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http://sourceforge.net/projects/cushaw2/files/CUSHAW2-GPU/

Software program (based on CUSHAW2) designed and optimized for Kepler-based GPUs, but still workable on earlier-generation Fermi-based ones.

Proper citation: CUSHAW2-GPU (RRID:SCR_005480) Copy   

•   Source: SciCrunch Registry
  

http://www.axa-research.org/

The core mission of the AXA Research Fund is to finance basic research contributing to understand and prevent risks. We support innovative and cutting-edge projects within three areas: environmental, life, and socio-economic risks. Only research institutions may submit applications. * Funding for research projects, postdoc and graduate fellowships, and long and short term projects * Encourages international applicants and research around the world Research projects funded by the Fund must fall within the scope of one of the themes identified by the AXA Scientific Board. The themes for 2011 are identified below: * Life risks ** Aging & Long-term care ** Biomedical risks ** Addictions and risky behaviors * Socio-economic risks ** Geopolitical risks ** Macroeconomic and financial systemic risks ** Individual and collective behaviors when facing uncertainties ** Large corporate risks * Environmental risks ** Climate change ** Natural hazards ** Human driven environmental changes

Proper citation: AXA Research Fund (RRID:SCR_005276) Copy   

•   Source: SciCrunch Registry
  

http://nebc.nerc.ac.uk/tools/bio-linux/bio-linux-7-info

A free, fully featured, powerful, configurable and easy to maintain bioinformatics workstation that provides more than 500 bioinformatics programs on an Ubuntu Linux 12.04 LTS base. Install it or run it live. There is a graphical menu for bioinformatics programs, as well as easy access to the Bio-Linux bioinformatics documentation system and sample data useful for testing programs. You can run a Bio-Linux system on Amazon EC2 or other cloud computing architectures by using CloudBioLinux.

Proper citation: Bio-Linux (RRID:SCR_005399) Copy   

•   Source: SciCrunch Registry
  

http://www.neuron.yale.edu

NEURON is a simulation environment for modeling individual neurons and networks of neurons. It provides tools for conveniently building, managing, and using models in a way that is numerically sound and computationally efficient. It is particularly well-suited to problems that are closely linked to experimental data, especially those that involve cells with complex anatomical and biophysical properties. NEURON has benefited from judicious revision and selective enhancement, guided by feedback from the growing number of neuroscientists who have used it to incorporate empirically-based modeling into their research strategies. NEURON's computational engine employs special algorithms that achieve high efficiency by exploiting the structure of the equations that describe neuronal properties. It has functions that are tailored for conveniently controlling simulations, and presenting the results of real neurophysiological problems graphically in ways that are quickly and intuitively grasped. Instead of forcing users to reformulate their conceptual models to fit the requirements of a general purpose simulator, NEURON is designed to let them deal directly with familiar neuroscience concepts. Consequently, users can think in terms of the biophysical properties of membrane and cytoplasm, the branched architecture of neurons, and the effects of synaptic communication between cells. * helps users focus on important biological issues rather than purely computational concerns * has a convenient user interface * has a user-extendable library of biophysical mechanisms * has many enhancements for efficient network modeling * offers customizable initialization and simulation flow control * is widely used in neuroscience research by experimentalists and theoreticians * is well-documented and actively supported * is free, open source, and runs on (almost) everything

Proper citation: NEURON (RRID:SCR_005393) Copy   

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http://www.genoscope.cns.fr/externe/gmorse/

Software aimed at using RNA-Seq short reads to build de novo gene models. First, candidate exons are built directly from the positions of the reads mapped on the genome (without any ab initio assembly of the reads), and all the possible splice junctions between those exons are tested against unmapped reads : the testing of junctions is directed by the information available in the RNA-Seq dataset rather than a priori knowledge about the genome. Exons can thus be chained into stranded gene models.

Proper citation: G-Mo.R-Se (RRID:SCR_005273) Copy   

•   Source: SciCrunch Registry
  

http://www.incf.org/resources/training

The International Neuroinformatics Coordinating Facility (INCF) Training in Neuroinformatics section provides a broad listing of educational and training opportunities in fields related to neuroinformatics.

Proper citation: INCF Training in Neuroinformatics (RRID:SCR_005395) Copy   

•   Source: SciCrunch Registry
  

http://cistrome.org/BETA/

A software package that integrates ChIP-seq of transcription factors or chromatin regulators with differential gene expression data to infer direct target genes. BETA has three functions: (1) to predict whether the factor has activating or repressive function; (2) to infer the factor''''s target genes; and (3) to identify the motif of the factor and its collaborators which might modulate the factor''''s activating or repressive function. BETA requires ~2GB RAM and 1h for the whole procedure. BETA may run on the web server at Cistrome or may be downloaded.

