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http://ani.embl.de/4DXpress

This database provides a platform to query and compare gene expression data during the development of the major model animals (zebrafish, drosophila, medaka, mouse). The name 4DXpress stands for expression database in 4D. The 4D (four dimensions) of 4DXpress can be interpreted either as: 3 spatial dimensions plus time, or as 1. species 2. gene 3. developmental stage 4. anatomical structure. The major focus of this database lies in cross species comparison. The high resolution expression data was acquired through whole mount in situ hybridsation-, antibody- or transgenic experiments. Data was integrated from several species specific expression pattern databases, such as ZFIN, BDGP, GXD, MEPD as well as directly submitted by researchers of the participating groups at EMBL. The 4DXpress database is a project within the Centre for Computational Biology at EMBL. It is developed by Yannick Haudry, Thorsten Henrich and Ivica Letunic and coordinated by Thorsten Henrich. Hugo Berube is developing the 4D ArrayExpress Data Warehouse at EBI for integrating in situ data with microarray data.

Proper citation: Expression Database in 4D (RRID:SCR_007066) Copy   

•   Source: SciCrunch Registry

http://www.chr7.org

Database containing the DNA sequence and annotation of the entire human chromosome 7, encompassing nearly 158 million nucleotides of DNA and 1917 gene structures, are presented; the most up to date collation of sequence, gene, and other annotations from all databases (eg. Celera published, NCBI, Ensembl, RIKEN, UCSC) as well as unpublished data. To generate a higher order description, additional structural features such as imprinted genes, fragile sites, and segmental duplications were integrated at the level of the DNA sequence with medical genetic data, including 440 chromosome rearrangement breakpoints associated with disease. The objective of this project is to generate a comprehensive description of human chromosome 7 to facilitate biological discovery, disease gene research and medical genetic applications. There are over 360 disease-associated genes or loci on chromosome 7. A major challenge ahead will be to represent chromosome alterations, variants, and polymorphisms and their related phenotypes (or lack thereof), in an accessible way. In addition to being a primary data source, this site serves as a weighing station for testing community ideas and information to produce highly curated data to be submitted to other databases such as NCBI, Ensembl, and UCSC. Therefore, any useful data submitted will be curated and shown in this database. All Chromosome 7 genomic clones (cosmids, BACs, YACs) listed in GBrowser and in other data tables are freely distributed.

Proper citation: Chromosome 7 Annotation Project (RRID:SCR_007134) Copy   

•   Source: SciCrunch Registry

http://www.thebiogrid.org/

Curated protein-protein and genetic interaction repository of raw protein and genetic interactions from major model organism species, with data compiled through comprehensive curation efforts.

Proper citation: Biological General Repository for Interaction Datasets (BioGRID) (RRID:SCR_007393) Copy   

•   Source: SciCrunch Registry

http://enhancer.lbl.gov/

Resource for experimentally validated human and mouse noncoding fragments with gene enhancer activity as assessed in transgenic mice. Most of these noncoding elements were selected for testing based on their extreme conservation in other vertebrates or epigenomic evidence (ChIP-Seq) of putative enhancer marks. Central public database of experimentally validated human and mouse noncoding fragments with gene enhancer activity as assessed in transgenic mice. Users can retrieve elements near single genes of interest, search for enhancers that target reporter gene expression to particular tissue, or download entire collections of enhancers with defined tissue specificity or conservation depth.

Proper citation: VISTA Enhancer Browser (RRID:SCR_007973) Copy   

•   Source: SciCrunch Registry

https://www.ncbi.nlm.nih.gov/genbank/dbest/

Database as a division of GenBank that contains sequence data and other information on single-pass cDNA sequences, or Expressed Sequence Tags, from a number of organisms.

Proper citation: dbEST (RRID:SCR_008132) 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.baderlab.org/Software/ActiveDriver

A statistical method for interpreting variations in protein sequence (e.g. coding SNPs in the population, SNVs in cancer genomes) in the context of protein post-translational signaling modifications.

Proper citation: ActiveDriver (RRID:SCR_008104) Copy   

•   Source: SciCrunch Registry

http://qiime.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 23,2023.Software package for comparison and analysis of microbial communities, primarily based on high-throughput amplicon sequencing data, but also supporting analysis of other types of data. QIMME analyzes and transforms raw sequencing data generated on Illumina or other platforms to publication quality graphics and statistics.

