Features

GFGenome:

• GFGenome.get_consensus_Kozak_seq(seq=False)

  Consensus Kozak sequence

• GFGenome.get_Kozak_matrix()

  Kozak sequence matrix (rows - transcripts)

• GFGenome.get_consensus_stop_codon_context(seq=False)

  Consensus stop codon context sequence

• GFGenome.get_stop_codon_context_matrix()

  Stop codon context matrix (rows - transcripts)

• GFGenome.get_codon_pair_bias()

  Codon pair bias

• GFGenome.get_nucleobase_mutation_table()

  Nucleobase mutation matrix (table with positions of variants and their type in Kozak sequence and stop codon context)

GFTranscript:

• GFTranscript.codon_counts()

  Coding sequence codon count

• GFTranscript.utr5_motif_counts(pattern)

  5’UTR motif count

• GFTranscript.utr3_motif_counts(pattern)

  3’UTR motif count

• GFTranscript.codon_usage()

  Coding sequence codon usage

• GFTranscript.gc_content(region)

  Coding sequence, 5’UTR or 3’UTR G and C content

• GFTranscript.get_Kozak_seq()

  Kozak sequence

• GFTranscript.get_stop_codon_context()

  Stop codon context

• GFTranscript.get_codon_pairs_frequency()

  Coding sequence codon pair frequency

Upstream AUG:

• UpstreamAUG.predict_on_sample(seq)

  Predict on sample (get all AUGs with the information whether they are in-frame or not and whether they have a stop codon or not)

• UpstreamAUG.predict_on_sample_with_pos(seq)

  Predict on sample with position (predict on sample plus positions of AUGs)

• UpstreamAUG.predict_on_sample_with_pos_pandas()

  Predict on sample with position and appending to the passed dictionary

• UpstreamAUG.predict_on_batch(seq_list)

  Predict on batch

5’UTR class:

• FivePrimeUTRSeq(data, reverse_complement_bool, contig=None, strand=None)

  An object contains list of all 5’UTR sequences, their positions, exons, positions of exons and corresponding transcripts

Auxiliary functions:

• VCFMutator.mutate_sequence(interval, fasta=None, seq_whole=None)

  Mutate sequence

• VCFMutator.mutate_codon_context(intervals, seqs, column_names)

  Mutate codon context

• reverse_complement(dna)

  Reverse complement

• PCA_with_standard_sample_deviation_scaling(df, n_comp=2)

  Pca with scaling

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template. gfeat is based on pyensembl package and can be partially viewed as its extension.

0.1.0 (2018-09-27)

• First release on PyPI.

1.0.0 (2018-09-28)

• First stable release on PyPI.