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http://martinos.org/mne/

Software suite for processing magnetoencephalography and electroencephalography data. Open source Python software for exploring, visualizing, and analyzing human neurophysiological data including MEG, EEG, sEEG, ECoG . Implements all functionality of MNE Matlab tools in Python and extends capabilities of MNE Matlab tools to, e.g., frequency-domain and time-frequency analyses and non-parametric statistics.

Proper citation: MNE software (RRID:SCR_005972) Copy   

•   Source: SciCrunch Registry

http://divvy.ucsd.edu/

Software application for performing unsupervised machine learning and visualization with a focus on the clustering (separating data into groups) and dimensionality reduction (finding low dimensional structure in high dimensional data) subfields of machine learning. For visualization we provide support for both the whole dataset (e.g. a scatter plot) and points (e.g. transforming a particular point into an image). * Endlessly extensible. Every clusterer, reducer, point visualizer and dataset visualizer in Divvy is a plugin. We''ve provided a few big ones (K-means, PCA, scatter plot, &c.) and we''re hoping the community will use our plugin protocol to build many more. Each plugin defines its own UI, so your algorithm can look and behave the way that you want it to without top-down constraints. * Have lots of cores? Divvy is both task and data parallel. No longer will you be waiting for one algorithm to complete before you start another. Start as many as you want and keep using the UI. Only started one? With data parallelism we''ll still push your new MacBook Pro to 800% CPU utilization. * Part of your workflow: Export your clusterings and reductions to .csv and your visualizations to .png. Use your Matlab or R data with our Matlab/R to Divvy export tools available at http://github.com/jmlewis/divvy.

Proper citation: Divvy (RRID:SCR_006336) Copy   

•   Source: SciCrunch Registry

http://datastar.mannlib.cornell.edu/

A single library software prototype transitioning to a to an open-source platform ready for adoption and extension at other institutions wishing to provide research data sharing and discovery services. Datastar''''s ability to expose metadata about research datasets in a standard semantic format called Linked Data will be enhanced to support selective interchange of related information with VIVO, an open-source semantic researcher networking tool gaining prominence through adoption at multiple U.S. universities, in the federal government, and internationally.

Proper citation: DataStaR (RRID:SCR_006381) Copy   

•   Source: SciCrunch Registry

http://galaxyproject.org/

Open, web-based platform providing bioinformatics tools and services for data intensive genomic research. Platform may be used as a service or installed locally to perform, reproduce, and share complete analyses. Galaxy automatically tracks and manages data provenance and provides support for capturing the context and intent of computational methods. Galaxy Community has created Galaxy instances in many different forms and for many different applications including Galaxy servers, cloud services that support Galaxy instances, and virtual machines and containers that can be easily deployed for your own server.The Galaxy team is a part of BX at Penn State, and the Biology and Mathematics and Computer Science departments at Emory University.Training Infrastructure as a Service (TIaaS) is a service offered by some UseGalaxy servers to specifically support training use cases.

Proper citation: Galaxy (RRID:SCR_006281) Copy   

•   Source: SciCrunch Registry

https://sites.google.com/site/functionalconnectivitytoolbox/

MATLAB toolbox for performing functional connectivity analyses includes many of the most commonly-used approaches researchers have utilized to date for the identification of condition-dependent functional interactions between fMRI time-series obtained from two or more brain regions. The approaches are either bivariate or multivariate methods defined in time or frequency domains that emphasize distinct features of relationships among the time-series.

