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http://www.plafornea.com.ar/

Software allows estimating growth and production at stand level of main forest species implanted in Argentine Mesopotamia such as Pino taeda, Eucalyptus grandis, Pino elliottii and Araucaria angustifolia. Areas for which models were adjusted correspond to province of Misiones, northeast of Corrientes and Concordia in province of Entre Ríos.

Proper citation: PlaForNEA (RRID:SCR_022027) Copy   

•   Source: SciCrunch Registry

https://microbiomedata.org

Platform facilitates comprehensive discovery of and access to multidisciplinary microbiome data in order to unlock new possibilities with microbiome data science. Multi organizational effort to integrate microbiome data across diverse areas in medicine, agriculture, bioenergy, and environment. Founded to support long term advancement of microbiome science.

Proper citation: National Microbiome Data Collaborative (RRID:SCR_022161) Copy   

•   Source: SciCrunch Registry

https://osf.io/xfpn4/

MDAR Framework establishes minimum set of requirements in transparent reporting applicable to studies in life sciences. MDAR checklist is tool for authors, editors and others seeking to adopt MDAR framework for transparent reporting in manuscripts and other outputs and designed to provide harmonizing principle for reporting requirements currently in use at various journals.

Proper citation: MDAR (RRID:SCR_022038) Copy   

•   Source: SciCrunch Registry

https://elucidata.io/el-maven/

Open source LC-MS data processing engine for simplifying metabolomics analysis. Mass spectrometry data processing engine that is optimal for isotopomer labeling and global metabolomic profiling experiments. Interactive software platform that accelerates analysis of LC-MS, GC-MS, and LC-MS/MS datasets.

Proper citation: EL MAVEN (RRID:SCR_022159) Copy   

•   Source: SciCrunch Registry

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

Software R package for cell type deconvolution of heterogeneous tissues based on bulk RNAseq data or single cell RNAseq expression profiles.Provides unified testing interface to rapidly run and benchmark multiple deconvolution methods.

Proper citation: granulator (RRID:SCR_022158) Copy   

•   Source: SciCrunch Registry

http://www.theseed.org/wiki/Home_of_the_SEED

The SEED is a framework to support comparative analysis and annotation of genomes. The cooperative effort focuses on the development of the comparative genomics environment and, more importantly, on the development of curated genomic data. Curation of genomic data (annotation) is done via the curation of subsystems by an expert annotator across many genomes, not on a gene by gene basis. From the curated subsystems we extract a set of freely available protein families (FIGfams). These FIGfams form the core component of our RAST automated annotation technology. Answering numerous requests for automatic Seed-Quality annotations for more or less complete bacterial and archaeal genomes, we have established the free RAST-Server (RAST=Rapid Annotation using Subsytems Technology). Using similar technology, we make the Metagenomics-RAST-Server freely available. We also provide a SEED-Viewer that allows read-only access to the latest curated data sets. We currently have 58 Archaea, 902 Bacteria, 562 Eukaryota, 1254 Plasmids and 1713 Viruses in our database. All tools and datasets that make up the SEED are in the public domain and can be downloaded at ftp://ftp.theseed.org

Proper citation: SEED (RRID:SCR_002129) Copy   

•   Source: SciCrunch Registry

http://bioinfo.jialab-ucr.org/CancerMIRNome/

Web server for cancer miRNome interactive analysis and visualization based on human miRNome data of cancer types from The Cancer Genome Atlas, and public cancer circulating miRNome profiling datasets from NCBI Gene Expression Omnibus and ArrayExpress. Comprehensive database for interactive analysis and visualization of miRNA expression profiles.

Proper citation: CancerMIRNome (RRID:SCR_022092) Copy   

•   Source: SciCrunch Registry

http://www.khri.med.umich.edu/research/lesperance_lab/low_freq.php

This web site lists the disease causing mutations and polymorphisms found in the Wolfram syndrome (WFS1) gene. Sponsors: This resource is supported by the University of Michigan at Ann Arbor.

