| class="wikitable sortable" |
| scope="col" data-sort-type="text" | Name
! scope="col" class="unsortable" | Description
! scope="col" class="unsortable" | References
! scope="col" class="unsortable" | URL
! scope="col" data-sort-type="number" | Year |
|---|
| AAIndexLoc | Machine-learning-based algorithm that uses amino acid index to predict protein subcellular localization based on its sequence. ([https://bio.tools/AAIndexLoc bio.tools entry]) | [{{cite journal | vauthors = Tantoso E, Li KB | title = AAIndexLoc: predicting subcellular localization of proteins based on a new representation of sequences using amino acid indices | journal = Amino Acids | volume = 35 | issue = 2 | pages = 345–53 | date = August 2008 | pmid = 18163182 | doi = 10.1007/s00726-007-0616-y | s2cid = 712299 }}] | http://aaindexloc.bii.a-star.edu.sg/
|2008 |
| APSLAP | Prediction of apoptosis protein sub cellular Localization
| [{{cite journal | vauthors = Saravanan V, Lakshmi PT | title = APSLAP: an adaptive boosting technique for predicting subcellular localization of apoptosis protein | journal = Acta Biotheoretica | volume = 61 | issue = 4 | pages = 481–97 | date = December 2013 | pmid = 23982307 | doi = 10.1007/s10441-013-9197-1 | s2cid = 23858443 }}]
|
|2013 |
| AtSubP | A highly accurate subcellular localization prediction tool for annotating the Arabidopsis thaliana proteome. ([https://bio.tools/AtSubP bio.tools entry]) | [{{cite journal | vauthors = Kaundal R, Saini R, Zhao PX | title = Combining machine learning and homology-based approaches to accurately predict subcellular localization in Arabidopsis | journal = Plant Physiology | volume = 154 | issue = 1 | pages = 36–54 | date = September 2010 | pmid = 20647376 | pmc = 2938157 | doi = 10.1104/pp.110.156851 }}] | http://bioinfo3.noble.org/AtSubP/
|2010 |
| BaCelLo | BaCelLo is a predictor for the subcellular localization of proteins in eukaryotes. ([https://bio.tools/bacello bio.tools entry]) | [{{cite journal | vauthors = Pierleoni A, Martelli PL, Fariselli P, Casadio R | title = BaCelLo: a balanced subcellular localization predictor | journal = Bioinformatics | volume = 22 | issue = 14 | pages = e408–16 | date = July 2006 | pmid = 16873501 | doi = 10.1093/bioinformatics/btl222 | doi-access = free }}] | http://gpcr.biocomp.unibo.it/bacello/index.htm
|2006 |
| BAR+ | BAR+ is a server for the structural and functional annotation of protein sequences ([https://bio.tools/bar bio.tools entry]) | [{{cite journal | vauthors = Piovesan D, Martelli PL, Fariselli P, Zauli A, Rossi I, Casadio R | title = BAR-PLUS: the Bologna Annotation Resource Plus for functional and structural annotation of protein sequences | journal = Nucleic Acids Research | volume = 39 | issue = Web Server issue | pages = W197–202 | date = July 2011 | pmid = 21622657 | pmc = 3125743 | doi = 10.1093/nar/gkr292 }}] | http://bar.biocomp.unibo.it/bar2.0/
|2011 |
| BAR | BAR 3.0 is a server for the annotation of protein sequences relying on a comparative large-scale analysis on the entire UniProt. With BAR 3.0 and a sequence you can annotate when possible: function (Gene Ontology), structure (Protein Data Bank), protein domains (Pfam). Also if your sequence falls into a cluster with a structural/some structural template/s we provide an alignment towards the template/templates based on the Cluster-HMM (HMM profile) that allows you to directly compute your 3D model. Cluster HMMs are available for downloading. ([https://bio.tools/BAR8984 bio.tools entry]) | [{{cite journal | vauthors = Profiti G, Martelli PL, Casadio R | title = The Bologna Annotation Resource (BAR 3.0): improving protein functional annotation | journal = Nucleic Acids Research | volume = 45 | issue = W1 | pages = W285–W290 | date = July 2017 | pmid = 28453653 | pmc = 5570247 | doi = 10.1093/nar/gkx330 }}] | https://bar.biocomp.unibo.it/bar3/
|2017 |
| BASys | BASys (Bacterial Annotation System) is a tool for automated annotation of bacterial genomic (chromosomal and plasmid) sequences including gene/protein names, GO functions, COG functions, possible paralogues and orthologues, molecular weights, isoelectric points, operon structures, subcellular localization, signal peptides, transmembrane regions, secondary structures, 3-D structures, reactions, and pathways. ([https://bio.tools/basys bio.tools entry]) | [{{cite journal | vauthors = Van Domselaar GH, Stothard P, Shrivastava S, Cruz JA, Guo A, Dong X, Lu P, Szafron D, Greiner R, Wishart DS|author10-link=David S. Wishart | title = BASys: a web server for automated bacterial genome annotation | journal = Nucleic Acids Research | volume = 33 | issue = Web Server issue | pages = W455–9 | date = July 2005 | pmid = 15980511 | pmc = 1160269 | doi = 10.1093/nar/gki593 }}] | http://basys.ca
|2005 |
| BOMP | The beta-barrel Outer Membrane protein Predictor (BOMP) takes one or more fasta-formatted polypeptide sequences from Gram-negative bacteria as input and predicts whether or not they are beta-barrel integral outer membrane proteins. ([https://bio.tools/bomp bio.tools entry]) | [{{cite journal | vauthors = Berven FS, Flikka K, Jensen HB, Eidhammer I | title = BOMP: a program to predict integral beta-barrel outer membrane proteins encoded within genomes of Gram-negative bacteria | journal = Nucleic Acids Research | volume = 32 | issue = Web Server issue | pages = W394–9 | date = July 2004 | pmid = 15215418 | pmc = 441489 | doi = 10.1093/nar/gkh351 }}] | http://www.bioinfo.no/tools/bomp
|2004 |
| BPROMPT | Bayesian PRediction Of Membrane Protein Topology (BPROMPT) uses a Bayesian Belief Network to combine the results of other membrane protein prediction methods for a protein sequence. ([https://bio.tools/bprompt bio.tools entry]) | [{{cite journal | vauthors = Taylor PD, Attwood TK, Flower DR | title = BPROMPT: A consensus server for membrane protein prediction | journal = Nucleic Acids Research | volume = 31 | issue = 13 | pages = 3698–700 | date = July 2003 | pmid = 12824397 | pmc = 168961 | doi = 10.1093/nar/gkg554 }}] | http://www.ddg-pharmfac.net/bprompt/BPROMPT/BPROMPT.html
|2003 |
| BUSCA | BUSCA (Bologna Unified Subcellular Component Annotator) is a web-server for predicting protein subcellular localization. BUSCA integrates different tools to predict localization-related protein features as well as tools for discriminating subcellular localization of both globular and membrane proteins. ([https://bio.tools/busca bio.tools entry]) | [{{cite journal | vauthors = Savojard C, Martelli PL, Fariselli P, Profiti G, Casadio R | title = BUSCA: an integrative web server to predict subcellular localization of proteins | journal = Nucleic Acids Research | volume = 46 | issue = W1 | pages = W459–W466 | date = July 2018 | pmid = 29718411 | pmc = 6031068 | doi = 10.1093/nar/gky320 }}] | https://busca.biocomp.unibo.it/
|2018 |
| Cell-PLoc | A package of web-servers for predicting subcellular localization of proteins in various organisms. | [{{cite journal | vauthors = Chou KC, Shen HB | title = Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms | journal = Nature Protocols | volume = 3 | issue = 2 | pages = 153–62 | date = 2008-01-01 | pmid = 18274516 | doi = 10.1038/nprot.2007.494 | s2cid = 226104 }}]
|
|2008 |
| CELLO | CELLO uses a two-level Support Vector Machine system to assign localizations to both prokaryotic and eukaryotic proteins. | [{{cite journal | vauthors = Yu CS, Lin CJ, Hwang JK | title = Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions | journal = Protein Science | volume = 13 | issue = 5 | pages = 1402–6 | date = May 2004 | pmid = 15096640 | pmc = 2286765 | doi = 10.1110/ps.03479604 }}][{{cite journal | vauthors = Yu CS, Chen YC, Lu CH, Hwang JK | title = Prediction of protein subcellular localization | journal = Proteins | volume = 64 | issue = 3 | pages = 643–51 | date = August 2006 | pmid = 16752418 | doi = 10.1002/prot.21018 | s2cid = 24245346 }}]
|
|2006 |
| ClubSub-P | ClubSub-P is a database of cluster-based subcellular localization (SCL) predictions for Archaea and Gram negative bacteria. | [{{cite journal | vauthors = Paramasivam N, Linke D | title = ClubSub-P: Cluster-Based Subcellular Localization Prediction for Gram-Negative Bacteria and Archaea | journal = Frontiers in Microbiology | volume = 2 | pages = 218 | year = 2011 | pmid = 22073040 | pmc = 3210502 | doi = 10.3389/fmicb.2011.00218 | doi-access = free }}]
|
|2011 |
| CoBaltDB | CoBaltDB is a novel powerful platform that provides easy access to the results of multiple localization tools and support for predicting prokaryotic protein localizations. | [{{cite journal | vauthors = Goudenège D, Avner S, Lucchetti-Miganeh C, Barloy-Hubler F | title = CoBaltDB: Complete bacterial and archaeal orfeomes subcellular localization database and associated resources | journal = BMC Microbiology | volume = 10 | pages = 88 | date = March 2010 | pmid = 20331850 | pmc = 2850352 | doi = 10.1186/1471-2180-10-88 | doi-access = free }}]
|
|2010 |
| ComiR | ComiR is a web tool for combinatorial microRNA (miRNA) target prediction. Given an messenger RNA (mRNA) in human, mouse, fly or worm genomes, ComiR predicts whether a given mRNA is targeted by a set of miRNAs. ([https://bio.tools/comir bio.tools entry]) | [{{cite journal | vauthors = Coronnello C, Benos PV | title = ComiR: Combinatorial microRNA target prediction tool | journal = Nucleic Acids Research | volume = 41 | issue = Web Server issue | pages = W159–64 | date = July 2013 | pmid = 23703208 | pmc = 3692082 | doi = 10.1093/nar/gkt379 }}] | http://www.benoslab.pitt.edu/comir/
|2013 |
