hlatyping
rnaseq
hlatyping | rnaseq | |
---|---|---|
1 | 14 | |
52 | 780 | |
- | 2.4% | |
0.0 | 9.4 | |
about 2 months ago | 4 days ago | |
Nextflow | Nextflow | |
MIT License | MIT License |
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hlatyping
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Ask HN: How to be my own genetic disease researcher for my partner?
Here's a post I made on reddit about how to do exactly this:
https://www.reddit.com/r/Nebulagenomics/comments/nhjfpa/how_...
You use the VCF and a java project called the Exomiser, and it will give you output files with all the pathogenic variants marked
In my case and is the case with a lot of rare diseases you could have unique pathology and mutations in a certain gene but that don't show up as pathogenic in clin var. For example my family has a lot of autoimmune diseases and as expected my HLA genes are totally trashed. However none of these mutations have ever been seen and flagged before especially was WGS is so new.
If you only have a list of genes and the genomizer will give you a list of the genes that are the most heaviy affected, you can put them into this app to get some further data and idea about what kind of tissue expression or rare disease spectrums you may be dealing with: https://maayanlab.cloud/Enrichr/
sadly the reality is though you can have all that and it almost puts you at a disadvatnage with doctors because you look crazy and sus claiming you have some HLA mutation or whatever. Who told you that? Oh well I data mined it...uh huh sure....honestly to get it back into the medical system and to be taken seriously you'd probably have to get a doctor to retest it, for example I can can spin this up to get my HLA alleles from my fastq https://github.com/nf-core/hlatyping
But no doctor is going to put that in my medical record until I convince them to run a blood test for the same damn thing.
if anyone wants to help me with my own genetic search woes and help me out or know solutions please let me know. if you want to help me publish or add to that guide somewhere let me know - i asked nebula if they wanted to print it on the blog and they said the'd be interested but I just never cleaned it up
rnaseq
- R pipelines for bulk RNA-seq analyses
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Point of using Hisat2 build to index reference genomes when working with known genomes mouse/human?
Just run something like this and don’t worry about it: https://nf-co.re/rnaseq
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I used featureCounts to quantify RNA-seq reads and got a low successful alignment percentage. Is this a problem?
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
- What are some good examples of well-engineered bioinformatics pipelines?
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How to know where to align if I have RNAseq 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
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Semi Budget-Friendly High-Thread Count Options?
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
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How to get NGS programming experience?
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.
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Illumina: can I use it on my laptop?
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
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What is the preferred way of documenting a Nextflow pipeline?
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.
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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.