There are a few quick'n'dirty ways depending on the type of data.
In any case you want to align your files to a reference genome and then check the distribution of reads, either on a genome browser or with tools such as RSEQC
which calcualtes the fraction of reads aligning to exon, intron, intergenic etc.
RNA-seq, if you use a standard aligner such as ...
Transcript abundance quantification is a tricky topic since a read often could belong to several transcripts, so any "count" is a best guess as to which transcript it actually originates from. That being said, there are tools that can help you here:
salmon (as you mentioned) to quanitfy. Run it with --numGibbsSamples 50 (or higher if your computer ...
In two words: incidence and funding
I'm not an expert on this topic, but I assume it has something to do with the incidence of breast cancer itself:
Breast cancer is the most common cancer in American women, except for skin cancers. Currently, the average risk of a woman in the United States developing breast cancer sometime in her life is about 13%. This ...
This is a common question, and the answer is while you can calculate this, it's not statistically robust.
You're likely to arrive at false conclusions.
Why is this?
Because while TPM is something you can calculate from a single sample, your question involves multiple replicates and those replicates vary between each other.
Counts in units of TPM are missing ...
Honestly, ask who made them. If the reads are from an organism without splicing, it will not be trivial to figure it out with 100% certainty.
If you are calling variants, I don't think it matters much, except that higher organisms have such a small % of their genome transcribed, it should be obvious very quickly if you have the desired coverage across the ...
The salmon output log contains a line with [info] Mapping rate which allows to calculate how many reads went mapped or unmapped. Is that sufficient? It should be in the logs folder of the directory salmon creates per sample.
Going VCF to mpileup is not really something one does or can do. The mpileup should be generated from a BAM or SAM file or something else that has raw, unfiltered read alignments in it. The VCF just has data on variant sites, and usually just variant sites that passed a certain likelihood threshold at that. With the VCF, the best you can do is generate a ...
I usually convert SRA files to FASTQ before doing any further processing, but the tooling for converting SRA files is cumbersome.
To simplify things, I recommend you download the FASTQ file directly. The ENA currently has a nicer interface for doing these things. Here is a link to download the FASTQ files for the SRA sequencing run associated with the SRA ...
There are several methods/tools that take into account multi-mappers and deal with them in reasonable ways (e.g. pseudo-alignment methods like salmon and more traditional alignment-based methods like RSEM). If you completely ignore multi-mappers, then yes, you will be losing information, which may or may not be valuable to you.
Both of those methods utilize ...
Use raw counts as much as you can. Add in various relevant factors as covariates in DESeq2. RNAseq metrics have come a long way but are still misused by people because it's more convenient to compare some sort of normalized metric that give out relevant confounding factors, I guess.
The naive per-million scaling methods do not properly correct for the compositional bias between samples. This is especially true if the groups you compare are expected to be very different, e.g. different organs, see here demonstrated on some GTEx data: https://www.biostars.org/p/9465851/#9465854
Why do people use flawed metrics? Well, because some are very ...
When I try this as a regular user on our Ubuntu 16 LTS system, I end up with ~/.local/bin/CIRIquant from the pip install, so maybe just add that directory to your path:
$ export PATH="$HOME/.local/bin:$PATH"
$ CIRIquant -h
usage: CIRIquant [-h] [--config FILE] [-1 MATE1] [-2 MATE2] [-o DIR]
[-p PREFIX] [-t INT] [-a INT] [-l INT] [-...
These algorithms are used quite a lot, and I will touch only a few aspects:
Genetic sequence alignment. Different (but still closely related) organisms have very similar genetic sequences, which differ only at some positions due to point mutations, which can be nucleotide substitutions (another letter in a sequence) or insertion/deletion of a few ...
The simplest thing to do is to trim the 150 bp fasts so that they are 100 long. I don't think there is an easy way to correct for the fact that the 150 bp long reads will have a higher unambiguous rate of alignment and gene assignment than the 100 bp long reads.
If you have a mix of all the experimental conditions across all the lengths, you can include ...
You could use samtools coverage as explained in the manual of samtoools.
Here is a example which is also described on the manual site.
samtools coverage -r chr1:1M-12M input.bam
#rname startpos endpos numreads covbases coverage meandepth meanbaseq meanmapq
chr1 1000000 12000000 528695 1069995 9.72723 3.50281 34.4 55.8
You could try the PBMC 3k data from the Satija lab:
For well-annotated data, there's the Single-cell proteo-genomic reference map:
Data for that are available here.
If these datasets are too large (i.e. too good), you can use the existing ...
First of all, RNASeq is extremely sensitive to batch effects. Are these matched controls processed at the same time as the tumors? IF they are not, lots of your differences will be caused by batch effect, and not cancer.
I believe the best strategy is to merge the FPKM columns and perform
correlation analyses of my gene of interest (GOI) versus all other
I do not know this package but it seems to call scran so why bothering and not call scran directly, see for an extended discussion + code:
According to DESeq2, you cannot analyze your data without having replicates.
In short, you have an experiment where you compare A to B because, in your theory, A is more expressed than B. However, you do the experiment only once. Without repeating the same experiment again under the same circumstances, there's no way to confirm you will have the same results ...
In this situation, you can apply the strategy in section 2.12 in the edgeR manual which basically "makes up" a dispersion estimate and then run the normal DE strategy. This is obviously neither reliable, nor publishable but at least it gives you a list of genes you can use for validation. Treat results with care, focus on genes with decent counts (...
DESeq2 works for that. Unless you literally have only two samples.
There is no statistically sound way of analyzing DE genes with literally only two samples. This is a bad experimental design, suitable for only the roughest of exploratory purposes. Only the most obvious differences (that you probably knew already) are likely to be borne out in a ...
How to figure out what error messages are trying to tell you is one of the great mysteries… :-)
What version of Snakemake are you using? I get slightly different messages. With an ancient version (3.13.3) on one machine:
SyntaxError in line 14 of /path/to/Snakefile:
With a more up-to-date version (6.9.1, running on Python 3.9):
SyntaxError in ...
Not familiar with the protocol, but I guess RF means different things here. If I am right, with your RF protocol, the first read comes from the reverse strand of the transcript and the second read from the forward strand, but the pair orientation is still FR:
R2 ----> <---- R1
The RF pair orientation should be
Yes, but there will be a batch effect.
Not that I know if, you'll need to use sva or something along those lines to handle the batch effect.
Crossing your fingers and hoping the batch effect is small is pretty much your only other option if the data isn't setup in a way that you can use sva or similar. So yeah, redoing the experiment may be the only real ...
The batch effect is expected to be the same across all samples - therefore you want to estimate its effect across all of the samples. Just add batch as an additional parameter to the linear model (this is presuming samples sequenced in the two batches have been randomised).
You can show the effect of "batch effect" removal with a PCA plot by ...
Something like this book chapter might help on understanding FDR. The packages or functions you call differ in their estimation of pi0 or the proportion of null hypothesis.
Basically, the p.adjust(..method = "BH") is a more conservative method with Benjamini-Hochberg, you are assuming the proportion of null features to be 1.
When you use fdrtool, ...