I have sequencing data of a few samples of a `DNA genome virus`. I'd like to learn `de novo assembly` of the `short reads`, making scaffolds from it, and then `counting the abundance` of each strain in the data. I heard about `SPAdes` as a good choice for these kinds of very short genomes. and also `BBmap` for statistics related to contigs.
I am a complete newbie in everything related to de novo assembly. So, I wonder if you could recommend some good `courses/tutorials/papers` for learning all the steps, including units, special vocabularies, analysis steps, and everything else.
I have some experience with analyzing `RNA-seq` and also `small RNA-seq` data. I learned these two using free online resources, however, it is very hard to find even one full workflow post/paper on `genome assembly`.
Note: I know that I should read the documentation on `SPAdes` and `BBmap` and I will. However, I'd like to first get to a good idea of what are the steps, test it on some sample data, and then go through learning different tools. So, it is like saying that I need to learn RNA-seq analysis not read a particular `aligner` documentation.
cross-posted in bioinformatics.stackexchange and biostars.
I am a complete newbie in everything related to de novo assembly. So, I wonder if you could recommend some good `courses/tutorials/papers` for learning all the steps, including units, special vocabularies, analysis steps, and everything else.
I have some experience with analyzing `RNA-seq` and also `small RNA-seq` data. I learned these two using free online resources, however, it is very hard to find even one full workflow post/paper on `genome assembly`.
Note: I know that I should read the documentation on `SPAdes` and `BBmap` and I will. However, I'd like to first get to a good idea of what are the steps, test it on some sample data, and then go through learning different tools. So, it is like saying that I need to learn RNA-seq analysis not read a particular `aligner` documentation.
cross-posted in bioinformatics.stackexchange and biostars.