This notebook describes the analysis and interpretation of EM-Seq data generated for developmental timepoints. EM-seq data was generated from 0-9 hrs, 11-12 hrs and 24-60 hrs embryos, aiming to elucidate the epigenetic dynamics during Parhyale embryogenesis
In 3_9 DNA methylation during Parhyale embryogensis.nb.html are the files and scripts used to analyse EM-seq data from raw reads to data interpretation and visualisaiton. The performed tasks include:
- Quality check of raw methylation reads using using FASTQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
- Reads trimming using TrimGalore (https://github.com/FelixKrueger/TrimGalore)
- Reads mapping, deduplication and methylation calling using Bismark (https://www.bioinformatics.babraham.ac.uk/projects/bismark/)
- Summarising CpG mapping count, coverage and methylation levels for each stage
- Plot fraction of methylated CpGs per genomic feature including genic, intergenic and TE regions
- Differential methylation analysis using Methylkit package (https://bioconductor.org/packages/devel/bioc/vignettes/methylKit/inst/doc/methylKit.html)
- Description of gene-body methyalation patterns
- Definition of threshold to define methylated genes
- Integratation of expression and methylation data to explore potential relationships between expigenetic and transcriptional states
- Estimate statistical differences between developmenatl genes based on their methylation state.
In 3_10 Impact of DNA methylation loss on Parhyale embryogenesis.nb.html are the files and scripts used to analyse the impact of DNA methyaltion loss on embryo development. Expression changes weere analysed on embryos treated with the methylation inhibitor 5-aza-2'-deoxycytidine (5-AzadC). The analyses performed include:
- Analysis of embryo surival rates under different conditions
- Expression quantification of 5AZA-treated embryos using FeatureCounts R funciotn (https://www.rdocumentation.org/packages/Rsubread/versions/1.22.2/topics/featureCounts)
- PAC analysis of wild-type and treated embryos
- Differential gene expression (DGE) analysis. Used DESeq2 library to call statistically significant differentially expressed genes, setting were padj < 0.01 and |log10FC| > 1.
- Cluster of expression using genes differentially expressed
- Catgeorisation of genes affected by drug treatment based on developmental categories like matnerla, zygotic and maternal-zygotic.
- Visualisation of results was performed using pheatmap, ggplot , ggbreak, ggarrange R packages, among others.
- Gene ontology analysis using topGO R package (https://bioconductor.org/packages/release/bioc/html/topGO.html)
Please email [email protected] if there is any problem, thanks! (Manuel)
This is a tool to view the html files.