{"id":44379443,"url":"https://github.com/matthiasblum/idrpred","last_synced_at":"2026-02-15T10:44:11.055Z","repository":{"id":205283344,"uuid":"713631556","full_name":"matthiasblum/idrpred","owner":"matthiasblum","description":"A consensus-based predictor of intrinsically disordered regions in proteins","archived":false,"fork":false,"pushed_at":"2025-07-29T10:59:06.000Z","size":305978,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-07-29T12:56:24.844Z","etag":null,"topics":["bioinformatics","sequence-analysis"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/matthiasblum.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2023-11-02T23:21:14.000Z","updated_at":"2025-07-29T10:59:12.000Z","dependencies_parsed_at":"2024-08-21T19:19:24.010Z","dependency_job_id":"692be012-1217-4429-8d9b-639502718f9a","html_url":"https://github.com/matthiasblum/idrpred","commit_stats":{"total_commits":6,"total_committers":1,"mean_commits":6.0,"dds":0.0,"last_synced_commit":"d5bb19d950cc06afce01cbbe410e6be5977e6f0f"},"previous_names":["matthiasblum/idrpred"],"tags_count":4,"template":false,"template_full_name":null,"purl":"pkg:github/matthiasblum/idrpred","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matthiasblum%2Fidrpred","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matthiasblum%2Fidrpred/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matthiasblum%2Fidrpred/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matthiasblum%2Fidrpred/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/matthiasblum","download_url":"https://codeload.github.com/matthiasblum/idrpred/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matthiasblum%2Fidrpred/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29476045,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-15T10:25:47.032Z","status":"ssl_error","status_checked_at":"2026-02-15T10:25:01.815Z","response_time":118,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bioinformatics","sequence-analysis"],"created_at":"2026-02-11T22:00:26.197Z","updated_at":"2026-02-15T10:44:11.050Z","avatar_url":"https://github.com/matthiasblum.png","language":"Python","funding_links":[],"categories":["EMEA"],"sub_categories":["Italy"],"readme":"# IDRPred\n\n[![DOI](https://zenodo.org/badge/713631556.svg)](https://zenodo.org/doi/10.5281/zenodo.13356721)\n[![Docker Image Version (tag)](https://img.shields.io/docker/v/matblum/idrpred/latest?label=Docker)](https://hub.docker.com/r/matblum/idrpred)\n\nIDRPred is a modern implementation of [MobiDB-lite](https://github.com/BioComputingUP/MobiDB-lite)[1], \na method for identifying intrinsically disordered regions (IDRs) in proteins.\nMobiDB-lite uses multiple predictors to derive a consensus, which is filtered \nfor spurious short predictions in a second step.\n\nThe main advantage of IDRPred is that it only requires Python 3 while MobiDB-lite requires both Python 2 and 3.\n\n## Installation\n\n```sh\npip install git+https://github.com/matthiasblum/idrpred\n```\n\n### Docker\n\nA Docker image of `idrpred` is available from [Docker Hub](https://hub.docker.com/r/matblum/idrpred).\n\n## Usage\n\n```sh\nidrpred [options] [infile] [outfile]\n```\n\nPositional arguments:\n\n* `infile`: The FASTA file of sequences to process. If `-` or not specified, read from standard input.\n* `outfile`: The TSV file of predicted intrinsically disordered regions. If `-` or not specified, write to standard output.