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Diploidy has profound implications for population genetics and susceptibility to genetic diseases. Although two copies are present for most genes in the human genome, they are not necessarily both active or active at the same level in a given individual. Genomic imprinting, resulting in exclusive or biased expression in favor of the allele of paternal or maternal origin, is now believed to affect hundreds of human genes. A far greater number of genes display unequal expression of gene copies due to cis-acting genetic variants that perturb gene expression. The availability of data generated by RNA sequencing applied to large numbers of individuals and tissue types has generated unprecedented opportunities to assess the contribution of genetic variation to allelic imbalance in gene expression. Here we review the insights gained through the analysis of these data about the extent of the genetic contribution to allelic expression imbalance, the tools and statistical models for gene expression imbalance, and what the results obtained reveal about the contribution of genetic variants that alter gene expression to complex human diseases and phenotypes. C.S. and S.C. are funded by Science Foundation Ireland grant number 16/IA/4612. peer-reviewed

Gene set analysis (GSA) remains a common step in genome-scale studies because it can reveal insights that are not apparent from results obtained for individual genes. Many different computational tools are applied for GSA, which may be sensitive to different types of signals; however, most methods test whether there are differences in the distribution of the effect of some experimental condition between genes in gene sets of interest. We have developed a unifying framework for GSA that first fits effect size distributions, and then tests for differences in these distributions between gene sets. These differences can be in the proportions of genes that are perturbed or in the sign or size of the effects. Inspired by statistical meta-analysis, we take into account the uncertainty in effect size estimates to reduce the influence of genes with greater uncertainty in effect size estimate on distribution parameters. We demonstrate, using simulation and by application to real data, that this approach provides significant gains in performance over existing methods. Furthermore, the statistical tests carried out are defined in terms of effect sizes, rather than the results of prior statistical tests measuring these changes, which leads to improved interpretability and greater robustness to variation in sample sizes. We also show that the approach naturally suggests alternative test types that are not usually considered in GSA; it can, for example, be applied to identify differences in effect size distributions between sample subgroups in a gene set of interest. Applying this approach to an analysis of gene expression changes between matched colon tumour and normal samples, we found several gene sets that showed distinct behaviour in patient subgroups with different prognoses. These may help to explain the clinical differences that have been reported between these patient groups.Author summaryThe role of gene set analysis is to identify groups of genes that are perturbed in a genomics experiment. There are many tools available for this task and they do not all test for the same types of changes. Here we propose a new way to carry out gene set analysis that involves first working out the distribution of the group effect in the gene set and then comparing this distribution to the equivalent distribution in other genes. Tests performed by existing tools for gene set analysis can be related to different comparisons in these distributions of group effects. A unified framework for gene set analysis provides for more explicit null hypotheses against which to test sets of genes for different types of responses to the experimental conditions. These results are more interpretable, because the group effect distributions can be compared visually, providing an indication of how the experimental effect differs between the gene sets. We can also apply this method to identify sets of genes that behave atypically in subgroups of samples. This enabled us to identify differences in the expression of several gene sets in colon cancer samples between individuals with reduced mortality and those without this benefit.

Background Tumour mutation burden (TMB), defined as the number of somatic mutations per megabase within the sequenced region in the tumour sample, has been used as a biomarker for predicting response to immune therapy. Several studies have been conducted to assess the utility of TMB for various cancer types; however, methods to measure TMB have not been adequately evaluated. In this study, we identified two sources of bias in current methods to calculate TMB. Methods We used simulated data to quantify the two sources of bias and their effect on TMB calculation, we down-sampled sequencing reads from exome sequencing datasets from TCGA to evaluate the consistency in TMB estimation across different sequencing depths. We analyzed data from ten cancer cohorts to investigate the relationship between inferred TMB and sequencing depth. Results We found that TMB, estimated by counting the number of somatic mutations above a threshold frequency (typically 0.05), is not robust to sequencing depth. Furthermore, we show that, because only mutations with an observed frequency greater than the threshold are considered, the observed mutant allele frequency provides a biased estimate of the true frequency. This can result in substantial over-estimation of the TMB, when the cancer sample includes a large number of somatic mutations at low frequencies, and exacerbates the lack of robustness of TMB to variation in sequencing depth and tumour purity. Conclusion Our results demonstrate that care needs to be taken in the estimation of TMB to ensure that results are unbiased and consistent across studies and we suggest that accurate and robust estimation of TMB could be achieved using statistical models that estimate the full mutant allele frequency spectrum.

Genomics is revolutionizing biomedical research, medicine and healthcare globally in academic, public and industry sectors alike. Concrete examples around the world show that huge benefits for patients, society and economy can be accrued through effective and responsible genomic research and clinical applications. Unfortunately, Ireland has fallen behind and needs to act now in order to catch up. Here, we identify key issues that have resulted in Ireland lagging behind, describe how genomics can benefit Ireland and its people and outline the measures needed to make genomics work for Ireland and Irish patients. There is now an urgent need for a national genomics strategy that enables an effective, collaborative, responsible, well-regulated, and patient centred environment where genome research and clinical genomics can thrive. We present eight recommendations that could be the pillars of a national genomics health strategy.

