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http://sourceforge.net/projects/bio-rainbow/

Software developed to provide an ultra-fast and memory-efficient solution to clustering and assembling short reads produced by RAD-seq.

Proper citation: Rainbow (RRID:SCR_002724) Copy   

•   Source: SciCrunch Registry

http://biohealth.snu.ac.kr/software/TRAP/

A comprehensive software package integrating all necessary tasks such as mapping short reads, measuring gene expression levels, finding differentially expressed genes (DEGs), clustering and pathway analysis for time-series data in a single environment.

Proper citation: Time-series RNA-seq Analysis Package (RRID:SCR_002935) Copy   

•   Source: SciCrunch Registry

http://www.fmrib.ox.ac.uk/fsl/

Software library of image analysis and statistical tools for fMRI, MRI and DTI brain imaging data. Include registration, atlases, diffusion MRI tools for parameter reconstruction and probabilistic taractography, and viewer. Several brain atlases, integrated into FSLView and Featquery, allow viewing of structural and cytoarchitectonic standard space labels and probability maps for cortical and subcortical structures and white matter tracts. Includes Harvard-Oxford cortical and subcortical structural atlases, Julich histological atlas, JHU DTI-based white-matter atlases, Oxford thalamic connectivity atlas, Talairach atlas, MNI structural atlas, and Cerebellum atlas.

Proper citation: FSL (RRID:SCR_002823) Copy   

•   Source: SciCrunch Registry

http://cran.r-project.org/web/packages/gap/

GAP is designed as an integrated package for genetic data analysis of both population and family data. Currently, it contains functions for sample size calculations of both population-based and family-based designs, classic twin models, probability of familial disease aggregation, kinship calculation, some statistics in linkage analysis, and association analysis involving one or more genetic markers including haplotype analysis with or without environmental covariates.

Proper citation: Genetic Analysis Package (RRID:SCR_003006) Copy   

•   Source: SciCrunch Registry

https://code.google.com/p/sasqpcr/

All-in-one computer program for robust and rapid analysis of quantitative reverse transcription real-time polymerase chain reaction (RT-qPCR) data in SAS. It incorporates all functions important for RT-qPCR data analysis including assessment of PCR efficiencies, validation of internal reference genes and normalizers, normalization of confounding variations across samples and statistical comparisons of target gene expression in parallel samples. The program is highly automatic in data analyses and result output. The input data have no limitations for the number of genes or cDNA samples. Users can simply change the macro variables to test various analytical strategies, optimize results and customize the analytical processes. The program is also extendable allowing advanced SAS users to develop particular statistical tests appropriate for their experimental designs. Thus users are the actual decision-makers controlling RT-qPCR data analyses. The program has to be used in SAS software; however, extensive SAS programming knowledge is not required.

Proper citation: SASqPCR (RRID:SCR_003056) Copy   

•   Source: SciCrunch Registry

http://www.nematodes.org/

Nematode & Neglected Genomics (at) The Blaxter Lab is a nematode related portal including databases and services. Resources include genomic and transcriptomic databases for nematodes and other metazoan phyla and freely downloadable software tools for expressed sequence tag analysis, DNA barcode analysis and phylogenomics. Major categories include: * GenePool * 959 Nematode Genomes * Teaching * Research Projects * Bioinformatics Software Tools * Lab Personnel * Lab Wiki * Genomics Databases * NEMBASE4 * Tardigrada: Hypsibius dujardini * Earthworm: Lumbricus rubellus * MolluscDB * ArthropodDB * other Neglected Genomes

Proper citation: nematodes.org (RRID:SCR_003267) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/cancer/software/genepattern

A powerful genomic analysis platform that provides access to hundreds of tools for gene expression analysis, proteomics, SNP analysis, flow cytometry, RNA-seq analysis, and common data processing tasks. A web-based interface provides easy access to these tools and allows the creation of multi-step analysis pipelines that enable reproducible in silico research.

