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Next-generation Sequencing03:00

Next-generation Sequencing

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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....
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Evolutionary Relationships through Genome Comparisons02:54

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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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Sanger Sequencing01:57

Sanger Sequencing

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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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Multi-species Conserved Sequences02:51

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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...
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RNA-seq03:21

RNA-seq

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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...
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Related Experiment Video

Updated: Jul 6, 2025

Ultra-long Read Sequencing for Whole Genomic DNA Analysis
10:34

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Published on: March 15, 2019

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FMAlign2: a novel fast multiple nucleotide sequence alignment method for ultralong datasets.

Pinglu Zhang1,2, Huan Liu3, Yanming Wei4

  • 1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, China.

Bioinformatics (Oxford, England)
|January 10, 2024
PubMed
Summary
This summary is machine-generated.

FMAlign2 improves multiple sequence alignment (MSA) for ultralong DNA sequences. This bioinformatics tool uses a vertical division strategy and suffix arrays for faster, accurate alignment of massive datasets.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple Sequence Alignment (MSA) is fundamental in bioinformatics.
  • Conventional MSA methods face challenges with ultralong sequences.
  • Existing methods struggle to scale for sequences billions of characters in length.

Purpose of the Study:

  • To develop an efficient and accurate MSA method for ultralong sequences.
  • To enhance existing MSA tools for handling massive genomic datasets.
  • To improve the scalability and speed of bioinformatics sequence alignment.

Main Methods:

  • FMAlign2 employs a vertical division strategy for parallel processing.
  • It utilizes suffix arrays to identify maximal exact matches.
  • Sequence-profile alignment and refinement are used for subset concatenation.

Main Results:

  • FMAlign2 significantly reduces alignment time for ultralong sequences compared to FMAlign.
  • The method maintains high accuracy on massive datasets.
  • It enables the alignment of sequences reaching billions in length within acceptable timeframes.

Conclusions:

  • FMAlign2 offers a scalable solution for multiple sequence alignment of ultralong sequences.
  • The tool enhances the capabilities of existing bioinformatics methods.
  • It provides an efficient approach for analyzing large-scale genomic data.