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https://bioweb.pasteur.fr/packages/pack@Tracer@v1.6

Open source software tool for analysing trace files generated by Bayesian MCMC runs. Software package for visualising and analysing MCMC trace files generated through Bayesian phylogenetic inference. Provides kernel density estimation, multivariate visualisation, demographic trajectory reconstruction, conditional posterior distribution summary and more.

Proper citation: Tracer (RRID:SCR_019121) Copy   

•   Source: SciCrunch Registry

https://github.com/datatagsuite

Software suite to enable discoverability of datasets. Enables submission of metadata on datasets to DataMed. Has core set of elements, which are generic and applicable to any type of dataset, and extended set that can accommodate more specialized data types. Platform independent model developed by NIH BD2K bioCADDIE project for DataMed Data Discovery Index prototype being developed. Also available as annotated serialization in schema.org, which in turn is widely used by major search engines like Google, Microsoft, Yahoo and Yandex.

Proper citation: DatA Tag Suite (RRID:SCR_019236) Copy   

•   Source: SciCrunch Registry

https://www.archrproject.com/

Software R package for processing and analyzing single-cell ATAC-seq data. Used for integrative single cell chromatin accessibility analysis.Provides intuitive, user focused interface for complex single cell analysis, including doublet removal, single cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing.

Proper citation: ArchR (RRID:SCR_020982) Copy   

•   Source: SciCrunch Registry

https://cedar.metadatacenter.org/

Web application for creating, collecting, testing, and sharing metadata. It provides templates for metadata models or structures, and is capable of testing those models quickly using real data.

Proper citation: CEDAR Workbench (RRID:SCR_016270) Copy   

•   Source: SciCrunch Registry

https://software.broadinstitute.org/software/discovar/blog/

Software tool for variant calling with reference and de novo assembly of genomes. The heart of DISCOVAR is a de novo genome assembler which can generate de novo assemblies for both large and small genomes.

Proper citation: Discovar assembler (RRID:SCR_016755) Copy   

•   Source: SciCrunch Registry

https://niaid.github.io/spice/

Software application for data mining and visualization. Used for analyzes of large FLOWJO data sets from polychromatic flow cytometry and organizing the normalized data graphically.

Proper citation: SPICE (RRID:SCR_016603) Copy   

•   Source: SciCrunch Registry

https://github.com/marbl/salsa

Software tool for scaffold long read assemblies with Hi-C data.

Proper citation: SALSA (RRID:SCR_022013) Copy   

•   Source: SciCrunch Registry

https://github.com/JamieHeather/stitchr

Software Python tool for stitching coding T cell receptors nucleotide sequences from V,J,CDR3 information. Produces complete coding sequences representing fully spliced TCR cDNA given minimal V,J,CDR3 information.

Proper citation: Stitchr (RRID:SCR_022139) Copy   

•   Source: SciCrunch Registry

https://github.com/immunogenomics/harmony

Software R package to project cells into shared embedding in which cells group by cell type rather than dataset specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. Used for integration of single cell data.

Proper citation: Harmony (RRID:SCR_022206) Copy   

•   Source: SciCrunch Registry

https://github.com/wyp1125/MCScanx

Software toolkit for detection and evolutionary analysis of gene synteny and collinearity.

Proper citation: MCScanX (RRID:SCR_022067) Copy   

•   Source: SciCrunch Registry

https://masst.gnps2.org/microbemasst/

Web taxonomically informed mass spectrometry search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging database of over 60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns.

