KO
|
EN
gitlite — search
Search
#typescript
#ai-agents
#deepseek-harness
#dsh-plugin
#open-source
#ai
#cli
#dsh
#claude-code
#codex
#developer-tools
#react
orfipy
★ 82
Open GitHub ↗
Fast and flexible ORF finder
Download README (.md)
Explore Similar Repositories
rowhammer-tester
:
No description available.
skywalking-rust
:
Apache SkyWalking Rust Agent
jasyncapi
:
/jay-sync-api/ is a Java code-first tool for AsyncAPI specification
Wei.Repository
:
基于.Net6,EFCore+Dapper 封装Repository，实现UnitOfWork,提供基本的CURD操作，支持多数据库，多DbContext
mailersend-php
:
The official MailerSend PHP SDK
// repository documentation
Was this content helpful?
★ 0
(0 ratings)
Select Rating:
★
★
★
★
★
Submit Feedback
Recent Feedback
×
Download README
Do you want to download the
README.md
file for
orfipy
?
Download (.md)
[](https://travis-ci.org/urmi-21/orfipy)  [](https://anaconda.org/bioconda/orfipy) [](https://anaconda.org/bioconda/orfipy)  [](https://pepy.tech/project/orfipy) [](https://doi.org/10.1093/bioinformatics/btab090) # Introduction orfipy is a tool written in python/cython to extract ORFs in an extremely and fast and flexible manner. Other popular ORF searching tools are [OrfM](https://github.com/wwood/OrfM) and [getorf](http://emboss.sourceforge.net/apps/cvs/emboss/apps/getorf.html). 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 the [paper here](https://doi.org/10.1093/bioinformatics/btab090). 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/btab090 ## Installation ### Install latest stable version ``` pip install orfipy ``` Or install via conda ``` conda config --add channels defaults conda config --add channels bioconda conda config --add channels conda-forge conda create -n orfipy -c bioconda orfipy ``` ### Install the development version from source ``` git clone https://github.com/urmi-21/orfipy.git cd orfipy pip install . ``` or use pip ``` pip install git+git://github.com/urmi-21/orfipy.git ``` ## Examples Details of `orfipy` algorithm are in the <a href=https://doi.org/10.1093/bioinformatics/btab090> paper. Please go through the <a href=https://github.com/urmi-21/orfipy/tree/master/supplementary_data>SI</a></em> if you are interested to know differences between `orfipy` and other ORF finder tools and how to set `orfipy` parameters to match the output of other tools. Below are some usage examples for `orfipy` To see full list of options use the command: ``` orfipy -h ``` ### Input `orfipy` version 0.0.3 and above, supports sequences in Fasta/Fastq format (orfipy uses [pyfastx](https://github.com/lmdu/pyfastx)). Input files can be in .gz format. **Extract ORF sequences and write ORF sequences in orfs.fa file** ``` orfipy input.fasta --dna orfs.fa --min 10 --max 10000 --procs 4 --table 1 --outdir orfs_out ``` **Use [standard codon table](https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi?chapter=cgencodes) but use only ATG as start codon** ``` orfipy input.fa.gz --dna orfs.fa --start ATG ``` **Note:** Users can also provide their own translation table, as a .json file, to `orfipy` using `--table` option. Example of json file containing a valid translation table is [here](https://github.com/urmi-21/orfipy/blob/master/scripts/example_user_table.json) **See available codon tables** ``` orfipy --show-table ``` **Extract ORFs BED file** ``` orfipy input.fasta --bed orfs.bed --min 50 --procs 4 or orfipy input.fasta --min 50 --procs 4 > orfs.bed ``` **Extract ORFs BED12 file** **Note**: Add `--include-stop` for orfipy output to be consistent with Transdecoder.Predict output .bed file. ``` orfipy testseq.fa --min 100 --bed12 of.bed --partial-5 --partial-3 --include-stop ``` **Extract ORFs peptide sequences using default translation table** ``` orfipy input.fasta --pep orfs_peptides.fa --min 50 --procs 4 ``` ### API Users 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:TAG ``` `orfipy_core.orfs` function can take following arguments - seq: 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 OrfM <p> <img src="https://raw.githubusercontent.com/urmi-21/orfipy/master/scripts/comparison.png" alt> <em>Comparison 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 the <a href=https://doi.org/10.1093/bioinformatics/btab090> publication</a> and <a href=https://doi.org/10.1093/bioinformatics/btab090>SI</a></em> </p> - orfipy 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 limited - getorf is memory efficient but slower, no Fastq support. Provides some flexibility in ORF searches. ## Funding This 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).
