Hello everyone,
I am new in bioinformatics, I have a several tasks to do, but I am really confused how can I do that.
What is needed is (given from my prof):
Given a set of gene expression data (let's use RNAseq-data for fly to keep the memory and CPU efforts down), you map them to the genome. This gives, for each sample/data set, a single signal "expression value", i.e., coverage f(x) as a function of the genomic coordinate x.
Now that task is to compute segmentations of this signal, i.e., find a set of intervals on which f is approximately constant.
First do this for every dataset separately.
Now we have a more difficult problem. Given the f_i(x) for each data set i, find a segmentation so that EACH f_i is approximately constant on each interval.
Of course, you want segmentations that have as few intervals as possible.
I would suggest to do two things:
(1) find a set of about 12 different RNAseq data sets from the fruitfly and map them to the genome.
(2) re-implement the simplest segmentation algorithms for time series-like data and test them.
(3) check how consistent are the results.
(4) how can we combine the different signal f_i to define a single criterion for segmenting the signal jointly.
The point now is that, of course, we want that the number of segments that we are defining only slowly grows with I and eventually saturates, since otherwise you just wind up with every genomic position being its owninterval -- which is of course a useless segmentation.
Can anyone explain what this tasks mean exactly:
- From where I can get the RNAseq (GEO, SRA, FLYBase...)
- What is "single signal expression value" and coverage?
- The sequences from databases do not contains coverage? should I calculate the coverage? If so, from where I get number of reads!!
- What are the segmentation algorithm that should be used?
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