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A tool to visualize read-depth and pair-end information as it originally comes from NGS machines at various resolutions. Its main purpose is to provide intuitive visualizations for an easier exploration or detections of structural variations. With Meander, users can explore variations at different levels of resolution and simultaneously compare up to four different individuals against a common reference.

This tool allows to display very long data vectors in a space-efficient manner, allowing the user to visually judge the large scale structure and distribution of features simultaneously with the rough shape and intensity of individual features.

Hilbert curves enable high-resolution visualization of genomic data on a chromosome- or genome-wide scale. The HilbertCurve package provides an easy-to-use interface for mapping genomic data to Hilbert curves. The package transforms the curve as a virtual axis, thereby hiding the details of the curve construction from the user. HilbertCurve supports multiple-layer overlay that makes it a powerful tool to correlate the spatial distribution of multiple feature types. HilbertCurve greatly facilitates the visualization and interpretation of the ever increasing number of genome-wide datasets generated by next-generation sequencing and other omics techniques.

Visualizes long vectors of integer data by means of Hilbert curves. HilbertVisGUI allows to display very long data vectors in a space-efficient manner, allowing the user to visually judge the large-scale structure and distribution of features simultaneously with the rough shape and intensity of individual features. A typical use case is ChIP-Chip and ChIP-Seq, or basically all the kinds of genomic data, that are conventionally displayed as quantitative track ("wiggle data") in genome browsers such as those provided by Ensemble or UCSC.

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Big data scientist National institute of Plant Gneome Reseaech (NIPGR)

Research Interests Gene fusion detection in Plants Fusion transcripts (i.e., chimeric RNAs) resulting from gene fusions are well known in case of human. But, in plants, this phenomenon is not yet explored. We are planning to discover the fusion transcripts/gene fusions in different type of plants by using RNA-Seq datasets. Further, we are planning to understand the mechanism of gene fusion formation and significance of fusions in plants. Whole genome and transcriptome sequencing data analysis of Plants In this era of Next Generation Sequencing (NGS), there is huge amount of sequencing data available in the public domain. Any novel finding from these available datasets is major challenge for a computational biologist. We are interested in the analysis of whole genome and transcriptome sequencing data of different plants to fetch out the useful information from those datasets, with the help of bioinformatics tools. Currently, we are planning to study the gene clusters of secondary metabolite pathways in different plants. Development of webservers, databases and computational pipelines for plant research Development of database is necessary to compile and share the information with scientific community. We are dedicated to develop useful databases and webserver for plant research. Another area of interest is to develop automated pipelines and tools for the analysis of high throughput genomics data, generated by NGS technologies. Professional & Academic Background Staff Scientist II (May 2017- present): National Institute of Plant Genome Research (NIPGR), New Delhi, India Postdoctoral Research Associate (2015-2017): University Of Virginia, Charlottesville, VA, USA Research Scientist (2014-2015): Sir Ganga Ram Hospital, New Delhi, India PhD Bioinformatics (2009-2014): Bioinformatics Centre, Institute of Microbial Technology (IMTECH), Chandigarh under Jawaharlal Nehru University (JNU), New Delhi, India M.Sc. Life Sciences (2007-2009): Jawaharlal Nehru University (JNU), New Delhi, India B.Sc. Biotechnology (2004-2007): Jamia Millia Islamia (JMI), New Delhi, India Awards and Fellowships Junior and Senior Research Fellowship (2009-2014): Council of Scientific and Industrial Research (CSIR), New Delhi, India GATE (Graduate Aptitude Test in Engineering): Qualified in years 2008 and 2009 Scientific Contributions/ Recognitions Associate editor: Journal of Translational Medicine. Editorial Board Member of Journal: Theoretical Biology and Medical Modelling. Reviewer: PloS One, BMC Genomics, BMC Bioinformatics, BMC Biology, BMC Biotechnology, Frontiers in Physiology and several other journals. Web Resources/ Databases (Developed/ Contributed) A Platform for Designing Genome-Based Personalized Immunotherapy or Vaccine against Cancer (http://www.imtech.res.in/raghava/cancertope/) GenomeABC: A webserver for benchmarking of genome assemblers. (http://crdd.osdd.net/raghava/genomeabc/). Genomics web portal page. (http://crdd.osdd.net/raghava/genomesrs/). Map/Alignment module of CancerDr: Cancer Drug Resistance Database. (http://crdd.osdd.net/raghava/cancerdr/). Short reads and contigs alignment module of PCMDB: Pancreatic cancer methylation database. (http://crdd.osdd.net/raghava/pcmdb/). Burkholderia sp. SJ98 database. (http://crdd.osdd.net/raghava/genomesrs/burkholderia/). Rhodococcus imtechensis RKJ300 database. (http://crdd.osdd.net/raghava/genomesrs/rkj300/). Genotrick: A pipeline for whole genome assembly and annotation of Genomes (http://crdd.osdd.net/raghava/genomesrs/genotrick/) Development of Debian packages in OSDDlinux: A Customized Operating System for Drug Discovery. (http://osddlinux.osdd.net/). A Web-Based Platform for Designing Vaccines against Existing and Emerging Strains of Mycobacterium tuberculosis. (http://crdd.osdd.net/raghava/mtbveb/).

FusionFinder comrad

Mahantesh Biradar

‎Graduate PhD researcher focusing on Cardiovascular Epidemiology & Social Media Enthusiast Research Stash

I'm a Graduate PhD researcher in the area of Biomedical Science and focussing on the Pharmacogenomics/Pharmacogenomics of Diabetes and related complications

Krithika Bhuvaneshwar

Bioinformatics Georgetown University

G-DOC

Hilbert curve generation software tools | Genome annotation

Hilbert curves can be used to effectively visualize genomic data, because they can provide a global overview of genome-scale datasets while still revealing the spatial distribution of features at high resolution (Anders, 2009). Hilbert curve visualization has been utilized to compare the sequence difference between human and other primates (Wong, 2014), to show the spatial organization of different chromatin states (Kharchenko et al., 2011), and to investigate the genome-wide distribution of histone modifications (Anders 2009, Henry et al., 2012).

References:
(Anders, 2009) Visualization of genomic data with the Hilbert curve. Bioinformatics.
(Wong, 2014) Tiny genetic difference between humans and other primates pervade the genome. Sci. Am.
(Kharchenko et al., 2011) Comprehensive analysis of the chromatin landscape in Drosophila melanogaster. Nature.
(Henry et al., 2012) Cell type-specific genomics of Drosophila neurons. Nucleic Acids Res.

Source text:
(Gu et al., 2016) HilbertCurve: an R/Bioconductor package for high-resolution visualization of genomic data. Bioinformatics.

Parent category

Genome annotation

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