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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://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://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://katahdin.mssm.edu/kismeth/revpage.pl

A web-based tool for bisulfite sequencing analysis that was designed to be used with plants, since it considers potential cytosine methylation in any sequence context (CG, CHG, and CHH). It provides a tool for the design of bisulfite primers as well as several tools for the analysis of the bisulfite sequencing results. Kismeth is not limited to data from plants, as it can be used with data from any species.

Proper citation: Kismeth (RRID:SCR_005444) 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

http://omicslab.genetics.ac.cn/GOEAST/

Gene Ontology Enrichment Analysis Software Toolkit (GOEAST) is a web based software toolkit providing easy to use, visualizable, comprehensive and unbiased Gene Ontology (GO) analysis for high-throughput experimental results, especially for results from microarray hybridization experiments. The main function of GOEAST is to identify significantly enriched GO terms among give lists of genes using accurate statistical methods. Compared with available GO analysis tools, GOEAST has the following unique features: * GOEAST supports analysis for data from various resources, such as expression data obtained using Affymetrix, illumina, Agilent or customized microarray platforms. GOEAST also supports non-microarray based experimental data. The web-based feature makes GOEAST very user friendly; users only have to provide a list of genes in correct formats. * GOEAST provides visualizable analysis results, by generating graphs exhibiting enriched GO terms as well as their relationships in the whole GO hierarchy. * Note that GOEAST generates separate graph for each of the three GO categories, namely biological process, molecular function and cellular component. * GOEAST allows comparison of results from multiple experiments (see Multi-GOEAST tool). The displayed color of each GO term node in graphs generated by Multi-GOEAST is the combination of different colors used in individual GOEAST analysis. Platform: Online tool

Proper citation: GOEAST - Gene Ontology Enrichment Analysis Software Toolkit (RRID:SCR_006580) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.istge.it/cldb/indexes.html

Hypertext on cell culture availability extracted from the Cell Line Data Base of the Interlab Project. HyperCLDB includes links to records of OMIM, the Online Mendelian Inheritance in Man Catalogue, and now also links to the PubMed, database of bibliographic biomedical references, which are drawn primarily from MEDLINE and PREMEDLINE.

Proper citation: Hyper Cell Line Database (RRID:SCR_007730) 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://bioinformatics.intec.ugent.be/magic/

Web based interface for exploring and analyzing a comprehensive maize-specific cross-platform expression compendium. This compendium was constructed by collecting, homogenizing and formally annotating publicly available microarrays from Gene Expression Omnibus (GEO), and ArrayExpress.

Proper citation: Magic (RRID:SCR_006406) Copy   

•   Source: SciCrunch Registry

http://gump.qimr.edu.au/general/daleN/SNPSpD/

SNPSpD is a method of correcting for non-independance of single nucleotide polymorphisms (SNPs) in linkage disequilibrium (LD) with each other, on the basis of the spectral decomposition (SpD) of matrices of LD between SNP''s. Additionally, output from SNPSpD includes eigenvalues, principal-component coefficients, and factor loadings after varimax rotation, enabling the selection of a subset of SNPs that optimize the information in a genomic region.

Proper citation: Single Nucleotide Polymorphism Spectral Decomposition (SNPSpD) (RRID:SCR_008621) Copy   

•   Source: SciCrunch Registry

http://wwwmgs.bionet.nsc.ru/mgs/programs/panalyst/

WebProAnalyst provides web-accessible analysis for scanning the quantitative structure-activity relationships in protein families. It searches for a sequence region, whose substitutions are correlated with variations in the activities of a homologous protein set, the so-called activity modulating sites. WebProAnalyst allows users to search for the key physicochemical characteristics of the sites that affect the changes in protein activities. It enables the building of multiple linear regression and neural networks models that relate these characteristics to protein activities. WebProAnalyst implements multiple linear regression analysis, back propagation neural networks and the Structure-Activity Correlation/Determination Coefficient (SACC/SADC). A back propagation neural network is implemented as a two-layered network, one layer as input, the other as output (Rumelhart et al, 1986). WebProAnalyst uses alignment of amino acid sequences and data on protein activity (pK, Km, ED50, among others). The input data are the numerical values for the physicochemical characteristics of a site in the multiple alignment given by a slide window. The output data are the predicted activity values. The current version of WebProAnalyst handles a single activity for a single protein. The SACC/SADC may be defined as an estimate of the strongest multiple correlation between the physicochemical characteristics of a site in a multiple alignment and protein activities. The SACC/SADC coefficient makes possible the calculation of the possible highest correlation achievable for the quantitative relationship between the physicochemical properties of sites and protein activities. The SACC/SADC is a convenient means for an arrangement of positions by their functional significance. WebProAnalyst outputs a list of multiple alignment positions, the respective correlation values, also regression analysis parameters for the relationships between the amino acid physicochemical characteristics at these positions and the protein activity values.

