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Sanger Sequencing

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...

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HISEA: HIerarchical SEed Aligner for PacBio data.

Nilesh Khiste¹, Lucian Ilie²

¹Department of Computer Science, University of Western Ontario, LondonOntario, N6A 5B7, Canada.

BMC Bioinformatics

|December 21, 2017

Summary

This summary is machine-generated.

HISEA, a new aligner for Single Molecule, Real-Time (SMRT) sequencing data, offers superior sensitivity for genome assembly. It improves upon existing methods, enabling faster and more cost-effective genome assembly with high accuracy.

Keywords:

Genome assembly PacBio sequencing Read aligner Read overlapper

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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Published on: June 23, 2012

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Next-generation sequencing (NGS) requires robust genome assembly algorithms.
Illumina sequencing's short reads limit assembly of repetitive regions.
Single Molecule, Real-Time (SMRT) sequencing offers long reads but has a high error rate.

Purpose of the Study:

To develop a novel algorithm for efficient and accurate genome assembly from SMRT sequencing data.
To address the challenges posed by high error rates and long reads in SMRT data.
To improve the sensitivity and performance of SMRT genome assembly pipelines.

Main Methods:

Developed HISEA (Hierarchical SEed Aligner), a pairwise read aligner for SMRT data.
HISEA employs a two-step k-mer search with clustering, filtering, and alignment extension.
Evaluated HISEA against state-of-the-art aligners using real datasets and a new evaluation metric.

Main Results:

HISEA demonstrated superior alignment detection sensitivity compared to existing methods.
Benchmarking in the Canu pipeline showed HISEA-based assemblies outperform those using MHAP.
HISEA enabled comparable assemblies at lower coverage and faster runtimes.

Conclusions:

HISEA significantly enhances SMRT data alignment sensitivity.
Integrating HISEA into the Canu pipeline yields improved genome assemblies.
HISEA offers a more efficient and cost-effective solution for SMRT genome assembly.