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http://bmsr.usc.edu/software/targetgene/

MATLAB tool to effectively identify potential therapeutic targets and drugs in cancer using genetic network-based approaches. It can rapidly extract genetic interactions from a precompiled database stored as a MATLAB MAT-file without the need to interrogate remote SQL databases. Millions of interactions involving thousands of candidate genes can be mapped to the genetic network within minutes. While TARGETgene is currently based on the gene network reported in (Wu et al.,Bioinformatics 26:807-813, 2010), it can be easily extended to allow the optional use of other developed gene networks. The simple graphical user interface also enables rapid, intuitive mapping and analysis of therapeutic targets at the systems level. By mapping predictions to drug-target information, TARGETgene may be used as an initial drug screening tool that identifies compounds for further evaluation. In addition, TARGETgene is expected to be applicable to identify potential therapeutic targets for any type or subtype of cancers, even those rare cancers that are not genetically recognized. Identification of Potential Therapeutic Targets * Prioritize potential therapeutic targets from thousands of candidate genes generated from high-throughput experiments using network-based metrics * Validate predictions (prioritization) using user-defined benchmark genes and curated cancer genes * Explore biologic information of selected targets through external databases (e.g., NCBI Entrez Gene) and gene function enrichment analysis Initial Drug Screening * Identify for further evaluation existing drugs and compounds that may act on the potential therapeutic targets identified by TARGETgene * Explore general information on identified drugs of interest through several external links Operating System: Windows XP / Vista / 7

Proper citation: TARGETgene (RRID:SCR_001392) Copy   

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http://faculty.washington.edu/browning/beagle/beagle.html

Software package for analysis of large-scale genetic data sets with hundreds of thousands of markers genotyped on thousands of samples. BEAGLE can * phase genotype data (i.e. infer haplotypes) for unrelated individuals, parent-offspring pairs, and parent-offspring trios. * infer sporadic missing genotype data. * impute ungenotyped markers that have been genotyped in a reference panel. * perform single marker and haplotypic association analysis. * detect genetic regions that are homozygous-by-descent in an individual or identical-by-descent in pairs of individuals. Beagle can also be used in conjunction with PRESTO, a program for fast and flexible permutation testing. PRESTO can compute empirical distributions of order statistics, analyze stratified data, and determine significance levels for one-stage and two-stage genetic association studies. BEAGLE is written in Java and runs on any computing platform with a Java version 1.6 interpreter (e.g. Windows, Unix, Linux, Solaris, Mac).

Proper citation: BEAGLE (RRID:SCR_001789) Copy   

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http://www.nitrc.org/projects/pestica/

Software tool to detect physiologic signals from the data itself as well as an adaptive physiologic noise removal tool (Impulse Response Function or IRF-RETROICOR) that zooms in on noise with only 6 regressors, getting all the noise that 5th order RETROICOR gets. These tools will allow you to correct your data for physiologic noise with what you currently have. These signals are equivalent to a parallel monitored pulse signal and a respiratory chest-bellows signal. Do you have 3D+time EPI data (BOLD or perfusion) but no usable physio signals for pulse and respiration? Are you concerned about the effect of physio noise on your data but don't know what to do but regress data-derived signals that mix unknown functional signal with possible physio noise signal? Are you concerned about the number of regressors you're incorporating once you add 5th order RETROICOR (20 more regressors!)? This is for you.

Proper citation: PESTICA fMRI Physio Detection/Correction (RRID:SCR_002513) Copy   

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http://openmeeg.gforge.inria.fr

A C++ package for low-frequency bio-electromagnetism solving forward problems in the field of EEG and MEG with very high accuracy.

Proper citation: OpenMEEG (RRID:SCR_002510) Copy   

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http://clip.med.yale.edu/presto/

Software toolkit for processing raw reads from high-throughput sequencing of lymphocyte repertoires.

Proper citation: pRESTO (RRID:SCR_001782) Copy   

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http://www.crl.med.harvard.edu/software/STAPLE/index.php

An algorithm for the Simultaneous Truth and Performance Level Estimation, which estimates a reference standard and segmentation generator performance from a set of segmentations. It has been widely applied for the validation of image segmentation algorithms, and to compare the performance of different algorithms and experts. It has also found application in the identification of a consensus segmentation, by combination of the output of a group of segmentation algorithms, and for segmentation by registration and template fusion., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: STAPLE (RRID:SCR_002590) Copy   

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http://www.math.hkbu.edu.hk/~mng/CLUSTAG/CLUSTAG.html

Software application that uses hierarchical clustering and graph methods for selecting tag SNPs (single nucleotide polymorphisms). Cluster and set-cover algorithms are developed to obtain a set of tag SNPs that can represent all the known SNPs in a chromosomal region, subject to the constraint that all SNPs must have a squared correlation R2 > C with at least one tag SNP, where C is specified by the user. The program is implemented with Java, and it can run in Windows platform as well as the Unix environment.

