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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...
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...
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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: May 30, 2026

Optimization and Comparative Analysis of Plant Organellar DNA Enrichment Methods Suitable for Next-generation Sequencing
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Optimization and Comparative Analysis of Plant Organellar DNA Enrichment Methods Suitable for Next-generation Sequencing

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SEED: efficient clustering of next-generation sequences.

Ergude Bao1, Tao Jiang, Isgouhi Kaloshian

  • 1Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA.

Bioinformatics (Oxford, England)
|August 4, 2011
PubMed
Summary

SEED is a new algorithm for clustering next-generation sequences (NGS) that efficiently handles large datasets. It significantly speeds up genome assembly and improves contig length, outperforming existing tools.

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Collection and Extraction of Saliva DNA for Next Generation Sequencing
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Last Updated: May 30, 2026

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12:33

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Collection and Extraction of Saliva DNA for Next Generation Sequencing
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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-generation sequencing (NGS) data analysis requires efficient clustering to manage large datasets and reduce redundancy.
  • Existing sequence clustering algorithms struggle with scalability, limiting their use on datasets with millions of reads.

Purpose of the Study:

  • Introduce SEED, an efficient algorithm for clustering very large next-generation sequence datasets.
  • Evaluate SEED's performance as a standalone tool and as a preprocessing step for genome assembly.

Main Methods:

  • SEED utilizes a modified spaced seed method called block spaced seeds.
  • Clustering is performed using hash tables to identify virtual center sequences and their neighbors.
  • The algorithm allows for clusters with up to three mismatches and three overhanging residues.

Main Results:

  • SEED can cluster 100 million short read sequences in under 4 hours with linear time and memory performance.
  • As a preprocessing tool, SEED reduced Velvet/Oasis assembler time and memory by 60-85% and 21-41%, respectively.
  • SEED improved assembly contiguity, indicated by 12-27% larger N50 values, and showed 2- to 10-fold better time performance compared to other clustering tools.

Conclusions:

  • SEED is a highly efficient and scalable algorithm for clustering large NGS datasets.
  • SEED significantly enhances genome and transcriptome assembly processes, improving speed, memory usage, and contiguity.
  • SEED demonstrates effectiveness as a standalone tool for small RNA sequence discovery in unsequenced organisms.