Utilities for working with pogigwasc

Scripts to help prepare data for the pogigwasc gene predictor, and to reformat or process output files, and to work with output from Intronarrator.

Requires python 3.6+, Biopython, pybedtools

pogigwasc is a gene predictor for eukaryotic genomes with ambiguous stop codons, developed by David Vetter.

Scripts

• split_scaffolds.py - Split scaffolds on Ns into contigs, and record original coordinates in a JSON file (used later to recombine the contigs). This is because pogigwasc does accepts only contigs (no Ns) as input
• recombine_contigs.py - Recombine annotations performed on split contigs to original scaffold coordinates. Requires JSON output from split_scaffolds.py
• add_attributes_pogigwasc.py - Add parent gene features and ID, Parent attributes to pogigwasc predictions and extend CDSs to include adjacent stop codons.
• add_realtrons_pogigwasc_intronless.py - Combine pogigwasc gene predictions and "realtrons" (empirically predicted introns from RNAseq mapping) predicted by Intronarrator. Intronarrator must first be used to identify realtrons and artificially remove them from the assembly. Gene prediction then run with pogigwasc on the intronless assembly in intronless mode.
• pogigwasc_utils_shared.py - Functions shared by more than one script (not used as a standalone script)
• testing.py - Test module for these scripts

Feature IDs have the format {seqid}.g{number} for genes, {seqid}.c{number} for CDSs, and {seqid}.s{number} for stop codons, where the CDS and stop codon features have the same number as their parent gene feature. CDSs are directly linked to parent genes, without intervening transcript ("mRNA") feature, because these are purely computational predictions, and other gene features like UTRs are not predicted.

TODO:

• How to split and handle hard masks at beginning or end of contigs?
• How to deal with empirical introns that are exactly at the boundaries of a CDS?
• Enhancement: split scaffolds into contigs on soft masks or lower case 'n'
• Estimate model parameters from training data - standalone script
• Empirical correction of mispredicted introns from RNAseq data