[{"_1":2,"_155":-5,"_156":-5},"loaderData",{"_3":4,"_13":14,"_21":22,"_134":135},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","routes/_layout",{"_15":-5,"_16":17,"_18":17,"_19":11,"_20":11},"username","isAskGordonEnabled",true,"isUsingNewTemplate","showMinimalLanding","showHoneypotTrap","routes/_layout.r.$namespace.$reponame",{"_23":24,"_25":-5,"_26":11,"_27":11,"_28":29,"_30":31,"_32":-5,"_33":11,"_34":35,"_84":-5,"_85":86,"_125":-5,"_126":11,"_127":17,"_128":11,"_129":-5,"_130":11,"_131":-5,"_132":133},"canonicalUrl","https://hub.docker.com/r/bioacc/chorus/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","bioacc","reponame","chorus","cloudBuildSource","isAutobuildsAvailable","repo",{"_36":37,"_38":39,"_41":42,"_43":44,"_45":11,"_46":47,"_48":49,"_50":51,"_53":31,"_28":29,"_54":55,"_56":40,"_57":58,"_59":60,"_61":62,"_63":-5,"_64":58,"_65":66,"_67":11,"_68":29,"_69":70,"_75":11,"_76":77,"_81":29,"_82":83},"categories",[],"content_types",[40],"image","date_registered","2023-08-22T19:22:54.609176Z","description","Chorus: Heterogeneous GPU+CPU Multiple Protein Sequences Alignment Search for Large Database","is_private","last_updated","2023-08-22T20:11:54.775389Z","last_modified","2024-10-16T13:48:34.145251Z","media_types",[52],"application/vnd.docker.container.image.v1+json","name","pull_count",70,"repository_type","star_count",0,"status",1,"status_description","active","source","collaborator_count","full_description","# Chorus: Heterogeneous GPU+CPU Multiple Protein Sequences Alignment Search for Large Database\n\nChorus is an efficient protein-protein local alignment search tool for multiple query sequences and large databases. Our method is up to 300x faster than NCBI-BLASTP when inputting multiple query seqs at a time, and can stay ahead of the state-of-the-art database-index methods when there are less than 5000 input query seqs (2.2-27x faster than fastest MMseqs2, 1.1-9x than fastest DIAMOND), while maintaining a low memory footprint. \n\n## Requirements\n\n\nHardware:\n\nA linux (ubuntu) server with a NVIDIA GPU and support nvidia-container \n\nSoftware: \n\n * docker\n * nvidia-container-toolkit\n\n## Quick Start\n\n### 1. Fetch container image \n\n```bash\ndocker pull bioacc/chorus:1.0.0\n```\n\n### 2. Tantan the database and query sequences (Optional)\n\n2.1. ~~Download and build tantan~~ Integrated in `chorus` container image now\n\n2.2. Tantan the database\n\n # suppose host file directory is /HOST_INPUT & /HOST_OUTPUT\n docker run -i -u $(id -u):$(id -g) --gpus=all -v /HOST_INPUT:/HOST_INPUT \\\n bioacc/chorus:1.0.0 tantan -p /HOST_INPUT/ > /HOST_OUTPUT/\n\n2.3. Tantan the query sequences\n\n docker run -i -u $(id -u):$(id -g) --gpus=all -v /HOST_INPUT:/HOST_INPUT \\\n bioacc/chorus:1.0.0 tantan -p /HOST_INPUT/ > /HOST_OUTPUT/\n\n### 3. Create database (process only once for each database)\n\nInput: A protein database file (in fasta format).\n\n docker run -i -u $(id -u):$(id -g) --gpus=all \\\n -v /HOST_INPUT:/HOST_INPUT \\\n -v /HOST_OUTPUT:/HOST_OUTPUT \\\n bioacc/chorus:1.0.0 createDB /HOST_INPUT/ /HOST_OUTPUT/ \n\nOutput: This step will create database files for each part, including \"dbnameX.seq\", \"dbnameX.name\", \"dbnameX.sofs\" and \"dbnameX.nofs\", to the same directory with the original db file.\n\nE.g.\n\n ./createDB ../db/nr.fasta ../db/nr 4\n\n### 4. Query the sequences\n\nInput: The pre-processed database in step 2. And a query sequences file (in fasta format).\n\n docker run -i -u $(id -u):$(id -g) --gpus=all \\\n -v /HOST_INPUT_QR:/HOST_INPUT_QR \\\n -v /HOST_INPUT_DB:/HOST_INPUT_DB \\\n -v /HOST_OUTPUT:/HOST_OUTPUT \\\n bioacc/chorus:1.0.0 query -q /HOST_INPUT_QR/ -d /HOST_INPUT_DB/ [db2 ... (optional)] -o /HOST_OUTPUT/\n\nChorus will automatically detect all parts of the database created in the same directory, iteratively process them, and finally show all results in the output file. \n\n\nE.g.