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http://www.psb.ugent.be/cbd/papers/BiNGO/Home.html

The Biological Networks Gene Ontology tool (BiNGO) is an open-source Java tool to determine which Gene Ontology (GO) terms are significantly overrepresented in a set of genes. BiNGO can be used either on a list of genes, pasted as text, or interactively on subgraphs of biological networks visualized in Cytoscape. BiNGO maps the predominant functional themes of the tested gene set on the GO hierarchy, and takes advantage of Cytoscape''''s versatile visualization environment to produce an intuitive and customizable visual representation of the results. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: BiNGO: A Biological Networks Gene Ontology tool (RRID:SCR_005736) Copy   

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http://etest.vbi.vt.edu/etblast3/

eTBLAST is a unique search engine for searching biomedical literature. Our service is very different from PubMed. While PubMed searches for keywords, our search engine lets you input an entire paragraph and returns MEDLINE abstracts that are similar to it. This is something like PubMed''s Related Articles feature, only better because it runs on your unique set of interests. For example, input the abstract of an unpublished paper or a grant proposal into our engine, and with the touch of a button you''ll be able to find every abstract in MEDLINE dealing with your topic. No more guessing whether your set of keywords has found all the right papers. No more sorting through hundreds of papers you don''t care about to find the handful you were looking for--our search engine does it for you. When most people use PubMed to search MEDLINE they pick one or two keywords to describe their topic, then browse through a long list of results. When they find a paper that looks interesting they click on its Related Articles, in hopes of finding more papers like that one. If they find another relevant paper, they explore it''s related articles--and so on. This process of culling long lists of documents by hand makes literature searching tedious and time consuming. We make it easier for you by providing better results the first time, and then allowing you to automatically combine the papers you care about for a second round. Our Iterate feature allows you to checkmark the abstracts you found interesting in the first round and combine them all to create a new query. It''s like rolling several Related Articles lists into one. * We sort our results by relevance, while PubMed sorts by date. * We save you the time and effort of creating a complicated query. * We let you iterate your search over several good papers to narrow your focus. * We provide you the full MEDLINE abstract in our results, and a link to the PubMed page. * We can send your results straight to your email so you never lose a reference or forget where you found it. * This absolutely free service is provided by the University of Texas Southwestern Medical Center. No registration necessary!

Proper citation: eTBlast (RRID:SCR_008188) Copy   

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http://ophid.utoronto.ca/navigator/

A software package for visualizing and analyzing protein-protein interaction networks. NAViGaTOR can query OPHID / I2D - online databases of interaction data - and display networks in 2D or 3D. To improve scalability and performance, NAViGaTOR combines Java with OpenGL to provide a 2D/3D visualization system on multiple hardware platforms. NAViGaTOR also provides analytical capabilities and supports standard import and export formats such as GO and the Proteomics Standards Initiative (PSI). NAViGaTOR can be installed and run on Microsoft Windows, Linux / UNIX, and Mac OS systems. NAViGaTOR is written in Java and uses JOGL (Java bindings for OpenGL) to support scalability, highlighting or suppressing of information, and other advanced graphic approaches.

Proper citation: Network Analysis, Visualization and Graphing TORonto (RRID:SCR_008373) Copy   

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http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi

This server provides programs, web services, and databases, related to our work on RNA secondary structures. For general information and other offerings from our group see the main TBI web server. With the 1st of May 2009 we updated our servers to the Vienna RNA package version 1.8.2! The Vienna RNA Servers: * RNAfold server predicts minimum free energy structures and base pair probabilities from single RNA or DNA sequences. * RNAalifold server predicts consensus secondary structures from an alignment of several related RNA or DNA sequences. You need to upload an alignment. * RNAinverse server allows you to design RNA sequences for any desired target secondary structure. * RNAcofold server allows you to predict the secondary structure of a dimer. * RNAup server allows you to predict the accessibility of a target region. * LocARNA server generates structural alignments from a set of sequences. In collaboration with the Bioinformatics Group Freiburg. * barriers server allows you to get insights into RNA folding kinetics. * RNAz server will assist you in detecting thermodynamically stable and evolutionarily conserved RNA secondary structures in multiple sequence alignments. * Structure conservation analysis server will assist you in detecting evolutionarily conserved RNA secondary structures in multiple sequence alignments. * RNAstrand server allows you to predict the reading direction of evolutionarily conserved RNA secondary structures. * RNAxs server assists you in siRNA design. * Bcheck predicts rnpB genes Downloads Get the Source code for: * the Vienna RNA Package, our basic RNA secondary structure analysis software. * The ALIDOT package for finding conserved structure motifs (add-on) * The barriers program for analysis of RNA folding landscapes. Databases * Atlas of conserved Viral RNA Structures found by ALIDOT

Proper citation: Vienna RNA (RRID:SCR_008550) Copy   

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http://lowelab.ucsc.edu/tRNAscan-SE

Web server to search for tRNA genes in genomic sequence. If you would like to run tRNAscan-SE locally, you can get the UNIX source code (gzip''d tar file).

Proper citation: tRNAscan-SE (RRID:SCR_008637) Copy   

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http://steps.sourceforge.net/STEPS/default.php

STEPS is a package for exact stochastic simulation of reaction-diffusion systems in realistic, complex 3D geometries. Our core simulation algorithm is an efficient implementation of a variation on Gillespie''s SSA, extended to deal with diffusion of molecules over the elements of a 3D tetrahedral mesh. While it was mainly developed for simulating detailed models of neuronal signaling pathways in dendrites and around synapses, it is a general tool and can be used for studying any biochemical pathway in which spatial gradients and morphology are thought to play a role. We have implemented STEPS as a set of Python modules, which means STEPS users can use Python scripts to control all aspects of setting up the model, generating a mesh, controlling the simulation and generating and analyzing output. The core computational routines are still implemented as C/C++ extension modules for maximal speed of execution.

Proper citation: STEPS (RRID:SCR_008742) Copy   

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http://iubio.bio.indiana.edu:8089/

Provides summary of gene and genomic information from eukaryotic organism databases. This includes gene symbol and full name, chromosome, genetic and molecular map information, Gene Ontology (Function/Location/Process) and gene homology, product information, links to extended gene information.

Proper citation: Eukaryote Genes (RRID:SCR_008617) Copy   

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https://github.com/uclinfectionimmunity/Decombinator

Software suite for analysis of T cell receptor repertoire data. Used for fast, efficient analysis of T cell receptor (TcR) repertoire samples, designed to be accessible to those with no previous programming experience.

Proper citation: Decombinator (RRID:SCR_006732) Copy   

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http://dgidb.genome.wustl.edu/

A database of drug-gene relationships that provides drug-gene interactions and potential druggability data given list of genes. There are about 15 data sources that are being aggregated by DGIdb, with update date and these data sources are listed on this page: http://dgidb.genome.wustl.edu/sources, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: DGIdb (RRID:SCR_006608) Copy   

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http://www.pseudomonas.com/

Database of peer-reviewed, continually updated annotation for the Pseudomonas aeruginosa PAO1 reference strain genome expanded to include all Pseudomonas species to facilitate cross-strain and cross-species genome comparisons with high quality comparative genomics. The database contains robust assessment of orthologs, a novel ortholog clustering method, and incorporates five views of the data at the sequence and annotation levels (Gbrowse, Mauve and custom views) to facilitate genome comparisons. Other features include more accurate protein subcellular localization predictions and a user-friendly, Boolean searchable log file of updates for the reference strain PAO1. The current annotation is updated using recent research literature and peer-reviewed submissions by a worldwide community of PseudoCAP (Pseudomonas aeruginosa Community Annotation Project) participating researchers. If you are interested in participating, you are invited to get involved. Many annotations, DNA sequences, Orthologs, Intergenic DNA, and Protein sequences are available for download.

