MERIT reveals the impact of genomic context on sequencing error rate in ultra-deep applications

Zoonotic origin of the human malaria parasite Plasmodium malariae from African apes

The human parasite Plasmodium malariae has relatives infecting African apes (Plasmodium rodhaini) and New World monkeys (Plasmodium brasilianum), but its origins remain unknown. Using a novel approach to characterise P. malariae-related sequences in wild and captive African apes, we found that this group comprises three distinct lineages, one of which represents a previously unknown, highly divergent species infecting chimpanzees, bonobos and gorillas across central Africa. A second ape-derived lineage is much more closely related to the third, human-infective lineage P. malariae, but exhibits little evidence of genetic exchange with it, and so likely represents a separate species. Moreover, the levels and nature of genetic polymorphisms in P. malariae indicate that it resulted from the zoonotic transmission of an African ape parasite, reminiscent of the origin of P. falciparum. In contrast, P. brasilianum falls within the radiation of human P. malariae, and thus reflects a recent anthroponosis.

Plasmodium malariae is a cause of malaria in humans and related species have been identified in non-human primates. Here, the authors use genomic analyses to establish that human P. malariae arose from a host switch of an ape parasite whilst a species infecting New World monkeys can be traced to a reverse zoonosis.

SimuSCoP: reliably simulate Illumina sequencing data based on position and context dependent profiles

BACKGROUND

A number of simulators have been developed for emulating next-generation sequencing data by incorporating known errors such as base substitutions and indels. However, their practicality may be degraded by functional and runtime limitations. Particularly, the positional and genomic contextual information is not effectively utilized for reliably characterizing base substitution patterns, as well as the positional and contextual difference of Phred quality scores is not fully investigated. Thus, a more effective and efficient bioinformatics tool is sorely required.

RESULTS

Here, we introduce a novel tool, SimuSCoP, to reliably emulate complex DNA sequencing data. The base substitution patterns and the statistical behavior of quality scores in Illumina sequencing data are fully explored and integrated into the simulation model for reliably emulating datasets for different applications. In addition, an integrated and easy-to-use pipeline is employed in SimuSCoP to facilitate end-to-end simulation of complex samples, and high runtime efficiency is achieved by implementing the tool to run in multithreading with low memory consumption. These features enable SimuSCoP to gets substantial improvements in reliability, functionality, practicality and runtime efficiency. The tool is comprehensively evaluated in multiple aspects including consistency of profiles, simulation of genomic variations and complex tumor samples, and the results demonstrate the advantages of SimuSCoP over existing tools.

CONCLUSIONS

SimuSCoP, a new bioinformatics tool is developed to learn informative profiles from real sequencing data and reliably mimic complex data by introducing various genomic variations. We believe that the presented work will catalyse new development of downstream bioinformatics methods for analyzing sequencing data.