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MethMotif: An integrative cell specific database of transcription factor binding motifs coupled with DNA methylation profiles

ABSTRACT: MethMotif: An integrative cell specific database of transcription factor binding motifs coupled with DNA methylation profiles

PROVIDER: PRJNA484119 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR7637726_1.fastq.gz	Fastqsanger.gz
	SRR7637726_2.fastq.gz	Fastqsanger.gz
	SRR8165160_1.fastq.gz	Fastqsanger.gz
	SRR8165160_2.fastq.gz	Fastqsanger.gz

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MethMotif: an integrative cell specific database of transcription factor binding motifs coupled with DNA methylation profiles.

Project description:Several recent studies have portrayed DNA methylation as a new player in the recruitment of transcription factors (TF) within chromatin, highlighting a need to connect TF binding sites (TFBS) with their respective DNA methylation profiles. However, current TFBS databases are restricted to DNA binding motif sequences. Here, we present MethMotif, a two-dimensional TFBS database that records TFBS position weight matrices along with cell type specific CpG methylation information computed from a combination of ChIP-seq and whole genome bisulfite sequencing datasets. Integrating TFBS motifs with TFBS DNA methylation better portrays the features of DNA loci recognised by TFs. In particular, we found that DNA methylation patterns within TFBS can be cell specific (e.g. MAFF). Furthermore, for a given TF, different DNA methylation profiles are associated with different DNA binding motifs (e.g. REST). To date, MethMotif database records over 500 TFBSs computed from over 2000 ChIP-seq datasets in 11 different cell types. MethMotif portal is accessible through an open source web interface (https://bioinfo-csi.nus.edu.sg/methmotif) that allows users to intuitively explore the entire dataset and perform both single, and batch queries.

| S-EPMC6323897 | biostudies-literature

MethMotif: An integrative cell specific database of transcription factor binding motifs coupled with DNA methylation profiles

Project description:Integration of whole-genome bisulfite sequencing with ChIPseq datasets.

2018-08-10 | GSE118030 | GEO

PiceaGenExpress database of transcription profiles

Project description:PiceaGenExpress database of transcription profiles

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YM500: An integrative small RNA sequencing (smRNA-seq) database for microRNA research

Project description:MicroRNAs (miRNAs) are small RNAs of ~22 nucleotides in length that are involved in the regulation of a variety of physiological and pathological processes. Advances in high-throughput small RNA sequencing (smRNA-seq), one of the next generation sequencing applications, have reshaped the miRNA research landscape. In this study, we established an integrative database containing analysis pipelines and analysis results of 609 human and mice smRNA-seq results, including public data from GEO and private ones. YM500 collects analysis results for miRNA quantification, for isomiR identification (incl. RNA editing), for arm switching discovery, and, more importantly, for novel miRNA predictions. Wetlab validation on >100 miRNAs confirmed high correlation between miRNA profiling and RT-qPCR results (R=0.84). This database allows researchers to search these 4 different types of analysis results via our interactive web interface. YM500 allows researchers to define the criteria of isomiRs, as well as integrates the information of dbSNP to help researchers to distinguish isomiRs from SNPs. A user-friendly interface is provided to integrate miRNA-related information and existing evidences from hundreds of sequencing datasets. The identified novel miRNAs and isomiRs hold the potentials for both basic research and biotech applications. YM500 is now available on http://ngs.ym.edu.tw/ym500/. There are 34 in-house datasets in YM500 . They are 9 human embryonal tumors, 9 human germ cell tumors, and 16 human gliomas.

2012-12-03 | E-GEOD-39841 | biostudies-arrayexpress

YM500: An integrative small RNA sequencing (smRNA-seq) database for microRNA research

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Project description:Purpose: 224 GSM Samples form GSE32970 and GSE29692 was reanalyzed to find the binding sites of 542 TFs. Methods: 1. iFORM (incorporating Find Occurrence of Regulatory Motifs) is a tool we designed to scan through sequences in search of binding sites that match given motifs. 2. We mapped motif-binding protein information found in TRANSFAC, JASPAR, and UniPROBE databases to 542 coding genes, using GeneCards (Rebhan et al., 1997) and UniProt Knowledgebase (Magrane and Consortium, 2011). The position-specific weight matrices of the 542 TFs, corresponding to 796 motif models, were collected from these databases. 3.We used a uniform set of open chromatin elements (DNaseI hypersensitive sites, DHSs) in 133 ENCODE cell types, based on DNase-seq data produced by the "Open Chromatin" (Duke/UNC/UT-A)(GSE32970) and University of Washington (UW) ENCODE groups (GSE29692)from the project inception in 2007 through the ENCODE January 2011 data freeze. Processing pipeline: Combined all replicates for a given cell type; Subsampled the result at a level of 30 million tags; Ran results through the Stam Lab hotspot pipeline. Hotspots were identified using a relaxed threshold. NarrowPeaks were generated by first thresholding hotspots at FDR 1%, and then locating local maxima of the tag density within the hotspots. The 133 narrowPeaks are base sequences for the given motifs scan. Result: We used our iFORM with a custom library of all 796 motifs to scan for motif instances in 133 ENCODE DHSs cell types separately. Result in 133 files correspond to 133 cell types, each file contains binding sites of 542 TFs in a cell type.

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LIN28 binds messenger RNAs at GGAGA motifs and regulates splicing factor abundance

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2012-09-10 | E-GEOD-39873 | biostudies-arrayexpress

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