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http://www.jiffynet.org/

Web based instant protein network modeler for newly sequenced species. Web server designed to instantly construct genome scale protein networks using protein sequence data. Provides network visualization, analysis pages and solution for instant network modeling of newly sequenced species.

Proper citation: JiffyNet (RRID:SCR_011954) Copy   

•   Source: SciCrunch Registry

http://genomejack.net/

A genome browser specialized in next-generation sequencing data.

Proper citation: GenomeJack (RRID:SCR_012026) Copy   

•   Source: SciCrunch Registry

http://flj.hinv.jp/

A human full-length cDNA sequence analysis database focused on mRNA varieties caused by variations of transcription start site (TSS) and splicing. Also available is ATGpr, a program for identifying the translational initiation codons in cDNA sequences. Data are derived from several full-length cDNA studies in Japan. Human gene number was estimated to be 20-25 thousand. However, the number of human mRNA varieties was predicted to be about 100 thousand. The varieties are thought to be caused by variations of TSS and splicing. In their previous human cDNA project, about 30 thousand of FLJ human full-length sequenced cDNAs were deposited to DDBJ/GenBank/EMBL, and they obtained about 1.4 million of 5''-end sequences (5''-EST) of FLJ full-length cDNAs from about 100 kinds of cDNA libraries consist of human tissues and cells constructed by oligo-capping method. The majority of the insert cDNA sizes were over 2 kb and the full-length rate of 5''-end was 90. And our FLJ cDNAs were covered about 80 of human genes. About 22 thousand of finished grades of full-length sequenced cDNAs were obtained in this project. The sequence analysis databases is focused on mRNA variations using human genome and cDNA sequences, FLJ full-length sequenced cDNAs, 5-ESTs of FLJ full-length cDNAs and other cDNA sequences described below. After those sequences were mapped onto the human genome sequences, clustering of the cDNA sequences were done based on the mapping results.

Proper citation: FLJ Human cDNA Database (RRID:SCR_008253) Copy   

•   Source: SciCrunch Registry

http://www.biodas.org

The Distributed Annotation System (DAS) defines a communication protocol used to exchange annotations on genomic or protein sequences. It is motivated by the idea that such annotations should not be provided by single centralized databases, but should instead be spread over multiple sites. Data distribution, performed by DAS servers, is separated from visualization, which is done by DAS clients. The advantages of this system are that control over the data is retained by data providers, data is freed from the constraints of specific organisations and the normal issues of release cycles, API updates and data duplication are avoided. DAS is a client-server system in which a single client integrates information from multiple servers. It allows a single machine to gather up sequence annotation information from multiple distant web sites, collate the information, and display it to the user in a single view. Little coordination is needed among the various information providers. DAS is heavily used in the genome bioinformatics community. Over the last years we have also seen growing acceptance in the protein sequence and structure communities. A DAS-enabled website or application can aggregate complex and high-volume data from external providers in an efficient manner. For the biologist, this means the ability to plug in the latest data, possibly including a user''s own data. For the application developer, this means protection from data format changes and the ability to add new data with minimal development cost. Here are some examples of DAS-enabled applications or websites for end users: :- Dalliance Experimental Web/Javascript based Genome Viewer :- IGV Integrative Genome Viewer java based browser for many genomes :- Ensembl uses DAS to pull in genomic, gene and protein annotations. It also provides data via DAS. :- Gbrowse is a generic genome browser, and is both a consumer and provider of DAS. :- IGB is a desktop application for viewing genomic data. :- SPICE is an application for projecting protein annotations onto 3D structures. :- Dasty2 is a web-based viewer for protein annotations :- Jalview is a multiple alignment editor. :- PeppeR is a graphical viewer for 3D electron microscopy data. :- DASMI is an integration portal for protein interaction data. :- DASher is a Java-based viewer for protein annotations. :- EpiC presents structure-function summaries for antibody design. :- STRAP is a STRucture-based sequence Alignment Program. Hundreds of DAS servers are currently running worldwide, including those provided by the European Bioinformatics Institute, Ensembl, the Sanger Institute, UCSC, WormBase, FlyBase, TIGR, and UniProt. For a listing of all available DAS sources please visit the DasRegistry. Sponsors: The initial ideas for DAS were developed in conversations with LaDeana Hillier of the Washington University Genome Sequencing Center.

