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Related Concept Videos

Generation Time01:22

Generation Time

Bacterial generation time, the period required for a bacterial population to double during its exponential growth phase, serves as a critical measure of microbial growth dynamics under optimal conditions. This parameter varies significantly across bacterial species and can be influenced by factors such as temperature, pH, and the availability of nutrients. For example, Escherichia coli can achieve a generation time of approximately 20 minutes, while Mycobacterium tuberculosis exhibits a much...
Crossing over01:34

Crossing over

Unlike mitosis, meiosis aims for genetic diversity in its creation of haploid gametes. Dividing germ cells first begin this process in prophase I, where each chromosome—replicated in S phase—is now composed of two sister chromatids (identical copies) joined centrally.
The homologous pairs of sister chromosomes—one from the maternal and one from the paternal genome—then begin to align alongside each other lengthwise, matching corresponding DNA positions in a process called synapsis.
In order to...
Crossing Over01:30

Crossing Over

Crossing over is the exchange of genetic information between homologous chromosomes during prophase I of meiosis I. Genetic recombination gives rise to allelic diversity in the newly formed daughter cells. In humans, crossing over produces genetically distinct haploid egg and sperm cells that undergo fertilization to produce unique offspring. Before cell division starts, the germ cell’s chromosome(s) undergo duplication in the S phase of the cell cycle. As the cells enter prophase I, duplicated...
Crossing Over01:34

Crossing Over

Unlike mitosis, meiosis aims for genetic diversity in its creation of haploid gametes. Dividing germ cells first begin this process in prophase I, where each chromosome—replicated in S phase—is now composed of two sister chromatids (identical copies) joined centrally.
The homologous pairs of sister chromosomes—one from the maternal and one from the paternal genome—then begin to align alongside each other lengthwise, matching corresponding DNA positions in a process called synapsis.
In order to...
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.
Gene Flow02:39

Gene Flow

Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.

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Related Experiment Video

Updated: Jun 19, 2026

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation
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Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

Published on: September 19, 2019

Next-generation gap.

John D McPherson1

  • 1Ontario Institute for Cancer Research, Toronto, Ontario, Canada. john.mcpherson@oicr.on.ca

Nature Methods
|October 22, 2009
PubMed
Summary
This summary is machine-generated.

The increasing volume of sequencing data outpaces analysis capabilities, creating a significant bottleneck for researchers. Addressing this challenge is crucial to prevent high data analysis costs from hindering genomic research.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Massively parallel sequencing generates vast amounts of data.
  • Current data processing and analysis tools are complex and fragmented.
  • Incomplete documentation and lack of validation methods hinder new users.

Purpose of the Study:

  • To highlight the growing disparity between sequencing data generation and analysis.
  • To emphasize the challenges faced by new researchers in data interpretation.
  • To underscore the need for improved bioinformatics infrastructure and support.

Main Methods:

  • The study identifies key challenges in the bioinformatics pipeline.
  • It reviews the current landscape of available analysis tools.
  • It discusses the implications of inadequate data analysis support.

Main Results:

  • A significant gap exists between sequencing output and analytical capacity.
  • New users struggle with a complex array of tools for base calling, alignment, assembly, and analysis.
  • Lack of standardized validation methods exacerbates the problem.

Conclusions:

  • Bridging the analysis gap is essential for realizing the potential of low-cost sequencing.
  • Failure to address this bottleneck will lead to prohibitive data analysis costs.
  • Improved bioinformatics resources and training are urgently needed.