trinityrnaseq VS sage

The screen shot suggests you are not able to actually compile/install trinity. The cmake command is not used to run it. After you download and unpack the trinity software package: https://github.com/trinityrnaseq/trinityrnaseq/releases/download/Trinity-v2.13.2/trinityrnaseq-v2.13.2.FULL.tar.gz, you should look at the INSTALL file, which tells you to:

I'm trying commands from the Docker site and the trinity site. When I try to run the command, it behaves like the code is incomplete (hitting enter just gives me a new line). To my understanding, the issue can be either in the pathway/to/files part of the code, of in the trinityrnaseq/trinityrnaseq part.

For my analysis I am trying to do variant calling on my transcriptome. I am following the Trinity github pipeline for variant calling, in which they provided a python script for GATK for the variant calling. I use SLURM to run this job for me:

Since you're an R user, I would recommend Bioconductor. Here's an excellent tutorial on how to analyze RNA-seq data (make sure that you're R version is up to date!): https://www.bioconductor.org/packages/devel/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html . However, if you want to assemble raw transcriptome data, then Trinity is a great tool (https://github.com/trinityrnaseq/trinityrnaseq/wiki).

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