Proper citation: Distributed Annotation System (RRID:SCR_008427) Copy   

•   Source: SciCrunch Registry

http://cgems.cancer.gov

The project began as a pilot study to identify inherited genetic susceptibility to prostate and breast cancer. CGEMS has developed into a robust research program involving genome-wide association studies (GWASs) for a number of cancers to identify common genetic variants that affect a person''s risk of developing cancer. In collaboration with extramural scientists, NCI''s Division of Cancer Epidemiology and Genetics (DCEG) has carried out genome-wide scans for breast, prostate, pancreatic, and lung cancers, while a GWAS of bladder cancer is currently underway. By making the data available to both intramural and extramural research scientists, as well as those in the private sector through rapid posting, NIH can leverage its resources to ensure that the dramatic advances in genomics are incorporated into rigorous population-based studies. Ultimately, findings from these studies may yield new preventive, diagnostic, and therapeutic interventions for cancer. Sponsors: This resource is supported by the U.S. National Institues Of Health.

Proper citation: CGEMS (RRID:SCR_008445) Copy   

•   Source: SciCrunch Registry

http://plantgrn.noble.org/LegumeIP/

LegumeIP is an integrative database and bioinformatics platform for comparative genomics and transcriptomics to facilitate the study of gene function and genome evolution in legumes, and ultimately to generate molecular based breeding tools to improve quality of crop legumes. LegumeIP currently hosts large-scale genomics and transcriptomics data, including: * Genomic sequences of three model legumes, i.e. Medicago truncatula, Glycine max (soybean) and Lotus japonicus, including two reference plant species, Arabidopsis thaliana and Poplar trichocarpa, with the annotation based on UniProt TrEMBL, InterProScan, Gene Ontology and KEGG databases. LegumeIP covers a total 222,217 protein-coding gene sequences. * Large-scale gene expression data compiled from 104 array hybridizations from L. japonicas, 156 array hybridizations from M. truncatula gene atlas database, and 14 RNA-Seq-based gene expression profiles from G. max on different tissues including four common tissues: Nodule, Flower, Root and Leaf. * Systematic synteny analysis among M. truncatula, G. max, L. japonicus and A. thaliana. * Reconstruction of gene family and gene family-wide phylogenetic analysis across the five hosted species. LegumeIP features comprehensive search and visualization tools to enable the flexible query on gene annotation, gene family, synteny, relative abundance of gene expression.

Proper citation: LegumeIP (RRID:SCR_008906) Copy   

•   Source: SciCrunch Registry

http://hymenopteragenome.org/beebase/

Gene sequences and genomes of Bombus terrestris, Bombus impatiens, Apis mellifera and three of its pathogens, that are discoverable and analyzed via genome browsers, blast search, and apollo annotation tool. The genomes of two additional species, Apis dorsata and A. florea are currently under analysis and will soon be incorporated.BeeBase is an archive and will not be updated. The most up-to-date bee genome data is now available through the navigation bar on the HGD Home page.

Proper citation: BeeBase (RRID:SCR_008966) Copy   

•   Source: SciCrunch Registry

http://rgd.mcw.edu/rgdCuration/?module=portal&func=show&name=renal

An integrated resource for information on genes, QTLs and strains associated with a variety of kidney and renal system conditions such as Renal Hypertension, Polycystic Kidney Disease and Renal Insufficiency, as well as Kidney Neoplasms.

Proper citation: Renal Disease Portal (RRID:SCR_009030) Copy   

•   Source: SciCrunch Registry

http://compbio.cs.brown.edu/projects/gasv/

Software tool combining both paired read and read depth signals into probabilistic model which can analyze multiple alignments of reads. Used to find structural variation in both normal and cancer genomes using data from variety of next-generation sequencing platforms. Used to predict structural variants directly from aligned reads in SAM/BAM format.Combines read depth information along with discordant paired read mappings into single probabilistic model two common signals of structural variation. When multiple alignments of read are given, GASVPro utilizes Markov Chain Monte Carlo procedure to sample over the space of possible alignments.