Proper citation: Binding and Expression Target Analysis (RRID:SCR_005396) Copy   

•   Source: SciCrunch Registry
  

http://www.icn.ucl.ac.uk/motorcontrol/

Using robotic devices to investigate human motor behavior, this group develops computational models to understand the underlying control and learning processes. By simulating novel objects or dynamic environments they study how the brain recalibrates well-learned motor skills or acquires new ones. These insights are used to design fMRI studies to investigate how these processes map onto the brain. They have developed a number of novel techniques of how to study motor control in the MRI environment, and how to analyze MRI data of the human cerebellum. They also study patients with stroke or neurological disease to further determine how the brain manages to control the body.

Proper citation: UCL Motor Control Group (RRID:SCR_005271) Copy   

•   Source: SciCrunch Registry
  

http://pubbrain.org/

A literature search and visualization tool that allows end users to enter any PubMed query and see that query rendered as a heatmap illustrating which regions of interest are most commonly mentioned within the search results. To use PubBrain, simply enter any valid PubMed search in the search box.

Proper citation: PubBrain (RRID:SCR_005387) Copy   

•   Source: SciCrunch Registry
  

http://oilspilltopics.wordpress.com/

The Gulf of Mexico Oil Spill Incident has impacted many aspects of the coastal environment and the people living in the coastal states. The general public, government officials, and journalists would like to get a general, big picture of the impact, and Gulf-based researchers would like to investigate the fate and effects of oil, dispersed oil, and dispersant on the ecosystems of the Gulf of Mexico and affected coastal states. The topic map we have developed serves to facilitate not only the understanding of the impacts of the incident but also knowledge discovery through interdisciplinary knowledge fusion. Although the topic map focuses on the Gulf of Mexico Oil Spill Incident, it also covers oil spill incidents and concerns elsewhere. A topic map has three constructs: (1) topics (or concepts), (2) associations (or relationships, links) between the topics, (3) information resources relevant to a given topic. You will be able to see all three of these on the topic map. By scanning the topics and their relationships, you can get a big picture of the topics and focus on the information resources that relate most significantly to your interest, and hopefully discover new knowledge by integrating the knowledge from multiple disciplines. Please visit our User Guides to get an idea of how to navigate through the topic map, and its textual interface. The topic map is posted at: http://topicmap.lsu.edu The project is funded by Gulf of Mexico Research Initiative through Louisiana State University.

Proper citation: Oil Spill Topic Map (RRID:SCR_005388) Copy   

•   Source: SciCrunch Registry
  

http://ingap.sourceforge.net/

Software mining pipeline guided by a Bayesian principle to detect single nucleotide polymorphisms, insertion and deletions by comparing high-throughput pyrosequencing reads with a reference genome of related organisms. This pipeline is extended to identify and visualize large-size structural variations, including insertions, deletions, inversions and translocations.

Proper citation: inGAP (RRID:SCR_005261) Copy   

•   Source: SciCrunch Registry
  

http://sv.gersteinlab.org/pemer/

Software package as computational framework with simulation-based error models for inferring genomic structural variants from massive paired-end sequencing data. Package is composed of three modules, PEMer workflow, SV-Simulation and BreakDB. PEMer workflow is a sensitive software for detecting SVs from paired-end sequence reads. SV-Simulation randomly introduces SVs into a given genome and generates simulated paired-end reads from novel genome.

Proper citation: PEMer (RRID:SCR_005263) Copy   

•   Source: SciCrunch Registry
  

http://phenotype.mc.vanderbilt.edu/

Collaborative environment of building and validating electronic phenotype algorithms using electronic medical records (EMRs) and natural language processing (NLP) for use in genome-wide association studies (GWAS). On this site you can: View existing algorithms, Enter or create new algorithms, Collaborate with others to create or review algorithms, View implementation details for existing algorithms. The Electronic Medical Records and Genomics Network (eMERGE) has investigated whether data captured through routine clinical care using electronic medical records (EMRs) can identify disease phenotypes with sufficient positive and negative predictive values for use in genome-wide association studies (GWAS). Most EMRs captured key information (diagnoses, medications, laboratory tests) used to define phenotypes in a structured format; in addition, natural language processing has also been shown to improve case identification rates. PheKB is an outgrowth of that validation effort. Phenotype algorithms can be viewed by data modalities or methods used: CPT codes, ICD 10 codes, ICD 9 codes, Laboratories, Medications, Vital Signs, Natural Language Processing Algorithms can also be viewed by: * Implementation results (positive predictive value, sensitivity, publications) * Institution * Work Group

Proper citation: PheKB (RRID:SCR_005292) Copy   

•   Source: SciCrunch Registry
  

http://omrf.org/

A biomedical research institute that aims to understand and develop more effective treatments for human disease, focusing on critical research areas such as heart disease, cancer, lupus and Alzheimer's disease.

