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https://software.broadinstitute.org/software/discovar/blog/

Software tool for variant calling with reference and de novo assembly of genomes. The heart of DISCOVAR is a de novo genome assembler which can generate de novo assemblies for both large and small genomes.

Proper citation: Discovar assembler (RRID:SCR_016755) Copy   

•   Source: SciCrunch Registry

https://github.com/schatzlab/genomescope

Open source software package for fast genome analysis from unassembled short reads. Used to estimate genome heterozygosity, repeat content, and size from sequencing reads using a kmer-based statistical approach.

Proper citation: GenomeScope (RRID:SCR_017014) Copy   

•   Source: SciCrunch Registry

https://omicssimla.sourceforge.io

Software tool for generating multi omics data with disease status. Simulates genomics (SNPs and copy number variations), epigenomics ( whole genome bisulphite sequencing), transcriptomics ( RNA seq), and proteomics (normalized reverse phase protein array) data at the whole genome level. Available as desktop and web application version.

Proper citation: OmicsSIMLA (RRID:SCR_017011) Copy   

•   Source: SciCrunch Registry

https://github.com/tseemann/shovill

Software tool to assemble bacterial isolate genomes from Illumina paired end reads. Used for isolate data only, primarily small haploid organisms. Supports assemblers like SKESA, Velvet and Megahit.

Proper citation: shovill (RRID:SCR_017077) Copy   

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https://github.com/PGB-LIV/VAPPER

Software tool for analysis of variant antigens in African trypanosomes. Used for quantitative analysis of antigenic diversity in systems data of genomes, transcriptomes, and proteomes, called Variant Antigen Profiling to understand how antigenic diversity relates to clinical outcome, how antigen genes may be used as epidemiological markers of virulence, and in measuring gene expression during experimental infections.

Proper citation: VAPPER (RRID:SCR_016993) Copy   

•   Source: SciCrunch Registry

http://cab.spbu.ru/software/rnaspades/

Software tool for assembling transcripts from RNA-Seq data. Explores surprising computational parallels between assembly of transcriptomes and single cell genomes. Suitable for all kind of organisms. Part of SPAdes package since version 3.9.

Proper citation: rnaSPAdes (RRID:SCR_016992) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/goseq.html

Software application for performing Gene Ontology analysis on RNAseq data and other length biased data. Used to reduce complexity and highlight biological processes in genome wide expression studies.

Proper citation: Goseq (RRID:SCR_017052) Copy   

•   Source: SciCrunch Registry

https://proteomics.cancer.gov/programs/cptac

Clinical proteomic tumor analysis consortium to systematically identify proteins that derive from alterations in cancer genomes and related biological processes, in order to understand molecular basis of cancer that is not possible through genomics and to accelerate translation of molecular findings into clinic. Operates through Proteome Characterization Centers, Proteogenomic Translational Research Centers, and Proteogenomic Data Analysis Centers. CPTAC investigators collaborate, share data and expertise across consortium, and participate in consortium activities like developing standardized workflows for reproducible studies.

Proper citation: CPTAC (RRID:SCR_017135) Copy   

•   Source: SciCrunch Registry

https://www.trophoblast.cam.ac.uk/Resources/BioInformatics

Core provides assistance with experimental design, RNA sequencing, whole genome and targeted sequencing, methylation sequencing, protein alignment, microscopy image analysis, and training.

Proper citation: University of Cambridge Centre for Trophoblast Research Bioinformatics Core Facility (RRID:SCR_017192) Copy   

•   Source: SciCrunch Registry

https://github.com/Sethupathy-Lab/miRquant

Software tool for accurate annotation and quantification of microRNAs and their isomiRs from small RNA-sequencing data. Provides information on quality of sequencing data, genome mapping statistics, abundance of other types of small RNAs such as tDRs and yDRs, prevalence of post transcriptional modifications.

Proper citation: miRquant (RRID:SCR_017261) Copy   

•   Source: SciCrunch Registry

http://pathwaynet.princeton.edu/

Web user interface for interaction predictions of human gene networks and integrative analysis of user data types that takes advantage of data from diverse tissue and cell-lineage origins. Predicts presence of functional association and interaction type among human genes or its protein products on whole genome scale. Used to analyze experimetnal gene in context of interaction networks.

Proper citation: PathwayNet (RRID:SCR_017353) Copy   

•   Source: SciCrunch Registry

https://github.com/brentp/peddy

Software package that evaluates correspondence between stated sexes, relationships, and ancestries in pedigree file and those inferred from genotypes in VCF file resulting from human whole genome sequencing or whole exome sequencing studies. Facilitates both automated and interactive, visual detection of sample swaps, poor sequencing quality, and other indicators of sample problems.