Phylogenetic models of the evolution of protein-coding sequences can provide insights into the selection pressures that have shaped them. In the application of these models synonymous nucleotide substitutions, which do not alter the encoded amino acid, are often assumed to have limited functional consequences and used as a proxy for the neutral rate of evolution. The ratio of nonsynonymous to synonymous substitution rates is then used to categorize the selective regime that applies to the protein (e.g. purifying selection, neutral evolution, diversifying selection). Here, we extend these models to explore the extent of purifying selection acting on substitutions between synonymous stop codons. Using a large collection of coding sequence alignments we estimate that a high proportion (approximately 57%) of mammalian genes are affected by selection acting on stop codon preference. This proportion varies substantially by codon, with UGA stop codons far more likely to be conserved. Genes with evidence of selection acting on synonymous stop codons have distinctive characteristics, compared to unconserved genes with the same stop codon, including enrichment for specific functional properties. Notably, genes with conserved stop codons have longer 3′ UTRs and are associated with shorter mRNA half-life than other genes. The coding regions of these genes are also much more likely to be under strong purifying selection pressure. Our results suggest that the preference for UGA stop codons in many multicellular eukaryotes is selective rather than mutational in origin. Author summary Three codons can specify the termination of translation of protein-coding genes, but the efficiency of translation termination differs between them. Readthrough translation, in which the ribosome fails to terminate at the stop codon, can result in aberrant proteins but can also have a regulatory role. By extending statistical models used to study the evolution of sense codons, we performed a large-scale analysis of selection acting on mutations between the three stop codons in mammals. We estimate that more than half of mammalian protein-coding genes are affected by selection that reduces the rate of substitution between synonymous stop codons, compared to what we would expect if the stop codons were functionally equivalent. Our results suggest that the choice of stop codon is affected by evolutionary selection and impacts the function or regulation of a substantial proportion of mammalian genes.

MHC molecules are capable of presenting neoantigens resulting from somatic mutations on cell surfaces, potentially directing immune responses against cancer. This led to the hypothesis that cancer driver mutations may occur in gaps in the capacity to present neoantigens that are dependent on MHC genotype. If this is correct, it may help to predict the driver mutations that are likely to be observed in an individual from genotype data. Two papers supporting this hypothesis were published in Cell, reporting that driver mutations that occur frequently tend to be poorly presented by common MHC alleles and proposing that MHC genotype is predictive of the driver mutations observed in a patient. Here we demonstrate that these results are a consequence of unjustified statistical assumptions and that the data on which they are based do not provide any evidence of an effect of MHC genotype on the oncogenic mutation landscape.

Summary DNA replication initiates from multiple origins, and selective CDC7 kinase inhibitors (CDC7is) restrain cell proliferation by limiting origin firing. We have performed a CRISPR-Cas9 genome-wide screen to identify genes that, when lost, promote the proliferation of cells treated with sub-efficacious doses of a CDC7i. We have found that the loss of function of ETAA1, an ATR activator, and RIF1 reduce the sensitivity to CDC7is by allowing DNA synthesis to occur more efficiently, notably during late S phase. We show that partial CDC7 inhibition induces ATR mainly through ETAA1, and that if ATR is subsequently inhibited, origin firing is unleashed in a CDK- and CDC7-dependent manner. Cells are then driven into a premature and highly defective mitosis, a phenotype that can be recapitulated by ETAA1 and TOPBP1 co-depletion. This work defines how ATR mediates the effects of CDC7 inhibition, establishing the framework to understand how the origin firing checkpoint functions.

Expression quantitative trait loci (eQTLs) identified using tumor gene expression data could affect gene expression in cancer cells, tumor-associated normal cells, or both. Here, we have demonstrated a method to identify eQTLs affecting expression in cancer cells by modeling the statistical interaction between genotype and tumor purity. Only one third of breast cancer risk variants, identified as eQTLs from a conventional analysis, could be confidently attributed to cancer cells. The remaining variants could affect cells of the tumor microenvironment, such as immune cells and fibroblasts. Deconvolution of tumor eQTLs will help determine how inherited polymorphisms influence cancer risk, development, and treatment response. Electronic supplementary material The online version of this article (10.1186/s13059-018-1507-0) contains supplementary material, which is available to authorized users.

Do human societies from around the world exhibit similarities in the way that they are structured, and show commonalities in the ways that they have evolved? These are long-standing questions that have proven difficult to answer. To test between competing hypotheses, we constructed a massive repository of historical and archaeological information known as "Seshat: Global History Databank." We systematically coded data on 414 societies from 30 regions around the world spanning the last 10,000 years. We were able to capture information on 51 variables reflecting nine characteristics of human societies, such as social scale, economy, features of governance, and information systems. Our analyses revealed that these different characteristics show strong relationships with each other and that a single principal component captures around three-quarters of the observed variation. Furthermore, we found that different characteristics of social complexity are highly predictable across different world regions. These results suggest that key aspects of social organization are functionally related and do indeed coevolve in predictable ways. Our findings highlight the power of the sciences and humanities working together to rigorously test hypotheses about general rules that may have shaped human history.

Cell subtype proportion variability between samples contributes significantly to the variation of functional genomic properties such as gene expression or DNA methylation. Although the impact of the variation of cell subtype composition on measured genomic quantities is recognized, and some innovative tools have been developed for the analysis of heterogeneous samples, most functional genomics studies using samples with mixed cell types still ignore the influence of cell subtype proportion variation, or just deal with it as a nuisance variable to be eliminated. Here we demonstrate how harvesting information about cell subtype proportions from functional genomics data can provide insights into cellular changes associated with phenotypes. We focused on two types of mixed cell populations, human blood and mouse kidney. Cell type prediction is well developed in the former, but not currently in the latter. Estimating the cellular repertoire is easier when a reference dataset from purified samples of all cell types in the tissue is available, as is the case for blood. However, reference datasets are not available for most other tissues, such as the kidney. In this study, we showed that the proportion of alterations attributable to changes in the cellular composition varies strikingly in the two disorders (asthma and systemic lupus erythematosus), suggesting that the contribution of cell subtype proportion changes to functional genomic properties can be disease-specific. We also showed that a reference dataset from a single-cell RNA-seq study successfully estimated the cell subtype proportions in mouse kidney and allowed us to distinguish altered cell subtype differences between two different knock-out mouse models, both of which had reported a reduced number of glomeruli compared to their wild-type counterparts. These findings demonstrate that testing for changes in cell subtype proportions between conditions can yield important insights in functional genomics studies.

