Machine Learning Techniques for Classifying the Mutagenic Origins of Point Mutations.

There is increasing interest in developing diagnostics that discriminate individual mutagenic mechanisms in a range of applications that include identifying population-specific mutagenesis and resolving distinct mutation signatures in cancer samples. Analyses for these applications assume that mutagenic mechanisms have a distinct relationship with neighboring bases that allows them to be distinguished. Direct support for this assumption is limited to a small number of simple cases, e.g., CpG hypermutability. We have evaluated whether the mechanistic origin of a point mutation can be resolved using only sequence context for a more complicated case. We contrasted single nucleotide variants originating from the multitude of mutagenic processes that normally operate in the mouse germline with those induced by the potent mutagen N-ethyl-N-nitrosourea (ENU). The considerable overlap in the mutation spectra of these two samples make this a challenging problem. Employing a new, robust log-linear modeling method, we demonstrate that neighboring bases contain information regarding point mutation direction that differs between the ENU-induced and spontaneous mutation variant classes. A logistic regression classifier exhibited strong performance at discriminating between the different mutation classes. Concordance between the feature set of the best classifier and information content analyses suggest our results can be generalized to other mutation classification problems. We conclude that machine learning can be used to build a practical classification tool to identify the mutation mechanism for individual genetic variants. Software implementing our approach is freely available under an open-source license.

Species abundance information improves sequence taxonomy classification accuracy.

Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that classification accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate a significant increase in the species-level classification accuracy across common sample types. At the species level, overall average error rates decline from 25% to 14%, which is favourably comparable to the error rates that existing classifiers achieve at the genus level (16%). Our findings indicate that for most practical purposes, the assumption that reference species are equally likely to be observed is untenable. q2-clawback provides a straightforward alternative for samples from common environments.

Author Correction: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Did aculeate silk evolve as an antifouling material?

Many of the challenges we currently face as an advanced society have been solved in unique ways by biological systems. One such challenge is developing strategies to avoid microbial infection. Social aculeates (wasps, bees and ants) mitigate the risk of infection to their colonies using a wide range of adaptations and mechanisms. These adaptations and mechanisms are reliant on intricate social structures and are energetically costly for the colony. It seems likely that these species must have had alternative and simpler mechanisms in place to ensure the maintenance of hygienic domicile conditions prior to the evolution of these complex behaviours. Features of the aculeate coiled-coil silk proteins are reminiscent of those of naturally occurring α-helical antimicrobial peptides (AMPs). In this study, we demonstrate that peptides derived from the aculeate silk proteins have antimicrobial activity. We reconstruct the predicted ancestral silk sequences of an aculeate ancestor that pre-dates the evolution of sociality and demonstrate that these ancestral sequences also contained peptides with antimicrobial properties. It is possible that the silks evolved as an antifouling material and facilitated the evolution of sociality. These materials serve as model materials for consideration in future biomaterial development.

Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin.

BACKGROUND: Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. RESULTS: We present q2-feature-classifier ( https://github.com/qiime2/q2-feature-classifier ), a QIIME 2 plugin containing several novel machine-learning and alignment-based methods for taxonomy classification. We evaluated and optimized several commonly used classification methods implemented in QIIME 1 (RDP, BLAST, UCLUST, and SortMeRNA) and several new methods implemented in QIIME 2 (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods based on VSEARCH, and BLAST+) for classification of bacterial 16S rRNA and fungal ITS marker-gene amplicon sequence data. The naive-Bayes, BLAST+-based, and VSEARCH-based classifiers implemented in QIIME 2 meet or exceed the species-level accuracy of other commonly used methods designed for classification of marker gene sequences that were evaluated in this work. These evaluations, based on 19 mock communities and error-free sequence simulations, including classification of simulated "novel" marker-gene sequences, are available in our extensible benchmarking framework, tax-credit ( https://github.com/caporaso-lab/tax-credit-data ). CONCLUSIONS: Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for these classifiers under a range of standard operating conditions. q2-feature-classifier and tax-credit are both free, open-source, BSD-licensed packages available on GitHub.

q2-sample-classifier: machine-learning tools for microbiome classification and regression.

q2-sample-classifier is a plugin for the QIIME 2 microbiome bioinformatics platform that facilitates access, reproducibility, and interpretation of supervised learning (SL) methods for a broad audience of non-bioinformatics specialists.

