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Related Concept Videos

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
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In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Sequences01:29

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Sequences are fundamental mathematical objects consisting of ordered lists of numbers that follow a specific rule or pattern. Sequences are critical in various mathematical concepts, including calculus, series, and number theory. They can model real-world phenomena such as population growth, financial investments, and physical processes like the diminishing height of a bouncing ball.Each number in a sequence is referred to as a term. Typically, the terms are denoted as a1, a2, a3,…, where...
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Related Experiment Video

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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
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FastMotif: spectral sequence motif discovery.

Nicoló Colombo1, Nikos Vlassis2

  • 1Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg and.

Bioinformatics (Oxford, England)
|April 19, 2015
PubMed
Summary

FastMotif, a novel motif discovery algorithm, offers a computationally efficient solution for analyzing large sequencing datasets. This machine learning-based tool significantly accelerates Transcription Factor binding site identification compared to existing methods.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine learning

Background:

  • Transcription Factor binding studies are crucial in computational biology.
  • Existing motif finding algorithms struggle with large datasets from high-throughput sequencing.
  • There is a need for faster, more scalable sequence discovery tools.

Purpose of the Study:

  • To introduce FastMotif, a novel and highly efficient motif discovery algorithm.
  • To address the computational demands of analyzing large-scale sequencing data.
  • To improve the speed and scalability of Transcription Factor binding site analysis.

Main Methods:

  • Developed FastMotif based on the Method of Moments, a machine learning technique.
  • Utilized spectral decompositions for motif profile extraction.
  • Analyzed algorithm robustness and parameter sensitivity theoretically and numerically.

Main Results:

  • FastMotif achieves results comparable to state-of-the-art algorithms on HT-Selex data.
  • The algorithm is significantly faster, operating one order of magnitude quicker.
  • Demonstrated robustness to model misspecifications and avoidance of locally optimal solutions.

Conclusions:

  • FastMotif provides a computationally efficient and robust solution for motif discovery.
  • The algorithm is well-suited for the analysis of big sequencing data.
  • Accelerates Transcription Factor binding site identification in large biological datasets.