Data from: Genome-wide errant targeting by Hairy

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### Microarray_raw_data.tar 基因表达谱分析：本数据集采用8×15K定制化果蝇基因组寡核苷酸微阵列（Agilent），获取了4次生物学重复的转录组数据。使用安捷伦Feature Extraction软件对芯片图像数据进行定量分析。 ### diffReps_files.tar 本数据集使用diffReps程序鉴定组蛋白修饰的差异变化基因组区域：该程序采用滑动窗口法扫描基因组，筛选出读取计数存在显著差异的区域。通过匹配最近的RefSeq转录起始位点（Transcription Start Site, TSS），将diffReps检测到的区域关联至对应基因，并注释为基因间区、内含子、外显子等基因组特征。 ### HOMER_peak_files.tar 采用默认参数运行HOMER软件，分别以H3染色质免疫沉淀测序（ChIP-Seq）信号与输入样本信号作为背景，鉴定组蛋白修饰与Flag标签标记的Hairy蛋白结合峰。通过匹配最近的RefSeq转录起始位点（TSS），将HOMER鉴定得到的结合峰关联至对应基因，并注释为基因间区、内含子、外显子等基因组特征。 ### bedgraph_files.tar 本研究采用整合基因组浏览器（Integrative Genomics Viewer, IGV，Broad研究所开发）将染色质免疫沉淀测序（ChIP-Seq）实验结果可视化为自定义轨道。使用HOMER软件将唯一比对的总标签数归一化至1000万读取数，以生成可视化轨道。 ### Machine_learning_CodeANDResults 为构建机器学习模型以预测基因的抑制与非抑制类别，我们编写了Python与Java代码，将数据集划分为10个分区，结合Weka机器学习软件完成特征选择与10折交叉验证分类任务。本文件夹包含本次分析所用代码与正文汇总的原始结果。WekaCode文件夹下提供了readme文件以供进一步查阅说明。 ### bed_files.tar.gz part a 原始测序读数按"材料与方法"中描述的流程比对至参考基因组。将输出的BAM格式文件转换为BED格式文件并压缩为"bed_files.tar.gz"，随后通过split命令将压缩包拆分为5个分片（part a、b、c、d、e）。可使用cat命令将这些分片重新合并为完整的"bed_files.tar.gz"。 ### bed_files.tar.gz part b 原始测序读数按"材料与方法"中描述的流程比对至参考基因组。将输出的BAM格式文件转换为BED格式文件并压缩为"bed_files.tar.gz"，随后通过split命令将压缩包拆分为5个分片（part a、b、c、d、e）。可使用cat命令将这些分片重新合并为完整的"bed_files.tar.gz"。 ### bed_files.tar.gz part c 原始测序读数按"材料与方法"中描述的流程比对至参考基因组。将输出的BAM格式文件转换为BED格式文件并压缩为"bed_files.tar.gz"，随后通过split命令将压缩包拆分为5个分片（part a、b、c、d、e）。可使用cat命令将这些分片重新合并为完整的"bed_files.tar.gz"。 ### bed_files.tar.gz part d 原始测序读数按"材料与方法"中描述的流程比对至参考基因组。将输出的BAM格式文件转换为BED格式文件并压缩为"bed_files.tar.gz"，随后通过split命令将压缩包拆分为5个分片（part a、b、c、d、e）。可使用cat命令将这些分片重新合并为完整的"bed_files.tar.gz"。 ### bed_files.tar.gz part e 原始测序读数按"材料与方法"中描述的流程比对至参考基因组。将输出的BAM格式文件转换为BED格式文件并压缩为"bed_files.tar.gz"，随后通过split命令将压缩包拆分为5个分片（part a、b、c、d、e）。可使用cat命令将这些分片重新合并为完整的"bed_files.tar.gz"。