Standard Codon Substitution Models Overestimate Purifying Selection for Nonstationary Data.

Estimation of natural selection on protein-coding sequences is a key comparative genomics approach for de novo prediction of lineage-specific adaptations. Selective pressure is measured on a per-gene basis by comparing the rate of nonsynonymous substitutions to the rate of synonymous substitutions. All published codon substitution models have been time-reversible and thus assume that sequence composition does not change over time. We previously demonstrated that if time-reversible DNA substitution models are applied in the presence of changing sequence composition, the number of substitutions is systematically biased towards overestimation. We extend these findings to the case of codon substitution models and further demonstrate that the ratio of nonsynonymous to synonymous rates of substitution tends to be underestimated over three data sets of mammals, vertebrates, and insects. Our basis for comparison is a nonstationary codon substitution model that allows sequence composition to change. Goodness-of-fit results demonstrate that our new model tends to fit the data better. Direct measurement of nonstationarity shows that bias in estimates of natural selection and genetic distance increases with the degree of violation of the stationarity assumption. Additionally, inferences drawn under time-reversible models are systematically affected by compositional divergence. As genomic sequences accumulate at an accelerating rate, the importance of accurate de novo estimation of natural selection increases. Our results establish that our new model provides a more robust perspective on this fundamental quantity.

Statistical Methods for Identifying Sequence Motifs Affecting Point Mutations.

Mutation processes differ between types of point mutation, genomic locations, cells, and biological species. For some point mutations, specific neighboring bases are known to be mechanistically influential. Beyond these cases, numerous questions remain unresolved, including: what are the sequence motifs that affect point mutations? How large are the motifs? Are they strand symmetric? And, do they vary between samples? We present new log-linear models that allow explicit examination of these questions, along with sequence logo style visualization to enable identifying specific motifs. We demonstrate the performance of these methods by analyzing mutation processes in human germline and malignant melanoma. We recapitulate the known CpG effect, and identify novel motifs, including a highly significant motif associated with A[Formula: see text]G mutations. We show that major effects of neighbors on germline mutation lie within [Formula: see text] of the mutating base. Models are also presented for contrasting the entire mutation spectra (the distribution of the different point mutations). We show the spectra vary significantly between autosomes and X-chromosome, with a difference in T[Formula: see text]C transition dominating. Analyses of malignant melanoma confirmed reported characteristic features of this cancer, including statistically significant strand asymmetry, and markedly different neighboring influences. The methods we present are made freely available as a Python library https://bitbucket.org/pycogent3/mutationmotif.

Transcriptome sequencing of two phenotypic mosaic Eucalyptus trees reveals large scale transcriptome re-modelling.

Phenotypic mosaic trees offer an ideal system for studying differential gene expression. We have investigated two mosaic eucalypt trees from two closely related species (Eucalyptus melliodora and E. sideroxylon), which each support two types of leaves: one part of the canopy is resistant to insect herbivory and the remaining leaves are susceptible. Driving this ecological distinction are differences in plant secondary metabolites. We used these phenotypic mosaics to investigate genome wide patterns of foliar gene expression with the aim of identifying patterns of differential gene expression and the somatic mutation(s) that lead to this phenotypic mosaicism. We sequenced the mRNA pool from leaves of the resistant and susceptible ecotypes from both mosaic eucalypts using the Illumina HiSeq 2000 platform. We found large differences in pathway regulation and gene expression between the ecotypes of each mosaic. The expression of the genes in the MVA and MEP pathways is reflected by variation in leaf chemistry, however this is not the case for the terpene synthases. Apart from the terpene biosynthetic pathway, there are several other metabolic pathways that are differentially regulated between the two ecotypes, suggesting there is much more phenotypic diversity than has been described. Despite the close relationship between the two species, they show large differences in the global patterns of gene and pathway regulation.

