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http://ccb.jhu.edu/software/glimmerhmm/

A gene finder based on a Generalized Hidden Markov Model (GHMM). Although the gene finder conforms to the overall mathematical framework of a GHMM, additionally it incorporates splice site models adapted from the GeneSplicer program and a decision tree adapted from GlimmerM. It also utilizes Interpolated Markov Models for the coding and noncoding models . Currently, GlimmerHMM's GHMM structure includes introns of each phase, intergenic regions, and four types of exons (initial, internal, final, and single).

Proper citation: GlimmerHMM (RRID:SCR_002654) Copy   

•   Source: SciCrunch Registry

http://bowtie-bio.sourceforge.net/recount/

RNA-seq gene count datasets built using the raw data from 18 different studies. The raw sequencing data (.fastq files) were processed with Myrna to obtain tables of counts for each gene. For ease of statistical analysis, they combined each count table with sample phenotype data to form an R object of class ExpressionSet. The count tables, ExpressionSets, and phenotype tables are ready to use and freely available. By taking care of several preprocessing steps and combining many datasets into one easily-accessible website, we make finding and analyzing RNA-seq data considerably more straightforward.

Proper citation: ReCount - A multi-experiment resource of analysis-ready RNA-seq gene count datasets (RRID:SCR_001774) Copy   

•   Source: SciCrunch Registry

http://www.ucl.ac.uk/cardiovasculargeneontology/

Full Gene Ontology annotation to genes associated with cardiovascular processes. Every GO annotation made, is attributed to an identified source, such as a publication identifier (PMID), and an indication of the type of evidence which supports the association between the gene product and the GO term. Over 4,000 cardiovascular associated genes have been identified. A variety of tools have been provided to enable cardiovascular scientists to review the annotation of their ''''favorite'''' gene and suggest information that may be missing, inaccurate or incomplete in these annotations. Annotation suggestions can be sent through the feedback form or by email. The Gene Ontology (GO) vocabulary is the established standard for the functional annotation of gene products. By using GO to curate scientific literature and by integrating results from high-quality high-throughput experiments they will create an information-rich resource for the cardiovascular-research community, enabling researchers to rapidly evaluate and interpret existing data and generate hypotheses to guide future research.

Proper citation: Cardiovascular Gene Ontology Annotation Initiative (RRID:SCR_004795) Copy   

•   Source: SciCrunch Registry

http://www.broad.mit.edu/mpr/lung

Data set of a molecular taxonomy of lung carcinoma, the leading cause of cancer death in the United States and worldwide. Using oligonucleotide microarrays, researchers analyzed mRNA expression levels corresponding to 12,600 transcript sequences in 186 lung tumor samples, including 139 adenocarcinomas resected from the lung. Hierarchical and probabilistic clustering of expression data defined distinct sub-classes of lung adenocarcinoma. Among these were tumors with high relative expression of neuroendocrine genes and of type II pneumocyte genes, respectively. Retrospective analysis revealed a less favorable outcome for the adenocarcinomas with neuroendocrine gene expression. The diagnostic potential of expression profiling is emphasized by its ability to discriminate primary lung adenocarcinomas from metastases of extra-pulmonary origin. These results suggest that integration of expression profile data with clinical parameters could aid in diagnosis of lung cancer patients.

Proper citation: Classification of Human Lung Carcinomas by mRNA Expression Profiling Reveals Distinct Adenocarcinoma Sub-classes (RRID:SCR_003010) Copy   

•   Source: SciCrunch Registry

http://eyebrowse.cit.nih.gov/

EyeBrowse displays expressed sequence tag (EST) cDNA clones from eye tissues (derived from NEIBank and other sources) aligned with current versions of the human, rhesus, mouse, rat, dog, cow, chicken, or zebrafish genomes, including reference sequences for known genes. This gives a simplified view of gene expression activity from different parts of the eye across the genome. The data can be interrogated in several ways. Specific gene names can be entered into the search window. Alternatively, regions of the genome can be displayed. For example, entering two STS markers separated by a semicolon (e.g. RH18061;RH80175) allows the display of the entire chromosomal region associated with the mapping of a specific disease locus. ESTs for each tissue can then be displayed to help in the selection of candidate genes. In addition, sequences can be entered into a BLAT search and rapidly aligned on the genome, again showing eye derived ESTs for the same region. EyeBrowse includes a custom track display SAGE data for human eye tissues derived from the EyeSAGE project. The track shows the normalized sum of SAGE tag counts from all published eye-related SAGE datasets centered on the position of each identifiable Unigene cluster. This indicates relative activity of each gene locus in eye. Clicking on the vertical count bar for a particular location will bring up a display listing gene details and linking to specific SAGE counts for each eye SAGE library and comparisons with normalized sums for neural and non-neural tissues. To view or alter settings for the EyeSAGE track on EyeBrowse, click on the vertical gray bar at the left of the display. Other custom tracks display known eye disease genes and mapped intervals for candidate loci for retinal disease, cataract, myopia and cornea disease. These link back to further information at NEIBank. For mouse, there is custom track data for ChIP-on-Chip of RNA-Polymerase-II during photoreceptor maturation.

