DOI: 10.5061/dryad.cv323
title: Data from: Genome-wide errant targeting by Hairy

description: Metazoan transcriptional repressors regulate chromatin through diverse
 histone modifications. Contributions of individual factors to the
 chromatin landscape in development is difficult to establish, as global
 surveys reflect multiple changes in regulators. Therefore, we studied the
 conserved Hairy/Enhancer of Split family repressor Hairy, analyzing
 histone marks and gene expression in Drosophila embryos. This long-range
 repressor mediates histone acetylation and methylation in large blocks,
 with highly context-specific effects on target genes. Most strikingly,
 Hairy exhibits biochemical activity on many loci that are uncoupled to
 changes in gene expression. Rather than representing inert binding sites,
 as suggested for many eukaryotic factors, many regions are targeted
 errantly by Hairy to modify the chromatin landscape. Our findings
 emphasize that identification of active cis-regulatory elements must
 extend beyond the survey of prototypical chromatin marks. We speculate
 that this errant activity may provide a path for creation of new
 regulatory elements, facilitating the evolution of novel transcriptional
 circuits.
description: Microarray_raw_data.tarExpression profiling analysis: Transcriptome data from four biological replicates were generated using 8x15K Customized Drosophila Genome Oligo Microarrays (Agilent). Slide image data was quantified using Agilent's Feature Extraction software.diffReps_files.tarDifferentially changed genomic regions for histone marks were identified using the diffReps program, which uses a sliding window approach to scan the genome and find regions showing read count differences. Regions detected by diffReps were associated with genes by identifying the nearest RefSeq TSS and annotated to a genomic feature such as intergenic, intron, exon etc.HOMER_peak_files.tarUsing HOMER with default settings, peaks for histone marks and Flag tagged Hairy protein were identified using signals from H3 ChIP and input respectively as background. Peaks called by HOMER were associated with genes by identifying the nearest RefSeq TSS and annotated to a genomic feature such as intergenic, intron, exon etc.bedgraph_files.tarChIP-Seq experiments were visualized as custom tracks using Integrative Genomics Viewer (Broad Institute). Total uniquely mapped tags were normalized to 10 million reads to generate tracks using HOMER.Machine_learning_CodeANDResultsTo perform machine learning analysis to predict genes in the repressed and not-repressed categories, we wrote Python and Java codes to partition our dataset into 10 parts to perform feature selection and 10-fold cross validation classification utilizing the Weka machine learning software. The folder includes the codes used in this analysis and raw results that are summarized in the main text. For further description, a readme file is provided under WekaCode folder.bed_files.tar.gz part aRaw sequencing reads were mapped to genome as described in Materials and methods. The output bam files were converted to bed files and compressed in bed_files.tar.gz folder, which were broken into five pieces (part a, b, c, d and e) using split command. These files can be recombined to bed_files.tar.gz using cat command.bed_files.tar.gz_abed_files.tar part bRaw sequencing reads were mapped to genome as described in Materials and methods. The output bam files were converted to bed files and compressed in bed_files folder, which were broken into five pieces (part a, b, c, d and e) using split command. These files can be recombined to bed_files.tar.gz using cat command.bed_files.tar.gz_bbed_files.tar part cRaw sequencing reads were mapped to genome as described in Materials and methods. The output bam files were converted to bed files and compressed in bed_files folder, which were broken into five pieces (part a, b, c, d and e) using split command. These files can be recombined to bed_files.tar.gz using cat command.bed_files.tar.gz_cbed_files.tar part dRaw sequencing reads were mapped to genome as described in Materials and methods. The output bam files were converted to bed files and compressed in bed_files folder, which were broken into five pieces (part a, b, c, d and e) using split command. These files can be recombined to bed_files.tar.gz using cat command.bed_files.tar.gz_dbed_files.tar part eRaw sequencing reads were mapped to genome as described in Materials and methods. The output bam files were converted to bed files and compressed in bed_files folder, which were broken into five pieces (part a, b, c, d and e) using split command. These files can be recombined to bed_files.tar.gz using cat command.bed_files.tar.gz_e