Proper citation: GenePattern (RRID:SCR_003201) Copy   

•   Source: SciCrunch Registry

http://wiki.c2b2.columbia.edu/honiglab_public/index.php/Main_Page

Laboratory portal, including software, web-based tools, databases and data sets, related to their research that focuses on the development and application of biophysical and bioinformatics methods aimed at understanding the structural and energetic origins of protein-protein, protein-nucleic acid, and protein-membrane interactions. Their work includes fundamental theoretical research, the development of software tools, and applications to problems of biological importance. In this regard they maintain an active collaborative computational and experimental research program on the molecular basis of cell-cell adhesion. Other problems of current interest include protein structure prediction, the organization of protein sequence/structure space, the prediction of protein function based on protein structure, the structural origins of specificity in protein-DNA interactions, RNA function and, more generally, the electrostatic properties of biological macromolecules.

Proper citation: Honig Lab (RRID:SCR_003410) Copy   

•   Source: SciCrunch Registry

http://www.decode.com/

A biopharmaceutical company applying its discoveries in human genetics to develop drugs and diagnostics for common diseases. They specialize in gene discovery - their population approach and resources have enabled them to isolate key genes contributing to major public health challenges from cardiovascular disease to cancer. The company's genotyping capacity is now one of the highest in the world. They have a large population-based biobank containing whole blood and DNA samples with extensive relevant phenotypic information from around 120.000 Icelanders. In the company's work in more than 50 disease projects, their statistical and informatics departments have established themselves in data processing and analysis. deCODE genetics is widely recognized as a center of excellence in genetic research.

Proper citation: deCODE genetics (RRID:SCR_003334) Copy   

•   Source: SciCrunch Registry

https://code.google.com/p/bpipe/

Software tool for running and managing bioinformatics pipelines. It specializes in enabling users to turn existing pipelines based on shell scripts or command line tools into highly flexible, adaptable and maintainable workflows with a minimum of effort. Bpipe ensures that pipelines execute in a controlled and repeatable fashion and keeps audit trails and logs to ensure that experimental results are reproducible. Requiring only Java as a dependency, it is fully self-contained and cross-platform, making it very easy to adopt and deploy into existing environments.

Proper citation: Bpipe (RRID:SCR_003471) Copy   

•   Source: SciCrunch Registry

http://cng.gmu.edu:8080/Lm

A freely available software tool available for the Windows and Linux platform, as well as the Online version Applet, for the analysis, comparison and search of digital reconstructions of neuronal morphologies. For the quantitative characterization of neuronal morphology, LM computes a large number of neuroanatomical parameters from 3D digital reconstruction files starting from and combining a set of core metrics. After more than six years of development and use in the neuroscience community, LM enables the execution of commonly adopted analyses as well as of more advanced functions, including: (i) extraction of basic morphological parameters, (ii) computation of frequency distributions, (iii) measurements from user-specified subregions of the neuronal arbors, (iv) statistical comparison between two groups of cells and (v) filtered selections and searches from collections of neurons based on any Boolean combination of the available morphometric measures. These functionalities are easily accessed and deployed through a user-friendly graphical interface and typically execute within few minutes on a set of 20 neurons. The tool is available for either online use on any Java-enabled browser and platform or may be downloaded for local execution under Windows and Linux.

Proper citation: L-Measure (RRID:SCR_003487) Copy   

•   Source: SciCrunch Registry

http://www.idoimaging.com/program/280

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 6, 2023.Comprised of a large array of sophisticated programs, this comprehensive software package with tools based around the MINC file format. Utilities are provided for conversion, viewing, editing, registering, segmentation, and a wide array of analysis. Many programs are in Perl. MINC software tools for neurological imaging are free. Input format: Analyze, DICOM, Minc

Proper citation: MINC Brain Imaging Toolbox (RRID:SCR_003519) Copy   

•   Source: SciCrunch Registry

http://www.nanostring.com/products/nSolver

Data analysis software program that offers nCounter users the ability to QC, normalize, and analyze data without having to purchase additional software packages.