| cropPAL | A data portal to access the compendium of data on crop protein subcellular locations. ([https://bio.tools/croppal bio.tools entry]) | [{{cite journal | vauthors = Hooper CM, Castleden IR, Aryamanesh N, Jacoby RP, Millar AH | title = Finding the Subcellular Location of Barley, Wheat, Rice and Maize Proteins: The Compendium of Crop Proteins with Annotated Locations (cropPAL) | journal = Plant & Cell Physiology | volume = 57 | issue = 1 | pages = e9 | date = January 2016 | pmid = 26556651 | doi = 10.1093/pcp/pcv170 | doi-access = free }}] | http://crop-pal.org/
|2016 |
| DAS-TMfilter | DAS (Dense Alignment Surface) is based on low-stringency dot-plots of the query sequence against a set of library sequences - non-homologous membrane proteins - using a previously derived, special scoring matrix. The method provides a high precision hydrophobicity profile for the query from which the location of the potential transmembrane segments can be obtained. The novelty of the DAS-TMfilter algorithm is a second prediction cycle to predict TM segments in the sequences of the TM-library. ([https://bio.tools/DAS-TMfilter bio.tools entry]) | [{{Cite journal|last1=Cserzö|first1=Miklos|last2=Eisenhaber|first2=Frank|last3=Eisenhaber|first3=Birgit|last4=Simon|first4=Istvan|date=Sep 2002|title=On filtering false positive transmembrane protein predictions|journal=Protein Engineering, Design and Selection|volume=15|issue=9|pages=745–752|doi=10.1093/protein/15.9.745|pmid=12456873|issn=1741-0134|doi-access=free}}]
| http://mendel.imp.ac.at/sat/DAS/DAS.html {{Webarchive|url=https://web.archive.org/web/20180205151802/http://mendel.imp.ac.at/sat/DAS/DAS.html |date=2018-02-05 }}
|2002 |
| DeepLoc | Prediction of eukaryotic protein subcellular localization using deep learning ([https://bio.tools/DeepLoc bio.tools entry]) | [{{cite journal | vauthors = Almagro Armenteros JJ, Sønderby CK, Sønderby SK, Nielsen H, Winther O | title = DeepLoc: prediction of protein subcellular localization using deep learning | journal = Bioinformatics | volume = 33 | issue = 21 | pages = 3387–3395 | date = November 2017 | pmid = 29036616 | doi = 10.1093/bioinformatics/btx431 | doi-access = free }}] | http://www.cbs.dtu.dk/services/DeepLoc/
|2017 |
| Light Attention | Deep learning architecture for predicting eukaryotic subcellular localization and web server which predicts 10 locations for arbitrary amounts of sequences that can be uploaded as .fasta or copy-pasted ([https://bio.tools/light_attention_subcellular_localization bio.tools entry]) | [{{cite journal | vauthors = Stärk H, Dallago C, Heinzinger M, Rost B | title = Light Attention Predicts Protein Location from the Language of Life | journal = Bioinformatics Advances | volume = 1 | issue = 1 | date = November 2021 | pages = vbab035 | doi = 10.1093/bioadv/vbab035 | pmid = 36700108 | pmc = 9710637 | doi-access = free }}] | https://github.com/HannesStark/protein-localization
|2021 |
| DIANA-microT v5.0 | Web server which predicts targets for miRNAs and provides functional information on the predicted miRNA:target gene interaction from various online biological resources. Updates enable the association of miRNAs to diseases through bibliographic analysis and connection to the UCSC genome browser. Updates include sophisticated workflows. ([https://bio.tools/diana-microt bio.tools entry]) | [{{cite journal | vauthors = Maragkakis M, Reczko M, Simossis VA, Alexiou P, Papadopoulos GL, Dalamagas T, Giannopoulos G, Goumas G, Koukis E, Kourtis K, Vergoulis T, Koziris N, Sellis T, Tsanakas P, Hatzigeorgiou AG | title = DIANA-microT web server: elucidating microRNA functions through target prediction | journal = Nucleic Acids Research | volume = 37 | issue = Web Server issue | pages = W273–6 | date = July 2009 | pmid = 19406924 | pmc = 2703977 | doi = 10.1093/nar/gkp292 }}][{{cite journal | vauthors = Paraskevopoulou MD, Georgakilas G, Kostoulas N, Vlachos IS, Vergoulis T, Reczko M, Filippidis C, Dalamagas T, Hatzigeorgiou AG | title = DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows | journal = Nucleic Acids Research | volume = 41 | issue = Web Server issue | pages = W169–73 | date = July 2013 | pmid = 23680784 | pmc = 3692048 | doi = 10.1093/nar/gkt393 }}] | http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=MicroT_CDS/index
|2013 |
| DrugBank | DrugBank is a unique bioinformatics/cheminformatics resource that combines detailed drug (i.e. chemical) data with comprehensive drug target (i.e. protein) information. The database contains >4100 drug entries including >800 FDA approved small molecule and biotech drugs as well as >3200 experimental drugs. Additionally, >14,000 protein or drug target sequences are linked to these drug entries. ([https://bio.tools/drugbank bio.tools entry]) | [{{cite journal | vauthors = Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J | title = DrugBank: a comprehensive resource for in silico drug discovery and exploration | journal = Nucleic Acids Research | volume = 34 | issue = Database issue | pages = D668–72 | date = January 2006 | pmid = 16381955 | pmc = 1347430 | doi = 10.1093/nar/gkj067 }}] | http://redpoll.pharmacy.ualberta.ca/drugbank/index.html
|2006 |
| E.Coli Index | Comprehensive guide of information relating to E. coli; home of Echobase: a database of E. coli genes characterized since the completion of the genome. ([https://bio.tools/E.Coli_Index bio.tools entry]) | [{{cite journal | vauthors = Horler RS, Butcher A, Papangelopoulos N, Ashton PD, Thomas GH | title = EchoLOCATION: an in silico analysis of the subcellular locations of Escherichia coli proteins and comparison with experimentally derived locations | journal = Bioinformatics | volume = 25 | issue = 2 | pages = 163–6 | date = January 2009 | pmid = 19015139 | doi = 10.1093/bioinformatics/btn596 | doi-access = free }}] | http://www.york.ac.uk/res/thomas/
|2009 |
| ePlant | A suite of open-source world wide web-based tools for the visualization of large-scale data sets from the model organism Arabidopsis thaliana. It can be applied to any model organism. Currently has 3 modules: a sequence conservation explorer that includes homology relationships and single nucleotide polymorphism data, a protein structure model explorer, a molecular interaction network explorer, a gene product subcellular localization explorer, and a gene expression pattern explorer. ([https://bio.tools/eplant bio.tools entry]) | [{{cite journal | vauthors = Fucile G, Di Biase D, Nahal H, La G, Khodabandeh S, Chen Y, Easley K, Christendat D, Kelley L, Provart NJ | title = ePlant and the 3D data display initiative: integrative systems biology on the world wide web | journal = PLOS ONE | volume = 6 | issue = 1 | pages = e15237 | date = January 2011 | pmid = 21249219 | pmc = 3018417 | doi = 10.1371/journal.pone.0015237 | bibcode = 2011PLoSO...615237F | doi-access = free }}] | http://bar.utoronto.ca/eplant/
|2011 |
| ESLpred | ESLpred is a tool for predicting subcellular localization of proteins using support vector machines. The predictions are based on dipeptide and amino acid composition, and physico-chemical properties. ([https://bio.tools/eslpred bio.tools entry]) | [{{cite journal | vauthors = Bhasin M, Raghava GP | title = ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST | journal = Nucleic Acids Research | volume = 32 | issue = Web Server issue | pages = W414–9 | date = July 2004 | pmid = 15215421 | pmc = 441488 | doi = 10.1093/nar/gkh350 }}] | http://www.imtech.res.in/raghava/eslpred/
|2004 |
| Euk-mPLoc 2.0 | Predicting the subcellular localization of eukaryotic proteins with both single and multiple sites. | [{{cite journal | vauthors = Chou KC, Shen HB | title = A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0 | journal = PLOS ONE | volume = 5 | issue = 4 | pages = e9931 | date = April 2010 | pmid = 20368981 | pmc = 2848569 | doi = 10.1371/journal.pone.0009931 | bibcode = 2010PLoSO...5.9931C | doi-access = free }}]
|
|2010 |
| HIT | A comprehensive and fully curated database for Herb Ingredients?? Targets (HIT). Those herbal ingredients with protein target information were carefully curated. The molecular target information involves those proteins being directly/indirectly activated/inhibited, protein binders and enzymes whose substrates or products are those compounds. Those up/down regulated genes are also included under the treatment of individual ingredients. In addition, the experimental condition, observed bioactivity and various references are provided as well for user??s reference. The database can be queried via keyword search or similarity search. Crosslinks have been made to TTD, DrugBank, KEGG, PDB, Uniprot, Pfam, NCBI, TCM-ID and other databases. ([https://bio.tools/hit bio.tools entry]) | [{{cite journal | vauthors = Ye H, Ye L, Kang H, Zhang D, Tao L, Tang K, Liu X, Zhu R, Liu Q, Chen YZ, Li Y, Cao Z | title = HIT: linking herbal active ingredients to targets | journal = Nucleic Acids Research | volume = 39 | issue = Database issue | pages = D1055–9 | date = January 2011 | pmid = 21097881 | pmc = 3013727 | doi = 10.1093/nar/gkq1165 }}] | http://lifecenter.sgst.cn/hit/
|2011 |
| HMMTOP | Prediction of transmembranes helices and topology of proteins. ([https://bio.tools/HMMTOP bio.tools entry]) | [{{Cite journal|last1=Tusnády|first1=Gábor E.|last2=Simon|first2=István|date=Oct 1998|title=Principles governing amino acid composition of integral membrane proteins: application to topology prediction 1 1Edited by J. Thornton|journal=Journal of Molecular Biology|volume=283|issue=2|pages=489–506|doi=10.1006/jmbi.1998.2107|pmid=9769220|issn=0022-2836}}][{{Cite journal|last1=Tusnady|first1=G. E.|last2=Simon|first2=I.|date=2001-09-01|title=The HMMTOP transmembrane topology prediction server|journal=Bioinformatics|volume=17|issue=9|pages=849–850|doi=10.1093/bioinformatics/17.9.849|pmid=11590105|issn=1367-4803|doi-access=free}}]
| http://www.enzim.hu/hmmtop/
|2001 |
