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Related Concept Videos

DNA Packaging

DNA Packaging

DNA Packaging

DNA Packaging

The Nucleosome

The Nucleosome

Human DNA is almost two meters long. However, it is compressed inside a tiny nucleus measuring only a few microns in diameter. To make this degree of compaction possible, DNA is organized into several sequential levels so that it can fit into such a tiny space. The most compact form of DNA is a chromosome that can be seen under a microscope in a dividing cell.
In a chromosome, DNA is wound twice around a protein complex called a histone octamer core, which consists of 8 histone proteins. This...

The Nucleosome

The Nucleosome

DNA in a human cell is almost 2m long and it is packed inside a tiny nucleus that is only a few microns in diameter. The level of compaction of DNA inside the nucleus is astonishing. It is organized into several sequentially higher levels of compaction to fit into such a tiny space. The most compact form of DNA is a chromosome that can be seen under a microscope in a dividing cell.
DNA is wound twice around a protein complex called histone core, that consist of 8 histone proteins. This complex...

Genomic DNA in Eukaryotes

Genomic DNA in Eukaryotes

Eukaryotes have large genomes compared to prokaryotes. To fit their genomes into a cell, eukaryotic DNA is packaged extraordinarily tightly inside the nucleus. To achieve this, DNA is tightly wound around proteins called histones, which are packaged into nucleosomes that are joined by linker DNA and coil into chromatin fibers. Additional fibrous proteins further compact the chromatin, which is recognizable as chromosomes during certain phases of cell division.

Chromatin Packaging

Chromatin Packaging

Each human somatic cell contains 6 billion base pairs of DNA. Each base pair is 0.34 nm long, meaning each diploid cell contains a staggering 2 meters of DNA. This long DNA strand is packed inside a nucleus measuring only 10-20 microns in diameter with the help of specialized DNA-binding proteins called histones. Together they form a compact DNA-protein complex called chromatin. The chromatin is further compacted into higher-order structures. The highest level of compaction is achieved during...

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Related Experiment Video

Updated: May 5, 2026

DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation

DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation

Published on: December 29, 2021

DNA-COMPACT: DNA COMpression based on a pattern-aware contextual modeling technique.

Pinghao Li¹, Shuang Wang, Jihoon Kim

¹Division of Biomedical Informatics, University of California San Diego, La Jolla, California, United States of America ; Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.

|November 28, 2013

Summary

This summary is machine-generated.

Storing large genome data is a challenge. A new lossless genome compression algorithm offers improved performance for both reference-free and reference-based compression, significantly reducing file sizes for biomedical researchers.

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Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

The rapid advancement of DNA sequencing technologies generates massive amounts of genome data.
Efficient storage and transfer of large-scale genomic datasets pose significant challenges for biomedical research.
Existing genome compression algorithms face limitations in handling the ever-increasing volume of sequence data.

Purpose of the Study:

To develop an advanced lossless genome compression algorithm that improves compression efficiency.
To address the critical need for effective storage and data transfer solutions for large genome datasets.
To provide a method that performs well for both reference-free and reference-based genome compression.

Main Methods:

A novel two-pass lossless genome compression algorithm was designed.
The algorithm utilizes the synthesis of complementary contextual models to enhance compression performance.
The framework was evaluated for both reference-free and reference-based compression scenarios.

Main Results:

Reference-free compression achieved bit rates of 1.720 bits/base (bacteria) and 1.838 bits/base (yeast), outperforming state-of-the-art methods by 3.7% and 2.6%, respectively.
Reference-based compression on a personal genome dataset yielded a 189-fold compression rate, reducing file size from 2986.8 MB to 15.8 MB.
The proposed method demonstrated comparable decompression costs to existing algorithms.

Conclusions:

The developed two-pass lossless genome compression algorithm offers significant advantages over current methods.
The algorithm effectively reduces the storage and data transfer burden for large genome datasets.
This advancement supports the growing demands of genomic data in modern medicine and biomedical research.