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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.
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...
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 30, 2026

Novel Sequence Discovery by Subtractive Genomics
09:40

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Multiple sequence assembly from reads alignable to a common reference genome.

Qian Peng1, Andrew D Smith

  • 1Department of Computer Science & Engineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0404, La Jolla, CA 92093-0114, USA. qpeng@cs.ucsd.edu

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|July 23, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces efficient algorithms for genome resequencing assembly, focusing on assembling known read positions. We developed a linear-time algorithm for minimum superstring sets and explored read removal strategies for k-superstring consistency.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Genome resequencing experiments involve assembling short DNA sequences (reads) into longer contiguous sequences.
  • Assembly challenges arise when multiple distinct sequences need reconstruction, especially with known relative read positions from a reference genome.

Purpose of the Study:

  • To develop novel computational algorithms for genome resequencing assembly problems.
  • To address the specific challenge of assembling multiple distinct sequences with known relative positions.
  • To optimize the process of identifying minimum superstrings and managing read consistency.

Main Methods:

  • Developed an O(N) time complexity algorithm for the minimum superstring set problem, where N is the total length of reads.
  • Investigated the problem of removing a minimum number of reads for k-superstring consistency.
  • Utilized the minimum cost flow problem to solve the read removal problem in polynomial time for non-nested reads.

Main Results:

  • Achieved a significant improvement in time complexity for the minimum superstring problem compared to previous methods.
  • Demonstrated polynomial-time solvability for read removal with k-superstring consistency when nested reads are excluded.
  • Established the NP-hard nature of these problems when nested reads are permitted or when mismatches are allowed.

Conclusions:

  • The developed algorithms offer substantial efficiency gains for specific genome resequencing assembly tasks.
  • The study highlights the computational complexity introduced by nested reads and mismatches in assembly.
  • Findings provide a theoretical foundation for developing more robust and scalable genome assembly tools.