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Genomics02:02

Genomics

39.9K
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...
39.9K
Control Volume and System Representations01:16

Control Volume and System Representations

1.5K
Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
The control volume approach considers a stationary region in space through which fluid flows. This region is bounded by a control surface.  For instance, in the case of water...
1.5K
State Space Representation01:27

State Space Representation

547
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
547
Graphical and Analytic Representation of Sinusoids01:20

Graphical and Analytic Representation of Sinusoids

938
Analyzing two sinusoidal voltages with equal amplitude and period but different phases on an oscilloscope, an instrument used to display and analyze waveforms, involves a three-step process.
The first step is measuring the peak-to-peak value, which is twice the amplitude of the sinusoid. This provides information about the maximum voltage swing of the waveform.
Secondly, the period and angular frequency are determined. The period is the time taken for one complete cycle of the waveform, while...
938
Genomic Imprinting and Inheritance02:30

Genomic Imprinting and Inheritance

36.9K
Diploid organisms inherit genetic material through chromosomes from both parents. Copies of the same gene are known as alleles. In most cases, both alleles are simultaneously expressed and allow various cellular processes to function optimally. If one of the alleles is missing or mutated, the expression of the other allele can compensate; however, this is not true for all genes.
The expression of some genes depends on which parent passed the gene to the offspring, through a phenomenon known as...
36.9K
Vector Representation of Complex Numbers01:16

Vector Representation of Complex Numbers

521
Complex numbers, represented in Cartesian coordinates, can also be visualized as vectors. These vectors can be expressed in polar form, emphasizing their magnitude and angle. When a complex number is input into a function, the output is another complex number, highlighting the function's zero point from which the vector representation can originate.
Consider a function defined as the product of the complex factors in the numerator divided by the product of the complex factors in the...
521

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Related Experiment Video

Updated: Jan 27, 2026

Ultra-long Read Sequencing for Whole Genomic DNA Analysis
10:34

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Published on: March 15, 2019

23.9K

Low-dimensional representation of genomic sequences.

Richard C Tillquist1, Manuel E Lladser2

  • 1Department of Computer Science, The University of Colorado, Boulder, CO, 80309-0526, USA.

Journal of Mathematical Biology
|April 1, 2019
PubMed
Summary
This summary is machine-generated.

We introduce multilateration, a novel graph-based method for embedding biological sequences into Euclidean space. This approach efficiently handles long genomic sequences, overcoming limitations of existing methods like Multidimensional Scaling (MDS) and Node2Vec.

Keywords:
Feature extractionGraph embeddingsHamming graphMetric dimensionReadsResolving set

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Mining

Background:

  • Data analysis and mining often require data embedding in Euclidean space.
  • Conventional methods like binary and k-mer vectors can be too high-dimensional for biological sequence data.
  • Existing techniques like Multidimensional Scaling (MDS) and Node2Vec struggle with large datasets and new data points.

Purpose of the Study:

  • To develop an efficient and scalable method for embedding biological sequence data.
  • To address the limitations of existing embedding techniques for high-throughput sequencing data.
  • To create low-dimensional embeddings for arbitrary length sequences using graph theory.

Main Methods:

  • Amended the graph-theoretic notion of "metric dimension" to "multilateration."
  • Specialized multilateration to Hamming graphs for biological sequence representation.
  • Applied Multidimensional Scaling (MDS), Node2Vec, and multilateration for classifying DNA 20-mers.

Main Results:

  • Multilateration provides an efficient means of mapping long genomic sequences.
  • The proposed method generates low-dimensional embeddings for sequences of arbitrary length.
  • Multilateration embeddings offer comparable performance to MDS and Node2Vec in classification tasks.

Conclusions:

  • Multilateration is a scalable and efficient technique for embedding biological sequences.
  • This method overcomes the scalability issues associated with MDS and Node2Vec for long sequences.
  • Multilateration offers a promising approach for analyzing large-scale genomic data.