Resistify v1.0.0

No changes!
Accidentally released a v0.70.0 on conda instead of v0.7.0, so releasing v1 to fix.

Resistify v0.7.0

• HMMER searches now use recommended per-profile thresholds rather than a generic cutoff. Expect minor changes in NLR classification for partial or highly degraded NLRs, and the PRR classification will be much more stringent! As a result of this change, the E-value argument has been dropped.
• Long processes now have dynamic progress bars, and output static % progress logs.
• More verbose warning if running PRR pipeline CPU-only mode.
• Will now dynamically assign chunksize if not set manually for improved performance.
• Extracellular length of PRRs is now reported.
• Can now take gzipped fasta files as input
• If no NLRs/PRRs are detected, Resistify will no longer produce an error code 1. It will instead exit without error and produce empty output files.
• Removed bioconda as a dependency - should reduce dependency resolving time slightly and improve image size
• Fixed a significant bug where nlrexpress multiprocessing could silently freeze for "reasons". As a result of this change, debug logging for all nlrexpress subprocesses is disabled.

Resistify v0.6.3

• Fixed a bug where '.' characters would result in a crash. '.' is now treated the same as '*' - removed if at end of sequence and sequence skipped if internal

Resistify v0.6.2

• FASTA parser now warns and continues if internal stop codons detected
• Sequences <28aa now rejected as they could sometimes cause errors
• TMbed now runs prior to NLRexpress, filtering out non-PRR sequences prior
• Signal peptide status reported in results.tsv for PRRs
• Length of total LRR domain now reported under LRR_Length in results.tsv
• Improved logging

Resistify v0.6.1

Reduced dependency restrictions, particularly the restriction on scikit-learn which allows python<3.13 to be used.
Added warning suppression for scikit-learn

Full Changelog: v0.6.0...v0.6.1

Resistify v0.6.0

What's new in v0.6.0?

The release of v0.6.0 has brought a number of changes to Resistify.
First, you'll note that there are now two modes available - NLR and PRR - which identify NLRs and PRRs respectively.

The NLR pipeline is largely the same, but has received multiple performance improvements which should allow it to utilise more threads simultaneously and significantly reduce memory usage.
As a result of these changes, the --threads mode has now been removed which was a bit of a lie anyway, as numpy would use them all regardless.
The --ultra setting has been renamed as --retain.

The PRR pipeline is new to Resistify and is currently in development.
It uses a re-implementation of TMbed to predict transmembrane domains, from which it will identify and classify RLP/RLKs according to a recently described classification system.
Feel free to give it a try and offer suggestions!
Due to other commitments I can't currently benchmark this properly and make no guarantees to its accuracy yet.

Resistify v0.5.2

• Resistify now uses all available threads by default
• Dependencies have been reduced due to switch away from PyPi
• Improved argument descriptions
• Performance improvements

Resistify v0.5.1

Fixed bug where --ultra could pass empty N-terminals to --coconat resulting in error.

Resistify v0.5.0

• Resistify can now use CoCoNat to improve coiled-coil domain annotations. Currently, this mode is experimental and optional.
• Classification system revised to be more consistent and to prevent certain edge cases.
• domains.tsv has now been split into domains.tsv and annotations.tsv, which represent the merged and raw annotations respectively.
• --batch mode has been provided to allow users to run Resistify in smaller batches which should help limit memory usage on larger inputs.
• A bunch of refactoring and improved internal file handling.

Resistify v0.4.0

Fixed a regression where TIR HMM profiles were incorrectly handled.
Fixed a regression where input fasta files larger than 100k sequences were rejected - should have been sequences more than 100k in length!
Adjacent domains are now collapsed prior to classification (e.g., TNNL would now be TNL rather than TN).
Sequences classified as NLRs are now reported in nlrs.fasta for ease-of-use.
Improved README.md.