CHANGELOG.md

Change Log

All notable changes to this project will be documented in this file. This project adheres to Semantic Versioning.

[Unreleased]

Fixed

• Abundance list reported by kevlar filter now correctly show re-computed proband k-mer abundances, not pre-filtering abundances as before (see #111).
• The kevlar localize and kevlar call procedures now handle multiple assembled contigs and multiple reference matches (see #124 and #126).

Added

• New abundance screen now a part of kevlar novel. If any k-mer in a read is below some abundance threshold, the entire read is discarded (see #106).
• Better error reporting and handling of various issues with assembly, localization, and alignment (see #113, #114).
• Preliminary support for VCF output.

Changed

• The kevlar filter procedure now handles both contamination and reference matches under a single mask interface (see #103).
• Explicitly dropped support for Python 2.7. Now supports only Python >=3.5 (see #125).

Removed

• The kevlar collect command and associated tests. Its functionality has now been fully distributed to other subcommands.
  • Read filtering to kevlar filter
  • Junction count contig assembly to kevlar filter as an optional mode

[0.2.0] - 2017-07-21

Added

• New subcommands
  • partition: group reads by shared interesting k-mers
  • localize: determine an assembled contig's location in the reference genome
  • call: align assembled contigs to reference and call variant
• Documentation suite in docs/, hosted at https://kevlar.readthedocs.io
• New third-party dependency ksw2 for computing alignments. Wrapped with Cython, which is a new development-time dependency (but not install or run time).
• The pandas package is now a dependency, and pysam and networkx are now hard dependencies (rather than conditional).

Fixed

• Bug with assembly when the order of a read pair was swapped and they had the opposite orientation (see #85).

[0.1.0] - 2017-05-13

Added

• Command-line interface with 8 subcommands
  • dump: discard reads that match reference completely
  • count: compute k-mer abundances for all samples
  • novel: identify "interesting" (potentially novel) k-mers
  • filter: re-compute k-mer abundances, discard false positives and contamination
  • assemble: assemble reads for a single variant
  • collect: collect and filter (legacy)
  • mutate: simulate variants on a genome
  • reaugment: re-attach interesting k-mer annotations to reads
• Extensive test suite
• Continuous integration configuration