| HSLpred | Allows predicting the subcellular localization of human proteins. This is based on various type of residue composition of proteins using SVM technique. ([https://bio.tools/HSLpred bio.tools entry]) | [{{cite journal | vauthors = Garg A, Bhasin M, Raghava GP | title = Support vector machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search | journal = The Journal of Biological Chemistry | volume = 280 | issue = 15 | pages = 14427–32 | date = April 2005 | pmid = 15647269 | doi = 10.1074/jbc.M411789200 | doi-access = free }}] | http://www.imtech.res.in/raghava/hslpred/
|2005 |
| idTarget | idTarget is a web server for identifying biomolecular targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach. idTarget screens against protein structures in PDB. ([https://bio.tools/idtarget bio.tools entry]) | [{{cite journal | vauthors = Wang JC, Chu PY, Chen CM, Lin JH | title = idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach | journal = Nucleic Acids Research | volume = 40 | issue = Web Server issue | pages = W393–9 | date = July 2012 | pmid = 22649057 | pmc = 3394295 | doi = 10.1093/nar/gks496 }}] | http://idtarget.rcas.sinica.edu.tw
|2012 |
| iLoc-Cell | Predictor for subcellular locations of human proteins with multiple sites. ([https://bio.tools/iLoc-Cell bio.tools entry]) | [{{cite journal | vauthors = Chou KC, Wu ZC, Xiao X | title = iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites | journal = Molecular BioSystems | volume = 8 | issue = 2 | pages = 629–41 | date = February 2012 | pmid = 22134333 | doi = 10.1039/c1mb05420a }}] | http://www.jci-bioinfo.cn/iLoc-Hum
|2012 |
| KnowPredsite | A knowledge-based approach to predict the localization site(s) of both single-localized and multi-localized proteins for all eukaryotes. | [{{cite journal | vauthors = Lin HN, Chen CT, Sung TY, Ho SY, Hsu WL | title = Protein subcellular localization prediction of eukaryotes using a knowledge-based approach | journal = BMC Bioinformatics | volume = 10 | pages = S8 | date = December 2009 | issue = Suppl 15 | pmid = 19958518 | pmc = 2788359 | doi = 10.1186/1471-2105-10-S15-S8 | doi-access = free }}]
|
|2009 |
| lncRNAdb | lncRNAdb database contains a comprehensive list of long noncoding RNAs (lncRNAs) that have been shown to have, or to be associated with, biological functions in eukaryotes, as well as messenger RNAs that have regulatory roles. Each entry contains referenced information about the RNA, including sequences, structural information, genomic context, expression, subcellular localization, conservation, functional evidence and other relevant information. lncRNAdb can be searched by querying published RNA names and aliases, sequences, species and associated protein-coding genes, as well as terms contained in the annotations, such as the tissues in which the transcripts are expressed and associated diseases. In addition, lncRNAdb is linked to the UCSC Genome Browser for visualization and Noncoding RNA Expression Database (NRED) for expression information from a variety of sources. ([https://bio.tools/lncrnadb bio.tools entry]) | [{{cite journal | vauthors = Amaral PP, Clark MB, Gascoigne DK, Dinger ME, Mattick JS | title = lncRNAdb: a reference database for long noncoding RNAs | journal = Nucleic Acids Research | volume = 39 | issue = Database issue | pages = D146–51 | date = January 2011 | pmid = 21112873 | pmc = 3013714 | doi = 10.1093/nar/gkq1138 }}] | http://arquivo.pt/wayback/20160516021755/http://www.lncrnadb.org/
|2011 |
| Loc3D | LOC3D is a database of predicted subcellular localization for eukaryotic proteins of known three-dimensional (3D) structure and includes tools to predict the subcellular localization for submitted protein sequences. ([https://bio.tools/loc3d bio.tools entry]) | [{{cite journal | vauthors = Nair R, Rost B | title = LOC3D: annotate sub-cellular localization for protein structures | journal = Nucleic Acids Research | volume = 31 | issue = 13 | pages = 3337–40 | date = July 2003 | pmid = 12824321 | pmc = 168921 | doi = 10.1093/nar/gkg514 }}][{{cite journal | vauthors = Nair R, Rost B | title = Better prediction of sub-cellular localization by combining evolutionary and structural information | journal = Proteins | volume = 53 | issue = 4 | pages = 917–30 | date = December 2003 | pmid = 14635133 | doi = 10.1002/prot.10507 | citeseerx = 10.1.1.217.389 | s2cid = 3142627 }}] | http://cubic.bioc.columbia.edu/db/LOC3d/
|2005 |
| LOCATE | LOCATE is a curated database that houses data describing the membrane organization and subcellular localization of mouse proteins. ([https://bio.tools/locate bio.tools entry]) | [{{cite journal | vauthors = Fink JL, Aturaliya RN, Davis MJ, Zhang F, Hanson K, Teasdale MS, Kai C, Kawai J, Carninci P, Hayashizaki Y, Teasdale RD | title = LOCATE: a mouse protein subcellular localization database | journal = Nucleic Acids Research | volume = 34 | issue = Database issue | pages = D213–7 | date = January 2006 | pmid = 16381849 | pmc = 1347432 | doi = 10.1093/nar/gkj069 }}] | https://web.archive.org/web/20171231015119/http://locate.imb.uq.edu.au/
|2006 |
| LocDB | LocDB is a manually curated database with experimental annotations for the subcellular localizations of proteins in Homo sapiens (HS, human) and Arabidopsis thaliana (AT, thale cress). Each database entry contains the experimentally derived localization in Gene Ontology (GO) terminology, the experimental annotation of localization, localization predictions by state-of-the-art methods and, where available, the type of experimental information. LocDB is searchable by keyword, protein name and subcellular compartment, as well as by identifiers from UniProt, Ensembl and TAIR resources. ([https://bio.tools/locdb bio.tools entry]) | [{{cite journal | vauthors = Rastogi S, Rost B | title = LocDB: experimental annotations of localization for Homo sapiens and Arabidopsis thaliana | journal = Nucleic Acids Research | volume = 39 | issue = Database issue | pages = D230–4 | date = January 2011 | pmid = 21071420 | pmc = 3013784 | doi = 10.1093/nar/gkq927 }}] | http://www.rostlab.org/services/locDB/
|2011 |
| LOCtarget | LOCtarget is a tool for predicting, and a database of pre-computed predictions for, sub-cellular localization of eukaryotic and prokaryotic proteins. Several methods are employed to make the predictions, including text analysis of SWISS-PROT keywords, nuclear localization signals, and the use of neural networks. ([https://bio.tools/loctarget bio.tools entry]) | [{{cite journal | vauthors = Nair R, Rost B | title = LOCnet and LOCtarget: sub-cellular localization for structural genomics targets | journal = Nucleic Acids Research | volume = 32 | issue = Web Server issue | pages = W517–21 | date = July 2004 | pmid = 15215440 | pmc = 441579 | doi = 10.1093/nar/gkh441 }}] | http://www.rostlab.org/services/LOCtarget/
|2004 |
| LOCtree | Prediction based on mimicking the cellular sorting mechanism using a hierarchical implementation of support vector machines. LOCtree is a comprehensive predictor incorporating predictions based on PROSITE/PFAM signatures as well as SwissProt keywords. | [{{cite journal | vauthors = Nair R, Rost B | title = Mimicking cellular sorting improves prediction of subcellular localization | journal = Journal of Molecular Biology | volume = 348 | issue = 1 | pages = 85–100 | date = April 2005 | pmid = 15808855 | doi = 10.1016/j.jmb.2005.02.025 }}]
|
|2005 |
| LocTree2 | Framework to predict localization in life's three domains, including globular and membrane proteins (3 classes for archaea; 6 for bacteria and 18 for eukaryota). The resulting method, LocTree2, works well even for protein fragments. It uses a hierarchical system of support vector machines that imitates the cascading mechanism of cellular sorting. The method reaches high levels of sustained performance (eukaryota: Q18=65%, bacteria: Q6=84%). LocTree2 also accurately distinguishes membrane and non-membrane proteins. In our hands, it compared favorably with top methods when tested on new data ([https://bio.tools/loctree2 bio.tools entry]) | [{{cite journal | vauthors = Goldberg T, Hamp T, Rost B | title = LocTree2 predicts localization for all domains of life | journal = Bioinformatics | volume = 28 | issue = 18 | pages = i458–i465 | date = September 2012 | pmid = 22962467 | pmc = 3436817 | doi = 10.1093/bioinformatics/bts390 }}] | https://rostlab.org/owiki/index.php/Loctree2
|2012 |
| LocTree3 | Prediction of protein subcellular localization in 18 classes for eukaryota, 6 for bacteria and 3 for archaea ([https://bio.tools/loctree3 bio.tools entry]) | [{{cite journal | vauthors = Goldberg T, Hecht M, Hamp T, Karl T, Yachdav G, Ahmed N, Altermann U, Angerer P, Ansorge S, Balasz K, Bernhofer M, Betz A, Cizmadija L, Do KT, Gerke J, Greil R, Joerdens V, Hastreiter M, Hembach K, Herzog M, Kalemanov M, Kluge M, Meier A, Nasir H, Neumaier U, Prade V, Reeb J, Sorokoumov A, Troshani I, Vorberg S, Waldraff S, Zierer J, Nielsen H, Rost B | title = LocTree3 prediction of localization | journal = Nucleic Acids Research | volume = 42 | issue = Web Server issue | pages = W350–5 | date = July 2014 | pmid = 24848019 | pmc = 4086075 | doi = 10.1093/nar/gku396 }}] | https://rostlab.org/services/loctree3/
|2014 |
| MARSpred | Prediction method  for discrimination between Mitochondrial-AARSs and Cytosolic-AARSs. ([https://bio.tools/MARSpred bio.tools entry]) | [{{cite journal | vauthors = Panwar B, Raghava GP | title = Predicting sub-cellular localization of tRNA synthetases from their primary structures | journal = Amino Acids | volume = 42 | issue = 5 | pages = 1703–13 | date = May 2012 | pmid = 21400228 | doi = 10.1007/s00726-011-0872-8 | s2cid = 2996097 }}] | http://www.imtech.res.in/raghava/marspred/
|2012 |
