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Releases: antigenomics/vdjmatch

vdjmatch 0.0.1

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@mikessh mikessh released this 21 Jun 05:17

First PyPI release of the Python rewrite of vdjmatch (built on the seqtree search core).

Single-chain annotator (Phase A):

  • Fetch/cache the latest VDJdb release; parse to a canonical schema.
  • AIRR rearrangement + paired (cell/TCRvdb) readers via polars.
  • Fuzzy CDR3 search (seqtree) with the bundled TCR-specific VDJAM matrix; ranked hits with CIGAR + alignment + edit counts.
  • Control-calibrated E-values (BLAST-style significance vs a background repertoire).
  • Per-query best epitope call + epitope-level enrichment summary; pairwise sample overlap.
  • CLI: vdjmatch update, vdjmatch match.

The legacy Java/Groovy tool is preserved on the legacy-java branch (tags 1.1.4–1.3.1).

Paired α/β scoring, a re-derived segment-aware VDJAM, and tool comparisons are in progress (see roadmap-iter-1.md).

1.3.1

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@mikessh mikessh released this 12 Nov 09:14

Update VDJtools

1.2.2

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@mikessh mikessh released this 16 Aug 20:47
License upd

1.2.1

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@mikessh mikessh released this 02 Apr 09:39

jitpack tag

VDJmatch algorithm

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@mikessh mikessh released this 02 Apr 09:23

Implemented TCR similarity metric and smart algorithms for VDJdb querying.

Java 9 compatibility

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@mikessh mikessh released this 06 Dec 07:21
  • Now should work with Java 9
  • Other minor fixes

N.B Please ignore An illegal reflective access operation has occurred warnings occurring when running with Java 9, they have no effect on software performance/output whatsoever

Server compatibility release

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@mikessh mikessh released this 03 Aug 14:10
1.1.6

Upd sh script

Better summary stats

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@mikessh mikessh released this 13 Feb 22:31

Minor fixes, update VDJtools version, better annotation summary output

Cleanup release

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@mikessh mikessh released this 10 Jan 12:28

Removed some experimental functions & fixed sever bugs

Scoring update

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@mikessh mikessh released this 17 Jul 15:22

Critical fix for scoring system:

  • Re-named and modified presets: hamming (hamming distance based); 5,2,2,7 search scope followed by high-recall (precision corresponding to recall of 80%), optimal (precision and recall corresponding to highest F1 measure)
  • Scoring selection by precision and recall (selects nearest pre-trained preset matching specified value)
  • Ensure that exact matches are always reported disregarding score threshold