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https://qgis.org/en/site/

Open source cross platform desktop geographic information system application that supports viewing, editing, and analysis of geospatial data. Functions as geographic information system software, allowing users to analyze and edit spatial information, in addition to composing and exporting graphical maps.

Proper citation: QGIS (RRID:SCR_018507) Copy   

•   Source: SciCrunch Registry

http://bios.unc.edu/~weisun/software/asSeq.htm

Software that establishes a statistical framework for future developments of eQTL (expression quantitative trait locus) mapping methods using RNA-seq data (e.g., linkage-based eQTL mapping), and the joint study of multiple genetic markers and/or multiple genes. This R package has been submitted to R/bioconductor. It will be available on bioconductor soon. It is recommended to install this R package from bioconductor. You can also install this R package from the source code by yourself. Since the R package contains C code, a C complier is required for installation. With both R and appropriate c complier installed, this R package can be installed using the following command (in Mac Terminal window or Windows command window) R CMD INSTALL asSeq

Proper citation: asSeq (RRID:SCR_001625) Copy   

•   Source: SciCrunch Registry

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

Software R package for fast and accurate precision recall and ROC curve calculations. Calculates accurate precision recall and Receiver Operator Characteristics curves.

Proper citation: precrec (RRID:SCR_018659) Copy   

•   Source: SciCrunch Registry

http://steps.sourceforge.net/STEPS/default.php

STEPS is a package for exact stochastic simulation of reaction-diffusion systems in realistic, complex 3D geometries. Our core simulation algorithm is an efficient implementation of a variation on Gillespie''s SSA, extended to deal with diffusion of molecules over the elements of a 3D tetrahedral mesh. While it was mainly developed for simulating detailed models of neuronal signaling pathways in dendrites and around synapses, it is a general tool and can be used for studying any biochemical pathway in which spatial gradients and morphology are thought to play a role. We have implemented STEPS as a set of Python modules, which means STEPS users can use Python scripts to control all aspects of setting up the model, generating a mesh, controlling the simulation and generating and analyzing output. The core computational routines are still implemented as C/C++ extension modules for maximal speed of execution.

Proper citation: STEPS (RRID:SCR_008742) Copy   

•   Source: SciCrunch Registry

https://knights-lab.github.io/MLRepo/

Machine learning repository for microbiome datasets.

Proper citation: ML Repo (RRID:SCR_017079) Copy   

•   Source: SciCrunch Registry

https://hnn.brown.edu/

Open source software package for circuit level interpretation of human EEG/MEG data. Software tool for interpreting cellular and network origin of human MEG/EEG data. Simulates electrical activity of neocortical cells and circuits that generate primary electrical currents underlying EEG/MEG recordings. Designed for researchers and clinicians, without computational neural modeling experience, to develop and test hypothesis on circuit origin of their data.

Proper citation: Human Neocortical Neurosolver (RRID:SCR_017437) Copy   

•   Source: SciCrunch Registry

https://planttfdb.gao-lab.org/

Comprehensive plant transcription factor database. Interface to allow users to search the database by IDs or free texts, to make sequence similarity search against TFs of all or individual species, and to download TF sequences for local analysis.PlantTFDB 3.0: a portal for the functional and evolutionary study of plant transcription factors

Proper citation: PLANTTFDB (RRID:SCR_003362) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/genomation/

Software R package for simplfiying common tasks in genomic feature analysis. Toolkit to summarize, annotate and visualize genomic intervals. Provides functions for reading BED and GFF files as GRanges objects, summarizing genomic features over predefined windows so users can make average enrichment of features over defined regions or produce heatmaps. Can annotate given regions with other genomic features such as exons,introns and promoters.

