ProNTo								User Commands					ProNTo(1)

Name
	ProNTo::enzymeDigestion : Proteomics eNzymology Toolkit ::  Open Reading Frame prediction module

DESCRIPTION
	ProNTo: ProNTo guides selection of enzymes for proteomics tailored to the target organism's genome.

	The ORF prediction module reads genome data (~3000 line FASTAs maximum for efficiency) and outputs
	summary statistics for all 6 reading frames along with predicted ORFs to STDOUT in FASTA format.

	The module may be used individually while the pipeline is under development, placed on your $PATH or
	run locally on Unix file systems (tested on Linux Mint 17 / Ubuntu 14.04).

    Usage:
	./ORFprediction.pm --sequence-file ../../data/seq1_out.fasta --summary > example_output.fasta
	
	Segmented FASTA files are provided in the `/data/` directory at https://github.com/lmmx/ProNTo

	STDOUT may be redirected to a file to store the output, and further flags are available:

	--help
		Module-wide help text where used with the ProNTo::Methods module rather than standalone

	-V
		Verbose output, noting the lines and ORF coordinates being recorded

	--orf-list 1 2 3 4 5 6
		One to (all) six space-separated reading frames to filter ORF predictions within

	--use-longest-orf
		Select only the longest ORF (total nucleotides) to filter ORF predictions within

	--summary
		Present a summary table (as shown for the segmented genome.fasta file in accompanying
		report (and data/orf_preds.md file on file repository).

	--min-orf INTEGER
		Minimum ORF size (in nucleotide length), default setting 150 to filter ORF predictions

	--debug
		Provide additional information to the verbose option for closer analysis of ORF prediction

	-t
		Translation off (when provided, strings are FASTA formatted in output but not translated,
		for observation of the source of equivalent predictions in the FASTA output)

Developer notes

	Written and maintained by Louis Maddox as part of the ProNTo pipeline exercise at the University of
	Manchester, 2015. Copyright to the author under MIT license, other modules licensing unassociated.

	The algorithm attempts to remain memory efficient by keeping track of all ORF start and end sites,
	skipping any 'seen' for a given ORF as it loops around. The algorithm needs development, and to catch
	errors it currently translates sequences before confirming as a potential ORF to avoid observed
	behaviour leading to ORFs containing STOP codons internally. The putative ORFs are translated,
	any stop codon interruptions are separated, and the ORF only used if it remains larger than the
	minimum ORF size indicated by the user (default 150 nucleotides, or 50 codons - STOP inclusive).


						November 2015

Add man page for ORF predictions module
Add man page for ORF predictions module