Proper citation: Human Genetics Laboratory: WFS1 Gene Mutation and Polymorphism Database (RRID:SCR_001113) Copy   

•   Source: SciCrunch Registry

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

R package that takes a list of p-values resulting from the simultaneous testing of hypotheses and estimates their q-values. It is designed to measure the proportion of false positives when a test is significant. The software is capable of generating plots for visualization. It can be applied to problems in genomics, brain imaging, astrophysics, and data mining.

Proper citation: Qvalue (RRID:SCR_001073) Copy   

•   Source: SciCrunch Registry

http://www.frontiersin.org/10.3389/conf.fninf.2013.09.00111/event_abstract

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 17, 2016. ASP.NET AJAX .NET 4, Telerik controls enabled application designed to be used with the books presented, papers and web content to help with the acceleration of learning and software development for the neurosciences. All content developed at The Cromwell Workshop is intended to accelerate the implementation of biomedical knowledge, help with EEG clinical certifications and provide the opportunity for students, software developers, neuroscientists, neurologists and neurosurgeons to collaborate globally with knowledge content. e-NeoTutor relies on a subscription model to build software components in multiple languages and access book material. Tutor designed to complement free online course content in neuroscience, work with the Society of Neuroscience, The Human Connectome Project and assist researchers with CNIM certification, biomedical informatics, neuroinformatics and neuroscience training programs.

Proper citation: eNeoTutor (RRID:SCR_001590) Copy   

•   Source: SciCrunch Registry

http://toppcluster.cchmc.org/

A tool for performing multi-cluster gene functional enrichment analyses on large scale data (microarray experiments with many time-points, cell-types, tissue-types, etc.). It facilitates co-analysis of multiple gene lists and yields as output a rich functional map showing the shared and list-specific functional features. The output can be visualized in tabular, heatmap or network formats using built-in options as well as third-party software. It uses the hypergeometric test to obtain functional enrichment achieved via the gene list enrichment analysis option available in ToppGene.

Proper citation: ToppCluster (RRID:SCR_001503) Copy   

•   Source: SciCrunch Registry

https://sourceforge.net/projects/viste/

Open source, platform-independent application for the visualization and analysis of complex, high-dimensional imaging data such as Diffusion Tensor Imaging (DTI) and High Angular Resolution Diffusion Imaging (HARDI). It has a plugin-based architecture which allows third parties to develop new plugins to extend the tool. Overview of the many features: * vIST/e is programmed in C++. It uses the Visualization Toolkit for visualization and pipelined data processing, as well as the cross-platform toolkit Qt Framework for an easy-to-use Graphical User Interface. * vIST/e introduces a powerful new plugin system, which allows for modular development with increased extensibility and stability. * Powerful GPU-based visualization techniques allow for smooth, real-time visualization of large data sets. Using custom ray tracing algorithms created with OpenGL, vIST/e can render DTI ellipsoids and HARDI spherical harmonics glyphs up to 4th order. The high frame rates offered by modern GPU technology allows for interactive exploration of this complex data. * Diffusion Tensor Imaging data can be visualized and interactively explored in a number of ways, including multiple cross-sections, volume rendering, and tensor glyphs. Derived scalar volumes, including various different anisotropy measures, can be computed and visualized. Data from other modalities, such as structural MRI, can be shown alongside the DTI data. * Various fiber tracking methods allow for fast and accurate reconstruction of fiber pathways. Interactively defined Regions of Interest (ROIs) can be used for seeding and filtering of fibers. Fibers are visualized either as lines, optionally using a powerful, GPU-based lighting engine, or as 3D structures such as tubes. * Scalar volumes, glyphs, and fibers can be colored using a wide array of coloring option. Customizable color loop-up tables allow for highly flexible visualization of scalar data. * Visualization and processing of various different HARDI formats is supported. HARDI data is interactively visualized using highly detailed glyphs rendered on the GPU. HARDI glyphs can be visualized in combination with DTI glyphs, for a better overview of complex diffusion data. * vIST/e includes support for NVIDIA's Compute Unified Device Architecture (CUDA), which enables highly parallel, GPU-based data processing, allowing for significant speed-up of computationally expensive algorithms.

