Question: Algorithms to know when starting out analyzing transcriptome data
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gravatar for dickson8988
2.7 years ago by
dickson89880 wrote:

I'm still very much a newbie at bioinformatics; I've been transitioning to doing more in silico work in my area. I've got some good resources already on general bioinformatically-relevant algorithms, but which ones should I focus in on when doing transcriptomics work, and what purposes are they generally used for?

For example: There are a LOT of sequence algorithms. Beyond, say, the Smith-Waterman and Needleman-Wunsch algorithms for sequence alignment, which ones do you find the most useful for what task? Bonus points if they're algorithms that one might not immediately think are relevant to transcriptomics.

(I realize your answer might be 'it depends on the question', to which my answer is 'I'm formulating some questions, if you can tell me what types of questions an algorithm has been generally applicable to, that would be helpful'.)

Thank you for any and all advice.

ADD COMMENTlink modified 2.7 years ago • written 2.7 years ago by dickson89880
1

BLAST is the biggie. Got sequences, first step is probably to BLAST them.

ADD REPLYlink written 2.7 years ago by dylan.lawrence20

I think BLAST itself is based on the Smith-Waterman algorithm.

ADD REPLYlink written 2.7 years ago by dickson89880
1

Burrows Wheeler transform which many NGS aligners use.
A review of the algorithms for NGS data.

ADD REPLYlink modified 2.7 years ago • written 2.7 years ago by genomax91k
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Since you mention transcriptomics, this is another review paper that I would suggest Alignment-free sequence comparison: benefits, applications, and tools. These alignment-free methods have become quite popular especially in RNA-Seq analysis.

ADD REPLYlink written 2.7 years ago by dariober11k

Alignment-free? This will probably blow my non-programmer PI's mind.

Thank you!

ADD REPLYlink written 2.7 years ago by dickson89880
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A: Alignment and mapping

ADD REPLYlink written 2.7 years ago by genomax91k

I would encourage you to read the following wonderful work: Microarray data normalization and transformation. Microarrays are still being widely used.

Then you may branch into RNA-seq transcriptomics and then other types, like high throughput PCR and NanoString.

Kevin

ADD REPLYlink modified 2.7 years ago • written 2.7 years ago by Kevin Blighe66k
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