Folding behavior of four silks of giant honey bee reflects the evolutionary conservation of aculeate silk proteins.

Multiple gene duplication events in the precursor of the Aculeata (bees, ants, hornets) gave rise to four silk genes. Whilst these homologs encode proteins with similar amino acid composition and coiled coil structure, the retention of all four homologs implies they each are important. In this study we identified, produced and characterized the four silk proteins from Apis dorsata, the giant Asian honeybee. The proteins were readily purified, allowing us to investigate the folding behavior of solutions of individual proteins in comparison to mixtures of all four proteins at concentrations where they assemble into their native coiled coil structure. In contrast to solutions of any one protein type, solutions of a mixture of the four proteins formed coiled coils that were stable against dilution and detergent denaturation. The results are consistent with the formation of a heteromeric coiled coil protein complex. The mechanism of silk protein coiled coil formation and evolution is discussed in light of these results.

Draft Genome of Australian Environmental Strain WM 09.24 of the Opportunistic Human Pathogen Scedosporium aurantiacum.

We report here the first genome assembly and annotation of the human-pathogenic fungus Scedosporium aurantiacum, with a predicted 10,525 genes, and 11,661 transcripts. The strain WM 09.24 was isolated from the environment at Circular Quay, Sydney, New South Wales, Australia.

Genetic distance for a general non-stationary markov substitution process.

The genetic distance between biological sequences is a fundamental quantity in molecular evolution. It pertains to questions of rates of evolution, existence of a molecular clock, and phylogenetic inference. Under the class of continuous-time substitution models, the distance is commonly defined as the expected number of substitutions at any site in the sequence. We eschew the almost ubiquitous assumptions of evolution under stationarity and time-reversible conditions and extend the concept of the expected number of substitutions to nonstationary Markov models where the only remaining constraint is of time homogeneity between nodes in the tree. Our measure of genetic distance reduces to the standard formulation if the data in question are consistent with the stationarity assumption. We apply this general model to samples from across the tree of life to compare distances so obtained with those from the general time-reversible model, with and without rate heterogeneity across sites, and the paralinear distance, an empirical pairwise method explicitly designed to address nonstationarity. We discover that estimates from both variants of the general time-reversible model and the paralinear distance systematically overestimate genetic distance and departure from the molecular clock. The magnitude of the distance bias is proportional to departure from stationarity, which we demonstrate to be associated with longer edge lengths. The marked improvement in consistency between the general nonstationary Markov model and sequence alignments leads us to conclude that analyses of evolutionary rates and phylogenies will be substantively improved by application of this model.

Whole exome sequencing of extreme morbid obesity patients: translational implications for obesity and related disorders.

Whole-exome sequencing (WES) is a new tool that allows the rapid, inexpensive and accurate exploration of Mendelian and complex diseases, such as obesity. To identify sequence variants associated with obesity, we performed WES of family trios of one male teenager and one female child with severe early-onset obesity. Additionally, the teenager patient had hypopituitarism and hyperprolactinaemia. A comprehensive bioinformatics analysis found de novo and compound heterozygote sequence variants with a damaging effect on genes previously associated with obesity in mice (LRP2) and humans (UCP2), among other intriguing mutations affecting ciliary function (DNAAF1). A gene ontology and pathway analysis of genes harbouring mutations resulted in the significant identification of overrepresented pathways related to ATP/ITP (adenosine/inosine triphosphate) metabolism and, in general, to the regulation of lipid metabolism. We discuss the clinical and physiological consequences of these mutations and the importance of these findings for either the clinical assessment or eventual treatment of morbid obesity.

The embedding problem for markov models of nucleotide substitution.

