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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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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://www.plantagora.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 18,2025. A web-based plant genome assembly simulation platform whose resources include out of the box scripts for analyzing assembly data, an on-demand web graphing tool to model your experiment, and a downloadable database with metrics and parameters from over 3,000 simulated genome assemblies.

Proper citation: Plantagora (RRID:SCR_001227) Copy   

•   Source: SciCrunch Registry

https://www.sanger.ac.uk/collaboration/sequencing-idd-regions-nod-mouse-genome/

Genetic variations associated with type 1 diabetes identified by sequencing regions of the non-obese diabetic (NOD) mouse genome and comparing them with the same areas of a diabetes-resistant C57BL/6J reference mouse allowing identification of single nucleotide polymorphisms (SNPs) or other genomic variations putatively associated with diabetes in mice. Finished clones from the targeted insulin-dependent diabetes (Idd) candidate regions are displayed in the NOD clone sequence section of the website, where they can be downloaded either as individual clone sequences or larger contigs that make up the accession golden path (AGP). All sequences are publicly available via the International Nucleotide Sequence Database Collaboration. Two NOD mouse BAC libraries were constructed and the BAC ends sequenced. Clones from the DIL NOD BAC library constructed by RIKEN Genomic Sciences Centre (Japan) in conjunction with the Diabetes and Inflammation Laboratory (DIL) (University of Cambridge) from the NOD/MrkTac mouse strain are designated DIL. Clones from the CHORI-29 NOD BAC library constructed by Pieter de Jong (Children's Hospital, Oakland, California, USA) from the NOD/ShiLtJ mouse strain are designated CHORI-29. All NOD mouse BAC end-sequences have been submitted to the International Nucleotide Sequence Database Consortium (INSDC), deposited in the NCBI trace archive. They have generated a clone map from these two libraries by mapping the BAC end-sequences to the latest assembly of the C57BL/6J mouse reference genome sequence. These BAC end-sequence alignments can then be visualized in the Ensembl mouse genome browser where the alignments of both NOD BAC libraries can be accessed through the Distributed Annotation System (DAS). The Mouse Genomes Project has used the Illumina platform to sequence the entire NOD/ShiLtJ genome and this should help to position unaligned BAC end-sequences to novel non-reference regions of the NOD genome. Further information about the BAC end-sequences, such as their alignment, variation data and Ensembl gene coverage, can be obtained from the NOD mouse ftp site.

Proper citation: Sequencing of Idd regions in the NOD mouse genome (RRID:SCR_001483) Copy   

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http://ccr.coriell.org/Sections/Collections/NHGRI/?SsId=11

DNA samples and cell lines from fifteen populations, including the samples used for the International HapMap Project, the HapMap 3 Project and the 1000 Genomes Project (except for the CEPH samples). All of the samples were contributed with consent to broad data release and to their use in many future studies, including for extensive genotyping and sequencing, gene expression and proteomics studies, and all other types of genetic variation research. NHGRI led the contribution of the NIH to the International HapMap Project, which developed a haplotype map of the human genome. This haplotype map, called the HapMap is a publicly available tool that allows researchers to find genes and genetic variations that affect health and disease. The samples from four populations used to develop the HapMap were initially housed in the Human Genetic Cell Repository of the National Institute of General Medical Sciences (NIGMS). Except for the Utah CEPH samples that were in the NIGMS Repository before the initiation of the HapMap Project and remain there, the NHGRI Repository now houses all of the HapMap samples. The NHGRI repository also houses the extended set of HapMap samples, which includes additional samples from the HapMap populations and samples from seven additional populations. All of the samples were collected with extensive community engagement, including discussions with members of the donor communities about the ethical and social implications of human genetic variation research. These samples were studied as part of the HapMap 3 Project. The NHGRI repository also houses the samples for the International 1000 Genomes Project. This Project is lightly sequencing genome-wide 2500 samples from 27 populations. This project aims to provide a detailed map of human genetic variation, including common and rare SNPs and structural variants. This map will allow more precise localization of genomic regions that contribute to health and disease. The 1000 Genomes Project includes many of the samples from the HapMap and extended set of HapMap samples, as well as samples being collected from additional populations. Currently, samples from five additional populations are available; the others will become available during 2011 and 2012. No identifying or phenotypic information is available for the samples. Donors gave broad consent for use of the samples, including for genotyping, sequencing, and cellular phenotype studies. Samples collected from other populations for the study of human genetic variation may be added to the collection in the future. The NHGRI Repository distributes high quality lymphoblastoid cell lines and DNA from the samples to researchers. DNA is provided in plates or panels of 70 to 100 samples or as individual samples. Cell cultures and DNA samples are distributed only to qualified professional persons who are associated with recognized research, medical, educational, or industrial organizations engaged in health-related research or health delivery.

