Today molecular biology researchers allow generating massive quantities of multiple types of data with Nextgeneration sequencing, qPCR, microRNA, and Proteomics in less time.
For an example, if the researcher is dealing with understanding complex cause and effect relationships of differentially expressed genes, lethal or loss-of-function mutations can be challenging to identify systematically without having very high quality curated knowledge.
Obviously, data analysis depends on the research, and statistical analysis may consider the first step. However, researchers have to filter down their massive quantities of initial data in order to comprehensive biological analysis, to figure out the most interesting and relevant information from their experimental results.
With your experiences how do you effectively sort through and better understand these data for meaningful biological discovery?
What are your experiences of understanding your experimental data for making a cohesive biological story based on the known supporting evidence /knowledge?
What makes you perfect? What are your personal experiences regarding this?
Hi Aila,
I think that I am not perfect at all, to reply to your second last question. Regarding the big data analysis, and I try to keep it short: First the design of the design of the study must be laid out carefully to enable a robust interpretation (and an interesting reletavant question).
Once you generated the big data (I work mostly on parasites, genomic and transcriptomic), you search for something that is different, like a gene that is deleted or very different (in 90% of the cases those are false positive due to noise in the data), or look for statistical differentially expression genes. You can do enrichment analysis, GO or pathways... but then starts the real work: You need to reads around the findings, what is known, discuss results with the colleagues, maybe look into other datasets...
If possible you should go back to the wet lab to confirm your hypothesis and if all spins out, you can write the paper...
The interpretation of the resutls and setting it in context with know findings is the most challenging in terms of time and the feeling that there is so much out there - to strike the right balance.
I hope that gives you general idea the way I work.
One if the first things you can do is to search for long open reading frames (with start and stop codons). These are possible proteins expressed by the organism under study. Then translate it to amino acids. You now have a protein sequence which you can analyze for structure and function using many publicly available tools out there.
Dear Dr. Thomas and Dr. Vicente Thank you very much for sharing your personal experiences. It helps me a lot to understand.
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