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http://montana.eagle-i.net/i/0000012b-00be-4e65-df3b-3fdc80000000

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 27, 2023. Core for Microarray analysis, Database development, Systems biology analysis, Genome assembly, Pathway data analysis, Expression data analysis, Metagenomics analysis. To maintain equipment and software for bioinformatic research, promote bioinformatics education on the MSU campus, and provide training and support to biologists implementing bioinformatics tools in their research.

Proper citation: Montana State University Bioinformatics Core Facility (RRID:SCR_009937) Copy   

•   Source: SciCrunch Registry

http://www.stsiweb.org/SWGR/

Whole genome sequencing data for 454 unrelated Scripps Wellderly Study participants with European ancestry from a project that is studying the genetic architecture of exceptional healthspan from a cohort comprised of more than 1300 healthy individuals over the age of 80 years. SWGR_v1.0 includes chromosome-specific VCF4.1 bgzipped and tabix indexed files. Annotations for each variant can be found at Scripps Genome ADVISER (SG-ADVISER, http://genomics.scripps.edu/) Additional data releases are expected.

Proper citation: Scripps Wellderly Genome Reference (RRID:SCR_010250) Copy   

•   Source: SciCrunch Registry

http://www.scripps.edu/florida/technologies/cbs/index.html

Core facility that provides access to genome-wide collections of cDNAs and siRNAs that can be used to interrogate cellular models of signal transduction pathways and phenotypes. Services include cell lines, hit-picking clones and various screening sets, and access to equipment.Provides instruments:Analyst Molecular Devices,Embla Molecular Devices, Envision Perkin Elmer, Platemate Matrix, Tecan M200, Wellmate Matrix.

Proper citation: Scripps Research Institute Florida Cell Based High Throughput Screening Core Facility (RRID:SCR_014877) Copy   

•   Source: SciCrunch Registry

https://github.com/TransDecoder/TransDecoder

Software tool to identify candidate coding regions within transcript sequences, such as those generated by de novo RNA-Seq transcript assembly using Trinity, or constructed based on RNA-Seq alignments to genome using Tophat and Cufflinks.Starts from FASTA or GFF file. Can scan and retain open reading frames (ORFs) for homology to known proteins by using BlastP or Pfam search and incorporate results into obtained selection. Predictions can then be visualized by using genome browser such as IGV.

Proper citation: TransDecoder (RRID:SCR_017647) Copy   

•   Source: SciCrunch Registry

https://github.com/shendurelab/LACHESIS

Software tool for chromosome scale scaffolding of de novo genome assemblies based on chromatin interactions.Method exploits signal of genomic proximity in Hi-C datasets for ultra long range scaffolding of de novo genome assemblies.

Proper citation: LACHESIS (RRID:SCR_017644) Copy   

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https://bitbucket.org/mroachawri/purge_haplotigs/src

Pipeline for reassigning primary contigs that should be labelled as haplotigs. Used for third generation sequencing based assemblies to automate reassignment of allelic contigs, and to assist in manual curation of genome assemblies.

Proper citation: Purge_haplotigs (RRID:SCR_017616) Copy   

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https://www.sanger.ac.uk/science/tools/reapr

Software tool to identify errors in genome assemblies without need for reference sequence. Can be used in any stage of assembly pipeline to automatically break incorrect scaffolds and flag other errors in assembly for manual inspection. Reports mis-assemblies and other warnings, and produces new broken assembly based on error calls.

Proper citation: Recognition of Errors in Assemblies using Paired Reads (RRID:SCR_017625) Copy   

•   Source: SciCrunch Registry

https://openwetware.org/wiki/HughesLab:JTK_Cycle

Software R package for Detecting Rhythmic Components in Genome-Scale Data Sets. Non-parametric algorithm to identify rhythmic components in large datasets. Identifies and characterizes cycling variables in large datasets.

Proper citation: JTK_CYCLE (RRID:SCR_017962) Copy   

•   Source: SciCrunch Registry

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

Software tool for rapid and accurate annotation of organelle genomes, in particular chloroplast genomes.

Proper citation: GeSeq (RRID:SCR_017336) Copy   

•   Source: SciCrunch Registry

https://github.com/lufuhao/GeneSyntenyPipeline

Software pipeline was designed to draw gene synteny plot between genomes and obtain 1 to 1 gene pairs from each genome.

Proper citation: GeneSyntenyPipeline (RRID:SCR_018198) 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

http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/genomic_curated_CDF.asp

Brainarray custom CDFs for processing raw Affymetrix data. Used to map probe to probesets. Oligonucleotide probes on GeneChips are reorganized based on latest genome and transcriptome information.

