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Genome graphs and the evolution of genome inference
Benedict Paten1, Adam M Novak1, Jordan M Eizenga1
1Genomics Institute, CBSE, 501C Engineering 2, University of California Santa Cruz, Santa Cruz, California 95064, USA.
Genome Research
|April 1, 2017
Summary
Genome graphs address limitations in the human reference genome by incorporating common human genetic variation. These graph-based structures are expected to improve genomic analysis, including read mapping and variant calling.
Area of Science:
- Genomics
- Bioinformatics
Background:
- The human reference genome is a foundational tool in biology but excludes significant human genetic variation, leading to reference bias.
- This bias impacts the accuracy and completeness of genomic studies.
Purpose of the Study:
- To survey current projects developing genome graphs.
- To discuss the anticipated improvements in genomic analysis offered by genome graphs.
Main Methods:
- Review of existing projects utilizing graph-based models for genomic data.
- Conceptual framework for genome graphs.
Main Results:
- Genome graphs represent collections of human genomes, integrating common variation.
- Expected improvements in read mapping, variant calling, and haplotype determination.
Conclusions:
- Genome graphs offer a solution to reference bias in human genomics.
- These structures are poised to enhance the precision and scope of genomic analyses.