A Gene Finder Framework for the Julia Programming Language.

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Overview

This is a species-agnostic, algorithm extensible, sequence-anonymous (genome, metagenomes) gene finder library framework for the Julia Language.

The GeneFinder package provides a versatile framework for finding Open Reading Frames (ORFs) in DNA sequences. It offers a common interface for different gene finding algorithms and returns results as ORFCollection objects that bundle ORFs with their source sequence for clean sequence extraction.

Warning

This package is currently under development and the API is subject to change.

Installation

You can install GeneFinder from the Julia REPL. Press ] to enter pkg mode:

julia> add GeneFinder

Quick Start

julia> using BioSequences, GeneFinder

seq = dna"ATGATGCATGCATGCATGCTAGTAACTAGCTAGCTAGCTAGTAA";
collection = findorfs(seq) # Find all ORFs - returns an ORFCollection

ORFCollection{NaiveFinder} with 5 ORFs in 44bp sequence:
 ORF{NaiveFinder}(1:33, '+', 1)
 ORF{NaiveFinder}(4:33, '+', 1)
 ORF{NaiveFinder}(8:22, '+', 2)
 ORF{NaiveFinder}(12:29, '+', 3)
 ORF{NaiveFinder}(16:33, '+', 1)

Finding ORFs

The main function is findorfs, which uses the NaiveFinder algorithm by default:

julia> using BioSequences, GeneFinder

julia> seq = dna"AACCAGGGCAATATCAGTACCGCGGGCAATGCAACCCTGACTGCCGGCGGTAACCTGAACAGCACTGGCAATCTGACTGTGGGCGGTGTTACCAACGGCACTGCTACTACTGGCAACATCGCACTGACCGGTAACAATGCGCTGAGCGGTCCGGTCAATCTGAATGCGTCGAATGGCACGGTGACCTTGAACACGACCGGCAATACCACGCTCGGTAACGTGACGGCACAAGGCAATGTGACGACCAATGTGTCCAACGGCAGTCTGACGGTTACCGGCAATACGACAGGTGCCAACACCAACCTCAGTGCCAGCGGCAACCTGACCGTGGGTAACCAGGGCAATATCAGTACCGCAGGCAATGCAACCCTGACGGCCGGCGACAACCTGACGAGCACTGGCAATCTGACTGTGGGCGGCGTCACCAACGGCACGGCCACCACCGGCAACATCGCGCTGACCGGTAACAATGCACTGGCTGGTCCTGTCAATCTGAACGCGCCGAACGGCACCGTGACCCTGAACACAACCGGCAATACCACGCTGGGTAATGTCACCGCACAAGGCAATGTGACGACTAATGTGTCCAACGGCAGCCTGACAGTCGCTGGCAATACCACAGGTGCCAACACCAACCTGAGTGCCAGCGGCAATCTGACCGTGGGCAACCAGGGCAATATCAGTACCGCGGGCAATGCAACCCTGACTGCCGGCGGTAACCTGAGC";

julia> collection = findorfs(seq, finder = NaiveFinder)
ORFCollection{NaiveFinder} with 12 ORFs in 726bp sequence:
 ORF{NaiveFinder}(29:40, '+', 2)
 ORF{NaiveFinder}(137:145, '+', 2)
 ORF{NaiveFinder}(164:184, '+', 2)
 ORF{NaiveFinder}(173:184, '+', 2)
 ORF{NaiveFinder}(236:241, '+', 2)
 ORF{NaiveFinder}(248:268, '+', 2)
 ORF{NaiveFinder}(362:373, '+', 2)
 ORF{NaiveFinder}(470:496, '+', 2)
 ORF{NaiveFinder}(551:574, '+', 2)
 ORF{NaiveFinder}(569:574, '+', 2)
 ⋮
 ORF{NaiveFinder}(695:706, '+', 2)

Note

The minlen kwarg defaults to 6nt, so short ORFs like dna"ATGTGA" → aa"M*" may be detected.

Working with ORFCollection

The ORFCollection bundles ORFs with their source sequence:

# Access ORFs
julia> collection[1]           # First ORF
ORF{NaiveFinder}(29:40, '+', 2)

julia> collection[1:5]         # Range of ORFs
5-element Vector{OpenReadingFrame{NaiveFinder}}: # Note that subseting returns a vector
 ORF{NaiveFinder}(29:40, '+', 2)
 ORF{NaiveFinder}(137:145, '+', 2)
 ORF{NaiveFinder}(164:184, '+', 2)
 ORF{NaiveFinder}(173:184, '+', 2)
 ORF{NaiveFinder}(236:241, '+', 2)

julia> length(collection)      # Number of ORFs
12


julia> src = source(collection) # Get source sequence
726nt DNA Sequence:
AACCAGGGCAATATCAGTACCGCGGGCAATGCAACCCTG…GCGGGCAATGCAACCCTGACTGCCGGCGGTAACCTGAGC