Aberrant DNA methylation patterns have been reported in inflamed tissues and may play a role in disease. We studied DNA methylation and gene expression profiles of purified intestinal epithelial cells from ulcerative colitis patients, comparing inflamed and non-inflamed areas of the colon. We identified 577 differentially methylated sites (false discovery rate

Yu, Y., R. Ceredig, and C. Seoighe. 2017. A database of human immune receptor alleles recovered from population sequencing data. J . Immunol . 198: [2202–2210][1]. The affiliation for the second author was incorrect as published. The corrected affiliation footnote is shown below. The author line

We share the view of Dr. Watson and colleagues on the importance of germline databases (GLDB) of immune receptor genes and thank them for their interest in our work ([1][1]). Whereas we fully agree with them on many of the caveats that apply to alleles inferred from genome sequence data (discussed

Longitudinal allele frequency data are becoming increasingly prevalent. Such samples permit statistical inference of the population genetics parameters that influence the fate of mutant variants. To infer these parameters by maximum likelihood, the mutant frequency is often assumed to evolve according to the Wright–Fisher model. For computational reasons, this discrete model is commonly approximated by a diffusion process that requires the assumption that the forces of natural selection and mutation are weak. This assumption is not always appropriate. For example, mutations that impart drug resistance in pathogens may evolve under strong selective pressure. Here, we present an alternative approximation to the mutant-frequency distribution that does not make any assumptions about the magnitude of selection or mutation and is much more computationally efficient than the standard diffusion approximation. Simulation studies are used to compare the performance of our method to that of the Wright–Fisher and Gaussian diffusion approximations. For large populations, our method is found to provide a much better approximation to the mutant-frequency distribution when selection is strong, while all three methods perform comparably when selection is weak. Importantly, maximum-likelihood estimates of the selection coefficient are severely attenuated when selection is strong under the two diffusion models, but not when our method is used. This is further demonstrated with an application to mutant-frequency data from an experimental study of bacteriophage evolution. We therefore recommend our method for estimating the selection coefficient when the effective population size is too large to utilize the discrete Wright–Fisher model.

Objective(s): There is limited information on full-length genome sequences and the early evolution of transmitted HIV-1 subtype C viruses, which constitute the majority of viruses spread in Africa. The purpose of this study was to characterize the earliest changes across the genome of subtype C viruses following transmission, to better understand early control of viremia. Design: We derived the near full-length genome sequence responsible for clinical infection from five HIV subtype C-infected individuals with different disease progression profiles and tracked adaptation to immune responses in the first 6 months of infection. Methods: Near full-length genomes were generated by single genome amplification and direct sequencing. Sequences were analyzed for amino acid mutations associated with cytotoxic T lymphocyte (CTL) or antibody-mediated immune pressure, and for reversion. Results: Fifty-five sequence changes associated with adaptation to the new host were identified, with 38% attributed to CTL pressure, 35% to antibody pressure, 16% to reversions and the remainder were unclassified. Mutations in CTL epitopes were most frequent in thefirst 5 weeks of infection, with the frequency declining over time with the decline in viral load. CTL escape predominantly occurred in nef, followed by pol and env. Shuffling/toggling of mutations was identified in 81% of CTL epitopes, with only 7% reaching fixation within the 6-month period. Conclusion: There was rapid virus adaptation following transmission, predominantly driven by CTL pressure, with most changes occurring during high viremia. Rapid escape and complex escape pathways provide further challenges for vaccine protection. 2013 Wolters Kluwer Health | Lippincott Williams & Wilkins AIDS 2013, 27:507‐518

Nonnegative Matrix Factorization (NMF) has proved to be an effective method for unsupervised clustering analysis of gene expression data. By the nonnegativity constraint, NMF provides a decomposition of the data matrix into two matrices that have been used for clustering analysis. However, the decomposition is not unique. This allows different clustering results to be obtained, resulting in different interpretations of the decomposition. To alleviate this problem, some existing methods directly enforce uniqueness to some extent by adding regularization terms in the NMF objective function. Alternatively, various normalization methods have been applied to the factor matrices; however, the effects of the choice of normalization have not been carefully investigated. Here we investigate the performance of NMF for the task of cancer class discovery, under a wide range of normalization choices. After extensive evaluations, we observe that the maximum norm showed the best performance, although the maximum norm has not previously been used for NMF. Matlab codes are freely available from: http://maths.nuigalway.ie/~haixuanyang/pNMF/pNMF.htm.

Although they do not contribute directly to the proteome, introns frequently contain regulatory elements and can extend the protein coding potential of the genome through alternative splicing. For some genes, the contribution of introns to the time required for transcription can also be functionally significant. We have previously shown that intron length in genes associated with developmental patterning is often highly conserved. In general, sets of genes that require precise coordination in the timing of their expression may be sensitive to changes in transcript length. A prediction of this hypothesis is that evolutionary changes in intron length, when they occur, may be correlated between sets of coordinately expressed genes. To test this hypothesis, we analyzed intron length coevolution in alignments from nine eutherian mammals. Overall, genes that belong to the same protein complex or that are coexpressed were significantly more likely to show evidence of intron length coevolution than matched, randomly sampled genes. Individually, protein complexes involved in the cell cycle showed the strongest evidence of coevolution of intron lengths and clusters of coexpressed genes enriched for cell cycle genes also showed significant evidence of intron length coevolution. Our results reveal a novel aspect of gene coevolution and provide a means to identify genes, protein complexes and biological processes that may be particularly sensitive to changes in transcriptional dynamics.

Flowering plants, angiosperms, can be divided into two major clades, monocots and dicots, and while differences in amino acid composition in different species from the two clades have been reported, a systematic analysis of amino acid content and distribution remains outstanding. Here, we show that monocot and dicot proteins have developed distinct amino acid content. In Arabidopsis thaliana and poplar, as in the ancestral moss Physcomitrella patens, the average mass per amino acid appears to be independent of protein length, while in the monocots rice, maize and sorghum, shorter proteins tend to be made of lighter amino acids. An examination of the elemental content of these proteomes reveals that the difference between monocot and dicot proteins can be largely attributed to their different carbon signatures. In monocots, the shorter proteins, which comprise the majority of all proteins, are made of amino acids with less carbon, while the nitrogen content is unchanged in both monocots and dicots. We hypothesise that this signature could be the result of carbon use and energy optimisation in fast-growing annual Poaceae (grasses).