Proper citation: Webproanalyst (RRID:SCR_008348) Copy   

•   Source: SciCrunch Registry

http://www.bioinformatics.org/go2msig/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on April 24, 2020. Software tool as automated Gene Ontology based multi species gene set generator for gene set enrichment analysis. Used to generate gene sets required for Gene Set Enrichment Analysis for almost any organism for which GO term association data exists.
Gene set collections can be automatically created for wide variety of species.

Proper citation: GO2MSIG (RRID:SCR_018359) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.oxfordjournals.org/content/early/2012/05/10/bioinformatics.bts271.full.pdf

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on March 7,2024. Software for somatic single nucleotide variant (SNV) and small indel detection from sequencing data of matched tumor-normal samples. The method employs a novel Bayesian approach which represents continuous allele frequencies for both tumor and normal samples, whilst leveraging the expected genotype structure of the normal. This is achieved by representing the normal sample as a mixture of germline variation with noise, and representing the tumor sample as a mixture of the normal sample with somatic variation. A natural consequence of the model structure is that sensitivity can be maintained at high tumor impurity without requiring purity estimates. The method has superior accuracy and sensitivity on impure samples compared to approaches based on either diploid genotype likelihoods or general allele-frequency tests.

Proper citation: Strelka (RRID:SCR_005109) Copy   

•   Source: SciCrunch Registry

http://mendel.stanford.edu/sidowlab/downloads/quest/

A Kernel Density Estimator-based package for analysis of massively parallel sequencing data from chromatin immunoprecipitation (ChIP-seq) experiments.

Proper citation: Quantitative Enrichment of Sequence Tags (RRID:SCR_004065) Copy   

•   Source: SciCrunch Registry

http://www.brl.bcm.tmc.edu/pash/pashDownload.rhtml

Performs sequence comparison and read mapping and can be employed as a module within diverse configurable analysis pipelines, including ChIP-Seq and methylome mapping by whole-genome bisulfite sequencing.

Proper citation: Pash 3.0 (RRID:SCR_004078) Copy   

•   Source: SciCrunch Registry

http://epigraph.mpi-inf.mpg.de/WebGRAPH/

A software for genome and epigenome analysis., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: EpiGRAPH (RRID:SCR_004326) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/insertionmapper/

A pipeline tool for the identification of targeted sequences from multidimensional high throughput sequencing data. It consists of four independently working modules: Data Preprocessing, Database Modeling, Dimension Deconvolution and Element Mapping. This pipeline tool is applicable to scenarios requiring analysis of the tremendous output of short reads produced in NGS sequencing experiments of targeted genome sequences.

Proper citation: InsertionMapper (RRID:SCR_004163) Copy   

•   Source: SciCrunch Registry

https://bcbio-nextgen.readthedocs.org/en/latest/

A python toolkit providing best-practice pipelines for fully automated high throughput sequencing analysis.

Proper citation: bcbio-nextgen (RRID:SCR_004316) Copy   

•   Source: SciCrunch Registry

http://genome.gsc.riken.jp/osc/english/dataresource/

A program to eliminate artifactual reads from next-generation sequencing data sets.

Proper citation: TagDust (RRID:SCR_004175) Copy   

•   Source: SciCrunch Registry

http://www.sanger.ac.uk/resources/software/artemis/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Free genome browser and annotation tool that allows visualization of sequence features, next generation data and the results of analyses within the context of the sequence, and also its six-frame translation. Artemis is free software and is distributed under the terms of the GNU General Public License. Artemis is written in Java, and is available for UNIX, Macintosh and Windows systems. It can read EMBL and GENBANK database entries or sequence in FASTA, indexed FASTA or raw format. Other sequence features can be in EMBL, GENBANK or GFF format.

Proper citation: Artemis: Genome Browser and Annotation Tool (RRID:SCR_004267) Copy   

•   Source: SciCrunch Registry