RLS Version 1.4.0

Long reads binning!

Full ChangeLog

* Provide binning algorithm for assemblies from long read
* Add `--allow-missing-mmseqs2` flag to `check_install` subcommand
* Run Prodigal in multiple jobs without multiprocessing (#106)
* Better command line arguments
* Better error checking

Author: luispedro

ChangeLog

@@ -1,9 +1,12 @@
 Version 1.4.0 Dec  2022 by BigDataBiology
 	* Provide binning algorithm for assemblies from long read
+	* Add `--allow-missing-mmseqs2` flag to `check_install` subcommand
+	* Run Prodigal in multiple jobs without multiprocessing (#106)
+	* Better command line arguments
+	* Better error checking
 
 Version 1.3.1 Dec 9 2022 by BigDataBiology
 	* Make `--training-type` argument optional
-	* Add `--allow-missing-mmseqs2` flag to `check_install` subcommand
 
 Version 1.3.0 Nov 4 2022 by BigDataBiology
 	* Add self-supervised learning

docs/whatsnew.md

@@ -1,12 +1,31 @@
 # What's New
 
-## Version 1.4
+## Version 1.4.0: long reads binning!
 
-*Release December , 2022*
+*Released December 15, 2022*
 
-### User visible improvements
+Big change is the added binning algorithm for assemblies from long-read datasets.
+
+The overall structure of the pipeline is still similar to what was [manuscript](https://www.nature.com/articles/s41467-022-29843-y), but when clustering, it does not use infomap, but another procedure (an iterative version of DBSCAN).
+
+Use the flag `--sequencing-type=long_read` to enable an alternative clustering that works better with long reads.
+
+### Other user-visible improvements
+
+- Better error checking at multiple steps in the pipeline so that processes that will crash are caught as early as possible
+- Add `--allow-missing-mmseqs2` flag to `check_install` subcommand (eventually, self-supervision will be the default and mmseqs2 will be an optional dependency)
+
+### Command line parameter deprecations
+
+The previous arguments should continue to work, but going forward, the newer arguments are probably a better API.
+
+- Selecting self-supervised learning is now done with the `--self-supervised` flag (instead of `--training-type=self`)
+- Training from multiple samples is now enabled with the `--train-from-many` flag (instead of `--mode=several`)
+
+### Bugfixes
 
-- Added binning algorithm for assemblies from long-read datasets.
+- The output table sometimes had the wrong path in `v1.3`. This has been fixed
+- Prodigal is now run in a more robust manner when using multiple threads ([#106](https://github.com/BigDataBiology/SemiBin/issues/106))
 
 ## Version 1.3.1