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Rnaseq Alternatives
Similar projects and alternatives to rnaseq
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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fastp
An ultra-fast all-in-one FASTQ preprocessor (QC/adapters/trimming/filtering/splitting/merging...)
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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configs
Config files used to define parameters specific to compute environments at different Institutions (by nf-core)
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gatk4-genome-processing-pipeline-azure
Workflows used for processing whole genome sequence data + germline variant calling.
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sarek
Analysis pipeline to detect germline or somatic variants (pre-processing, variant calling and annotation) from WGS / targeted sequencing
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rnaseq reviews and mentions
- 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.
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www.saashub.com | 25 Apr 2024
Stats
nf-core/rnaseq is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of rnaseq is Nextflow.
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