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

Mathelier, Anthony; Fornés, Oriol; Arenillas, David J.; Chen, Chih-Yu; Denay, Grégoire; Lee, Jessica J. Y.; Shi, Wenqiang; Shyr, Casper; Tan, Ge; Worsley-Hunt, Rebecca; Zhang, Allen W.; Parcy, François; Lenhard, Boris; Sandelin, Albin; Wasserman, Wyeth W.

doi:10.1093/nar/gkv1176

Cited by 952 publications

(922 citation statements)

References 28 publications

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“…3. All predicted motifs matched experimentally determined yeast TFBS sequences in the JASPAR database 39 , which suggests strong biological relevance to our predictions, despite the large phylogenetic distance between S. cerevisiae and A. ostoyae. Taken together, the detected co-expressed genes and the presence of shared motifs in their promoters could suggest common regulatory mechanisms for rhizomorphs and fruiting bodies and probably represent elements of the regulatory networks that govern multicellular development as well as cap and stipe differentiation in Armillaria.…”

Section: Nature Ecology and Evolutionsupporting

confidence: 67%

Genome expansion and lineage-specific genetic innovations in the forest pathogenic fungi Armillaria

et al. 2017

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T he genus Armillaria causes root rot disease in both gymnoand angiosperms, in forests, parks, and even vineyards in more than 500 host plant species 1 across the world. Most Armillaria species are facultative necrotrophs, which, after colonizing and killing the root cambium, transition to a saprobic phase, decomposing dead woody tissues of the host. As saprotrophs, Armillaria spp. are white rot (WR) fungi, which can efficiently decompose all components of plant cell walls, including lignin, (hemi-)cellulose and pectin 2 . They produce fleshy fruiting bodies (honey mushrooms) that appear in large clumps around infected plants and produce sexual spores. The vegetative phase of Armillaria is predominantly diploid rather than dikaryotic like most basidiomycetes.Individuals of Armillaria can reach immense sizes and include the 'humongous fungus' , one of the largest terrestrial organisms on Earth 3 , measuring up to 965 hectares and 600 tons 4 , and can display a mutation rate ≅ 3 orders of magnitude lower than most filamentous fungi 5 . Individuals reach this immense size via growing rhizomorphs, dark mycelial strings 1-4 mm wide that allow the fungus to bridge gaps between food sources or host plants 1,6 (hence the name shoestring root rot). Rhizomorphs develop through the aggregation and coordinated parallel growth of hyphae, similar to some fruiting body tissues 7,8 . As migratory and exploratory organs, rhizomorphs can grow approximately 1 m yr −1 and cross several metres underground in search for new hosts, although roles in uptake and longrange translocation of nutrients have also been proposed 1,9,10 . Root contact by rhizomorphs is the main mode of infection by the fungus, which makes the prevention of recurrent infection in Armillariacontaminated areas particularly difficult 1 . Despite their huge impact on forestry, horticulture and agriculture, the genetics of the pathogenicity of Armillaria species is poorly understood. The only -omics data published so far have highlighted a substantial repertoire of plant cell wall degrading enzymes (PCWDE) and secreted proteins, among others, in A. mellea and A. solidipes 11,12 , while analyses of the genomes of other pathogenic basidiomycetes (such as Moniliophthora 13,14 , Heterobasidion 15 and Rhizoctonia 16 ) identified genes coding for PCWDEs, secreted and effector proteins or secondary metabolism (SM) as putative pathogenicity factors. However, the lifecycle and unique dispersal strategy of Armillaria prefigure other evolutionary routes to pathogenicity, which, along with other potential genomic factors (such as transposable elements 17 ) are not yet known.Here, we investigate genome evolution and the origin of pathogenicity in Armillaria using comparative genomics, transcriptomics

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Section: Nature Ecology and Evolutionsupporting

confidence: 67%

Genome expansion and lineage-specific genetic innovations in the forest pathogenic fungi Armillaria

et al. 2017

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“…Specifically, we used the human CTCF motif from the JASPAR 2016 database (Mathelier et al 2016) to define a position weight matrix (PWM) score sequence of the same size as the motif . A background probability of 30% for A and T and 20% for C and G were used for the PWM score calculation.…”

Section: Cold Spring Harbor Laboratory Press On May 12 2018 -Publishmentioning

confidence: 99%

Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq data

Teng

Irizarry

2017

Genome Res.

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The main application of ChIP-seq technology is the detection of genomic regions that bind to a protein of interest. A large part of functional genomics' public catalogs is based on ChIP-seq data. These catalogs rely on peak calling algorithms that infer protein-binding sites by detecting genomic regions associated with more mapped reads (coverage) than expected by chance, as a result of the experimental protocol's lack of perfect specificity. We find that GC-content bias accounts for substantial variability in the observed coverage for ChIP-seq experiments and that this variability leads to false-positive peak calls. More concerning is that the GC effect varies across experiments, with the effect strong enough to result in a substantial number of peaks called differently when different laboratories perform experiments on the same cell line. However, accounting for GC content bias in ChIP-seq is challenging because the binding sites of interest tend to be more common in high GC-content regions, which confounds real biological signals with unwanted variability. To account for this challenge, we introduce a statistical approach that accounts for GC effects on both nonspecific noise and signal induced by the binding site. The method can be used to account for this bias in binding quantification as well to improve existing peak calling algorithms. We use this approach to show a reduction in false-positive peaks as well as improved consistency across laboratories.

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“…Using machine learning methods, 43 these predictions can be used to identify TFs acting as key regulators [4,46,47]. 44 In addition to open-chromatin data, also Histone Modification (HM) ChIP-seq data 45 was used for the prediction of TF binding [4,5,13,24,51,67]. In these studies, HMs were 46 used either exclusively or along with open-chromatin data.…”

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confidence: 99%

“…From ENCODE, we downloaded DNaseI-seq data, gene 108 expression data, H3K4me3 ChIP-seq data, H3K27ac ChIP-seq data, and TF-ChIP-seq 109 files for K562, GM12878, and H1-hESC. For HepG2, we downloaded H3K4me3 different sets of position weight matrices (pwms): Using Jaspar [44] and Uniprobe [30], 115 we created a set of 439 pwms for usage within TEPIC. We downloaded the set of non 116 redundant TFs for vertebrates from Jaspar (version of 26.10.2015) and vertebrate TF 117 data from Uniprobe.…”

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confidence: 99%

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

Schmidt

Gasparoni

et al. 2016

Preprint

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The binding and contribution of transcription factors (TF) to cell specific gene Using machine learning, we find low affinity binding sites to improve our ability to 10 explain gene expression variability compared to the standard presence/absence 11 classification of binding sites. Further, we show that both footprints and peaks capture 12 essential TF binding events and lead to a good prediction performance. In our nearly reach the quality of several TF ChIP-seq datasets. Finally, these scores correctly 15

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

Cited by 952 publications

References 28 publications

Genome expansion and lineage-specific genetic innovations in the forest pathogenic fungi Armillaria

Genome expansion and lineage-specific genetic innovations in the forest pathogenic fungi Armillaria

Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq data

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

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