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https://github.com/BDI-pathogens/phyloscanner

Software tool for analysing pathogen genetic diversity and relationships between and within hosts at once, in windows along genome. Inferring transmission from within and between host pathogen genetic diversity.

Proper citation: phyloscanner (RRID:SCR_017400) Copy   

•   Source: SciCrunch Registry

https://www.drive5.com/piler/

Software tool for analyzing repetitive DNA found in genome sequences. Software package for identification and classification of genomic repeats. Used for identifying patterns of local alignments induced by certain classes of repeats.

Proper citation: PILER (RRID:SCR_017333) Copy   

•   Source: SciCrunch Registry

https://github.com/tanghaibao/mcscan

Software package to simultaneously scan multiple genomes to identify homologous chromosomal regions and subsequently align these regions using genes as anchors.Used to identify conserved gene arrays both within same genome and across different genomes. Command line program to wrap dagchainer and combine pairwise results into multi alignments in column format.

Proper citation: MCScan (RRID:SCR_017650) Copy   

•   Source: SciCrunch Registry

https://blobtools.readme.io/docs

Software tool as modular command line solution for visualisation, quality control and taxonomic partitioning of genome datasets. Used for interrogation of genome assemblies. Assists in primary partitioning of data, leading to improved assemblies, and screening of final assemblies for potential contaminants.

Proper citation: Blobtools (RRID:SCR_017618) Copy   

•   Source: SciCrunch Registry

https://github.com/philres/ngmlr

Software tool as long read mapper designed to align PacBio or Oxford Nanopore reads to reference genome and optimized for structural variation detection.

Proper citation: Ngmlr (RRID:SCR_017620) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/TCGAbiolinks/

Software R Bioconductor package for integrative analysis with TCGA data.TCGAbiolinks is able to access National Cancer Institute Genomic Data Commons thorough its GDC Application Programming Interface to search, download and prepare relevant data for analysis in R.

Proper citation: TCGAbiolinks (RRID:SCR_017683) Copy   

•   Source: SciCrunch Registry

http://refgenie.databio.org

Software tool to organize, retrieve, and share genome analysis resources. Reference genome assembly asset manager. In addition to genome indexes, can manage any files related to reference genomes, including sequences and annotation files. Includes command line interface and server application that provides RESTful API, so it is useful for both tool development and analysis.

Proper citation: refgenie (RRID:SCR_017574) Copy   

•   Source: SciCrunch Registry

https://crispy.secondarymetabolites.org

Web tool to design sgRNAs for CRISPR applications. Web tool based on CRISPy to design sgRNAs for any user-provided microbial genome. Implemented as standalone web application for Cas9 target prediction.

Proper citation: CRISPy-web (RRID:SCR_017970) Copy   

•   Source: SciCrunch Registry

https://github.com/slimsuite/pafscaff

Software as Pairwise mApping Format reference based Scaffold anchoring and super scaffolding tool. Dsigned for mapping genome assembly scaffolds to closely related chromosome level reference genome assembly.

Proper citation: PAFScaff (RRID:SCR_017976) Copy   

•   Source: SciCrunch Registry

https://github.com/santeripuranen/SpydrPick

Software command line tool for performing direct coupling analysis of aligned categorical datasets. Used for analysis at scale of pan genomes of many bacteria. Incorporates correction for population structure, which adjusts for phylogenetic signal in data without requiring explicit phylogenetic tree.

Proper citation: SpydrPick (RRID:SCR_018176) Copy   

•   Source: SciCrunch Registry

https://github.com/santeripuranen/SuperDCA

Software tool for global direct coupling analysis of input genome alignments. Implements variant of pseudolikelihood maximization direct coupling analysis, with emphasis on optimizations that enable its use on genome scale. May be used to discover co evolving pairs of loci.Used for genome wide epistasis analysis.

Proper citation: SuperDCA (RRID:SCR_018175) Copy   

•   Source: SciCrunch Registry

https://metacpan.org/pod/Bio::CUA

Software tool as flexible and comprehensive codon usage analyzer. Used to analyze codon usage bias (CUB) and relevant problems.

