Related Experiment Video
Updated: Aug 11, 2026
10:24
Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons
Published on: August 29, 2014
Gap statistics for whole genome shotgun DNA sequencing projects
Michael C Wendl1, Shiaw-Pyng Yang
1Genome Sequencing Center, Washington University School of Medicine, Box 8501, 4444 Forest Park Blvd., Saint Louis, MO 63108 USA. mwendl@wustl.edu
Bioinformatics (Oxford, England)
|February 14, 2004
Summary
Standard DNA sequencing theories overestimate gaps. Our study reveals actual gaps are significantly smaller, especially in eukaryotes, due to factors like genome complexity and assembly challenges, improving gap prediction accuracy.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Current DNA sequencing gap estimation relies on theories assuming independent sequence distribution.
- These standard theories often lead to significant under-prediction of actual gaps in sequencing projects.
Purpose of the Study:
- To develop more accurate gap estimation methods for DNA sequencing projects.
- To investigate the correlation between sequence coverage and gap size in different genome types.
- To identify factors contributing to discrepancies between theoretical and observed gap predictions.
Main Methods:
- Utilized a statistical scaling factor and data from 20 whole genome shotgun sequencing projects.
- Constructed regression equations to correlate genome coverage with a normalized gap measure.
- Analyzed prokaryotic and eukaryotic genomes, considering factors like genome complexity and assembly artifacts.
Main Results:
- Eukaryotic genomes show a strong correlation between coverage and gap size when non-essential data ('chaff') is excluded.
- Observed gap reduction rates are approximately one-third of theoretical predictions.
- Assembly difficulties in repeat-rich genomes and coverage anomalies contribute to prediction inaccuracies.
Conclusions:
- Standard theoretical models for DNA sequencing gap prediction are often inaccurate.
- Genome complexity, particularly repetitive elements, significantly impacts gap formation and prediction.
- Accurate a priori gap prediction has inherent limitations due to uncharacterized genomic factors.
Related Concept Videos
01:57Sanger Sequencing
DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
03:00Next-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.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
01:05Maxam-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...
Challenges of the Maxam-Gilbert Method
The...