Proper citation: nSolver Analysis Software (RRID:SCR_003420) Copy   

•   Source: SciCrunch Registry

http://www.minituba.org

miniTUBA is a web-based modeling system that allows clinical and biomedical researchers to perform complex medical/clinical inference and prediction using dynamic Bayesian network analysis with temporal datasets. The software allows users to choose different analysis parameters (e.g. Markov lags and prior topology), and continuously update their data and refine their results. miniTUBA can make temporal predictions to suggest interventions based on an automated learning process pipeline using all data provided. Preliminary tests using synthetic data and laboratory research data indicate that miniTUBA accurately identifies regulatory network structures from temporal data. miniTUBA represents in a network view possible influences that occur between time varying variables in your dataset. For these networks of influence, miniTUBA predicts time courses of disease progression or response to therapies. minTUBA offers a probabilistic framework that is suitable for medical inference in datasets that are noisy. It conducts simulations and learning processes for predictive outcomes. The DBN analysis conducted by miniTUBA describes from variables that you specify how multiple measures at different time points in various variables influence each other. The DBN analysis then finds the probability of the model that best fits the data. A DBN analysis runs every combination of all the data; it examines a large space of possible relationships between variables, including linear, non-linear, and multi-state relationships; and it creates chains of causation, suggesting a sequence of events required to produce a particular outcome. Such chains of causation networks - are difficult to extract using other machine learning techniques. DBN then scores the resulting networks and ranks them in terms of how much structured information they contain compared to all possible models of the data. Models that fit well have higher scores. Output of a miniTUBA analysis provides the ten top-scoring networks of interacting influences that may be predictive of both disease progression and the impact of clinical interventions and probability tables for interpreting results. The DBN analysis that miniTUBA provides is especially good for biomedical experiments or clinical studies in which you collect data different time intervals. Applications of miniTUBA to biomedical problems include analyses of biomarkers and clinical datasets and other cases described on the miniTUBA website. To run a DBN with miniTUBA, you can set a number of parameters and constrain results by modifying structural priors (i.e. forcing or forbidding certain connections so that direction of influence reflects actual biological relationships). You can specify how to group variables into bins for analysis (called discretizing) and set the DBN execution time. You can also set and re-set the time lag to use in the analysis between the start of an event and the observation of its effect, and you can select to analyze only particular subsets of variables.

Proper citation: miniTUBA (RRID:SCR_003447) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/vanator-cvr/

A Perl pipeline utilising a large variety of common alignment, assembly and analysis tools to assess the metagenomic profiles of Illumina deep sequencing samples. The emphasis is on the discovery of novel viruses in clinical and environmental samples.

Proper citation: Vanator (RRID:SCR_004370) Copy   

•   Source: SciCrunch Registry

http://www.genmapp.org/

GenMAPP is a free computer application designed to visualize gene expression and other genomic data on maps representing biological pathways and groupings of genes. Integrated with GenMAPP are programs to perform a global analysis of gene expression or genomic data in the context of hundreds of pathway MAPPs and thousands of Gene Ontology Terms (MAPPFinder), import lists of genes/proteins to build new MAPPs (MAPPBuilder), and export archives of MAPPs and expression/genomic data to the web. The main features underlying GenMAPP are: *Draw pathways with easy to use graphics tools *Color genes on MAPP files based on user-imported genomic data *Query data against MAPPs and the GeneOntology Enhanced features include the simultaneous view of multiple color sets, expanded species-specific gene databases and custom database options.

Proper citation: Gene Map Annotator and Pathway Profiler (RRID:SCR_005094) Copy   

•   Source: SciCrunch Registry

https://phenogen.org

Website for analyzing microarray data. Software toolbox for storing, analyzing and integrating microarray data and related genotype and phenotype data. The site is particularly suited for combining QTL and microarray data to search for candidate genes contributing to complex traits. In addition, the site allows, if desired by the investigators, sharing of the data. Investigators can conduct in-silico microarray experiments using their own and/or shared data. There are five major sections of the site: Genome/Transcriptome Data Browser, Microarray Analysis Tools, Gene List Analysis Tools, QTL Tools, and Downloads. The genome/transcriptome data browser combines a genome browser with all the microarray, RNA-Seq, and Genomic Sequencing data. This provides an effective platform to view all of this data side by side. Source code is available on GitHub.