\n\n### Available options\n\n| Options           | Description                                                                                     |\n|-------------------|-------------------------------------------------------------------------------------------------|\n| `--force`         | Derive a consensus as long as one predictor did not fail                                        |\n| `--skip-features` | Do not indentify sequence features, such as domains of low complexity                           |\n| `--round`         | Round scores reported by individual predictors, like MobiDB-lite does                           |\n| `--tempdir PATH`  | Create temporary files in PATH, instead of the default temporary directory (most likely `/tmp`) |\n| `--threads N`     | Process up to `N` sequences concurrently, default: `1`                                          |\n\n## Predictors\n\nOnly predictors whose licence authorises distribution have been included in IDRPred.\n\n| Method           | Reference |  Available  |\n|------------------|----------:|:-----------:|\n| ANCHOR           |       [2] |      ❌      |\n| DisEMBL-465 \t    |       [3] |      ✔      |\n| DisEMBL-HotLoops |       [3] |      ✔      |\n| DynaMine         |       [4] |      ❌      |\n| ESpritz-DisProt  |       [5] |      ✔      |\n| ESpritz-NMR      |       [5] |      ✔      |\n| ESpritz-Xray     |       [5] |      ✔      |\n| FeSS             |       [6] |      ❌      |\n| GlobPlot         |       [7] |      ✔      |\n| IUPred-Long      |       [8] |      ✔      |\n| IUPred-Short     |       [8] |      ✔      |\n| JRONN            |       [9] |      ❌      |\n| Pfilt            |      [10] |      ❌      |\n| SEG              |      [11] |      ✔      |\n| VSL2b            |      [12] |      ❌      |\n\n## Comparison\n\n### Annotations\n\n| Reference proteome | Sequences | Default options                                | IDRPred: `--round` option                            |\n|--------------------|----------:|------------------------------------------------|------------------------------------------------------|\n| *A. thaliana*      |    39,320 | ![](benchmarks/a-thaliana/predictions.png)     | ![](benchmarks/a-thaliana/predictions-round.png)     |\n| *D. melanogaster*  |    26,706 | ![](benchmarks/d-melanogaster/predictions.png) | ![](benchmarks/d-melanogaster/predictions-round.png) |\n| *E. Coli*          |     4,403 | ![](benchmarks/e-coli/predictions.png)         | ![](benchmarks/e-coli/predictions-round.png)         |\n| *H. Sapiens*       |    82,492 | ![](benchmarks/h-sapiens/predictions.png)      | ![](benchmarks/h-sapiens/predictions-round.png)      |\n| *S. cerevisiae*    |     6,060 | ![](benchmarks/s-cerevisiae/predictions.png)   | ![](benchmarks/s-cerevisiae/predictions-round.png)   |\n\n### Performances\n\n#### Single-threaded\n\nWall clock time to annotate common proteomes using one thread:\n\n\u003cp align=\"center\"\u003e\n    \u003cimg alt=\"single-thread-benchmark\" src=\"benchmarks/runtime-1-thread.png\" style=\"width: 80%;\"\u003e\n\u003c/p\u003e\n\n#### Multithreaded\n\nWall clock time to annotate common proteomes using eight threads:\n\n\u003cp align=\"center\"\u003e\n    \u003cimg alt=\"multi-thread-benchmark\" src=\"benchmarks/runtime-8-threads.png\" style=\"width: 80%;\"\u003e\n\u003c/p\u003e\n\nWall clock time to annotate one million sequences \nrandomly selected from [UniParc](https://www.uniprot.org/uniparc/) \nusing sixteen threads:\n\n\u003cp align=\"center\"\u003e\n    \u003cimg alt=\"multi-thread-benchmark\" src=\"benchmarks/runtime-16-threads.png\" style=\"width: 80%;\"\u003e\n\u003c/p\u003e\n\n## References\n\n1. Necci M, Piovesan D, Clementel D, Dosztányi Z, Tosatto SCE. MobiDB-lite 3.0: fast consensus annotation of intrinsic disorder flavors in proteins. Bioinformatics. 