Genome Spot

In 2020, Dr Antony Kaspi and I published a method called "mitch" [1] which is like GSEA, but was specifically designed for multi-contrast analysis, and based on rank-MANOVA statistics inspired by a 2012 paper by Cox and Mann. Mitch worked well for various types of omics data downstream of commonly used differential abundance tools like DESeq2, edgeR, DiffBind, etc, but we didn't consider at the time how mitch could be applied to microarray data.  You might think that microarrays are outdated, but they are still used extensively for epigenome-wide association studies (EWASs), which are frequently used to understand disease processes and to identify biomarkers of disease. To demonstrate, there are 1536 publicly available methylation array studies on NCBI GEO, and probably many more thousands that are restricted access. The tools available for pathway analysis of methylation array data are a bit limited. There's an over-representation method that take into consideration

KEGG is the Kyoto Encyclopedia of Genes and Genomes, a compendium of gene functional annotations which are commonly used for pathway enrichment analysis. KEGG has been around since 1997 (PMID: 9390290) but in 2000, as the draft human genome sequence was released, KEGG became a key database involved in the curation of literature data to catalog the function of all human genes, many of which were newly sequenced and previously uncharacterised. This invigorated interest in KEGG is demonstrated by one of their articles in 2000 (PMID: 10592173) accruing 24,236 citations according to Dimensions , 1270 fold higher than other articles from that time. A PubMed search using "KEGG" keyword shows 27,033 results, with matches in abstracts alone and the tragectory is increasing at a rapid rate. Despite the popularity of this tool, I'm urging you not to use it for your research. Here I will lay out the reasons. 1. It isn't comprehensive I did an analysis of KEGG and other pathway se

BLAST is a stalwart in the bioinformatics space. I've used it in multiple contexts and it is a good way to introduce students to bioinformatics principles and the process of pipeline development. Although there is a web interface, it is still good to use it locally if you need to run a large number of queries. As my group needed to use BLAST again this week, I thought I'd share a small example script which I shared with my Masters research students just getting into bioinformatics. The script (shown below) downloads the E. coli gene coding sequences and then extracts by random a few individual sequences. These undergo random mutagenesis and then we can use the mutated sequences as a query to find the original gene with BLAST. The output format is tabular which suits downstream large scale data analysis. The script also includes steps for generating the blast index. The script is uploaded as a gist here . It requires prerequesites: sudo apt install  ncbi-blast+ emboss unwrap_fas