Proper citation: Functional Connectivity Toolbox (RRID:SCR_006394) Copy   

•   Source: SciCrunch Registry

http://organelledb.lsi.umich.edu/

Database of organelle proteins, and subcellular structures / complexes from compiled protein localization data from organisms spanning the eukaryotic kingdom. All data may be downloaded as a tab-delimited text file and new localization data (and localization images, etc) for any organism relevant to the data sets currently contained in Organelle DB is welcomed. The data sets in Organelle DB encompass 138 organisms with emphasis on the major model systems: S. cerevisiae, A. thaliana, D. melanogaster, C. elegans, M. musculus, and human proteins as well. In particular, Organelle DB is a central repository of yeast protein localization data, incorporating results from both previous and current (ongoing) large-scale studies of protein localization in Saccharomyces cerevisiae. In addition, we have manually curated several recent subcellular proteomic studies for incorporation in Organelle DB. In total, Organelle DB is a singular resource consolidating our knowledge of the protein composition of eukaryotic organelles and subcellular structures. When available, we have included terms from the Gene Ontologies: the cellular component, molecular function, and biological process fields are discussed more fully in GO. Additionally, when available, we have included fluorescent micrographs (principally of yeast cells) visualizing the described protein localization. Organelle View is a visualization tool for yeast protein localization. It is a visually engaging way for high school and undergraduate students to learn about genetics or for visually-inclined researchers to explore Organelle DB. By revealing the data through a colorful, dimensional model, we believe that different kinds of information will come to light.

Proper citation: Organelle DB (RRID:SCR_007837) Copy   

•   Source: SciCrunch Registry

http://virome.diagcomputing.org/#view=home

A web-application designed for scientific exploration of metagenome sequence data collected from viral assemblages occurring within a number of different environmental contexts. The VIROME informatics pipeline focuses on the classification of predicted open-reading frames (ORFs) from viral metagenomes. The portal allows you to submit your viral metagenome to be processed through the VIROME analysis pipeline, and enable you to investigate your data via the VIROME user interface., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: VIROME (RRID:SCR_004362) Copy   

•   Source: SciCrunch Registry

http://vamps.mbl.edu/overview.php

A publicly-accessible website to measure and visualize similarities and differences between molecular profiles of complex microbial communities. The project includes visualization tools such as heat maps that simultaneously compare the taxonomic distributions of multiple datasets and 3-D charts of the frequency distributions of 16S rRNA tags. Analytical tools include Chao diversity estimates and rarefaction curves. As a service to the community, researchers have the opportunity to upload their own data to the site for private viewing with the full range of data and analysis tools. Public data can be downloaded for further analysis locally.

Proper citation: VAMPS (RRID:SCR_004483) Copy   

•   Source: SciCrunch Registry

http://www.aftol.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented Jan 13, 2022; To enhance the understanding of the evolution of the Kingdom Fungi, 1500+ species were sampled for eight gene loci across all major fungal clades, plus a subset of taxa for a suite of morphological and ultrastructural characters with resulting data: AFTOL Molecular Database (generated by WASABI - Web Accessible Sequence Analysis for Biological Inference), Blast search the AFTOL Database (generated by WASABI), AFTOL primers (generated by WASABI), AFTOL primers by species (generated by WASABI), AFTOL alignments, and the AFTOL Structural and Biochemical Database. Users may submit samples to the AFTOL project. AFTOL is a collaboration centered around four universities in the United States: Duke University (Francois Lutzoni and Rytas Vilgalys), Clark University (David Hibbett), Oregon State University (Joey Spatafora), and University of Minnesota (David McLaughlin). Participants throughout the world have donated vouchers, taxon samples, and gene sequences. The aim of the project is to reconstruct the fungal tree of life using all available data for eight loci (nuclear ribosomal DNA: LSU, SSU, ITS (including 5.8s, ITS1 and ITS2); RNA polymerase II: RPB1, RPB2; elongation factor 1-alpha; mitochondrial SSU rDNA, and mitochondrial ATP synthase protein subunit 6). A further objective of this study is to summarize and integrate current knowledge regarding fungal subcellular features within this new phylogenetic framework. The name of the bioinformatic package developed for AFTOL is WASABI which provides an efficient communication platform to facilitate the collection and dissemination of molecular data to (and from) the laboratories and participants. All molecular data can be viewed, downloaded, verified, and corrected by the participants of AFTOL. A central goal of the WASABI interface is to establish an automated analysis framework that includes basecalling of newly generated chromatograms, contig assembly, quality verification of sequences (including a local BLAST), sequence alignment, and congruence test. Gene sequences that pass all tests and are finally verified by their authors will undergo automated phylogenetic analysis on a regular schedule. Although all steps are initially carried out noninteractively, the users can verify and correct the results at any step and thus initiate the reanalysis of dependent data.