Genome-wide association studies (GWAS) have identified hundreds of inherited genetic variants that increase cancer risk. However, most cancer risk variants are in non-coding regions of the genome and modulate risk by affecting gene regulation. Thus, determining how inherited genetic variation affects gene expression in cancer is critically important to understanding disease development. Consequently, by leveraging large genomics datasets like The Cancer Genome Atlas (TCGA), previous studies have examined the effect of inherited genetic variation on gene expression in tumors; an approach known as expression quantitative trait locus (eQTL) mapping. However, tumors are mixtures of both cancer and normal cells, for example, immune cells and stroma. No previous cancer eQTL study has accounted for this mixture of cell types, essentially having treated bulk tumor expression as representative of gene expression in cancer cells. Building on research in deconvolution of mixtures of cell types, we have developed a new approach that can accurately account for the effect of tumor-infiltrating normal cells on cancer eQTLs. The approach involves first estimating the proportion of tumor-infiltrating normal cells (tumor purity) using a combined estimate from genomics data and H&E staining. Then, we developed a statistical model that can account for the effect of tumor purity on eQTLs by modeling the interaction of the tumor purity estimate and genotype. Intuitively, this works by determining how the magnitude of the association between gene expression and genotype changes as a function of tumor purity, then extrapolating this effect to 100% cancer cells. The model's accuracy was validated using simulated data and on expression profiles from purified cell types. We used this model to map eQTLs in the TCGA breast cancer cohort. Remarkably, while 57,189 eQTLs were identified in TCGA breast cancer patients using a conventional model, only 8,833 (15.4%) could be attributed to cancer cells when tumor purity was accounted for. Analysis of the Genotype-Tissue Expression (GTEx) data provided evidence that almost 50% of tumor eQTLs inferred using a conventional approach affect gene expression in tumor-infiltrating immune cells and fibroblasts. We also investigated the eQTL profiles of cancer risk variants from a GWAS for breast cancer risk. Strikingly, for 33% of breast cancer risk variants identified as eQTLs using a conventional approach, we found strong evidence of an effect in tumor-infiltrating normal cells, but no evidence of an effect in cancer cells. This suggests cancer risk is mediated by the effect of inherited genetic variation on gene regulation in the cells of the tumor microenvironment, as well as in cancer and pre-cancer cells. Our findings profoundly challenge the current interpretation of inherited genetic regulation in cancer and should be considered in functional validation of results from all cancer risk GWAS. Citation Format: Paul Geeleher, Fan Wang, Zheny Zhang, Robert L. Grossman, Cathal Seoighe, R. Stephanie Huang. Most expression quantitative trait loci discovered in tumors cannot be attributed to cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4271.

Admixed populations present unique opportunities to discover the genetic factors underlying many multifactorial diseases. The geographical position and complex history of South Africa has led to the establishment of the unique admixed population known as the South African Coloured. Not much is known about the genetic make-up of this population, and the historical record is patchy. We genotyped 959 individuals from the Western Cape area, self-identified as belonging to this population, using the Affymetrix 500k genotyping platform. This resulted in nearly 75,000 autosomal SNPs that could be compared with populations represented in the International HapMap Project and the Human Genome Diversity Project. Analysis by means of both the admixture and linkage models in STRUCTURE revealed that the major ancestral components of this population are predominantly Khoesan (32-43%), Bantu-speaking Africans (20-36%), European (21-28%) and a smaller Asian contribution (9-11%), depending on the model used. This is consistent with historical data. While of great historical and genealogical interest, this information is also essential for future admixture mapping of disease genes in this population.

It is unresolved whether recently transmitted human immunodeficiency viruses (HIV) have genetic features that specifically favour their transmissibility. To identify potential “transmission signatures”, we compared 20 full-length HIV-1 subtype C genomes from primary infections, with 66 sampled from ethnically and geographically matched individuals with chronic infections. Controlling for recombination and phylogenetic relatedness, we identified 39 sites at which amino acid frequency spectra differed significantly between groups. These sites were predominantly located within Env, Pol and Gag (14/39, 9/39 and 6/39 respectively) and were significantly clustered (33/39) within known immunoreactive peptides. Within 6 months of infection, we detected reversion-to-consensus mutations at 14 sites and potential CTL escape mutations at seven. Here we provide evidence that frequent reversion mutations probably allows the virus to recover replicative fitness which, together with immune escape driven by the HLA alleles of the new hosts, differentiate sequences from chronic infections from those sampled shortly after transmission.

The Mesembryanthemoideae and Ruschioideae subfamilies are a major component of the Greater Cape Floristic Region in southern Africa. The Ruschioideae show an astonishing diversity of leaf shape and growth forms. Although 1,585 species are recognised within the morphologically diverse Ruschioideae, these species show minimal variation in plastid DNA sequence. We have investigated whether changes in selected leaf development transcription factors underpin the recent, rapid diversification of this large group of succulent plants. Degenerate primers designed to conserved regions of Asymmetric Leaves1/Rough Sheath 2/Phantastica (ARP) and the Class III HD-ZIP family of genes, were used to amplify sequences corresponding to these genes from several species within the Mesembryanthemoideae and Ruschioideae subfamilies. Two members of the Class III HD-ZIP family were identified in both the Mesembryanthemoideae and Ruschioideae, and were derived from an ancient gene duplication event that preceded the divergence of gymnosperms and angiosperms. While a single ARP orthologue was identified in the Mesembryanthemoideae, two paralogues, ARPa and ARPb, were identified in the Ruschioideae subfamily. ARPa was present in all species of Ruschioideae analysed in this study. ARPb has been lost from the Apatesieae and Dorotheantheae tribes, which form an early evolutionary branch from the Ruschieae tribe, as well as from selected species within the Ruschieae. The recent duplication and subsequent selected gene loss of the ARP transcription factor correlates with the rapid diversification of plant forms in the Ruschioideae.