Proper citation: microbeMASST (RRID:SCR_024713) Copy   

•   Source: SciCrunch Registry

https://www.sanger.ac.uk/collaboration/sequencing-idd-regions-nod-mouse-genome/

Genetic variations associated with type 1 diabetes identified by sequencing regions of the non-obese diabetic (NOD) mouse genome and comparing them with the same areas of a diabetes-resistant C57BL/6J reference mouse allowing identification of single nucleotide polymorphisms (SNPs) or other genomic variations putatively associated with diabetes in mice. Finished clones from the targeted insulin-dependent diabetes (Idd) candidate regions are displayed in the NOD clone sequence section of the website, where they can be downloaded either as individual clone sequences or larger contigs that make up the accession golden path (AGP). All sequences are publicly available via the International Nucleotide Sequence Database Collaboration. Two NOD mouse BAC libraries were constructed and the BAC ends sequenced. Clones from the DIL NOD BAC library constructed by RIKEN Genomic Sciences Centre (Japan) in conjunction with the Diabetes and Inflammation Laboratory (DIL) (University of Cambridge) from the NOD/MrkTac mouse strain are designated DIL. Clones from the CHORI-29 NOD BAC library constructed by Pieter de Jong (Children's Hospital, Oakland, California, USA) from the NOD/ShiLtJ mouse strain are designated CHORI-29. All NOD mouse BAC end-sequences have been submitted to the International Nucleotide Sequence Database Consortium (INSDC), deposited in the NCBI trace archive. They have generated a clone map from these two libraries by mapping the BAC end-sequences to the latest assembly of the C57BL/6J mouse reference genome sequence. These BAC end-sequence alignments can then be visualized in the Ensembl mouse genome browser where the alignments of both NOD BAC libraries can be accessed through the Distributed Annotation System (DAS). The Mouse Genomes Project has used the Illumina platform to sequence the entire NOD/ShiLtJ genome and this should help to position unaligned BAC end-sequences to novel non-reference regions of the NOD genome. Further information about the BAC end-sequences, such as their alignment, variation data and Ensembl gene coverage, can be obtained from the NOD mouse ftp site.

Proper citation: Sequencing of Idd regions in the NOD mouse genome (RRID:SCR_001483) Copy   

•   Source: SciCrunch Registry

http://www.sb.cs.cmu.edu/drem

The Dynamic Regulatory Events Miner (DREM) allows one to model, analyze, and visualize transcriptional gene regulation dynamics. The method of DREM takes as input time series gene expression data and static transcription factor-gene interaction data (e.g. ChIP-chip data), and produces as output a dynamic regulatory map. The dynamic regulatory map highlights major bifurcation events in the time series expression data and transcription factors potentially responsible for them. DREM 2.0 was released and supports a number of new features including: * new static binding data for mouse, human, D. melanogaster, A. thaliana * a new and more flexible implementation of the IOHMM supports dynamic binding data for each time point or as a mix of static/dynamic TF input * expression levels of TFs can be used to improve the models learned by DREM * the motif finder DECOD can be used in conjuction with DREM and help find DNA motifs for unannotated splits * new features for the visualization of expressed TFs, dragging boxes in the model view, and switching between representations

Proper citation: Dynamic Regulatory Events Miner (RRID:SCR_003080) Copy   

•   Source: SciCrunch Registry

http://www.cs.cmu.edu/~jernst/stem/

The Short Time-series Expression Miner (STEM) is a Java program for clustering, comparing, and visualizing short time series gene expression data from microarray experiments (~8 time points or fewer). STEM allows researchers to identify significant temporal expression profiles and the genes associated with these profiles and to compare the behavior of these genes across multiple conditions. STEM is fully integrated with the Gene Ontology (GO) database supporting GO category gene enrichment analyses for sets of genes having the same temporal expression pattern. STEM also supports the ability to easily determine and visualize the behavior of genes belonging to a given GO category or user defined gene set, identifying which temporal expression profiles were enriched for these genes. (Note: While STEM is designed primarily to analyze data from short time course experiments it can be used to analyze data from any small set of experiments which can naturally be ordered sequentially including dose response experiments.) Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: Short Time-series Expression Miner (STEM) (RRID:SCR_005016) Copy   

