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http://coordinatingcenter.ucsf.edu/pride/

Randomized controlled trial being conducted at two clinical centers in the United States to learn more about the effects of weight loss on urinary incontinence. About 330 overweight women aged 30 or older will participate and will be followed for 18 months. Efficacy of weight reduction as a treatment for urinary incontinence will be examined at 6 months following the intensive weight control program, and the sustained impact of the intervention will be examined at 18 months. To increase the maintenance of weight reduction and facilitate evaluation of the enduring impact of weight loss on urinary incontinence, they propose to study a motivation-based weight maintenance program. At the end of the intensive weight control program, women randomized to the weight loss program will be randomized to either a 12-month skill-based maintenance intervention or to a motivation-based maintenance intervention. The maintenance interventions maximize the potential for sustained weight loss and will allow them to determine if long-term weight reduction will produce continued improvement in urinary incontinence.

Proper citation: Program to Reduce Incontinence by Diet and Exercise (RRID:SCR_009018) Copy   

•   Source: SciCrunch Registry

https://www.accordtrial.org/public

Study testing whether strict glucose control lowers the risk of heart disease and stroke in adults with type 2 diabetes. In addition the study is exploring: 1) Whether in the context of good glycemic control the use of different lowering lipid drugs will further improve these outcomes and 2) If strict control of blood pressure will also have additional beneficial effects on reducing cardiovascular disease. The design was a randomized, multicenter, double 2 X 2 factorial trial in 10,251 patients with type 2 diabetes mellitus. It was designed to test the effects on major CVD events of intensive glycemia control, of fibrate treatment to increase HDL-cholesterol and lower triglycerides (in the context of good LDL-C and glycemia control), and of intensive blood pressure control (in the context of good glycemia control), each compared to an appropriate control. All 10,251 participants were in an overarching glycemia trial. In addition, one 2 X 2 trial addressed the lipid question in 5,518 of the participants and the other 2 X 2 trial addressed the blood pressure question in 4,733 of the participants. The glycemia trial was terminated early due to higher mortality in the intensive compared with the standard glycemia treatment strategies. The results were published in June 2008 (N Eng J Med 2008;358:2545-59). Study-delivered treatment for all ACCORD participants was stopped on June 30, 2009, and the participants were assisted as needed in transferring their care to a personal physician. The lipid and blood pressure results (as well as the microvascular outcomes and eye substudy results) were published in 2010. All participants are continuing to be followed in a non-treatment observational study.

Proper citation: ACCORD (RRID:SCR_009015) Copy   

•   Source: SciCrunch Registry

https://github.com/compbiolabucf/PTNet

Graph based learning model for protein expression estimation by considering miRNA-mRNA interactions. Estimates protein levels by considering miRNA-mRNA interaction network, mRNA expression and miRNA expression.

Proper citation: PTNet (RRID:SCR_022975) Copy   

•   Source: SciCrunch Registry

https://github.com/aametwally/Metabolic_Subphenotype_Predictor

Software repository contains code for Inference of T2D metabolic subphenotypes (MuscleIR, Beta-cell Function, Incretin Effect, Hepatic IR), Identification of dominant metabolic subphenotype, Feature extraction from glucose tiemseries, Extraction of reduced representation of glucose tiemseries,Visualization of metabolic phenotypes based on various glucose-related metrics,Concordance between CGM and Venous glucose values from at home and at clinical setting, Classification of metabolic subphenotypes.

Proper citation: Metabolic Subphenotype Predictor (RRID:SCR_027192) Copy   

•   Source: SciCrunch Registry

http://www.ngsp.org

Project that aims to standardize Hemoglobin A1c test results to those of the Diabetes Control and Complications Trial (DCCT) and United Kingdom Prospective Diabetes Study (UKPDS) which established the direct relationships between HbA1c levels and outcome risks in patients with diabetes.

Proper citation: National Glycohemoglobin Standardization Program (RRID:SCR_015885) Copy   

•   Source: SciCrunch Registry

https://github.com/EpistasisLab/ReBATE

Open source software Python package to compare relief based feature selection algorithms used in data mining. Used for feature selection in any bioinformatics problem with potentially predictive features and target outcome variable, to detect feature interactions without examination of all feature combinations, to detect features involved in heterogeneous patterns of association such as genetic heterogeneity .

