GeMo: a web-based platform for the visualization and curation of genome ancestry mosaics.

In silico chromosome painting is a technique by which contributions of distinct genetic groups are represented along chromosomes of hybrid individuals. This type of analysis is used to study the mechanisms by which these individuals were formed. Such techniques are well adapted to identify genetic groups contributing to these individuals as well as hybridization events. It can also be used to follow chromosomal recombinations that occurred naturally or were generated by selective breeding. Here, we present GeMo, a novel interactive web-based and user-oriented interface to visualize in a linear-based fashion results of in silico chromosome painting. To facilitate data input generation, a script to execute analytical commands is provided and an interactive data curation mode is supported to ensure consistency of the automated procedure. GeMo contains preloaded datasets from published studies on crop domestication but can be applied to other purposes, such as breeding programs Although only applied so far on plants, GeMo can handle data from animals as well. Database URL: https://gemo.southgreen.fr/.

PLAZA 5.0: extending the scope and power of comparative and functional genomics in plants.

PLAZA is a platform for comparative, evolutionary, and functional plant genomics. It makes a broad set of genomes, data types and analysis tools available to researchers through a user-friendly website, an API, and bulk downloads. In this latest release of the PLAZA platform, we are integrating a record number of 134 high-quality plant genomes, split up over two instances: PLAZA Dicots 5.0 and PLAZA Monocots 5.0. This number of genomes corresponds with a massive expansion in the number of available species when compared to PLAZA 4.0, which offered access to 71 species, a 89% overall increase. The PLAZA 5.0 release contains information for 5 882 730 genes, and offers pre-computed gene families and phylogenetic trees for 5 274 684 protein-coding genes. This latest release also comes with a set of new and updated features: a new BED import functionality for the workbench, improved interactive visualizations for functional enrichments and genome-wide mapping of gene sets, and a fully redesigned and extended API. Taken together, this new version offers extended support for plant biologists working on different families within the green plant lineage and provides an efficient and versatile toolbox for plant genomics. All PLAZA releases are accessible from the portal website: https://bioinformatics.psb.ugent.be/plaza/.

Accessing NCBI data using the NCBI Sequence Viewer and Genome Data Viewer (GDV).

The National Center for Biotechnology Information (NCBI) is an archive providing free access to a wide range and large volume of biological sequence data and literature. Staff scientists at NCBI analyze user-submitted data in the archive, producing gene and SNP annotation and generating sequence alignment tools. NCBI's flagship genome browser, Genome Data Viewer (GDV), displays our in-house RefSeq annotation; is integrated with other NCBI resources such as Gene, dbGaP, and BLAST; and provides a platform for customized analysis and visualization. Here, we describe how members of the biomedical research community can use GDV and the related NCBI Sequence Viewer (SV) to access, analyze, and disseminate NCBI and custom biomedical sequence data. In addition, we report how users can add SV to their own web pages to create a custom graphical sequence display without the need for infrastructure investments or back-end deployments.

Viewing RNA-seq data on the entire human genome.

RNA-Seq Viewer is a web application that enables users to visualize genome-wide expression data from NCBI's Sequence Read Archive (SRA) and Gene Expression Omnibus (GEO) databases. The application prototype was created by a small team during a three-day hackathon facilitated by NCBI at Brandeis University. The backend data pipeline was developed and deployed on a shared AWS EC2 instance. Source code is available at https://github.com/NCBI-Hackathons/rnaseqview.