| MDLoc | Dependency-Based Protein Subcellular Location Predictor. ([https://bio.tools/MDLoc bio.tools entry]) | [{{cite journal | vauthors = Simha R, Briesemeister S, Kohlbacher O, Shatkay H | title = Protein (multi-)location prediction: utilizing interdependencies via a generative model | journal = Bioinformatics | volume = 31 | issue = 12 | pages = i365–74 | date = June 2015 | pmid = 26072505 | pmc = 4765880 | doi = 10.1093/bioinformatics/btv264 }}] | http://128.4.31.235/
|2015 |
| MemLoci | Predictor for the subcellular localization of proteins associated or inserted in eukaryotes membranes. ([https://bio.tools/memloci bio.tools entry]) | [{{cite journal | vauthors = Pierleoni A, Martelli PL, Casadio R | title = MemLoci: predicting subcellular localization of membrane proteins in eukaryotes | journal = Bioinformatics | volume = 27 | issue = 9 | pages = 1224–30 | date = May 2011 | pmid = 21367869 | doi = 10.1093/bioinformatics/btr108 | doi-access = free }}] | https://mu2py.biocomp.unibo.it/memloci
|2011 |
| MemPype | Prediction of topology and subcellular localization of Eukaryotic membrane proteins. ([https://bio.tools/mempype bio.tools entry]) | [{{cite journal | vauthors = Pierleoni A, Indio V, Savojardo C, Fariselli P, Martelli PL, Casadio R | title = MemPype: a pipeline for the annotation of eukaryotic membrane proteins | journal = Nucleic Acids Research | volume = 39 | issue = Web Server issue | pages = W375–80 | date = July 2011 | pmid = 21543452 | pmc = 3125734 | doi = 10.1093/nar/gkr282 }}] | https://mu2py.biocomp.unibo.it/mempype
|2011 |
| MetaLocGramN | Meta subcellular localization predictor of Gram-negative protein. MetaLocGramN is a gateway to a number of primary prediction methods (various types: signal peptide, beta-barrel, transmembrane helices and subcellular localization predictors). In author's benchmark, MetaLocGramN performed better in comparison to other SCL predictive methods, since the average Matthews correlation coefficient reached 0.806 that enhanced the predictive capability by 12% (compared to PSORTb3). MetaLocGramN can be run via SOAP. | [{{cite journal | vauthors = Magnus M, Pawlowski M, Bujnicki JM | title = MetaLocGramN: A meta-predictor of protein subcellular localization for Gram-negative bacteria | journal = Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics | volume = 1824 | issue = 12 | pages = 1425–33 | date = December 2012 | pmid = 22705560 | doi = 10.1016/j.bbapap.2012.05.018 }}]
|
|2012 |
| MirZ | MirZ is a web server that for evaluation and analysis of miRNA. It integrates two miRNA resources: the smiRNAdb miRNA expression atlas and the E1MMo miRNA target prediction algorithm. ([https://bio.tools/mirz bio.tools entry]) | [{{cite journal | vauthors = Hausser J, Berninger P, Rodak C, Jantscher Y, Wirth S, Zavolan M | title = MirZ: an integrated microRNA expression atlas and target prediction resource | journal = Nucleic Acids Research | volume = 37 | issue = Web Server issue | pages = W266–72 | date = July 2009 | pmid = 19468042 | pmc = 2703880 | doi = 10.1093/nar/gkp412 }}] | http://www.mirz.unibas.ch
|2009 |
| MitPred | Web-server specifically trained to predict the proteins which are destined to localized in mitochondria in yeast and animals particularly. ([https://bio.tools/MitPred bio.tools entry]) | [{{cite journal | vauthors = Kumar M, Verma R, Raghava GP | title = Prediction of mitochondrial proteins using support vector machine and hidden Markov model | journal = The Journal of Biological Chemistry | volume = 281 | issue = 9 | pages = 5357–63 | date = March 2006 | pmid = 16339140 | doi = 10.1074/jbc.M511061200 | doi-access = free }}] | http://www.imtech.res.in/raghava/mitpred/
|2006 |
| MultiLoc | An SVM-based prediction engine for a wide range of subcellular locations. | [{{cite journal | vauthors = Höglund A, Dönnes P, Blum T, Adolph HW, Kohlbacher O | title = MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition | journal = Bioinformatics | volume = 22 | issue = 10 | pages = 1158–65 | date = May 2006 | pmid = 16428265 | doi = 10.1093/bioinformatics/btl002 | doi-access = free }}]
|
|2006 |
| Mycosub | This web-server was used to predict the subcellular localizations of mycobacterial proteins based on optimal tripeptide compositions. ([https://bio.tools/Mycosub bio.tools entry]) | [{{cite journal | vauthors = Zhu PP, Li WC, Zhong ZJ, Deng EZ, Ding H, Chen W, Lin H | title = Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition | journal = Molecular BioSystems | volume = 11 | issue = 2 | pages = 558–63 | date = February 2015 | pmid = 25437899 | doi = 10.1039/c4mb00645c | s2cid = 8130819 }}] | http://lin.uestc.edu.cn/server/Mycosub
|2015 |
| NetNES | Prediction of the leucine-rich nuclear export signals (NES) in eukaryotic proteins ([https://bio.tools/netnes bio.tools entry]) | [{{cite journal | vauthors = la Cour T, Kiemer L, Mølgaard A, Gupta R, Skriver K, Brunak S | title = Analysis and prediction of leucine-rich nuclear export signals | journal = Protein Engineering, Design & Selection | volume = 17 | issue = 6 | pages = 527–36 | date = June 2004 | pmid = 15314210 | doi = 10.1093/protein/gzh062 | doi-access = free }}] | http://cbs.dtu.dk/services/NetNES/
|2004 |
| ngLOC | ngLOC is an n-gram-based Bayesian classifier that predicts subcellular localization of proteins both in prokaryotes and eukaryotes. The overall prediction accuracy varies from 85.3% to 91.4% across species. ([https://bio.tools/ngLOC bio.tools entry]) | [{{Cite journal|last1=King|first1=Brian R|last2=Guda|first2=Chittibabu|date=2007|title=ngLOC: an n-gram-based Bayesian method for estimating the subcellular proteomes of eukaryotes|journal=Genome Biology|volume=8|issue=5|pages=R68|doi=10.1186/gb-2007-8-5-r68|pmid=17472741|pmc=1929137|issn=1465-6906 |doi-access=free }}]
| http://genome.unmc.edu/ngLOC/index.html
|2007 |
| OBCOL | Software we designed to perform organelle-based colocalisation analysis from multi-fluorophore microscopy 2D, 3D and 4D cell imaging. ([https://bio.tools/OBCOL bio.tools entry]) | [{{cite journal | vauthors = Woodcroft BJ, Hammond L, Stow JL, Hamilton NA | title = Automated organelle-based colocalization in whole-cell imaging | journal = Cytometry. Part A | volume = 75 | issue = 11 | pages = 941–50 | date = November 2009 | pmid = 19746416 | doi = 10.1002/cyto.a.20786 | s2cid = 25068671 | doi-access = free }}] | http://obcol.imb.uq.edu.au/
|2009 |
| PA-SUB | PA-SUB (Proteome Analyst Specialized Subcellular Localization Server) can be used to predict the subcellular localization of proteins using established machine learning techniques. ([https://bio.tools/PA-SUB bio.tools entry]) | [{{cite journal | vauthors = Lu Z, Szafron D, Greiner R, Lu P, Wishart DS, Poulin B, Anvik J, Macdonell C, Eisner R | title = Predicting subcellular localization of proteins using machine-learned classifiers | journal = Bioinformatics | volume = 20 | issue = 4 | pages = 547–56 | date = March 2004 | pmid = 14990451 | doi = 10.1093/bioinformatics/btg447 | citeseerx = 10.1.1.216.1493 }}][{{cite journal | vauthors = Szafron D, Lu P, Greiner R, Wishart DS, Poulin B, Eisner R, Lu Z, Anvik J, Macdonell C, Fyshe A, Meeuwis D | title = Proteome Analyst: custom predictions with explanations in a web-based tool for high-throughput proteome annotations | journal = Nucleic Acids Research | volume = 32 | issue = Web Server issue | pages = W365–71 | date = July 2004 | pmid = 15215412 | pmc = 441623 | doi = 10.1093/nar/gkh485 }}] | http://www.cs.ualberta.ca/~bioinfo/PA/Sub/
|2004 |
| PharmMapper | PharmMapper is a web server that identifies potential drug targets from its PharmTargetDB for a given input molecule. Potential targets are identified from a prediction of the spatial arrangement of features essential for a given molecule to interact with a target. ([https://bio.tools/pharmmapper bio.tools entry]) | [{{cite journal | vauthors = Liu X, Ouyang S, Yu B, Liu Y, Huang K, Gong J, Zheng S, Li Z, Li H, Jiang H | title = PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach | journal = Nucleic Acids Research | volume = 38 | issue = Web Server issue | pages = W609–14 | date = July 2010 | pmid = 20430828 | pmc = 2896160 | doi = 10.1093/nar/gkq300 }}] | http://59.78.96.61/pharmmapper
|2010 |
| PlantLoc | PlantLoc is a web server for predicting plant protein subcellular localization by substantiality motif. ([https://bio.tools/plantloc bio.tools entry]) | [{{cite journal | vauthors = Tang S, Li T, Cong P, Xiong W, Wang Z, Sun J | title = PlantLoc: an accurate web server for predicting plant protein subcellular localization by substantiality motif | journal = Nucleic Acids Research | volume = 41 | issue = Web Server issue | pages = W441–7 | date = July 2013 | pmid = 23729470 | pmc = 3692052 | doi = 10.1093/nar/gkt428 }}] | http://cal.tongji.edu.cn/PlantLoc/
|2013 |
| PRED-TMBB | PRED-TMBB is a tool that takes a Gram-negative bacteria protein sequence as input and predicts the transmembrane strands and the probability of it being an outer membrane beta-barrel protein. The user has a choice of three different decoding methods. ([https://bio.tools/pred-tmbb bio.tools entry]) | [{{cite journal | vauthors = Bagos PG, Liakopoulos TD, Spyropoulos IC, Hamodrakas SJ | title = PRED-TMBB: a web server for predicting the topology of beta-barrel outer membrane proteins | journal = Nucleic Acids Research | volume = 32 | issue = Web Server issue | pages = W400–4 | date = July 2004 | pmid = 15215419 | pmc = 441555 | doi = 10.1093/nar/gkh417 }}][{{cite journal | vauthors = Bagos PG, Liakopoulos TD, Spyropoulos IC, Hamodrakas SJ | title = A Hidden Markov Model method, capable of predicting and discriminating beta-barrel outer membrane proteins | journal = BMC Bioinformatics | volume = 5 | pages = 29 | date = March 2004 | pmid = 15070403 | pmc = 385222 | doi = 10.1186/1471-2105-5-29 | doi-access = free }}] | http://bioinformatics.biol.uoa.gr/PRED-TMBB/
|2004 |
