Jindan Guo1, Erli Pang1, Hongtao Song1, Kui Lin2. 1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, China. 2. State Key Laboratory of Earth Surface Processes and Resource Ecology, Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, China. linkui@bnu.edu.cn.
Abstract
BACKGROUND: With the rapid development of accurate sequencing and assembly technologies, an increasing number of high-quality chromosome-level and haplotype-resolved assemblies of genomic sequences have been derived, from which there will be great opportunities for computational pangenomics. Although genome graphs are among the most useful models for pangenome representation, their structural complexity makes it difficult to present genome information intuitively, such as the linear reference genome. Thus, efficiently and accurately analyzing the genome graph spatial structure and coordinating the information remains a substantial challenge. RESULTS: We developed a new method, a colored superbubble (cSupB), that can overcome the complexity of graphs and organize a set of species- or population-specific haplotype sequences of interest. Based on this model, we propose a tri-tuple coordinate system that combines an offset value, topological structure and sample information. Additionally, cSupB provides a novel method that utilizes complete topological information and efficiently detects small indels (< 50 bp) for highly similar samples, which can be validated by simulated datasets. Moreover, we demonstrated that cSupB can adapt to the complex cycle structure. CONCLUSIONS: Although the solution is made suitable for increasingly complex genome graphs by relaxing the constraint, the directed acyclic graph, the motif cSupB and the cSupB method can be extended to any colored directed acyclic graph. We anticipate that our method will facilitate the analysis of individual haplotype variants and population genomic diversity. We have developed a C + + program for implementing our method that is available at https://github.com/eggleader/cSupB .
BACKGROUND: With the rapid development of accurate sequencing and assembly technologies, an increasing number of high-quality chromosome-level and haplotype-resolved assemblies of genomic sequences have been derived, from which there will be great opportunities for computational pangenomics. Although genome graphs are among the most useful models for pangenome representation, their structural complexity makes it difficult to present genome information intuitively, such as the linear reference genome. Thus, efficiently and accurately analyzing the genome graph spatial structure and coordinating the information remains a substantial challenge. RESULTS: We developed a new method, a colored superbubble (cSupB), that can overcome the complexity of graphs and organize a set of species- or population-specific haplotype sequences of interest. Based on this model, we propose a tri-tuple coordinate system that combines an offset value, topological structure and sample information. Additionally, cSupB provides a novel method that utilizes complete topological information and efficiently detects small indels (< 50 bp) for highly similar samples, which can be validated by simulated datasets. Moreover, we demonstrated that cSupB can adapt to the complex cycle structure. CONCLUSIONS: Although the solution is made suitable for increasingly complex genome graphs by relaxing the constraint, the directed acyclic graph, the motif cSupB and the cSupB method can be extended to any colored directed acyclic graph. We anticipate that our method will facilitate the analysis of individual haplotype variants and population genomic diversity. We have developed a C + + program for implementing our method that is available at https://github.com/eggleader/cSupB .
Authors: Goran Rakocevic; Vladimir Semenyuk; Wan-Ping Lee; James Spencer; John Browning; Ivan J Johnson; Vladan Arsenijevic; Jelena Nadj; Kaushik Ghose; Maria C Suciu; Sun-Gou Ji; Gülfem Demir; Lizao Li; Berke Ç Toptaş; Alexey Dolgoborodov; Björn Pollex; Iosif Spulber; Irina Glotova; Péter Kómár; Andrew L Stachyra; Yilong Li; Milos Popovic; Morten Källberg; Amit Jain; Deniz Kural Journal: Nat Genet Date: 2019-01-14 Impact factor: 38.330
Authors: Michael A Brockhurst; Ellie Harrison; James P J Hall; Thomas Richards; Alan McNally; Craig MacLean Journal: Curr Biol Date: 2019-10-21 Impact factor: 10.834