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Related Concept Videos

Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
Parallel-axis Theorem01:06

Parallel-axis Theorem

The parallel-axis theorem provides a convenient and quick method of finding the moment of inertia of an object about an axis parallel to the axis passing through its center of mass. Consider a thin rod as an example. There is a striking similarity between the process of finding the moment of inertia of a thin rod about an axis through its middle, where the center of mass lies, and about an axis through its end using the conventional method. In the conventional method, the concept of linear mass...
Theorems of Pappus and Guldinus: Problem Solving01:12

Theorems of Pappus and Guldinus: Problem Solving

Pappus and Guldinus's theorems are powerful mathematical principles that are used for finding the surface area and volume of composite shapes. For example, consider a cylindrical storage tank with a conical top. Finding the surface area or volume can be challenging for such complex shapes. These theorems are particularly useful in calculating the volume and surface area of such systems. Here, the cylindrical storage tank with a conical top can be broken down into two simple shapes: a cylinder...
Graphs of Functions01:30

Graphs of Functions

Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
Graphs of Equations in Two Variables01:30

Graphs of Equations in Two Variables

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Graphical Representation of Inequalities

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Updated: Jun 6, 2026

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Efficient parallel and out of core algorithms for constructing large bi-directed de Bruijn graphs.

Vamsi K Kundeti1, Sanguthevar Rajasekaran, Hieu Dinh

  • 1Department of Computer Science and Engineering, University of Connecticut, 371 Fairfield Way, U-2155, Storrs, CT 06269, USA. vamsik@engr.uconn.edu

BMC Bioinformatics
|November 17, 2010
PubMed
Summary

This study presents a faster parallel algorithm for constructing bi-directed de Bruijn graphs, essential for genomic sequence assembly. The new method is memory-efficient and outperforms existing approaches in both speed and resource usage.

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Last Updated: Jun 6, 2026

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Genomic sequence assembly relies on efficient graph construction algorithms.
  • De Bruijn graphs are crucial for short-read sequencing assembly.
  • Previous parallel algorithms for de Bruijn graphs had high communication complexity.

Purpose of the Study:

  • To develop a more efficient parallel algorithm for constructing bi-directed de Bruijn graphs.
  • To improve upon the communication complexity and memory usage of existing methods.
  • To provide scalable solutions for large-scale sequencing projects.

Main Methods:

  • Developed a parallel algorithm with optimal communication complexity, insensitive to alphabet size.
  • Extended the algorithm to an out-of-core model with optimal I/O complexity.
  • Implemented and tested the algorithm on a SGI/Altix computer for scalability.

Main Results:

  • The new parallel algorithm achieves Θ(n/p) time complexity with communication complexity similar to parallel sorting.
  • The out-of-core version offers optimal I/O complexity: Θ(nlog(n/B)Blog(M/B)).
  • Demonstrated superior asymptotic and practical performance compared to previous algorithms and VELVET, with constant memory usage.

Conclusions:

  • Bi-directed de Bruijn graphs are fundamental for Eulerian-based sequence assembly.
  • The developed parallel and out-of-core algorithms are efficient for constructing large-scale graphs.
  • The new algorithms offer improved performance and memory efficiency, outperforming existing methods like VELVET.