Proper citation: CustomCDF (RRID:SCR_018527) Copy   

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http://projects.tcag.ca/xenodup/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. It contains information about segmental duplications in the genomes of chimpanzee, mouse, and rat. The criteria used to identify regions of segmental duplication are: * Sequence identity of at least 90% * Sequence length of at least 5 kb * Not be entirely composed of repetitive elements. BACKGROUND: The high quality of the mouse genome draft sequence and its associated annotations are an invaluable biological resource. Identifying recent duplications in the mouse genome, especially in regions containing genes, may highlight important events in recent murine evolution. In addition, detecting recent sequence duplications can reveal potentially problematic regions of the genome assembly. We use BLAST-based computational heuristics to identify large (>/= 5 kb) and recent (>/= 90% sequence identity) segmental duplications in the mouse genome sequence. Here we present a database of recently duplicated regions of the mouse genome found in the mouse genome sequencing consortium (MGSC) February 2002 and February 2003 assemblies. RESULTS: We determined that 33.6 Mb of 2,695 Mb (1.2%) of sequence from the February 2003 mouse genome sequence assembly is involved in recent segmental duplications, which is less than that observed in the human genome (around 3.5-5%). From this dataset, 8.9 Mb (26%) of the duplication content consisted of "unmapped" chromosome sequence. Moreover, we suspect that an additional 18.5 Mb of sequence is involved in duplication artifacts arising from sequence misassignment errors in this genome assembly. By searching for genes that are located within these regions, we identified 675 genes that mapped to duplicated regions of the mouse genome. Sixteen of these genes appear to have been duplicated independently in the human genome. From our dataset we further characterized a 42 kb recent segmental duplication of Mater, a maternal-effect gene essential for embryogenesis in mice. CONCLUSION: Our results provide an initial analysis of the recently duplicated sequence and gene content of the mouse genome. Many of these duplicated loci, as well as regions identified to be involved in potential sequence misassignment errors, will require further mapping and sequencing to achieve accuracy. A Genome Browser database was set up to display the identified duplication content presented in this work. This data will also be relevant to the growing number of investigators who use the draft genome sequence for experimental design and analysis. The segmental duplication data and summary statistics are available for download and can also be visualized in a genome browser in the GBrowse section. Selected annotation tracks (except the segmental duplication track) have also been obtained from UCSC and loaded into the genome browser. Detailed information (e.g. overlapping genes, overlapping clones, detailed alignment) can be obtained by clicking on a duplication cluster in GBrowse. Both keyword search and BLAT search are available. Analyses based on previous genome assemblies can be found in the Previous Analyses section. Recent Developments The Non-Human Genome Segmental Duplication Database is continually updated including the archived copies of the analysis of all previous genome assemblies and will include all new species as they become available. Acknowledgments We thank The Centre for Applied Genomics at the Hospital for Sick Children (HSC) as well as collaborators worldwide. Supported by Genome Canada the Howard Hughes Medical Institute International Scholar Program (to S.W.S.) and the HSC Foundation.

Proper citation: Non-Human Genome Segmental Duplication Database (RRID:SCR_000470) Copy   

•   Source: SciCrunch Registry

http://bovinegenome.org/

Database and integrated tools to improve annotation of the bovine genome and to integrate the genome sequence with other genomics data.

Proper citation: Bovine Genome Database (RRID:SCR_000148) Copy   

•   Source: SciCrunch Registry

http://www.cbs.dtu.dk/services/gwBrowser/

An interactive web application for visualizing genomic data of sequenced prokaryotic chromosomes. It allows users to carry out various analyses such as mapping alignments of homologous genes to other genomes, mapping of short sequencing reads to a reference chromosome, and calculating DNA properties such as curvature or stacking energy along the chromosome. The GeneWiz browser produces an interactive graphic that enables zooming from a global scale down to single nucleotides, without changing the size of the plot. Its ability to disproportionally zoom provides optimal readability and increased functionality compared to other browsers. The tool allows the user to select the display of various genomic features, color setting and data ranges. Custom numerical data can be added to the plot allowing, for example, visualization of gene expression and regulation data. Further, standard atlases are pre-generated for all prokaryotic genomes available in GenBank, providing a fast overview of all available genomes, including recently deposited genome sequences.

Proper citation: GeneWiz browser (RRID:SCR_001454) Copy   

•   Source: SciCrunch Registry

http://www.worm.mpi-cbg.de/phenobank/cgi-bin/ProjectInfoPage.py

A database that provides primary data from two high-content screens that profile the set of ~900 essential C. elegans genes (~5% of the genome) required for embryo production and/or events during the first two embryonic divisions. Phenobank houses the movies, scored defects, and phenotypic classification data for the embryo-filming and gonad morphology screens.

