rnaseq VS sage

Just run something like this and don’t worry about it: https://nf-co.re/rnaseq

Try https://nf-co.re/rnaseq ! I know it was a lot of work to get to featurecounts, but it actually has been depreciated in favor of either salmon or RSEM quantification. In my experience, STAR-RSEM is the best way to get the most accurate quantification of RNA-Seq data

Consider looking into NFCore's RNAseq pipeline. I haven't tried this one myself, but it looks very comprehensive and has nice documentation: https://nf-co.re/rnaseq

my go-to benchmark for performance is the standard nf-core RNA-Seq pipeline; https://nf-co.re/rnaseq keep in mind that the included test profiles pull sample data down from the internet so that can end up bottlenecking your PC if you dont have a fast connection

I would suggest the nf-core/rnaseq pipeline. It's used by many core facilities around the world. Also, there are many more pipelines from nf-core, e.g. Sarek for variant calling.

You’ll have a batch effect if you use a different pipeline, but you can quantify RNA easily on a laptop. https://nf-co.re/rnaseq

Hi u/_Fallen_Azazel_, thank you for the answer. I took a look at their stuff but couldn't really find how they handle the documentation. For instance, `nf-core/rnaseq` is a model pipeline from the nf-core community, still, the documentation rendered on the nf-core website doesn't have any correlated markdown file at their repo (at least not that I could find). It is not clear for me how I should ideally do it.

First lets recognize that the framework presented has new features that don't exist in the previous DSLs you mention. Many developers highly value these additions and they along could justify a new stab at a workflow language: and for many the represent tradeoff * Interface generation * Declarative cloud resource provisionment * Static typing * Native python support This workflow has a similar level of complexity to nf-core/rnaseq (not the same, but similar in number of constituent tasks for the purpose of counting transcript abundance). It ingests raw sequencing reads, runs QC + trimming, does psuedo-alignment, recovers counts from abundance estimates, and aggregates counts over a many samples for direct use by diff-exp tools. (It is not 'running salmon'. I think that is a reductionist take.) It does this in addition to dynamically building React.js interfaces, adding static type validation to input parameters, and deploying cloud infrastructure in a simpler way. For the lines of code comparison, I think it is a weird way to compare software as the number of lines of code is no proxy for legibility, ease of development, likelihood of long-term maintenance (many more people know python than nextflow). Nonetheless nf-core/rnaseq has nearly 1000 lines alone in its workflow entrypoint alone - https://github.com/nf-core/rnaseq/blob/master/workflows/rnaseq.nf . With imported modules + subworkflows, LOC actually reaches the many thousands.. (Now I understand it is more complex and mature, but I highlight why I think the comparison is weird and wonder what you are even comparing to.) Whereas the entire logic of the presented pipeline is actually neatly encapsulated in 1200 lines of a single file. Overall this feels like a that doesn't come from a place of rational discourse, rather group dislike for something new and different. What I would like to do is address and talk about specific technical points (preferably over issues on github) so conversations can be productive and improve the tools I am working on.

If you want to implement LDA from scratch, you could check out how Sage is doing it.

You could check out https://github.com/lazear/sage - it's a near comprehensive program/pipeline for analyzing DDA/shotgun proteomics data. Most proteomics pipelines consist of running multiple, separate tools in sequence (search, spectrum rescoring, retention time prediction, quantification), but sage performs all of these. This cuts down on the need for disk space for storing intermediate results (none required), the need for IO (files are read once), and results in a proteomics pipeline that is >10-1000x faster than anything else, including commercial solutions

You can also check out the intro blog post if you're interesting in learning more about the algorithm behind Sage. Beyond being fast, it also includes integrated machine learning (linear discriminant analysis, KDE) for rescoring spectral matches.

You could try out Sage, if you're looking for speed - I don't think you'll find anything faster. https://github.com/lazear/sage