Proper citation: CLUSTAG (RRID:SCR_001816) Copy   

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http://nipy.org/nipype/

A package for writing fMRI analysis pipelines and interfacing with external analysis packages (SPM, FSL, AFNI). Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. However, this has resulted in a heterogeneous collection of specialized applications without transparent interoperability or a uniform operating interface. Nipype, an open-source, community-developed initiative under the umbrella of Nipy, is a Python project that solves these issues by providing a uniform interface to existing neuroimaging software and by facilitating interaction between these packages within a single workflow. Nipype provides an environment that encourages interactive exploration of algorithms from different packages (e.g., SPM, FSL), eases the design of workflows within and between packages, and reduces the learning curve necessary to use different packages. Nipype is creating a collaborative platform for neuroimaging software development in a high-level language and addressing limitations of existing pipeline systems.

Proper citation: Nipype (RRID:SCR_002502) Copy   

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http://www.people.fas.harvard.edu/~junliu/genotype/

Software application (entry from Genetic Analysis Software)

Proper citation: GS-EM (RRID:SCR_003992) Copy   

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http://www.nihtoolbox.org/WhatAndWhy/Motor/Locomotion/Pages/NIH-Toolbox-4--Meter-Walk-Gait-Speed-Test.aspx

Assessment test to measure gait speed where participants are asked to walk a short distance (4 meters) at their usual pace. Participants complete one practice and then two timed trials. Raw scores are recorded as the time in seconds required to walk 4 meters on each of the two trials, with the better trial used for scoring. The 4-Meter Walk Gait Speed Test is adapted from the 4-meter walk test in the Short Physical Performance Battery. The test takes approximately 3 minutes to administer (including instructions and practice). This test is recommended for ages 7-85.

Proper citation: NIH Toolbox 4-Meter Walk Gait Speed Test (RRID:SCR_003632) Copy   

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http://www.kcl.ac.uk/ioppn/depts/neuroimaging/research/imaginganalysis/Software/PIPR.aspx

Software toolbox designed to provide machine learning methods for pre-processed imaging data allowing for two (or more) class classification in the context of drug development. The Toolbox includes implementations of Gaussian Process Classification, Support Vector Machines, Ordinal Regression and Sparse Multinomial Logistic Regression for fMRI, Structural and ASL imaging data.

Proper citation: Pharmacological Imaging and Pattern Recognition toolbox (RRID:SCR_003874) Copy   

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http://www.nihtoolbox.org/WhatAndWhy/Motor/Endurance/Pages/NIH-Toolbox-2-Minute-Walk-Endurance-Test.aspx

Assessment test that measures sub-maximal cardiovascular endurance by recording the distance that the participant is able to walk on a 50-foot (out and back) course in 2 minutes. The participant's raw score is the distance in feet and inches walked in 2 minutes. The test is adapted from the American Thoracic Society's 6-Minute Walk Test Protocol. The test overall takes approximately 4 minutes to administer (with instructions and practice). This test is recommended for ages 3-85.

Proper citation: NIH Toolbox 2-Minute Walk Endurance Test (RRID:SCR_003631) Copy   

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http://www.nihtoolbox.org/WhatAndWhy/Motor/Balance/Pages/Balance.aspx

A measure to assess static standing balance that involves the participant assuming and maintaining up to 5 poses for 50 seconds each. The sequence of poses is: eyes open on a solid surface, eyes closed on solid surface, eyes open on foam surface, eyes closed on foam surface, eyes open in tandem stance. Detailed stopping rules are in place to ensure participant safety with these progressively demanding poses. Postural sway is recorded for each pose using an accelerometer that the participant wears at waist level. This test takes approximately 7 minutes to administer and is recommended for ages 3-85.

Proper citation: NIH Toolbox Standing Balance Test (RRID:SCR_003628) Copy   

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https://neuro-jena.github.io/software.html#tom

Software toolbox for creating customized pediatric templates. It provides reference data based on imaging data from the NIH study of normal brain development. Using the general linear model, they statistically isolate the influence of external variables of interest on brain structure, allowing us to generate high-quality matched templates for any given group of subjects. The toolbox offers two options: # to create pediatric templates (T1) and tissue maps (GM, WM, and CSF) based on the objective 1 NIH data (n = 404), in the age range of 5-18 years, or # to assess a new reference population with regard to your variables of interest. Of note, this approach is generally applicable and in no way restricted to analyzing pediatric imaging data: for example, if you aim at investigating the effects of aging in elderly subjects, the toolbox will also allow you to create more appropriate reference (if your group is large enough to isolate such effects).