Proper citation: EyeBrowse (RRID:SCR_008000) Copy   

•   Source: SciCrunch Registry

http://cistrome.org/finder

Data portal that can help query, evaluate and visualize publicly available Chromatin immunoprecipitation and DNase I hypersensitivity assays with high-throughput sequencing data in human and mouse. The database currently contains 6378 samples over 4391 datasets, 313 factors and 102 cell lines or cell populations (May 2013). Each dataset has gone through a consistent analysis and quality control pipeline; therefore, users could evaluate the overall quality of each dataset before examining binding sites near their genes of interest. CistromeFinder is integrated with UCSC genome browser for visualization, Primer3Plus for ChIP-qPCR primer design and CistromeMap for submitting newly available datasets. It also allows users to leave comments to facilitate data evaluation and update.

Proper citation: CistromeFinder (RRID:SCR_005405) Copy   

•   Source: SciCrunch Registry

http://www.stanford.edu/~rnusse/pathways/targets.html

A list of target genes of Wnt/beta-catenin signaling. Suggestions for additions are welcome. Direct targets are defined as those with Tcf binding sites and demonstrating that these sites are important.

Proper citation: Target genes of Wnt/beta-catenin signaling (RRID:SCR_007022) Copy   

•   Source: SciCrunch Registry

http://www.icmic.net/

Imaging core offering multiple small animal imaging modalities including MicroSPECT, Optical imaging, MicroCT and Ultrasound have been centralized together with a unit for probe development and a vivarium for the housing of animals in one laboratory.

Proper citation: In-Vivo Cellular and Molecular Imaging Center, Brussels (RRID:SCR_008047) Copy   

•   Source: SciCrunch Registry

http://gerg01.gsc.riken.jp/cage/mm5

A web system, which could search and display to current CAGE library information in CAGE Database.

Proper citation: CAGE Basic Viewer for Mus musculus (RRID:SCR_000451) Copy   

•   Source: SciCrunch Registry

http://anya.igsb.anl.gov/Geneways/GeneWays.html

System for automatically extracting, analzying, visualizing and integrating molecular pathway data from the research literature. System focuses on interactions between molecular substances and actions, providing a graphical consensus view on the collected information. GeneWays is designed as open platform, allowing researchers to query, review and critique integrated information.

Proper citation: GeneWays (RRID:SCR_000572) Copy   

•   Source: SciCrunch Registry

http://www.molecular-haplotype.org/profiler/profiler_intro.htm

A flexible software tool to generate the probability distribution of joint multilocus genotypes defined by sets of individuals within the pedigree and sets of markers within the framework map. (entry from Genetic Analysis Software), THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: PROFILER (RRID:SCR_009339) Copy   

•   Source: SciCrunch Registry

https://github.com/gaow/genetic-analysis-software/blob/master/pages/PREPLINK.md

Software application that is part of the LINKAGE auxiliary program (entry from Genetic Analysis Software)

Proper citation: PREPLINK (RRID:SCR_009335) Copy   

•   Source: SciCrunch Registry

http://fisher.utstat.toronto.edu/sun/Software/Prest or previously, http://galton.uchicago.edu/~mcpeek/software/prest

Software program that detects pedigree errors in general outbred pedigrees by use of genome-screen data. When a potential pedigree error is detected, our companion program, ALTERTEST, determines which relationships are compatible with the observed genotype data. Both programs are freely available on the web. (entry from Genetic Analysis Software)

Proper citation: PREST (RRID:SCR_009336) Copy   

•   Source: SciCrunch Registry

http://www.mds.qmw.ac.uk/statgen/dcurtis/software.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 16,2023. Excel spreadsheet to calculate power of affected sibpairs and TDT analyses (entry from Genetic Analysis Software)

Proper citation: POWTEST (RRID:SCR_009334) Copy   

•   Source: SciCrunch Registry

http://www.geneticepi.com/Research/software/software.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 16,2023. Software application (entry from Genetic Analysis Software), THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: POOL STR (RRID:SCR_009331) Copy   

•   Source: SciCrunch Registry

http://alla.cs.gsu.edu/~software/tagging/tagging.html

Software application that can be used for tagging SNP selection and genotype prediction (entry from Genetic Analysis Software)

Proper citation: MLR-TAGGING (RRID:SCR_009299) Copy   

•   Source: SciCrunch Registry

http://statgen.ncsu.edu/powermarker/

A comprehensive set of statistical methods for genetic marker data analysis, designed especially for SSR/SNP data analysis. PowerMarker builds a powerful user interface around both new and traditional statistical methods for population genetic analysis. See analysis to check out the versatility of PowerMarker. PowerMarker is also a 2D Viewer - which was used intensively for visualizing linkage disequilibria results. (entry from Genetic Analysis Software)

Proper citation: POWERMARKER (RRID:SCR_009332) Copy   

•   Source: SciCrunch Registry

http://www.emboss.co.nz/products.php?pid=2

Software application to draw chromosome maps from the output of MAPMAKER/EXP. The output format is a Enhanced Metafile, which can be imported into most Windows-based presentation or document editing programs. (entry from Genetic Analysis Software)

Proper citation: MMDRAWER (RRID:SCR_009296) Copy   

•   Source: SciCrunch Registry

http://cedar.genetics.soton.ac.uk/pub/PROGRAMS/pointer

Software application for complex segregation analysis with the mixed model (major locus and polygenes). (entry from Genetic Analysis Software)

Proper citation: POINTER (RRID:SCR_009330) Copy   

•   Source: SciCrunch Registry

http://www.stat.washington.edu/stephens/software.html

Software program that implements a new statistical method for reconstructing haplotypes from population genotype data (entry from Genetic Analysis Software)

Proper citation: PHASE (RRID:SCR_009327) Copy   

•   Source: SciCrunch Registry