Proper citation: Pseudomonas Genome Database (RRID:SCR_006590) Copy   

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http://sammeth.net/confluence/display/FLUX/Home

Software to recontruct abundances of known transcript forms from RNAseq data. The algorithm works by distributing the reads mapping to a given exonic region (or splice junction) among the transcripts including the exon (or splice junction). The input is the annotation of a reference transcriptome and reads from RNAseq technologies aligned to the genome. From the reference annotation, splicing graphs are produced and reads are mapped to corresponding edges in these graphs according to the position where they align in the genomic sequence. The resulting graph with edges labelled by the number of reads can be interpreted as a flow network where each transcript representing a transportation path from its start to its end and consequently each edge a possibly shared segment of transportation along which a certain number of reads per nucleotide -- i.e., a flux -- is observed. Given a density function of reads along a transcript, the expected participation of each transcript in an edge under consideration can be estimated. The basic idea is to cast back from these latter participations and the observed number of reads - allowing for a certain amount of noise - to the original transcript abundancies. To do so, a linear constraint is formalized for each edge, and an optimal solution for the complete set of constraints is found by a standard linear program solver.

Proper citation: FLUX CAPACITOR (RRID:SCR_006651) Copy   

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http://www.maizegdb.org

Collection of data related to crop plant and model organism Zea mays. Used to synthesize, display, and provide access to maize genomics and genetics data, prioritizing mutant and phenotype data and tools, structural and genetic map sets, and gene models and to provide support services to the community of maize researchers. Data stored at MaizeGDB was inherited from the MaizeDB and ZmDB projects. Sequence data are from GenBank. Data are searchable by phenotype, traits, Pests, Gel Pattern, and Mutant Images.

Proper citation: MaizeGDB (RRID:SCR_006600) Copy   

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http://www.proteinatlas.org/

Open access resource for human proteins. Used to search for specific genes or proteins or explore different resources, each focusing on particular aspect of the genome-wide analysis of the human proteins: Tissue, Brain, Single Cell, Subcellular, Cancer, Blood, Cell line, Structure and Interaction. Swedish-based program to map all human proteins in cells, tissues, and organs using integration of various omics technologies, including antibody-based imaging, mass spectrometry-based proteomics, transcriptomics, and systems biology. All the data in the knowledge resource is open access to allow scientists both in academia and industry to freely access the data for exploration of the human proteome.

Proper citation: The Human Protein Atlas (RRID:SCR_006710) Copy   

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http://www.niehs.nih.gov/research/resources/software/biostatistics/eagleview/

An information-rich viewer for next-generation genome assembles with data integration capability. EagleView can display a dozen different types of information including base qualities, machine specific trace signals, and genome feature annotations. It provides an easy way for inspecting visually the quality of a genome assembly and validating polymorphism candidate sites (e.g., SNPs) reported by polymorphism discovery tools. It can also facilitate data interpretation and hypothesis generation. EagleView is a multi-platform application developed with C++ and is available for all three major platforms: Windows, Linux, and Mac OS.

Proper citation: EagleView (RRID:SCR_006859) Copy   

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http://bar.utoronto.ca/welcome.htm

Web-based tools for working with functional genomics and other data, including Gene Expression and Protein Tools, Molecular Markers and Mapping Tools, and Other Genomic Tools. Most are designed with the plant (mainly Arabidopsis) researcher in mind, but a couple of them can be useful to the wider research community, e.g. Mouse eFP Browser or BlastDigester. The associated paper for most tools is available.

Proper citation: BAR (RRID:SCR_006748) Copy   

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http://sourceforge.net/projects/bigpre/

A quality assessment software package for next-genomics sequencing data.

Proper citation: BIGpre (RRID:SCR_006781) Copy   

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http://bioconductor.org/packages/2.12/bioc/html/seqbias.html

Software package that implements a model of per-position sequencing bias in high-throughput sequencing data using a simple Bayesian network, the structure and parameters of which are trained on a set of aligned reads and a reference genome sequence.

