MultiQC
rnaseq
MultiQC | rnaseq | |
---|---|---|
5 | 14 | |
1,173 | 780 | |
1.5% | 2.4% | |
9.7 | 9.4 | |
5 days ago | 5 days ago | |
JavaScript | Nextflow | |
GNU General Public License v3.0 only | MIT License |
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MultiQC
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R pipelines for bulk RNA-seq analyses
fastp + multiQC + Salmon + DESeq2 all some nextflow workflow. It is a good exercise (not complicated) to create the pipeline from scratch the first time to properly understand each tool.
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RNA-seq analysis
I would recommend looking at the pages for FastQC and MultiQC. I run FastQC on my fastq files, then MultiQC on them to collect all that individual data into one report. You can also use MultiQC to analyze the quality of your alignments, at least after using STAR aligner (probably others too, I just have only used STAR aligned).
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How to use MultiQC? I am trying to run it to compile the summary from my FASTQC but I keep getting the "Sample has no read" error.
If that all checks out then I would have to see more of your files in order to help, sorry. Submitting the issue at https://github.com/ewels/MultiQC/issues would help you more
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How can I keep Docker MultiQC from ignoring my large files?
I have seen this Github thread, but it is not applicable for me because I am not running MultiQC natively, I am running a docker version of it in HPC.
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.
What are some alternatives?
finviz - Unofficial API for finviz.com
mag - Assembly and binning of metagenomes
Sooty - The SOC Analysts all-in-one CLI tool to automate and speed up workflow.
diffexpr - Porting DESeq2 into python via rpy2
awesome-single-cell - Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
sage - Proteomics search & quantification so fast that it feels like magic
tm_calculator_gui - Calculates the melting temperature(in Celsius) of a user imported forward and reverse primer based on primer blast default parameters & pcr additives
HomeBrew - 🍺 The missing package manager for macOS (or Linux)
pyrodigal - Cython bindings and Python interface to Prodigal, an ORF finder for genomes and metagenomes. Now with SIMD!
configs - Config files used to define parameters specific to compute environments at different Institutions
Osintgram - Osintgram is a OSINT tool on Instagram. It offers an interactive shell to perform analysis on Instagram account of any users by its nickname
patterns - A curated collection of Nextflow implementation patterns