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The two big changes are: 1. the ability to use Yaml files to specify samples, 2. the introduction of `run_for_all` (and `run_for_all_samples`) functions to simplify the usage of the `parallel` module. Several of the other changes were then to support these two features. Additionally, some minor fixes and improvements were made. Full ChangeLog: - Add `load_sample_list` function to load samples in YAML format. - Add `compress_level` argument to `write` function to specify the compression level. - Added `name()` method to `ReadSet` objects, so you can do: input = load_fastq_directory("my-sample") print(input.name()) which will print `my-sample`. - Added `println` function which works like `print` but prints a newline after the output. - Make `print()` accept ints and doubles as well as strings. - Added `run_for_all` function to `parallel` module, simplifying its API. - When using the `parallel` module and a job fails, writes the log to the corresponding `.failed` file. - External modules can now use the `sequenceset` type to represent a FASTA file. - The `load_fastq_directory` function now supports `.xz` compressed files. - The `parallel` module now checks for stale locks **before** re-trying failed tasks. The former model could lead to a situation where a particular sample failed deterministically and then blocked progress even when some locks were stale. Bugfixes - The `parallel` module should generate a `.failed` file for each failed job, but this was not happening in every case. - Fixed parsing of GFF files to support negative values (reported by Josh Sekela on the mailing-list).
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