Proper citation: seqbias (RRID:SCR_006832) Copy   

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http://phenom.ccbr.utoronto.ca/index.jsp

Database of morphological phenotypes caused by mutation of essential genes in Saccharomyces cerevisiae, it allows storing, retrieving, visualizing and data mining the quantitative single-cell measurements extracted from micrographs of the temperature-sensitive (ts) mutant cells. PhenoM allows users to rapidly search and retrieve raw images and their quantified morphological data for genes of interest. The database also provides several data-mining tools, including a PhenoBlast module for phenotypic comparison between mutant strains and a Gene Ontology module for functional enrichment analysis of gene sets showing similar morphological alterations. About one-fifth of the genes in the budding yeast are essential for haploid viability and cannot be functionally assessed using standard genetic approaches such as gene deletion. To facilitate genetic analysis of essential genes, we and others have assembled collections of yeast strains expressing temperature-sensitive (ts) alleles of essential genes. To explore the phenotypes caused by essential gene mutation we used a panel of genetically engineered fluorescent markers to explore the morphology of cells in the ts strain collection using high-throughput microscopy. The database contains quantitative measurements of 1,909,914 cells and 78,194 morphological images for 775 temperature-sensitive mutants spanning 491 different essential genes in permissive temperature (26* C) and restrictive temperature (32* C). The morphological images were generated by high-content screening (HCS) technology.

Proper citation: PhenoM - Phenomics of yeast Mutants (RRID:SCR_006970) Copy   

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http://tritrypdb.org/tritrypdb/

An integrated genomic and functional genomic database providing access to genome-scale datasets for kinetoplastid parasites, and supporting a variety of complex queries driven by research and development needs. Currently, TriTrypDB integrates datasets from Leishmania braziliensis, L. infantum, L. major, L. tarentolae, Trypanosoma brucei and T. cruzi. Users may examine individual genes or chromosomal spans in their genomic context, including syntenic alignments with other kinetoplastid organisms. Data within TriTrypDB can be interrogated utilizing a sophisticated search strategy system that enables a user to construct complex queries combining multiple data types. All search strategies are stored, allowing future access and integrated searches. ''''User Comments'''' may be added to any gene page, enhancing available annotation; such comments become immediately searchable via the text search, and are forwarded to curators for incorporation into the reference annotation when appropriate. TriTrypDB provides programmatic access to its searches, via REST Web Services. The result of a web service request is a list of records (genes, ESTs, etc) in either XML or JSON format. REST services can be executed in a browser by typing a specific URL. TriTrypDB and its continued development are possible through the collaborative efforts between EuPathDB, GeneDB and colleagues at the Seattle Biomedical Research Institute (SBRI).

Proper citation: TriTrypDB (RRID:SCR_007043) Copy   

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http://bioinfo.cau.edu.cn/agriGO/

A web-based tool and database for the gene ontology analysis. Its focus is on agricultural species and is user-friendly. The agriGO is designed to provide deep support to agricultural community in the realm of ontology analysis. Compared to other available GO analysis tools, unique advantages and features of agriGO are: # The agriGO especially focuses on agricultural species. It supports 45 species and 292 datatypes currently. And agriGO is designed as an user-friendly web server. # New tools including PAGE (Parametric Analysis of Gene set Enrichment), BLAST4ID (Transfer IDs by BLAST) and SEACOMPARE (Cross comparison of SEA) were developed. The arrival of these tools provides users with possibilities for data mining and systematic result exploration and will allow better data analysis and interpretation. # The exploratory capability and result visualization are enhanced. Results are provided in different formats: HTML tables, tabulated text files, hierarchical tree graphs, and flash bar graphs. # In agriGO, PAGE and SEACOMPARE can be used to carry out cross-comparisons of results derived from different data sets, which is very important when studying multiple groups of experiments, such as in time-course research. Platform: Online tool, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: agriGO (RRID:SCR_006989) Copy   

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