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.