Quantifying mitochondrial heteroplasmy diversity: A computational approach

Biodiversity plays a pivotal role in sustaining ecosystem processes, encompassing diverse biological species, genetic types and the intricacies of ecosystem composition. However, the precise definition of biodiversity at the individual level remains a challenging endeavour. Hill numbers, derived from Rényi's entropy, have emerged as a popular measure of diversity, with a recent unified framework extending their application across various levels, from genetics to ecosystems. In this study, we employ a computational approach to exploring the diversity of mitochondrial heteroplasmy using real-world data. By adopting Hill numbers with q = 2, we demonstrate the feasibility of quantifying mitochondrial heteroplasmy diversity within and between individuals and populations. Furthermore, we investigate the alpha diversity of mitochondrial heteroplasmy among different species, revealing heterogeneity at multiple levels, including mitogenome components and protein-coding genes (PCGs). Our analysis explores large-scale mitochondrial heteroplasmy data in humans, examining the relationship between alpha diversity at the mitogenome components and PCGs level. Notably, we do not find a significant correlation between these two levels. Additionally, we observe significant correlations in alpha diversity between mothers and children in blood samples, exceeding the reported R2 value for allele frequency correlations. Moreover, our investigation of beta diversity and local overlay similarity demonstrates that heteroplasmy variant distributions in different tissues of children more closely resemble those of their mothers. Through systematic quantification and analysis of mitochondrial heteroplasmy diversity, this study enhances our understanding of heterogeneity at multiple levels, from individuals to populations, providing new insights into this fundamental dimension of biodiversity.

The Utilization of Reference-Guided Assembly and In Silico Libraries Improves the Draft Genome of Clarias batrachus and Culter alburnus

Long-read sequencing technologies can generate highly contiguous genome assemblies compared to short-read methods. However, their higher cost often poses a significant barrier. To address this, we explore the utilization of mapping-based genome assembly and reference-guided assembly as cost-effective alternative approaches. We assess the efficacy of these approaches in improving the contiguity of Clarias batrachus and Culter alburnus draft genomes. Our findings demonstrate that employing an iterative mapping strategy leads to a reduction in assembly errors. Specifically, after three iterations, the Mismatches per 100 kbp value for the C. batrachus genome decreased from 2447.20 to 2432.67, reaching a minimum of 2422.67 after two iterations. Additionally, the N50 value for the C. batrachus genome increased from 362,143 to 1,315,126 bp, with a maximum of 1,315,403 bp after two iterations. Furthermore, we achieved Mismatches per 100 kbp values of 3.70 for the reference-guided assembly of C. batrachus and 0.34 for C. alburnus. Correspondingly, the N50 value for the C. batrachus and C. alburnus genomes increased from 362,143 bp and 3,686,385 bp to 2,026,888 bp and 43,735,735 bp, respectively. Finally, we successfully utilized the improved C. batrachus and C. alburnus genomes to compare genome studies using the combined approach of Ragout and Ragtag. Through a comprehensive comparative analysis of mapping-based and reference-guided genome assembly methods, we shed light on the specific contributions of reference-guided assembly in reducing assembly errors and improving assembly continuity and integrity. These advancements establish reference-guided assembly and the utilization of in silico libraries as a promising and suitable approach for comparative genomics studies.