Proper citation: QIIME (RRID:SCR_008249) Copy   

•   Source: SciCrunch Registry

http://griffin.cbrc.jp/

Griffin (G-protein-receptor interacting feature finding instrument) is a high-throughput system to predict GPCR - G-protein coupling selectively with the input of GPCR sequence and ligand molecular weight. This system consists of two parts: 1) HMM section using family specific multiple alignment of GPCRs, 2) SVM section using physico-chemical feature vectors in GPCR sequence. G-protein coupled receptors (GPCR), which is composed of seven transmembrane helices, play a role as interface of signal transduction. The external stimulation for GPCR, induce the coupling with G-protein (Gi/o, Gq/11, Gs, G12/13) followed by different kinds of signal transduction to inner cell. About half of distributed drugs are intending to control this GPCR - G-protein binding system, and therefore this system is important research target for the development of effective drug. For this purpose, it is necessary to monitor, effectively and comprehensively, of the activation of G-protein by identifying ligand combined with GPCR. Since, at present, it is difficult to construct such biochemical experiment system, if the answers for experimental results can be prepared beforehand by using bioinformatics techniques, large progress is brought to G-protein related drug design. Previous works for predicting GPCR-G protein coupling selectivity are using sequence pattern search, statistical models, and HMM representations showed high sensitivity of predictions. However, there are still no works that can predict with both high sensitivity and specificity. In this work we extracted comprehensively the physico-chemical parameters of each part of ligand, GPCR and G-protein, and choose the parameters which have strong correlation with the coupling selectivity of G-protein. These parameters were put as a feature vector, used for GPCR classification based on SVM.

Proper citation: G protein receptor interaction feature finding instrument (RRID:SCR_008343) Copy   

•   Source: SciCrunch Registry

http://avogadro.cc/

Software for semantic chemical editing, visualization, and analysis. It is designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas.

Proper citation: Avogadro (RRID:SCR_015983) Copy   

•   Source: SciCrunch Registry

http://bioinformaticstools.mayo.edu/research/hybrid-denovo/

Software for a de novo OTU-picking pipeline integrating single- and paired-end 16S sequence tags. It is designed to take Illumina paired-end sequencing reads as input and output the OTU BIOM table, together with their representative sequences and a phylogenetic tree of OTUs.

Proper citation: Hybrid-denovo (RRID:SCR_015866) Copy   

•   Source: SciCrunch Registry

https://github.com/pjmark/NiftyPET

Python software package that offers quantitative PET image reconstruction and analysis with high accuracy and precision. It is written in CUDA C and embedded in Python C extensions.

Proper citation: NiftyPET (RRID:SCR_015873) Copy   

•   Source: SciCrunch Registry

https://github.com/EvolBioInf/andi

Software tool for rapidly computing and estimating evolutionary distance between closely related genomes. Because andi does not compute full alignments it scales even up to thousands of bacterial genomes.

Proper citation: andi (RRID:SCR_015971) Copy   

•   Source: SciCrunch Registry

https://github.com/larvalign/larvalign

Software package including computational methods for aligning gene expression patterns from the larval brain of Drosophila melanogaster. Its method includes evaluation of the registration framework involved in template generation and mapping.

Proper citation: larvalign (RRID:SCR_015815) Copy   

•   Source: SciCrunch Registry

http://bioconductor.org/packages/release/bioc/html/SC3.html

Software tool for the unsupervised clustering of cells from single cell RNA-Seq experiments. SC3 is capable of identifying subclones from the transcriptomes of neoplastic cells collected from patients.

Proper citation: SC3 (RRID:SCR_015953) Copy   

•   Source: SciCrunch Registry

https://github.com/marbl/canu

Software for scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Canu is a fork of the Celera Assembler and is designed for high-noise single-molecule sequencing (such as the PacBio RS II/Sequel or Oxford Nanopore MinION).

Proper citation: Canu (RRID:SCR_015880) Copy   

•   Source: SciCrunch Registry

https://genome.tugraz.at/genesisclient/genesisclient_description.shtml

Software for cluster analysis of microarray data. Genesis is a platform independent Java package of tools to simultaneously visualize and analyze a whole set of gene expression experiments.

Proper citation: Genesis (RRID:SCR_015775) Copy   

•   Source: SciCrunch Registry

https://github.com/sanger-pathogens/Bio-Tradis

Analysis software for the output from TraDIS (Transposon Directed Insertion Sequencing) analyses of dense transposon mutant libraries. The Bio-Tradis analysis pipeline is implemented as an extensible Perl library which can either be used as is, or as a basis for the development of more advanced analysis tools.

Proper citation: Bio-tradis (RRID:SCR_015993) Copy   

•   Source: SciCrunch Registry

http://www.vicbioinformatics.com/software.barrnap.shtml

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Software to predict the location of ribosomal RNA genes in genomes. It supports bacteria, archaea, mitochondria, and eukaryotes. It takes FASTA DNA sequence as input, writes GFF3 as output, and supports multithreading., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Barrnap (RRID:SCR_015995) Copy   

•   Source: SciCrunch Registry

http://hyphy.org/

Open source software package for comparative sequence analysis using stochastic evolutionary models. Used for analysis of genetic sequence data in particular the inference of natural selection using techniques in phylogenetics, molecular evolution, and machine learning.

Proper citation: HyPhy (RRID:SCR_016162) Copy   

•   Source: SciCrunch Registry