It is unknown whether patterns of human immunodeficiency virus (HIV)-specific T-cell responses during acute infection may influence the viral set point and the course of disease. We wished to establish whether the magnitude and breadth of HIV type 1 (HIV-1)-specific T-cell responses at 3 months postinfection were correlated with the viral-load set point at 12 months and hypothesized that the magnitude and breadth of HIV-specific T-cell responses during primary infection would predict the set point. Gamma interferon (IFN-γ) enzyme-linked immunospot (ELISPOT) assay responses across the complete proteome were measured in 47 subtype C HIV-1-infected participants at a median of 12 weeks postinfection. When corrected for amino acid length and individuals responding to each region, the order of recognition was as follows: Nef > Gag > Pol > Rev > Vpr > Env > Vpu > Vif > Tat. Nef responses were significantly ( P < 0.05) dominant, targeted six epitopic regions, and were unrelated to the course of viremia. There was no significant difference in the magnitude and breadth of responses for each protein region with disease progression, although there was a trend of increased breadth (mean, four to seven pools) in rapid progressors. Correlation of the magnitude and breadth of IFN-γ responses with the viral set point at 12 months revealed almost zero association for each protein region. Taken together, these data demonstrate that the magnitude and breadth of IFN-γ ELISPOT assay responses at 3 months postinfection are unrelated to the course of disease in the first year of infection and are not associated with, and have low predictive power for, the viral set point at 12 months.

Probabilistic models of sequence evolution are in widespread use in phylogenetics and molecular sequence evolution. These models have become increasingly sophisticated and combined with statistical model comparison techniques have helped to shed light on how genes and proteins evolve. Models of codon evolution have been particularly useful, because, in addition to providing a significant improvement in model realism for protein-coding sequences, codon models can also be designed to test hypotheses about the selective pressures that shape the evolution of the sequences. Such models typically assume a phylogeny and can be used to identify sites or lineages that have evolved adaptively. Recently some of the key assumptions that underlie phylogenetic tests of selection have been questioned, such as the assumption that the rate of synonymous changes is constant across sites or that a single phylogenetic tree can be assumed at all sites for recombining sequences. While some of these issues have been addressed through the development of novel methods, others remain as caveats that need to be considered on a case-by-case basis. Here, we outline the theory of codon models and their application to the detection of positive selection. We review some of the more recent developments that have improved their power and utility, laying a foundation for further advances in the modeling of coding sequence evolution.

Activation of macrophages and subsequent “killing” effector functions against infectious pathogens are essential for the establishment of protective immunity. NF-IL6 is a transcription factor downstream of IFN-γ and TNF in the macrophage activation pathway required for bacterial killing. Comparison of microarray expression profiles ofListeria monocytogenes(LM)-infected macrophages from WT and NF-IL6-deficient mice enabled us to identify candidate genes downstream of NF-IL6 involved in the unknown pathways of LM killing independent of reactive oxygen intermediates and reactive nitrogen intermediates. One differentially expressed gene, PKCδ, had higher mRNA levels in the LM-infected NF-IL6-deficient macrophages as compared with WT. To define the role of PKCδ during listeriosis, we infected PKCδ-deficient mice with LM. PKCδ-deficient mice were highly susceptible to LM infection with increased bacterial burden and enhanced histopathology despite enhanced NF-IL6 mRNA expression. Subsequent studies in PKCδ-deficient macrophages demonstrated that, despite elevated levels of proinflammatory cytokines and NO production, increased escape of LM from the phagosome into the cytoplasm and uncontrolled bacterial growth occurred. Taken together these data identified PKCδ as a critical factor for confinement of LM within macrophage phagosomes.

Background RNA:DNA hybrids represent a non-canonical nucleic acid structure that has been associated with a range of human diseases and potential transcriptional regulatory functions. Mapping of RNA:DNA hybrids in human cells reveals them to have a number of characteristics that give insights into their functions. Results We find RNA:DNA hybrids to occupy millions of base pairs in the human genome. A directional sequencing approach shows the RNA component of the RNA:DNA hybrid to be purine-rich, indicating a thermodynamic contribution to their in vivo stability. The RNA:DNA hybrids are enriched at loci with decreased DNA methylation and increased DNase hypersensitivity, and within larger domains with characteristics of heterochromatin formation, indicating potential transcriptional regulatory properties. Mass spectrometry studies of chromatin at RNA:DNA hybrids shows the presence of the ILF2 and ILF3 transcription factors, supporting a model of certain transcription factors binding preferentially to the RNA:DNA conformation. Conclusions Overall, there is little to indicate a dependence for RNA:DNA hybrids forming co-transcriptionally, with results from the ribosomal DNA repeat unit instead supporting the intriguing model of RNA generating these structures intrans. The results of the study indicate heterogeneous functions of these genomic elements and new insights into their formation and stability in vivo. Electronic supplementary material The online version of this article (doi:10.1186/s13072-015-0040-6) contains supplementary material, which is available to authorized users.