2021 Apr 1;36(22-23):5533-5534. DOI: [10.1093/bioinformatics/btaa1045](https://doi.org/10.1093/bioinformatics/btaa1045). PMID: [33325498](https://europepmc.org/article/MED/33325498).\n2. Dosztányi Z, Mészáros B, Simon I. ANCHOR: web server for predicting protein binding regions in disordered proteins. Bioinformatics. 2009 Oct 15;25(20):2745-6. DOI: [10.1093/bioinformatics/btp518](https://doi.org/10.1093/bioinformatics/btp518). Epub 2009 Aug 28. PMID: [19717576](https://europepmc.org/article/MED/19717576); PMCID: PMC2759549.\n3. Linding R, Jensen LJ, Diella F, Bork P, Gibson TJ, Russell RB. Protein disorder prediction: implications for structural proteomics. Structure. 2003 Nov;11(11):1453-9. DOI: [10.1016/j.str.2003.10.002](https://doi.org/10.1016/j.str.2003.10.002). PMID: [14604535](https://europepmc.org/article/MED/14604535).\n4. Cilia E, Pancsa R, Tompa P, Lenaerts T, Vranken WF. From protein sequence to dynamics and disorder with DynaMine. Nat Commun. 2013;4:2741. DOI: [10.1038/ncomms3741](https://doi.org/10.1038/ncomms3741). PMID: [24225580](https://europepmc.org/article/MED/24225580).\n5. Walsh I, Martin AJ, Di Domenico T, Tosatto SC. ESpritz: accurate and fast prediction of protein disorder. Bioinformatics. 2012 Feb 15;28(4):503-9. DOI: [10.1093/bioinformatics/btr682](https://doi.org/10.1093/bioinformatics/btr682). Epub 2011 Dec 20. PMID: [22190692](https://europepmc.org/article/MED/22190692).\n6. Piovesan D, Walsh I, Minervini G, Tosatto SCE. FELLS: fast estimator of latent local structure. Bioinformatics. 2017 Jun 15;33(12):1889-1891. DOI: [10.1093/bioinformatics/btx085](https://doi.org/10.1093/bioinformatics/btx085). PMID: [28186245](https://europepmc.org/article/MED/28186245).\n7. Linding R, Russell RB, Neduva V, Gibson TJ. GlobPlot: Exploring protein sequences for globularity and disorder. Nucleic Acids Res. 2003 Jul 1;31(13):3701-8. DOI: [10.1093/nar/gkg519](https://doi.org/10.1093/nar/gkg519). PMID: [12824398](https://europepmc.org/article/MED/12824398); PMCID: PMC169197.\n8. Mészáros B, Erdos G, Dosztányi Z. IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding. Nucleic Acids Res. 2018 Jul 2;46(W1):W329-W337. DOI: [10.1093/nar/gky384](https://doi.org/10.1093/nar/gky384). PMID: [29860432](https://europepmc.org/article/MED/29860432); PMCID: PMC6030935.\n9. Yang ZR, Thomson R, McNeil P, Esnouf RM. RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins. Bioinformatics. 2005 Aug 15;21(16):3369-76. DOI: [10.1093/bioinformatics/bti534](https://doi.org/10.1093/bioinformatics/bti534). Epub 2005 Jun 9. PMID: [15947016](https://europepmc.org/article/MED/15947016).\n10. Jones DT, Swindells MB. Getting the most from PSI-BLAST. Trends Biochem Sci. 2002 Mar;27(3):161-4. DOI: [10.1016/s0968-0004(01)02039-4](https://doi.org/10.1016/s0968-0004(01)02039-4). PMID: [11893514](https://europepmc.org/article/MED/11893514).\n11. Wootton JC. Non-globular domains in protein sequences: automated segmentation using complexity measures. Comput Chem. 1994 Sep;18(3):269-85. DOI: [10.1016/0097-8485(94)85023-2](https://doi.org/10.1016/0097-8485(94)85023-2). PMID: [7952898](https://europepmc.org/article/MED/7952898).\n12. Peng K, Radivojac P, Vucetic S, Dunker AK, Obradovic Z. Length-dependent prediction of protein intrinsic disorder. BMC Bioinformatics. 2006 Apr 17;7:208. DOI: [10.1186/1471-2105-7-208](https://doi.org/10.1186/1471-2105-7-208). PMID: [16618368](https://europepmc.org/article/MED/16618368); PMCID: PMC1479845.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatthiasblum%2Fidrpred","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmatthiasblum%2Fidrpred","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatthiasblum%2Fidrpred/lists"}