Proper citation: Codon Usage Analyzer (RRID:SCR_018500) Copy   

•   Source: SciCrunch Registry

http://gmdd.shgmo.org/Computational-Biology/GRS/

A compression tool for efficient storage of Genome Re-Sequencing data. GRS processes genome sequence data without use of reference SNPs and other variants. It can also automatically rebuild the individual genome sequence data using the reference genome sequence.

Proper citation: GRS (RRID:SCR_001008) Copy   

•   Source: SciCrunch Registry

http://ntap.cbi.pku.edu.cn/usage.php

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Software for tiling array data analysis to survey the genome-wide binding sites of transcription factor HY5 in Arabidopsis and the genome-wide histone modifications/DNA methylation level in rice. It was developed in the process of generating NimbleGen analysis. Written in R and Perl.

Proper citation: NTAP (RRID:SCR_001488) Copy   

•   Source: SciCrunch Registry

http://www.patricbrc.org/portal/portal/patric/Home

A Bioinformatics Resource Center bacterial bioinformatics database and analysis resource that provides researchers with an online resource that stores and integrates a variety of data types (e.g. genomics, transcriptomics, protein-protein interactions (PPIs), three-dimensional protein structures and sequence typing data) and associated metadata. Datatypes are summarized for individual genomes and across taxonomic levels. All genomes, currently more than 10 000, are consistently annotated using RAST, the Rapid Annotations using Subsystems Technology. Summaries of different data types are also provided for individual genes, where comparisons of different annotations are available, and also include available transcriptomic data. PATRIC provides a variety of ways for researchers to find data of interest and a private workspace where they can store both genomic and gene associations, and their own private data. Both private and public data can be analyzed together using a suite of tools to perform comparative genomic or transcriptomic analysis. PATRIC also includes integrated information related to disease and PPIs. The PATRIC project includes three primary collaborators: the University of Chicago, the University of Manchester, and New City Media. The University of Chicago is providing genome annotations and a PATRIC end-user genome annotation service using their Rapid Annotation using Subsystem Technology (RAST) system. The National Centre for Text Mining (NaCTeM) at the University of Manchester is providing literature-based text mining capability and service. New City Media is providing assistance in website interface development. An FTP server and download tool are available.

Proper citation: Pathosystems Resource Integration Center (RRID:SCR_004154) Copy   

•   Source: SciCrunch Registry

http://www.GeneWeaver.org

Freely accessible phenotype-centered database with integrated analysis and visualization tools. It combines diverse data sets from multiple species and experiment types, and allows data sharing across collaborative groups or to public users. It was conceived of as a tool for the integration of biological functions based on the molecular processes that subserved them. From these data, an empirically derived ontology may one day be inferred. Users have found the system valuable for a wide range of applications in the arena of functional genomic data integration.

Proper citation: Gene Weaver (RRID:SCR_003009) Copy   

•   Source: SciCrunch Registry

http://ww2.sanbi.ac.za/Dbases.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The STACKdb is knowledgebase generated by processing EST and mRNA sequences obtained from GenBank through a pipeline consisting of masking, clustering, alignment and variation analysis steps. The STACK project aims to generate a comprehensive representation of the sequence of each of the expressed genes in the human genome by extensive processing of gene fragments to make accurate alignments, highlight diversity and provide a carefully joined set of consensus sequences for each gene. The STACK project is comprised of the STACKdb human gene index, a database of virtual human transcripts, as well as stackPACK, the tools used to create the database. STACKdb is organized into 15 tissue-based categories and one disease category. STACK is a tool for detection and visualization of expressed transcript variation in the context of developmental and pathological states. The data system organizes and reconstructs human transcripts from available public data in the context of expression state. The expression state of a transcript can include developmental state, pathological association, site of expression and isoform of expressed transcript. STACK consensus transcripts are reconstructed from clusters that capture and reflect the growing evidence of transcript diversity. The comprehensive capture of transcript variants is achieved by the use of a novel clustering approach that is tolerant of sub-sequence diversity and does not rely on pairwise alignment. This is in contrast with other gene indexing projects. STACK is generated at least four times a year and represents the exhaustive processing of all publicly available human EST data extracted from GenBank. This processed information can be explored through 15 tissue-specific categories, a disease-related category and a whole-body index

