐related genes. We also identified a small number of conserved noncoding elements.Sexual reproduction impacted on cell cycle progression and induced an asymmetric response of the opposite mating types. G protein‐coupled receptors and cyclic guanosine monophosphate (cGMP) are implicated in the response to sexual cues, which overall entails a modulation of cell cycle, meiosis‐related and nutrient transporter genes, suggesting a fine control of nutrient uptake even under nutrient‐replete conditions.The controllable life cycle and the genome sequence of P. multistriata allow the reconstruction of changes occurring in diatoms in a key phase of their life cycle, providing hints on the evolution and putative function of their genes and empowering studies on sexual reproduction.

show abstract

JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles

Cited by 932 publications

References 32 publications

Transcription factor NFE2L2/NRF2 modulates chaperone-mediated autophagy through the regulation of LAMP2A

Transcription factor NFE2L2/NRF2 modulates chaperone-mediated autophagy through the regulation of LAMP2A

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

Finding a partner in the ocean: molecular and evolutionary bases of the response to sexual cues in a planktonic diatom

Contact Info

Product

Resources

About