Next-generation Sequencing Data Visualization Research Articles

Bigtools: a high-performance BigWig and BigBed library in Rust.

The BigWig and BigBed file formats were originally designed for the visualization of next-generation sequencing data through a genome browser. Due to their versatility, these formats have long since become ubiquitous for the storage of processed sequencing data and regularly serve as the basis for downstream data analysis. As the number and size of sequencing experiments continues to accelerate, there is an increasing demand to efficiently generate and query BigWig and BigBed files in a scalable and robust manner, and to efficiently integrate these functionalities into data analysis environments and third-party applications. Here, we present Bigtools, a feature-complete, high-performance, and integrable software library for generating and querying both BigWig and BigBed files. Bigtools is written in the Rust programming language and includes a flexible suite of command line tools as well as bindings to Python. Bigtools is cross-platform and released under the MIT license. It is distributed on Crates.io, Bioconda, and the Python Package Index, and the source code is available at https://github.com/jackh726/bigtools.

SNPitty: An Intuitive Web Application for Interactive B-Allele Frequency and Copy Number Visualization of Next-Generation Sequencing Data

Exploration and visualization of next-generation sequencing data are crucial for clinical diagnostics. Software allowing simultaneous visualization of multiple regions of interest coupled with dynamic heuristic filtering of genetic aberrations is, however, lacking. Therefore, the authors developed the web application SNPitty that allows interactive visualization and interrogation of variant call format files by using B-allele frequencies of single-nucleotide polymorphisms and single-nucleotide variants, coverage metrics, and copy numbers analysis results. SNPitty displays variant alleles and allelic imbalances with a focus on loss of heterozygosity and copy number variation using genome-wide heterozygous markers and somatic mutations. In addition, SNPitty is capable of generating predefined reports that summarize and highlight disease-specific targets of interest. SNPitty was validated for diagnostic interpretation of somatic events by showcasing a serial dilution series of glioma tissue. Additionally, SNPitty is demonstrated in four cancer-related scenarios encountered in daily clinical practice and on whole-exome sequencing data of peripheral blood from a Down syndrome patient. SNPitty allows detection of loss of heterozygosity, chromosomal and gene amplifications, homozygous or heterozygous deletions, somatic mutations, or any combination thereof in regions or genes of interest. Furthermore, SNPitty can be used to distinguish molecular relationships between multiple tumors from a single patient. On the basis of these data, the authors demonstrate that SNPitty is robust and user friendly in a wide range of diagnostic scenarios.

Ngs.plot: Quick mining and visualization of next-generation sequencing data by integrating genomic databases.

BackgroundUnderstanding the relationship between the millions of functional DNA elements and their protein regulators, and how they work in conjunction to manifest diverse phenotypes, is key to advancing our understanding of the mammalian genome. Next-generation sequencing technology is now used widely to probe these protein-DNA interactions and to profile gene expression at a genome-wide scale. As the cost of DNA sequencing continues to fall, the interpretation of the ever increasing amount of data generated represents a considerable challenge.ResultsWe have developed ngs.plot – a standalone program to visualize enrichment patterns of DNA-interacting proteins at functionally important regions based on next-generation sequencing data. We demonstrate that ngs.plot is not only efficient but also scalable. We use a few examples to demonstrate that ngs.plot is easy to use and yet very powerful to generate figures that are publication ready.ConclusionsWe conclude that ngs.plot is a useful tool to help fill the gap between massive datasets and genomic information in this era of big sequencing data.