| PredictNLS | Prediction and analysis of nuclear localization signals ([https://bio.tools/predictnls bio.tools entry]) | [{{cite journal | vauthors = Cokol M, Nair R, Rost B | title = Finding nuclear localization signals | journal = EMBO Reports | volume = 1 | issue = 5 | pages = 411–5 | date = November 2000 | pmid = 11258480 | pmc = 1083765 | doi = 10.1093/embo-reports/kvd092 }}] | https://www.rostlab.org/owiki/index.php/PredictNLS
|2000 |
| PredictProtein Open | Prediction of various aspects of protein structure and function. A user may submit a query to the server without registration. ([https://bio.tools/predictprotein_open bio.tools entry]) | [{{cite journal | vauthors = Yachdav G, Kloppmann E, Kajan L, Hecht M, Goldberg T, Hamp T, Hönigschmid P, Schafferhans A, Roos M, Bernhofer M, Richter L, Ashkenazy H, Punta M, Schlessinger A, Bromberg Y, Schneider R, Vriend G, Sander C, Ben-Tal N, Rost B | title = PredictProtein--an open resource for online prediction of protein structural and functional features | journal = Nucleic Acids Research | volume = 42 | issue = Web Server issue | pages = W337–43 | date = July 2014 | pmid = 24799431 | pmc = 4086098 | doi = 10.1093/nar/gku366 }}][{{cite journal | vauthors = Kaján L, Yachdav G, Vicedo E, Steinegger M, Mirdita M, Angermüller C, Böhm A, Domke S, Ertl J, Mertes C, Reisinger E, Staniewski C, Rost B | title = Cloud prediction of protein structure and function with PredictProtein for Debian | journal = BioMed Research International | volume = 2013 | pages = 1–6 | year = 2013 | pmid = 23971032 | pmc = 3732596 | doi = 10.1155/2013/398968 | doi-access = free }}][{{cite journal | vauthors = Rost B, Liu J | title = The PredictProtein server | journal = Nucleic Acids Research | volume = 31 | issue = 13 | pages = 3300–4 | date = July 2003 | pmid = 12824312 | pmc = 168915 | doi = 10.1093/nar/gkg508 }}][{{cite journal | vauthors = Rost B, Yachdav G, Liu J | title = The PredictProtein server | journal = Nucleic Acids Research | volume = 32 | issue = Web Server issue | pages = W321–6 | date = July 2004 | pmid = 15215403 | pmc = 441515 | doi = 10.1093/nar/gkh377 }}] | http://ppopen.informatik.tu-muenchen.de/
|2014 |
| PREP Suite | The PREP (Predictive RNA Editors for Plants) suite predicts sites of RNA editing based on the principle that editing in plant organelles increases the conservation of proteins across species. Predictors for mitochondrial genes, chloroplast genes, and alignments input by the user are included. ([https://bio.tools/prep_suite bio.tools entry]) | [{{cite journal | vauthors = Mower JP | title = The PREP suite: predictive RNA editors for plant mitochondrial genes, chloroplast genes and user-defined alignments | journal = Nucleic Acids Research | volume = 37 | issue = Web Server issue | pages = W253–9 | date = July 2009 | pmid = 19433507 | pmc = 2703948 | doi = 10.1093/nar/gkp337 }}][{{cite journal | vauthors = Mower JP | title = PREP-Mt: predictive RNA editor for plant mitochondrial genes | journal = BMC Bioinformatics | volume = 6 | pages = 96 | date = April 2005 | pmid = 15826309 | pmc = 1087475 | doi = 10.1186/1471-2105-6-96 | doi-access = free }}] | http://prep.unl.edu/
|2009 |
| ProLoc-GO | ProLoc-GO is an efficient sequence-based method by mining informative Gene Ontology terms for predicting protein subcellular localization. ([https://bio.tools/ProLoc-GO bio.tools entry]) | [{{cite journal | vauthors = Huang WL, Tung CW, Ho SW, Hwang SF, Ho SY | title = ProLoc-GO: utilizing informative Gene Ontology terms for sequence-based prediction of protein subcellular localization | journal = BMC Bioinformatics | volume = 9 | pages = 80 | date = February 2008 | pmid = 18241343 | pmc = 2262056 | doi = 10.1186/1471-2105-9-80 | doi-access = free }}] | http://140.113.239.45/prolocgo/
|2008 |
| ProLoc | Evolutionary support vector machine (ESVM) based classifier with automatic selection from a large set of physicochemical composition (PCC) features to design an accurate system for predicting protein subnuclear localization. ([https://bio.tools/ProLoc6694 bio.tools entry]) | [{{cite journal | vauthors = Huang WL, Tung CW, Huang HL, Hwang SF, Ho SY | title = ProLoc: prediction of protein subnuclear localization using SVM with automatic selection from physicochemical composition features | journal = Bio Systems | volume = 90 | issue = 2 | pages = 573–81 | year = 2007 | pmid = 17291684 | doi = 10.1016/j.biosystems.2007.01.001 }}] | http://140.113.239.45/proloc/
|2007 |
| Protegen | Protegen is a web-based database and analysis system that curates, stores and analyzes protective antigens. Protegen includes basic antigen information and experimental evidence curated from peer-reviewed articles. It also includes detailed gene/protein information (e.g. DNA and protein sequences, and COG classification). Different antigen features, such as protein weight and pI, and subcellular localizations of bacterial proteins are precomputed. ([https://bio.tools/protegen bio.tools entry]) | [{{cite journal | vauthors = Yang B, Sayers S, Xiang Z, He Y | title = Protegen: a web-based protective antigen database and analysis system | journal = Nucleic Acids Research | volume = 39 | issue = Database issue | pages = D1073–8 | date = January 2011 | pmid = 20959289 | pmc = 3013795 | doi = 10.1093/nar/gkq944 }}] | http://www.violinet.org/protegen
|2011 |
| Proteome Analyst | Proteome Analyst is a high-throughput tool for predicting properties for each protein in a proteome. The user provides a proteome in fasta format, and the system employs Psi-blast, Psipred and Modeller to predict protein function and subcellular localization. Proteome Analyst uses machine-learned classifiers to predict things such as GO molecular function. User-supplied training data can also be used to create custom classifiers. ([https://bio.tools/proteome_analyst bio.tools entry]) | | http://www.cs.ualberta.ca/~bioinfo/PA/
|2004 |
| ProTox | ProTox is a web server for the in silico prediction of oral toxicities of small molecules in rodents. ([https://bio.tools/ProTox bio.tools entry]) | [{{Cite journal|last1=Drwal|first1=Malgorzata N.|last2=Banerjee|first2=Priyanka|last3=Dunkel|first3=Mathias|last4=Wettig|first4=Martin R.|last5=Preissner|first5=Robert|date=2014-05-16|title=ProTox: a web server for the in silico prediction of rodent oral toxicity|journal=Nucleic Acids Research|volume=42|issue=W1|pages=W53–W58|doi=10.1093/nar/gku401|pmid=24838562|pmc=4086068|issn=1362-4962}}][{{Cite journal|last1=Banerjee|first1=Priyanka|last2=Eckert|first2=Andreas O|last3=Schrey|first3=Anna K|last4=Preissner|first4=Robert|date=2018-04-30|title=ProTox-II: a webserver for the prediction of toxicity of chemicals|journal=Nucleic Acids Research|volume=46|issue=W1|pages=W257–W263|doi=10.1093/nar/gky318|pmid=29718510|pmc=6031011|issn=0305-1048}}]
| http://tox.charite.de/tox
|2018 |
| PSLpred | Method for subcellular localization proteins belongs to prokaryotic genomes. ([https://bio.tools/PSLpred bio.tools entry]) | [{{cite journal | vauthors = Bhasin M, Garg A, Raghava GP | title = PSLpred: prediction of subcellular localization of bacterial proteins | journal = Bioinformatics | volume = 21 | issue = 10 | pages = 2522–4 | date = May 2005 | pmid = 15699023 | doi = 10.1093/bioinformatics/bti309 | doi-access = free }}] | http://www.imtech.res.in/raghava/pslpred/
|2005 |
| PSORTb | PSORTb (for “bacterial” PSORT) is a high-precision localization prediction method for bacterial proteins.PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. PSORTb version improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies. ([https://bio.tools/PSORTb bio.tools entry]) | [{{cite journal | vauthors = Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, Dao P, Sahinalp SC, Ester M, Foster LJ, Brinkman FS | title = PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes | journal = Bioinformatics | volume = 26 | issue = 13 | pages = 1608–15 | date = July 2010 | pmid = 20472543 | doi = 10.1093/bioinformatics/btq249 | pmc = 2887053 }}] | http://www.psort.org/psortb/
|2010 |
| PSORTdb | PSORTdb (part of the PSORT family) is a database of protein subcellular localizations for bacteria and archaea that contains both information determined through laboratory experimentation (ePSORTdb dataset) and computational predictions (cPSORTdb dataset). ([https://bio.tools/psortdb bio.tools entry]) | [{{cite journal | vauthors = Yu NY, Laird MR, Spencer C, Brinkman FS | title = PSORTdb--an expanded, auto-updated, user-friendly protein subcellular localization database for Bacteria and Archaea | journal = Nucleic Acids Research | volume = 39 | issue = Database issue | pages = D241–4 | date = January 2011 | pmid = 21071402 | pmc = 3013690 | doi = 10.1093/nar/gkq1093 }}][{{cite journal | vauthors = Rey S, Acab M, Gardy JL, Laird MR, deFays K, Lambert C, Brinkman FS | title = PSORTdb: a protein subcellular localization database for bacteria | journal = Nucleic Acids Research | volume = 33 | issue = Database issue | pages = D164–8 | date = January 2005 | pmid = 15608169 | pmc = 539981 | doi = 10.1093/nar/gki027 }}] | http://db.psort.org
|2010 |
| psRobot | psRobot is a web-based tool for plant small RNA meta-analysis. psRobot computes stem-loop small RNA prediction, which aligns user uploaded sequences to the selected genome, extracts their predicted precursors, and predicts whether the precursors can fold into stem-loop shaped secondary structure. psRobot also computes small RNA target prediction, which predict the possible targets of user provided small RNA sequences from the selected transcript library. ([https://bio.tools/psrobot bio.tools entry]) | [{{cite journal | vauthors = Wu HJ, Ma YK, Chen T, Wang M, Wang XJ | title = PsRobot: a web-based plant small RNA meta-analysis toolbox | journal = Nucleic Acids Research | volume = 40 | issue = Web Server issue | pages = W22–8 | date = July 2012 | pmid = 22693224 | pmc = 3394341 | doi = 10.1093/nar/gks554 }}] | http://omicslab.genetics.ac.cn/psRobot/
