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Related Concept Videos

Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Updated: Mar 2, 2026

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
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shinyheatmap: Ultra fast low memory heatmap web interface for big data genomics.

Bohdan B Khomtchouk1,2, James R Hennessy3, Claes Wahlestedt1,2

  • 1Center for Therapeutic Innovation, University of Miami Miller School of Medicine, 1501 NW 10th Ave., Miami, FL, 33136, United States of America.

Plos One
|May 12, 2017
PubMed
Summary
This summary is machine-generated.

shinyheatmap is a new, user-friendly software suite for creating customizable biological heatmaps. It efficiently visualizes large datasets in a web browser, overcoming previous computational limitations for omics data analysis.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Visualization

Background:

  • Next-generation sequencing technologies generate massive datasets, posing visualization challenges in biology.
  • Large-scale omics data, particularly from single-cell studies, strains computational resources and requires programming expertise.
  • Traditional heatmap generation struggles with large datasets (100k+ rows) due to memory and processing limitations.

Purpose of the Study:

  • To develop an advanced, user-friendly software suite for creating biological heatmaps.
  • To enable efficient visualization of large, high-dimensional omics datasets.
  • To overcome computational and accessibility barriers in biological data visualization.

Main Methods:

  • Development of shinyheatmap, a web-based software suite.
  • Integration of a high-performance web plug-in, fastheatmap, for rapid plotting.
  • Implementation in R, ensuring accessibility and customizability.

Main Results:

  • shinyheatmap efficiently generates highly customizable static and interactive heatmaps.
  • The software features a low memory footprint, suitable for extremely large datasets.
  • fastheatmap plots interactive heatmaps of up to 10^7 rows in seconds, surpassing previous benchmarks.

Conclusions:

  • shinyheatmap is freely available as a web server with an intuitive graphical user interface.
  • The source code for shinyheatmap and fastheatmap is publicly accessible on GitHub.
  • This tool democratizes the visualization of large biological datasets, enhancing omics data analysis.