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data-raw/ITFP/README.md

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+# ITFP: an integrated platform of mammalian transcription factors
+
+Guangyong Zheng,
+Kang Tu,
+Qing Yang,
+Yun Xiong,
+Chaochun Wei,
+Lu Xie,
+Yangyong Zhu,
+and Yixue
+
+<http://dx.doi.org/10.1093/bioinformatics/btn439>
+
+## Summary
+
+Investigation of transcription factors (TFs) and their downstream regulated
+genes (targets) is a significant issue in post-genome era, which can provide
+a brand new vision for some vital biological process. However, information of
+TFs and their targets in mammalian is far from sufficient. Here, we developed
+an integrated TF platform (ITFP), which included abundant TFs and their
+targets of mammalian. In current release, ITFP includes 4105 putative TFs and
+69 496 potential TF-target pairs for human, 3134 putative TFs and 37 040
+potential TF-target pairs for mouse, and 1114 putative TFs and 18 055
+potential TF-target pairs for rat. In short, ITFP will serve as an important
+resource for the research community of transcription and provide strong
+support for regulatory network study.
+
+- - -
+
+# ITFP Website
+
+<http://itfp.biosino.org/itfp>
+
 # Introduction
 
 Investigation of transcription factors (TFs) and their downstream regulated

data-raw/Neph2012/README.md

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 # Circuitry and Dynamics of Human Transcription Factor Regulatory Networks
 
-Shane Neph
-Andrew B. Stergachis
-Alex Reynolds
-Richard Sandstrom
-Elhanan Borenstein
-John A. Stamatoyannopoulos
+Shane Neph,
+Andrew B. Stergachis,
+Alex Reynolds,
+Richard Sandstrom,
+Elhanan Borenstein,
+John A. Stamatoyannopoulos,
+
+<http://dx.doi.org/10.1016/j.cell.2012.04.040>
 
 ## Highlights
 

data-raw/TRED/README.md

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-# Transcriptional Regulatory Element Database
+# TRED: a Transcriptional Regulatory Element Database and a platform for in silico gene regulation studies
 
-<https://cb.utdallas.edu/cgi-bin/TRED/>
+Fang Zhao,
+Zhenyu Xuan,
+Lihua Liu,
+Michael Q. Zhang
+
+<http://dx.doi.org/10.1093/nar/gki004>
+
+In order to understand gene regulation, accurate and comprehensive knowledge
+of transcriptional regulatory elements is essential. Here, we report our
+efforts in building a mammalian Transcriptional Regulatory Element Database
+(TRED) with associated data analysis functions. It collects cis- and
+trans-regulatory elements and is dedicated to easy data access and analysis
+for both single-gene-based and genome-scale studies. Distinguishing features
+of TRED include: (i) relatively complete genome-wide promoter annotation for
+human, mouse and rat; (ii) availability of gene transcriptional regulation
+information including transcription factor binding sites and experimental
+evidence; (iii) data accuracy is ensured by hand curation; (iv) efficient user
+interface for easy and flexible data retrieval; and (v) implementation of
+on-the-fly sequence analysis tools. TRED can provide good training datasets
+for further genome-wide cis-regulatory element prediction and annotation,
+assist detailed functional studies and facilitate the decipher of gene
+regulatory networks (http://rulai.cshl.edu/TRED).
+
+- - -
+
+# TRED Website
+
+<https://cb.utdallas.edu/cgi-bin/TRED/tred.cgi?process=home>
 
