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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

https://psbweb05.psb.ugent.be/conet/microbialnetworks/spieceasi.php

Software R package estimates inverse covariance matrix from sequencing data.Statistical method for inference of microbial ecological networks from amplicon sequencing datasets.

Proper citation: Sparse Inverse Covariance Estimation for Ecological Association Inference (RRID:SCR_022646) Copy   

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http://hiv-web.lanl.gov/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 4, 2023. HIV Sequence Database is a database of annotated HIV sequences, plus a variety of tools and information for researchers studying HIV and SIV. The main aim of this website is to provide easy access to our sequence database, alignments, and the tools and interfaces we have produced. The HIV Sequence Database focuses on five primary goals: * Collecting HIV and SIV sequence data (all sequences since 1987) * Curating and annotating this data, and making it available to the scientific community * Computer analysis of HIV and related sequences * Production of software for the analysis of (sequence) data * The data and analyses on this site and published in a yearly printed publication, the HIV sequence Compendium, which is available free of charge.

Proper citation: HIV Sequence Database (RRID:SCR_002906) Copy   

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http://www.nsrrc.missouri.edu/

Provides access to critically needed swine models of human health and disease as well as a central resource for reagents, creation of new genetically modified swine, and information and training related to use of swine models in biomedical research.

Proper citation: National Swine Resource and Research Center (RRID:SCR_006855) Copy   

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https://github.com/BioDepot/BioDepot-workflow-builder

Software tool to create and execute reproducible bioinformatics workflows using drag and drop interface. Graphical widgets represent Docker containers executing modular task. Widgets are linked graphically to build bioinformatics workflows that can be reproducibly deployed across different local and cloud platforms. Each widget contains form-based user interface to facilitate parameter entry and console to display intermediate results.

Proper citation: BioDepot-workflow-builder (RRID:SCR_017402) Copy   

•   Source: SciCrunch Registry

https://cran.r-project.org/web/packages/celltrackR/index.html

Software R package to analyze immune cell migration data. Supports pipeline for track analysis by providing methods for data management, quality control, extracting and visualizing migration statistics, clustering tracks, and simulating cell migration.Available measures include displacement, confinement ratio, autocorrelation, straightness, turning angle, and fractal dimension. Measures can be applied to entire tracks, steps, or subtracks with varying length.

Proper citation: celltrackR (RRID:SCR_021021) Copy   

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https://www.rdocumentation.org/packages/DGCA/versions/1.0.2

Software R package to perform differential gene correlation analysis. Performs differential correlation analysis on input matrices, with multiple conditions specified by design matrix.

Proper citation: Differential Gene Correlation Analysis (RRID:SCR_020964) Copy   

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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   

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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   

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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://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

http://david.abcc.ncifcrf.gov/content.jsp?file=/ease/ease1.htm&type=1

Windows(c) desktop software application, customizable and standalone, that facilitates the biological interpretation of gene lists derived from the results of microarray, proteomic, and SAGE experiments. Provides statistical methods for discovering enriched biological themes within gene lists, generates gene annotation tables, and enables automated linking to online analysis tools. Offers statistical models to deal with multi-test comparison problem. Platform: Windows compatible

Proper citation: EASE: the Expression Analysis Systematic Explorer (RRID:SCR_013361) Copy   

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https://github.com/JCVenterInstitute/NSForest/releases

Software tool as method that takes cluster results from single cell nuclei RNAseq experiments and generates lists of minimal markers needed to define each cell type cluster. Utilizes random forest of decision trees machine learning approach. Used to determine minimum set of marker genes whose combined expression identified cells of given type with maximum classification accuracy.

Proper citation: NS-Forest (RRID:SCR_018348) Copy   

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https://mibig.secondarymetabolites.org/

MIBiG is genomic standards consortium project and biosynthetic gene cluster database used as reference dataset. Provides community standard for annotations and metadata on biosynthetic gene clusters and their molecular products. Standardised data format that describes minimally required information to uniquely characterise biosynthetic gene clusters. MIBiG 2.0 is expended repository for biosynthetic gene clusters of known function. MIBiG 3.0 is database update comprising large scale validation and re-annotation of existing entries and new entries. Community driven effort to annotate experimentally validated biosynthetic gene clusters.

Proper citation: Minimum Information about Biosynthetic Gene cluster (RRID:SCR_023660) Copy   

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http://www.cbil.upenn.edu/apidots/

Note: ApiDots is currently unavailable. For data on apicomplexan EST assemblies, please see EuPathDB ApiDots is a database integrating mRNA/EST sequences from numerous Apicomplexan parasites. ESTs and mRNAs were clustered and further assembled to generate consensus sequences. These consensus sequences were then subjected to database searches against protein sequences and protein domain sequences. The underlying relational structure of this database allows researchers to analyze these data and pose biologically interesting questions.

Proper citation: ApiDots (RRID:SCR_001778) Copy   

•   Source: SciCrunch Registry

http://www.hiv.lanl.gov/content/immunology/index

An annotated, searchable collection of HIV-1 cytotoxic and helper T-cell epitopes and antibody binding sites, plus related tools and information. The goal of this database is to provide a comprehensive listing of defined HIV epitopes. These data are also printed in the HIV Molecular Immunology compendium, which is updated yearly and provided free of charge to scientific researchers, both by online download and as a printed copy. The data included in this database are extracted from the HIV immunology literature. HIV-specific B-cell and T-cell responses are summarized and annotated. Immunological responses are divided into three sections, CTL (CD8+), T helper (CD4+), and antibody. Within these sections, defined epitopes are organized by protein and binding sites within each protein, moving from left to right through the coding regions spanning the HIV genome. We include human responses to natural HIV infections, as well as vaccine studies in a range of animal models and human trials. Responses that are not specifically defined, such as responses to whole proteins or monoclonal antibody responses to discontinuous epitopes, are summarized at the end of each protein sub-section. Studies describing general HIV responses to the virus, but not to any specific protein, are included at the end of each section. The annotation includes information such as cross-reactivity, escape mutations, antibody sequence, TCR usage, functional domains that overlap with an epitope, immune response associations with rates of progression and therapy, and how specific epitopes were experimentally defined. Basic information such as HLA specificities for T-cell epitopes, isotypes of monoclonal antibodies, and epitope sequences are included whenever possible. All studies that we can find that incorporate the use of a specific monoclonal antibody are included in the entry for that antibody. A single T-cell epitope can have multiple entries, generally one entry per study. Finally, tables and maps of all defined linear epitopes relative to the HXB2 reference proteins are provided. Alignments of CTL, helper T-cell, and antibody epitopes are available through the search interfaces. Only responses to HIV-1 and HIV-2 are included in the database.

Proper citation: HIV Molecular Immunology Database (RRID:SCR_002893) 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   

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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