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http://www-bionet.sscc.ru/sitex/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 19,2019. Analyzing protein structure projection on exon-intron structure of corresponding gene through years led to several fundamental conclusions about structural and functional organization of the protein. According to these results we decided to map the protein functional sites. So we created the database SitEx that keep the information about this mapping and included the BLAST search and 3D similar structure search using PDB3DScan for the polypeptide encoded by one exon, participating in organizing the functional site. This will help: # to study the positions of the functional sites in exon structure; # to make the complex analysis of the protein function; # to exposure the exons that took part in exon shuffling and came from bacterial genomes; # to study the peculiarities of coding the polypeptide structures. Currently, SitEx contains information about 9994 functional sites presented in 2021 proteins described in proteomes of 17 organisms.

Proper citation: SitEx (RRID:SCR_006122) Copy   

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https://compbio.dfci.harvard.edu/predictivenetworks//

A flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these ''known'' interactions together with gene expression data to infer robust gene networks. The regression-based network inference algorithm creates a graph of gene interactions in which cycles may be present (but no self-loops). Based on information-theoretic techniques, a causal gene interaction network is inferred from both prior knowledge (interactions extracted from biomedical literature and structured biological databases) and gene expression data. A prediction model is fitted for each gene, given its parents, enabling assessment of the predictive ability of the network model.

Proper citation: Predictive Networks (RRID:SCR_006110) Copy   

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http://202.38.126.151:8080/SDisease/

Curated database of experimentally supported data of RNA Splicing mutation and disease. The RNA Splicing mutations include cis-acting mutations that disrupt splicing and trans-acting mutations that affecting RNA-dependent functions that cause disease. Information such as EntrezGeneID, gene genomic sequence, mutation (nucleotide substitutions, deletions and insertions), mutation location within the gene, organism, detailed description of the splicing mutation and references are also given. Users are able to submit new entries to the database. This database integrating RNA splicing and disease associations would be helpful for understanding not only the RNA splicing but also its contribution to disease. In SpliceDisease database, they manually curated 2337 splicing mutation disease entries involving 303 genes and 370 diseases, which have been supported experimentally in 898 publications. The SpliceDisease database provides information including the change of the nucleotide in the sequence, the location of the mutation on the gene, the reference PubMed ID and detailed description for the relationship among gene mutations, splicing defects and diseases. They standardized the names of the diseases and genes and provided links for these genes to NCBI and UCSC genome browser for further annotation and genomic sequences. For the location of the mutation, they give direct links of the entry to the respective position/region in the genome browser.

Proper citation: SpliceDisease (RRID:SCR_006130) Copy   

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https://bioconductor.org/packages//2.11/bioc/html/gprege.html

Software R package for Gaussian Process Ranking and Estimation of Gene Expression time-series. The software fits two Gaussian processes (GPs) with an radial basis function (RBF) (+ noise diagonal) kernel on each profile. One GP kernel is initialized wih a short lengthscale hyperparameter, signal variance as the observed variance and a zero noise variance. It is optimized via scaled conjugate gradients (netlab). A second GP has fixed hyperparameters: zero inverse-width, zero signal variance and noise variance as the observed variance. The log-ratio of marginal likelihoods of the two hypotheses acts as a score of differential expression for the profile. Comparison via receiver operating characteristic curves (ROC curves) is performed against Bayesian hierarchical model for the analysis of time-series (BATS) (Angelini et.al, 2007).

Proper citation: gprege (RRID:SCR_001324) Copy   

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http://sourceforge.net/projects/kanalyze/

A Java toolkit designed to convert DNA and RNA sequences into k-mers.

Proper citation: KAnalyze (RRID:SCR_001323) Copy   

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http://www.bioconductor.org/packages/release/bioc/html/beadarray.html

Software package to read bead-level data (raw TIFFs and text files) output by BeadScan as well as bead-summary data from BeadStudio. Methods for quality assessment and low-level analysis are provided.

Proper citation: beadarray (RRID:SCR_001314) Copy   

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http://www.bioconductor.org/packages/release/bioc/html/macat.html

Software library that contains functions to investigate links between differential gene expression and the chromosomal localization of the genes. It is motivated by the common observation of phenomena involving large chromosomal regions in tumor cells. MACAT is the implementation of a statistical approach for identifying significantly differentially expressed chromosome regions.

Proper citation: MACAT (RRID:SCR_001350) Copy   

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http://www.bioconductor.org/packages/release/bioc/html/lapmix.html

Software to identify differentially expressed genes. A hierarchical Bayesian approach is used, and the hyperparameters are estimated using empirical Bayes.

Proper citation: lapmix (RRID:SCR_001347) Copy   

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http://www.bioinf.jku.at/software/farms/farms.html

Software using a model-based technique for summarizing high-density oligonucleotide array data at probe level for Affymetrix GeneChips. It is based on a factor analysis model for which a Bayesian maximum a posteriori method optimizes the model parameters under the assumption of Gaussian measurement noise.

