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Related Concept Videos

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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...
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
Genomics02:02

Genomics

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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Related Experiment Video

Updated: Jun 2, 2026

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies
12:08

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Published on: August 20, 2021

Enhancing genome assemblies by integrating non-sequence based data.

Thomas N Heider1, James Lindsay, Chenwei Wang

  • 1Department of Molecular and Cellular Biology, University of Connecticut, 06269, Storrs CT, USA. andrew.pask@uconn.edu.

BMC Proceedings
|May 11, 2011
PubMed
Summary
This summary is machine-generated.

Integrating comparative genome maps with sequencing data significantly improved the tammar wallaby genome assembly. Map data alone minimally scaffolded contigs but greatly increased scaffold size, enhancing genome structure and utility.

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Novel Sequence Discovery by Subtractive Genomics
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High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)
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High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

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Related Experiment Videos

Last Updated: Jun 2, 2026

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies
12:08

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High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)
09:06

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

Published on: October 5, 2018

Area of Science:

  • Comparative genomics
  • Bioinformatics
  • Genome assembly

Background:

  • Genome projects utilize pre-existing data like linkage and physical maps.
  • Conserved synteny maps from related species can enhance genome assemblies.
  • High-throughput sequencing complements traditional mapping data.

Purpose of the Study:

  • To assess the utility of comparative genome maps in enhancing a low-coverage de novo genome assembly.
  • To compare map-based scaffolding with sequence-based scaffolding methods.
  • To evaluate the combined effect of map and sequence data on genome assembly quality.

Main Methods:

  • Utilized a Bioperl pipeline to align comparative map data to tammar wallaby contigs.
  • Constructed pseudo paired-end libraries from map data (5-10 MB intervals).
  • Employed Bambus software for scaffolding and compared results with Illumina paired-end sequencing data (0.5x coverage).

Main Results:

  • Map data alone scaffolded 2.2% of contigs, increasing N50 scaffold size to 39 KB.
  • Sequence data alone scaffolded 53% of contigs.
  • Combining both data sets increased contig scaffolding to 55% and boosted N50 scaffold size by 35% compared to sequence data alone.

Conclusions:

  • A bioinformatics pipeline integrating map data effectively enhances genome assembly structure.
  • While map data's contribution to initial contig scaffolding was minimal, it significantly improved scaffold size and overall assembly utility.
  • The developed pipeline offers a valuable approach for improving low-coverage genome reconstructions.