gcgc 0.10.0

GCGC is a preprocessing library for biological sequence model development.

GCGC

GCGC is a python package for feature processing on Biological Sequences.

Installation

Install GCGC via pip:

$ pip install gcgc

Documentation

The GCGC documentation is at gcgc.trenthauck.com, please see it for an example.

Citing GCGC

If you use GCGC in your research, cite it with the following:

@misc{trent_hauck_2018_2329966,
  author       = {Trent Hauck},
  title        = {GCGC},
  month        = dec,
  year         = 2018,
  doi          = {10.5281/zenodo.2329966},
  url          = {https://doi.org/10.5281/zenodo.2329966}
}

Changelog

0.10.0 (2019-11-09)

gcgc has been revamped quite a bit to better support existing processing pipelines for NLP without trying to do to much. See the docs for more information about how this works.

0.9.0 (2019-08-05)

Added

• Parser now outputs the length of the tensor not including padding. This is useful for packing and length based iteration.
• Generating masked output from the parse_record method is now available.
• Alphabet can include an optional mask token.

Changed

• Can now specify how large of kmer step size to generate when supplying a kmer value.
• Renames EncodedSeq.integer_encoded to EncodedSeq.get_integer_encoding which takes a kmer_step_size to specify how large of steps to take when encoding.
• Add parsed_seq_len to the SequenceParser object to control how much padding to apply to the end of the integer encoded sequence. This is useful since a batch of tensors is expected to have the same size.

0.8.0 (2019-07-04)

Fixed

• Broken test due to platform differences in Path.glob sorting.

Added

• User can specify to use start or end tokens optionally.

Removed

• Removed one_hot_encoding. The user can do that pretty easily if needed. E.g. see scatter in PyTorch.

0.7.0 (2019-06-22)

Added

• Properties to access the integer encodings of special tokens. (35cae2a)
  • Alphabet.encoded_start
  • Alphabet.encoded_end
  • Alphabet.encoded_padding
• Remove uniprot dataset creation. (e233162)
• Simplify index handling for GenomicDataset. (3213a9e)

0.6.1 (2019-06-10)

Added

• Updated package management so gcgc is easier to use with other version of torch.

0.6.0 (2019-04-04)

Added

• Ability for kmer size to be passed to an alphabet.

0.5.2 (2019-03-21)

Added

• Add Dockerfile and docker-compose.yml for development.
• EncodedSeq.shift, which will shift sequence by an offset integer.
• EncodedSeq.from_integer_encoded_seq will take a list of integers and an alphabet and return an EncodedSeq object.
• Add the ability to apply a function to the rollout_kmers yielded values.

Changed

• Alphabet special characters are now located at the start, rather than the end, of the letters and token sequence.

0.5.1 (2019-01-09)

Added

• Add extra css to make underline links in articles.
• Exit if the download directory doesn't exist in the call to download organism.
• Wording improvements in docs.

0.5.0 (2018-12-31)

Added

• Include seq_tensor_one_hot in the PyTorch Parser.
• Added a GCGCRecord.encoded_seq property.
• New gcgc.random module to start holding sequence data.
• New gcgc.rollout module to handle working through chunks of sequences.
  • rollout_kmers will roll out kmers.
  • rollout_seq_features will roll out the SeqFeatures from a SeqRecord.
• EncodingAlphabet now can optionally take a gap_characters set of characters to add to the alphabet letters. It also takes add_lower_case_for_inserts which will duplicate the alphabet, but convert the letters to lowercase.

Changed

Fixed

• Fixed bug in GenomicDataset.from_path where it still referred to init_from_path_generator.

0.4.0

Added

• EncodedSeq now supports iterating through kmers, see EncodedSeq.rollout_kmers for options.
• GCGC is citable.
• GCGC now has a CHANGELOG.md.