BackgroundMultiple Sequence Alignments (MSAs) are the starting point of molecular evolutionary analyses. Errors in MSAs generate a non-historical signal that can lead to incorrect inferences. Therefore, numerous efforts have been made to reduce the impact of alignment errors, by improving alignment algorithms and by developing methods to filter out poorly aligned regions. However, MSAs do not only contain alignment errors, but also primary sequence errors. Such errors may originate from sequencing errors, from assembly errors, or from erroneous structural annotations (such as incorrect intron/exon boundaries). Even though their existence is acknowledged, the impact of primary sequence errors on evolutionary inference is poorly characterized.ResultsIn a first step to fill this gap, we have developed a program called HmmCleaner, which detects and eliminates these errors from MSAs. It uses profile hidden Markov models (pHMM) to identify sequence segments that poorly fit their MSA and selectively removes them. We assessed its performances using > 700 amino-acid MSAs from prokaryotes and eukaryotes, in which we introduced several types of simulated primary sequence errors. The sensitivity of HmmCleaner towards simulated primary sequence errors was > 95%. In a second step, we compared the impact of segment filtering software (HmmCleaner and PREQUAL) relative to commonly used block-filtering software (BMGE and TrimAI) on evolutionary analyses. Using real data from vertebrates, we observed that segment-filtering methods improve the quality of evolutionary inference more than the currently used block-filtering methods. The formers were especially effective at improving branch length inferences, and at reducing false positive rate during detection of positive selection.ConclusionsSegment filtering methods such as HmmCleaner accurately detect simulated primary sequence errors. Our results suggest that these errors are more detrimental than alignment errors. However, they also show that stochastic (sampling) error is predominant in single-gene evolutionary inferences. Therefore, we argue that MSA filtering should focus on segment instead of block removal and that more studies are required to find the optimal balance between accuracy improvement and stochastic error increase brought by data removal.Electronic supplementary materialThe online version of this article (10.1186/s12862-019-1350-2) contains supplementary material, which is available to authorized users.

Neurodevelopmental disorders (NDDs) are caused by mutations in diverse genes involved in different cellular functions, although there can be crosstalk, or convergence, between molecular pathways affected by different NDDs. To assess molecular convergence, we generated human neural progenitor cell models of 9q34 deletion syndrome, caused by haploinsufficiency of EHMT1, and 18q21 deletion syndrome, caused by haploinsufficiency of TCF4. Using next-generation RNA sequencing, methylation sequencing, chromatin immunoprecipitation sequencing, and whole-genome miRNA analysis, we identified several levels of convergence. We found mRNA and miRNA expression patterns that were more characteristic of differentiating cells than of proliferating cells, and we identified CpG clusters that had similar methylation states in both models of reduced gene dosage. There was significant overlap of gene targets of TCF4 and EHMT1, whereby 8.3% of TCF4 gene targets and 4.2% of EHMT1 gene targets were identical. These data suggest that 18q21 and 9q34 deletion syndromes show significant molecular convergence but distinct expression and methylation profiles. Common intersection points might highlight the most salient features of disease and provide avenues for similar treatments for NDDs caused by different genetic mutations.

BackgroundBisulfite sequencing is the most efficient single nucleotide resolution method for analysis of methylation status at whole genome scale, but improved quality control metrics are needed to better standardize experiments.ResultsWe describe BisQC, a step-by-step method for multiplexed bisulfite-converted DNA library construction, pooling, spike-in content, and bioinformatics. We demonstrate technical improvements for library preparation and bioinformatic analyses that can be done in standard laboratories. We find that decoupling amplification of bisulfite converted (bis) DNA from the indexing reaction is an advantage, specifically in reducing total PCR cycle number and pre-selecting high quality bis-libraries. We also introduce a progressive PCR method for optimal library amplification and size-selection. At the sequencing stage, we thoroughly test the benefits of pooling non-bis DNA library with bis-libraries and find that BisSeq libraries can be pooled with a high proportion of non-bis DNA libraries with minimal impact on BisSeq output. For informatics analysis, we propose a series of optimization steps including the utilization of the mitochondrial genome as a QC standard, and we assess the validity of using duplicate reads for coverage statistics.ConclusionWe demonstrate several quality control checkpoints at the library preparation, pre-sequencing, post-sequencing, and post-alignment stages, which should prove useful in determining sample and processing quality. We also determine that including a significant portion of non-bisulfite converted DNA with bisulfite converted DNA has a minimal impact on usable bisulfite read output.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a generalist virus, infecting and evolving in numerous mammals, including captive and companion animals, free-ranging wildlife, and humans. Transmission among non-human species poses a risk for the establishment of SARS-CoV-2 reservoirs, makes eradication difficult, and provides the virus with opportunities for new evolutionary trajectories, including the selection of adaptive mutations and the emergence of new variant lineages. Here, we use publicly available viral genome sequences and phylogenetic analysis to systematically investigate the transmission of SARS-CoV-2 between human and non-human species and to identify mutations associated with each species. We found the highest frequency of animal-to-human transmission from mink, compared with lower transmission from other sampled species (cat, dog, and deer). Although inferred transmission events could be limited by sampling biases, our results provide a useful baseline for further studies. Using genome-wide association studies, no single nucleotide variants (SNVs) were significantly associated with cats and dogs, potentially due to small sample sizes. However, we identified three SNVs statistically associated with mink and 26 with deer. Of these SNVs, ~⅔ were plausibly introduced into these animal species from local human populations, while the remaining ~⅓ were more likely derived in animal populations and are thus top candidates for experimental studies of species-specific adaptation. Together, our results highlight the importance of studying animal-associated SARS-CoV-2 mutations to assess their potential impact on human and animal health.