Version 0.7.0
CNVkit now depends on pandas, scipy and pyvcf. The internals were largely
rewritten, so please report any bugs or other regressions you find.
Documentation is much improved.
export:
- VCF format is supported (#5, #41). The generated VCFs are compatible with
many third-party tools, including development versions of MetaSV. (Thanks
@chapmanb)
- Removed the "freebayes" sub-command; use "export bed" instead.
segment:
- The names of genes (or other targeted loci) covered by each segment are
now included in the output .cns file.
- The p-value or q-value threshold (depending on the method) can now be
specified with -t/--threshold.
- The "haar" method works properly now (#6). This segmentation algorithm is
implemented in Python and does not require R to run. It is a bit faster
than CBS, but not as accurate.
loh:
- Plot variant allele frequencies (VAFs) as their actual values, 0 to 1,
instead of the mirrored b-allele frequency (0.5 to 1). Draw segment mean
allele frequencies separately above and below 0.5. This matches how the
equivalent SNP array data are typically viewed.
antitarget:
- Generate off-target bins for all chromosomes present in the "access" BED
file, not just those where targeted regions occur. (#37)
coverage:
- A minimum read mapping quality (MAPQ) value can now be specified with
-q/--min-mapq. The default value is 0, i.e. reads are no longer excluded
for low MAPQ or ambiguous mapping location. This should generally improve
calling accuracy and avoid some spurious deletion calls.