Proper citation: AFTOL (RRID:SCR_004650) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.engineering.asu.edu/springs/Sprouts/index.html

SPROUTS is a database of predicted protein folding related data. It was designed to gather all the results from a study concerning the comparison between tools devoted to the prediction of stability changes upon point mutations. The second aim of this database is to offer simple and user-friendly tools to better visualize and analyze the results obtained. We are now able to propose three ways of visualization and analysis: the first one consists in getting raw Delta Delta G values in a table. The second one is a 2D graph representation of a computed stability score for each residue of a given sequence and for each tool. The last one is based on a Jmol applet (Jmol) with the possibility to represent the 3D structure of a given protein with symbols representing the information stored in the database. We assume that each visualization mode offers a different look on the data stored in the database and will suit to every scientists willing to query the database whether they are more used to handle 3D protein structure or 1D/2D sequence problems. Finally, the ultimate objective is to integrate these data and their analysis with other structural bioinformatic concepts in order to improve other methods that may be related to this concept. We are currently working at adding the information extracted from our other projects related to the prediction of protein folding nucleus in order to obtain a meta server devoted to the characterization of the folding core of proteins. As of today, this database has grown up and consists in more than 100 structures which have been computed for a total of around 16500 amino acids.

Proper citation: SPROUTS- Structural Prediction for Protein Folding Utility System (RRID:SCR_005118) Copy   

•   Source: SciCrunch Registry

http://www.musicianbrain.com/#index

The human brain has the remarkable ability to adapt in response to changes in the environment over the course of a lifetime. This is the mechanism for learning, growth, and normal development. Similar changes or adaptations can also occur in response to focal brain injuries, e.g., partially-adapted neighboring brain regions or functionally-related brain systems can either substitute for some of the lost function or develop alternative strategies to overcome a disability. Through ongoing research, the Music and Neuroimaging Laboratory''s mission is to: * Reveal the perceptual and cognitive aspects of music processing including the perception and memory for pitch, rhythmic, harmonic, and melodic stimuli. * Investigate the use of music and musical stimuli as an interventional tool for educational and therapeutic purposes. * Reveal the behavioral and neural correlates of learning, skill acquisition, and brain adaptation in response to changes in the environment or brain injury in the developing and adult brain. * Reveal the determinants and facilitators for recovery from brain injury. Project topics include: Aphasia Therapy, Singing and Speaking, Tone Deafness / Congenital Amusia, Motor Recovery Studies, Music and Emotions, Music and Autism, Children and Music Making, Brain Stimulation, Adult Musician Studies, Absolute Pitch Studies, Acute Stroke Studies

Proper citation: Music and Neuroimaging Laboratory (RRID:SCR_005447) Copy   

•   Source: SciCrunch Registry

https://github.com/slowkoni/rfmix

Software tool for local ancestry and admixture inference. Discriminative Modeling Approach for Rapid and Robust Local-Ancestry Inference.

Proper citation: RFMix (RRID:SCR_027030) Copy   

•   Source: SciCrunch Registry

http://www.cebm.brown.edu/openmee/index.html

Open-source, cross-platform software for ecological and evolutionary meta-analysis.