Motivation: Accurate detection of positive Darwinian selection can provide important insights to researchers investigating the evolution of pathogens. However, many pathogens (particularly viruses) undergo frequent recombination and the phylogenetic methods commonly applied to detect positive selection have been shown to give misleading results when applied to recombining sequences. We propose a method that makes maximum likelihood inference of positive selection robust to the presence of recombination. This is achieved by allowing tree topologies and branch lengths to change across detected recombination breakpoints. Further improvements are obtained by allowing synonymous substitution rates to vary across sites. Results: Using simulation we show that, even for extreme cases where recombination causes standard methods to reach false positive rates >90%, the proposed method decreases the false positive rate to acceptable levels while retaining high power. We applied the method to two HIV-1 datasets for which we have previously found that inference of positive selection is invalid owing to high rates of recombination. In one of these (env gene) we still detected positive selection using the proposed method, while in the other (gag gene) we found no significant evidence of positive selection. Availability: A HyPhy batch language implementation of the proposed methods and the HIV-1 datasets analysed are available at . The HyPhy package is available at , and it is planned that the proposed methods will be included in the next distribution. RDP2 is available at . Contact: konrad@cbio.uct.ac.za, cathal@science.uct.ac.za

Estimates of the number of human genes that produce more than one transcript isoform through alternative mRNA splicing depend on the assumption that the observation of multiple transcripts from a gene can be attributed entirely to alternative splicing. It is possible, however, that a substantial proportion of cases where multiple transcripts have been observed for a gene result from differences between alleles. Many examples of genes that are spliced differently from different alleles have been reported but no systematic estimate of the proportion of alternatively spliced genes that are affected by such polymorphisms has been carried out. We find that alternative transcript isoforms are non-randomly associated with closely linked nucleotide polymorphisms, based on an integrated analysis of the dbSNP, dbEST and ASAP databases. From the observed level of association between transcript isoforms and polymorphisms, we estimate that 21% of alternatively spliced genes are affected by polymorphisms that either completely determine which form of the transcript is observed or alter the relative abundances of some of the alternative isoforms. We provide a conservative lower bound of 6% on this estimate and point out that alternative splicing cannot be confirmed absolutely unless more than one transcript is observed from the same allele.

Complete genome sequence data led rapidly to the conclusion that ancient genome duplications had shaped the genomes of the model organisms Saccharomyces cerevisiae and Arabidopsis thaliana. Recent contributions have gone on to refine date estimates for these duplications and, in the case of Arabidopsis, to infer additional, more ancient, rounds of duplication by reconstructing gene order before the most recent duplication event. It is becoming widely accepted that an ancient duplication occurred before the radiation of the ray-finned fish. However, despite methodological advances and the availability of complete genome sequence data the debate over whether very ancient genome duplications have occurred early in the vertebrate lineage has not yet been fully resolved.

We have performed a large-scale analysis of amino acid sequence evolution after gene duplication by comparing evolution after gene duplication with evolution after speciation in over 1,800 phylogenetic trees constructed from manually curated alignments of protein domains downloaded from the PFAM database. The site-specific rate of evolution is significantly altered by gene duplication. A significant increase in the proportion of amino acid substitutions at constrained (slowly evolving) sites after duplication was observed. An increase in the proportion of replacements at normally constrained amino acid sites could result from relaxation of purifying selective pressure. However, the proportion of amino acid replacements involving radical changes in amino acid properties after duplication does not appear to be significantly increased by relaxed selective pressure. The increased proportion of replacements at constrained sites was observed over a relatively large range of protein change (up to 25% amino acid replacements per site). These findings have implications for our understanding of the nature of evolution after duplication and may help to shed light on the evolution of novel protein functions through gene duplication.

Using a large set of orthologous human and mouse gene pairs, we have characterized genes that have been retained in duplicate in human over timescales comparable to the time of speciation of human and mouse. Orthologous gene pairs for which a paralogous gene has been present for much or all of the time since speciation show an increased rate of nonsynonymous substitution. We have related rate of divergence to functional classification using the Gene Ontology terms. Protein function was found, in some cases, to have a larger impact on rate of evolution than the presence or absence of a paralog. No evidence was found that genes that have been retained in duplicate are weighted toward any functional categories. An increase in the ratio of nonsynonymous to synonymous changes following duplication has previously been reported. However, because amino acid sequences include conservative as well as more freely evolving sites, the ratio of nonsynonymous to synonymous changes tends to be higher for closely related pairs. By measuring the divergence of orthologs only and comparing between genes for which a paralogous gene is either present or absent, we have compared gene pairs that share a common divergence time. We have also found that shorter genes have a higher probability of being found duplicated in the human genome, possibly reflecting a mutational effect.

The differential abundance of transcripts from alternative alleles of a gene, for example in a hybrid plant or an outbred natural population, can provide information about the nature of interindividual or interstrain variation in gene expression. Allele-specific expression (ASE) can result from epigenetic phenomena, such as imprinting (when the overexpressed allele is inherited consistently from one parent) or allele-specific chromatin modifications. Alternatively, DNA sequence variants in the promoter or within the transcribed region of a gene can affect the rate of transcription or the rate of decay of the transcript, respectively. The existence of this allelic variation and the insights it provides into the nature of the gene regulation are of significant interest. With the recent widespread availability of sequencing based transcriptomics, the power to detect ASE has increased; however, inference of ASE from transcriptome sequencing data is subject to several caveats and potential biases and the results need to be interpreted with care.

peer-reviewed Background: Negative energy balance (NEB) is an altered metabolic state in high yielding cows that occurs during the first few weeks postpartum when energy demands for lactation and maintenance exceed the energy supply from dietary intake. NEB can, in turn, lead to metabolic disorders and to reduced fertility. Alterations in the expression of more than 700 hepatic genes have previously been reported in a study of NEB in postpartum dairy cows. miRNAs (microRNA) are known to mediate many alterations in gene expression post transcriptionally. To study the hepatic miRNA content of postpartum dairy cows, including their overall abundance and differential expression, in mild NEB (MNEB) and severe NEB (SNEB), short read RNA sequencing was carried out. To identify putative targets of differentially expressed miRNAs among differentially expressed hepatic genes reported previously in dairy cows in SNEB computational target identification was employed. Results: Our results indicate that the dairy cow liver expresses 53 miRNAs at a lower threshold of 10 reads per million. Of these, 10 miRNAs accounted for greater than 95% of the miRNAome (miRNA content). Of the highly expressed miRNAs, miR-122 constitutes 75% followed by miR-192 and miR-3596. Five out of thirteen let-7 miRNA family members are also among the highly expressed miRNAs. miR-143, down-regulated in SNEB, was found to have 4 putative up-regulated gene targets associated with SNEB including LRP2 (low density lipoprotein receptor-related protein 2), involved in lipid metabolism and up-regulated in SNEB. Conclusions: This is the first liver miRNA-seq profiling study of moderate yielding dairy cows in the early postpartum period. Tissue specific miR-122 and liver enriched miR-192 are two of the most abundant miRNAs in the postpartum dairy cow liver. miR-143 is significantly down-regulated in SNEB and putative targets of miRNA-143 which are up-regulated in SNEB, include a gene involved in lipid metabolism. Teagasc Walsh Fellowship Programme

