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Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
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Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
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The Lambda Select cII Mutation Detection System
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nala: text mining natural language mutation mentions.

Juan Miguel Cejuela1,2, Aleksandar Bojchevski1,2, Carsten Uhlig1

  • 1TUM, Department of Informatics, Bioinformatics & Computational Biology - i12, Garching, Munich, Germany.

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Summary

A new method, nala, effectively extracts sequence variants from scientific literature, capturing both standard and natural language mentions. This significantly improves upon existing tools, especially for natural language variant data.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Extracting sequence variants from literature is crucial but challenging.
  • Existing methods overlook natural language (NL) mentions, focusing only on standard (ST) notations.
  • A significant portion of articles contain sequence variant information solely in NL.

Purpose of the Study:

  • To develop a method for extracting both ST and NL sequence variant mentions.
  • To evaluate the performance of the new method against existing state-of-the-art tools.

Main Methods:

  • Developed 'nala', a method combining conditional random fields with unsupervised word embeddings from PubMed.
  • Created three new corpora to benchmark named-entity recognition (NER) for sequence variants.
  • Utilized unsupervised word embedding features learned from the entire PubMed database.

Main Results:

  • 'nala' substantially outperformed existing methods like SETH and tmVar.
  • Existing tools missed 33% of unique mentions, which 'nala' successfully identified.
  • For NL mentions, 'nala' achieved 100% unique detection, highlighting its strength in this area.

Conclusions:

  • 'nala' represents a significant advancement in extracting sequence variants from biomedical literature.
  • The method effectively addresses the underutilization of NL mentions.
  • The developed corpora and 'nala' tool are publicly available for research.