Download 139K Mail Access Amr Txt
DOWNLOAD https://ssurll.com/2tkYfd
As the number of publicly available methylomes rises, large-scale comparative analysis of methylation patterns across multiple samples calls for well-curated reference methylome databases. Some databases exist to address the need for accessing bisulfite sequencing datasets. The NCBI Sequence Read Archive (SRA) contains raw sequences of most BS-seq data [18]. The NGSmethDB database [19] and the NCBI Epigenomics Resources [20] provide pre-computed methylation level results at individual cytosines, saving both time and computational resources to download and analyze those raw reads,. However there are not sufficient effort to address the issue of higher-level methylation features. This creates a computational barrier for researchers who seek to use methylation-based features in large projects. Additionally, databases like SRA and NCBI Epigenetic Resources are designed to accommodate datasets generated from a variety of techniques, including ChIP-seq, RNA-seq and WGBS. As such, their annotation lacks metadata (e.g. bisulfite conversion rates) that are specific to bisulfite sequencing.
MethBase currently includes over one hundred methylomes from well-studied organisms, including human, mouse, chimpanzee and arabidopsis (studies: 28, 17, 3 and 8; methylomes: 169, 71, 5 and 32, respectively). These methylomes are organized by species and further grouped by the project or publication associated with the data. The processed data in MethBase can be accessed through a Track Hub that can be easily loaded into any mirror of the UCSC Genome Browser [28]. Visual examination of the methylation patterns at specific genes and genomic loci is an essential part of exploratory data analysis for investigators using WGBS data. Pre-computed high-level methylation features, including HMRs, HyperMRs, PMDs and AMRs, highlight potentially interesting regions (Figure 1). From the visualization interface, one may access detailed meta data and summary statistics through the track summary pages (Figure 2). For more detailed genome-scale analysis, researchers also have the option to download the methylation data and pre-computed methylation features through the UCSC Table Browser [29], in the form of either genomic interval annotations or single-site methylation levels. 59ce067264