rnaseq
configs
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rnaseq | configs | |
---|---|---|
14 | 1 | |
758 | 78 | |
4.9% | - | |
9.5 | 9.7 | |
7 days ago | 4 days ago | |
Nextflow | Nextflow | |
MIT License | MIT License |
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rnaseq
- R pipelines for bulk RNA-seq analyses
- What are some good examples of well-engineered bioinformatics pipelines?
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Generate GUIs and deploy bioinformatics workflows with python
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.
- I've been really frustrated with picking the right tools for bulk RNA-seq, so I did a long literature review and wrote this workflow
- Software repository and hackathons
- Introduction to RNAseq and microRNA?
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Tkinter for python 3.10 broken on MacOS?
Not really sure why it's a problem for you, I'm working on rnaseq and they use a very big input dataset, also outputs huge datasets too. It uses docker so you can deploy fast on VMs.
configs
We haven't tracked posts mentioning configs yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
argo-events - Event-driven Automation Framework for Kubernetes
mag - Assembly and binning of metagenomes
sage - Proteomics search & quantification so fast that it feels like magic
diffexpr - Porting DESeq2 into python via rpy2
HomeBrew - 🍺 The missing package manager for macOS (or Linux)
sarek - Analysis pipeline to detect germline or somatic variants (pre-processing, variant calling and annotation) from WGS / targeted sequencing
rnatoy - A proof of concept RNA-Seq pipeline with Nextflow
methylclock - DNA methylation-based clocks
patterns - A curated collection of Nextflow implementation patterns
seqkit - A cross-platform and ultrafast toolkit for FASTA/Q file manipulation
rnaseq-nf - A proof of concept of RNAseq pipeline