Proper citation: vIST/e (RRID:SCR_001627) Copy   

•   Source: SciCrunch Registry

http://ww2.sanbi.ac.za/Dbases.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The STACKdb is knowledgebase generated by processing EST and mRNA sequences obtained from GenBank through a pipeline consisting of masking, clustering, alignment and variation analysis steps. The STACK project aims to generate a comprehensive representation of the sequence of each of the expressed genes in the human genome by extensive processing of gene fragments to make accurate alignments, highlight diversity and provide a carefully joined set of consensus sequences for each gene. The STACK project is comprised of the STACKdb human gene index, a database of virtual human transcripts, as well as stackPACK, the tools used to create the database. STACKdb is organized into 15 tissue-based categories and one disease category. STACK is a tool for detection and visualization of expressed transcript variation in the context of developmental and pathological states. The data system organizes and reconstructs human transcripts from available public data in the context of expression state. The expression state of a transcript can include developmental state, pathological association, site of expression and isoform of expressed transcript. STACK consensus transcripts are reconstructed from clusters that capture and reflect the growing evidence of transcript diversity. The comprehensive capture of transcript variants is achieved by the use of a novel clustering approach that is tolerant of sub-sequence diversity and does not rely on pairwise alignment. This is in contrast with other gene indexing projects. STACK is generated at least four times a year and represents the exhaustive processing of all publicly available human EST data extracted from GenBank. This processed information can be explored through 15 tissue-specific categories, a disease-related category and a whole-body index

Proper citation: Sequence Tag Alignment and Consensus Knowledgebase Database (RRID:SCR_002156) Copy   

•   Source: SciCrunch Registry

https://cran.r-project.org/package=StAMPP

Software R package for statistical analysis of mixed ploidy populations.Used for calculation of population structure and differentiation based on single nucleotide polymorphism genotype data from populations of any ploidy level, and/or mixed ploidy levels.

Proper citation: StAMPP (RRID:SCR_022022) Copy   

•   Source: SciCrunch Registry

https://cran.r-project.org/package=hierfstat

Software R package for estimation and tests of hierarchical F statistics.Used to estimate hierarchical F-statistics from haploid or diploid genetic data with any numbers of levels in hierarchy.Intended for analysis of population structure using genetic markers.

Proper citation: hierfstat (RRID:SCR_022021) Copy   

•   Source: SciCrunch Registry

http://www.projects.roslin.ac.uk/sheepmap/front.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The project aims to apply genome mapping research to sheep, utilizing previous research in sheep (in other countries) and in other species (in the UK and abroad) to the benefit of the UK sheep industry. The project itself uses existing breeding structures, knowledge of the sheep genome and experimental resources. It has three main aims: i) To use the Suffolk, Texel and Charollais Sire Referencing Schemes to detect and verify quantitative trait loci (QTLs) for growth and carcass composition traits ii) To investigate candidate genes and/or chromosomal regions for associations with production traits. iii) To investigate approaches for optimizing future genotyping strategies within the sire referencing schemes for practical and cost effective application of marker-assisted selection By using commercial breeding populations for the research, immediate application of beneficial results is possible. Potential benefits include increased genetic progress through marker assisted selection which utilizes the genotype information, correction of possible parentage errors (ultimately leading to additional genetic progress) and opportunities for using marker information for product certification. The project will benefit the UK sheep industry by the use of Marker Assisted Selection (MAS) utilizing QTL or gene variants identified in the project. Additional benefits may arise from parentage verification and correction of errors e.g. misallocation of lamb to ewe. In the longer term, opportunities may exist to use markers for quality control, tracing products to their source. The major advantage of the design of this project is that the results are immediately applicable to the breeding schemes within which the QTLs and/or genes are detected. The time lag in the application of the results that is often seen with experimental populations is minimized. The project requires close involvement with the Sire Reference Schemes, in return for their assistance the results have immediate benefit to animals within these groups.