Proper citation: PhenoBank (RRID:SCR_000930) Copy   

•   Source: SciCrunch Registry

https://gtexportal.org/home/

Database and browser that provides a central resource to archive and display association between genetic variation and high-throughput molecular-level phenotypes. This effort originated with the NIH GTEx roadmap project: however the scope of this resource will be extended to include any available genotype/molecular phenotype datasets.

Proper citation: GTEx eQTL Browser (RRID:SCR_001618) Copy   

•   Source: SciCrunch Registry

https://fungi.ensembl.org/Neurospora_crassa/Info/Index

It's strategy involves Whole Genome Shotgun (WGS) sequencing, in which sequence from the entire genome is generated and reassembled. This method is standard for microbial genome sequencing, and has been successfully applied to Drosophila. Neurospora is an ideal candidate for this approach because of the low repeat content of the genome. Neurospora crassa Database has expanded the scope of its database by including a mitochondrial annotation, incorporating information from the Neurospora compendium, and assigning NCU numbers to tRNA and rRNAs. They have improved the annotation process to predict untranslated regions and to reduce the number of spurious predictions. As a result, version 3 contains 9,826 genes, 794 fewer than version 2. During the initial phase of a WGS project they sequence both ends of the 4 kb inserts from a plasmid library prepared using randomly sheared and sized-selected DNA. The shotgun reads are assembled by recognizing overlapping regions of sequence and making use of the knowledge of the orientation and distance of the paired reads from each plasmid. Obtaining deep sequence coverage though high levels of sequence redundancy assures that the majority of the genome is represented in the initial assembly and that the consensus sequence is of high quality. Their approach toward the initial assembly was conservative, meaning they would rather fail to join sequence contigs that might overlap each other than risk making false joins between two closely related but non-overlapping genomic regions. Hence, the initial assembly contains many sequence contigs and over time these contigs will increase in size and decrease in number as they are joined together. After shotgun sequencing and assembly there was a second phase of sequencing in which additional sequence was obtained from specific regions that were missing from the original assembly or are recognized to be of low quality in the consensus. The Neurospora crassa sequencing project reflects a close collaboration between the Broad Institute and the Neurospora research community. Principal investigators include Bruce Birren and Chad Nusbaum from the Broad Institute, Matt Sachs at the Oregon Graduate Institute of Science and Technology, Chuck Staben at the University of Kentucky and Jak Kinsey at the Fungal Genetics Stock Center at the University of Kansas Medical Center. In addition, we have a larger Advisory Board made up of a number of Neurospora researchers. Sponsors: They have been funded by the National Science Foundation to sequence the N. crassa genome and make the information publicly available.

Proper citation: Neurospora crassa Database (RRID:SCR_001372) Copy   

•   Source: SciCrunch Registry

http://igenbio.com/

A web-based genome analysis platform that integrates proprietary functional genomic data, metabolic reconstructions, expression profiling, and biochemical and microbiological data with publicly available information. Focused on microbial genomics, it provides better and faster identification of gene function across all organisms. Building upon a comprehensive genomic database integrated with a collection of microbial metabolic and non-metabolic pathways and using proprietary algorithms, it assigns functions to genes, integrates genes into pathways, and identifies previously unknown or mischaracterized genes, cryptic pathways and gene products. . * Automated and manual annotation of genes and genomes * Analysis of metabolic and non-metabolic pathways to understand organism physiology * Comparison of multiple genomes to identify shared and unique features and SNPs * Functional analysis of gene expression microarray data * Data-mining for target gene discovery * In silico metabolic engineering and strain improvement

Proper citation: ERGO (RRID:SCR_001243) Copy   

•   Source: SciCrunch Registry

https://chordate.bpni.bio.keio.ac.jp/chordate/faba/1.4/top.html

Image resource including ascidian's three-dimensional (3D) and cross-sectional images through the developmental time course. These images were reconstructed from more than 3,000 high-resolution real images collected by confocal laser scanning microscopy (CLSM) at newly defined 26 distinct developmental stages (stages 1-26) from fertilized egg to hatching larva, which were grouped into six periods named the zygote, cleavage, gastrula, neurula, tailbud, and larva periods. The data set will be helpful in standardizing developmental stages for morphology comparison as well as for providing guidelines for several functional studies of a body plan in chordate.

Proper citation: Four-dimensional Ascidian Body Atlas (RRID:SCR_001691) Copy   

•   Source: SciCrunch Registry