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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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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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Genome Annotation and Assembly03:36

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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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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Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...
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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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Collection and Extraction of Saliva DNA for Next Generation Sequencing
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Deep learning in next-generation sequencing.

Bertil Schmidt1, Andreas Hildebrandt1

  • 1Institut für Informatik, Johannes Gutenberg University Mainz, Germany.

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Summary
This summary is machine-generated.

Deep learning (DL) methods, using artificial neural networks (ANNs), are increasingly vital for analyzing complex next-generation sequencing (NGS) data. This review covers key DL architectures, applications, and frameworks for NGS research.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-generation sequencing (NGS) generates vast datasets crucial for biological and medical research.
  • There is a growing need for advanced computational methods to process and analyze NGS data.
  • Machine learning (ML), particularly deep learning (DL), offers powerful tools for complex biological data analysis.

Purpose of the Study:

  • To review the application of deep learning (DL) methods in the context of next-generation sequencing (NGS) data analysis.
  • To highlight important deep learning network architectures relevant to NGS.
  • To discuss various application areas and DL frameworks used in NGS research.

Main Methods:

  • Review of current literature on deep learning applications in bioinformatics.
  • Identification of key deep learning architectures (e.g., convolutional neural networks, recurrent neural networks).
  • Categorization of DL applications in NGS, including variant calling, metagenomics, and feature detection.

Main Results:

  • Deep learning methods are demonstrating significant success in various NGS data analysis tasks.
  • Specific network architectures are well-suited for different types of genomic data and research questions.
  • A range of DL frameworks are available to support these analyses.

Conclusions:

  • Deep learning is a transformative technology for advancing next-generation sequencing data interpretation.
  • The integration of DL into NGS workflows is essential for addressing complex biological and medical questions.
  • Further research into novel DL approaches will continue to enhance genomic data analysis capabilities.