Genome Spot

The year has gone so fast! Lets go through some of the major points of 2014 and predict what 2015 has in store. Sequencing hardware It began with announcements from Illumina of the HiSeqX10 as well as the release of the NextSeq500. Later, X-10 technology was included in V4 HiSeq2500 kits resulting in a substantial increase in sequence output from that instrument. There was also the announcement of the NeoPrep automated library preparation system that will be officially released in the 1st half of 2015. NeoPrep looks like an attempt to use microfluidics to perform generate libraries; if this is successful, it would be a major advance in reducing bottlenecks in sample preparation. Microfluidics are also able to reduce the volumes of reagents required, making the process cheaper. In addition, labour costs per library will be drastically reduced given that automation will be commonplace for routine protocols. Regarding 3rd gen platforms, we saw some mixed reviews from Oxford Nanopore

In this post, I'll provide a step-by-step guide to perform interspecies gene name conversion of gene expression data. This is a necessary step in the comparison of profiling data from two different experiments with different species (human and mouse), and allows us to use extensive human-centric gene set libraries in MSigDB when analysing non-human mammalian profiling data (such as mouse). I performed GEO2R analysis of mouse expression data ( GSE30192 ) to analyse the effect of azacitidine on mouse C2C12 myoblasts. The data looks like this: "ID" "adj.P.Val" "P.Value" "t" "B" "logFC" "Gene.symbol" "Gene.title" "1420647_a_at" "0.000346" "2.24e-08" "56.073665" "8.699524" "6.9755573" "Krt8" "keratin 8" "1423327_at" "0.000346" "2.32e-08" "55.685912" "8.686447" &q

There is a need to make bioinformatics tools more user friendly and accessible to a wider audience. We have seen that Galaxy , GEO2R ,  Genevestigator and GenePattern  have each developed a huge following in the molecular biology community, and this trend will continue with introduction of new RNA-seq analysis tools. Previously, I posted about differential gene expression analysis of RNA-seq performed by the DEB  online tool. In this post, I introduce Degust , an online app to analyse gene expression count data and determine which genes are differentially expressed. Degust was written by David R. Powell ( @d_r_powell ) and was Supported by Victorian Bioinformatics Consortium, Monash University and VLSCI's Life Sciences Computation Centre . In this test, I'll be using the azacitidine mRNA-seq data set that I have previously analysed.  To make the count matrix, I used featureCounts. First step in the process is to your RNA-seq count data. It can be done in tab or comma se