Proper citation: Oklahoma Medical Research Foundation (RRID:SCR_005287) Copy   

•   Source: SciCrunch Registry
  

http://seqware.github.io/

A portable software infrastructure designed to analyze massive genomics datasets produced by contemporary and emerging technologies, in particular Next Generation Sequencing (NGS) platforms. It consists of a comprehensive suite of infrastructure tools focused on enabling the end-to-end analysis of sequence data ? from from raw base calling to analyzed variants ready for interpretation by users. SeqWare is tool agnostic, it is a framework for building analysis workflows and does not provide specific implementations out-of-the-box. You use SeqWare to create high-throughput infrastructure for NGS analysis using whatever analysis tools you like. SeqWare currently provides 5 main tools specifically designed to support massively parallel sequencing technologies. All tools can be used together or separately: * MetaDB: provides a common database to store metadata used by all components. * Portal: a LIMS-like web application to manage samples, record computational events, and present results back to end users. * Pipeline: a workflow engine that is capable of wrapping and combining other tools (BFAST, BWA, SAMtools, etc) into complex pipelines, recording metadata about the analysis, and facilitates automation of pipelines based on metadata. * Web Service: a programmatic API that lets people build new tools on top of the project * Query Engine: a NoSQL database designed to store and query variants and other events inferred from sequence data.

Proper citation: SeqWare (RRID:SCR_005289) Copy   

•   Source: SciCrunch Registry
  

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

The FMRIB Centre is a multi-disciplinary neuroimaging research facility, which focuses on the use of Magnetic Resonance Imaging (MRI) for neuroscience research, along with related technologies such as Transcranial Magnetic Stimulation, transcranial Direct Cortical Stimulation and EEG. FMRIB is composed of research groups in all aspects of brain imaging research, including physics, analysis, basic science and clinical neuroscience. We were recently awarded 8 million pounds by the MRC, EPSRC, Wolfson Foundation and University of Oxford to purchase and install new 7T and 3T leading-edge MRI systems to enable us to image brain structure and function at even higher resolution than currently possible.

Proper citation: Oxford Centre for Functional MRI of the Brain (RRID:SCR_005283) Copy   

•   Source: SciCrunch Registry
  

http://afni.nimh.nih.gov/afni/doc/misc/AtlasMap

A sample script on how to map some numbers to brain regions, using the Talairach-Tournoux Atlas database. For example, put the value 0.379 in each hippocampus voxel, and the value 0.666 in each superior temporal gyrus voxel.

Proper citation: Mapping Data to the Talairach Atlas (RRID:SCR_005284) Copy   

•   Source: SciCrunch Registry
  

http://cmrm.med.jhmi.edu/

The goal of our laboratory is to develop new MR technologies to improve the resolution and contrast of MRI and apply them to observe brain anatomy to answer various types of biological questions. Currently we have three major research targets: Characterization of mouse brain development; Human white matter anatomy and development; and Development of diffusion tensor imaging technique and technology dissemination. The DTI database (Under the DTI Download Tab) contains raw and processed DTI data of normal population. Currently we have 2.5 mm isotropic resolution images and 2.2 mm isotropic resolution images. Only 2.5 mm data are available from this site. If you are interested in the high-resolution images, please contact susumu @ mri.jhu.edu. This database is open to public once the user is registered. Basic imaging parameters can be also downloaded.

Proper citation: Johns Hopkins Laboratory of Brain Anatomical MRI (RRID:SCR_005280) Copy   

•   Source: SciCrunch Registry
  

http://eqtl.rc.fas.harvard.edu/idcheck/

Software that allows assessment of concordance between genotype (from SNP arrays or DNA sequencing) and gene expression (RNA-seq) samples. IDCheck compares the identity of RNA-seq reads and SNP genotypes using a likelihood based method. Based on maximum likelihood estimates of relevant parameters, we can detect sample contamination and identify correct sample pairs when swapping occurs.

Proper citation: IdCheck (RRID:SCR_005510) Copy   

•   Source: SciCrunch Registry
  

http://www.well.ox.ac.uk/project-stampy

A software package for the mapping of short reads from illumina sequencing machines onto a reference genome. It''s recommended for most workflows, including those for genomic resequencing, RNA-Seq and Chip-seq. Stampy excels in the mapping of reads containing that contain sequence variation relative to the reference, in particular for those containing insertions or deletions. It can map reads from a highly divergent species to a reference genome for instance. Stampy achieves high sensitivity and speed by using a fast hashing algorithm and a detailed statistical model. Stampy has the following features: * Maps single, paired-end and mate pair Illumina reads to a reference genome * Fast: about 20 Gbase per hour in hybrid mode (using BWA) * Low memory footprint: 2.7 Gb shared memory for a 3Gbase genome * High sensitivity for indels and divergent reads, up to 10-15% * Low mapping bias for reads with SNPs * Well calibrated mapping quality scores * Input: Fastq and Fasta; gzipped or plain * Output: SAM, Maq''s map file * Optionally calculates per-base alignment posteriors * Optionally processes part of the input * Handles reads of up to 4500 bases

Proper citation: Stampy (RRID:SCR_005504) Copy   

•   Source: SciCrunch Registry