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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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Related Experiment Video

Updated: Nov 17, 2025

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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FiNGS: high quality somatic mutations using filters for next generation sequencing.

Christopher Paul Wardell1, Cody Ashby2, Michael Anton Bauer2

  • 1Department of Biomedical Informatics, University of Arkansas for Medical Sciences, 4301 W Markham St, Little Rock, AR, 72205, USA. cpwardell@uams.edu.

BMC Bioinformatics
|February 19, 2021
PubMed
Summary

Filters for Next Generation Sequencing (FiNGS) software improves somatic variant calling precision by reducing false positives in cancer sequencing data. This reproducible tool enhances downstream analysis by offering configurable filters and easy integration into existing pipelines.

Keywords:
CancerDNAFilteringGenomicsMutationsNext generation sequencingQuality controlSequence analysisSequencingSnvs

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Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Somatic variant callers identify mutations in cancer sequencing data, offering high sensitivity but often suffering from low precision and numerous false positives.
  • Current methods require ad hoc filtering post-variant calling, highlighting an unmet need for reproducible somatic variant filtering.
  • Filters for Next Generation Sequencing (FiNGS) was developed to address these specific challenges in somatic variant analysis.

Purpose of the Study:

  • To develop and evaluate a novel software tool, FiNGS, for reproducible filtering of somatic variants.
  • To improve the precision of somatic variant calls generated by standard callers.
  • To provide a user-friendly and configurable solution for somatic variant filtering.

Main Methods:

  • FiNGS software was developed using Python.
  • The tool was tested on publicly available sequencing data sets.
  • Performance was benchmarked against default variant caller outputs and other existing filtering tools.

Main Results:

  • FiNGS demonstrated reliable improvement in the precision of somatic variant calls.
  • The software outperformed other tools designed for somatic variant filtering.
  • FiNGS effectively reduces false positives while maintaining high recall.

Conclusions:

  • FiNGS offers a simple, deployable, and user-configurable tool for reproducible somatic variant filtering.
  • The software integrates seamlessly into existing sequencing pipelines by using standard Variant Call Format (VCF) files.
  • FiNGS empowers researchers to develop, implement, and share custom filtering strategies.