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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

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://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://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   

•   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

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

https://github.com/xavierdidelot/clonalorigin

Software package for comparative analysis of the sequences of a sample of bacterial genomes in order to reconstruct the recombination events that have taken place in their ancestry.

Proper citation: ClonalOrigin (RRID:SCR_016061) Copy   

•   Source: SciCrunch Registry

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

https://broadinstitute.github.io/warp/docs/Pipelines/Optimus_Pipeline/README

Optimus is a pipeline developed by the Data Coordination Platform (DCP) of the Human Cell Atlas (HCA) Project that supports processing of any 3' single-cell and single-nuclei expression data generated with the 10x Genomic v2 or v3 assay. It is an alignment and transcriptome quantification pipeline that corrects cell barcodes, aligns reads to the genome, corrects Unique Molecular Identifiers (UMIs), generates an expression matrix in a UMI-aware manner, calculates summary metrics for genes and cells, detects empty droplets, returns read outputs in BAM format, and returns gene counts in NumPy matrix and Loom matrix formats.

Proper citation: Optimus Pipeline (RRID:SCR_018908) Copy   

•   Source: SciCrunch Registry

http://crispr-era.stanford.edu/index.jsp

Software comprehensive design tool for CRISPR mediated gene editing, repression and activation. Fast and comprehensive guide RNA design tool for genome editing, repression and activation. Used for automated genome wide sgRNA design.

Proper citation: CRISPR-ERA (RRID:SCR_018710) Copy   

•   Source: SciCrunch Registry

http://www.bioinformatics.ucla.edu/ASAP2

THIS RESOURCE IS NO LONGER IN SERVICE, documented on 8/12/13. An expanded version of the Alternative Splicing Annotation Project (ASAP) database with a new interface and integration of comparative features using UCSC BLASTZ multiple alignments. It supports 9 vertebrate species, 4 insects, and nematodes, and provides with extensive alternative splicing analysis and their splicing variants. As for human alternative splicing data, newly added EST libraries were classified and included into previous tissue and cancer classification, and lists of tissue and cancer (normal) specific alternatively spliced genes are re-calculated and updated. They have created a novel orthologous exon and intron databases and their splice variants based on multiple alignment among several species. These orthologous exon and intron database can give more comprehensive homologous gene information than protein similarity based method. Furthermore, splice junction and exon identity among species can be valuable resources to elucidate species-specific genes. ASAP II database can be easily integrated with pygr (unpublished, the Python Graph Database Framework for Bioinformatics) and its powerful features such as graph query, multi-genome alignment query and etc. ASAP II can be searched by several different criteria such as gene symbol, gene name and ID (UniGene, GenBank etc.). The web interface provides 7 different kinds of views: (I) user query, UniGene annotation, orthologous genes and genome browsers; (II) genome alignment; (III) exons and orthologous exons; (IV) introns and orthologous introns; (V) alternative splicing; (IV) isoform and protein sequences; (VII) tissue and cancer vs. normal specificity. ASAP II shows genome alignments of isoforms, exons, and introns in UCSC-like genome browser. All alternative splicing relationships with supporting evidence information, types of alternative splicing patterns, and inclusion rate for skipped exons are listed in separate tables. Users can also search human data for tissue- and cancer-specific splice forms at the bottom of the gene summary page. The p-values for tissue-specificity as log-odds (LOD) scores, and highlight the results for LOD >= 3 and at least 3 EST sequences are all also reported.

Proper citation: Alternative Splicing Annotation Project II Database (RRID:SCR_000322) Copy   

