Hi, I unfortunately cannot apply to a FT residency program as I have a FT job (as I suspect most people here do).
However, I do have an interest in Bioinformatics/Computational Biology. Unfortunately, I find that most textbooks that cover these topics are very theoretical, e.g., going over how Blast algorithm works, explanations of NGS assembly and annotation.
I'm curious any peeps in the field have any simple learning projects/practical papers for a programmer interested in Bioinformatics?
Things I'm thinking of are, (would like to hear from the experts any hints, tips or links):
1) Downloading a GWAS (Genome Wide Association) data-set for a psychiatric or cancer disease and replicating the statistical techniques that scientists used to determine the clusters of responsible genes (e.g., http://www.nature.com/nature/journal/v511/n7510/full/nature1...)
2) Downloading different strains of Cannabis Indica and Sativa genome draft assemblies; running initial phylenlogy tree and run genome annotation pipelines to identify known genes; and using RNASeq data to isolate the potencies of different varieties of Cannabis and metabolic pathways to produce hemp (e.g., http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3359589/)
3) Downloading data-set of RNASeq data from synthetic biology experiments involving yeast and replicating the statistical technique that authors used to determine the gene regulators/promoters used to program the cells to produce plastic, fuel and drugs for them! (http://www.nature.com/nature/journal/v440/n7086/full/nature0...)
Hi, I unfortunately cannot apply to a FT residency program as I have a FT job (as I suspect most people here do).
However, I do have an interest in Bioinformatics/Computational Biology. Unfortunately, I find that most textbooks that cover these topics are very theoretical, e.g., going over how Blast algorithm works, explanations of NGS assembly and annotation.
I'm curious any peeps in the field have any simple learning projects/practical papers for a programmer interested in Bioinformatics?
Things I'm thinking of are, (would like to hear from the experts any hints, tips or links):
1) Downloading a GWAS (Genome Wide Association) data-set for a psychiatric or cancer disease and replicating the statistical techniques that scientists used to determine the clusters of responsible genes (e.g., http://www.nature.com/nature/journal/v511/n7510/full/nature1...)
2) Downloading different strains of Cannabis Indica and Sativa genome draft assemblies; running initial phylenlogy tree and run genome annotation pipelines to identify known genes; and using RNASeq data to isolate the potencies of different varieties of Cannabis and metabolic pathways to produce hemp (e.g., http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3359589/)
3) Downloading data-set of RNASeq data from synthetic biology experiments involving yeast and replicating the statistical technique that authors used to determine the gene regulators/promoters used to program the cells to produce plastic, fuel and drugs for them! (http://www.nature.com/nature/journal/v440/n7086/full/nature0...)