What's New
Version 1.3.0
Released November 4 2022
User visible improvements
- Added self-supervised learning mode (see [Training SemiBin models] for more details)
Bugfixes
- Fix output table to contain correct paths
- Fix mispelling in argument name
--epochs
(the old variation,--epoches
is still accepted for backwards compatibility, but should be considered deprecated)
Version 1.2.0
Released October 19 2022
User visible improvements
- Pretrained model from chicken caecum (contributed by Florian Plaza OƱate)
- Output table with basic information on bins (including N50 & L50)
- When reclustering is used (default), output the unreclusted bins into a directory called
output_prerecluster_bins
- Added
--verbose
flag and silented some of the output when it is not used - Use coloredlogs (if package is available)
Version 1.1.1
Released September 27 2022
Bugfixes
- Completely remove use of
atomicwrites
package (#97)
Version 1.1.0
Released September 21 2022
User-visible improvements
- Support .cram format input (#104)
- Support using depth file from Metabat2 (#103)
- More flexible specification of prebuilt models (case insensitive, normalize
-
and_
) - Better output message when no bins are produced
Bugfixes
- Fix bug using
atomicwrite
on certain network filesystems (#97)
Internal improvements
- Remove torch version restriction (and test on Python 3.10)
Version 1.0.3
Released August 3 2022
Bugfixes
- Fix coverage parsing when value is not an integer (#103)
- Fix multi_easy_bin with taxonomy file given on the command line (see discussion at #102)
Version 1.0.2
Released July 8 2022
Bugfixes
Version 1.0.1
Released May 9 2022
Bugfixes
- Fix edge case when calling prodigal with more threads than contigs (#93)
Version 1.0.0
Released April 29 2022
This coincides with the publication of the manuscript.
User-visible improvements
- More balanced file split when calling prodigal in parallel should take better advantage of multiple threads
- Fix bug when long stretches of Ns are present (#87]
- Better error messages (#90 & #91])
Bugfixes
- Fix bugs in training from multiple samples
- Fix bug in incorporating CAT results
Version 0.7
Released March 2 2022
This release solves issues running on Mac OS X.
User-visible improvements
- Improved
check_install
command: it now prints out paths and correctly handles optionality of FragGeneScan/prodigal - Add
concatenate_fasta
command to combine fasta files for multi-sample binning - Add option
--tmpdir
to set temporary directory - Substitute FragGeneScan with Prodigal (FragGeneScan can still be used with
--orf-finder
parameter). FragGeneScan caused issues, especially on Mac OSX
Internal improvements
- Reuse
markers.hmmout
file to make the training from several samples faster
Version 0.6
Released February 7 2022
User-visible improvements
- Provide pretrained models from soil, cat gut, human oral,pig gut, mouse gut, built environment, wastewater and global (training from all samples).
- Users can now pass in the output of running mmseqs2 directly and SemiBin will
use that instead of calling mmseqs itself (use option
--taxonomy-annotation-table
). - The subcommand to generate cannot links is now called
generate_cannot_links
. The old name (predict_taxonomy
) is kept as a deprecated alias. - Similarly, sequence features (k-mer and abundance) are generated using the
commands
generate_sequence_features_single
andgenerate_sequence_features_multi
(for single- and multi-sample modes, respectively). The old names (generate_data_single
/generate_data_multi
) are kept as deprecated aliases. - Add
check_install
command and runcheck_install
before easy command
Bugfixes
- Fix bug with non-standard characters in sample names (#68).
Version 0.5
Released January 7 2022
User-visible improvements
- Reclustering is now the default (use
--no-recluster
to disable it; the option--recluster
is deprecated and ignored) as the computational costs are much lower - GTDB lazy downloading is now performed even if a non-standard directory is used
- The CACHEDIR.TAG protocol was implemented (this is supported by several tools that perform tasks such as backups).
Bugfixes
- Fix bug with
--min-len
(minimal length). Previously, only contigs greater than the given minimal length were used (instead of greater-equal to the minimal length). - GTDB downloading was inconsistent in a few instances which have been fixed
Internal improvements
- Much more efficient code (including lower memory usage) for binning,
especially if a pretrained model is used. As an example, using a
deeply-sequenced ocean sample, generating the data (
generate_data_single
step) goes down from 14 to 9 minutes; while binning (bin
step, using--recluster
) goes down from 10m17s (using 20GB of RAM, at peak) to 4m33 (using 4.5 GB, at peak). Thus total time from BAM file to bins went down from 25 to 14 minutes (using 4 threads) and peak RAM is now 4.5GB, making it usable on a typical laptop.
Version 0.4.0
Released 27 October 2021
User-visible improvements
- Add support for
.xz
FASTA files as input
Internal improvements
- Removed BioPython dependency
Bug fixes
- Fix bug when uncompressing FASTA files (#42)
- Fix bug when splitting data
Version 0.3
Released 10 August 2021
User-visible improvements
- Support training from several samples
- Remove
output_bin_path
ifoutput_bin_path
exists - Make several internal parameters configuable: (1) minimum length of contigs to bin (
--min-len
parameter); (2) minimum length of contigs to break up in order to generate must-link constraints (--ml-threshold
parameter); (3) the ratio of the number of base pairs of contigs between 1000-2500 bp smaller than this value, the minimal length will be set as 1000bp, otherwise 2500bp (--ratio
parameter). - Add
-p
argument forpredict_taxonomy
mode
Internal improvements
- Better code overall
- Fix
np.concatenate
warning - Remove redundant matrix when clustering
- Better pretrained models
- Faster calculating dapth using Numpy
- Use correct number of threads in
kneighbors_graph()
Bugfixes
- Respect number of threads (
-p
argument) when training (issue 34)
Version 0.2
Release 27 May 2021
User-visible improvements
- Change name to
SemiBin
- Add support for training with several samples
- Test with Python 3.9
- Download mmseqs database with
--remove-tmp-file 1
- Better output names
- Fix bugs when paths have spaces
- Fix installation issues by listing all the dependencies
- Add
download_GTDB
command - Add
--recluster
option - Add
--environment
option - Add
--mode
option
Internal improvements
- All around more robust code by including more error checking & testing
- Better built-in models
Version 0.1.1
Released 21 March 2021
Bugfix release fixing an issue with minfasta-kbs
Version 0.1
Released 21 March 2021
- First release: testing version