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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.
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Sanger Sequencing01:57

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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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Maxam-Gilbert Sequencing01:05

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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.
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Genomics02:02

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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RNA-seq03:21

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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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RACE - Rapid Amplification of cDNA Ends02:35

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Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific...
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G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome
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GenSeq+: A Scalable High-Performance Accelerator for Genome Sequencing.

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    Gene sequencing generates massive data, requiring efficient algorithms. This study presents GeneKMP, a scalable FPGA-based accelerator for the Knuth-Morris-Pratt (KMP) algorithm, significantly speeding up genome analysis.

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    Area of Science:

    • Computational Biology
    • Bioinformatics
    • Hardware Acceleration

    Background:

    • Genome sequencing presents significant computational challenges due to massive data volumes.
    • Traditional string matching algorithms struggle with the efficiency demands of modern gene sequencing.
    • High-performance implementations of algorithms like Knuth-Morris-Pratt (KMP) are crucial for bioinformatics.

    Purpose of the Study:

    • To develop a scalable and high-performance KMP algorithm accelerator for gene sequencing.
    • To address the urgent need for efficient data processing in computational biology.
    • To introduce a novel programming model and partitioning algorithm for hardware/software co-design.

    Main Methods:

    • Implementation of a scalable KMP accelerator on Field-Programmable Gate Arrays (FPGA), named GeneKMP.
    • Design of pipelined computing units for enhanced throughput and scalability.
    • Development of a greedy-based partitioning algorithm for software/hardware co-design paradigms.
    • Utilizing a novel programming model to simplify high-level programming.

    Main Results:

    • The GeneKMP accelerator achieves significant speedup on state-of-the-art Xilinx FPGA hardware prototypes.
    • The implementation demonstrates high throughput and scalability through its pipelined architecture.
    • The accelerator offers a promising performance improvement with minimal hardware cost and power consumption.

    Conclusions:

    • The GeneKMP accelerator provides an efficient solution for accelerating string matching in genome sequencing.
    • FPGA-based acceleration offers a viable approach for handling large-scale bioinformatics data.
    • The developed programming model and partitioning algorithm facilitate easier implementation and optimization.