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http://blocks.fhcrc.org/blocks/codehop.html

This COnsensus-DEgenerate Hybrid Oligonucleotide Primer (CODEHOP) strategy has been implemented as a computer program that is accessible over the World-Wide Web and is directly linked from the BlockMaker multiple sequence alignment site for hybrid primer prediction beginning with a set of related protein sequences. This is a new primer design strategy for PCR amplification of unknown targets that are related to multiply-aligned protein sequences. Each primer consists of a short 3' degenerate core region and a longer 5' consensus clamp region. Only 3-4 highly conserved amino acid residues are necessary for design of the core, which is stabilized by the clamp during annealing to template molecules. During later rounds of amplification, the non-degenerate clamp permits stable annealing to product molecules. The researchers demonstrate the practical utility of this hybrid primer method by detection of diverse reverse transcriptase-like genes in a human genome, and by detection of C5 DNA methyltransferase homologs in various plant DNAs. In each case, amplified products were sufficiently pure to be cloned without gel fractionation. Sponsors: This work was supported in part by a grant from the M. J. Murdock Charitable Trust and by a grant from NIH. S. P. is a Howard Hughes Medical Institute Fellow of the Life Sciences Research Foundation., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 15,2026.

Proper citation: COnsensus-DEgenerate Hybride Oligonucleotide Primers (RRID:SCR_002875) Copy   

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http://www.nsrrc.missouri.edu/

Provides access to critically needed swine models of human health and disease as well as a central resource for reagents, creation of new genetically modified swine, and information and training related to use of swine models in biomedical research.

Proper citation: National Swine Resource and Research Center (RRID:SCR_006855) Copy   

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http://genome.jgi.doe.gov/programs/fungi/index.jsf

Fungal genomics database and interactive analytical tools that integrates all fungal genomes for diverse fungi that are important for energy and environment, the focus of the JGI Fungal program. It integrates genomics data from the DOE JGI and its users and promotes user community participation in data submission, annotation and analysis. Over 100 newly sequenced and annotated fungal genomes from JGI and elsewhere are available to the public through MycoCosm, and new annotated genomes are being added to this resource upon completion of annotation. MycoCosm offers web-based genome analysis tools for fungal biologists to ''navigate'' through sequenced genomes and explore them in the context of ''genome-centric'' and ''comparative views''.

Proper citation: MycoCosm (RRID:SCR_005312) Copy   

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http://last.cbrc.jp/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Software tool for aligning sequences, similar to BLAST 2 sequences that colour-codes the alignments by reliability. Another useful feature of LAST is that it can compare huge (vertebrate-genome-sized) datasets. Unfortunately, this only applies to the downloadable version of LAST, not the web service. The web service can just about handle bacterial genomes, but it will take a few minutes and the output will be large. LAST can: * Handle big sequence data, e.g: ** Compare two vertebrate genomes ** Align billions of DNA reads to a genome * Indicate the reliability of each aligned column. * Use sequence quality data properly. * Compare DNA to proteins, with frameshifts. * Compare PSSMs to sequences * Calculate the likelihood of chance similarities between random sequences. LAST cannot (yet): * Do spliced alignment., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: LAST (RRID:SCR_006119) Copy   

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https://www.facebase.org/

A web portal that provides access to data, tools and materials that will aid in craniofacial research. Included is access to genomic and imaging based data sets from a variety of species, including zebrafish, human and mouse.

