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Converting single nucleotide variants (SNVs) between human genome reference builds like GRCh37 and GRCh38 can be problematic. This study introduces an algorithm to identify unstable positions, ensuring more reliable data conversion for next-generation sequencing studies.

Keywords:
CrossMapGRCh37GRCh38genome build conversionliftOver

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) relies on high-quality reference genomes for accurate single nucleotide variant (SNV) calling.
  • Comparing genomic data across different human genome reference builds (e.g., GRCh37, GRCh38) is challenging due to non-comparable position information.
  • Existing conversion tools like liftOver and CrossMap may produce inaccurate SNV positions or chromosome changes.

Purpose of the Study:

  • To develop a novel algorithm for identifying unstable positions during human genome reference build conversions.
  • To provide a method for ensuring accurate SNV conversion between GRCh37 and GRCh38.
  • To improve the reliability of compiling sequencing resources and comparing results across studies.

Main Methods:

  • Developed a novel algorithm to detect unstable genomic positions independent of specific conversion tools.
  • Algorithm identifies unstable positions based on chain files that map contiguous positions between builds.
  • Provided a list of unstable positions for GRCh37 to GRCh38 conversion and demonstrated pre-exclusion strategy.

Main Results:

  • Identified specific positions that are unstable when converting between GRCh37 and GRCh38.
  • Pre-excluding SNVs at these unstable positions before conversion yields stable and reliable results.
  • The proposed method achieves the same high-confidence converted SNV list as post-conversion filtering.

Conclusions:

  • Careful consideration of genome build conversion is crucial for accurate genomic data analysis.
  • The novel algorithm and list of unstable positions offer a simple yet effective method for enhancing data confidence.
  • This approach facilitates more reliable comparisons of sequencing data across different human genome reference builds.