Proper citation: UK Sheep Genome Mapping Project (RRID:SCR_002272) Copy   

•   Source: SciCrunch Registry

http://coins.mrn.org/

A web-based neuroimaging and neuropsychology software suite that offers versatile, automatable data upload/import/entry options, rapid and secure sharing of data among PIs, querying and export all data, real-time reporting, and HIPAA and IRB compliant study-management tools suitable to large institutions as well as smaller scale neuroscience and neuropsychology researchers. COINS manages over over 400 studies, more than 265,000 clinical neuropsychological assessments, and 26,000 MRI, EEG, and MEG scan sessions collected from 18,000 participants at over ten institutions on topics related to the brain and behavior. As neuroimaging research continues to grow, dynamic neuroinformatics systems are necessary to store, retrieve, mine and share the massive amounts of data. The Collaborative Informatics and Neuroimaging Suite (COINS) has been created to facilitate communication and cultivate a data community. This tool suite offers versatile data upload/import/entry options, rapid and secure sharing of data among PIs, querying of data types and assessments, real-time reporting, and study-management tools suitable to large institutions as well as smaller scale researchers. It manages studies and their data at the Mind Research Network, the Nathan Kline Institute, University of Colorado Boulder, the Olin Neuropsychiatry Research Center (at) Hartford Hospital, and others. COINS is dynamic and evolves as the neuroimaging field grows. COINS consists of the following collaboration-centric tools: * Subject and Study Management: MICIS (Medical Imaging Computer Information System) is a centralized PostgreSQL-based web application that implements best practices for participant enrollment and management. Research site administrators can easily create and manage studies, as well as generate reports useful for reporting to funding agencies. * Scan Data Collection: An automated DICOM receiver collects, archives, and imports imaging data into the file system and COINS, requiring no user intervention. The database also offers scan annotation and behavioral data management, radiology review event reports, and scan time billing. * Assessment Data Collection: Clinical data gathered from interviews, questionnaires, and neuropsychological tests are entered into COINS through the web application called Assessment Manager (ASMT). ASMT's intuitive design allows users to start data collection with little or no training. ASMT offers several options for data collection/entry: dual data entry, for paper assessments, the Participant Portal, an online tool that allows subjects to fill out questionnaires, and Tablet entry, an offline data entry tool. * Data Sharing: De-identified neuroimaging datasets with associated clinical-data, cognitive-data, and associated meta-data are available through the COINS Data Exchange tool. The Data Exchange is an interface that allows investigators to request and share data. It also tracks data requests and keeps an inventory of data that has already been shared between users. Once requests for data have been approved, investigators can download the data directly from COINS.

Proper citation: Mind Research Network - COINS (RRID:SCR_000805) Copy   

•   Source: SciCrunch Registry

https://github.com/Benson-Genomics-Lab/TRF

Software tool to locate and display tandem repeats in DNA sequences. Program to analyze DNA sequences.

Proper citation: Tandem Repeats Finder (RRID:SCR_022193) Copy   

•   Source: SciCrunch Registry

http://www.patika.org/

The human pathway database which contains different biological entities and reactions and software tools for analysis. PATIKA Database integrates data from several sources, including Entrez Gene, UniProt, PubChem, GO, IntAct, HPRD, and Reactome. Users can query and access this data using the PATIKAweb query interface. Users can also save their results in XML or export to common picture formats. The BioPAX and SBML exporters can be used as part of this Web service.

Proper citation: Pathway Analysis Tool for Integration and Knowledge Acquisition (RRID:SCR_002100) Copy   

•   Source: SciCrunch Registry

https://github.com/bwa-mem2/bwa-mem2

Software tool for sequence mapping.The next version of BWA-MEM. Used for aligning sequencing reads against large reference genome.

Proper citation: BWA-MEM2 (RRID:SCR_022192) Copy   

•   Source: SciCrunch Registry