Genome

panX is a powerful interactive platform for pan-genome exploration. It provides multiple different views on annotated genomes and allows rapid search by gene name, diversity, duplications, etc. Strain-specific meta data is integrated into the phylogenetic tree viewer such that associations between gene presence and phenotypes can be spotted.

iobio uses immediate visual feedback to make understanding complex genomic datasets more intuitive, and analysis more interactive.  Applications include:

• gene.iobio.io: a web app for investigating potential disease-causing variants
• taxonomer.iobio.io: an ultra fast metagenomics classification and analysis app
• bam.iobio.io: an alignment data inspector tool that quickly samples bam files and visualizes a series of metrics
• vcf.iobio.io: a variant data inspector tool that quickly samples vcf files and visualizes a series of metrics

The inherent conformation of polysaccharides possess major challenge to determine/predict their 3D structure; despite their importance in various biological processes in all the organisms. Capsular polysaccharides (K antigen) are long-chain polysaccharides that make up the capsules of encapsulated Gram-negative bacteria. They help bacteria to escape from host defense mechanisms. We have developed a manually curated 3D structures’ database for 72 K antigens from various serotypes of Escherichia coli and developed an organized repository namely EK3D, which can be accessed by www.iith.ac.in/EK3D/. Using the Cartesian coordinate’s translation method, multimers of all 72 K antigens of E. coli can be generated of any desired length and torsion angles. Subsequently, generated modelscan be downloaded and used in docking studies or as starting model for NMR structure refinement.

Vertebrate genome annotation is a core part of the Sanger Institute's work and has a dedicated team of 20 plus annotators with many years of experience. As the volume of genomic data has increased so the sophistication of our software has had to improve with graphical signposts, dynamic filtering and other techniques showing what's important. Annotools comprises programs written in C++ to be fast, efficient and offer a more complex interface than is really possible in a browser. They make good use of screen space supporting multiple screens, multiple windows and both vertical (maximum tracks) and horizontal (maximum sequence) display modes. The amount of data displayed is controllable via dynamic loading to the whole window or parts of the window, zooming thresholds and column compression. Transcripts and alignments are displayed with indications of co-linearity, non-canonical splice sites and missing sequence. Built-in tools allow searching features and sequence, comparing feature splice sites and configurable linking out to other data sources. The software can be run standalone or integrated into other systems via a remote control peer-to-peer communication system.

Phandango is a fully interactive tool to allow visualization of population scale bacterial genomic data connected by a phylogeny. Population-scale genomic studies have been incredibly useful at understanding the evolution of bacteria and provided insights into clinically relevant information such as the spread of drug resistance and transmission patterns of infections diseases. The datasets produced by these projects often comprise hundreds of genomes and generate data such as predicted recombination regions, pan-genome contents, various metadata and, recently, genotype-phenotype association data. Currently these data are often explored using static visualizations generated by scripts. Phandango is a tool which allows real-time interactive visualization of these datasets simply by dragging and dropping files onto the browser. It has been found to be extremely useful to quickly interpret and compare the results generated from a number of published bioinformatics software such as GUBBINS, BRAT NextGen, ROARY, LS-BSR, TRADIS, PLINK and Seer, as well as user generated metadata.

NinjaViewer is an interactive web-based data analysis and visualisation platform designed to integrate multiple related biological datasets. The current prototype enables visual analysis and cross referencing of genomic, evolutionary and gene expression data from 21 species of mosquito and 7 species of the malaria parasite. The platform is being developed to be extensible and datatype agnostic. Several visualisation modes are already implemented including self-organising maps, hierarchical clustering and Voronoi maps, which enable analysis of tens of thousands of genes and orthologous gene groups at once. Several statistical analyses are also implemented including term enrichment and spatial correlation analyses in addition to violin and species matrix displays of cluster-specific properties. NinjaViewer is not just a viewer, it is a knowledge discovery tool that makes the most of the human-computer interaction: harnessing the user's intuitive visual processing power and the computer's ability to search data and perform statistical tests very rapidly.

Gene Expression Commons is an open platform for objective gene expression profiling of any cell type for any gene, rather than profiling of relative difference for limited number of genes between two samples. This was enabled by global-scale meta-analysis of thousands of microarray data in public repository to obtain dynamic-range and expression kinetics of every gene. Gene Expression Commons can tell what genes are actively expressed in what cell types. Now the platform has been expanded toward profiling of geneset activity. On Gene Expression Commons, users find interesting set of genes in many contexts by Gene Search or Pattern Search. By submitting such interesting set of genes as a Geneset, Gene Expression Commons automatically computes similarity to known genesets defined by Gene Ontology etc., and what cell types actively express the Geneset.

GenomeCRISPR is the world’s first database for high-throughput screens using the CRISPR/Cas-9 system. Searching for a gene of interest, users are able to find data about relevant screening experiments. Moreover, GenomeCRISPR provides information about phenotypic effects that were observed after perturbation of the gene. Comprehensive visualisations displaying applied sgRNAs and their effects in genomic context complement this information. This enables users to gain an unbiased understanding of sgRNA efficiency, facilitating the selection of sgRNAs for future experiments. As GenomeCRISPR holds data from high-throughput experiments, traditional ways of representing data using data tables and static figures quickly reach their limits. To guarantee the clear and intuitive representation of large amounts of data, we rely on modern JavaScript based frameworks such as BioJavaScript or D3. Exploiting the power of these cutting edge technologies data is visualised interactively, enabling users to explore and make sense of large amounts of information.

Sequencing techniques that probe the 3D organization of the genome generate large amounts of data whose processing, analysis and visualization is challenging. Hi-C Explorer is a set of tools for the analysis and visualization of chromosome conformation data. Hi-C explorer facilitates the creation of contact matrices, correction of contacts, TAD detection, merging, reordering or chromosomes, conversion from different formats and detection of long-range contacts. Moreover, it allows the visualization of multiple contact matrices along with other types of data like genes, compartments, ChIP-seq coverage tracks (and in general any type of genomic scores) and long range contacts.

Current genomics visualization tools are intended for a single node environment and lack computational resources to provide interactive speeds. Data from the 1000 Genomes Project provides 1.6 terabytes of variant data and over 14 terabytes of alignment data. However, typical genomic visualizations materialize less than 10 kbp, approximately 3.3e-7% of the genome. Mango is a visualization browser that selectively materializes and organizes genomic data to provide fast in memory queries. Mango materializes data from persistent storage as the user requests different regions of the genome. This data is efficiently partitioned and organized in memory using interval trees, which enables quick range queries over genomic data.