Proper citation: GASVPro (RRID:SCR_005259) Copy   

•   Source: SciCrunch Registry

http://bejerano.stanford.edu/prism/public/html/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: PRISM (Stanford database) (RRID:SCR_005375) Copy   

•   Source: SciCrunch Registry

http://ergatis.sourceforge.net/

A web interface and scalable software system for bioinformatics workflows that is used to create, run, and monitor reusable computational analysis pipelines. It contains pre-built components for common bioinformatics analysis tasks. These components can be arranged graphically to form highly-configurable pipelines. Each analysis component supports multiple output formats, including the Bioinformatic Sequence Markup Language (BSML). The current implementation includes support for data loading into project databases following the CHADO schema, a highly normalized, community-supported schema for storage of biological annotation data. Ergatis uses the Workflow engine to process its work on a compute grid. Workflow provides an XML language and processing engine for specifying the steps of a computational pipeline. It provides detailed execution status and logging for process auditing, facilitates error recovery from point of failure, and is highly scalable with support for distributed computing environments. The XML format employed enables commands to be run serially, in parallel, and in any combination or nesting level.

Proper citation: Ergatis (RRID:SCR_005377) Copy   

•   Source: SciCrunch Registry

http://toolshed.g2.bx.psu.edu/repository/display_tool?repository_id=5d0de444b1f9ac52&tool_config=database%2Fcommunity_files%2F000%2Frepo_136%2Fcrest.xml&changeset_revision=4f6952e0af48

An algorithm for detecting genomic structural variations at base-pair resolution using next-generation sequencing data. CREST uses pieces of DNA called soft clips to find structural variations. Soft clips are the DNA segments produced during sequencing that fail to properly align to the reference genome as the sample genome is reassembled. CREST uses the soft clips to precisely identify sites of chromosomal rearrangement or where pieces of DNA are inserted or deleted.

Proper citation: CREST (RRID:SCR_005257) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/molbiolib/

A compact, portable, and extensively tested C++11 software framework and set of applications tailored to the demands of next-generation sequencing data and applicable to many other applications. It is designed to work with common file formats and data types used both in genomic analysis and general data analysis. A central relational-database-like Table class is a flexible and powerful object to intuitively represent and work with a wide variety of tabular datasets, ranging from alignment data to annotations. MolBioLib includes programs to perform a wide variety of analysis tasks such as computing read coverage, annotating genomic intervals, and novel peak calling with a wavelet algorithm. This package assumes fluency in both UNIX and C++.

Proper citation: MolBioLib (RRID:SCR_005372) Copy   

•   Source: SciCrunch Registry

http://www.bioextract.org/GuestLogin

An open, web-based system designed to aid researchers in the analysis of genomic data by providing a platform for the creation of bioinformatic workflows. Scientific workflows are created within the system by recording tasks performed by the user. These tasks may include querying multiple, distributed data sources, saving query results as searchable data extracts, and executing local and web-accessible analytic tools. The series of recorded tasks can then be saved as a reproducible, sharable workflow available for subsequent execution with the original or modified inputs and parameter settings. Integrated data resources include interfaces to the National Center for Biotechnology Information (NCBI) nucleotide and protein databases, the European Molecular Biology Laboratory (EMBL-Bank) non-redundant nucleotide database, the Universal Protein Resource (UniProt), and the UniProt Reference Clusters (UniRef) database. The system offers access to numerous preinstalled, curated analytic tools and also provides researchers with the option of selecting computational tools from a large list of web services including the European Molecular Biology Open Software Suite (EMBOSS), BioMoby, and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The system further allows users to integrate local command line tools residing on their own computers through a client-side Java applet.

Proper citation: BioExtract (RRID:SCR_005397) Copy   

•   Source: SciCrunch Registry

http://splitread.sourceforge.net/

Software for detecting INDELs (small insertions and deletion with size less than 50bp) as well as large deletions that are within the coding regions from the exome sequencing data. It also can be applied to the whole genome sequencing data.

Proper citation: SPLITREAD (RRID:SCR_005264) Copy   

•   Source: SciCrunch Registry

http://code.google.com/p/hydra-sv/

Software that detects structural variation (SV) breakpoints by clustering discordant paired-end alignments whose signatures corroborate the same putative breakpoint. Hydra can detect breakpoints caused by all classes of structural variation. Moreover, it was designed to detect variation in both unique and duplicated genomic regions; therefore, it will examine paired-end reads having multiple discordant alignments. Hydra does not attempt to classify SV breakpoints based on the mapping distances and orientations of each breakpoint cluster, it merely detects and reports breakpoints. This is an intentional decision, as it was observed that in loci affected by complex rearrangements, the type of variant suggested by the breakpoint signature is not always correct. Hydra does report the orientations, distances, number of supporting read-pairs, etc., for each breakpoint. It is suggested that downstream methods be used to classify variants based on the genomic features that they overlap and the co-occurrence of other breakpoints. For example, they developed BEDTools for exactly this purpose and the breakpoints reported by Hydra are in the BEDPE format used by BEDTools. Future releases of Hydra will include scripts that assist in the classification process.