•   Source: SciCrunch Registry

http://ontodog.hegroup.org/index.php

Ontodog is a web-based ontology view generator. It can generate inSubset annotation ontology, user preferred label annotation ontology and subset of source ontology. Simply provide Ontodog input term file (Microsoft Excel file or tab-delimited text file), select one source ontology or enter your own source ontology and SPARQL endpoint, then set the settings for Ontodog output files and get the OWL (RDF/XML) Output files. Ontodog performs the basic ontology modularization-like function, i.e.,it automatically extracts all axioms and related terms associated with user-specified signature term(s). In addition, Ontodog includes extra features: (1) extracting all instance data associated with the retrieved class terms and annotations; and (2) recursively extracting all axioms and related terms indirectly associated with signature terms. More features are being added to Ontodog, such as relabeling preferred names for various ontology terms to fit in with the needs from a specific community. The Ontodog input data requires a source ontology and a list of user-specified signature terms in tab-delimited format. Ontodog provides the template files for generating the signature terms as the input terms file to download. There are several output options that the users can choose based on their needs. With more and more ontologies being developed, Ontodog offers a timely web-based package of solutions for ontology view generation. Ontodog provides an efficient approach to promote ontology sharing and interoperability. It is easy to use and does not require knowledge of SPARQL, script programming, and command line operation. Ontodog is developed to serve the ontology community for ontology reuse. It is freely available under the Apache License 2.0. The source code is made available under Apache License 2.0.

Proper citation: Ontodog: A Web-based Ontology View Generator (RRID:SCR_005061) Copy   

•   Source: SciCrunch Registry

https://www.beiresources.org/

Central data repository that supplies organisms and reagents to the broad community of microbiology and infectious diseases researchers.

Proper citation: BEI Resource Repository (RRID:SCR_013698) Copy   

•   Source: SciCrunch Registry

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

A DICOM image converter based on the ITK IO mechanism for reading and writing images. The formats currently supported by the converter are DICOM to: Analyze (*.hdr); MetaImage (*.mhd); Nrrd (*.nhdr, *.nrrd).

Proper citation: DICOMConvert (RRID:SCR_014100) Copy   

•   Source: SciCrunch Registry

https://cibersort.stanford.edu/

Software tool to provide an estimation of the abundances of member cell types in a mixed cell population, using gene expression data. Used for characterizing cell composition of complex tissues from their gene expression profiles, large scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets.

Proper citation: CIBERSORT (RRID:SCR_016955) Copy   

•   Source: SciCrunch Registry

http://automl.info/tpot/

Software Python package to automate building of ML pipelines by combining flexible expression tree representation of pipelines with stochastic search algorithms such as genetic programming.

Proper citation: Tree-Based Pipeline Optimization Tool (RRID:SCR_017531) Copy   

•   Source: SciCrunch Registry

http://coreimmunology.ucsf.edu/flow-cytometry

Flow cytometry facility offering training and services including:Access to two, 17-color BD LSR II analytical instruments with High Throughput Sampler (HTS) module,Configurations:LSRII 1,LSRII 2;Help with Flow Cytometry Panel Design;Fluorofinder (access our cytometers under CFAR Immunology Core);BD Panel designer;SFGH LSRII Flow Core Protocols;LSRII Startup and Shutdown;How to run the CST calibration assay;Access to a 17-color BD FACSAria II for fluorescence-activated cell sorting (FACS);4-way tube sorting;96 well plate sorting;Index sorting;SFGH ARIA Flow Core Protocols and configuration;ARIA Startup;Determining Drop Delay;Side Stream Set Up;Clog Procedure;ARIA Shutdown Protocol;ARIA Configuration;DNA analysis with standard dyes;Analysis of CFP, GFP, YFP, mRFP, mTomato, and mCherry gene expression proteins;Calcium flux measurements using Indo-1;Training of users on the operation of instruments and experimental design through the CIL Flow Cytometry Course;Maintaining and Upgrading Instruments;Research Support Services (study design, assay selection, grant and paper writing support).

Proper citation: University of California at San Francisco Division of Experimental Medicine Flow Core Facility (RRID:SCR_017903) Copy   

•   Source: SciCrunch Registry