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Related Concept Videos

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...
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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...
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.
Trimmed Mean01:10

Trimmed Mean

While measuring the mean of a data set, care needs to be taken when associating the mean to its central tendency. The same goes for the arithmetic mean, the geometric mean, or the harmonic mean. This is because the presence of a single outlier data value can significantly affect the mean. That is, the mean is sensitive to fluctuations in the data set.
Although certain measures of central tendency are not sensitive to outliers, there are alternative versions of the mean that get around the...

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Related Experiment Video

Updated: May 28, 2026

Introductory Analysis and Validation of CUT&RUN Sequencing Data
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ConDeTri--a content dependent read trimmer for Illumina data.

Linnéa Smeds1, Axel Künstner

  • 1Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden.

Plos One
|November 1, 2011
PubMed
Summary

ConDeTri is a new tool for DNA and RNA sequencing data quality filtering. It removes sequencing errors from reads, improving data standardization and analysis pipelines for next-generation sequencing.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • DNA and RNA sequencing are vital in biological and medical research.
  • Next-generation sequencing technologies, like Illumina/Solexa, generate vast amounts of data.
  • Current tools for quality filtering of sequencing data are lacking accuracy and ease of use.

Purpose of the Study:

  • To present ConDeTri, a novel method for content-dependent read trimming.
  • To improve the quality and standardization of next-generation sequencing data.
  • To integrate read trimming into existing sequencing data processing pipelines.

Main Methods:

  • ConDeTri utilizes base-specific quality scores for read trimming.
  • The method is designed for single-end and paired-end sequence data of any length.

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  • It operates independently of sequencing coverage and requires no user interaction.

    • Main Results:

    • ConDeTri effectively removes sequencing errors and low-quality reads.
    • The tool reduces computational time and memory usage in de novo assembly.
    • It is particularly beneficial for low-coverage or large genome sequencing projects.

    Conclusions:

    • ConDeTri offers an accurate and user-friendly solution for next-generation sequencing data quality filtering.
    • The method enhances the reliability of sequencing data for downstream analysis.
    • ConDeTri can be readily incorporated into standard bioinformatics pipelines for Illumina data.