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https://www.genomics.agilent.com/article.jsp?pageId=2100

Software that performs data analysis algorithms for QPCR data. The software is included with the purchase of the Agilent MxPro QPCR System., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: MxPro QPCR (RRID:SCR_016375) Copy   

•   Source: SciCrunch Registry

http://homer.ucsd.edu/homer/microarray/index.html

Software tool to analyze the promoters of genes and look for motifs that are enriched in the target gene promoters relative to other promoters. Used for gene based analysis to provide a list of genes that should contain the same elements, such as genes that are co-regulated. It includes gene ontology analysis and can be used to look for RNA motifs in mRNAs.

Proper citation: findMotif.pl (RRID:SCR_016417) Copy   

•   Source: SciCrunch Registry

https://github.com/sblanck/smagexp

Software toolkit for transcriptomics data meta-analysis. It integrates metaMA and metaRNAseq packages into Galaxy, carries out meta-analysis of gene expression data, handles microarray data from Gene Expression Omnibus (GEO) database, and more.

Proper citation: SMAGEXP (RRID:SCR_016360) Copy   

•   Source: SciCrunch Registry

https://ihg.helmholtz-muenchen.de/cgi-bin/hw/hwa1.pl

Software tool for performing tests for deviation from Hardy-Weinberg equilibrium and tests for association. Used in population-based genetic association studies to identify susceptibility genes for complex diseases.

Proper citation: Tests for deviation from Hardy-Weinberg equilibrium (RRID:SCR_016496) Copy   

•   Source: SciCrunch Registry

http://amp.pharm.mssm.edu/DGB/

Web based application to assist researchers with identifying drugs and small molecules that are predicted to maximally influence expression of mammalian gene of interest. Used to identify drugs and small molecules to regulate expression of target genes for research purpose only. Application for ranking drugs to modulate specific gene based on transcriptomic signatures.

Proper citation: Drug Gene Budger (RRID:SCR_016489) Copy   

•   Source: SciCrunch Registry

https://www.thermofisher.com/order/catalog/product/00-0210

Scanner for microarray analysis to scan next-generation higher-density arrays, including SNP arrays, tiling arrays for transcription and all-exon arrays for whole-genome analysis.

Proper citation: Thermo Fisher: GeneChip� Scanner 3000 7G (RRID:SCR_016522) Copy   

•   Source: SciCrunch Registry

https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000363.v16.p10

Project to generate extensive biomarker data from Framingham Heart Study participants using immunoassays, proteomics, metabolomics/lipomics, and gene expression and microRNA profiling to advance personalized medicine through biomarker discovery and validation.

Proper citation: SABRe CVD Initiative (RRID:SCR_016572) Copy   

•   Source: SciCrunch Registry

https://github.com/WGS-TB/MentaLiST

Software for a MLST (multi-locus sequence typing) caller, based on a k-mer counting algorithm and written in the Julia language. Designed and implemented to handle large typing schemes.

Proper citation: MentaLiST (RRID:SCR_016469) Copy   

•   Source: SciCrunch Registry

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

Software R package to convert between human and non-human gene orthologs/homologs. Integrates orthology assertion predictions sourced from multiple databases as compiled by the HGNC Comparison of Orthology Predictions (HCOP) (Wright et al. 2005 , Eyre et al. 2007 , Seal et al. 2011 ).

Proper citation: babelgene (RRID:SCR_027117) Copy   

•   Source: SciCrunch Registry

https://sc.edu/study/colleges_schools/pharmacy/research_and_practice/research_centers_and_facilities/functional_genomics_core/index.php

Core offers resources and solutions for conducting genomics, transcriptomics, epigenomics and functional genomics projects. We work with researchers to determine project goals and design custom solutions. We assist at all stages of the project, from support in grant development to generation of publication-quality data. Services include Consultations, Bioinformatics, Nucleic Acids purification, quantification and QC DNA Sequencing (SANGER and NGS), Library Constructions for NGS applications,Microarray Hybridization, Real Time PCR, Custom epigenomics applications, Lentiviral vectors and lentiviruses construction and production, CRIPR-CAS9sgRNAs and RNAi/shRNAs knockdown of individual genes and functional screening of sgRNA and shRNA libraries for target identification.