|2012 |
| pTARGET | pTARGET predicts the subcellular localization of eukaryotic proteins based on the occurrence patterns of location-specific protein functional domains and the amino acid compositional differences in proteins from nine distinct subcellular locations. ([https://bio.tools/ptarget bio.tools entry]) | [{{cite journal | vauthors = Guda C, Subramaniam S | title = pTARGET [corrected] a new method for predicting protein subcellular localization in eukaryotes | journal = Bioinformatics | volume = 21 | issue = 21 | pages = 3963–9 | date = November 2005 | pmid = 16144808 | doi = 10.1093/bioinformatics/bti650 | doi-access = free }}][{{cite journal | vauthors = Guda C | title = pTARGET: a web server for predicting protein subcellular localization | journal = Nucleic Acids Research | volume = 34 | issue = Web Server issue | pages = W210–3 | date = July 2006 | pmid = 16844995 | pmc = 1538910 | doi = 10.1093/nar/gkl093 }}] | http://bioinformatics.albany.edu/~ptarget
|2006 |
| PUPS (Predictions of Unseen Proteins’ Subcellular localization) | PUPS uses protein sequences and cellular landmark images to perform predictions of unseen proteins’ subcellular localization. | [{{cite journal | vauthors = Xinyi Z, Yitong T, Yunhao B, Fei C, Caroline U | title = Prediction of protein subcellular localization in single cells | journal = bioRxiv | date = July 2024 | doi = 10.1101/2024.07.25.605178| pmc = 11291118 }}] | https://pmc.ncbi.nlm.nih.gov/articles/PMC11291118/
|2024 |
| RegPhos | RegPhos is a database for exploration of the phosphorylation network associated with an input of genes/proteins. Subcellular localization information is also included. ([https://bio.tools/regphos bio.tools entry]) | [{{cite journal | vauthors = Lee TY, Bo-Kai Hsu J, Chang WC, Huang HD | title = RegPhos: a system to explore the protein kinase-substrate phosphorylation network in humans | journal = Nucleic Acids Research | volume = 39 | issue = Database issue | pages = D777–87 | date = January 2011 | pmid = 21037261 | pmc = 3013804 | doi = 10.1093/nar/gkq970 }}] | http://regphos.mbc.nctu.edu.tw/
|2011 |
| RepTar | RepTar is a database of miRNA target predictions, based on the RepTar algorithm that is independent of evolutionary conservation considerations and is not limited to seed pairing sites. ([https://bio.tools/reptar bio.tools entry]) | [{{cite journal | vauthors = Elefant N, Berger A, Shein H, Hofree M, Margalit H, Altuvia Y | title = RepTar: a database of predicted cellular targets of host and viral miRNAs | journal = Nucleic Acids Research | volume = 39 | issue = Database issue | pages = D188–94 | date = January 2011 | pmid = 21149264 | pmc = 3013742 | doi = 10.1093/nar/gkq1233 }}] | http://reptar.ekmd.huji.ac.il
|2011 |
| RNApredator | RNApredator is a web server for the prediction of bacterial sRNA targets. The user can choose from a large selection of genomes. Accessibility of the target to the sRNA is considered. ([https://bio.tools/rnapredator bio.tools entry]) | [{{cite journal | vauthors = Eggenhofer F, Tafer H, Stadler PF, Hofacker IL | title = RNApredator: fast accessibility-based prediction of sRNA targets | journal = Nucleic Acids Research | volume = 39 | issue = Web Server issue | pages = W149–54 | date = July 2011 | pmid = 21672960 | pmc = 3125805 | doi = 10.1093/nar/gkr467 }}] | http://rna.tbi.univie.ac.at/RNApredator
|2011 |
| S-PSorter | A novel cell structure-driven classifier construction approach for predicting image-based protein subcellular location by employing the prior biological structural information. ([https://bio.tools/S-PSorter bio.tools entry]) | [{{cite journal | vauthors = Shao W, Liu M, Zhang D | title = Human cell structure-driven model construction for predicting protein subcellular location from biological images | journal = Bioinformatics | volume = 32 | issue = 1 | pages = 114–21 | date = January 2016 | pmid = 26363175 | doi = 10.1093/bioinformatics/btv521 | doi-access = free }}] | https://github.com/shaoweinuaa/S-PSorter
|2016 |
| SChloro | Prediction of protein sub-chloroplastinc localization. ([https://bio.tools/schloro bio.tools entry]) | [{{cite journal | vauthors = Savojardo C, Martelli PL, Fariselli P, Casadio R | title = SChloro: directing Viridiplantae proteins to six chloroplastic sub-compartments | journal = Bioinformatics | volume = 33 | issue = 3 | pages = 347–353 | date = February 2017 | pmid = 28172591 | pmc = 5408801 | doi = 10.1093/bioinformatics/btw656 }}] | http://schloro.biocomp.unibo.it
|2017 |
| SCLAP | An Adaptive Boosting Method for Predicting Subchloroplast Localization of Plant Proteins. | [{{cite journal | vauthors = Saravanan V, Lakshmi PT | title = SCLAP: an adaptive boosting method for predicting subchloroplast localization of plant proteins | journal = OMICS | volume = 17 | issue = 2 | pages = 106–15 | date = February 2013 | pmid = 23289782 | doi = 10.1089/omi.2012.0070 }}]
|
|2013 |
| SCLPred | SCLpred protein subcellular localization prediction by N-to-1 neural networks. | [{{cite journal | vauthors = Mooney C, Wang YH, Pollastri G | title = SCLpred: protein subcellular localization prediction by N-to-1 neural networks | journal = Bioinformatics | volume = 27 | issue = 20 | pages = 2812–9 | date = October 2011 | pmid = 21873639 | doi = 10.1093/bioinformatics/btr494 | doi-access = free | hdl = 10197/3443 | hdl-access = free }}]
|
|2011 |
| SCLpred-EMS | Subcellular localization prediction of endomembrane system and secretory pathway proteins by Deep N-to-1 Convolutional Neural Networks | [{{cite journal |last1=Kaleel |first1=M |last2=Zheng |first2=Y |last3=Chen |first3=J |last4=Feng |first4=X |last5=Simpson |first5=JC |last6=Pollastri |first6=G |last7=Mooney |first7=C |title=SCLpred-EMS: subcellular localization prediction of endomembrane system and secretory pathway proteins by Deep N-to-1 Convolutional Neural Networks. |journal=Bioinformatics |date=6 March 2020 |volume=36 |issue=11 |pages=3343–3349 |doi=10.1093/bioinformatics/btaa156 |pmid=32142105 |hdl=10197/12182 |hdl-access=free }}] | http://distilldeep.ucd.ie/SCLpred2/
|2020 |
| SCLpred-MEM | Subcellular localization prediction of membrane proteins by deep N-to-1 convolutional neural networks | [{{cite journal |last1=Kaleel |first1=Manaz |last2=Ellinger |first2=Liam |last3=Lalor |first3=Clodagh |last4=Pollastri |first4=Gianluca |last5=Mooney |first5=Catherine |title=SCLpred-MEM: Subcellular localization prediction of membrane proteins by deep N-to-1 convolutional neural networks |journal=Proteins: Structure, Function, and Bioinformatics |year=2021 |volume=89 |issue=10 |pages=1233–1239 |language=en |doi=10.1002/prot.26144|pmid=33983651 |s2cid=234484678 |doi-access=free |hdl=2346/90320 |hdl-access=free }}] | http://distilldeep.ucd.ie/SCLpred-MEM/
|2021 |
| SecretomeP | Predictions of non-classical (i.e. not signal peptide triggered) protein secretion ([https://bio.tools/secretomep bio.tools entry]) | [{{cite journal | vauthors = Bendtsen JD, Jensen LJ, Blom N, Von Heijne G, Brunak S | title = Feature-based prediction of non-classical and leaderless protein secretion | journal = Protein Engineering, Design & Selection | volume = 17 | issue = 4 | pages = 349–56 | date = April 2004 | pmid = 15115854 | doi = 10.1093/protein/gzh037 | doi-access = free }}][{{cite journal | vauthors = Bendtsen JD, Kiemer L, Fausbøll A, Brunak S | title = Non-classical protein secretion in bacteria | journal = BMC Microbiology | volume = 5 | pages = 58 | date = October 2005 | pmid = 16212653 | pmc = 1266369 | doi = 10.1186/1471-2180-5-58 | doi-access = free }}] | http://cbs.dtu.dk/services/SecretomeP/
|2005 |
| SemiBiomarker | New semi-supervised protocol that can use unlabeled cancer protein data in model construction by an iterative and incremental training strategy.It can result in improved accuracy and sensitivity of subcellular location difference detection. ([https://bio.tools/SemiBiomarker bio.tools entry]) | [{{cite journal | vauthors = Xu YY, Yang F, Zhang Y, Shen HB | title = Bioimaging-based detection of mislocalized proteins in human cancers by semi-supervised learning | journal = Bioinformatics | volume = 31 | issue = 7 | pages = 1111–9 | date = April 2015 | pmid = 25414362 | pmc = 4382902 | doi = 10.1093/bioinformatics/btu772 }}] | http://www.csbio.sjtu.edu.cn/bioinf/SemiBiomarker/
|2015 |
| SherLoc | An SVM-based predictor combining MultiLoc with text-based features derived from PubMed abstracts. | [{{cite journal | vauthors = Shatkay H, Höglund A, Brady S, Blum T, Dönnes P, Kohlbacher O | title = SherLoc: high-accuracy prediction of protein subcellular localization by integrating text and protein sequence data | journal = Bioinformatics | volume = 23 | issue = 11 | pages = 1410–7 | date = June 2007 | pmid = 17392328 | doi = 10.1093/bioinformatics/btm115 | doi-access = free }}]
|
|2007 |
| SUBA3 | A subcellular localisation database for Arabidopsis proteins, with online search interface. ([https://bio.tools/suba3 bio.tools entry]) | [{{cite journal | vauthors = Tanz SK, Castleden I, Hooper CM, Vacher M, Small I, Millar HA | title = SUBA3: a database for integrating experimentation and prediction to define the SUBcellular location of proteins in Arabidopsis | journal = Nucleic Acids Research | volume = 41 | issue = Database issue | pages = D1185–91 | date = January 2013 | pmid = 23180787 | pmc = 3531127 | doi = 10.1093/nar/gks1151 }}][{{cite journal | vauthors = Hooper CM, Tanz SK, Castleden IR, Vacher MA, Small ID, Millar AH | title = SUBAcon: a consensus algorithm for unifying the subcellular localization data of the Arabidopsis proteome | journal = Bioinformatics | volume = 30 | issue = 23 | pages = 3356–64 | date = December 2014 | pmid = 25150248 | doi = 10.1093/bioinformatics/btu550 | doi-access = free }}] | http://suba3.plantenergy.uwa.edu.au/