Proper citation: FaceBase (RRID:SCR_005998) Copy   

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https://www.iscaconsortium.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on June 22, 2022. A rapidly growing group of clinical cytogenetics and molecular genetics laboratories committed to improving quality of patient care related to clinical genetic testing using new molecular cytogenetic technologies including array comparative genomic hybridization (aCGH) and quantitative SNP analysis by microarrays or bead chip technology. They improve clinical care by providing a large publicly available database and forum where clinicians and researchers can share knowledge to expedite the understanding of copy number variation (CNV) in an abnormal population. The ISCA database contains whole genome array data from a subset of the ISCA Consortium clinical diagnostic laboratories. Array analysis was carried out on individuals with phenotypes including intellectual disability, autism, and developmental delay. Efforts of the Consortium include: # Clinical Utility: The ISCA Consortium has made recommendations regarding the appropriate clinical indications for cytogenetic array testing (Miller et al. AJHG 2010, PMID: 20466091). Currently, discussions are focused on pediatric applications for children with unexplained developmental delay, intellectual disability, autism and other developmental disabilities. A separate committee has been developed to address appropriate cancer genetic applications (http://www.urmc.rochester.edu/ccmc/). # Evidence-based standards for cytogenomic array design: The Consortium will develop recommendations for standards for the design, resolution and content of cytogenomic arrays using an evidence-based process and an international panel of experts in clinical genetics, clinical laboratory genetics (cytogenetics and molecular genetics), genomics and bioinformatics. This design is intended to be platform and vendor-neutral (common denominator is genome sequence coordinates), and is a dynamic process with input from the broader genetics community and evidence-based review by the expert panel (which will evolve into a Standing Committee with international representation). # Public Database for clinical and research community: It is essential that publicly available databases be created and maintained for cytogenetic array data generated in clinical testing laboratories. The ISCA data will be held in dbGaP and dbVar at NCBI/NIH and curated by a committee of clinical genetics laboratory experts. The very high quality of copy number data (i.e., deletions and duplications) coming from clinical laboratories combined with expert curation will produce an invaluable resource to the clinical and research communities. # Standards for interpretation of cytogenetic array results: Using the ISCA Database, along with other genomic and genetics databases, the Consortium will develop recommendations for the interpretation and reporting of pathogenic vs. benign copy number changes as well as imbalances of unknown clinical significance.

Proper citation: ISCA Consortium (RRID:SCR_006168) Copy   

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https://www.synapse.org/

A cloud-based collaborative platform which co-locates data, code, and computing resources for analyzing genome-scale data and seamlessly integrates these services allowing scientists to share and analyze data together. Synapse consists of a web portal integrated with the R/Bioconductor statistical package and will be integrated with additional tools. The web portal is organized around the concept of a Project which is an environment where you can interact, share data, and analysis methods with a specific group of users or broadly across open collaborations. Projects provide an organizational structure to interact with data, code and analyses, and to track data provenance. A project can be created by anyone with a Synapse account and can be shared among all Synapse users or restricted to a specific team. Public data projects include the Synapse Commons Repository (SCR) (syn150935) and the metaGenomics project (syn275039). The SCR provides access to raw data and phenotypic information for publicly available genomic data sets, such as GEO and TCGA. The metaGenomics project provides standardized preprocessed data and precomputed analysis of the public SCR data.

Proper citation: Synapse (RRID:SCR_006307) Copy   

•   Source: SciCrunch Registry

http://coot.embl.de/g2d/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A database of candidate genes for mapped inherited human diseases. Candidate priorities are automatically established by a data mining algorithm that extracts putative genes in the chromosomal region where the disease is mapped, and evaluates their possible relation to the disease based on the phenotype of the disorder. Data analysis uses a scoring system developed for the possible functional relations of human genes to genetically inherited diseases that have been mapped onto chromosomal regions without assignment of a particular gene. Methodology can be divided in two parts: the association of genes to phenotypic features, and the identification of candidate genes on a chromosonal region by homology. This is an analysis of relations between phenotypic features and chemical objects, and from chemical objects to protein function terms, based on the whole MEDLINE and RefSeq databases.

Proper citation: Candidate Genes to Inherited Diseases (RRID:SCR_008190) Copy   

•   Source: SciCrunch Registry

http://www.genome.arizona.edu/

Their primary focus is in the area of structural, evolutionary and functional genomics of crop plants. AGI is divided into 5 Centers each lead by a Center Leader and a senior Manager (BAC Library Construction Center, BAC/EST Resource Center, Sequencing & Physical Mapping Center (including: production sequencing and fingerprinting, and sequence finishing), Bioinformatics Center and the Evolutionary and Functional Genomics Center). AGI is housed in the state of the art Thomas W. Keating Bioresearch Building on the northeast part of campus near the Medical School. AGI currently employees about 30 scientists and is primarily funded through federal grants, private contracts, and the Bud Antle Endowed Chair in Plant Molecular Genetics. Sponsors: AGI is supported by Bio5, Plant Sciences, National Science Foundation, National Institues oh Health, and USDA.

