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The emergence of viral variants with altered phenotypes is a public health challenge underscoring the need for advanced evolutionary forecasting methods. Given extensive epistatic interactions within viral genomes and known viral evolutionary history, efficient genomic surveillance necessitates early detection of emerging viral haplotypes rather than commonly targeted single mutations. Haplotype inference, however, is a significantly more challenging problem precluding the use of traditional approaches. Here, using SARS-CoV-2 evolutionary dynamics as a case study, we show that emerging haplotypes with altered transmissibility can be linked to dense communities in coordinated substitution networks, which become discernible significantly earlier than the haplotypes become prevalent. From these insights, we develop a computational framework for inference of viral variants and validate it by successful early detection of known SARS-CoV-2 strains. Our methodology offers greater scalability than phylogenetic lineage tracing and can be applied to any rapidly evolving pathogen with adequate genomic surveillance data.

 

Lyme disease (LD), the most prevalent tick-borne disease of humans in the Northern Hemisphere, is caused by the spirochetal bacterium of Borreliella burgdorferi ( Bb ) sensu lato complex. In nature, Bb spirochetes are continuously transmitted between Ixodes ticks and mammalian or avian reservoir hosts. Peromyscus leucopus mice are considered the primary mammalian reservoir of Bb in the United States. Earlier studies demonstrated that experimentally infected P. leucopus mice do not develop disease. In contrast, C3H mice, a widely used laboratory strain of Mus musculus in the LD field, develop severe Lyme arthritis. To date, the exact tolerance mechanism of P. leucopus mice to Bb -induced infection remains unknown. To address this knowledge gap, the present study has compared spleen transcriptomes of P. leucopus and C3H/HeJ mice infected with Bb strain 297 with those of their respective uninfected controls. Overall, the data showed that the spleen transcriptome of Bb -infected P. leucopus mice was much more quiescent compared to that of the infected C3H mice. To date, the current investigation is one of the few that have examined the transcriptome response of natural reservoir hosts to Borreliella infection. Although the experimental design of this study significantly differed from those of two previous investigations, the collective results of the current and published studies have consistently demonstrated very limited transcriptomic responses of different reservoir hosts to the persistent infection of LD pathogens. Importance The bacterium Borreliella burgdorferi ( Bb ) causes Lyme disease, which is one of the emerging and highly debilitating human diseases in countries of the Northern Hemisphere. In nature, Bb spirochetes are maintained between hard ticks of Ixodes spp. and mammals or birds. In the United States, the white-footed mouse, Peromyscus leucopus , is one of the main Bb reservoirs. In contrast to humans and laboratory mice (e.g., C3H mice), white-footed mice rarely develop clinical signs (disease) despite being (persistently) infected with Bb . How the white-footed mouse tolerates Bb infection is the question that the present study has attempted to address. Comparisons of genetic responses between Bb -infected and uninfected mice demonstrated that, during a long-term Bb infection, C3H mice reacted much stronger, whereas P. leucopus mice were relatively unresponsive. 

Abstract Aligning sequencing reads onto a reference is an essential step of the majority of genomic analysis pipelines. Computational algorithms for read alignment have evolved in accordance with technological advances, leading to today’s diverse array of alignment methods. We provide a systematic survey of algorithmic foundations and methodologies across 107 alignment methods, for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. We discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology. 

Abstract Rapidly evolving RNA viruses continuously produce minority haplotypes that can become dominant if they are drug-resistant or can better evade the immune system. Therefore, early detection and identification of minority viral haplotypes may help to promptly adjust the patient’s treatment plan preventing potential disease complications. Minority haplotypes can be identified using next-generation sequencing, but sequencing noise hinders accurate identification. The elimination of sequencing noise is a non-trivial task that still remains open. Here we propose CliqueSNV based on extracting pairs of statistically linked mutations from noisy reads. This effectively reduces sequencing noise and enables identifying minority haplotypes with the frequency below the sequencing error rate. We comparatively assess the performance of CliqueSNV using an in vitro mixture of nine haplotypes that were derived from the mutation profile of an existing HIV patient. We show that CliqueSNV can accurately assemble viral haplotypes with frequencies as low as 0.1% and maintains consistent performance across short and long bases sequencing platforms.