Going from a list of accession numbers to a formatted latex table

I just want to highlight two packages in R that really do something useful. mygene (https://bioconductor.org/packages/release/bioc/html/mygene.html) takes a list of accession numbers and gets back genbank records. This is something I'm always doing. There are probably lots of good ways of doing this. xtable (https://cran.r-project.org/web/packages/xtable/index.html) is a bit more unique. It can take an R … Continue reading Going from a list of accession numbers to a formatted latex table →

UpSetR: better than a venn diagram

Here are some links for you to explore the use of UpSet plots as an alternative to a venn diagram. https://caleydo.org/tools/upset/ https://cran.r-project.org/web/packages/UpSetR/vignettes/basic.usage.html Above the first graph shows 79 genes overlapping between reproductive and sterile maternal bias. In the second graph that overlap is only 73 as some of these genes also overlap with another category … Continue reading UpSetR: better than a venn diagram →

Confirmation of Differential or Allele-Specific Methylation

Here is a brief pipeline for confirming allele specific methylation (or differential meth with some modifications) in known genes (like qPCR for gene expression). The above protocol was specifically for confirming methylation in different alleles. However this could be adapted to confirm methylation levels in known genes between different samples. Also the TOPO closing says … Continue reading Confirmation of Differential or Allele-Specific Methylation →

Running out of memory on your Mac?

Twice now my mac has run low on memory. When I run omnisweeper the problem is over 50Gb of cores. Found this at https://discussions.apple.com/thread/7852353 The /cores folder is where OS X stores Core Dumps. These are files that are intended for developers to troubleshoot and diagnose faults in their software. They are generated as software … Continue reading Running out of memory on your Mac? →

RNA-seq – how many replicates?

I think we've discussed this paper before in lab meetings, but just thought I'd pop it here for posterity. How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? The authors performed an RNA-seq experiment with 48 replicates per condition, identified the significantly differentially expressed genes, and … Continue reading RNA-seq – how many replicates? →

On the optimal use of methylKit

This is a useful paper from 2017 titled Strategies for analyzing bisulfite sequencing data. It reviews sequencing and alignment approaches, general methylome analysis, differential methylation and annotation, as well as recommending analysis platforms for non-coders. Section 4.1 discusses differential methylation methods, comparing three different methods using a simulated dataset, including as a subset of one method … Continue reading On the optimal use of methylKit →

Permute? or no?

This post covers brief findings from exploring permutation tests when looking for differentially methylated genes from whole genome bisulfite sequencing. The idea behind it: if we randomly shuffle the data between control and treatment samples and see roughly the same number of genes coming out as differentially methylated then we can't say the genes we originally … Continue reading Permute? or no? →