ChIP-Seq

1. - Nice and ‘gentle’ introduction to ChIP-seq data analysis - Example of data analysis workflow (subjective) - No deep details about the pros and cons of tools - Notes on reproducibility and repeatability - Do’s and don’ts of ChIP data analysis (qc, IDR, duplicates reads ,normalisation etc ..)
2. - Published during the ENOCDE media hype - Epigenomics flavored paper - Focus on DNase HS and Open Chromatine region - Details about the usage of ChIP technology (use case) - Histone modifications and TFBS to explore the DHS - Example of Data integrations in action
3. - Guide to ChIP-seq pipeline development - Details about ‘details’ and focus on adapting the tools to the data analyzed - Tools/Seeds mini-benchmarking : example bwa/novoalign - Post analysis data exploration (annotation, visualization, filtering ..) - Irreproducible Discovery Rate .. again
4. - Nice overview of using both technologies to answer specific questions - Explaining the peak calling algorithms - Review of the models used by peak callers - More focused on peak calling than the details about other parts of the ChIP- seq data analysis modules
5. more papers here : https://goo.gl/EJdxUz
6. Data preprocessing Read mapping and visualization Global Reproducibility Peak Calling Analysis of quantitative changes Sequence quality Raw Read Counts Check Read Length Map/align reads Count the mapped reads Visualize Calculate reproducibility Peak Calling Peak Visualization Peak Conservation Enriched Regions Normalize data Quantitative change fastx_quality_stats fastx_quality_boxplot fastx_nucleotide_distribution HTSeq HTSeq pysam HTSeq bowtie2 samtools bedtools ucsc toolkit python/awk seeds MACS2 bedtools python/shell seeds bedtools pybedtools matplotlib /seaborn python IDR (R) picard tools