|2014 |
| SubChlo | Computational system for predicting protein subchloroplast locations from its primary sequence. It can locate the protein whose subcellular location is chloroplast in one of the four parts: envelope (which consists of outer membrane and inner membrane), thylakoid lumen, stroma and thylakoid membrane. ([https://bio.tools/SubChlo bio.tools entry]) | [{{cite journal | vauthors = Du P, Cao S, Li Y | title = SubChlo: predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm | journal = Journal of Theoretical Biology | volume = 261 | issue = 2 | pages = 330–5 | date = November 2009 | pmid = 19679138 | doi = 10.1016/j.jtbi.2009.08.004 | bibcode = 2009JThBi.261..330D }}] | http://bioinfo.au.tsinghua.edu.cn/software/subchlo/
|2009 |
| SuperPred | The SuperPred web server compares the structural fingerprint of an input molecule to a database of drugs connected to their drug targets and affected pathways. As the biological effect is well predictable, if the structural similarity is sufficient, the web-server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets. Such information can be useful in drug classification and target prediction. ([https://bio.tools/superpred bio.tools entry]) | [{{cite journal | vauthors = Dunkel M, Günther S, Ahmed J, Wittig B, Preissner R | title = SuperPred: drug classification and target prediction | journal = Nucleic Acids Research | volume = 36 | issue = Web Server issue | pages = W55–9 | date = July 2008 | pmid = 18499712 | pmc = 2447784 | doi = 10.1093/nar/gkn307 }}] | http://bioinformatics.charite.de/superpred
|2008 |
| SuperTarget | Web resource for analyzing drug-target interactions. Integrates drug-related info associated with medical indications, adverse drug effects, drug metabolism, pathways and Gene Ontology (GO) terms for target proteins. ([https://bio.tools/supertarget bio.tools entry]) | [{{cite journal | vauthors = Hecker N, Ahmed J, von Eichborn J, Dunkel M, Macha K, Eckert A, Gilson MK, Bourne PE, Preissner R | title = SuperTarget goes quantitative: update on drug-target interactions | journal = Nucleic Acids Research | volume = 40 | issue = Database issue | pages = D1113–7 | date = January 2012 | pmid = 22067455 | pmc = 3245174 | doi = 10.1093/nar/gkr912 }}] | http://bioinformatics.charite.de/supertarget/
|2012 |
| SwissTargetPrediction | SwissTargetPrediction is a web server for target prediction of bioactive small molecules. This website allows you to predict the targets of a small molecule. Using a combination of 2D and 3D similarity measures, it compares the query molecule to a library of 280 000 compounds active on more than 2000 targets of 5 different organisms. ([https://bio.tools/SwissTargetPrediction bio.tools entry]) | [{{Cite journal|last1=Gfeller|first1=David|last2=Michielin|first2=Olivier|last3=Zoete|first3=Vincent|date=2013-09-17|title=Shaping the interaction landscape of bioactive molecules|journal=Bioinformatics|volume=29|issue=23|pages=3073–3079|doi=10.1093/bioinformatics/btt540|pmid=24048355|issn=1460-2059|doi-access=free}}][{{Cite journal|last1=Gfeller|first1=David|last2=Grosdidier|first2=Aurélien|last3=Wirth|first3=Matthias|last4=Daina|first4=Antoine|last5=Michielin|first5=Olivier|last6=Zoete|first6=Vincent|date=2014-05-03|title=SwissTargetPrediction: a web server for target prediction of bioactive small molecules|journal=Nucleic Acids Research|volume=42|issue=W1|pages=W32–W38|doi=10.1093/nar/gku293|pmid=24792161|pmc=4086140|issn=1362-4962}}]
| http://www.swisstargetprediction.ch
|2014 |
| T3DB | The Toxin and Toxin-Target Database (T3DB) is a unique bioinformatics resource that compiles comprehensive information about common or ubiquitous toxins and their toxin-targets. Each T3DB record (ToxCard) contains over 80 data fields providing detailed information on chemical properties and descriptors, toxicity values, protein and gene sequences (for both targets and toxins), molecular and cellular interaction data, toxicological data, mechanistic information and references. This information has been manually extracted and manually verified from numerous sources, including other electronic databases, government documents, textbooks and scientific journals. A key focus of the T3DB is on providing ??depth?? over ??breadth?? with detailed descriptions, mechanisms of action, and information on toxins and toxin-targets. Potential applications of the T3DB include clinical metabolomics, toxin target prediction, toxicity prediction and toxicology education. ([https://bio.tools/t3db bio.tools entry]) | [{{cite journal | vauthors = Lim E, Pon A, Djoumbou Y, Knox C, Shrivastava S, Guo AC, Neveu V, Wishart DS | title = T3DB: a comprehensively annotated database of common toxins and their targets | journal = Nucleic Acids Research | volume = 38 | issue = Database issue | pages = D781–6 | date = January 2010 | pmid = 19897546 | pmc = 2808899 | doi = 10.1093/nar/gkp934 }}] | http://www.t3db.org
|2010 |
| TALE-NT | Transcription activator-like (TAL) Effector-Nucleotide Targeter 2.0 (TALE-NT) is a suite of web-based tools that allows for custom design of TAL effector repeat arrays for desired targets and prediction of TAL effector binding sites. ([https://bio.tools/TALE-NT bio.tools entry]) | [{{cite journal | vauthors = Doyle EL, Booher NJ, Standage DS, Voytas DF, Brendel VP, Vandyk JK, Bogdanove AJ | title = TAL Effector-Nucleotide Targeter (TALE-NT) 2.0: tools for TAL effector design and target prediction | journal = Nucleic Acids Research | volume = 40 | issue = Web Server issue | pages = W117–22 | date = July 2012 | pmid = 22693217 | pmc = 3394250 | doi = 10.1093/nar/gks608 }}] | https://boglab.plp.iastate.edu/
|2012 |
| TarFisDock | Target Fishing Dock (TarFisDock) is a web server that docks small molecules with protein structures in the Potential Drug Target Database (PDTD) in an effort to discover new drug targets. ([https://bio.tools/tarfisdock bio.tools entry]) | [{{cite journal | vauthors = Li H, Gao Z, Kang L, Zhang H, Yang K, Yu K, Luo X, Zhu W, Chen K, Shen J, Wang X, Jiang H | title = TarFisDock: a web server for identifying drug targets with docking approach | journal = Nucleic Acids Research | volume = 34 | issue = Web Server issue | pages = W219–24 | date = July 2006 | pmid = 16844997 | pmc = 1538869 | doi = 10.1093/nar/gkl114 }}] | http://www.dddc.ac.cn/tarfisdock/
|2006 |
| TargetRNA | TargetRNA is a web based tool for identifying mRNA targets of small non-coding RNAs in bacterial species. ([https://bio.tools/targetrna bio.tools entry]) | [{{cite journal | vauthors = Tjaden B | title = TargetRNA: a tool for predicting targets of small RNA action in bacteria | journal = Nucleic Acids Research | volume = 36 | issue = Web Server issue | pages = W109–13 | date = July 2008 | pmid = 18477632 | pmc = 2447797 | doi = 10.1093/nar/gkn264 }}] | http://cs.wellesley.edu/~btjaden/TargetRNA2/
|2008 |
| TargetP | Prediction of N-terminal sorting signals. | [{{cite journal | vauthors = Emanuelsson O, Nielsen H, Brunak S, Von Heijne G | title = Predicting subcellular localization of proteins based on their N-terminal amino acid sequence | journal = Journal of Molecular Biology | volume = 300 | issue = 4 | pages = 1005–16 | date = July 2000 | pmid = 10891285 | doi = 10.1006/jmbi.2000.3903 }}]
|
|2000 |
| TDR Targets | Tropical Disease Research (TDR) Database: Designed and developed to facilitate the rapid identification and prioritization of molecular targets for drug development, focusing on pathogens responsible for neglected human diseases. The database integrates pathogen specific genomic information with functional data for genes collected from various sources, including literature curation. Information can be browsed and queried. ([https://bio.tools/tdr_targets bio.tools entry]) | [{{cite journal | vauthors = Magariños MP, Carmona SJ, Crowther GJ, Ralph SA, Roos DS, Shanmugam D, Van Voorhis WC, Agüero F | title = TDR Targets: a chemogenomics resource for neglected diseases | journal = Nucleic Acids Research | volume = 40 | issue = Database issue | pages = D1118–27 | date = January 2012 | pmid = 22116064 | pmc = 3245062 | doi = 10.1093/nar/gkr1053 }}] | http://tdrtargets.org/
|2012 |
| TetraMito | Sequence-based predictor for identifying submitochondria location of proteins. ([https://bio.tools/TetraMito bio.tools entry]) | [{{cite journal | vauthors = Lin H, Chen W, Yuan LF, Li ZQ, Ding H | title = Using over-represented tetrapeptides to predict protein submitochondria locations | journal = Acta Biotheoretica | volume = 61 | issue = 2 | pages = 259–68 | date = June 2013 | pmid = 23475502 | doi = 10.1007/s10441-013-9181-9 | s2cid = 30809970 }}] | http://lin.uestc.edu.cn/server/TetraMito
|2013 |
| TMBETA-NET | Tool that predicts transmembrane beta strands in an outer membrane protein from its amino acid sequence. ([https://bio.tools/tmbeta-net bio.tools entry]) | [{{cite journal | vauthors = Gromiha MM, Ahmad S, Suwa M | title = Neural network-based prediction of transmembrane beta-strand segments in outer membrane proteins | journal = Journal of Computational Chemistry | volume = 25 | issue = 5 | pages = 762–7 | date = April 2004 | pmid = 14978719 | doi = 10.1002/jcc.10386 | s2cid = 3486330 }}][{{cite journal | vauthors = Gromiha MM, Ahmad S, Suwa M | title = TMBETA-NET: discrimination and prediction of membrane spanning beta-strands in outer membrane proteins | journal = Nucleic Acids Research | volume = 33 | issue = Web Server issue | pages = W164–7 | date = July 2005 | pmid = 15980447 | pmc = 1160128 | doi = 10.1093/nar/gki367 }}] | http://psfs.cbrc.jp/tmbeta-net/
|2005 |
| TMHMM | Prediction of transmembrane helices to identify transmembrane proteins.
|[{{Cite journal|last1=Krogh|first1=Anders|last2=Larsson|first2=Björn|last3=von Heijne|first3=Gunnar|author-link3=Gunnar von Heijne|last4=Sonnhammer|first4=Erik L.L|date=Jan 2001|title=Predicting transmembrane protein topology with a hidden markov model: application to complete genomes|journal=Journal of Molecular Biology|volume=305|issue=3|pages=567–580|doi=10.1006/jmbi.2000.4315|pmid=11152613|issn=0022-2836}}]
|
|2001 |
| TMPred | The TMpred program makes a prediction of membrane-spanning regions and their orientation. The algorithm is based on the statistical analysis of TMbase, a database of naturally occurring transmembrane proteins ([https://bio.tools/TMPred bio.tools entry]) | [{{Cite journal|last1=Hofmann|first1=K|last2=Stoffel|first2=W|date=1993|title=TMbase—A database of membrane spanning proteins segments|url=https://embnet.vital-it.ch/documentation/mfc-35.pdf|journal=Biol Chem Hoppe-Seyler|volume=374|pages=166}}]
| http://embnet.vital-it.ch/software/TMPRED_form.html
|1993 |
| TPpred 1.0 | Organelle targeting peptide prediction ([https://bio.tools/tppred_1.0 bio.tools entry]) | [{{cite journal | vauthors = Indio V, Martelli PL, Savojardo C, Fariselli P, Casadio R | title = The prediction of organelle-targeting peptides in eukaryotic proteins with Grammatical-Restrained Hidden Conditional Random Fields | journal = Bioinformatics | volume = 29 | issue = 8 | pages = 981–8 | date = April 2013 | pmid = 23428638 | doi = 10.1093/bioinformatics/btt089 | doi-access = free }}] | http://tppred.biocomp.unibo.it/tppred/default/index
|2013 |
| TPpred 2.0 | Mitochondrial targeting peptide prediction ([https://bio.tools/tppred_2.0 bio.tools entry]) | [{{cite journal | vauthors = Savojardo C, Martelli PL, Fariselli P, Casadio R | title = TPpred3 detects and discriminates mitochondrial and chloroplastic targeting peptides in eukaryotic proteins | journal = Bioinformatics | volume = 31 | issue = 20 | pages = 3269–75 | date = October 2015 | pmid = 26079349 | doi = 10.1093/bioinformatics/btv367 | doi-access = free }}] | https://tppred3.biocomp.unibo.it
|2015 |
| TPpred 3.0 | Organelle-targeting peptide detection and cleavage-site prediction ([https://bio.tools/tppred_3.0 bio.tools entry]) | | http://tppred3.biocomp.unibo.it/tppred3
|2015 |
| TTD | Therapeutic Target Database (TTD) has been developed to provide information about therapeutic targets and corresponding drugs. TTD includes information about successful, clinical trial and research targets, approved, clinical trial and experimental drugs linked to their primary targets, new ways to access data by drug mode of action, recursive search of related targets or drugs, similarity target and drug searching, customized and whole data download, and standardized target ID. ([https://bio.tools/ttd bio.tools entry]) | [{{cite journal | vauthors = Zhu F, Han B, Kumar P, Liu X, Ma X, Wei X, Huang L, Guo Y, Han L, Zheng C, Chen Y | title = Update of TTD: Therapeutic Target Database | journal = Nucleic Acids Research | volume = 38 | issue = Database issue | pages = D787–91 | date = January 2010 | pmid = 19933260 | pmc = 2808971 | doi = 10.1093/nar/gkp1014 }}] | http://bidd.nus.edu.sg/group/cjttd/
|2010 |
| UM-PPS | The University of Minnesota Pathway Prediction System (UM-PPS) is a web tool that recognizes functional groups in organic compounds that are potential targets of microbial catabolic reactions and predicts transformations of these groups based on biotransformation rules. Multi-level predictions are made. ([https://bio.tools/um-pps bio.tools entry]) | [{{cite journal | vauthors = Ellis LB, Gao J, Fenner K, Wackett LP | title = The University of Minnesota pathway prediction system: predicting metabolic logic | journal = Nucleic Acids Research | volume = 36 | issue = Web Server issue | pages = W427–32 | date = July 2008 | pmid = 18524801 | pmc = 2447765 | doi = 10.1093/nar/gkn315 }}] | http://eawag-bbd.ethz.ch/predict/aboutPPS.html
|2008 |
| WoLF PSORT | WoLF PSORT is an extension of the PSORT II program for protein subcellular location prediction. ([https://bio.tools/wolf_psort bio.tools entry]) | [{{cite journal | vauthors = Horton P, Park KJ, Obayashi T, Fujita N, Harada H, Adams-Collier CJ, Nakai K | title = WoLF PSORT: protein localization predictor | journal = Nucleic Acids Research | volume = 35 | issue = Web Server issue | pages = W585–7 | date = July 2007 | pmid = 17517783 | pmc = 1933216 | doi = 10.1093/nar/gkm259 }}] | https://wolfpsort.hgc.jp/
|2007 |
| YLoc | YLoc is a web server for the prediction of subcellular localization. Predictions are explained and biological properties used for the prediction highlighted. In addition, a confidence estimates rates the reliability of individual predictions. ([https://bio.tools/yloc bio.tools entry]) | [{{cite journal | vauthors = Briesemeister S, Rahnenführer J, Kohlbacher O | title = YLoc--an interpretable web server for predicting subcellular localization | journal = Nucleic Acids Research | volume = 38 | issue = Web Server issue | pages = W497–502 | date = July 2010 | pmid = 20507917 | pmc = 2896088 | doi = 10.1093/nar/gkq477 }}] | http://www.multiloc.org/YLoc
|2010 |
| Zinc Finger Tools | Zinc Finger Tools provides several tools for selecting zinc finger protein target sites and for designing the proteins that will target them. ([https://bio.tools/zinc_finger_tools bio.tools entry]) | [{{cite journal | vauthors = Blancafort P, Magnenat L, Barbas CF | title = Scanning the human genome with combinatorial transcription factor libraries | journal = Nature Biotechnology | volume = 21 | issue = 3 | pages = 269–74 | date = March 2003 | pmid = 12592412 | doi = 10.1038/nbt794 | s2cid = 9761535 }}][{{cite journal | vauthors = Dreier B, Fuller RP, Segal DJ, Lund CV, Blancafort P, Huber A, Koksch B, Barbas CF | title = Development of zinc finger domains for recognition of the 5'-CNN-3' family DNA sequences and their use in the construction of artificial transcription factors | journal = The Journal of Biological Chemistry | volume = 280 | issue = 42 | pages = 35588–97 | date = October 2005 | pmid = 16107335 | doi = 10.1074/jbc.M506654200 | doi-access = free }}][{{cite journal | vauthors = Dreier B, Segal DJ, Barbas CF | title = Insights into the molecular recognition of the 5'-GNN-3' family of DNA sequences by zinc finger domains | journal = Journal of Molecular Biology | volume = 303 | issue = 4 | pages = 489–502 | date = November 2000 | pmid = 11054286 | doi = 10.1006/jmbi.2000.4133 | s2cid = 11263372 }}][{{cite journal | vauthors = Mandell JG, Barbas CF | title = Zinc Finger Tools: custom DNA-binding domains for transcription factors and nucleases | journal = Nucleic Acids Research | volume = 34 | issue = Web Server issue | pages = W516–23 | date = July 2006 | pmid = 16845061 | pmc = 1538883 | doi = 10.1093/nar/gkl209 }}][{{cite journal | vauthors = Dreier B, Beerli RR, Segal DJ, Flippin JD, Barbas CF | title = Development of zinc finger domains for recognition of the 5'-ANN-3' family of DNA sequences and their use in the construction of artificial transcription factors | journal = The Journal of Biological Chemistry | volume = 276 | issue = 31 | pages = 29466–78 | date = August 2001 | pmid = 11340073 | doi = 10.1074/jbc.M102604200 | doi-access = free }}][{{cite journal | vauthors = Segal DJ, Dreier B, Beerli RR, Barbas CF | title = Toward controlling gene expression at will: selection and design of zinc finger domains recognizing each of the 5'-GNN-3' DNA target sequences | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 96 | issue = 6 | pages = 2758–63 | date = March 1999 | pmid = 10077584 | pmc = 15842 | bibcode = 1999PNAS...96.2758S | doi = 10.1073/pnas.96.6.2758 | doi-access = free }}] | http://www.scripps.edu/mb/barbas/zfdesign/zfdesignhome.php {{Webarchive|url=https://web.archive.org/web/20090917004316/http://www.scripps.edu/mb/barbas/zfdesign/zfdesignhome.php |date=2009-09-17 }}
|2006 |