Algorithms to know when starting out analyzing transcriptome data
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3.3 years ago

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.

RNA-Seq transcriptomics algorithms • 670 views
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BLAST is the biggie. Got sequences, first step is probably to BLAST them.

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

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

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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.

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

Thank you!

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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

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