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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
Although all next-generation methods use different technologies, they all share a set of standard features....
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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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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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Related Experiment Video

Updated: Aug 29, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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BaseFormer: Transformer based Base-Caller for Fast and Accurate Next Generation Sequencing.

Shuwei Li, Zhiru Guo, Ao Shen

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
    PubMed
    Summary

    A new deep learning pipeline improves gene sequencing accuracy and speed. BaseFormer enhances cluster quality and throughput for next-generation sequencing (NGS) data analysis.

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Next-generation sequencing (NGS) is crucial for modern biology and medicine.
    • Analyzing NGS fluorescent images is challenging due to weak signals, noise, and limitations in current methods' accuracy and speed.

    Purpose of the Study:

    • To develop a novel deep learning-based gene sequencing pipeline.
    • To improve the accuracy and speed of analyzing NGS data, particularly high-density datasets.

    Main Methods:

    • Proposed a novel deep learning pipeline for gene sequencing.
    • Implemented a semi-automatic labeling method for fluorescent image analysis.
    • Developed a model named BaseFormer for enhanced data processing.

    Main Results:

    • BaseFormer demonstrated superior cluster quality (Q30: 88%).
    • Achieved a 16.5% improvement in throughput compared to traditional methods.
    • Maintained a low average error rate (0.137%), with a best-case error rate of 0.068% on high-density data.

    Conclusions:

    • The proposed deep learning pipeline, BaseFormer, offers significant improvements in NGS data analysis.
    • The method shows particular promise for high-density sequencing data, overcoming limitations of traditional approaches.
    • This advancement contributes to more accurate and efficient gene sequencing, impacting biological and medical research.