Proper citation: AGI (RRID:SCR_007203) Copy   

•   Source: SciCrunch Registry

http://genes.toronto.edu/

The Frey Lab develops techniques that use large scale datasets to derive predictive models of how genes and many other genomic features act in combination to produce genetic messages that control cellular activities. We have most recently focused on how organisms use alternative splicing to generate a tremendous level of biological complexity that cannot be explained by gene expression alone (Nature, 2010). Some of the tools, software and databases provided by the Frey Lab are affinity propagation, splicing prediction, PTMClust - A Post-translational Modification Refinement Algorithm, the ''epitome'': A new model of patterns, transformation invariant clustering and subspaces, learning flexible sprites from images and videos, phase unwrapping by loopy belief propagation, useful Matlab scripts, bioinformatics links, and SeedSearcher: A motif finder.

Proper citation: Frey Lab (RRID:SCR_008859) Copy   

•   Source: SciCrunch Registry

http://bioinf.wehi.edu.au/software/linkdatagen

Perl tool that generates linkage mapping input files using data from HAPMAP Phase III populations. It provides rudimentary error checks and is easily amended for personal linkage mapping preferences.

Proper citation: LINKDATAGEN (RRID:SCR_015625) Copy   

•   Source: SciCrunch Registry

http://jjwanglab.org/gwasrap

GWASrap is a comprehensive web-based bioinformatics tool to systematically support variant representation, annotation and prioritization for data generated from genome-wide association studies (GWAS) and Next Generation Sequencing (NGS). Our web-based framework utilizes state-of-the-art web technologies to maximize user interaction and visualization of the results. For a given SNP dataset with its P-values, GWASrap will first provide a Circos-style plot to visualize any genetic variants at either the genome or chromosome level. The tool then combines different genomic features (SNP/CNV density, disease susceptibility loci, etc.) with comprehensive annotations that give the researcher an intuitive view of the functional significance of the different genomic regions. The detailed statistics of the underlying study are also displayed on the web page, including variant distribution in different functional categories, classic Manhattan plot and QQ plot. Users can perform interactive operations in the Manhattan panel, such as zooming in and out to search regions or markers of interest. The system can also display a comprehensive range of relevant information from variant genetic attributes to nearby genomic elements, such as enhancers or non-coding RNAs. Furthermore, researchers can obtain extensive functional predictions for various features including transcription factor-binding sites, miRNA and miRNA target sites, and their predicted changes caused by the genetic variants. Our system can re-prioritize genetic variants by combining the original statistical value and variant prioritization score based on a simple additive effect equation. Researchers can also re-evaluate the significance of a trait/disease-associated SNP (TAS) using the dynamic linkage disequilibrium (LD) panel or the tree-like network panel. The GWASrap supports input variants in different formats, not only common variants with a dbSNP rs ID but also rare variants from NGS data, which are represented by chromosome and locations. GWASrap provides a range of web services for data retrieving about the annotation information and effect prediction of each variant in dbSNP using the SOAP interface. The WSDL for each service is available in the API tab. Each service returns JSON string including all related information with key/value. GWASrap provides running results about some current published GWAS as well as a category view for each hot disease / trait. The dataset is brought from published database GWAS or curated from literature.

Proper citation: GWASrap (RRID:SCR_013144) Copy   

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https://github.com/broadinstitute/pilon/

Software tool to automatically improve draft assemblies and find variation among strains, including large event detection. FASTA files of genome along with one or more BAM files of reads aligned as input. Read alignment analysis is used to identify inconsistencies between input genome and evidence in reads, then attempts to make improvements to genome.

Proper citation: Pilon (RRID:SCR_014731) Copy   

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https://github.com/HingeAssembler/HINGE

Software application for long read genome assembly based on hinging. Used in long-read sequencing technologies in genome assemblies to achieve optimal repeat resolution.

Proper citation: Hinge (RRID:SCR_016135) Copy   

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https://chlorobox.mpimp-golm.mpg.de/OGDraw.html

Software package for graphical visualization of organellar genomes. Converts annotations in GenBank format into graphical maps. Used to create visual representations of circular and linear annotated genome sequences provided as GenBank files or accession numbers.

Proper citation: OGDraw (RRID:SCR_017337) Copy   

•   Source: SciCrunch Registry

http://www.youtube.com/ncbinlm

Videos from the National Center for Biotechnology Information including presentations and tutorials about NCBI biomolecular and biomedical literature databases and tools.