\n\n ./query -d ../db/nr -q ../example_query -o res.out\n\n\n## More Arguments for Seaching\n\nPlease use \n\n docker run -i \\\n bioacc/chorus:1.0.0 query --help\n\nto get help message.\n\n| Arg | Description | Default |\n| --------------- | ------------------------------------------------------------------------------------------------------------------------------- | ------- |\n| --outfmt | Output format: 0: m8 tabular (like blast+ outfmt 6); 1: detailed alignment; 2: tabular and ref seqs (fasta); 3: Opfi format; 4: a3m format; 5: Diamond benchmark format. | 0 |\n| -l, --filter-level | The filter level to pass a database sequence to smith waterman align. The smaller the value, the more sensitive the result. (\"sensitivity\" refers to the ability of a sequence alignment algorithm to correctly identify and align similar or homologous sequences, especially those that share low levels of similarity or are distantly related.) | 1 |\n| --min-score | The minimum score for each alignment to display. | 0 |\n| -e, --max-evalue | The maximum expectation value for each alignment to display | 1e1 |\n| --max-output-align | The maximum number of alignments to display for each query. (0 means no limit) | 0 |\n| --num-threads | Number of CPU threads. | Maximum threads available |\n| -k, --seed-length | Length of k-mer. (3, 4, or 5) | 5 |\n| -w, --qit-width | Width of query index table. (Number of indices for each row) The larger the value, the less likely the overflow occurs. | 4 |\n| -h, --hash-size | Voting hash table size ratio. The larger the value, the larger the GPU voting hash table (doubly for each increment). (0: maybe OK; 1: OK for most query seqs; 2: redundancy) | 2 | \n| --band-width | The bandwidth in banded smith-waterman. The larger the value, the more sensitive the result. | 8 |\n| --must-include | Requires the ref sequence must match a regular expression n times. (e.g. --must-include \"R[A-Z]{4,7}H\" 2) | No limit |","has_starred","hub_user","immutable_tags_settings",{"_71":11,"_72":73},"enabled","rules",[74],".*","is_automated","permissions",{"_78":17,"_79":11,"_80":11},"read","write","admin","user","storage_size",1171694459,"orgData","recentTags",[87],{"_88":40,"_89":90,"_91":92,"_93":94,"_95":96,"_97":98,"_46":114,"_115":90,"_116":29,"_117":52,"_53":118,"_119":120,"_121":105,"_122":114,"_123":62,"_124":17},"content_type","creator",22960259,"digest","sha256:86d07546af2589cf69e37231f654061fca9659f527bbf30b7c7ebb9769d19e85","full_size",1171633032,"id",498600019,"images",[99],{"_100":101,"_91":92,"_102":103,"_104":105,"_106":107,"_108":109,"_110":103,"_111":-5,"_112":94,"_59":62,"_113":-5},"architecture","amd64","features","","last_pulled","2026-09-12T09:17:34.52165339Z","last_pushed","2023-08-22T20:11:45Z","os","linux","os_features","os_version","size","variant","2023-08-22T20:11:54.633985Z","last_updater","last_updater_username","media_type","1.0.0","repository",21664794,"tag_last_pulled","tag_last_pushed","tag_status","v2","sbxKitMetadata","isPotentialSpam","isHubSbxKitsEnabled","showSbxLogoOverride","sbxTrademarkText","displaySecurityMetadata","tagAttestations","tagsHaveAttestations",[],"routes/_layout.r.$namespace.$reponame.tags",{"_34":136,"_143":144,"_132":153,"_154":11},{"_36":137,"_38":138,"_41":42,"_43":44,"_45":11,"_46":47,"_48":49,"_50":139,"_53":31,"_28":29,"_54":55,"_56":40,"_57":58,"_59":60,"_61":62,"_63":-5,"_64":58,"_65":66,"_67":11,"_68":29,"_69":140,"_75":11,"_76":142,"_81":29,"_82":83},[],[40],[52],{"_71":11,"_72":141},[74],{"_78":17,"_79":11,"_80":11},"tags",{"_145":60,"_146":-5,"_147":-5,"_148":149},"count","next","previous","results",[150],{"_88":40,"_89":90,"_91":92,"_93":94,"_95":96,"_97":151,"_46":114,"_115":90,"_116":29,"_117":52,"_53":118,"_119":120,"_121":105,"_122":114,"_123":62,"_124":17},[152],{"_100":101,"_91":92,"_102":103,"_104":105,"_106":107,"_108":109,"_110":103,"_111":-5,"_112":94,"_59":62,"_113":-5},[],"canDeleteTags","actionData","errors"]

bioacc/chorus

Tags for bioacc/chorus