Proper citation: University of South Carolina Functional Genomics Core Facility (RRID:SCR_026178) Copy   

•   Source: SciCrunch Registry

http://sherlock.ucsf.edu/

Service to discover disease genes in GWAS using eQTL signature matching by simply submitting your list of GWAS associations (SNPs and p-values). It is important to upload all SNPs in your association study, not just the top hits. Sherlock may be able to group multiple lower-confidence SNPs to discover functionally-important genes.

Proper citation: Sherlock (RRID:SCR_001628) Copy   

•   Source: SciCrunch Registry

http://www-personal.umich.edu/~jianghui/rseqdiff/

An R package that can detect differential gene and isoform expressions from RNA-seq data of multiple biological conditions. The approach considers three cases for each gene: 1) no differential expression, 2) differential expression without differential splicing and 3) differential splicing.

Proper citation: rSeqDiff (RRID:SCR_001683) Copy   

•   Source: SciCrunch Registry

https://cran.r-project.org/src/contrib/Archive/QuasiSeq/

Software package to apply the QL, QLShrink and QLSpline methods to quasi-Poisson or quasi-negative binomial models for identifying differentially expressed genes in RNA-seq data.

Proper citation: QuasiSeq (RRID:SCR_001715) Copy   

•   Source: SciCrunch Registry

http://datahub.io/dataset/kupkb

A collection of omics datasets (mRNA, proteins and miRNA) that have been extracted from PubMed and other related renal databases, all related to kidney physiology and pathology giving KUP biologists the means to ask queries across many resources in order to aggregate knowledge that is necessary for answering biological questions. Some microarray raw datasets have also been downloaded from the Gene Expression Omnibus and analyzed by the open-source software GeneArmada. The Semantic Web technologies, together with the background knowledge from the domain's ontologies, allows both rapid conversion and integration of this knowledge base. SPARQL endpoint http://sparql.kupkb.org/sparql The KUPKB Network Explorer will help you visualize the relationships among molecules stored in the KUPKB. A simple spreadsheet template is available for users to submit data to the KUPKB. It aims to capture a minimal amount of information about the experiment and the observations made.

Proper citation: Kidney and Urinary Pathway Knowledge Base (RRID:SCR_001746) Copy   

•   Source: SciCrunch Registry

http://gmod.org/wiki/Main_Page

A collection of open source software tools for creating and managing genome-scale biological databases. GMOD is made up databases, applications, and adaptor software that connects these components together. You can use it to create a small laboratory database of genome annotations, or a large web-accessible community database. At first GMOD just featured model organisms but now any organism with any kind of sequence associated with it is a good candidate as a subject for a GMOD database. There are GMOD databases with just protein sequence in them, with EST sequence only, those that are concerned primarily with gene expression, and even those dedicated to collections of RNA sequence. They have also heard of GMOD databases for oligonucleotides and plasmids.

Proper citation: Generic Model Organism Database Project (RRID:SCR_001731) Copy   

•   Source: SciCrunch Registry

http://www.cbgrits.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Time-series data sets spanning twelve time-points between E12-P9 for exploring cerebellar development of the mouse in time and space. The database contains a number of mutant / wildtype microarray datasets including two complete wildtype microarray time-series (C57BL/6 and DBA/2J). The dataset also includes in situ hybridization and bioinformatic analyses. Exploration of this dataset will allow the investigator to assess differential gene expression profiles from a developing mutant cerebella, to assess the temporal changes in gene expression in the wildtype, and to verify the cellular expression of these genes in images from our in situ hybridization library. Using the database, the investigator can explore the developmental expression or differential expression patterns of a particular gene, or create lists of similarly expression genes by building simple search algorithms. These lists can then be mined across all the datasets in both space and time. Cb GRiTS's current datasets represent gene expression analyses from multiple cerebellar mutant and wildtype single time-point and developmental series.

