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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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SETH detects and normalizes genetic variants in text.

Philippe Thomas1, Tim Rocktäschel2, Jörg Hakenberg3

  • 1Language Technology Lab, DFKI Berlin, Germany Knowledge Management in Bioinformatics, Institute for Computer Science, Humboldt-Universität Zu Berlin, Unter Den Linden 6, Berlin 10099, Germany.

Bioinformatics (Oxford, England)
|June 4, 2016
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Summary
This summary is machine-generated.

This study introduces SETH, a novel tool for recognizing and normalizing genetic variations mentioned in biomedical texts. SETH improves the discoverability of specific genetic variations and gene-related information within scientific literature.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Natural Language Processing

Background:

  • Biomedical literature contains extensive data on genetic variations and their effects.
  • Identifying specific genetic variations or all variations within a gene is challenging due to diverse textual descriptions.

Purpose of the Study:

  • To develop a tool, SETH, for automated recognition and normalization of genetic variations from text.
  • To enhance the retrieval and analysis of genetic variation data within biomedical literature.

Main Methods:

  • SETH employs text recognition techniques to identify genetic variations.
  • Normalization is performed against established databases like dbSNP and UniProt.

Main Results:

  • SETH demonstrates high precision and recall in identifying genetic variations across multiple corpora of PubMed abstracts.
  • The tool effectively normalizes identified variations to standard identifiers.

Conclusions:

  • SETH offers a robust solution for extracting and standardizing genetic variation information from scientific texts.
  • The tool's availability and documentation facilitate its integration into broader research applications.