a single tumor sample without matched normals

[songbowang125]

Hi,
I was using cnvkit to run a tumor sample, which has no matched normal samples. Following your docs, I ran "cnvkit.py reference -o FlatReference.cnn -f ucsc.hg19.fa -t targets.bed -a antitargets.bed" and a flat reference was generated. So, when I run'fix' command, I need to type 'cnvkit.py fix Sample.targetcoverage.cnn Sample.antitargetcoverage.cnn Flat_reference.cnn -o Sample.cnr' instand of 'Reference.cnn'. Was I right?

And, is cnvkit suitable for long reads, such as reads from Pacbio CCS or ONT technologies. I mean the models, including both segment methods and bias correction methods. Are they suitable for both short and long reads?

Thanks.

[tskir]

Hi @songbowang125! I'm not the author of CNVkit, but I could try to clarify these for you.

The fix command you list is correct for the flat reference. The only minor thing is that the filename FlatReference.cnn / Flat_reference.cnn is spelled slightly differently between them; it should be the same file.

As for the long reads; I don't think CNVkit was specifically optimised for them, and I couldn't find any benchmarking studies comparing its performance in short vs long read scenarios. Some bias correction methods, for example edge bias methods, implicitly assume paired end short reads with an insert size of about 250:

cnvkit/cnvlib/fix.py

Lines 182 to 191 in b55ef86

	def get_edge_bias(cnarr, margin):
	"""Quantify the "edge effect" of the target tile and its neighbors.
	The result is proportional to the change in the target's coverage due to
	these edge effects, i.e. the expected loss of coverage near the target
	edges and, if there are close neighboring tiles, gain of coverage due
	to "spill over" reads from the neighbor tiles.
	(This is not the actual change in coverage. This is just a tribute.)
	"""

cnvkit/cnvlib/params.py

Lines 7 to 8 in b55ef86

	# Fragment size for paired-end reads
	INSERT_SIZE = 250

Regardless of that, my intuition tells me that CNVkit should still perform reasonably well on long read data. Any minor problems with bias correction should probably be offset by the fact that long reads will have much better mapping quality throughtout the genome.

Please let me know if you have any further questions, I'll be happy to help!

[etal]

Answered; long-read support is speculative with no concrete task. Closing as answered — please reopen, or open a new issue, if a concrete problem remains.

For general usage questions, we now use GitHub Discussions (Q&A): https://github.com/etal/cnvkit/discussions