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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.
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...
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...
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...

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Related Experiment Video

Updated: Jun 13, 2026

Collection and Extraction of Saliva DNA for Next Generation Sequencing
06:58

Collection and Extraction of Saliva DNA for Next Generation Sequencing

Published on: August 27, 2014

A survey of sequence alignment algorithms for next-generation sequencing.

Heng Li1, Nils Homer

  • 1Broad Institute, Cambridge, MA 02142, USA. hengli@broadinstitute.org

Briefings in Bioinformatics
|May 13, 2010
PubMed
Summary
This summary is machine-generated.

Sequence alignment software has advanced significantly, making short-read alignment no longer a data analysis bottleneck. Future developments focus on long sequence reads and cloud computing for enhanced bioinformatics.

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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Related Experiment Videos

Last Updated: Jun 13, 2026

Collection and Extraction of Saliva DNA for Next Generation Sequencing
06:58

Collection and Extraction of Saliva DNA for Next Generation Sequencing

Published on: August 27, 2014

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons
10:24

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Published on: August 29, 2014

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Area of Science:

  • Bioinformatics and Computational Biology
  • Genomics and Next-Generation Sequencing

Background:

  • Next-generation sequencing technologies generate vast amounts of data, necessitating efficient analysis methods.
  • Sequence alignment, comparing sequence reads to a reference genome, is a critical step in analyzing this data.
  • The rapid evolution of sequencing necessitates continuous development of alignment algorithms.

Purpose of the Study:

  • To systematically review recent advancements in sequence alignment algorithms.
  • To introduce practical applications of these algorithms across various experimental data types.
  • To discuss future trends in sequence alignment, including long-read sequencing and cloud computing.

Main Methods:

  • Systematic literature review of sequence alignment algorithms developed in the past two years.
  • Analysis of practical applications using diverse experimental datasets.
  • Exploration of emerging trends and future directions in the field.

Main Results:

  • Short-read alignment algorithms have matured, and are no longer the primary bottleneck in large-scale sequencing data analysis.
  • A wide variety of alignment software is now available, catering to different experimental needs.
  • Current developments show a trend towards optimizing for long sequence reads and integrating cloud computing solutions.

Conclusions:

  • The bottleneck in sequencing data analysis has shifted from short-read alignment to other areas.
  • Future research and development should focus on efficient long-read alignment and scalable cloud-based solutions.
  • The field is rapidly evolving, with ongoing innovation in alignment algorithms and their applications.