HPV16-Genotyper: A Computational Tool for Risk-Assessment, Lineage Genotyping and Recombination Detection in HPV16 Sequences, Based on a Large-Scale Evolutionary Analysis

Previous analyses have identified certain but limited evidence of recombination among HPV16 genomes, in accordance with a general perception that DNA viruses do not frequently recombine. In this evolutionary/bioinformatics study we have analyzed more than 3600 publicly available complete and partial HPV16 genomes. By studying the phylogenetic incongruence, similarity plots and the distribution patterns of lineage-specific SNPs, we identify several potential recombination events between the two major HPV16 evolutionary clades. These two clades comprise the (widely considered) phenotypically more benign (lower risk) lineage A and the (widely considered) phenotypically more aggressive (higher risk) non-European lineages B, C and D. We observe a frequency of potential recombinant sequences ranging between 0.3 and 1.2% which is low, but nevertheless considerable. Our findings have clinical implications and highlight that HPV16 genotyping and risk assessment based only on certain genomic regions and not the entire genome may provide a false genotype and, therefore, its associated risk estimate. Finally, based on this analysis, we have developed a bioinformatics tool that automates the entire process of HPV16 lineage genotyping, recombination detection and further identifies, within the submitted sequences, SNPs that have been reported in the literature to increase the risk of cancer.

Revisiting the recombinant history of HIV-1 group M with dynamic network community detection

In previous work, we used a comprehensive sliding-window molecular clock analysis of near full length HIV-1 genomes to find that different regions of the virus genome yield significantly different estimates of the time to the most recent common ancestor. This finding, together with other evidence of the deep recombinant history of SIV and HIV, is not consistent with the standard model of the evolutionary history of HIV-1/M where non-recombinant subtypes are globally distributed through founder effects, followed by occasional inter-subtype recombination. We propose to re-examine the history of HIV-1/M using recombination detection methods that do not rely on predefined non-recombinant genomes. Here we describe an unsupervised non-parametric clustering approach to this problem by adapting a community detection method developed for the analysis of dynamic social networks. We compared our method to other reference-free recombination detection programs, namely GARD (HyPhy) and RDP (versions 4 and 5). We simulated recombination events by swapping randomly sampled branches in a time-scaled tree, which we reconstructed from subtype reference sequences in BEAST. Extant sequences were simulated for each tree using INDELible, and concatenated segments delimited by randomly sampled breakpoints from the resulting alignments to form the recombinant sequences. We show that our community detection method outperforms GARD, RDP4 and RDP5 in detecting recombinant breakpoints in simulated data, with a significantly lower mean error rate (Wilcoxon test, P < 0:05). Our method groups HIV-1 into 25 communities and detects evidence of inter-subtype recombination in pure subtype reference genomes obtained from Los Alamos HIV sequence database. For instance, we estimate that sub-subtypes A1 and A2 may contain large fragments from subtype C, while subtype C seems to contain fragments from subtype G. Our method provides a new reference-free framework for detecting recombination in viral genomes, and network communities may provide an alternative framework for HIV-1 classification.

Phylogenetic and recombination analyses of two deformed wing virus strains from different honeybee species in China

Background Deformed wing virus (DWV) is one of many viruses that infect honeybees and has been extensively studied because of its close association with honeybee colony collapse that is induced by Varroa destructor. However, virus genotypes, sequence characteristics, and genetic variations of DWV remain unknown in China. Methods Two DWV strains were isolated from Jinzhou and Qinhuangdao cities in China, and were named China1-2017 (accession number: MF770715) and China2-2018 (accession number: MH165180), respectively, and their complete genome sequences were analyzed. To investigate the phylogenetic relationships of the DWV isolates, a phylogenetic tree of the complete open reading frame (ORF), structural protein VP1, and non-structural protein 3C+RdRp of the DWV sequences was constructed using the MEGA 5.0 software program. Then, the similarity and recombinant events of the DWV isolated strains were analyzed using recombination detection program (RDP4) software and genetic algorithm for recombination detection (GARD). Results The complete genomic analysis showed that the genomes of the China1-2017 and China2-2018 DWV strains consisted of 10,141 base pairs (bp) and 10,105 bp, respectively, and contained a single, large ORF (China1-2017: 1,146–9,827 bp; China2-2018: 1,351–9,816 bp) that encoded 2,894 amino acids. The sequences were compared with 20 previously reported DWV sequences from different countries and with sequences of two closely related viruses, Kakugo virus (KV) and V. destructor virus-1. Multiple sequence comparisons revealed a nucleotide identity of 84.3–96.7%, and identity of 94.7–98.6% in amino acids between the two isolate strains and 20 reference strains. The two novel isolates showed 96.7% nucleotide identity and 98.1% amino acid identity. The phylogenetic analyses showed that the two isolates belonged to DWV Type A and were closely related to the KV-2001 strain from Japan. Based on the RDP4 and GARD analyses, the recombination of the China2-2018 strain was located at the 4,266–7,507 nt region, with Korea I-2012 as an infer unknown parent and China-2017 as a minor parent, which spanned the entire helicase ORF. To the best of our knowledge, this is the first study to the complete sequence of DWV isolated from Apis cerana and the possible DWV recombination events in China. Our findings are important for further research of the phylogenetic relationship of DWVs in China with DWV strains from other countries and also contribute to the understanding of virological properties of these complex DWV recombinants.

CleanRecomb, a quick tool for recombination detection in SNP based cluster analysis

We present CleanRecomb, a tool to quickly filter a SNP matrix for likely recombination events.MethodThe method evaluates segments with identical SNP profiles over the genome, based on the assumption that SNPs in the absense of recombination events are uniformly distributed across the genome. The method is evaluated on a set of 9 ST200 E. coli genome sequences.ResultsThe detected recombination events coincide with regions of elevated SNP density.

Synonymous and nonsynonymous distances help untangle convergent evolution and recombination

When estimating a phylogeny from a multiple sequence alignment, researchers often assume the absence of recombination. However, if recombination is present, then tree estimation and all downstream analyses will be impacted, because different segments of the sequence alignment support different phylogenies. Similarly, convergent selective pressures at the molecular level can also lead to phylogenetic tree incongruence across the sequence alignment. Current methods for detection of phylogenetic incongruence are not equipped to distinguish between these two different mechanisms and assume that the incongruence is a result of recombination or other horizontal transfer of genetic information. We propose a new recombination detection method that can make this distinction, based on synonymous codon substitution distances. Although some power is lost by discarding the information contained in the nonsynonymous substitutions, our new method has lower false positive probabilities than the comparable recombination detection method when the phylogenetic incongruence signal is due to convergent evolution. We apply our method to three empirical examples, where we analyze: (1) sequences from a transmission network of the human immunodeficiency virus, (2)