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Positional bias in variant calls against draft reference assemblies.

Roman V BriskineKentaro K Shimizu
Published in: BMC genomics (2017)
Draft genome sequences generated by several popular assemblers appear to be susceptible to the positional bias potentially affecting many resequencing projects in non-model species. The bias is inherent to the assembly algorithms and arises from their particular handling of repeated sequences. It is recommended to reduce the bias by filtering especially if higher-quality genome assembly cannot be achieved. Our findings can help other researchers to improve the quality of their variant data sets and reduce artefactual findings in downstream analyses.
Keyphrases
  • quality improvement
  • machine learning
  • genome wide
  • big data
  • electronic health record
  • gene expression
  • dna methylation
  • artificial intelligence