Unknown,Transcriptomics,Genomics,Proteomics

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Genotyping of E14 mouse embryonic stem cells by sequencing

ABSTRACT: More than 2x10E9 sequences made on Illumina platform derived from the genome of E14 embryonic stem cells cultured in our laboratory were used to build a database of about 2.7x10E6 single nucleotide variant. The database was validated using other two sequencing datasets from other laboratory and high overlap was observed. The identified variant are enriched on intergenic regions, but several thousands reside on gene exons and regulatory regions, such as promoters, enhancers, splicing site and untranslated regions of RNA, thus indicating high probability of an important functional impact on the molecular biology of this cells. We created a new E14 genome assembly including the new identified variants and used it to map reads from next generation sequencing data generated in our laboratory or in others on E14 cell line. We observed an increase in the number of mapped reads of about 5%. CpG dinucleotide showed the higher variation frequency, probably because of it could be target of DNA methylation. We performed a reduced representation bisulfite sequencing on E14 cell line to test our new genome assembly with respect to the mm9 genome reference. After mapping and methylation status calling, we obtained an increase of about 120,000 called CpG and we avoided about 20,000 wrong CpG calling. genotyping of E14 embryonic stem cells (ESCs) and Reduced representation Bisulfite Sequencing (RRBS) of E14 ESCs.

ORGANISM(S): Mus musculus

SUBMITTER: Francesco Neri

PROVIDER: E-GEOD-53149 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:DNA methylation is a defining feature of mammalian cellular identity and is essential for normal development. Most cell types, except germ cells and pre-implantation embryos, display relatively stable DNA methylation patterns, with 70-80% of all CpGs being methylated. Despite recent advances we still have a too limited understanding of when, where and how many CpGs participate in genomic regulation. Here we report the in depth analysis of 42 whole genome bisulfite sequencing (WGBS) data sets across 30 diverse human cell and tissue types. We observe dynamic regulation for only 21.8% of autosomal CpGs within a normal developmental context, a majority of which are distal to transcription start sites. These dynamic CpGs co-localize with gene regulatory elements, particularly enhancers and transcription factor binding sites (TFBS), which allow identification of key lineage specific regulators. In addition, differentially methylated regions (DMRs) often harbor SNPs associated with cell type related diseases as determined by GWAS. The results also highlight the general inefficiency of WGBS as 70-80% of the sequencing reads across these data sets provided little or no relevant information regarding CpG methylation. To further demonstrate the utility of our DMR set, we use it to classify unknown samples and identify representative signature regions that recapitulate major DNA methylation dynamics. In summary, although in theory every CpG can change its methylation state, our results confirm that only a fraction does so as part of coordinated regulatory programs. Therefore our selected DMRs can serve as a starting point to help guide novel, more effective reduced representation approaches to capture the most informative fraction of CpGs as well as further pinpoint putative regulatory elements. Analysis of 42 human WGBS libraries comprising 30 distinct primary cell lines, tissues, in vitro derived cell types and cell lines. BiSeq raw sequencing reads were aligned using maq in bisulfite mode (Li et al. 2008) or bsmap 2.7 (Xi et al. 2009) against human genome version hg19/GRCh37, discarding duplicate reads. DNA methylation calling was performed based on an extended custom software pipeline published previously for RRBS (Gu et al., 2010). The bed files contain all seen CpGs within the given library. The number of methylated reads/number of total reads is listed in the score column. This study includes a re-analysis of Samples from the NIH Roadmap Epigenomics Mapping Consortium (REMC; GSE16256, GSE17312), Hodges et al. 2011 (GSE31971), Molaro et al. 2011 (GSE30340), and Xi et al. 2013. WARNING: This submission is incomplete pending further deposits of raw data files from Meissner via EDACC.

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Genotyping of E14 mouse embryonic stem cells by sequencing

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