Proper citation: Template-O-Matic Toolbox (RRID:SCR_003220) Copy   

•   Source: SciCrunch Registry

http://www.nihtoolbox.org/WhatAndWhy/Cognition/Language/Pages/NIH-Toolbox-Oral-Reading-Recognition-Test.aspx

Assessment test that measures the ability of patients to identify words and letters. The participant is asked to read and pronounce letters and words as accurately as possible. The test administrator scores them as right or wrong. For the youngest children, the initial items require them to identify letters (as opposed to symbols) and to identify a specific letter in an array of 4 symbols. The test is given in a computerized adaptive format and requires approximately 3 minutes. This test is recommended for ages 7-85, but is available for use as young as age 3, if requested. Separate but parallel reading tests have been developed in English and in Spanish.

Proper citation: NIH Toolbox Oral Reading Recognition Test (RRID:SCR_003622) Copy   

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https://omictools.com/prolinks-tool

THIS RESOURCE IS NO LONGER IN SERVICE, documented July 7, 2017. Collection of inference methods used to predict functional linkages between proteins. These methods include the Phylogenetic Profile method which uses the presence and absence of proteins across multiple genomes to detect functional linkages; the Gene Cluster method which uses genome proximity to predict functional linkage; Rosetta Stone which uses a gene fusion event in a second organism to infer functional relatedness; and the Gene Neighbor method which uses both gene proximity and phylogenetic distribution to infer linkage.

Proper citation: ProLinks Database of Functional Linkages (RRID:SCR_003185) Copy   

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http://www.fmri.wfubmc.edu/cms/software

Research group based in the Department of Radiology of Wake Forest University School of Medicine devoted to the application of novel image analysis methods to research studies. The ANSIR lab also maintains a fully-automated functional and structural image processing pipeline supporting the image storage and analysis needs of a variety of scientists and imaging studies at Wake Forest. Software packages and toolkits are currently available for download from the ANSIR Laboratory, including: WFU Biological Parametric Mapping Toolbox, WFU_PickAtlas, and Adaptive Staircase Procedure for E-Prime.

Proper citation: Advanced Neuroscience Imaging Research Laboratory Software Packages (RRID:SCR_002926) Copy   

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http://www.nihtoolbox.org/WhatAndWhy/Cognition/EpisodicMemory/Pages/NIH-Toolbox-Picture-Sequence-Memory-Test.aspx

A measure for the assessment of episodic memory that involves recalling increasingly lengthy series of illustrated objects and activities that are presented in a particular order on the computer screen. The participants are asked to recall the sequence of pictures that is demonstrated over two learning trials; sequence length varies from 6-18 pictures, depending on age. Participants are given credit for each adjacent pair of pictures (i.e., if pictures in locations 7 and 8 and placed in that order and adjacent to each other anywhere such as slots 1 and 2 one point is awarded) they correctly place, up to the maximum value for the sequence, which is one less than the sequence length (if there are 18 pictures in the sequence, the maximum score is 17, because that is the number of adjacent pairs of pictures that exist). The test takes approximately 7 minutes to administer. This test is recommended for ages 3-85.

Proper citation: NIH Toolbox Picture Sequence Memory Test (RRID:SCR_003618) Copy   

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http://www.nihtoolbox.org/WhatAndWhy/Cognition/Attention/Pages/NIH-Toolbox-Flanker-Inhibitory-Control-and-Attention-Test.aspx

Assessment test that measures both a participant''s attention and inhibitory control. The test requires the participant to focus on a given stimulus while inhibiting attention to stimuli (fish for ages 3-7 or arrows for ages 8-85) flanking it. Sometimes the middle stimulus is pointing in the same direction as the flankers (congruent) and sometimes in the opposite direction (incongruent). Scoring is based on a combination of accuracy and reaction time, and the test takes approximately 3 minutes to administer. This test is recommended for ages 3-85.

Proper citation: NIH Toolbox Flanker Inhibitory Control and Attention Test (RRID:SCR_003617) Copy   

•   Source: SciCrunch Registry

http://www.nihtoolbox.org/WhatAndWhy/Cognition/ExecutiveFunction/Pages/NIH-Toolbox-Dimensional-Change-Card-Sort-Test.aspx

Assessment test that measures the cognitive flexibility of patients. Two target pictures are presented that vary along two dimensions (e.g., shape and color). Participants are asked to match a series of bivalent test pictures (e.g., yellow balls and blue trucks) to the target pictures, first according to one dimension (e.g., color) and then, after a number of trials, according to the other dimension (e.g., shape). Switch trials are also employed, in which the participant must change the dimension being matched. For example, after 4 straight trials matching on shape, the participant may be asked to match on color on the next trial and then go back to shape, thus requiring the cognitive flexibility to quickly choose the correct stimulus. Scoring is based on a combination of accuracy and reaction time, and the test takes approximately 4 minutes to administer. This test is recommended for ages 3-85.

Proper citation: NIH Toolbox Dimensional Change Card Sort Test (RRID:SCR_003616) Copy   

•   Source: SciCrunch Registry