 # Introduction
 

data-raw/UCSC/README.md

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+# Transcription Factor ChIP-seq (161 factors) from ENCODE with Factorbook Motifs
+
+<http://genome.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeRegTfbsClusteredV3>
+
+# Description
+
+This track shows regions of transcription factor binding derived from a large
+collection of ChIP-seq experiments performed by the ENCODE project, together
+with DNA binding motifs identified within these regions by the ENCODE
+Factorbook repository.
+
+Transcription factors (TFs) are proteins that bind to DNA and interact with RNA
+polymerases to regulate gene expression. Some TFs contain a DNA binding domain
+and can bind directly to specific short DNA sequences ('motifs'); others bind
+to DNA indirectly through interactions with TFs containing a DNA binding
+domain. High-throughput antibody capture and sequencing methods (e.g. chromatin
+immunoprecipitation followed by sequencing, or 'ChIP-seq') can be used to
+identify regions of TF binding genome-wide. These regions are commonly called
+ChIP-seq peaks.
+
+ENCODE TFBS ChIP-seq data were processed using the computational pipeline
+developed by the ENCODE Analysis Working Group to generate uniform peaks of TF
+binding. Peaks for 161 transcription factors in 91 cell types are combined here
+into clusters to produce a summary display showing occupancy regions for each
+factor and motif sites within the regions when identified. Additional views of
+the underlying ChIP-seq data and documentation on the methods used to generate
+it are available from the ENCODE Uniform TFBS track.
+
+# Display Conventions
+
+A gray box encloses each peak cluster of transcription factor occupancy, with
+the darkness of the box being proportional to the maximum signal strength
+observed in any cell line contributing to the cluster. The HGNC gene name for
+the transcription factor is shown to the left of each cluster. Within a
+cluster, a green highlight indicates the highest scoring site of a
+Factorbook-identified canonical motif for the corresponding factor. (NOTE:
+motif highlights are shown only in browser windows of size 50,000 bp or less,
+and their display can be suppressed by unchecking the highlight motifs box on
+the track configuration page). Arrows on the highlight designate the matching
+strand of the motif.
+
+The cell lines where signal was detected for the factor are identified by
+single-letter abbreviations shown to the right of the cluster. The darkness of
+each letter is proportional to the signal strength observed in the cell line.
+Abbreviations starting with capital letters designate ENCODE cell types
+identified for intensive study - Tier 1 and Tier 2 - while those starting with
+lowercase letters designate Tier 3 cell lines.
+
+Click on a peak cluster to see more information about the TF/cell assays
+contributing to the cluster, the cell line abbreviation table, and details
+about the highest scoring canonical motif in the cluster.
+
+# Methods
+
+Peaks of transcription factor occupancy from uniform processing of ENCODE
+ChIP-seq data by the ENCODE Analysis Working Group were filtered to exclude
+datasets that did not pass the integrated quality metric (see "Quality Control"
+section of Uniform TFBS) and then were clustered using the UCSC hgBedsToBedExps
+tool. Scores were assigned to peaks by multiplying the input signal values by a
+normalization factor calculated as the ratio of the maximum score value (1000)
+to the signal value at one standard deviation from the mean, with values
+exceeding 1000 capped at 1000. This has the effect of distributing scores up to
+mean plus one 1 standard deviation across the score range, but assigning all
+above to the maximum score. The cluster score is the highest score for any peak
+contributing to the cluster.
+
+The Factorbook motif discovery and annotation pipeline uses the MEME-ChIP and
+FIMO tools from the MEME software suite in conjunction with machine learning
+methods and manual curation to merge discovered motifs with known motifs
+reported in Jaspar and TransFac. Motif identifications reported in Wang et al.
+2012 (below) were supplemented in this track with more recent data (derived
+from newer ENCODE datasets - Jan 2011 through Mar 2012 freezes), provided by
+the Factorbook team. Motif identifications from all datasets were merged, with
+the most significant value (qvalue) reported being picked when motifs were
+duplicated in multiple cell lines. The scores for the selected best-scoring
+motif sites were then transformed to -log10.
+
+# Release Notes
+
+Release 4 (February 2014) of this track adds display of the Factorbook motifs.
+Release 3 (August 2013) added 124 datasets (690 total, vs. 486 in Release 2),
+representing all ENCODE TF ChIP-seq passing quality assessment through the
+ENCODE March 2012 data freeze. The peaks used to generate these clusters were
+called with less stringent thresholds than used during the January 2011 uniform
+processing shown in Release 2 of this track. The contributing datasets are
+displayed as individual tracks in the ENCODE Uniform TFBS track, which is
+available along with the primary data tracks in the ENC TF Binding Supertrack
+page. The clustering for V3/V4 is based on the transcription factor target, and
+so differs from V2 where clustering was based on antibody.
+
+For the V3/V4 releases, a new track table format, 'factorSource' was used to
+represent the primary clusters table and downloads file,
+wgEncodeRegTfbsClusteredV3. This format consists of standard BED5 fields (see
+File Formats) followed by an experiment count field (expCount) and finally two
+fields containing comma-separated lists. The first list field (expNums)
+contains numeric identifiers for experiments, keyed to the
+wgEncodeRegTfbsClusteredInputsV3 table, which includes such information as the
+experiment's underlying Uniform TFBS table name, factor targeted, antibody
+used, cell type, treatment (if any), and laboratory source. The second list
+field (expScores) contains the scores for the corresponding experiments. For
+convenience, the file downloads directory for this track also contains a BED
+file, wgEncodeRegTfbsClusteredWithCellsV3, that lists each cluster with the
+cluster score followed by a comma-separated list of cell types.
+
+# Credits
+
+This track shows ChIP-seq data from the Myers Lab at the HudsonAlpha Institute
+for Biotechnology and by the labs of Michael Snyder, Mark Gerstein, Sherman
+Weissman at Yale University, Peggy Farnham at the University of Southern
+California, Kevin Struhl at Harvard, Kevin White at the University of Chicago,
+and Vishy Iyer at the University of Texas, Austin. These data were processed
+into uniform peak calls by the ENCODE Analysis Working Group pipeline developed
+by Anshul Kundaje The clustering of the uniform peaks was performed by UCSC.
+The Factorbook motif identifications and localizations (and valuable assistance
+with interpretation) were provided by Jie Wang, Bong Hyun Kim and Jiali Zhuang
+of the Zlab (Weng Lab) at UMass Medical School.
+
+# References
+
+Gerstein MB, Kundaje A, Hariharan M, Landt SG, Yan KK, Cheng C, Mu XJ, Khurana
+E, Rozowsky J, Alexander R et al. Architecture of the human regulatory network
+derived from ENCODE data. Nature. 2012 Sep 6;489(7414):91-100. PMID: 22955619
+
+Wang J, Zhuang J, Iyer S, Lin X, Whitfield TW, Greven MC, Pierce BG, Dong X,
+Kundaje A, Cheng Y et al. Sequence features and chromatin structure around the
+genomic regions bound by 119 human transcription factors. Genome Res. 2012
+Sep;22(9):1798-812. PMID: 22955990; PMC: PMC3431495
+
+Wang J, Zhuang J, Iyer S, Lin XY, Greven MC, Kim BH, Moore J, Pierce BG, Dong
+X, Virgil D et al. Factorbook.org: a Wiki-based database for transcription
+factor-binding data generated by the ENCODE consortium. Nucleic Acids Res. 2013
+Jan;41(Database issue):D171-6. PMID: 23203885; PMC: PMC3531197
+
+# Data Release Policy
+
+While primary ENCODE data was subject to a restriction period as described in
+the ENCODE data release policy, this restriction does not apply to the
+integrative analysis results, and all primary data underlying this track have
+passed the restriction date. The data in this track are freely available.