The most commonly used software for performing transcriptomics analysis with RNA-seq these days is the Tuxedo suite, consisting of programs called Bowtie, TopHat and Cufflinks. This works pretty well for a well annotated genome, like that of mouse and human. But what happens with a genome that is incompletely assembled and whose transcript is not well annotated? I had the opportunity to work on an insect transcriptome and encountered a number of challenges. My task was to compare the transcriptomes of this insect in two different biological states, identify the differentially expressed transcripts, and then determine whether these were enriched for any biological processes. This was easier said than done.
I recently came across a video showing how a case of scientific fraud came to light as another research team tried to figure out how exactly certain published results were obtained.
One of the problems in published scientific studies that involve data processing is that they sometimes are not detailed enough. Another investigator starting with the same raw data may sometimes not get exactly the expected output. Often the differences are inconsequential but sometimes they are not. This may cause serious aggravation to others trying to understand the reason for the discrepancy. A detective work trying to figure out what the first group actually did may be necessary. Sometimes the reason for the discrepancy is just carelessness, but sometimes it may be purposeful choice of some parameter to produce desirable results; Efforts to replicate the computation have even resulted in the discovery of fraudulent data. The solution to these difficulties passes from full transparency, a thorough documentation of the steps taken in performing an analysis. This is the basis of reproducible research. Continue reading
I taught a 7-week course on Bioinformatics at Bosphorus University in Istanbul Turkey, this summer. Although I had previously given guest lectures at Yale, this was the first time I had the responsibility for a whole course. It was an interesting experience at several different levels. Continue reading
Studies of host-microbiome interactions always fascinate me. The biology of the host organism affects the composition of the microbial flora and vice-versa. For example obesity and diabetes are just two diseases that lead to changes in the gut microbiome. Several studies have shown that the host immune system is modulated by the gut microbiome. Continue reading
Recently I had the unusual experience of being an advisor for hire. My client was a clinical scientist specialized in molecular biology. One of his post-docs had started a project that required bioinformatics expertise. She already had a computer science background and had taken some training in transcriptomics data analysis but she was inexperienced in this methodology. The PI could not assess her work and guide her. So I became her advisor. Continue reading
When you compare the RNA or protein expression profile of a control and treated sample, you get a list of differentially expressed genes. It is interesting to look for interesting names in the list but there are a number of bioinformatics analysis methods that can tell you a lot more. These are pathway analysis or network analysis software. Continue reading