[{"_1":2,"_160":-5,"_161":-5},"loaderData",{"_3":4,"_17":18,"_25":26,"_139":140},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7,"_13":14,"_15":16},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","theme","system","contrast","normal","routes/_layout",{"_19":-5,"_20":21,"_22":21,"_23":11,"_24":21},"username","isAskGordonEnabled",true,"isUsingNewTemplate","showMinimalLanding","showHoneypotTrap","routes/_layout.r.$namespace.$reponame",{"_27":28,"_29":-5,"_30":11,"_31":11,"_32":33,"_34":35,"_36":-5,"_37":11,"_38":39,"_88":-5,"_89":90,"_128":-5,"_129":11,"_130":21,"_131":11,"_132":-5,"_133":11,"_134":-5,"_135":136,"_137":91,"_138":-7},"canonicalUrl","https://hub.docker.com/r/janka2012/digis/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","janka2012","reponame","digis","cloudBuildSource","isAutobuildsAvailable","repo",{"_40":41,"_42":43,"_45":46,"_47":48,"_49":11,"_50":51,"_52":53,"_54":55,"_57":35,"_32":33,"_58":59,"_60":44,"_61":62,"_63":64,"_65":66,"_67":-5,"_68":62,"_69":70,"_71":11,"_72":33,"_73":74,"_79":11,"_80":81,"_85":33,"_86":87},"categories",[],"content_types",[44],"image","date_registered","2019-08-22T08:45:52.944029Z","description","","is_private","last_updated","2021-06-10T19:18:04.373566Z","last_modified","2024-10-16T13:48:34.145251Z","media_types",[56],"application/vnd.docker.container.image.v1+json","name","pull_count",289,"repository_type","star_count",0,"status",1,"status_description","active","source","collaborator_count","full_description","# digIS\n\n\n**Table of content**\n\n\n * [Overview](#overview)\n * [Requirements](#requirements)\n * [Installation](#installation)\n * [Usage](#usage)\n * [Understanding Outputs](#understanding-outputs)\n * [Getting FASTA file using GFF file](#getting-fasta-file-using-gff-file)\n\n\n\n## Overview\ndigIS is a command-line tool developed in Python. It utilizes several external tools such as BLAST, HMMER, and Biopython library. As an input, digIS accepts contigs in FASTA format. Optionally, the user can provide a GenBank annotation file for a given input sequence(s). This annotation is later used to improve the classification of identified IS elements.\n\n\nThe digIS search pipeline operates in the following steps:\n1. The whole input nucleic acid sequence is translated into amino acid sequences (all six frames).\n2. The translated sequences are searched using manually curated pHMMs.\n3. Found hits, referred to as seeds, are filtered by domain bit score and e-value. Those that overlap or follow one another within a certain distance are merged.\n4. Each seed is matched against the database of known IS elements (ISfinder) and its genomic positions are extended according to the best hit.\n5. Extended seeds are filtered by noise cutoff score and length. Duplicates, corresponding to the same IS element, are removed.\n6. Remaining extended seeds are classified based on sequence similarity and GenBank annotation (if available) to assess their quality.\n7. Finally, the classified outputs are reported in the CSV and GFF3 format.\n\n## Requirements\n- HMMER 3.3 or higher, download the latest version from http://hmmer.org/download.html\n- ncbi-blast+ v 2.6.1 or higher, download the latest version from ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/\n- Python3\n- Biopython 1.73 or higher\n\n## Installation\n\n### Install dependencies using package manager (for Ubuntu)\n```bash\nsudo apt-get update\nsudo apt-get install hmmer\nsudo apt-get install ncbi-blast+\n\n# install python3 and pip3\nsudo apt-get install python3.7\nsudo apt install python3-pip\n\n# install Biopython, bcbio-gff, and numpy packages\npip3 install biopython\npip3 install bcbio-gff\npip3 install numpy\n```\n\n### Download digIS version from github repository\n```bash\n# download the latest version\ngit clone https://github.com/janka2012/digIS.git\n\n# or download specific release from https://github.com/janka2012/digIS/releases\nwget https://github.com/janka2012/digIS/archive/v1.0.tar.gz\ntar -xvzf v1.0.tar.gz\n\n# go to digIS directory\ncd digIS-v1.0\n```\n## Usage\n\n### Mode with GenBank annotation\n\n```bash\nexport PYTHONPATH=$PYTHONPATH:/path/to/digis/\npython3 digIS_search.py -i data/test_data/NC_002608.fasta -g data/test_data/NC_002608.gb -o digis_genbank\n```\n\n### Mode without GenBank annotation\n```bash\nexport PYTHONPATH=$PYTHONPATH:/path/to/digis/\npython3 digIS_search.py -i data/test_data/NC_002608.fasta -o digis_without_genbank\n```\n\n## Run digIS in docker container\n\n### Install docker\n\n```bash\n# update software repositories\nsudo apt-get update\n\n# uninstall older versions of docker\nsudo apt-get remove docker docker-engine docker.io\n\n# install docker\nsudo apt install docker.io\n\n# start and automate docker\nsudo systemctl start docker\nsudo systemctl enable docker\n\n# check docker version (optional)\ndocker --version\n```\n\n### Pull the docker image from Dockerhub\n\n```bash\ndocker pull janka2012/digis\n\n# List created docker images. You should see image with name digis listed.\ndocker images\nREPOSITORY TAG IMAGE ID CREATED SIZE\njanka2012/digis latest 1f09fc937ee1 14 minutes ago 765MB\n```\n\n### Run digIS using ```digis_docker_wrapper.sh```\nInstead of typing overwhelmingly long docker commands we are providing `digis_docker_wrapper.sh` script which allows you to use digIS docker image in really convinient way. This script takes same arguments as standard `digIS.py` script.\n\n```bash\nsh digis_docker_wrapper.sh -i data/test_data/NC_002608.fasta -g data/test_data/NC_002608.gb -o digis_genbank\n```\n\n## Understanding Outputs\n\n### digIS output directory structure\ndigIS stores all results in output directory you specify by using `-o` option. The default output directory name is set to `digIS_output`. The output directory has following structure:\n* `hmmer`: results from hmmer and phmmer search, separate file for each sequence in input (multi)fasta file \n* `logs`: \n* `pep`: translated protein sequences of the input genomic sequences\n* `results`: digIS results for all sequences in input (multi)fasta file in CSV and GFF3 format together with summarization statistics.\n * `input_filename.csv`: results in CSV format\n * `input_filename.gff`: results in GFF format\n * `input_filename.sum`: sumarization statistics\n\n### digIS output files in `results` subfolder\nFor a given input (multi)fasta file digIS generates three files with results: CSV file, GFF3 file and file with summary statistics.\n\n#### CSV output\n* `id`: unique record id\n* `level`: Extension level, possible values: IS, ORF, domain.\n* `qid`: name of the query profile\n* `qstart`: start position of the hit in the query profile\n* `qend`: end position of the hit in the query profile\n* `sid`: name of the subject sequence\n* `sstart`: start position of the hit in subject sequence\n* `send`: end position of the hit in subject sequence\n* `strand`: the strand on which the hit was found\n* `acc`: taken from HMMER. A measure of how reliable the overall alignment is (from 0 to 1, with 1.00 indicating a completely reliable alignment according to the model).\n* `score`: taken from HMMER. TODO\n* `evalue`: taken from HMMER. TODO\n* `ORF_sim`: TODO\n* `IS_sim`: TODO\n* `GenBank_class`: classification based on GenBank annotation. Possible values: is_related, no, other annotation. If GenBank annotation is not provided, this classification is not available and this field is empty.\n\n#### Example CSV output\n```\nid;level;qid;qstart;qend;sid;sstart;send;strand;acc;score;evalue;ORF_sim;IS_sim;GenBank_class\nNC_002608.1_000_is;is;IS200_IS605;1;113;NC_002608.1;181742;183592;-;0.98;105.7;4.9e-34;0.8923076923076924;0.8803465078505684;is_related\nNC_002608.1_001_is;is;IS200_IS605;1;113;NC_002608.1;154295;156130;-;0.98;117.5;1.1e-37;0.9922480620155039;1.0;is_related\n```\n\n#### GFF output\nThe GFF3 output file has the same content as the CSV output file, but in GFF3 format. Detailed description of GFF3 format can be found [here](http://gmod.org/wiki/GFF3).