Proper citation: Cerebellar Gene Regulation in Time and Space Database (RRID:SCR_001699) Copy   

•   Source: SciCrunch Registry

https://physiomeproject.org/

The Physiome Project is a worldwide public domain effort to provide a computational framework for understanding human and other eukaryotic physiology. It aims to develop integrative models at all levels of biological organization, from genes to the whole organism via gene regulatory networks, protein pathways, integrative cell function, and tissue and whole organ structure/function relations. Additionally, an important goal of the project is to develop applications for teaching physiology. Current projects include the development of: - ontologies to organize biological knowledge and access to databases - markup languages to encode models of biological structure and function in a standard format for sharing between different application programs and for re-use as components of more comprehensive models - databases of structure at the cell, tissue and organ levels - software to render computational models of cell function such as ion channel electrophysiology, cell signaling and metabolic pathways, transport, motility, the cell cycle, etc. in 2 & 3D graphical form - software for displaying and interacting with the organ models which will allow the user to move across all spatial scales Sponsors: This project is supported by the International Union of Physiological Sciences (IUPS), the IEEE Engineering. in Medicine and Biology (EMBS), and the International Federation for Medical and Biological Engineering (IFMBE)

Proper citation: International Union of Physiological Sciences: Physiome Project (RRID:SCR_001760) Copy   

•   Source: SciCrunch Registry

http://incf.org/about/programs/modeling/blue-gene-access

Through this site, INCF provides he neuroinformatics community with access to an IBM Blue Gene/L supercomputer. INCF owns a share of a BlueGene/L (BG/L) supercomputer located at the Parallel Computer Center (PDC) at The Royal Institute of Technology (KTH) in Stockholm. Allocations are now available through the INCF Secretariat. During an initial evaluation phase, a limited numbers of large-scale computing projects will be selected, based on the suitability of the project for supercomputing. Research groups with limited access to supercomputers at their home institutions are given priority. Approved projects are regularly re-evaluated. New projects are approved based on availability and usage load of the BG/L. The Blue Gene/L supercomputer project is aimed at expanding the horizon of high-performance computing to unprecedented levels of scale and performance. Blue Gene/L is the first supercomputer in the Blue Gene family. The full Blue Gene/L consists of 64 racks containing 65,536 high-performance compute nodes. Each node (nodes and chips are the same in the Blue Gene system) contains two embedded 32-bit PowerPC processors. Furthermore, the same chip that is used for compute nodes is also used for the 1,024 I/O nodes. A three-dimensional torus network and a collective network are used to interconnect all nodes. The full system contains 33 terabytes of main memory; it is designed to achieve 183.5 teraflops peak performance using one of the processors of each node for computation and the other processor for communication, and 367 teraflops using both processors for computation. Another key architectural feature of this supercomputer is the link chip component and five Blue Gene/L networks, the PowerPC 440 core and floating-point enhancements, the on-chip and off-chip distributed memory system, the node- and system-level design for high reliability, and the comprehensive approach to fault isolation. One of the key objectives in Blue Gene/L design is to achieve cost/performance comparable to the COTS (Commodity Off The Shelf) approach, while at the same time incorporating a processor and network combination so powerful that it revolutionizes the performance of supercomputer systems. Sponsors: This resource is supported by the INCF.

Proper citation: International Neuroinformatics Coordinating Facility: Blue Gene/L Access (RRID:SCR_001755) Copy   

•   Source: SciCrunch Registry

http://csg.sph.umich.edu//abecasis/MACH/index.html

A Markov Chain based software tool for haplotyping, genotype imputation and disease association analysis that can resolve long haplotypes or infer missing genotypes in samples of unrelated individuals.

Proper citation: MACH 1.0 (RRID:SCR_001759) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/projects/homology/maps/

This page provides quick access to the Comparative mapping functions available in the Map Viewer. Currently, comparative maps are calculated using HomoloGene orthology predictions. Once the gene pairs have been established, blocks of conserved syteny can be established using the positions of each gene object in their respective builds. Sponsors: This resource is supported by NCBI.

Proper citation: Homology Maps Page (RRID:SCR_001666) Copy   

•   Source: SciCrunch Registry