Proper citation: peddy (RRID:SCR_017287) Copy   

•   Source: SciCrunch Registry

https://github.com/ndierckx/NOVOPlasty

Software package as de novo assembler and heteroplasmy variance caller for short circular genomes. Used for de novo assembly of organelle genomes from whole genome data.

Proper citation: NOVOPlasty (RRID:SCR_017335) Copy   

•   Source: SciCrunch Registry

https://pachterlab.github.io/cgal/

Software tool for computing genome assembly likelihoods.Computes likelihood of reads with respect to assembly and statistical model which can be used as metric for evaluating assemblies. Novel likelihood based approach to assembly assessment in absence of ground truth.

Proper citation: Computing Genome Assembly Likelihoods (RRID:SCR_017624) Copy   

•   Source: SciCrunch Registry

http://www.informatics.jax.org/batch_data.shtml

Software tool to access various mouse genome information in batch format. Batch data and analysis tools.

Proper citation: Batch Data and Analysis Tool (RRID:SCR_017515) Copy   

•   Source: SciCrunch Registry

https://imputationserver.sph.umich.edu/

Web server to implement whole genotype imputation workflow for efficient parallelization of computationally intensive tasks. Service for imputation that facilitates access to new reference panels and greatly improves user experience and productivity. Used to find haplotype segments and reference panel of sequenced genomes, assign genotypes at untyped markers, improve genome coverage, facilitate comparison and combination of studies that use different marker panels, increase power to detect genetic association, and guide fine mapping.

Proper citation: Michigan Imputation Server (RRID:SCR_017579) 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

https://fgr.hms.harvard.edu/

Database that provides free online tools to users to allow the retrieval of information related to the Drosophila genome and allows access to genome-wide and related cell-based screening of Drosophila at Harvard Medical School (for a fee) . Tools available include SnapDragon, and RNAi designer, a heat map tool for viewing screen data, and gene and amplicon search and download tools. The DRSC mainly exists to provide Drosophila genome screening services, including help with assay development and optimization, data and image analysis, and planning of follow-up assays.

Proper citation: Drosophila RNAi Screening Center (RRID:SCR_000733) 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://www-abcdb.biotoul.fr

ABCdb is a public resource devoted to the ATP-binding Cassette (ABC) transporters encoded by completely sequenced prokaryotic genomes. In order to establish, in a complete genome, the repertory of ABC systems, we have to: i) identify the different partners, ii) assemble the partners in putative systems, and iii) classify the system into the correct functional subfamily (Quentin et al., 2002). The main pitfalls were the identification of loosely conserved domains and the assembly of partners encoded by genes dispersed over the chromosome. In order to face the avalanche of newly sequenced genomes, we decided to also feed into the database the raw prediction issued by this automatic procedure, before time consuming review by an expert occurs. Therefore, the database comprises two sections: CleanDb, for data checked by an expert and AutoDb for raw data. The ABC proteins are involved in a wide variety of physiological processes in Archaea, Bacteria and Eucaryota where they are encoded by large families of paralogous genes. The majority of ABC domains energize the transport of compounds across membranes. In bacteria, ABC transporters are involved in the uptake of a wide variety of molecules, as well as in mechanisms of virulence and antibiotic resistance. In eukaryotes, most of them are involved in drug resistance and in human cell, many are associated with diseases. Sequence analysis reveals that members of the ABC superfamily can be organized into sub-families, and suggests that they have diverged from common ancestral forms. A typical ABC transporter system is composed of an assembly of protein domains that serve different functions: i) two Nucleotide Binding Domains (NBD) that energize transport via ATP hydrolysis, ii) two Membrane Spanning Domains (MSD) that act as a membrane channel for the substrate, and iii) for the importer, a Solute Binding Protein (SBP) that confers substrates specificity on the transporter. The different partners of an ABC system are generally encoded by neighboring genes. The database includes information on: * ABC transporters * Protein partners * Protein domains (NBD, MSD and SBP) * Classification of ABC transporters and their protein partners * Taxonomy of the species Each model Protein includes a link to the Peptide sequence, general information extracted from EMBL files, and specific tags to store results of predictions. The results of the annotation procedure are reachable through the class Prediction. The origin of the proteins is modeled as a path through the classes Chromosome, Strain, Species, and Taxon. Assembly and protein compilation tables are also provided for each of the chromosomes ( Assembly and Protein ).

Proper citation: Archaeal and Bacterial ABC Transporter Database (RRID:SCR_001692) Copy   

•   Source: SciCrunch Registry