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Structural Classification of Joints01:20

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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Knowledge of anatomy is essential to understand human biology and medicine. Anatomists and health care professionals use standard terminology to describe the human body with more precision and no ambiguity. Anatomical terms have mostly Greek and Latin-derived roots. Because these languages are rarely used in conversation, the meaning of words remains the same. Each term is made up of a root in between the prefixes and suffixes. The root of a term often refers to an organ, tissue, or condition,...
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Domainoid: domain-oriented orthology inference.

Emma Persson1, Mateusz Kaduk1, Sofia K Forslund2,3

  • 1Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Box 1031, 17121, Solna, Sweden.

BMC Bioinformatics
|October 30, 2019
PubMed
Summary
This summary is machine-generated.

Domainoid infers orthologous relationships by analyzing individual protein domains, overcoming limitations of full-length sequence methods. This domain-based approach reveals evolutionary connections missed by traditional orthology inference tools.

Keywords:
Domain orthologOrthologyProtein domain

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Orthology inference traditionally relies on full-length protein sequences.
  • Proteins comprise domains with distinct evolutionary histories, challenging full-length methods.
  • Domain architecture variations impact protein function and evolutionary analysis.

Purpose of the Study:

  • To develop Domainoid, a novel tool for domain-level orthology inference.
  • To address limitations of full-length sequence-based orthology detection.
  • To identify orthologous relationships missed by conventional methods.

Main Methods:

  • Domainoid applies the InParanoid algorithm to individual protein domains.
  • It infers orthologous groups at the domain level.
  • Protein-level orthology is determined by the proportion of orthologous domains.

Main Results:

  • Domainoid detects discordant domain orthologs, highlighting varied evolutionary paths within proteins.
  • The tool identifies numerous orthologous relationships not found by full-length approaches.
  • Domainoid assignments were validated against a standard benchmark and compared with InParanoid.

Conclusions:

  • Domain-based orthology inference significantly expands the detection of evolutionary relationships.
  • Domainoid offers a complementary approach to full-length sequence analysis for orthology.
  • This method enhances our understanding of protein evolution and domain dynamics.