Proper citation: ReBATE (RRID:SCR_017139) Copy   

•   Source: SciCrunch Registry

https://github.com/zeyang-shen/maggie

Software Python package for identifying motifs mediating transcription factor binding and function. Links mutations of motif to changes of epigenomic feature without assuming linear relationship.

Proper citation: Motif Alteration Genome wide to Globally Investigate Elements (RRID:SCR_021903) Copy   

•   Source: SciCrunch Registry

https://spin.niddk.nih.gov/bax/software/TALOS-N/

Software package for prediction of protein backbone and sidechain torsion angles from NMR chemical shifts.

Proper citation: TALOS-N (RRID:SCR_022800) Copy   

•   Source: SciCrunch Registry

https://github.com/ParkerLab/ataqv

Software package for QC and visualization of ATAC-seq results. Used to examine aligned reads and report basic metrics, including reads mapped in proper pairs, optical or PCR duplicates, reads mapping to autosomal or mitochondrial references, ratio of short to mononucleosomal fragment counts, mapping quality, various kinds of problematic alignments.

Proper citation: ataqv (RRID:SCR_023112) Copy   

•   Source: SciCrunch Registry

https://github.com/qianli10000/mtradeR

Software R package implements Joint model with Matching and Regularization and simulation pipeline. Used to test association between taxa and disease risk, and adjusted for correlated taxa screened by pre-selection procedure in abundance and prevalence, individually.

Proper citation: mtradeR (RRID:SCR_022977) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/Maaslin2.html

SoftwareR package that identifies microbial taxa correlated with factors of interest using generalized linear models and mixed models.Used for efficiently determining multivariable association between clinical metadata and microbial meta'omic features.

Proper citation: MaAsLin2 (RRID:SCR_023241) Copy   

•   Source: SciCrunch Registry

https://pancreatlas.org/

Collection of human pancreas data and images. Platform to share data from human pancreas samples. Houses reference datasets from human pancreas samples, achieved through generosity of organ donors and their families.

Proper citation: Pancreatlas (RRID:SCR_018567) Copy   

•   Source: SciCrunch Registry

https://gitlab.com/rosen-lab/white-adipose-atlas

Single cell atlas of human and mouse white adipose tissue.

Proper citation: White Adipose Atlas (RRID:SCR_023625) Copy   

•   Source: SciCrunch Registry

https://huttenhower.sph.harvard.edu/picrust/

Software for predicting functional abundances based only on marker gene sequences.Used for prediction of metagenome functions. Contains updated and larger database of gene families and reference genomes, provides interoperability with any operational taxonomic unit (OTU)-picking or denoising algorithm, and enables phenotype predictions. Allows addition of custom reference databases.

Proper citation: PICRUSt2 (RRID:SCR_022647) Copy   

•   Source: SciCrunch Registry

https://picrust.github.io/picrust/

Software package to predict metagenome functional content from marker gene (e.g., 16S rRNA) surveys and full genomes. Used to predict which gene families are present and then combines gene families to estimate the composite metagenome.

Proper citation: PICRUSt (RRID:SCR_016855) Copy   

•   Source: SciCrunch Registry

http://www.cidr.jhmi.edu/

Next generation sequencing and genotyping services provided to investigators working to discover genes that contribute to disease. On-site statistical geneticists provide insight into analysis issues as they relate to study design, data production and quality control. In addition, CIDR has a consulting agreement with the University of Washington Genetics Coordinating Center (GCC) to provide statistical and analytical support, most predominantly in the areas of GWAS data cleaning and methods development. Completed studies encompass over 175 phenotypes across 530 projects and 620,000 samples. The impact is evidenced by over 380 peer-reviewed papers published in 100 journals. Three pathways exist to access the CIDR genotyping facility: * NIH CIDR Program: The CIDR contract is funded by 14 NIH Institutes and provides genotyping and statistical genetic services to investigators approved for access through competitive peer review. An application is required for projects supported by the NIH CIDR Program. * The HTS Facility: The High Throughput Sequencing Facility, part of the Johns Hopkins Genetic Resources Core Facility, provides next generation sequencing services to internal JHU investigators and external scientists on a fee-for-service basis. * The JHU SNP Center: The SNP Center, part of the Johns Hopkins Genetic Resources Core Facility, provides genotyping to internal JHU investigators and external scientists on a fee-for-service basis. Data computation service is included to cover the statistical genetics services provided for investigators seeking to identify genes that contribute to human disease. Human Genotyping Services include SNP Genome Wide Association Studies, SNP Linkage Scans, Custom SNP Studies, Cancer Panel, MHC Panels, and Methylation Profiling. Mouse Genotyping Services include SNP Scans and Custom SNP Studies.

