Biology subjects

Diesh, C.

Publications and source records attributed to Diesh, C..

4 recordsLinked to original sources

bioRxiv2022

JBrowse 2: A modular genome browser with views of synteny and structural variation

We present JBrowse 2, a general-purpose genome annotation browser offering enhanced visualization of complex structural variation and evolutionary relationships. JBrowse 2 retains the core features of the open-source JavaScript genome browser JBrowse while adding new views for synteny, dotplots, breakpoints, gene fusions, and whole-genome overviews. The software readily allows users to share sessions, open multiple genomes or views, and navigate quickly between these views. It can be embedded in a web page, used as a standalone desktop application, or run from Jupyter notebooks or R sessions. Using a plugin framework, developers can create new data adapters, track types, and visualizations. These improvements are enabled by a ground-up redesign of the JBrowse architecture using modern web technology. We describe application functionality, use cases, performance benchmarks, and implementation notes for web administrators and developers.

bioinformatics↗

bioRxiv2022

JBrowse Jupyter: A Python interface to JBrowse 2

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSMotivationC_ST_ABSJBrowse Jupyter is a package that aims to close the gap between Python programming and genomic visualization. Web-based genome browsers are routinely used for publishing and inspecting genome annotations. Historically they have been deployed at the end of bioinformatics pipelines, typically decoupled from the analysis itself. However, emerging technologies such as Jupyter notebooks enable a more rapid iterative cycle of development, analysis and visualization. ResultsWe have developed a package that provides a python interface to JBrowse 2s suite of embeddable components, including the primary Linear Genome View. The package enables users to quickly set up, launch and customize JBrowse views from Jupyter notebooks. In addition, users can share their data via Googles Colab notebooks, providing reproducible interactive views. AvailabilityJBrowse Jupyter is released under the Apache License and is available for download on PyPI. Source code and demos are available on GitHub at https://github.com/GMOD/jbrowse-jupyter. Contactihh@berkeley.edu

bioinformatics↗

bioRxiv2021

CNVpytor: a tool for CNV/CNA detection and analysis from read depth and allele imbalance in whole genome sequencing

Detecting copy number variations (CNVs) and copy number alterations (CNAs) based on whole genome sequencing data is important for personalized genomics and treatment. CNVnator is one of the most popular tools for CNV/CNA discovery and analysis based on read depth (RD). Herein, we present an extension of CNVnator developed in Python -- CNVpytor. CNVpytor inherits the reimplemented core engine of its predecessor and extends visualization, modularization, performance, and functionality. Additionally, CNVpytor uses B-allele frequency (BAF) likelihood information from single nucleotide polymorphism and small indels data as additional evidence for CNVs/CNAs and as primary information for copy number neutral losses of heterozygosity. CNVpytor is significantly faster than CNVnator--particularly for parsing alignment files (2 to 20 times faster)--and has (20-50 times) smaller intermediate files. CNV calls can be filtered using several criteria and annotated. Modular architecture allows it to be used in shared and cloud environments such as Google Colab and Jupyter notebook. Data can be exported into JBrowse, while a lightweight plugin version of CNVpytor for JBrowse enables nearly instant and GUI-assisted analysis of CNVs by any user. CNVpytor release and the source code are available on GitHub at https://github.com/abyzovlab/CNVpytor under the MIT license.

bioinformatics↗

bioRxiv2017

WikiGenomes: an open Web application for community consumption and curation of gene annotation data in Wikidata.

With the advancement of genome sequencing technologies, new genomes are being sequenced daily. While these sequences are deposited in publicly available data warehouses, their functional and genomic annotations (beyond genes which are predicted automatically) mostly reside in the text of primary publications. Professional curators are hard at work extracting those annotations from the literature for the most studied organisms and depositing them in structured databases. However, the resources dont exist to fund the comprehensive curation of the thousands of newly sequenced organisms in this manner. Here, we describe WikiGenomes (wikigenomes.org), a web application that facilitates the consumption and curation of genomic data by the entire scientific community. WikiGenomes is based on Wikidata, an openly editable knowledge graph with the goal of aggregating published knowledge into a free and open database. WikiGenomes empowers the individual genomic researcher to contribute their expertise to the curation effort and integrates the knowledge into Wikidata, enabling it to be accessed by anyone without restriction.

genomics↗