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Related Concept Videos

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.
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...
Sanger Sequencing01:57

Sanger 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...
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...
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.
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...

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Related Experiment Video

Updated: May 26, 2026

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome
06:40

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Published on: March 22, 2018

Computational techniques for human genome resequencing using mated gapped reads.

Paolo Carnevali1, Jonathan Baccash, Aaron L Halpern

  • 1Complete Genomics Inc., Mountain View, California 94043, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|December 20, 2011
PubMed
Summary
This summary is machine-generated.

Novel computational methods enable accurate variant calling from DNA nanoarray sequencing. These advanced techniques improve human genome resequencing by addressing limitations of existing assembly approaches.

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Last Updated: May 26, 2026

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome
06:40

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Published on: March 22, 2018

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Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

Ultra-long Read Sequencing for Whole Genomic DNA Analysis
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Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Published on: March 15, 2019

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Self-assembling DNA nanoarrays offer a promising avenue for low-cost, high-quality human genome resequencing.
  • Existing computational methods are inadequate for accurate variant calling due to the unique characteristics of DNA nanoarray reads.

Purpose of the Study:

  • To develop novel computational methods for accurate variant calling, including single nucleotide polymorphisms (SNPs), short insertions/deletions (indels), and structural variations.
  • To address the limitations of current resequencing assembly techniques for DNA nanoarray data.

Main Methods:

  • Utilized an iterative optimization process adjusting genome sequences to maximize a posteriori probability based on observed reads.
  • Employed Bayesian statistics with a simplified read generation model for computational tractability.
  • Incorporated a local de novo assembly procedure, generalizing De Bruijn graphs, to seed optimization and avoid local optima.
  • Applied a correlation-based filter to mitigate false positives arising from repetitive genomic regions.

Main Results:

  • Developed and validated novel computational methods for accurate SNP and indel calling (<100 bp) from DNA nanoarray data.
  • Extended applicability of these methods to evaluate larger, hypothesized structural variations.
  • Successfully reduced the false positive rate, particularly in repetitive genomic regions.

Conclusions:

  • The novel computational methods provide accurate variant calling for human genome resequencing using DNA nanoarray data.
  • These advancements overcome limitations of traditional methods, enabling more reliable genomic analysis.
  • The developed approach enhances the utility of DNA nanoarrays for cost-effective and high-quality genome resequencing.