Proper citation: PhenoGen Informatics (RRID:SCR_001613) Copy   

•   Source: SciCrunch Registry

http://www.farsight-toolkit.org/wiki/FARSIGHT_Toolkit

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23, 2022. A collection of software modules for image data handling, pre-processing, segmentation, inspection, editing, post-processing, and secondary analysis. These modules can be scripted to accomplish a variety of automated image analysis tasks. All of the modules are written in accordance with software practices of the Insight Toolkit Community. Importantly, all modules are accessible through the Python scripting language which allows users to create scripts to accomplish sophisticated associative image analysis tasks over multi-dimensional microscopy image data. This language works on most computing platforms, providing a high degree of platform independence. Another important design principle is the use of standardized XML file formats for data interchange between modules.

Proper citation: Farsight Toolkit (RRID:SCR_001728) Copy   

•   Source: SciCrunch Registry

http://dial.mc.duke.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. The Duke Image Analysis Laboratory (DIAL) is committed to providing comprehensive imaging support in research studies and clinical trials to various agencies. The capabilities of the lab include protocol development, site training and certification, and image archival and analysis for a variety of modalities including magnetic resonance imaging, magnetic resonance spectroscopy, computed tomography and nuclear medicine. DIAL uses the latest technologies to analyze Magnetic Resonance Imaging (MRI) data sets of the brain. Currently the lab is engaged in measurement of the hippocampus, amygdala, caudate, ventricular system, and other brain regional volumes. Each of these techniques have undergone a rigorous validation process. The measurements of brain structures provide a useful means of non-invasively testing for changes in the brain of the patient. Changes over time in the brain can be detected, and evaluated with respect to the treatment that the patient is receiving. Magnetic Resonance Spectroscopy (MRS) allows DIAL to obtain an accurate profile of the chemical content of the brain. This sensitive technique can detect small changes in the metabolic state of the brain; changes that vary in response to administration of therapeutic agents. The ability to detect these subtle shifts in brain chemistry allows DIAL to identify changes in the brain with more sensitivity than allowed by image analysis. In this respect, NMR spectroscopy can provide early detection of changes in the brain, and serves to compliment the data obtained from image analysis. Additionally, DIAL also contains SQUID (Scalable Query Utility and Image Database). It is an image management system developed to facilitate image management in research and clinical trials: SQUID offers secure, redundant image storage and organizational functions for sorting and searching digital images for a variety of modalities including MRI, MRS, CAT Scan, X-Ray and Nuclear Medicine. SQUID can access images directly from DUMC scanners. Data can also be loaded via DICOM CDs, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Duke University Medical Center: Duke Image Analysis Laboratory (RRID:SCR_001716) Copy   

•   Source: SciCrunch Registry

http://ilyinlab.org/friend/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Friend is a bioinformatics application designed for simultaneous analysis and visualization of multiple structures and sequences of proteins and/or DNA/RNA. The application provides basic functionalities such as: structure visualization with different rendering and coloring, sequence alignment, and simple phylogeny analysis, along with a number of extended features to perform more complex analyses of sequence structure relationships, including: structural alignment of proteins, investigation of specific interaction motifs, studies of protein-protein and protein-DNA interactions, and protein super-families. Friend is also useful for the functional annotation of proteins, protein modeling, and protein folding studies. Friend provides three levels of usage; 1) an extensive GUI for a scientist with no programming experience, 2) a command line interface for scripting for a scientist with some programming experience, and 3) the ability to extend Friend with user written libraries for an experienced programmer. The application is linked and communicates with local and remote sequence and structure databases.

Proper citation: An Integrated Multiple Structure Visualization and Multiple Sequence Alignment Application (RRID:SCR_001646) Copy   

•   Source: SciCrunch Registry