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http://www.greenphyl.org/

A database designed for plant comparative and functional genomics based on complete genomes. It comprises complete proteome sequences from the major phylum of plant evolution. The clustering of these proteomes was performed to define a consistent and extensive set of homeomorphic plant families. Based on this, lists of gene families such as plant or species specific families and several tools are provided to facilitate comparative genomics within plant genomes. The analyses follow two main steps: gene family clustering and phylogenomic analysis of the generated families. Once a group of sequences (cluster) is validated, phylogenetic analyses are performed to predict homolog relationships such as orthologs and ultraparalogs.

Proper citation: GreenPhylDB (RRID:SCR_002834) Copy   

•   Source: SciCrunch Registry

http://andestools.sourceforge.net/

Software library and a suite of applications, written in Perl and R, for deep sequencing statistical analyses.

Proper citation: ANDES (RRID:SCR_002791) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/gap

Database developed to archive and distribute clinical data and results from studies that have investigated interaction of genotype and phenotype in humans. Database to archive and distribute results of studies including genome-wide association studies, medical sequencing, molecular diagnostic assays, and association between genotype and non-clinical traits.

Proper citation: NCBI database of Genotypes and Phenotypes (dbGap) (RRID:SCR_002709) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.charite.de/superpred/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on November 24,2025. Publicly available web-server to predict medical indication areas based on properties and similarity of chemical compounds. The web-server translates a user-defined molecule into a structural fingerprint that is compared to about 6300 drugs, which are enriched by 7300 links to molecular targets of the drugs, derived through text mining followed by manual curation. Links to the affected pathways are provided. The similarity to the medical compounds is expressed by the Tanimoto coefficient that gives the structural similarity of two compounds. A similarity score higher than 0.85 results in correct ATC prediction for 81% of all cases. As the biological effect is well predictable, if the structural similarity is sufficient, the web-server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets. The combination of physicochemical property and similarity searching provides the possibility to detect new biologically active compounds and novel targets for drug-like compounds. SuperPred can be applied for drug repositioning purposes, too. A further intention of SuperPred is to find side effects elicited by drugs caused through off-target hits.

Proper citation: SuperPred: Drug classification and target prediction (RRID:SCR_002691) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/bio-rainbow/

Software developed to provide an ultra-fast and memory-efficient solution to clustering and assembling short reads produced by RAD-seq.

Proper citation: Rainbow (RRID:SCR_002724) Copy   

•   Source: SciCrunch Registry

http://cpc.cbi.pku.edu.cn/

A Support Vector Machine-based classifier to assesses the protein-coding potential of a transcript based on six biologically meaningful sequence features. CPC can discriminate coding from noncoding transcripts with high accuracy and speed. In addition to predicting the coding potential of the input transcripts, the CPC web server also graphically displays detailed sequence features and additional annotations of the transcript that may facilitate users' further investigation. The coding potential calculator tool reads FASTA data format as input.

Proper citation: Coding Potential Calculator (RRID:SCR_002764) Copy   

•   Source: SciCrunch Registry

http://cgap.nci.nih.gov/

Project to determine the gene expression profiles of normal, precancer, and cancer cells, whose generated resources are available to the cancer community. Interconnected modules provide access to all CGAP data, bioinformatic analysis tools, and biological resources allowing the user to find in silico answers to biological questions in a fraction of the time it once took in the laboratory. * Genes * Tissues * Pathways * RNAi * Chromosomes * SAGE Genie * Tools

Proper citation: Cancer Genome Anatomy Project (RRID:SCR_003072) Copy   

•   Source: SciCrunch Registry

https://rostlab.org/owiki/index.php/PredictNLS

Software automated tool for analysis and determination of Nuclear Localization Signals (NLS). Predicts that your protein is nuclear or finds out whether your potential NLS is found in our database. The program also compiles statistics on the number of nuclear/non-nuclear proteins in which your potential NLS is found. Finally, proteins with similar NLS motifs are reported, and the experimental paper describing the particular NLS are given.

Proper citation: PredictNLS (RRID:SCR_003133) Copy   

•   Source: SciCrunch Registry

http://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi

A web interface to the Primer3 primer design program as an enhanced alternative for the CGI- scripts that come with Primer3.

Proper citation: Primer3Plus (RRID:SCR_003081) Copy   

•   Source: SciCrunch Registry

http://www.aetionomy.eu/

Consortium founded to establish mechanism-based taxonomies for Alzheimer's and Parkinson's disease and other neurodegenerative disorders (NDD), with the goal of facilitating development of more effective and targeted treatments. To do this, the consortium collects and analyzes data to: * Create new ways to combine underutilized data currently available in the literature, public databases, and from private companies * Determine how to dynamically organize and structure different types of knowledge about NDD * Determine how to apply this knowledge to construct new patient group classification * Identify correlations between disease features at molecular, tissue or organ-specific, and clinical levels * Identify sub-groups of patients based on the molecular cause of their disease, as opposed to the nature and location of their symptoms * Deliver data, tools, and recommendations for the biomedical community in the treatment of NDD A mechanism-based taxonomy is hoped to advance the: # Description and organization of the indication-specific data # Linking of data to disease models, based on causal and correlative relationships The expected outcome of AETIONOMY is a new NDD taxonomy system that distinguishes mixed pathologies, allowing for new features or classes to be added into the taxonomy, all with the goal of aiding drug and biomarker discovery.

