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Related Concept Videos

Initiation of Translation02:33

Initiation of Translation

39.2K
Initiating translation is complex because it involves multiple molecules. Initiator tRNA, ribosomal subunits, and eukaryotic initiation factors (eIFs) are all required to assemble on the initiation codon of mRNA. This process consists of several steps that are mediated by different eIFs.
First, the initiator tRNA must be selected from the pool of elongator tRNAs by eukaryotic initiation factor 2 (eIF2). The initiator tRNA (Met-tRNAi) has conserved sequence elements including modified bases at...
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Signal Sequences and Sorting Receptors01:41

Signal Sequences and Sorting Receptors

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Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
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Translation01:31

Translation

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Lesson: Translation
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of...
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Translation01:31

Translation

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Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of Life
Proteins are...
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Nucleotide Excision Repair01:08

Nucleotide Excision Repair

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Overview
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Nucleotide Excision Repair01:38

Nucleotide Excision Repair

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DNA Distortion and Damage
Cells are regularly exposed to mutagens—factors in the environment that can damage DNA and generate mutations. UV radiation is one of the most common mutagens and is estimated to introduce a significant number of changes in DNA. These include bends or kinks in the structure, which can block DNA replication or transcription. If these errors are not fixed, the damage can cause mutations, which in turn can result in cancer or disease depending on which sequences are...
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Related Experiment Video

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Sequencing of mRNA from Whole Blood using Nanopore Sequencing
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Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning.

Haotian Teng1, Minh Duc Cao1, Michael B Hall1

  • 1Institute for Molecular Bioscience, University of Queensland, St Lucia, Brisbane, QLD 4072, Australia.

Gigascience
|April 13, 2018
PubMed
Summary

Chiron, a new deep learning model, deciphers DNA sequences from nanopore signals with high accuracy. This breakthrough in DNA sequencing technology offers faster and cheaper analysis without complex signal processing steps.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Machine Learning

Background:

  • Nanopore sequencing offers faster and cheaper DNA analysis.
  • Accurate DNA sequence deciphering from noisy nanopore signals remains a challenge.

Purpose of the Study:

  • To develop a deep learning model for end-to-end basecalling in nanopore sequencing.
  • To directly translate raw nanopore signals into DNA sequences without segmentation.

Main Methods:

  • Developed Chiron, a novel deep learning model for basecalling.
  • Trained Chiron using a limited dataset of 4,000 reads.
  • Utilized graphics processing units for accelerated basecalling.

Main Results:

  • Chiron achieves state-of-the-art basecalling accuracy, even on novel species.
  • The model directly translates raw signals to DNA sequences, bypassing segmentation.
  • Achieved basecalling speeds exceeding 2,000 bases per second.

Conclusions:

  • Chiron represents a significant advancement in nanopore DNA sequencing.
  • The model offers a more accurate and efficient method for basecalling.
  • Deep learning can overcome challenges in interpreting complex biological signals.