Proper citation: NCBI YouTube Channel (RRID:SCR_006084) Copy   

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http://wego.genomics.org.cn/cgi-bin/wego/index.pl

Web Gene Ontology Annotation Plot (WEGO) is a simple but useful tool for plotting Gene Ontology (GO) annotation results. Different from other commercial software for chart creating, WEGO is designed to deal with the directed acyclic graph (DAG) structure of GO to facilitate histogram creation of GO annotation results. WEGO has been widely used in many important biological research projects, such as the rice genome project and the silkworm genome project. It has become one of the useful tools for downstream gene annotation analysis, especially when performing comparative genomics tasks. Platform: Online tool

Proper citation: WEGO - Web Gene Ontology Annotation Plot (RRID:SCR_005827) Copy   

•   Source: SciCrunch Registry

http://recountdb.cbrc.jp/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. Database for corrected read counts and genome mapping on NCBI's Short Read Archive. The corrected count was done using RECOUNT and the mapping with LAST. We also provide information of reference genome to which we aligned the short reads. We focus on transcriptomic data, specifically TSS-Seq and RNA-Seq. Because this is the type of data for which sequence count correction is most important. Hence we do not include the genomic reads. The current version contains 2,265 entries from 45 organisms, with read lengths from 17 to 100bp. Via a searchable and browseable interface users can obtain corrected data in formats useful for transcriptomic analysis. We provide the data grouped according to the genome, type of studies and submitter in TAB , PSL and BAM format. They contain the mapping position and annotation of reads observed and corrected counts.

Proper citation: RecountDB (RRID:SCR_006117) Copy   

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http://operons.ibt.unam.mx/OperonPredictor/

The Prokaryotic Operon DataBase (ProOpDB) constitutes one of the most precise and complete repository of operon predictions in our days. Using our novel and highly accurate operon algorithm, we have predicted the operon structures of more than 1,200 prokaryotic genomes. ProOpDB offers diverse alternatives by which a set of operon predictions can be retrieved including: i) organism name, ii) metabolic pathways, as defined by the KEGG database, iii) gene orthology, as defined by the COG database, iv) conserved protein motifs, as defined by the Pfam database, v) reference gene, vi) reference operon, among others. In order to limit the operon output to non-redundant organisms, ProOpDB offers an efficient protocol to select the more representative organisms based on a precompiled phylogenetic distances matrix. In addition, the ProOpDB operon predictions are used directly as the input data of our Gene Context Tool (GeConT) to visualize their genomic context and retrieve the sequence of their corresponding 5�� regulatory regions, as well as the nucleotide or amino acid sequences of their genes. The prediction algorithm The algorithm is a multilayer perceptron neural network (MLP) classifier, that used as input the intergenic distances of contiguous genes and the functional relationship scores of the STRING database between the different groups of orthologous proteins, as defined in the COG database. Nevertheless, the operon prediction of our method is not restricted to only those genes with a COG assignation, since we successfully defined new groups of orthologous genes and obtained, by extrapolation, a set of equivalent STRING-like scores based on conserved gene pairs on different genomes. Since the STRING functional relationships scores are determined in an un-bias manner and efficiently integrates a large amount of information coming from different sources and kind of evidences, the prediction made by our MLP are considerably less influenced by the bias imposed in the training procedure using one specific organism.

Proper citation: ProOpDB (RRID:SCR_006111) Copy   

•   Source: SciCrunch Registry

http://tbdb.org

Database providing integrated access to genome sequence, expression data and literature curation for Tuberculosis (TB) that houses genome assemblies for numerous strains of Mycobacterium tuberculosis (MTB) as well assemblies for over 20 strains related to MTB and useful for comparative analysis. TBDB stores pre- and post-publication gene-expression data from M. tuberculosis and its close relatives, including over 3000 MTB microarrays, 95 RT-PCR datasets, 2700 microarrays for human and mouse TB related experiments, and 260 arrays for Streptomyces coelicolor. (July 2010) To enable wide use of these data, TBDB provides a suite of tools for searching, browsing, analyzing, and downloading the data.

Proper citation: Tuberculosis Database (RRID:SCR_006619) Copy   

•   Source: SciCrunch Registry