Extracting Sequences

julia> orfseq = sequence(collection, 1) # By index
12nt DNA Sequence:
ATGCAACCCTGA


julia> orf = collection[1]; orfseq = sequence(collection, orf) # By ORF
12nt DNA Sequence:
ATGCAACCCTGA

julia> all_orfs_seqs = sequence.(Ref(collection), collection.orfs) # All sequences at once
12-element Vector{LongSubSeq{DNAAlphabet{4}}}:
 ATGCAACCCTGA
 ATGCGCTGA
 ATGCGTCGAATGGCACGGTGA
 ATGGCACGGTGA
 ATGTGA
 ATGTGTCCAACGGCAGTCTGA
 ATGCAACCCTGA
 ATGCACTGGCTGGTCCTGTCAATCTGA
 ATGTCACCGCACAAGGCAATGTGA
 ATGTGA
 ATGTGTCCAACGGCAGCCTGA
 ATGCAACCCTGA

Translation

Using BioSequences.translate on extracted sequences:

julia> protein = translate(sequence(collection, 1)) # Single ORF
4aa Amino Acid Sequence:
MQP*

julia> proteins = translate.(sequence.(Ref(collection), collection)) # All ORFs
12-element Vector{LongAA}:
 MQP*
 MR*
 MRRMAR*
 MAR*
 M*
 MCPTAV*
 MQP*
 MHWLVLSI*
 MSPHKAM*
 M*
 MCPTAA*
 MQP*

Scoring ORFs

ORF sequences can be scored using the log-odds ratio approach from BioMarkovChains.jl:

julia> using BioMarkovChains

julia> orfseqs = sequence.(Ref(collection), collection); # Extract sequences

julia> scores = log_odds_ratio_score.(orfseqs)
12-element Vector{Float64}:
 0.469404606944017
 1.0174520899042823
 1.5914902556997463
 0.9772187907841964
 0.6106494455192994
 0.7089167973379216
 0.469404606944017
 1.5523291911446804
 0.5282685400427601
 0.6106494455192994
 0.7405746713921604
 0.469404606944017

Check if sequences are likely coding using a conding decision rule, check the iscoding function:

julia> iscoding(sequence(collection, 1)) # Check individual sequence
true

julia> orfseqs = sequence.(Ref(collection), collection); # Check all sequences
julia> coding_bit_mask = iscoding.(orfseqs)
12-element BitVector:
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1

# Filter for coding ORFs
julia> codingorfs = collection[coding_bit_mask]

12-element Vector{OpenReadingFrame{NaiveFinder}}:
 ORF{NaiveFinder}(29:40, '+', 2)
 ORF{NaiveFinder}(137:145, '+', 2)
 ORF{NaiveFinder}(164:184, '+', 2)
 ORF{NaiveFinder}(173:184, '+', 2)
 ORF{NaiveFinder}(236:241, '+', 2)
 ORF{NaiveFinder}(248:268, '+', 2)
 ORF{NaiveFinder}(362:373, '+', 2)
 ORF{NaiveFinder}(470:496, '+', 2)
 ORF{NaiveFinder}(551:574, '+', 2)
 ORF{NaiveFinder}(569:574, '+', 2)
 ORF{NaiveFinder}(581:601, '+', 2)
 ORF{NaiveFinder}(695:706, '+', 2)

Writing to Files

GeneFinder supports exporting ORFs in multiple formats:

Function	Description
write_orfs_fna	Nucleotide sequences (FASTA)
write_orfs_faa	Amino acid sequences (FASTA)
write_orfs_bed	BED format
write_orfs_gff	GFF3 format

Example:

julia> open("output.fna", "w") do io
          write_orfs_fna(seq, io; finder=NaiveFinder) # Write nucleotide FASTA
       end

julia> open("output.faa", "w") do io
          write_orfs_faa(seq, io; finder=NaiveFinder) # Write amino acid FASTA
       end


julia> open("output.gff", "w") do io
          write_orfs_gff(seq, io; finder=NaiveFinder, seqname="myseq") # Write GFF3
       end

Output (FASTA):

>ORF01 id=01 start=29 stop=40 strand=+ frame=2 features=[]
ATGCAACCCTGA
>ORF02 id=02 start=137 stop=145 strand=+ frame=2 features=[]
ATGCGCTGA
⋮

Alternative Start Codons

Use alternative_start=true to include GTG, TTG, and CTG as start codons:

collection = findorfs(seq, finder=NaiveFinder, alternative_start=true)

Available Algorithms

Algorithm	Description
NaiveFinder	Regex-based ORF detection (default)
NaiveFinderLazy	Memory-optimized variant with pre-allocation
NaiveCollector	Uses simple eachmatch for non-overlapping ORF detection

Documentation

For more details, see the full documentation.

Citation

If you use GeneFinder in your research, please cite: