Tools   Select Another Resource Report Type

http://xldb.fc.ul.pt/biotools/rebil/goa/

A tool for assisting the GO annotation of UniProt entries by linking the GO terms present in the uncurated annotations with evidence text automatically extracted from the documents linked to UniProt entries. Platform: Online tool

Proper citation: GoAnnotator (RRID:SCR_005792) Copy   

•   Source: SciCrunch Registry

https://www.facebase.org/node/252

THIS RESOURCE IS NO LONGER IN SERVICE,documented on January,18, 2022. FaceBase Biorepository is now collecting biological samples from people with cleft lip/palate and their family members. Information for Prospective Cases: Clefts of the lip and/or palate can be caused by a wide range of genetic, environmental and other factors. The FaceBase Biorepository will serve as a common source of both biological samples and information that can be made available to investigators trying to determine the underlying cause of these common birth defects. Genetic studies, in particular, will benefit from both family history information and having samples from affected individuals as well as their family members. DNA is the information containing molecules found in all the cells of our body and can be easily obtained from material such as blood or saliva samples. As part of the FaceBase Biorepository, we are requesting families to submit biological samples from specific family members as well as information from other family members that might be affected with either the same condition or a similar condition. The medical and family history information that is collected includes other relevant information such as exposure to possible environmental causes during pregnancy. The biorepository is managed by Nichole Nidey, a research study coordinator, and Jeff Murray, a pediatric clinical geneticist and researcher. They are available to speak with family members regarding questions they may have, including providing information about the biorepository and making arrangements for the collection of samples for those who wish to participate. All participation is voluntary. Your name or other personally identifiable information (name, address, etc) will be removed before information is placed in the biorepository. Summary data to show how the database itself has been used overall as well as updates on whether specific findings might have been made using this database will be available on the FaceBase website at www.facebase.org. A newsletter containing this information will also be given to families and referring clinicians so that they may discuss the specifics with the families if there appears to be information that might be relevant in a particular case. Families will also need to sign a consent form that has been approved by the Institutional Review Board at the University of Iowa. Also, any submitted samples or data can also be removed from the database at any time should the family no longer wish to participate. Investigators interested in requesting DNA samples or for more information, please contact cleftresearch (at) uiowa.edu, Nichole Nidey, nichole-nidey (at) uiowa.edu or (319) 353-4365, or Jeff Murray, jeff-murray (at) uiowa.edu.

Proper citation: FaceBase Biorepository (RRID:SCR_006001) Copy   

•   Source: SciCrunch Registry

http://pipeclip.qbrc.org/

A Galaxy framework-based online pipeline for reliable analysis of data generated by three types of CLIP-seq protocols: HITS-CLIP, PAR-CLIP and iCLIP. It provides both data processing and statistical analysis to determine candidate cross-linking regions, which are comparable to those regions identified from the original studies or using existing computational tools., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: PIPE-CLIP (RRID:SCR_005820) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/expressionprofiler/

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 2, 2016. The EP:GO browser is built into EBI's Expression Profiler, a set of tools for clustering, analysis and visualization of gene expression and other genomic data. With it, you can search for GO terms and identify gene associations for a node, with or without associated subnodes, for the organism of your choice.

Proper citation: Expression Profiler (RRID:SCR_005821) Copy   

•   Source: SciCrunch Registry

http://www.snubi.org/software/GOChase/

GOChase is a set of web-based utilities to detect and correct the errors in GO-based annotations. # GOChase-History resolves the whole modification history of GO IDs. # GOChase-Correct highlights merged GO IDs and redirects to the correct primary term into which the secondary ID was merged. For obsolete GO terms, the nearest non-discarded parent term is recommended by GOChase. This function may be used by GO browsers such as AmiGO and QuickGO to fix broken hyperlinks. # A whole database (such as LocusLink) as a flat file can be loaded into GOChase, reporting the annotation errors and GOChase corrections. # When one inputs a GO ID, GOChase will resolve all gene products annotated with the GO ID across all the major databases. Platform: Online tool

Proper citation: GOChase (RRID:SCR_005822) Copy   

•   Source: SciCrunch Registry

http://www.sapientaproject.com/

Software to help researchers process scientific papers faster and get the information they are interested in out of them. This is achieved by automating the recognition of core scientific concepts such as Motivation, Method, Result, Conclusion in papers and uses them to generate automatic summaries. This SAPIENTA tool adds additional functionality to the SAPIENT tool, an annotation tool implemented as a web application which enables experts to annotate scientific papers, sentence by sentence manually, according to the Core Scientific Concept (CSC) schema.

Proper citation: Sapienta (RRID:SCR_006993) Copy   

•   Source: SciCrunch Registry

http://athina.biol.uoa.gr/bioinformatics/mcmbb/

A web tool used in the discrimination of beta-barrel outer membrane proteins with a Markov chain model. MCMBB is a fast algorithm, which discriminates beta-barrel outer membrane proteins from globular proteins and from alpha-helical membrane proteins. The algorithm is based on a 1st order Markov Chain model, which captures the alternating pattern of hydrophilic-hydrophobic residues occurring in the membrane-spanning beta-strands of beta-barrel outer membrane proteins. The model achieves high accuracy in discriminating outer membrane proteins, since it can discriminate beta-barrel outer membrane with a correct classification rate of 90.08% and the globular proteins with a correct classification rate of 92.67%. When submitting alpha-helical membrane proteins, the method shows an accuracy of 100%. A score greater than zero, indicates that the protein is more likely to be a beta-barrel outer membrane protein, whereas a result lower than zero, indicates that the protein is probable not a beta-barrel. You may enter up to 1000 sequences in Fasta format.

Proper citation: MCMBB (RRID:SCR_006198) Copy   

•   Source: SciCrunch Registry

http://athina.biol.uoa.gr/bioinformatics/waveTM/

A web tool for the prediction of transmembrane segments in alpha-helical membrane proteins. A sliding window of 20 residues is used in order to calculate an average residue hydrophobicity profile, using a hydrophobicity scale. Discrete Wavelet Transform is applied on the average residue hydrophobicity signal and the different frequency coefficients produced are adaptively thresholded so that a denoised signal is reconstructed. A dynamic programming algorithm processes the denoised signal to provide the optimal model for the number, the length and the location of membrane-spanning segments. The end points of the predicted segments are extended to include flanking hydrophobic residues. Topology prediction can also be obtained in conjunction with OrienTM (Liakopoulos et al, 2001). Analysis of a non-redundant test set, provides a ~95% per segment accuracy and ~90% per residue accuracy. Now, you can: * Run waveTM on a sequence * Browse the results obtained with the algorithm * View additional material concerning the hydrophobicity scale

Proper citation: waveTM (RRID:SCR_006199) Copy   

•   Source: SciCrunch Registry

http://athina.biol.uoa.gr/bioinformatics/PRED-COUPLE2/

A tool that predicts the coupling specificity of G-protein coupled receptors to G-proteins. We present a method that combines hidden Markov models and a feed-forward artificial neural network to overcome these limitations, while producing the most accurate predictions currently available. Using an up-to-date curated dataset, our method yields a 94% correct classification rate in a 5-fold cross-validation test. The method predicts also promiscuous coupling preferences, including coupling to G12/13, whereas unlike other methods avoids overpredictions (false positives) when non-GPCR sequences are encountered. * The PRED-COUPLE 2.00 system predicts coupling specificty of GPCRs to all families of G-proteins (including G12/13). * Coupling to more than one G-protein families can also be predicted with this tool. * No membrane topology information is required. Furthermore, no membrane topology prediction is executed by this method. * The method is based on a refined library of highly-discriminative Hidden Markov Models. Hits from individual profiles are combined by a feed-forward Artificial Neural Network to produce the final output. * Seven (7) transmembrane receptor signatures from the Pfam database version 17.00 are also applied in order to verify a true GPCR sequence. When a query sequence is not recognized as a 7 transmembrane receptor a message is shown.

Proper citation: PRED-COUPLE 2 (RRID:SCR_006193) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.biol.uoa.gr/PRED-TMBB/

A web tool, based on a Hidden Markov Model, capable of predicting the transmembrane beta-strands of the gram-negative bacteria outer membrane proteins, and of discriminating such proteins from water-soluble ones when screening large datasets. The model is trained in a discriminative manner, aiming at maximizing the probability of the correct prediction rather than the likelihood of the sequences. The training is performed on a non-redundant database consisting of 16 outer membrane proteins (OMP''s) with their structures known at atomic resolution. We show that we can achieve predictions at least as good comparing with other existing methods, using as input only the amino-acid sequence, without the need of evolutionary information included in multiple alignments. The method is also powerful when used for discrimination purposes, as it can discriminate with a high accuracy the outer membrane proteins from water soluble in large datasets, making it a quite reliable solution for screening entire genomes. This web-server can help you run a discriminating process on any amino-acid sequence and thereafter localize the transmembrane strands and find the topology of the loops.

Proper citation: PRED-TMBB (RRID:SCR_006190) Copy   

•   Source: SciCrunch Registry

http://www.uniprot.org/uniparc/

Database that contains publicly available protein sequences with stable and unique identifiers (UPI) which are never removed, changed or reassigned. UniParc tracks sequence changes in the source databases and archives the history of all changes. Information other than protein sequence must be retrieved from the UniParc source databases using the database cross-references.

Proper citation: UniParc (RRID:SCR_005818) Copy   

•   Source: SciCrunch Registry

http://lussierlab.org/GO-Module/GOModule.cgi

GO-Module provides an interface to reduce the dimensionality of GO enrichment results and produce interpretable biomodules of significant GO terms organized by hierarchical knowledge that contain only true positive results. Users can download a text file of GO terms annotated with their significance and identified biomodules, a network visualization of resultant GO IDs or terms in PDF format, and view results in an online table. Platform: Online tool

Proper citation: GO-Module (RRID:SCR_005813) Copy   

•   Source: SciCrunch Registry

http://python-xy.github.io/

Scientific and engineering development software for numerical computations, data analysis and data visualization based on Python programming language, Qt graphical user interfaces and Spyder interactive scientific development environment. Used to interpreted languages (such as MATLAB or IDL) or compiled languages (C/C++ or Fortran) to switch to Python.

Proper citation: Pythonxy (RRID:SCR_006903) Copy   

•   Source: SciCrunch Registry

http://iresite.org/

Database of experimentally verified IRES structures. Presents information about experimentally studied Internal Ribosome Entry Site segments.

Proper citation: IRESite (RRID:SCR_007753) Copy   

•   Source: SciCrunch Registry

http://gtlinker.cnb.csic.es/

Web application that filters and links enriched output data identifying sets of associated genes and terms, producing metagroups of coherent biological significance. The method uses fuzzy reciprocal linkage between genes and terms to unravel their functional convergence and associations. It can also be accessed through its web service.

Proper citation: GeneTerm Linker (RRID:SCR_006385) Copy   

•   Source: SciCrunch Registry

http://atlasgeneticsoncology.org/

Online journal and database devoted to genes, cytogenetics, and clinical entities in cancer, and cancer-prone diseases. Its aim is to cover the entire field under study and it presents concise and updated reviews (cards) or longer texts (deep insights) concerning topics in cancer research and genomics.

Proper citation: Atlas of Genetics and Cytogenetics in Oncology and Haematology (RRID:SCR_007199) Copy   

•   Source: SciCrunch Registry

http://bioapps.rit.albany.edu/MITOPRED/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. It predicts nuclear-encoded mitochondrial proteins from all eukaryotic species including plants. Prediction is based on the occurrence patterns of Pfam domains (version 16.0) in different cellular locations, amino acid composition and pI value differences between mitochondrial and non-mitochondrial locations. Additionally, you may download MITOPRED predictions for complete proteomes. Re-calculated predictions are instantly accessible for proteomes of Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila, Homo sapiens, Mus musculus and Arabidopsis species as well as all the eukaryotic sequences in the Swiss-Prot and TrEMBL databases. Queries, at different confidence levels, can be made through four distinct options: (i) entering Swiss-Prot/TrEMBL accession numbers; (ii) uploading a local file with such accession numbers; (iii) entering protein sequences; (iv) uploading a local file containing protein sequences in FASTA format. The Mitopred algorithm works based on the differences in the Pfam domain occurrence patters and amino acid composition differences in different cellular compartments. Location specific Pfam domains have been determined from the entire eukaryotic set of Swissprot database. Similarly, differences in the amino acid composition between mitochondrial and non-mitochondrial sequences were pre-calculated. This information is used to calculate location-specific amino acid weights that are used to calculate amino acid score. Similarly, pI average values of the N-terminal 25 residues in different cellular location were also determined. This knowledge-base is accessed by the program during execution.

Proper citation: mitopred (RRID:SCR_006135) Copy   

•   Source: SciCrunch Registry

http://igs-server.cnrs-mrs.fr/mgdb/Rickettsia/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 18, 2016. Rickettsia are obligate intracellular bacteria living in arthropods. They occasionally cause diseases in humans. To understand their pathogenicity, physiologies and evolutionary mechanisms, RicBase is sequencing different species of Rickettsia. Up to now we have determined the genome sequences of R. conorii, R. felis, R. bellii, R. africae, and R. massiliae. The RicBase aims to organize the genomic data to assist followup studies of Rickettsia. This website contains information on R. conorii and R. prowazekii. A R. conorii and R. prowazekii comparative genome map is also available. Images of genome maps, dendrogram, and sequence alignment allow users to gain a visualization of the diagrams.

Proper citation: Rickettsia Genome Database (RRID:SCR_007102) Copy   

•   Source: SciCrunch Registry

http://www.nactem.ac.uk/medie/

An intelligent search engine to retrieve biomedical correlations from MEDLINE, based on indexing by Natural Language Processing and Text Mining techniques. You can find abstracts/sentences in MEDLINE by specifying semantics of correlations; for example, What activates p53 and What causes colon cancer. Semantic search uses a semantic query for finding biomedical correlations. Input a subject, a verb, and an object of a concept (or either of them) into a form. Results of the query will be shown in a second. (E.g., What does p53 activate? (subject=p53, verb=activate)) Reference: Miyao, Yusuke, Tomoko Ohta, Katsuya Masuda, Yoshimasa Tsuruoka, Kazuhiro Yoshida, Takashi Ninomiya and Jun''''ichi Tsujii (2006) Semantic Retrieval for the Accurate Identification of Relational Concepts in Massive Textbases. Proceedings COLING-ACL 2006. Sydney, Australia, pp. 1017--1024.

Proper citation: MEDIE (RRID:SCR_006254) Copy   

•   Source: SciCrunch Registry

http://genetrail.bioinf.uni-sb.de/

A web-based application that analyzes gene sets for statistically significant accumulations of genes that belong to some functional category. Considered category types are: KEGG Pathways, TRANSPATH Pathways, TRANSFAC Transcription Factor, GeneOntology Categories, Genomic Localization, Protein-Protein Interactions, Coiled-coil domains, Granzyme-B clevage sites, and ELR/RGD motifs. The web server provides two statistical approaches, "Over-Representation Analysis" (ORA) comparing a reference set of genes to a test set, and "Gene Set Enrichment Analysis" (GSEA) scoring sorted lists of genes., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GeneTrail (RRID:SCR_006250) Copy   

•   Source: SciCrunch Registry