> For the complete documentation index, see [llms.txt](https://nks.gitbook.io/rna-seq/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://nks.gitbook.io/rna-seq/1.-preface/genomic-data-science-as-a-tool-to-biologist.md).

# Genomic data science as a tool to biologist

A concept that I believe to crack the bottle neck of connecting NGS and conventional science

I believe that science is all about hypothesis testing.  But the hypothesis that I am talking about here is a bit different from the way statisticians would like to put.  For example,

![](https://user-images.githubusercontent.com/30200862/136685805-b14ea82b-ece5-4376-b5d8-3c2a1eee8452.png)

Wait a second, what's the deal here?  They could be essentially the same at some level. So the criticality lies in how to validate the conclusion.  For the former I might want to perform any sort of quantifiable PCR to validate the expression, but for the latter I would like to see what if I use other treatment and what if I assess at different molecular level such as protein and/or other clinically/physiologically related phenotype.

The way you phrase your hypothesis dictates your research directions and methods, and thus output.

I am not saying who is better than who, I am saying that we should all work together.  In my journey of self-educating to use the NGS tools, Mathematicians tends to create new terminology to suit their needs on some well-known "things", such as the "summarizing transcriptome to gene level" conversion used widely in DESeq2 related discussion.  So sometimes I am also confused, in which line of professions or methodology that I am using to solve a natural science question which is universal to every single living things on the Earth.  I mean, should I even need to worry about that when if we all are heading to the same Rome?