\n\n#### Example GFF3 output record\n```\nNC_002608.1 digIS transposable_element 309812 311213 1.0 - . id=NC_002608.1_13_is;level=is;qid=IS4Sa_ISH8_IS231_IS4;qstart=1;qend=226;class_sim_orf=strong;class_sim_is=strong;class_sim_all=strong;class_genebank=None;class_level=None\n```\n\n#### Summary statistics\n\nThis file is in tab-delimited format and contains six coloumns:\n * `seqid`: sequence ID\n * `family`: IS family name\n * `nIS`: number of IS elements per individual family\n * `bps`: overall number of base pairs of input sequence occupied by IS elements\n * `dnaLen`: length of the input sequence\n * `%dna`: overall percentage of input sequence occupied by IS elements\n\n## Getting FASTA file using GFF file\n\nThe [GFF](http://gmod.org/wiki/GFF3) is a standard format for storing of genome features. This file can be used as an input for other tools to process or visualize appropriate genomic features. \n\nFor instance, FASTA sequences of IS elements (their catalytic domains) can be obtained using [Bedtools](https://bedtools.readthedocs.io/en/latest/) and command `getfasta` as follows: \n\n```bash\nbedtools getfasta -fi -bed -fo \n```\nwhere _input.fasta_ represents FASTA file used for searching, _input.gff_ is the digIS output GFF file and _output.fasta_ is required output file.\n\n### Getting Flank Regions using GFF file\n\n```bash\nbedtools flank -i -g -l -r \n```\nwhere _genome.size_ is a text file containing information about chromosomes and their sizes in form: `chromosome_namechromosome_size`. For more information about _genome.size_ file format please see Bedtools [documentation](https://bedtools.readthedocs.io/en/latest/).\n\nAs the output a new GFF file with positions of flank regions is generated. Then, the appropriate FASTA file with flank sequences can be obtained using `bedtools getfasta` command described above. \n","has_starred","hub_user","immutable_tags_settings",{"_75":11,"_76":77},"enabled","rules",[78],".*","is_automated","permissions",{"_82":21,"_83":11,"_84":11},"read","write","admin","user","storage_size",1758637305,"orgData","recentTags",[91],{"_92":44,"_93":94,"_95":96,"_97":98,"_99":100,"_50":117,"_118":94,"_119":33,"_120":56,"_57":121,"_122":123,"_124":108,"_125":117,"_126":66,"_127":21},"content_type","creator",2596678,"full_size",247082392,"id",66401782,"images",[101],{"_102":103,"_104":105,"_106":48,"_107":108,"_109":110,"_111":112,"_113":48,"_114":-5,"_115":96,"_63":66,"_116":-5},"architecture","amd64","digest","sha256:ab8918e16428bef00728fa8ecc55e73f720601f853e38a549ba9bba850029cb4","features","last_pulled","2026-10-01T12:51:30.866672148Z","last_pushed","2021-06-10T19:18:05Z","os","linux","os_features","os_version","size","variant","2021-06-10T19:18:04.054778Z","last_updater","last_updater_username","media_type","latest","repository",7588574,"tag_last_pulled","tag_last_pushed","tag_status","v2","sbxKitMetadata","isPotentialSpam","isHubSbxKitsEnabled","showSbxLogoOverride","sbxTrademarkText","displaySecurityMetadata","tagAttestations","tagsHaveAttestations",[],"selectedTag","attestationsUrl","routes/_layout.r.$namespace.$reponame.tags",{"_38":141,"_148":149,"_135":158,"_159":11},{"_40":142,"_42":143,"_45":46,"_47":48,"_49":11,"_50":51,"_52":53,"_54":144,"_57":35,"_32":33,"_58":59,"_60":44,"_61":62,"_63":64,"_65":66,"_67":-5,"_68":62,"_69":70,"_71":11,"_72":33,"_73":145,"_79":11,"_80":147,"_85":33,"_86":87},[],[44],[56],{"_75":11,"_76":146},[78],{"_82":21,"_83":11,"_84":11},"tags",{"_150":64,"_151":-5,"_152":-5,"_153":154},"count","next","previous","results",[155],{"_92":44,"_93":94,"_95":96,"_97":98,"_99":156,"_50":117,"_118":94,"_119":33,"_120":56,"_57":121,"_122":123,"_124":108,"_125":117,"_126":66,"_127":21},[157],{"_102":103,"_104":105,"_106":48,"_107":108,"_109":110,"_111":112,"_113":48,"_114":-5,"_115":96,"_63":66,"_116":-5},[],"canDeleteTags","actionData","errors"]

janka2012/digis

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