This manual is intended for users who have a basic knowledge of the R environment, and would like to use R/Bioconductor to perform general or HT sequencing analysis. For new users, it is recommended to first review the material covered in the ‘R Basics’ section (see below). To obtain a more comprehensive overview of R’s sequence bioinformatics utilities, Robert Gentleman’s book ‘R Programming for Bioinformatics’ is an excellent choice. The packages introduced here are a ‘personal selection’ of the authors of this manual that does not reflect the full utility spectrum of the Bioconductor project for this field of application. The introduced packages were chosen, because the authors use them often for their own teaching and research. To obtain a broad overview of available Bioconductor packages, it is strongly recommended to consult its official project site (http://bioconductor.org). Due to the rapid development of many packages, it is also important to be aware that this manual will often not be fully up-to-date. Because of this and many other reasons, it is absolutely critical to use the original documentation of each package (PDF manual or vignette) as primary source of documentation. Users are welcome to send suggestions for improving this manual directly to the authors. Readers of this manual might also be interested in the recently released systemPipeR package which provides convenience utilities for building end-to-end analysis pipelines with automated report generation for a variety next generation sequence (NGS) applications such as RNA-Seq, ChIP-Seq, VAR-Seq and Ribo-Seq. Workshops on HT sequence data analysis using BioC-Seq resources are announced on the Bioconductor workshop site. The Institute for Integrative Genome Biology, IIGB at UCR also offers workshops in this field. In addition, there are many more institutions that organize similar workshops. The best way to find out about upcoming events in a certain location is to run a Google search on this topic. The R & BioConductor manual provides an introduction on the usage of the R environment and its basic command syntax, and the Programming in R manual introduces a more advanced overview of R’s programming syntax. If you are working on a remote server, you will need to copy this file locally before you can upload it to a genome browser. If you have trouble with the copy, you can download a sample here: chromsome1.wig Go to an genome browser, upload your .wig file, and view it. You can try the Arabidopsis UCSC browser (http://epigenomics.mcdb.ucla.edu/cgi-bin/hgGateway?org=A.+thaliana&db=araTha1), by uploading your .wig file via the ‘add your own custom tracks’ button. After uploading this, browse to the region you would like to see. A read pileup in the test dataset for the workshop can be found at chr1:17,825,707-17,825,775. Also, try inspecting regions of high coverage you found in the previous step. Note: In this case, the UCSC genome browser is not using the same version of the genome (TAIR9) so your read loci may not correlate with the correct annotation features. Bioconductor provides various packages for analyzing and visualizing ChIP-Seq data. Only a small selection of these packages is introduced here. Additional useful introductions to this topic are: BioC ChIP-seq Case Study and BioC ChIP-Seq. BayesPeak is a peak calling package for identifying DNA binding sites of proteins in ChIP-Seq experiments. Its algorithm uses hidden Markov models (HMM) and Bayesian statistical methods. The following sample code introduces the identification of peaks with the BayesPeak package as well as the incorporation of read coverage information obtained by the chipseq package. The PICS package applies probabilistic inference to aligned-read ChIP-Seq data in order to identify regions bound by transcription factors. PICS identifies enriched regions by modeling local concentrations of directional reads, and uses DNA fragment length prior information to discriminate closely adjacent binding events via a Bayesian hierarchical t-mixture model. The following sample code uses the test data set from the above BayesPeak package in order to compare the results from both methods by identifying their consensus peak set. The ChIPpeakAnno package provides. batch annotation of the peaks identified from either ChIP-seq or ChIP-chip experiments. It includes functions to retrieve the sequences around peaks, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites supplied by users. The package leverages the biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest and stat packages. A variety of additional R packages are available for normalizing RNA-Seq read count data and identifying differentially expressed genes (DEG): Source.