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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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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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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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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

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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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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DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Updated: Mar 22, 2026

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
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Visual programming for next-generation sequencing data analytics.

Franco Milicchio1, Rebecca Rose2, Jiang Bian3

  • 1Department of Engineering, Roma Tre University, Rome, Italy.

Biodata Mining
|April 30, 2016
PubMed
Summary
This summary is machine-generated.

Developing integrated software frameworks for next-generation sequencing (NGS) data analysis is crucial. Combining template and visual programming can bridge the gap between data generation and hypothesis testing for improved diagnostics.

Keywords:
Big dataGeneric programmingGraphical user interfaceHigh-throughput sequencingNext-generation sequencingSoftware suiteTemplate libraryVisual programming

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-generation sequencing (NGS) generates large, complex datasets.
  • Current NGS data analysis tools face challenges in software development speed and user accessibility.
  • A gap exists between NGS data generation capabilities and effective analysis methods.

Purpose of the Study:

  • To scrutinize state-of-the-art software libraries and graphical tools for NGS data analytics.
  • To identify ideal characteristics for an NGS developing environment.
  • To discuss a computational framework addressing current limitations in NGS data analysis.

Main Methods:

  • Review of low-level software libraries specifically for NGS.
  • Analysis of graphical tools for NGS analytics.
  • Discussion of a computational framework integrating template and visual programming.

Main Results:

  • An ideal NGS developing environment should be modular, scalable, transparent, interoperable, and usable.
  • Generic software template libraries and visual programming can address software development and user "cultural" gaps.
  • A blended framework offers potential for standardized pipelines and new workflow development.

Conclusions:

  • A well-developed software framework is essential to bridge the gap between NGS data generation and hypothesis testing.
  • This framework will facilitate the development of novel diagnostic tools for routine healthcare.
  • Advancements in NGS data analysis software are critical for biological and medical research progress.