Bisulfite sequencing (BS-seq) technology measures DNA methylation at single nucleotide resolution. A key task in BS-seq data analysis is to identify differentially methylation (DM) under different conditions. Here we provide a tutorial for BS-seq DM analysis using Bioconductor package DSS. DSS uses a beta-binomial model to characterize the sequence counts from BS-seq, and implements rigorous statistical method for hypothesis testing. It provides flexible functionalities for a variety of DM analyses.

The Cistrome Data Browser (DB) at the website (cistrome.org/db) provides about 56,000 published human and mouse ChIP-seq, DNase-seq, and ATAC-seq chromatin profiles, which we have processed using uniform analysis and quality control pipelines. The Cistrome DB Toolkit at the website (dbtoolkit.cistrome.org) was developed to allow users to investigate fundamental questions using this data collection. In this tutorial, we describe how to use the Cistrome DB to search for publicly available chromatin profiles, to assess sample quality, to access peak results, to visualize signal intensities, to explore DNA sequence motifs, and to identify putative target genes. We also describe the use of the Toolkit module to seek the factors most likely to regulate a gene of interest, the factors that bind to a given genomic interval (enhancer, SNP, etc.), and samples that have significant peak overlaps with user-defined peak sets. This tutorial guides biomedical researchers in the use of Cistrome DB resources to rapidly obtain valuable insights into gene regulatory questions

This tutorial presents a mathematical theory that relates the probability of sample frequencies, of M phenotypes in an isogenic population of N cells, to the probability distribution of the sample mean of a quantitative biomarker, when the N is very large. An analogue to the statistical mechanics of canonical ensemble is discussed.