rnaseq VS HomeBrew

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.

Homebrew is a highly popular package manager on macOS and Linux systems, enabling users to easily install, update, and uninstall command-line tools and applications. Its design philosophy focuses on simplifying the software installation process on macOS, eliminating the need for manual downloads and compilations of software packages.

Homebrew - package manager for linux-based OSs.

Package Manager: Homebrew

Hopping from one distro to another with a different package manager might require some time to adapt. Using a package manager that can be installed on most distro is one way to help you get to work faster. Flatpak is one of them; other alternative are Snap, Nix or Homebrew. Flatpak is a good starter, and if you have a bunch of free time, I suggest trying Nix.

Are you using SQLite that ships with macOS, or SQLite installed from homebrew?

I had a different problem in the past with the SQLite that ships with macOS, and have been using SQLite from homebrew since.

So if it’s the one that comes with macOS that gives you this problem that you are having, try using SQLite from homebrew instead.

https://brew.sh/

Before we begin, make sure you have Homebrew installed on your Mac. Homebrew is a package manager that makes it easy to install software and dependencies. You can install Homebrew by following the instructions on their website: https://brew.sh/

I’m on MacOS and erlang.org, elixir-lang.org, and postgresql.org all suggest installation via Homebrew, which is a very popular package manager for MacOS.

I have always either installed Node from the installer provided by the Nodejs website or, via Brew in macOS. I have also used nvm in the past but did not know that there was a best practice to guide us.

A running Rails application needs a database to connect to. You may already have your database of choice installed, but if not, I recommend PostgreSQL, or Postgres for short. On a Mac, probably the easiest way to install it is with Posrgres.app. Another option, the one I prefer, is to use Homebrew. With Homebrew installed, this command will install PostgreSQL version 16 along with libpq:

On a macOS machine, you can use homebrew by running the command.