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Related Concept Videos

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
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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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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. 
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High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq
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FANSe2: a robust and cost-efficient alignment tool for quantitative next-generation sequencing applications.

Chuan-Le Xiao1, Zhi-Biao Mai1, Xin-Lei Lian1

  • 1Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou, China.

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|April 19, 2014
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FANSe2 is a novel algorithm for deep sequencing data analysis. It offers accurate and fast read mapping for RNA-seq, improving gene identification and expression quantification with efficient parallelization.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Accurate interpretation of deep sequencing data is crucial for quantitative RNA-seq.
  • Existing mapping algorithms have trade-offs between speed and accuracy (seed-based vs. BWT-based).

Purpose of the Study:

  • To develop a novel algorithm, FANSe2, that combines the speed of BWT-based methods with the robustness of seed-based methods.
  • To improve the accuracy and efficiency of read mapping for deep sequencing applications.

Main Methods:

  • Developed FANSe2 algorithm with an iterative mapping strategy.
  • Utilized statistics of real-world sequencing error distribution for acceleration.
  • Implemented a scalable parallelization method for multi-computer utilization.

Main Results:

  • FANSe2 demonstrated higher sensitivity and accuracy than BWT-based algorithms on prokaryotic and eukaryotic datasets.
  • Experimental validation confirmed FANSe2's superior gene identification with fewer false positives/negatives.
  • FANSe2 showed better consistency with microarray data for gene expression quantification.
  • Mapped an entire Illumina HiSeq 2000 flowcell dataset in 4.1 hours using three office computers.

Conclusions:

  • FANSe2 provides a practical tool for deep sequencing, offering robust accuracy, full indel sensitivity, and speed.
  • The algorithm's efficient parallelization enables economical computational utilization.
  • FANSe2 is freely available for research applications.