Proper citation: NHGRI Sample Repository for Human Genetic Research (RRID:SCR_004528) Copy   

•   Source: SciCrunch Registry

http://regulatorygenomicsgroup.org/chicago

Statistical pipeline for detecting significant chromosomal interactions in Capture Hi-C data. CHiCAGO uses a convolution background model accounting for both random Brownian collisions between chromatin fragments and technical noise. CHiCAGO then performs a p-value weighting procedure based on the expected true positive rates at different distance ranges, with scores representing soft-thresholded -log weighted p-values., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: CHiCAGO (RRID:SCR_014941) Copy   

•   Source: SciCrunch Registry

http://www.cdc.gov/genomics/hugenet/default.htm

Human Genome Epidemiology Network, or HuGENet, is a global collaboration of individuals and organizations committed to the assessment of the impact of human genome variation on population health and how genetic information can be used to improve health and prevent disease. Its goals include: establishing an information exchange that promotes global collaboration in developing peer-reviewed information on the relationship between human genomic variation and health and on the quality of genetic tests for screening and prevention; providing training and technical assistance to researchers and practitioners interested in assessing the role of human genomic variation on population health and how such information can be used in practice; developing an updated and accessible knowledge base on the World Wide Web; and promoting the use of this knowledge base by health care providers, researchers, industry, government, and the public for making decisions involving the use of genetic information for disease prevention and health promotion. HuGENet collaborators come from multiple disciplines such as epidemiology, genetics, clinical medicine, policy, public health, education, and biomedical sciences. Currently, there are 4 HuGENet Coordinating Centers for the implementation of HuGENet activities: CDC''s Office of Public Health Genomics, Atlanta, Georgia; HuGENet UK Coordinating Center, Cambridge, UK; University of Ioannina, Greece; University of Ottawa , Ottawa, Canada. HuGENet includes: HuGE e-Journal Club: The HuGE e-Journal Club is an electronic discussion forum where new human genome epidemiologic (HuGE) findings, published in the scientific literature in the CDC''s Office of Public Health Genomics Weekly Update, will be abstracted, summarized, presented, and discussed via a newly created HuGENet listserv. HuGE Reviews: A HuGE Review identifies human genetic variations at one or more loci, and describes what is known about the frequency of these variants in different populations, identifies diseases that these variants are associated with and summarizes the magnitude of risks and associated risk factors, and evaluates associated genetic tests. Reviews point to gaps in existing epidemiologic and clinical knowledge, thus stimulating further research in these areas. HuGE Fact Sheets: HuGE Fact Sheets summarize information about a particular gene, its variants, and associated diseases. HuGE Case Studies: An on-line presentation designed to sharpen your epidemiological skills and enhance your knowledge on genomic variation and human diseases. Its purpose is to train health professionals in the practical application of human genome epidemiology (HuGE), which translates gene discoveries to disease prevention by integrating population-based data on gene-disease relationships and interventions. Students will acquire conceptual and practical tools for critically evaluating the growing scientific literature in specific disease areas. HUGENet Publications: Articles related to the HuGENet movement written by our HuGENet collaborators. HuGE Navigator: An integrated, searchable knowledge base of genetic associations and human genome epidemiology, including information on population prevalence of genetic variants, gene-disease associations, gene-gene and gene- environment interactions, and evaluation of genetic tests. HuGE Workshops: HuGENet has sponsored meetings and workshops with national and international partners since 2001. Available are detailed summaries, agendas or the ability to download speaker slides. HuGE Book: Human Genome Epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent Disease. (The findings and conclusions in this book are those of the author(s) and do not necessarily represent the views of the funding agency.) HuGENet Collaborators: HuGENet is interested in establishing collaborations with individuals and organizations working on population based research involving genetic information. HuGE Funding: Funding opportunities for specific population-based genetic epidemiology research projects are available. Research initiatives whose aims include assessing the prevalence of human genetic variation, the association between genetic variants and human diseases, the measurement of gene-gene or gene-environment interaction, and the evaluation of genetic tests for screening and prevention are compiled to create a posted listing. Additional information and application details can be found by clicking on the respective links.

