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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Related Experiment Video

Updated: May 12, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

TRAINER: a general-purpose trainable short biosequence classifer.

Hasan Ogul1, Alper T Kalkan, Sinan U Umu

  • 1Department of Computer Engineering, Baskent University, Ankara, Turkey. hogul@baskent.edu.tr

Protein and Peptide Letters
|April 3, 2013
PubMed
Summary

TRAINER is a novel online platform for generic sequence classification, allowing custom datasets and alphabets. It offers flexible machine learning options and enables model sharing for DNA and protein sequence analysis.

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Published on: January 25, 2019

Area of Science:

  • Computational Biosciences
  • Bioinformatics
  • Machine Learning in Biology

Background:

  • Sequence classification is a core challenge in computational biosciences.
  • Existing tools are often problem-specific and limited by predefined alphabets.
  • A versatile platform for diverse sequence classification tasks is needed.

Purpose of the Study:

  • To introduce TRAINER, a generic online sequence classification platform.
  • To enable users to define their own training data and alphabets.
  • To provide a flexible framework for various sequence classification problems.

Main Methods:

  • TRAINER offers multiple feature representation schemes.
  • Users can select from various supervised machine learning algorithms.
  • Trained models can be saved and reused by other users.

Main Results:

  • Demonstrated effective use of TRAINER for DNA and protein sequences.
  • Successfully performed candidate effector prediction.
  • Accurately predicted nucleolar localization signals.

Conclusions:

  • TRAINER provides a flexible and generic solution for sequence classification.
  • The platform supports diverse biological sequence data and custom alphabets.
  • It facilitates reproducible research through model sharing.