v0.1-r9

Added the --enable_long_indel option to output indel variant calls >50bp (#64), Click here to see more benchmarking results.

v0.1-r8

1. Added the --enable_phasing option that adds a step after Clair3 calling to output variants phased by Whatshap (#63).
2. Fixed unexpected program termination on successful runs.

v0.1-r7

1. Increased var_pct_full in ONT mode from 0.3 to 0.7. Indel F1-score increased ~0.2%, but took ~30 minutes longer to finish calling a ~50x ONT dataset.
2. Expand fall through to next most likely variant if network prediction has insufficient read coverage (#53 commit 09a7d18, contributor @ftostevin-ont), accuracy improved on complex Indels.
3. Streamized pileup and full-alignment training workflows. Reduce diskspace demand in model training (#55 commit 09a7d18, contributor @ftostevin-ont).
4. Added mini_epochs option in Train.py, performance slightly improved in training a model for ONT Q20 data using mini-epochs(#60, contributor @ftostevin-ont).
5. Massively reduced disk space demand when outputting GVCF. Now compressing GVCF intermediate files with lz4, five times smaller with little speed penalty.
6. Added --remove_intermediate_dirto remove intermediate files as soon as no longer needed (#48).
7. Renamed ONT pre-trained models with Medaka's naming convention.
8. Fixed training data spilling over to validation data (#57).

v0.1-r6

1. Reduced memory footprint at the SortVcf stage(#45).
2. Reduced ulimit -n (number of files simultaneously opened) requirement (#45, #47).
3. Added Clair3-Illumina package in bioconda(#42)

v0.1-r5

1. Modified data generator in model training to avoid memory exhaustion and unexpected segmentation fault by Tensorflow (contributor @ftostevin-ont ).
2. Simplified dockerfile workflow to reuse container caching (contributor @amblina).
3. Fixed ALT output for reference calls (contributor @wdecoster).
4. Fixed a bug in multi-allelic AF computation (AF of [ACGT]Del variants was wrong before r5).
5. Added AD tag to the GVCF output.
6. Added the --call_snp_only option to only call SNP only (#40).
7. Added pileup and full-alignment output validity check to avoid workflow crashing (#32, #38).

v0.1-r4

1. Install via bioconda.
2. Added an ONT Guppy2 model to the images (ont_guppy2). Click here for more benchmarking results. The results show you have to use the Guppy2 model for Guppy2 or earlier data. 3. Added google colab notebooks for quick demo. 4. Fixed a bug then there are too few variant candidates (#28).

v0.1-r3

1. Added ulimit -u (max user processes) check (lowers the THREADS if the resource is insufficient) and automatic retries on failed jobs (#20, #23, #24).
2. Added support for user-defined pileup model prefix --pileup_model_prefix and full-alignment model prefix --fa_model_prefix.
3. Added contig validity check. Contigs specified in BED but not in BAM won't be used.
4. Added an ONT Guppy5 model (ont_guppy5).

v0.1-r2

1. Fixed BED file out of range error (#12)
2. Added support for both .bam.bai and .bai BAM index filename (#10).
3. Added some boundary checks on inputs.
4. Added version checks on required packages and utilities.
5. Increased pipeline robustness.

v0.1-r1

1. Support relative path in Conda, but Docker and Singularity still require absolute path (#5).
2. Fix taskset CPU-core visibility and provide a Singularity image (#6).

v0.1

Initial release