Proper citation: Hydra (RRID:SCR_005260) Copy   

•   Source: SciCrunch Registry

http://www.yandell-lab.org/software/mwas.html

The MAKER Web Annotation Service (MWAS) is an easily configurable web-accessible genome annotation pipeline. It''''s purpose is to allow research groups with small to intermediate amounts of eukaryotic and prokaryotic genome sequence (i.e. BAC clones, small whole genomes, preliminary sequencing data, etc.) to independently annotate and analyze their data and produce output that can be loaded into a genome database. MWAS is build on the stand alone genome annotation pipeline MAKER, and users who wish to annotate larger datasets and whole genomes are free to download MAKER for use on their own systems. MWAS identifies repeats, aligns ESTs and proteins to a genome, produces ab-initio gene predictions and automatically synthesizes these data into gene annotations having evidence-based quality values. MWAS can also automatically train popular gene prediction algorithms for use on new genomes for which pre-existing information is limited. MAKER is a member of the Generic Model Organism Database (GMOD) project and output produced by this site can be directly used with other GMOD tools. Annotations can be directly viewed online by the user via GBrowse, JBrowse, and Apollo, or they can be downloaded for local analysis and integration into a genome database. MWAS also supplies summary statistics on sequence features via the Sequence Ontology tool SOBA. MWAS should prove especially useful for emerging model organism genome projects with minimal bioinformatics expertise and computer resources, since a user can produce final genome annotations without having to install and configure any software locally.

Proper citation: MAKER Web Annotation Service (RRID:SCR_005318) Copy   

•   Source: SciCrunch Registry

http://cgs.csail.mit.edu/gem/

Java software for studying protein-DNA interaction using ChIP-seq / ChIP-exo data. It links binding event discovery and motif discovery with positional priors in the context of a generative probabilistic model of ChIP data and genome sequence, resolves ChIP data into explanatory motifs and binding events at unsurpassed spatial resolution. GEM reciprocally improves motif discovery using binding event locations, and binding event predictions using discovered motifs.

Proper citation: GEM (RRID:SCR_005339) Copy   

•   Source: SciCrunch Registry

http://pringlelab.stanford.edu/projects.html

Software to collapse identical and near-identical Illumina and 454 reads (such as those from PCR clones) into single error-corrected sequences; it can process paired-end as well as single-end reads. Fulcrum is customizable and can be deployed on a single machine, a local network or a commercially available MapReduce cluster, and it has been optimized to maximize ease-of-use, cross-platform compatibility and future scalability. Sequence datasets have been collapsed by up to 71%, and the reduced number and improved quality of the resulting sequences allow assemblers to produce longer contigs while using less memory.

Proper citation: Fulcrum (RRID:SCR_005523) Copy   

•   Source: SciCrunch Registry

http://microbesonline.org/

MicrobesOnline is designed specifically to facilitate comparative studies on prokaryotic genomes. It is an entry point for operon, regulons, cis-regulatory and network predictions based on comparative analysis of genomes. The portal includes over 1000 complete genomes of bacteria, archaea and fungi and thousands of expression microarrays from diverse organisms ranging from model organisms such as Escherichia coli and Saccharomyces cerevisiae to environmental microbes such as Desulfovibrio vulgaris and Shewanella oneidensis. To assist in annotating genes and in reconstructing their evolutionary history, MicrobesOnline includes a comparative genome browser based on phylogenetic trees for every gene family as well as a species tree. To identify co-regulated genes, MicrobesOnline can search for genes based on their expression profile, and provides tools for identifying regulatory motifs and seeing if they are conserved. MicrobesOnline also includes fast phylogenetic profile searches, comparative views of metabolic pathways, operon predictions, a workbench for sequence analysis and integration with RegTransBase and other microbial genome resources. The next update of MicrobesOnline will contain significant new functionality, including comparative analysis of metagenomic sequence data. Programmatic access to the database, along with source code and documentation, is available at http://microbesonline.org/programmers.html.

Proper citation: MicrobesOnline (RRID:SCR_005507) Copy   

•   Source: SciCrunch Registry