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 ...
So, I think there are a few potential options here. Alevin is using selective-alignment internally to determine the mappings for the reads. So, even if you have a k-mer supporting the mapping, if you have gibberish for the rest of the read, the mapping score is going to be very poor and you're not going to recover that mapping locus. There are some ...
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.
A sample code is given in the salmon documentation as follows. Source
for i in `seq 25 40`;
All transcripts should always be returned in the output, whether there's evidence supporting their expression or not. This is for the sake of simple convenience, since typically one would be merging these values across samples into a matrix. Missing values will then tend to become either NA or cause an error, when what would be appropriate would be to have ...
The solution was to use a different library as I expected. It should be library(TxDb.Hsapiens.UCSC.hg19.ensGene). And also, as specified in the question, to remove everything after the dot in the Name column of the quant.sf files.
There has been a detailed comparison of kallisto and salmon for lncRNA transcripts by Zheng et al https://www.biorxiv.org/content/biorxiv/early/2018/01/09/241869.full.pdf
It shows them to be near identical. This suggests there is some problem with the way you made the comparison/plot.
You provide Salmon with a transcriptome fasta... so merging the human transcriptome with the pox virus genome fasta file should work. You don't mention that in your post but the viral genome should exist as a fasta. You can extract the sequences from genbank or maybe you can find our virus here: https://www.ebi.ac.uk/genomes/virus.html
You can find the ...
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 ...
Make a list.txt file containing a single column of SRA numbers to download.
for i in $(cat list.txt); do echo $i; date; fasterq-dump -S $i; done
It works well to use NCBI's web interface to find SRA samples of interest, download and open findings in Excel, then copy single column containing SRA numbers and paste into list.txt using document editor such ...
From looking at the salmon documentation here, it is correct that both of these are essential for single end experiments:
Since the empirical fragment length distribution cannot be estimated from the mappings of single-end reads
From the discussion here it would appear that you can't estimate these parameters at all via the FASTQ files alone.
Do your ids in the expression file (from gencode) match up with the gene IDs in the transcript file (from UCSC?) I'm willing to bet that they don't. Stay consistent throughout your process. Either pull down the gencode transcript to gene translations, or do your pseudoalignments to UCSC transcripts.
The issue was caused by the corrupted input .fastq files that were damaged somehow upon uploading them from the local machine. We figured it out by md5sum command output comparison. We have not yet managed to actually run all of the script still, it is failing, but for another reason now.