The differential abundance of transcripts from alternative alleles of a gene, for example in a hybrid plant or an outbred natural population, can provide information about the nature of interindividual or interstrain variation in gene expression. Allele-specific expression (ASE) can result from epigenetic phenomena, such as imprinting (when the overexpressed allele is inherited consistently from one parent) or allele-specific chromatin modifications. Alternatively, DNA sequence variants in the promoter or within the transcribed region of a gene can affect the rate of transcription or the rate of decay of the transcript, respectively. The existence of this allelic variation and the insights it provides into the nature of the gene regulation are of significant interest. With the recent widespread availability of sequencing based transcriptomics, the power to detect ASE has increased; however, inference of ASE from transcriptome sequencing data is subject to several caveats and potential biases and the results need to be interpreted with care.

Completion of the human genome sequence provides evidence for a gene count with lower bound 30,000–40,000. Significant protein complexity may derive in part from multiple transcript isoforms. Recent EST based studies have revealed that alternate transcription, including alternative splicing, polyadenylation and transcription start sites, occurs within at least 30–40% of human genes. Transcript form surveys have yet to integrate the genomic context, expression, frequency, and contribution to protein diversity of isoform variation. We determine here the degree to which protein coding diversity may be influenced by alternate expression of transcripts by exhaustive manual confirmation of genome sequence annotation, and comparison to available transcript data to accurately associate skipped exon isoforms with genomic sequence. Relative expression levels of transcripts are estimated from EST database representation. The rigorous in silico method accurately identifies exon skipping using verified genome sequence. 545 genes have been studied in this first hand-curated assessment of exon skipping on chromosome 22. Combining manual assessment with software screening of exon boundaries provides a highly accurate and internally consistent indication of skipping frequency. 57 of 62 exon skipping events occur in the protein coding regions of 52 genes. A single gene, (FBXO7) expresses an exon repetition. 59% of highly represented multi-exon genes are likely to express exon-skipped isoforms in ratios that vary from 1:1 to 1:>100. The proportion of all transcripts corresponding to multi-exon genes that exhibit an exon skip is estimated to be 5%.

Gene order evolution in two eukaryotes was studied by comparing the Saccharomyces cerevisiae genome sequence to extensive new data from whole-genome shotgun and cosmid sequencing of Candida albicans . Gene order is substantially different between these two yeasts, with only 9% of gene pairs that are adjacent in one species being conserved as adjacent in the other. Inversion of small segments of DNA, less than 10 genes long, has been a major cause of rearrangement, which means that even where a pair of genes has been conserved as adjacent, the transcriptional orientations of the two genes relative to one another are often different. We estimate that about 1,100 single-gene inversions have occurred since the divergence between these species. Other genes that are adjacent in one species are in the same neighborhood in the other, but their precise arrangement has been disrupted, probably by multiple successive multigene inversions. We estimate that gene adjacencies have been broken as frequently by local rearrangements as by chromosomal translocations or long-distance transpositions. A bias toward small inversions has been suggested by other studies on animals and plants and may be general among eukaryotes.

We have updated the map of duplicated chromosomal segments in the Saccharomyces cerevisiae genome originally published by Wolfe and Shields in 1997 (Nature 387, 708‐713). The new analysis is based on the more sensitive Smith‐Waterman search method instead of BLAST. The parameters used to identify duplicated chromosomal regions were optimized such as to maximize the amount of the genome placed into paired regions, under the assumption that the hypothesis that the entire genome was duplicated in a single event is correct. The core of the new map, with 52 pairs of regions containing three or more duplicated genes, is largely unchanged from our original map. 39 tRNA gene pairs and one snRNA pair have been added. To find additional pairs of genes that may have been formed by whole genome duplication, we searched through the parts of the genome that are not covered by this core map, looking for putative duplicated chromosomal regions containing only two duplicate genes instead of three, or having lower-scoring gene pairs. This approach identified a further 32 candidate paired regions, bringing the total number of protein-coding genes on the duplication map to 905 (16% of the proteome). The updated map suggests that a second copy of the ribosomal DNA array has been deleted from chromosome IV. © 1999 Elsevier Science B.V. All rights reserved.

Whole-genome duplication approximately 10 8 years ago was proposed as an explanation for the many duplicated chromosomal regions in Saccharomyces cerevisiae . Here we have used computer simulations and analytic methods to estimate some parameters describing the evolution of the yeast genome after this duplication event. Computer simulation of a model in which 8% of the original genes were retained in duplicate after genome duplication, and 70–100 reciprocal translocations occurred between chromosomes, produced arrangements of duplicated chromosomal regions very similar to the map of real duplications in yeast. An analytical method produced an independent estimate of 84 map disruptions. These results imply that many smaller duplicated chromosomal regions exist in the yeast genome in addition to the 55 originally reported. We also examined the possibility of determining the original order of chromosomal blocks in the ancestral unduplicated genome, but this cannot be done without information from one or more additional species. If the genome sequence of one other species (such as Kluyveromyces lactis ) were known it should be possible to identify 150–200 paired regions covering the whole yeast genome and to reconstruct approximately two-thirds of the original order of blocks of genes in yeast. Rates of interchromosome translocation in yeast and mammals appear similar despite their very different rates of homologous recombination per kilobase.

