I am looking for informal guidance to speed up my bioinformatics dissertation focusing on analyzing genomic data.
I am looking for an informal bioinformatics research adviser, trainer and tutor. I urgently need a publication to remain in the program. But since I am almost blind I won't be able to submit one on time unless I can get some help. My vocational rehabilitation agency is giving me funds to pay for assistants and tutors to help me compensating for the shortcomings caused by my visual impairment.
My research involves yeast because my adviser has a yeast lab. We are interested in understanding how and why caloric restriction extends lifespan. We are specifically interested in changes in membrane composition because it is a marker for aging. We would like to improve our understanding of the mechanisms underplaying and driving the aging process so that we can eventually reverse it.
I am especially looking for help in learning the most relevant Bioconductor packages for analyzing time series microarray, RNA Seq. Chip-Seq. Proteome and any kind of epigenetic, metabolomic and transcription-factor-binding-site data for constructing highly predictive causality-inferring co-expression, regulatory and protein binding networks. My aim is to use computational methods for predicting a novel lifespan extending intervention for yeast, which I hope that my adviser can biologically validate in his yeast lab. I have stayed up all night looking for good tutorials, datasets and review articles, which I thought might be good for learning and developing these skills and techniques, but unfortunately my Firefox browser crashed. This has caused me to have lost all my many open browser windows, in which I had opened this kind of learning material, i.e. tutorials, review articles and sample datasets. If youâ€™d like a list of articles, which employ the skills and techniques Iâ€™d like to gain, Iâ€™d gladly repeat this literature search again. Maybe after having posted this text I will start working on a second post listing the references to the resources I thought could be helpful for us and also make a note for each publication explaining why I think it is useful. But feel free to refer to and use any information, which has helped you to learn all of this. For example, I have never seen a regulatory network for transcription-factors and therefore, I am still not sure whether the colorful circular figure 6 of one particular article is actually considered a transcription-factor-binding regulatory network and how to read it.