Proper citation: Human Genome Epidemiology Network (RRID:SCR_013117) Copy   

•   Source: SciCrunch Registry

Ratings or validation data are available for this resource

https://www.gencodegenes.org

Human and mouse genome annotation project which aims to identify all gene features in the human genome using computational analysis, manual annotation, and experimental validation.

Proper citation: GENCODE (RRID:SCR_014966) Copy   

•   Source: SciCrunch Registry

http://david.abcc.ncifcrf.gov/content.jsp?file=/ease/ease1.htm&type=1

Windows(c) desktop software application, customizable and standalone, that facilitates the biological interpretation of gene lists derived from the results of microarray, proteomic, and SAGE experiments. Provides statistical methods for discovering enriched biological themes within gene lists, generates gene annotation tables, and enables automated linking to online analysis tools. Offers statistical models to deal with multi-test comparison problem. Platform: Windows compatible

Proper citation: EASE: the Expression Analysis Systematic Explorer (RRID:SCR_013361) Copy   

•   Source: SciCrunch Registry

http://www-sequence.stanford.edu/group/yeast_deletion_project/

Database and project to reveal open reading frames (ORFs) in the yeast genome in order to discover their functions. A PCR-based gene deletion strategy is used to assign functions through phenotypic analysis of mutants.

Proper citation: Saccharomyces Genome Deletion Project (RRID:SCR_014961) Copy   

•   Source: SciCrunch Registry

http://kmer.sourceforge.net

Alignment analysis software tool for comparative mapping between two genome assemblies or between two different genomes. It can cache intermediate results to speed a comparisons of multiple sequences.

Proper citation: Atac (RRID:SCR_015980) Copy   

•   Source: SciCrunch Registry

http://sing.ei.uvigo.es/ALTER/

Web application to perform program-oriented conversion of DNA and protein alignments and transform between multiple sequence alignment formats. ALTER focuses on the specifications of mainstream alignment and analysis programs rather than on the conversion among more or less specific formats.

Proper citation: ALTER (RRID:SCR_015968) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/arden/

Software for specificity control of read alignments using an artificial reference. It estimates error rates based on real experimental reads and an additionally generated artificial reference genome. It can be used to optimize parameters for read mappers, to select read mappers for a specific problem or also to filter alignments based on quality estimation.

Proper citation: Arden (RRID:SCR_015975) Copy   

•   Source: SciCrunch Registry

https://cell-innovation.nig.ac.jp/maser/Tools/visualization_top_en.html

One stop platform for NGS big data from analysis to visualization. There are about 400 analysis pipelines integrated on Maser. List of all analysis pipelines, including descriptions and approximate execution times, can be found on page for ‘All pipelines’ in the User Guide. loadGtfToGe_db software loads GTF files to a database for Genome Explorer. It allows the user to browse the results through the GE.

Proper citation: loadGtfToGe_db (RRID:SCR_015998) Copy   

•   Source: SciCrunch Registry

http://sanger-pathogens.github.io/circlator/

Software that automates assembly circularization and produces accurate linear representations of circular sequences. It is used for assembling of DNA sequence data of complete bacterial and small eukaryotic genomes.

Proper citation: Circlator (RRID:SCR_016058) Copy   

•   Source: SciCrunch Registry

https://github.com/genomeannotation/GAG

Command line program to read, modify, annotate and generate genomic data. Can write files to .gff3 or to the NCBI's .tbl format.

Proper citation: Genome Annotation Generator (RRID:SCR_016053) Copy   

•   Source: SciCrunch Registry

http://genometools.org

Software toolkit for biological sequence analysis and -presentation combined into a single binary. It is used for genome analysis, efficient processing of structured genome annotations and contains binaries for sequence and annotation handling, sequence compression, index structure generation and access, annotation visualization.

Proper citation: GenomeTools (RRID:SCR_016120) Copy   

•   Source: SciCrunch Registry

https://gemma.msl.ubc.ca/phenotypes.html

Database that consolidates information on genes and phenotypes across multiple resources and allows tracking and exploring of the associations. Part of Gemma, a web site, database and a set of tools for the meta-analysis, re-use and sharing of genomics data.

Proper citation: Phenocarta (RRID:SCR_016273) Copy   

•   Source: SciCrunch Registry