•   Source: SciCrunch Registry

http://genome.jgi.doe.gov/programs/bacteria-archaea/index.jsf

Mission: Dynamically evolve sequencing, finishing, annotation and analysis processes, exploit new technologies, and develop expertise to deliver high quality and high throughput sequence-based microbial science by listening to and responding to DOE Users and scientific community needs. GOALS 1. Expand product catalog and increase sample throughput while maintaining highest quality The MGP has been expanding its product catalog beyond a finished microbial genome and has projected to significantly up ramp throughput for the majority of its current products namely Draft Genomes, Single Cell Genomes, Quick Draft Genomes, Resequencing projects and RNAseq Project. This projected increase in microbial genomes is going hand-in-hand with and has been stimulated by new high throughput technologies and capabilities (de novo microbial Illumina assemblies, single cell genomics, Genologic sample tracking). The increased throughput will support the user community as well as JGI scientists by enabling DOE-relevant science at a grander scale. As the Program aims to generate hundreds of microbial genomes per year, our goal is to scale our production efficiency and maintain our trademark quality to best support our science mission. 2. Expand sequence space One of the ongoing missions of the MGP is to expand the coverage of the phylogenomic sequence space by generating reference genome datasets from highly diverse braches in bacterial and archaeal tree of life. The value of such effort includes the generation of phylogenetic anchors for metagenomic datasets, the improvement of annotation, an increased insight into phylogenetic distribution of functions, the discovery of novel genes, protein families, pathways and a better understanding on evolutionary diversication. 3. Make Single Cell Genomes a robust User product As the vast majority of microbes are uncultured to date, single cell genomics will be a crucial component of the MGP over the next several years to drive not only JGI science but also User community proposed single cell research. Going hand-in-hand are R&D efforts in selective single cell isolations, testing the effects of fixation of single cell sequencing, as well as single cell transcriptomics. 4. Sequence Pangenomes Combining similar genomes together creating pangenomes will allow more compact genome sequence storage and visualization and expedite analysis and annotation. Moreover, the pangenome as a representation of the whole group of organisms may be more representative of a given species within the environment. The MGP thus thrives to enable the sequencing and analysis of pangenomes. Current technology allows the sequencing of one organism strain at a time. Assuming that for most cases, several dozen strains may need to be sequenced in order to generate a more accurate pangenome for every microbial species, it becomes evident that the cost for doing so may be prohibitively high. Our goal here will be to explore new approaches and technologies for generating these pangenomes at a very low cost and analogous to what is the cost today for a single strain. 5. Expand and improve microbial annotation using transcriptomic data To improve annotation of gene structure, establish accurate transcription level and timing, provide information on gene regulation and generate information for expanding understanding of systems biology, the MGP thieves to generate transcriptomics data for larger sets of Bacteria and/or Archaea. This will enable the identification of novel regulator RNAs, as well as facilitate the understanding of uncharacterized protein families. 6. Maintain and evolve a top quality data management system To enable state of the art and world class comparative analysis of internal and external scientific projects, the JGI data integration and visualization management system for comparative analysis of microbial genomes, namely IMG, needs to be maintained and continuously evolved. The system needs to be able to support and integrate all data generated by JGI (WGS, reseq, RNAseq, -other omics data), as well as by the user community, enabling annotation and manual curation of the annotation, comparative analysis, gene-centric and pathway centric analyzes. The system should also facilitate the interation of associated metadata, enable data sharing and distribution, as well as automated data GenBank submissions. Lastly, the system needs to have the ability to scale enabling the annotation of thousands of genomes per year. 7. Drive Flagship projects To stay at the forefront of microbial genomic research, be recognized as such and enable the development new methods and tools, the MGP aims to drive DOE mission relevant flagship projects. Novel tools and methods developed will ultimately serve the user community if proven useful and implemented as part of a larger pipeline. MGP flagship projects are the GEBA and GEBA uncultured projects, as well as the GEBA-RNB, the proposed Microbial Earth and the Microbial Dark Matter Projects.

Proper citation: Microbial Genetics Resource at JGI (RRID:SCR_000570) Copy   

•   Source: SciCrunch Registry

http://genome.igib.res.in/tbvar/

Database of the variome of Mycobacterium tuberculosis (Mtb) comprising of over 29,000 single nucleotide variations created from re-analyzed data sets corresponding to over 400 isolates of Mtb. Using a systematic computational pipeline, potential functional variants and drug-resistance associated variants have been annotated. The database has an option to annotate variants from clinical re-sequencing of Mtb.

Proper citation: tbvar (RRID:SCR_001178) Copy   

•   Source: SciCrunch Registry

http://mirna.imbb.forth.gr/SSCprofiler.html

Tool which can be used to identify novel miRNA gene candidates in the human genome.

Proper citation: SSCprofiler (RRID:SCR_001282) Copy   

•   Source: SciCrunch Registry

http://hipipe.ncgm.sinica.edu.tw/

Tool that provides high performance NGS (next-generation sequencing) data analysis pipelines so that researchers with minimum IT or bioinformatics knowledge can perform common analyses on NGS data. 3 TB of storage space is reserved for each task.

Proper citation: HiPipe (RRID:SCR_001215) Copy   

•   Source: SciCrunch Registry

http://metazoa.ensembl.org/index.html

Ensembl Genomes project produces genome databases for important species from across taxonomic range, using Ensembl software system. Five sites are now available, one of which is Ensembl Metazoa, which houses metazoan species.

Proper citation: Ensembl Metazoa (RRID:SCR_000800) Copy   

•   Source: SciCrunch Registry