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Genome Size and the Evolution of New Genes

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.

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Related Experiment Video

Updated: Feb 3, 2026

Novel Sequence Discovery by Subtractive Genomics

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Discovery of potential causative mutations in human coding and noncoding genome with the interactive software

Riku Katainen^1,2, Iikki Donner^3,4, Tatiana Cajuso^3,4

¹Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland. riku.katainen@helsinki.fi.

Nature Protocols

|October 17, 2018

Summary

This summary is machine-generated.

Next-generation sequencing (NGS) generates vast genomic data. BasePlayer software simplifies variant analysis for disease genetics, enabling rapid mutation discovery without extensive bioinformatics expertise.

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Published on: June 19, 2014

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Next-generation sequencing (NGS) generates large genomic datasets, posing interpretation challenges.
Identifying causative DNA alterations is crucial for understanding genetic disorders like cancer and rare diseases.
Current workflows for variant analysis involve multiple complex steps and tools.

Purpose of the Study:

To introduce BasePlayer, a user-friendly software for interactive variant analysis.
To enable scientists, irrespective of bioinformatics background, to perform disease genetics variant analysis.
To facilitate rapid genome-wide mutation cluster identification in regulatory regions.

Main Methods:

Development of BasePlayer, a multi-platform-compatible software.
Implementation of an interactive interface for variant analysis.
Utilizing BasePlayer for genome-wide scans of mutation clusters.

Main Results:

BasePlayer allows scientists to conduct variant analysis in disease genetics settings.
A genome-wide scan of regulatory regions for mutation clusters can be performed efficiently.
Analysis of 3 million somatic variants in 200 whole-genome-sequenced cancers takes approximately 10 minutes on a desktop computer.

Conclusions:

BasePlayer significantly simplifies the interpretation of massive genomic data from NGS.
The software empowers researchers to accelerate variant analysis in disease genetics.
BasePlayer enhances the accessibility of genomic data analysis for a broader scientific audience.