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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

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.
RNA-seq03:21

RNA-seq

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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Genomics02:02

Genomics

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...
Sanger Sequencing01:57

Sanger Sequencing

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: Jun 18, 2026

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
13:24

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

BING: biomedical informatics pipeline for Next Generation Sequencing.

Jeffrey Kriseman1, Christopher Busick, Szabolcs Szelinger

  • 1Department of Biomedical Informatics, Arizona State University, Phoenix, AZ 85004-2157, USA. Jeffrey.Kriseman@asu.edu

Journal of Biomedical Informatics
|November 21, 2009
PubMed
Summary
This summary is machine-generated.

A new biomedical informatics pipeline (BING) enhances next-generation sequencing (NGS) analysis. BING

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) generates vast amounts of data, creating a bottleneck in computational analysis.
  • Existing informatics pipelines face challenges in image processing, computational performance, and accuracy for NGS data.

Purpose of the Study:

  • To introduce a novel biomedical informatics pipeline (BING) for enhanced NGS data analysis.
  • To present new computational approaches for image alignment, signal processing, and base calling in NGS.
  • To benchmark BING against established analysis tools like the Illumina Genome Analysis Pipeline.

Main Methods:

  • Development of a biomedical informatics pipeline (BING) utilizing novel algorithms.
  • Implementation of pixel-based analysis for NGS data.
  • Benchmarking BING's performance against the Illumina Genome Analysis Pipeline for key metrics.

Main Results:

  • BING employs novel computational approaches for image alignment, signal processing, and base calling.
  • BING's pixel-based analysis significantly increases the number of sequence reads compared to Illumina's tool.
  • BING reduces computational time and achieves an error rate below 2%.

Conclusions:

  • BING offers a significant advancement in NGS data analysis, addressing key informatics challenges.
  • The pixel-based approach in BING has the potential to increase the density and throughput of NGS technologies.
  • BING provides a more accurate and computationally efficient solution for analyzing high-throughput genomic sequencing data.