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https://dbaasp.org

Collection of manually curated data regarding structure and antimicrobial activity of natural and synthetic peptides. Provides the information and analytical resources to develop antimicrobial compounds with the high therapeutic index.

Proper citation: Database of Antimicrobial Activity and Structure of Peptides (RRID:SCR_016600) Copy   

•   Source: SciCrunch Registry

https://www.immgen.org/

Project combines immunology and computational biology laboratories in effort to establish complete road map of gene-expression and regulatory networks in all immune cells. Project will generate, with rigorously standardized conditions, complete compendium of genome-wide data sets showing expression of protein-coding genes for all defined cell populations of mouse immune system.

Proper citation: ImmGen (RRID:SCR_021792) Copy   

•   Source: SciCrunch Registry

https://github.com/immunogenomics/masc

Software tool for testing whether specified covariate influences membership of single cells in any of multiple cellular subsets while accounting for technical confounds and biological variation.

Proper citation: Mixed effects association testing for single cells (RRID:SCR_025632) 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://ibeximagingcommunity.github.io/ibex_imaging_knowledge_base/

Open, global repository as central resource for reagents, protocols, panels, publications, software, and datasets. In addition to IBEX, we support standard, single cycle multiplexed imaging (Multiplexed 2D imaging), volume imaging of cleared tissues with clearing enhanced 3D (Ce3D), highly multiplexed 3D imaging (Ce3D-IBEX), and extension of the IBEX dye inactivation protocol to the Leica Cell DIVE (Cell DIVE-IBEX). Committed to sharing knowledge related to multiplexed imaging. Antibody validation community knowledgebase.

Proper citation: IBEX Knowledge Base (RRID:SCR_025296) Copy   

•   Source: SciCrunch Registry

https://github.com/sxf296/drug_targeting

Software tool to detect phenotypically relevant drug targets through unique transcriptomic enrichment that emphasizes biological directionality of drug-derived gene sets. Exploratory tool to screen for possible drug targeting molecules.

Proper citation: drug perturbation Gene Set Enrichment Analysis (RRID:SCR_025351) Copy   

•   Source: SciCrunch Registry

http://www.jcvi.org/charprotdb/index.cgi/home

The Characterized Protein Database, CharProtDB, is designed and being developed as a resource of expertly curated, experimentally characterized proteins described in published literature. For each protein record in CharProtDB, storage of several data types is supported. It includes functional annotation (several instances of protein names and gene symbols) taxonomic classification, literature links, specific Gene Ontology (GO) terms and GO evidence codes, EC (Enzyme Commisssion) and TC (Transport Classification) numbers and protein sequence. Additionally, each protein record is associated with cross links to all public accessions in major protein databases as ��synonymous accessions��. Each of the above data types can be linked to as many literature references as possible. Every CharProtDB entry requires minimum data types to be furnished. They are protein name, GO terms and supporting reference(s) associated to GO evidence codes. Annotating using the GO system is of importance for several reasons; the GO system captures defined concepts (the GO terms) with unique ids, which can be attached to specific genes and the three controlled vocabularies of the GO allow for the capture of much more annotation information than is traditionally captured in protein common names, including, for example, not just the function of the protein, but its location as well. GO evidence codes implemented in CharProtDB directly correlate with the GO consortium definitions of experimental codes. CharProtDB tools link characterization data from multiple input streams through synonymous accessions or direct sequence identity. CharProtDB can represent multiple characterizations of the same protein, with proper attribution and links to database sources. Users can use a variety of search terms including protein name, gene symbol, EC number, organism name, accessions or any text to search the database. Following the search, a display page lists all the proteins that match the search term. Click on the protein name to view more detailed annotated information for each protein. Additionally, each protein record can be annotated.

Proper citation: CharProtDB: Characterized Protein Database (RRID:SCR_005872) Copy   

•   Source: SciCrunch Registry

http://www.cpc.unc.edu/projects/addhealth

Longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States during the 1994-95 school year. Public data on about 21,000 people first surveyed in 1994 are available on the first phases of the study, as well as study design specifications. It also includes some parent and biomarker data. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood. The restricted-use contract includes four hours of free consultation with appropriate staff; after that, there''s a fee for help. Researchers can also share information through a listserv devoted to the database.

Proper citation: Add Health (National Longitudinal Study of Adolescent Health) (RRID:SCR_007434) Copy   

•   Source: SciCrunch Registry

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://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   

•   Source: SciCrunch Registry

https://www.bioconductor.org/packages/release/bioc/html/SingleR.html

Software R package for unbiased cell type recognition of scRNA-seq data. Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer cell of origin of each single cell independently.

Proper citation: SingleR (RRID:SCR_023120) Copy   

•   Source: SciCrunch Registry

https://benjjneb.github.io/dada2/

Open source software R package for modeling and correcting Illumina sequenced amplicon errors. Fast and accurate sample inference from amplicon data with single nucleotide resolution.

Proper citation: DADA2 (RRID:SCR_023519) Copy   

•   Source: SciCrunch Registry

https://github.com/cliu32/athlates

Software package for determining HLA genotypes for individuals from Illumina exome sequencing data. Program applies assembly, allele identification and allelic pair inference to short read sequences, and applies it to data from Illumina platforms.

Proper citation: ATHLATES (RRID:SCR_023689) 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

http://www.hiv.lanl.gov/content/vaccine/home.html

An overview of HIV and SIV vaccine trials and their outcomes. It was developed as a tool for compilation, search and comparison of published studies on SIV, HIV and SHIV vaccine trials in nonhuman primates. We used a set of criteria to scan Pubmed for relevant studies to enter into the database. In selecting studies for entry, priority was given to recently published studies in journals generally regarded as the primary source of information pertaining to HIV and SIV vaccine research in nonhuman primates. In most cases, we give priority to challenge studies, where the animals received a live virus to measure the "efficacy" of the immunogen(s) inoculated during the course of the investigation. 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 *Publication of the data and analyses on this site and in a yearly printed publication, the HIV sequence Compendium, which is available free of charge

Proper citation: Nonhuman Primate HIV/SIV Vaccine Trials Database (RRID:SCR_002274) Copy   

•   Source: SciCrunch Registry

http://www.immuneprofiling.org/

Consortium established to capitalize on recent advances in immune profiling methods in order to create a novel public resource that characterizes diverse states of the human immune system following infection; prior to and following vaccination against an infectious disease; or prior to and following treatment with an immune adjuvant that targets a known innate immune receptor(s). Through this program, well-characterized human cohorts are studied using a variety of modern analytic tools, including multiplex transcriptional, cytokine, and proteomic assays; multiparameter phenotyping of leukocyte subsets; assessment of leukocyte functional status; and multiple computational methods. Centralized research resources and a comprehensive, centralized database will be constructed for use by the greater scientific community. The information gained from the program will provide a comprehensive understanding of the human immune system and its regulation, and will reveal novel associations between components of the immune system and other biological systems, identify novel immune mediators and pathways, establish predictors of vaccine safety in different populations, and enable the rapid evaluation of different vaccine formulations and administration regimens in human populations.

Proper citation: Human Immunology Project Consortium (RRID:SCR_001491) 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

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