Proper citation: genomation (RRID:SCR_003435) Copy   

•   Source: SciCrunch Registry

http://www.neuromatic.thinkrandom.com/

NeuroMatic is a collection of Igor Pro functions for analyzing electrophysiological data. By allowing users to organize their data into Sets and Groups, NeuroMatic makes it relatively easy to compute transformations and statistical analyses on their data, including scaling, alignment averaging, baseline subtraction, spike detection, stationarity analysis, rise-time computations, etc. Being open source and modular designed, NeuroMatic also allows users to develop their own analysis functions that can be easily incorporated into NeuroMatic's framework. Note, if you have reached this page in search of a freeware tool for neuronal reconstructions, you are more likely to be interested in Neuromantic, a software package that sounds like NeuroMatic, but is not quite the same. Features of NeuroMatic Include * Sorting, Scaling, Averaging, Interpolation * Max / Min / Mean / Level / Rise Time / FWHM / Slope Measurements * Stability / Stationarity Analysis * Event Detection * Waveform Template Matching * Spike Raster Plots * Interspike-Interval and Peri-Stimulus Time (PST) Histograms * Compact Easy-to-Use Interface * Modular design as a basis for your own procedures * Extra space for your own buttons and controls * Import functions for Axograph and Pclamp data * Automatic macro generation for batch processing Supporting Agencies: MRC, Wellcome Trust Spike, Event, Fit, NClamp, Acquisition, spike train, EPSP, IPSP, IPSC, EPSC

Proper citation: NeuroMatic (RRID:SCR_004186) Copy   

•   Source: SciCrunch Registry

http://ratmine.mcw.edu/ratmine/begin.do

RatMine integrates data from RGD, UniProtKB, NCBI, KEGG and other sources to form a web-based data warehouse and tool set tailored for rat based data research. Search RatMine by entering names, identifiers, or keywords for genes, proteins, pathways, papers, etc. Additionally, we support programmatic access to our data through Application Programming Interface - choose from Perl or Java API. RatMine is a data warehouse that integrates many diverse biological data sets. The main focus is R. norvegicus genomics and proteomics. By integrating such data into one place it is possible to construct queries across domains of biological knowledge. The RatMine user interface is designed to go beyond simply looking up an identifier and viewing a report page. Some of the features include: * Quick Search is available just like on other sites, type in an identifier to see a report page. * Template queries are ''canned'' queries that provide a simple form to perform a specific task. You can create your own templates if you log in. * Lists lets you operate on whole lists of data at once. You can upload lists or save them from results tables. We also create useful public lists for everyone to use. * MyMine lets you create an account to save your own queries, bags and templates, as well as marking public templates as favorites.

Proper citation: RatMine (RRID:SCR_004190) Copy   

•   Source: SciCrunch Registry

http://biorxiv.org/

A free archive and distribution service for unpublished preprints in the life sciences allowing authors to make their findings immediately available to the scientific community and receive feedback on draft manuscripts before they are submitted to journals. An article may be posted prior to, or concurrently with, submission to a journal but should not be posted if it has already been published. Once an article is published in a journal, bioRxiv will update the preprint with a link to the published version.

Proper citation: bioRxiv (RRID:SCR_003933) Copy   

•   Source: SciCrunch Registry

http://git-scm.com/

A free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. Git is easy to learn and has a tiny footprint with lightning fast performance. It outclasses SCM tools like Subversion, CVS, Perforce, and ClearCase with features like cheap local branching, convenient staging areas, and multiple workflows.

Proper citation: Git (RRID:SCR_003932) Copy   

•   Source: SciCrunch Registry

http://pubchem.ncbi.nlm.nih.gov/

Collection of information about chemical structures and biological properties of small molecules and siRNA reagents hosted by the National Center for Biotechnology Information (NCBI).

Proper citation: PubChem (RRID:SCR_004284) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/pdbe/

The European resource for the collection, organization and dissemination of data on biological macromolecular structures. In collaboration with the other worldwide Protein Data Bank (wwPDB) partners - the Research Collaboratory for Structural Bioinformatics (RCSB) and BioMagResBank (BMRB) in the USA and the Protein Data Bank of Japan (PDBj) - they work to collate, maintain and provide access to the global repository of macromolecular structure data. The main objectives of the work at PDBe are: * to provide an integrated resource of high-quality macromolecular structures and related data and make it available to the biomedical community via intuitive user interfaces. * to maintain in-house expertise in all the major structure-determination techniques (X-ray, NMR and EM) in order to stay abreast of technical and methodological developments in these fields, and to work with the community on issues of mutual interest (such as data representation, harvesting, formats and standards, or validation of structural data). * to provide high-quality deposition and annotation facilities for structural data as one of the wwPDB deposition sites. Several sophisticated tools are also available for the structural analysis of macromolecules.