Continuous-time Markov processes are often used to model the complex natural phenomenon of sequence evolution. To make the process of sequence evolution tractable, simplifying assumptions are often made about the sequence properties and the underlying process. The validity of one such assumption, time-homogeneity, has never been explored. Violations of this assumption can be found by identifying non-embeddability. A process is non-embeddable if it can not be embedded in a continuous time-homogeneous Markov process. In this study, non-embeddability was demonstrated to exist when modelling sequence evolution with Markov models. Evidence of non-embeddability was found primarily at the third codon position, possibly resulting from changes in mutation rate over time. Outgroup edges and those with a deeper time depth were found to have an increased probability of the underlying process being non-embeddable. Overall, low levels of non-embeddability were detected when examining individual edges of triads across a diverse set of alignments. Subsequent phylogenetic reconstruction analyses demonstrated that non-embeddability could impact on the correct prediction of phylogenies, but at extremely low levels. Despite the existence of non-embeddability, there is minimal evidence of violations of the local time homogeneity assumption and consequently the impact is likely to be minor.

Infection with a Virulent Strain of Wolbachia Disrupts Genome Wide-Patterns of Cytosine Methylation in the Mosquito Aedes aegypti.

BACKGROUND: Cytosine methylation is one of several reversible epigenetic modifications of DNA that allow a greater flexibility in the relationship between genotype and phenotype. Methylation in the simplest models dampens gene expression by modifying regions of DNA critical for transcription factor binding. The capacity to methylate DNA is variable in the insects due to diverse histories of gene loss and duplication of DNA methylases. Mosquitoes like Drosophila melanogaster possess only a single methylase, DNMT2. DESCRIPTION: Here we characterise the methylome of the mosquito Aedes aegypti and examine its relationship to transcription and test the effects of infection with a virulent strain of the endosymbiont Wolbachia on the stability of methylation patterns. CONCLUSION: We see that methylation in the A. aegypti genome is associated with reduced transcription and is most common in the promoters of genes relating to regulation of transcription and metabolism. Similar gene classes are also methylated in aphids and honeybees, suggesting either conservation or convergence of methylation patterns. In addition to this evidence of evolutionary stability, we also show that infection with the virulent wMelPop Wolbachia strain induces additional methylation and demethylation events in the genome. While most of these changes seem random with respect to gene function and have no detected effect on transcription, there does appear to be enrichment of genes associated with membrane function. Given that Wolbachia lives within a membrane-bound vacuole of host origin and retains a large number of genes for transporting host amino acids, inorganic ions and ATP despite a severely reduced genome, these changes might represent an evolved strategy for manipulating the host environments for its own gain. Testing for a direct link between these methylation changes and expression, however, will require study across a broader range of developmental stages and tissues with methods that detect splice variants.

Histone H2A.Z inheritance during the cell cycle and its impact on promoter organization and dynamics.

Although it has been clearly established that well-positioned histone H2A.Z-containing nucleosomes flank the nucleosome-depleted region (NDR) at the transcriptional start site (TSS) of active mammalian genes, how this chromatin-based information is transmitted through the cell cycle is unknown. We show here that in mouse trophoblast stem cells, the amount of histone H2A.Z at promoters decreased during S phase, coinciding with homotypic (H2A.Z-H2A.Z) nucleosomes flanking the TSS becoming heterotypic (H2A.Z-H2A). To our surprise these nucleosomes remained heterotypic at M phase. At the TSS, we identified an unstable heterotypic histone H2A.Z-containing nucleosome in G1 phase that was lost after DNA replication. These dynamic changes at the TSS mirror a global expansion of the NDR at S and M phases, which, unexpectedly, is unrelated to transcriptional activity. Coincident with the loss of histone H2A.Z at promoters, histone H2A.Z is targeted to the centromere when mitosis begins.

Dynamic evolution of venom proteins in squamate reptiles.

