Package: orfipy
Introductionorfipy is a tool written in python/cython to extract ORFs in an extremely and fast and flexible manner.
Other popular ORF searching tools areOrfMandgetorf.
Compared to OrfM and getorf, orfipy provides the most options to fine tune ORF searches.
orfipy uses multiple CPU cores and is particularly faster for data containing multiple smaller fasta sequences such as de-novo transcriptome assemblies.
Please read thepaper here.Please cite as: Urminder Singh, Eve Syrkin Wurtele, orfipy: a fast and flexible tool for extracting ORFs, Bioinformatics, 2021;, btab090,https://doi.org/10.1093/bioinformatics/btab090InstallationInstall latest stable versionpip install orfipyOr install via condaconda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge

conda create -n orfipy -c bioconda orfipyInstall the development version from sourcegit clone https://github.com/urmi-21/orfipy.git
cd orfipy
pip install .or use pippip install git+git://github.com/urmi-21/orfipy.gitExamplesDetails oforfipyalgorithm are in thepaper. Please go through theSIif you are interested to know differences betweenorfipyand other ORF finder tools and how to setorfipyparameters to match the output of other tools.Below are some usage examples fororfipyTo see full list of options use the command:orfipy -hInputorfipyversion 0.0.3 and above, supports sequences in Fasta/Fastq format (orfipy usespyfastx). Input files can be in .gz format.Extract ORF sequences and write ORF sequences in orfs.fa fileorfipy input.fasta --dna orfs.fa --min 10 --max 10000 --procs 4 --table 1 --outdir orfs_outUsestandard codon tablebut use only ATG as start codonorfipy input.fa.gz --dna orfs.fa --start ATGNote:Users can also provide their own translation table, as a .json file, toorfipyusing--tableoption. Example of json file containing a valid translation table ishereSee available codon tablesorfipy --show-tableExtract ORFs BED fileorfipy input.fasta --bed orfs.bed --min 50 --procs 4
or
orfipy input.fasta --min 50 --procs 4 > orfs.bedExtract ORFs BED12 fileNote: Add--include-stopfor orfipy output to be consistent with Transdecoder.Predict output .bed file.orfipy testseq.fa --min 100 --bed12 of.bed --partial-5 --partial-3 --include-stopExtract ORFs peptide sequences using default translation tableorfipy input.fasta --pep orfs_peptides.fa --min 50 --procs 4APIUsers can directly import the ORF search algorithm, written in cython, in their python ecosystem.>>> import orfipy_core 
>>> seq='ATGCATGACTAGCATCAGCATCAGCAT'
>>> for start,stop,strand,description in orfipy_core.orfs(seq,minlen=3,maxlen=1000):
...     print(start,stop,strand,description)
... 
0 9 + ID=Seq_ORF.1;ORF_type=complete;ORF_len=9;ORF_frame=1;Start:ATG;Stop:TAGorfipy_core.orfsfunction can take following argumentsseq: Required input sequence (str)name ['Seq'] Name (str)minlen [0] min length (int)maxlen [1000000] max length (int)strand ['b'] Strand to use, (b)oth, (f)wd or (r)ev (char)starts [['TTG','CTG','ATG']] Start codons to use (list)stops=['TAA','TAG','TGA'] Stop codons to use (list)include_stop [False] Include stop codon in ORF (bool)partial3 [False] Report ORFs without a stop (bool)partial5 [False] Report ORFs without a start (bool)between_stops [False] Report ORFs defined as between stops (bool)Comparison with getorf and OrfMComparison of orfipy features and performance with getorf and OrfM. Tools were run on different data and ORFs were output to both nucleotide and peptide Fasta files (fasta), only peptide Fasta (peptide) and BED (bed).
    For details see thepublicationandSIorfipy is most flexible, particularly faster for data containing multiple smaller fasta sequences such as de-novo transcriptome assemblies or collection of microbial genomes.OrfM is fast (faster for Fastq), uses less memory, but ORF search options are limitedgetorf is memory efficient but slower, no Fastq support. Provides some flexibility in ORF searches.FundingThis work is funded in part by the National Science Foundation award IOS 1546858, "Orphan Genes: An Untapped Genetic Reservoir of Novel Traits".
This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number
ACI-1548562 (Bridges HPC environment through allocations TG-MCB190098 and TG-MCB200123 awarded from XSEDE and HPC Consortium).