Proper citation: OpenMEE (RRID:SCR_027300) Copy   

•   Source: SciCrunch Registry

https://simtk.org/home/foldvillin

An archive of hundreds of all-atom, explicit solvent molecular dynamics simulations that were performed on a set of nine unfolded conformations of a variant of the villin headpiece subdomain (HP-35 NleNle). It includes scripts for accessing the archive of villin trajectories as well as a VMD plug-in for viewing the trajectories. In addition, all starting structures used in the trajectories are also provided. The simulations were generated using a distributed computing method utilizing the symmetric multiprocessing paradigm for individual nodes of the Folding_at_home distributed computing network. The villin trajectories in the archive are divided into two projects: PROJ3036 and PROJ3037. PROJ3036 contains trajectories starting from nine non-folded configurations. PROJ3037 contains trajectories starting from the native (folded) state. Runs 0 through 8 (in PROJ3036) correspond to starting configurations 0 through 8 discussed in the paper in J. Mol. Biol. (2007) 374(3):806-816 (see the publications tab for a full reference), whereas RUN9 uses the same starting configuration as RUN8. Each run contains 100 trajectories (named clone 0-99), each with the same starting configuration but different random velocities. Trajectories vary in their length of time and are subdivided into frames, also known as a generation. Each frame contains around 400 configurational snapshots, or timepoints, of the trajectory, with the last configurational snapshot of frame i corresponding to the first configurational snapshot of generation i+1. The goal is to allow researchers to analyze and benefit from the many trajectories produced through the simulations.

Proper citation: Molecular Simulation Trajectories Archive of a Villin Variant (RRID:SCR_002704) Copy   

•   Source: SciCrunch Registry

https://zenodo.org/records/11095105

Software label transfer tool for single-cell RNA sequencing analysis. Scalable, Interpretable Modeling for Single-cell RNA-seq data classification.

Proper citation: SIMS (RRID:SCR_025787) Copy   

•   Source: SciCrunch Registry

https://github.com/mwang87/MassQueryLanguage

Software application for universal searching of Mass Spectrometry data. Open source MS query language for flexible and mass spectrometer manufacturer-independent mining of MS data. Implements common MS terminology to build consensus vocabulary to search for MS patterns in single mass spectrometry run. Enables set of mass spectrometry patterns to be queried directly from raw data.

Proper citation: MassQL (RRID:SCR_025106) Copy   

•   Source: SciCrunch Registry

https://fmug.amaral.northwestern.edu/

Software data-driven tool to identify understudied genes and characterize their tractability. Users submit list of human genes and can filter these genes down based on list of factors. Code to generate Find My Understudied Genes app for Windows, iOS and macOS platforms.

Proper citation: Find My Understudied Genes (RRID:SCR_025047) Copy   

•   Source: SciCrunch Registry

https://github.com/bmvdgeijn/WASP/

Software allele-specific pipeline for unbiased read mapping and molecular QTL discovery. Allele-specific software for robust molecular quantitative trait locus discovery.

Proper citation: WASP (RRID:SCR_025497) Copy   

•   Source: SciCrunch Registry

http://bmcmicrobiol.biomedcentral.com/articles/10.1186/1471-2180-9-S1-S1

THIS RESOURCE IS NO LONGER IN SERVICE, documented on June 10, 2016. A consortium that created universal descriptors to describe functionally similar gene products and their attributes across all organisms. In 2004, the PAMGO interest group joined the GO consortium to extend the GO to include terms describing various processes related to microbe-host interactions. The organization uses a controlled vocabulary to set a process in place to describe plant associated microbes and their interactions with their plant-hosts. These higher order terms can describe gene products of all types of symbionts (e.g. parasites, commensals, and mutualists), including prokaryotes and eukaryotes that associate with plant or animal hosts. This initiative is a multi-institutional collaborative effort to pool information and research in: the bacteria Dickeya dadantii, Pseudomonas syringae pv tomato and Agrobacterium tumefaciens, the fungus Magnaporthe grisea, the oomycetes Phytophthora sojae and Phytophthora ramorum, and the nematode Meloidogyne hapla.

Proper citation: PAMGO (RRID:SCR_000022) Copy   

•   Source: SciCrunch Registry

http://www.stanford.edu/group/Urchin/contents.html

Laboratory modules designed for high school students covering sea urchin embryology including fertilization and development.

Proper citation: Sea Urchin Embryology (RRID:SCR_000460) Copy   

•   Source: SciCrunch Registry