Proper citation: AETIONOMY (RRID:SCR_000232) Copy   

•   Source: SciCrunch Registry

http://deweylab.biostat.wisc.edu/rsem/

Software package for quantifying gene and isoform abundances from single end or paired end RNA Seq data. Accurate transcript quantification from RNA Seq data with or without reference genome. Used for accurate quantification of gene and isoform expression from RNA-Seq data.

Proper citation: RSEM (RRID:SCR_000262) Copy   

•   Source: SciCrunch Registry

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

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 30,2023. Software for differential gene expression analysis based on the negative binomial distribution. It estimates variance-mean dependence in count data from high-throughput sequencing assays and tests for differential expression.

Proper citation: DESeq (RRID:SCR_000154) Copy   

•   Source: SciCrunch Registry

http://code.google.com/p/gasv/

Software tool for identifying structural variants (SVs) from paired-end sequencing data.GASV distribution includes three components that are typically run in succession: the BAM file of unique paired-read mappings is processed; structural variants are identified by clustering discordant fragments; and a probabilistic algorithm improves the specificity of GASV predictions.

Proper citation: GASV (RRID:SCR_000061) Copy   

•   Source: SciCrunch Registry

https://github.com/Brazelton-Lab/seq-annot

Software Python package for annotating and counting genomic features in genomes and metagenomes. Software tools to facilitate annotation and comparison of genomes and metagenomes.

Proper citation: seq-annot (RRID:SCR_018731) Copy   

•   Source: SciCrunch Registry

https://github.com/almorlio/CiLiQuant

Software tool to separate junction reads based on their linear or circular origin. Only non ambiguous junction reads are used to compare relative linear and circular transcript abundance.

Proper citation: CiLiQuant (RRID:SCR_019319) Copy   

•   Source: SciCrunch Registry

https://qupath.github.io/

Open Source software package for digital pathology image analysis. Used for whole slide image analysis and digital pathology. Provides researchers with batch processing and scripting functionality, and extensible platform with which to develop and share new algorithms to analyze complex tissue images.

Proper citation: QuPath (RRID:SCR_018257) Copy   

•   Source: SciCrunch Registry

http://wwwmgs.bionet.nsc.ru/mgs/systems/rsnp/

A system of databases which stores information on the influence of mutations in regulatory gene regions . This tool helps recognize protein binding sites that are being altered by mutation. It has four cross-linked sub databases that focus on specific aspects including: (1) the effect of single nucleotide mutations in regulatory gene regions and their interaction with nuclear proteins; (2) references to original publications on the subject; (3) the experimental details of these publications; and (4) the protocols of these experiments. This resource is aimed at providing information to further research on the influence of specific sequence alterations on disease susceptibility, drug resistance and healthcare.

Proper citation: rSNP Guide (RRID:SCR_000087) Copy   

•   Source: SciCrunch Registry

http://www.proglycprot.org/

Manually curated, comprehensive repository of experimentally characterized bacterial glycoproteins and archaeal glycoproteins, generated from an exhaustive literature search. This is the focused effort to provide concise relevant information derived from rapidly expanding literature on prokaryotic glycoproteins, their glycosylating enzyme(s), glycosylation linked genes, and genomic context thereof, in a cross-referenced manner. The database is arranged into two sections namely, ProCGP and ProUGP. ProCGP is the main section containing characterized prokaryotic glycoproteins, defined as entries with at least one experimentally known glycosylated residue (glycosite). Whereas, ProUGP is the supplementary section, presenting uncharacterized prokaryotic glycoproteins, defined as entries with experimentally identified glycosylation but unidentified glycosites. The ProGlycProt has been developed with to aid and advance the emerging scientific interests in understanding the mechanisms, implications, and novelties of protein glycosylation in prokaryotes that include many pathogenic as well as economically important bacterial species. The website supports a dedicated structure gallery of homology models and crystal structures of characterized glycoproteins in addition to two new tools developed in view of emerging information about prokaryotic sequons (conserved sequences of amino acids around glycosites) that are never or rarely seen in eukaryotic glycoproteins. ProGlycProt provides an extensive compilation of experimentally identified glycosites (334) and glycoproteins (340) of prokaryotes that could serve as an information resource for research and technology applications in glycobiology. A general data update policy is once in three months. Existing entries are updated in real-time.

Proper citation: ProGlycProt (RRID:SCR_000622) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/dbSTS/

THIS RESOURCE IS NO LONGER IN SERVICE, as of October 1, 2013; however, the site is still accessible. NCBI resource that contains sequence and mapping data on short genomic landmark sequences or Sequence Tagged Sites. STS sequences are incorporated into the STS Division of GenBank. The dbSTS database offers a route for submission of STS sequences to GenBank. It is designed especially for the submission of large batches of STS sequences.

Proper citation: dbSTS (RRID:SCR_000400) Copy   

•   Source: SciCrunch Registry

http://interolog.gersteinlab.org/

Interolog/Regulog quantitatively assess the degree to which interologs can be reliably transferred between species as a function of the sequence similarity of the corresponding interacting proteins.

Proper citation: Interolog/Regulog Database (RRID:SCR_000755) Copy   

•   Source: SciCrunch Registry