Proper citation: Center for Inherited Disease Research (RRID:SCR_007339) Copy   

•   Source: SciCrunch Registry

http://t1dbase.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 26,2019. In October 2016, T1DBase has merged with its sister site ImmunoBase (https://immunobase.org). Documented on March 2020, ImmunoBase ownership has been transferred to Open Targets (https://www.opentargets.org). Results for all studies can be explored using Open Targets Genetics (https://genetics.opentargets.org). Database focused on genetics and genomics of type 1 diabetes susceptibility providing a curated and integrated set of datasets and tools, across multiple species, to support and promote research in this area. The current data scope includes annotated genomic sequences for suspected T1D susceptibility regions; genetic data; microarray data; and global datasets, generally from the literature, that are useful for genetics and systems biology studies. The site also includes software tools for analyzing the data.

Proper citation: T1DBase (RRID:SCR_007959) Copy   

•   Source: SciCrunch Registry

http://www.broad.mit.edu/mpg/grail/

A tool to examine relationships between genes in different disease associated loci. Given several genomic regions or SNPs associated with a particular phenotype or disease, GRAIL looks for similarities in the published scientific text among the associated genes. As input, users can upload either (1) SNPs that have emerged from a genome-wide association study or (2) genomic regions that have emerged from a linkage scan or are associated common or rare copy number variants. SNPs should be listed according to their rs#''s and must be listed in HapMap. Genomic Regions are specified by a user-defined identifier, the chromosome that it is located on, and the start and end base-pair positions for the region. Grail can take two sets of inputs - Query regions and Seed regions. Seed regions are definitely associated SNPs or genomic regions, and Query regions are those regions that the user is attempting to evaluate agains them. In many applications the two sets are identical. Based on textual relationships between genes, GRAIL assigns a p-value to each region suggesting its degree of functional connectivity, and picks the best candidate gene. GRAIL is developed by Soumya Raychaudhuri in the labs of David Altshuler and Mark Daly at the Center for Human Genetic Research of Massachusetts General Hospital and Harvard Medical School, and the Broad Institute. GRAIL is described in manuscript, currently in preparation.

Proper citation: Gene Relationships Across Implicated Loci (RRID:SCR_008537) Copy   

•   Source: SciCrunch Registry

http://www.jneurosci.org/supplemental/18/12/4570/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on January 29, 2013. Supplemental data for the paper Changes in mitochondrial function resulting from synaptic activity in the rat hippocampal slice, by Vytautas P. Bindokas, Chong C. Lee, William F. Colmers, and Richard J. Miller that appears in the Journal of Neuroscience June 15, 1998. You can view digital movies of changes in fluorescence intensity by clicking on the title of interest.

Proper citation: Hippocampal Slice Wave Animations (RRID:SCR_008372) Copy   

•   Source: SciCrunch Registry

https://github.com/vlink/marge

Software package that integrates genome wide genetic variation with epigenetic data to identify collaborative transcription factor pairs. Optimized to work with chromatin accessibility assays such as ATAC-seq or DNase I hypersensitivity, as well as transcription factor binding data collected by ChIP-seq. Used to identify combinations of cell type specific transcription factors while simultaneously interpreting functional effects of non-coding genetic variation.

Proper citation: Motif Mutation Analysis for Regulatory Genomic Elements (RRID:SCR_021902) Copy   

•   Source: SciCrunch Registry