Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Locus of Control01:26

Locus of Control

228
Locus of control describes how individuals perceive the causes of events in their lives, influencing motivation and well-being. Introduced by Julian Rotter in 1954, it is categorized into internal and external locus of control.Internal Locus of ControlIndividuals with an internal locus of control believe their actions determine outcomes, fostering responsibility, self-efficacy, and motivation. For example, an employee may attribute career success to hard work. Research links this mindset to...
228
Root-Locus Method01:19

Root-Locus Method

518
A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
This system can be represented by a block...
518
Construction of Root Locus01:15

Construction of Root Locus

421
The construction of a root locus involves several key steps to analyze and visualize the behavior of a system's poles with varying gain. The number of branches in the root locus equals the number of closed-loop poles and is symmetrical about the real axis.
For positive gain values, the root locus exists on the real axis to the left of an odd number of finite open-loop poles or zeros. The root locus starts at the open-loop poles and traces the paths of the closed-loop poles as the gain...
421
Properties of the Root Locus01:05

Properties of the Root Locus

308
The root locus method is an invaluable tool for analyzing higher-order systems without needing to factor the denominator of the transfer function. A pole of the system is identified when the characteristic polynomial in the transfer function's denominator equals zero.
To determine if a point lies on the root locus, the criterion involves the sum of angles contributed by all poles and zeros to that point. Specifically, this sum must be an odd multiple of 180 degrees. The gain at any point on...
308
Rotter's Locus of Control01:14

Rotter's Locus of Control

963
Julian Rotter introduced the concept of locus of control, a cognitive factor that significantly influences personality development and learning. Locus of control refers to an individual's beliefs about the extent of control they have over events in their lives. According to Rotter, this belief system can be categorized into two types: internal and external locus of control.
Individuals with an internal locus of control believe that their personal efforts and decisions directly affect their...
963
Plotting and Calibrating the Root Locus01:19

Plotting and Calibrating the Root Locus

477
Root loci often diverge as system poles shift from the real axis to the complex plane. Key points in this transition are the breakaway and break-in points, indicating where the root locus leaves and reenters the real axis. The branches of the root locus form an angle of 180/n degrees with the real axis, where n is the number of branches at a breakaway or break-in point.
The maximum gain occurs at the breakaway points between open-loop poles on the real axis, while the minimum gain is...
477

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A pragmatist approach to bridging tables and ontologies through LinkML and punning.

Journal of biomedical semantics·2026
Same author

Pleistocene Forest Stability Predicts Patterns of Frog Diversity in Central Africa.

Ecology and evolution·2026
Same author

CONVERGENT EVOLUTION BETWEEN ENDOSYMBIOTIC BACTERIA IN FEATHER-FEEDING LICE.

The Journal of parasitology·2026
Same author

Nine changes needed to deliver a radical transformation in biodiversity measurement.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Phylogenomics of the woodland salamanders (Plethodon): Reticulate evolution and indistinct species boundaries.

Molecular phylogenetics and evolution·2026
Same author

A riddle wrapped in an enigma: parasitic lice as clues to the evolutionary puzzle of Sapayoa (Aves).

Biology letters·2026

Related Experiment Video

Updated: Feb 9, 2026

Mycobacterial DNA Extraction using Bead Beating in Custom Buffer Followed by NGS Workflow
09:18

Mycobacterial DNA Extraction using Bead Beating in Custom Buffer Followed by NGS Workflow

Published on: June 13, 2025

964

aTRAM 2.0: An Improved, Flexible Locus Assembler for NGS Data.

Julie M Allen1, Raphael LaFrance1, Ryan A Folk1

  • 1Florida Museum of Natural History and University of Florida, Gainesville, FL, USA.

Evolutionary Bioinformatics Online
|June 9, 2018
PubMed
Summary
This summary is machine-generated.

aTRAM 2.0 is a significantly improved software for assembling genome sequencing data. This enhanced tool offers faster read retrieval and greater flexibility for various analytical workflows in genomics.

Keywords:
aTRAMlocus assemblymassively parallel sequencingshort-read sequencingsoftware

More Related Videos

Author Spotlight: Advancing Chromatin Research and Overcoming Limitations with a High-Enrichment Locus-Specific Chromatin Isolation Protocol
10:33

Author Spotlight: Advancing Chromatin Research and Overcoming Limitations with a High-Enrichment Locus-Specific Chromatin Isolation Protocol

Published on: November 17, 2023

1.7K
Localization of the Locus Coeruleus in the Mouse Brain
07:44

Localization of the Locus Coeruleus in the Mouse Brain

Published on: March 7, 2019

19.2K

Related Experiment Videos

Last Updated: Feb 9, 2026

Mycobacterial DNA Extraction using Bead Beating in Custom Buffer Followed by NGS Workflow
09:18

Mycobacterial DNA Extraction using Bead Beating in Custom Buffer Followed by NGS Workflow

Published on: June 13, 2025

964
Author Spotlight: Advancing Chromatin Research and Overcoming Limitations with a High-Enrichment Locus-Specific Chromatin Isolation Protocol
10:33

Author Spotlight: Advancing Chromatin Research and Overcoming Limitations with a High-Enrichment Locus-Specific Chromatin Isolation Protocol

Published on: November 17, 2023

1.7K
Localization of the Locus Coeruleus in the Mouse Brain
07:44

Localization of the Locus Coeruleus in the Mouse Brain

Published on: March 7, 2019

19.2K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-scale data collection technologies have advanced significantly.
  • Efficient and flexible tools for assembling raw sequencing data into analytical workflows are still developing.
  • The previous version, aTRAM 1.0, could assemble any locus but lacked efficiency and comprehensive functionality.

Purpose of the Study:

  • To re-implement and enhance the aTRAM software for improved efficiency and functionality in assembling genome sequencing data.
  • To provide a versatile toolkit for diverse downstream analytical workflows.
  • To accelerate read retrieval and broaden assembly capabilities.

Main Methods:

  • Complete re-implementation of the aTRAM software architecture.
  • Optimization of databasing strategies for faster read retrieval.
  • Addition of new features including single- or paired-end data assembly, utilization of both read directions, a de novo assembly module, and automated phylogenomic pipelines.

Main Results:

  • aTRAM 2.0 demonstrates substantially increased speed across all applications compared to aTRAM 1.0 due to optimized databasing.
  • The new version offers enhanced flexibility and functionality, supporting a wider range of assembly tasks.
  • Integrated pipelines streamline common workflows in phylogenomics.

Conclusions:

  • aTRAM 2.0 represents a major advancement in genome data assembly tools.
  • The software provides a faster, more flexible, and comprehensive solution for assembling genome sequencing data.
  • aTRAM 2.0 is well-suited for various applications in genomics and phylogenomics research.