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Sign up1.2 Million Identical Warnings is a bit Verbose #915
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I was under the impression that I would need to account for GC bias in order to achieve accurate measurements of ploidy. The manuscript by Benjamini proposed a reasonable means to do so, and I assumed that was implemented in this tool here. I'm curious what you mean when you ask if this is necessary with modern data. Does modern data not suffer from GC bias? Furthermore, if the tool is deprecated, do you have suggestions for alternatives? |
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The tool is deprecated only because modern data rarely suffers from any major GC bias. Otherwise it should work fine.
…Sent from my iPhone
On 20. Feb 2020, at 16:40, bklein345 ***@***.***> wrote:
I was under the impression that I would need to account for GC bias in order to achieve accurate measurements of ploidy. The manuscript by Benjamini proposed a reasonable means to do so, and I assumed that was implemented in this tool here. I'm curious what you mean when you ask if this is necessary with modern data. Does modern data not suffer from GC bias? Furthermore, if the tool is deprecated, do you have suggestions for alternatives?
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Search whether this issue (or a similar issue) has been solved before
using the search tab above. Link the previous issue if appropriate below.
Paste your deepTools version (
deeptools --version) and your pythonversion (
python --version) below.deeptools 3.3.2
Python 3.6.0
Paste the full deepTools command that produces the issue below
(ignore if you simply spotted the issue in the code/documentation).
computeGCBias
--bamfile $bam
--effectiveGenomeSize $genome_size
--genome $fasta_2bit
--GCbiasFrequenciesFile $output
--blackListFileName $blacklist_bed
--verbose
--biasPlot $plot_out
--regionSize $region_size
Paste the output printed on screen from the command that produces the issue
below (ignore if you simply spotted the issue in the code/documentation).
WARNING: too many NNNs present in 1:0-176
WARNING: too many NNNs present in 1:62-238
WARNING: too many NNNs present in 1:124-300
WARNING: too many NNNs present in 1:186-362
WARNING: too many NNNs present in 1:248-424
WARNING: too many NNNs present in 1:310-486
WARNING: too many NNNs present in 1:372-548
....
Repeated ~1.2million times.