Phylogenetic analyses of toxin gene families have revolutionised our understanding of the origin and evolution of reptile venoms, leading to the current hypothesis that venom evolved once in squamate reptiles. However, because of a lack of homologous squamate non-toxin sequences, these conclusions rely on the implicit assumption that recruitments of protein families into venom are both rare and irreversible. Here we use sequences of homologous non-toxin proteins from two snake species to test these assumptions. Phylogenetic and ancestral-state analyses revealed frequent nesting of 'physiological' proteins within venom toxin clades, suggesting early ancestral recruitment into venom followed by reverse recruitment of toxins back to physiological roles. These results provide evidence that protein recruitment into venoms from physiological functions is not a one-way process, but dynamic, with reversal of function and/or co-expression of toxins in different tissues. This requires a major reassessment of our previous understanding of how animal venoms evolve.

A unique H2A histone variant occupies the transcriptional start site of active genes.

Transcriptional activation is controlled by chromatin, which needs to be unfolded and remodeled to ensure access to the transcription start site (TSS). However, the mechanisms that yield such an 'open' chromatin structure, and how these processes are coordinately regulated during differentiation, are poorly understood. We identify the mouse (Mus musculus) H2A histone variant H2A.Lap1 as a previously undescribed component of the TSS of active genes expressed during specific stages of spermatogenesis. This unique chromatin landscape also includes a second histone variant, H2A.Z. In the later stages of round spermatid development, H2A.Lap1 dynamically loads onto the inactive X chromosome, enabling the transcriptional activation of previously repressed genes. Mechanistically, we show that H2A.Lap1 imparts unique unfolding properties to chromatin. We therefore propose that H2A.Lap1 coordinately regulates gene expression by directly opening the chromatin structure of the TSS at genes regulated during spermatogenesis.

Statistical methods for detecting periodic fragments in DNA sequence data.

BACKGROUND: Period 10 dinucleotides are structurally and functionally validated factors that influence the ability of DNA to form nucleosomes, histone core octamers. Robust identification of periodic signals in DNA sequences is therefore required to understand nucleosome organisation in genomes. While various techniques for identifying periodic components in genomic sequences have been proposed or adopted, the requirements for such techniques have not been considered in detail and confirmatory testing for a priori specified periods has not been developed. RESULTS: We compared the estimation accuracy and suitability for confirmatory testing of autocorrelation, discrete Fourier transform (DFT), integer period discrete Fourier transform (IPDFT) and a previously proposed Hybrid measure. A number of different statistical significance procedures were evaluated but a blockwise bootstrap proved superior. When applied to synthetic data whose period-10 signal had been eroded, or for which the signal was approximately period-10, the Hybrid technique exhibited superior properties during exploratory period estimation. In contrast, confirmatory testing using the blockwise bootstrap procedure identified IPDFT as having the greatest statistical power. These properties were validated on yeast sequences defined from a ChIP-chip study where the Hybrid metric confirmed the expected dominance of period-10 in nucleosome associated DNA but IPDFT identified more significant occurrences of period-10. Application to the whole genomes of yeast and mouse identified ~21% and ~19% respectively of these genomes as spanned by period-10 nucleosome positioning sequences (NPS). CONCLUSIONS: For estimating the dominant period, we find the Hybrid period estimation method empirically to be the most effective for both eroded and approximate periodicity. The blockwise bootstrap was found to be effective as a significance measure, performing particularly well in the problem of period detection in the presence of eroded periodicity. The autocorrelation method was identified as poorly suited for use with the blockwise bootstrap. Application of our methods to the genomes of two model organisms revealed a striking proportion of the yeast and mouse genomes are spanned by NPS. Despite their markedly different sizes, roughly equivalent proportions (19-21%) of the genomes lie within period-10 spans of the NPS dinucleotides {AA, TT, TA}. The biological significance of these regions remains to be demonstrated. To facilitate this, the genomic coordinates are available as Additional files 1, 2, and 3 in a format suitable for visualisation as tracks on popular genome browsers.