Comments (1)
Thank you for your interest in our tool, and for this informative post. The observation is correct: what is required is the fragment length/std and not the read length, which is what we are supplying for single-end reads. That is an error, and fixing it could also improve performance due to omitting the running of analyze_reads(...). However, it appears that fragment length information is only used in kallisto quantification to account for sequence-specific biases as a result of nonrandom priming of fragments (see kallisto paper). In fact, we have our own quantification step in arcasHLA that does not directly use kallisto quant. Additionally, in kallisto quantification step, the mean/std are only used to generate weights from a Gaussian distribution with these specified parameters. We expect the impact of using mean/std of reads as opposed to mean/std of fragments to be minimal, if any at all, on our results.
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Related Issues (20)
- quant.py error: FileNotFoundError: [Errno 2] No such file or directory: 'dat/ref/hla.p' HOT 1
- gene without cDNA sequence causes error when running quant.py
- what's the difference between the genotype and partial parameters ?
- hla.p.json error HOT 1
- intermediate alignment issue
- Getting empty fastq files after partial typing HOT 1
- scipy.stats.mode() function update results in failed reference construction HOT 2
- Deprecation of kallisto pseudo HOT 3
- empty .fa.gz files when running "extract" HOT 1
- arcasHLA customize not working HOT 1
- [reference] Error: dat/IMGTHLA/hla.dat empty or corrupted.
- Frequent reporting of a null allele for 10X data
- Some alles alleles did not be inferred ? HOT 1
- [IndexError] arcasHLA `reference` -- initial set up HOT 13
- Problems running `customize` HOT 2
- Running paired data results in empty fastq HOT 1
- 0 reads mapped to a single HLA gene
- [KeyError] arcasHLA `convert` - using "group" HOT 4
- Donor/Recipient
- TypeError: only integer scalar arrays can be converted to a scalar index HOT 1
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