Proper citation: FARMS (RRID:SCR_001344) Copy   

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https://bioconductor.org/packages//2.11/bioc/html/bridge.html

Software package to test for differentially expressed genes with microarray data. It can be used with both cDNA microarrays or Affymetrix chip. The packge fits a robust Bayesian hierarchical model for testing for differential expression. Outliers are modeled explicitly using a $t$-distribution. The model includes an exchangeable prior for the variances which allow different variances for the genes but still shrink extreme empirical variances. The model can be used for testing for differentially expressed genes among multiple samples, and can distinguish between the different possible patterns of differential expression when there are three or more samples. Parameter estimation is carried out using a novel version of Markov Chain Monte Carlo that is appropriate when the model puts mass on subspaces of the full parameter space.

Proper citation: bridge (RRID:SCR_001343) Copy   

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http://www.bioconductor.org/packages/release/bioc/html/vsn.html

Software package that implements a method for normalizing microarray intensities, both between colours within array, and between arrays. The method uses a robust variant of the maximum-likelihood estimator for the stochastic model of microarray data described in the references. The model incorporates data calibration (a.k.a. normalization), a model for the dependence of the variance on the mean intensity, and a variance stabilizing data transformation. Differences between transformed intensities are analogous to normalized log-ratios. However, in contrast to the latter, their variance is independent of the mean, and they are usually more sensitive and specific in detecting differential transcription.

Proper citation: vsn (RRID:SCR_001459) Copy   

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https://www.unafold.org/

Software package for nucleic acid folding and hybridization prediction. It has capabilities to predict folding for single-stranded RNA or DNA through a combination of free energy minimization, partition function calculations and stochastic sampling. The program runs on Unix and Linux platforms as well as Mac OS X and Windows.

Proper citation: UNAFold (RRID:SCR_001360) Copy   

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http://www.bioconductor.org/packages/release/bioc/html/aroma.light.html

Light-weight software package for normalization and visualization of microarray data using only basic R data types. Software can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes.

Proper citation: aroma.light (RRID:SCR_001312) Copy   

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http://www.bioconductor.org/packages/2.13/bioc/html/BeadDataPackR.html

Software that provides functionality for the compression and decompression of raw bead-level data from the Illumina BeadArray platform.

Proper citation: BeadDataPackR (RRID:SCR_001310) Copy   

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https://www.bioconductor.org/packages/release/bioc/html/OLIN.html

Software functions for normalization of two-color microarrays by optimised local regression and for detection of artifacts in microarray data.

Proper citation: OLIN (RRID:SCR_001304) Copy   

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http://www.bioconductor.org/packages/release/bioc/html/qcmetrics.html

Software package that provides a framework for generic quality control of data. It permits to create, manage and visualise individual or sets of quality control metrics and generate quality control reports in various formats.

Proper citation: qcmetrics (RRID:SCR_001303) Copy   

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https://www.bioconductor.org/packages//2.12/bioc/html/dexus.html

Software package that identifies differentially expressed genes in RNA-Seq data under all possible study designs such as studies without replicates, without sample groups, and with unknown conditions. It works also for known conditions, for example for RNA-Seq data with two or multiple conditions. RNA-Seq read count data can be provided both by the S4 class Count Data Set and by read count matrices. Differentially expressed transcripts can be visualized by heatmaps, in which unknown conditions, replicates, and samples groups are also indicated. This software is fast since the core algorithm is written in C. For very large data sets, a parallel version of DEXUS is provided in this package. DEXUS is a statistical model that is selected in a Bayesian framework by an EM algorithm. It does not need replicates to detect differentially expressed transcripts, since the replicates (or conditions) are estimated by the EM method for each transcript. The method provides an informative/non-informative value to extract differentially expressed transcripts at a desired significance level or power.

Proper citation: DEXUS (RRID:SCR_001309) Copy   

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http://amp.pharm.mssm.edu/Enrichr/

A web-based gene list enrichment analysis tool that provides various types of visualization summaries of collective functions of gene lists. It includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes / proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries.

Proper citation: Enrichr (RRID:SCR_001575) Copy   

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http://www.biostat.jhsph.edu/~hji/cisgenome/index.htm

Integrated software tool for tiling array, ChIP-seq, genome and cis-regulatory element analysis.

Proper citation: CisGenome (RRID:SCR_001558) Copy   

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http://intermine.org/

An open source data warehouse system built for the integration and analysis of complex biological data that enables the creation of biological databases accessed by sophisticated web query tools. Parsers are provided for integrating data from many common biological data sources and formats, and there is a framework for adding data. InterMine includes a user-friendly web interface that works "out of the box" and can be easily customized for specific needs, as well as a powerful, scriptable web-service API to allow programmatic access to data.

Proper citation: InterMine (RRID:SCR_001772) Copy   

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