The lack of virus fossilization precludes any references or ancestors for inferring evolutionary processes, and viruses have no cell structure, metabolism, or space to reproduce outside host cells. Most mutations yielding high pathogenicity become removed from the population, but adaptive mutations could be epidemically transmitted and fixed in the population. Therefore, determining how viruses originated, how they diverged and how an infectious disease was transmitted are serious challenges. To predict potential epidemic outbreaks, we tested our strategy, Epi-Clock, which applies the ZHU algorithm on different SARS-CoV-2 datasets before outbreaks to search for real significant mutational accumulation patterns correlated with the outbreak events. We imagine that specific amino acid substitutions are triggers for outbreaks. Surprisingly, some inter-species genetic distances of Coronaviridae were shorter than the intra-species distances, which may represent the intermediate states of different species or subspecies in the evolutionary history of Coronaviridae. The insertions and deletions of whole genome sequences between different hosts were separately associated with new functions or turning points, clearly indicating their important roles in the host transmission and shifts of Coronaviridae. Furthermore, we believe that non-nucleosomal DNA may play dominant roles in the divergence of different lineages of SARS-CoV-2 in different regions of the world because of the lack of nucleosome protection. We suggest that strong selective variation among different lineages of SARS-CoV-2 is required to produce strong codon usage bias, significantly appear in B.1.640.2 and B.1.617.2 (Delta). Interestingly, we found that an increasing number of other types of substitutions, such as those resulting from the hitchhiking effect, have accumulated, especially in the pre-breakout phase, even though some previous substitutions were replaced by other dominant genotypes. From most validations, we could accurately predict the potential pre-phase of outbreaks with a median interval of 5 days before. Using our pipeline, users may review updated information on the website https://bioinfo.liferiver.com.cn with easy registration.

Background The lack of virus fossilization precludes any references or ancestors for inferring evolutionary processes, and viruses have no cell structure, metabolism, or space to reproduce outside host cells. Most mutations yielding high pathogenicity go extinct from the population, but adaptive mutations could be epidemically transmitted and fixed in the population. Therefore, determining how viruses originated, how they diverged and how an infectious disease was transmitted are serious challenges. Methods To predict potential epidemic outbreaks, we tested our strategy, Epi-Clock, which applies the ZHU algorithm on different SARS-CoV-2 datasets before outbreaks to search for real significant mutational accumulation patterns correlated with the outbreak events. We imagine that specific amino acid substitutions would be triggers for outbreaks. Results Surprisingly, some inter-species genetic distances of Coronaviridae were shorter than the intra-species distances, which may represent the intermediate states of different species or subspecies in the evolutionary history of Coronaviridae. The insertions and deletions of whole genome sequences between different hosts were separately associated with new functions or turning points, clearly indicating their important roles in the host transmission and shifts of Coronaviridae. Furthermore, we believe that non-nucleosomal DNA may play dominant roles in the divergence of different lineages of SARS-CoV-2 in different regions of the world because of the lack of nucleosome protection. We suggest that strong selective variation among different lineages of SARS-CoV-2 is required to produce strong codon usage bias. Interestingly, we found that an increasing number of other types of substitutions, such as those resulting from the hitchhiking effect, have accumulated, especially in the pre-breakout phase, even though some previous substitutions were replaced by other dominant genotypes. From most validations, we could accurately predict the potential pre-phase of outbreaks with a median interval of 5 days before. Using our pipeline, users may review updated information on the website https://bioinfo.liferiver.com.cn with easy registration. Conclusions Here, we propose Epi-Clock, a sensitive platform to help understand pathogenic disease outbreaks and facilitate the response to future outbreaks, similar to a clock that can signal the need to assist individuals at focal locations by using diagnostics, isolation control, vaccines or therapy at any time.

BackgroundSince DNA concentrations of pathogenic microorganisms in biological samples were mostly very low and close to the detection limit with time consuming and costly with poor accuracy, pathogen detection was lack of shared universal phylogenetic repetitive biomarkers and utilization has become one of the most challenging aspects in clinical applications. Limited by failing to cover updated epidemic testing samples, some service was hard to practice in clinical applications without customized settings in order to lower the quality of nucleic acid detection reagents. Furthermore, in order to maintain the high conservations, biomarker combinations may not be cost effective and could cause several experimental mistakes in actual mechanical processes in many clinical settings. With the limit of recent developing technology and knowledge, 16s rRNA was too conservative to distinguish the closely related species, and likely mosaic plasmid was always not effective because of its unevenly distributed across prokaryotic taxa. ResultsHere we provided a new strategy, Shine, to explore specific, sensitive and conserved biomarkers from massive microbial genomic data within intra-populations in order to improve detection sensitivity and accuracy. Distinguished with simple concatenations with blast-based filtering, our method was constructed by a de novo genome alignment-based pipeline to explore the original and specific repetitive biomarkers of the defined intra-population to cover all members to detect newly discovered multi-copy conserved species-specific or even subspecies-specific target probes and primers sets, which has been successfully applied on a number of clinical projects and have the overwhelming advantages of quantitative systematic and automated detection of all pathogenic microorganisms without limits of genome annotation and incompletely assembled motifs. Based on our strategy, users may select different configuration parameters depending on the purpose of the project to realize routine clinical detection practices on the website https://bioinfo.liferiver.com.cn with easy-to-go registration.ConclusionsIt is recommended that our strategy is suitable to identify shared universal phylogenetic markers with few false positive or false negative errors and to automate the design of minimal high effective primers and probes to detect pathogenic communities with cost-effective predictive power.

Determining how viruses originated and diverged and how infectious diseases are transmitted are serious challenges. Surprisingly, some inter-species genetic distances among Coronaviridae were shorter than intra-species distances, possibly representing the intermediate states of different species or subspecies in the evolutionary history of Coronaviridae. The indels of whole genome sequences between different hosts were separately associated with new functions or turning points, clearly indicating their important roles in the host transmission and shifts of Coronaviridae. Furthermore, we believe that non-nucleosomal DNA may play dominant roles in the divergence of different lineages of SARS-CoV-2 in different regions of the world because of the lack of nucleosome protection. We suggest that strong selective variation among different lineages of SARS-CoV-2 is required to produce strong codon usage bias. Interestingly, we found that an increasing number of other types of substitutions, such as those resulting from the hitchhiking effect, have accumulated, especially in the pre-breakout phase. To predict potential epidemic outbreaks, we tested our strategy, Epi-Clock, which applies the ZHU algorithm on different SARS-CoV-2 datasets to search for real significant mutational accumulation patterns correlated with the outbreak events. We could accurately predict the potential pre-phase of outbreaks with a median interval of 5 days before the outbreaks. Using our pipeline, users may review updated information on the website https://bioinfo.liferiver.com.cn with easy registration. Therefore, we propose Epi-Clock, a sensitive platform similar to a clock that can signal the need to assist individuals at focal locations by using diagnostics, isolation control, vaccines or therapy at any time.