Proper citation: PDBe - Protein Data Bank in Europe (RRID:SCR_004312) Copy   

•   Source: SciCrunch Registry

http://discovery.hsci.harvard.edu/

An online database of curated cancer stem cell (CSC) experiments coupled to the Galaxy analytical framework. Driven by a need to improve our understanding of molecular processes that are common and unique across cancer stem cells (CSCs), the SCDE allows users to consistently describe, share and compare CSC data at the gene and pathway level. The initial focus has been on carefully curating tissue and cancer stem cell-related experiments from blood, intestine and brain to create a high quality resource containing 53 public studies and 1098 assays. The experimental information is captured and stored in the multi-omics Investigation/Study/Assay (ISA-Tab) format and can be queried in the data repository. A linked Galaxy framework provides a comprehensive, flexible environment populated with novel tools for gene list comparisons against molecular signatures in GeneSigDB and MSigDB, curated experiments in the SCDE and pathways in WikiPathways. Investigation/Study/Assay (ISA) infrastructure is the first general-purpose format and freely available desktop software suite targeted to experimentalists, curators and developers and that: * assists in the reporting and local management of experimental metadata (i.e. sample characteristics, technology and measurement types, sample-to-data relationships) from studies employing one or a combination of technologies; * empowers users to uptake community-defined minimum information checklists and ontologies, where required; * formats studies for submission to a growing number of international public repositories endorsing the tools, currently ENA (genomics), PRIDE (proteomics) and ArrayExpress (transcriptomics). Galaxy allows you to do analyses you cannot do anywhere else without the need to install or download anything. You can analyze multiple alignments, compare genomic annotations, profile metagenomic samples and much much more. Best of all, Galaxy''''s history system provides a complete analyses record that can be shared. Every history is an analysis workflow, which can be used to reproduce the entire experiment. The code for this Galaxy instance is available for download from BitBucket.

Proper citation: Stem Cell Discovery Engine (RRID:SCR_004453) Copy   

•   Source: SciCrunch Registry

http://www.trex.uqam.ca/

A web server dedicated to the reconstruction of phylogenetic trees, reticulation networks and to the inference of horizontal gene transfer (HGT) events.

Proper citation: Tree and reticulogram REConstruction (RRID:SCR_004497) Copy   

•   Source: SciCrunch Registry

https://github.com/alyssafrazee/derfinder

R package for differential expression analysis of RNA-seq data.

Proper citation: DER Finder (RRID:SCR_004250) Copy   

•   Source: SciCrunch Registry

http://www.tcdb.org/

Curated, relational database containing sequence, classification, structural, functional and evolutionary information about transport systems from variety of living organisms based on IUBMB-approved transporter classification (TC) system. Descriptions, TC numbers, and examples of over 600 families of transport proteins are provided. TC system is analogous to Enzyme Commission (EC) system for classification of enzymes, except that it incorporates both functional and phylogenetic information. TCDB users may submit their own sequenced proteins and descriptions for inclusion into database. The software tools used are all freely available for download. These programs are used for analysis of Protein and DNA sequences. Programs require UNIX server to run.

Proper citation: Transporter Classification Database (RRID:SCR_004490) Copy   

•   Source: SciCrunch Registry

http://blast.ncbi.nlm.nih.gov/Blast.cgi

Web search tool to find regions of similarity between biological sequences. Program compares nucleotide or protein sequences to sequence databases and calculates statistical significance. Used for identifying homologous sequences.

Proper citation: NCBI BLAST (RRID:SCR_004870) Copy   

•   Source: SciCrunch Registry

http://noble.gs.washington.edu/proj/philius/

Web server that predicts protein transmembrane topology and signal peptides. Hidden Markov models (HMM) have been successfully applied to the tasks of transmembrane protein topology prediction and signal peptide prediction. They expand upon this work by making use of the more powerful class of dynamic Bayesian networks (DBN). Their model, Philius, is inspired by a previously published HMM, Phobius, and combines a signal peptide sub-model with a transmembrane sub-model. They introduce a two-stage DBN decoder which combines the power of posterior decoding with the grammar constraints of Viterbi-style decoding. Philius also provides protein type, segment, and topology confidence metrics to aid in the interpretation of the predictions.

Proper citation: Philius (RRID:SCR_004625) Copy   

•   Source: SciCrunch Registry