Proper citation: Sequence Tag Alignment and Consensus Knowledgebase Database (RRID:SCR_002156) Copy   

•   Source: SciCrunch Registry

http://www.projects.roslin.ac.uk/sheepmap/front.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The project aims to apply genome mapping research to sheep, utilizing previous research in sheep (in other countries) and in other species (in the UK and abroad) to the benefit of the UK sheep industry. The project itself uses existing breeding structures, knowledge of the sheep genome and experimental resources. It has three main aims: i) To use the Suffolk, Texel and Charollais Sire Referencing Schemes to detect and verify quantitative trait loci (QTLs) for growth and carcass composition traits ii) To investigate candidate genes and/or chromosomal regions for associations with production traits. iii) To investigate approaches for optimizing future genotyping strategies within the sire referencing schemes for practical and cost effective application of marker-assisted selection By using commercial breeding populations for the research, immediate application of beneficial results is possible. Potential benefits include increased genetic progress through marker assisted selection which utilizes the genotype information, correction of possible parentage errors (ultimately leading to additional genetic progress) and opportunities for using marker information for product certification. The project will benefit the UK sheep industry by the use of Marker Assisted Selection (MAS) utilizing QTL or gene variants identified in the project. Additional benefits may arise from parentage verification and correction of errors e.g. misallocation of lamb to ewe. In the longer term, opportunities may exist to use markers for quality control, tracing products to their source. The major advantage of the design of this project is that the results are immediately applicable to the breeding schemes within which the QTLs and/or genes are detected. The time lag in the application of the results that is often seen with experimental populations is minimized. The project requires close involvement with the Sire Reference Schemes, in return for their assistance the results have immediate benefit to animals within these groups.

Proper citation: UK Sheep Genome Mapping Project (RRID:SCR_002272) Copy   

•   Source: SciCrunch Registry

http://vmd.vbi.vt.edu/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. Database covering a range of plant pathogenic oomycetes, fungi and bacteria primarily those under study at Virginia Bioinformatics Institute. The data comes from different sources and has genomes of 3 oomycetes pathogens: Phytophthora sojae, Phytophthora ramorum and Hyaloperonospora arabidopsidis. The genome sequences (95 MB for P.sojae and 65 MB for P.ramorum) were annotated with approximately 19,000 and approximately 16,000 gene models, respectively. Two different statistical methods were used to validate these gene models, Fickett''''s and a log-likelihood method. Functional annotation of the gene models is based on results from BlastX and InterProScan screens. From the InterProScan results, putative functions to 17,694 genes in P.sojae and 14,700 genes in P.ramorum could be assigned. An easy-to-use genome browser was created to view the genome sequence data, which opens to detailed annotation pages for each gene model. A community annotation interface is available for registered community members to add or edit annotations. There are approximately 1600 gene models for P.sojae and approximately 700 models for P.ramorum that have already been manually curated. A toolkit is provided as an additional resource for users to perform a variety of sequence analysis jobs.

Proper citation: VMD (RRID:SCR_004905) Copy   

•   Source: SciCrunch Registry

http://soybase.org

Professionally curated repository for genetics, genomics and related data resources for soybean that contains the most current genetic, physical and genomic sequence maps integrated with qualitative and quantitative traits. SoyBase includes annotated Williams 82 genomic sequence and associated data mining tools. The genetic and sequence views of the soybean chromosomes and the extensive data on traits and phenotypes are extensively interlinked. This allows entry to the database using almost any kind of available information, such as genetic map symbols, soybean gene names or phenotypic traits. The repository maintains controlled vocabularies for soybean growth, development, and traits that are linked to more general plant ontologies. Contributions to SoyBase or the Breeder''s Toolbox are welcome.

Proper citation: SoyBase (RRID:SCR_005096) Copy   

•   Source: SciCrunch Registry