The extent to which the order of genes along chromosomes is conserved between Saccharomyces cerevisiae and related species was studied by analysing data from DNA sequence databases. As expected, the extent of gene order conservation decreases with increasing evolutionary distance. About 59% of adjacent gene pairs in Kluyveromyces lactis or K. marxianus are also adjacent in S. cerevisiae, and a further 16% of Kluyveromyces neighbours can be explained in terms of the inferred ancestral gene order in Saccharomyces prior to the occurrence of an ancient whole-genome duplication. Only 13% of Candida albicans linkages, and no Schizosaccharomyces pombe linkages, are conserved. Analysis of gene order arrangements, chromosome numbers, and ribosomal RNA sequences suggests that genome duplication occurred before the divergence of the four species in Saccharomyces sensu stricto (all of which have 16 chromosomes), but after this lineage had diverged from Saccharomyces kluyveri and the Kluyveromyces lactis/marxianus species assemblage. © 1998 John Wiley & Sons, Ltd.

The short arms of the five acrocentric human chromosomes harbor sequences that direct the assembly and function of the nucleolus, one of the key functional domains of the nucleus, yet they are absent from the current human genome assembly. Here we describe the genomic architecture of these human nucleolar organizers. Sequences distal and proximal to ribosomal gene arrays are conserved among the acrocentric chromosomes, suggesting they are sites of frequent recombination. Although previously believed to be heterochromatic, characterization of these two flanking regions reveals that they share a complex genomic architecture similar to other euchromatic regions of the genome, but they have distinct genomic characteristics. Proximal sequences are almost entirely segmentally duplicated, similar to the regions bordering centromeres. In contrast, the distal sequence is predominantly unique to the acrocentric short arms and is dominated by a very large inverted repeat. We show that the distal element is localized to the periphery of the nucleolus, where it appears to anchor the ribosomal gene repeats. This, combined with its complex chromatin structure and transcriptional activity, suggests that this region is involved in nucleolar organization. Our results provide a platform for investigating the role of NORs in nucleolar formation and function, and open the door for determining the role of these regions in the well-known empirical association of nucleoli with pathology.

Motivation: DNA methylation is an epigenetic mark that can stably repress gene expression. Because of its biological and clinical significance, several methods have been developed to compare genome-wide patterns of methylation between groups of samples. The application of gene set analysis to identify relevant groups of genes that are enriched for differentially methylated genes is often a major component of the analysis of these data. This can be used, for example, to identify processes or pathways that are perturbed in disease development. We show that gene-set analysis, as it is typically applied to genome-wide methylation assays, is severely biased as a result of differences in the numbers of CpG sites associated with different classes of genes and gene promoters. Results: We demonstrate this bias using published data from a study of differential CpG island methylation in lung cancer and a dataset we generated to study methylation changes in patients with long-standing ulcerative colitis. We show that several of the gene sets that seem enriched would also be identified with randomized data. We suggest two existing approaches that can be adapted to correct the bias. Accounting for the bias in the lung cancer and ulcerative colitis datasets provides novel biological insights into the role of methylation in cancer development and chronic inflammation, respectively. Our results have significant implications for many previous genome-wide methylation studies that have drawn conclusions on the basis of such strongly biased analysis. Contact: cathal.seoighe@nuigalway.ie Supplementary information: Supplementary data are available at Bioinformatics online.

Background microRNAs (miRNAs) have been shown to regulate the expression of a large number of genes and play key roles in many biological processes. Several previous studies have quantified the inhibitory effect of a miRNA indirectly by considering the expression levels of genes that are predicted to be targeted by the miRNA and this approach has been shown to be robust to the choice of prediction algorithm. Given a gene expression dataset, Cheng et al. defined the regulatory effect score (RE-score) of a miRNA as the difference in the gene expression rank of targets of the miRNA compared to non-targeted genes. Results Using microarray data from parent-offspring trios from the International HapMap project, we show that the RE-score of most miRNAs is correlated between parents and offspring and, thus, inter-individual variation in RE-score has a genetic component in humans. Indeed, the mean RE-score across miRNAs is correlated between parents and offspring, suggesting genetic differences in the overall efficiency of the miRNA biogenesis pathway between individuals. To explore the genetics of this quantitative trait further, we carried out a genome-wide association study of the mean RE-score separately in two HapMap populations (CEU and YRI). No genome-wide significant associations were discovered; however, a SNP rs17409624, in an intron of DROSHA, was significantly associated with mean RE-score in the CEU population following permutation-based control for multiple testing based on all SNPs mapped to the canonical miRNA biogenesis pathway; of 244 individual miRNA RE-scores assessed in the CEU, 214 were associated (p < 0.05) with rs17409624. The SNP was also nominally significantly associated (p = 0.04) with mean RE-score in the YRI population. Interestingly, the same SNP was associated with 17 (8.5% of all expressed) miRNA expression levels in the CEU. We also show here that the expression of the targets of most miRNAs is more highly correlated with global changes in miRNA regulatory effect than with the expression of the miRNA itself. Conclusions We present evidence that miRNA regulatory effect is a heritable trait in humans and that a polymorphism of the DROSHA gene contributes to the observed inter-individual differences.

Background We carried out an analysis of intron length conservation across a diverse group of nineteen mammalian species. Motivated by recent research suggesting a role for time delays associated with intron transcription in gene expression oscillations required for early embryonic patterning, we searched for examples of genes that showed the most extreme conservation of total intron content in mammals. Results Gene sets annotated as being involved in pattern specification in the early embryo or containing the homeobox DNA-binding domain, were significantly enriched among genes with highly conserved intron content. We used ancestral sequences reconstructed with probabilistic models that account for insertion and deletion mutations to distinguish insertion and deletion events on lineages leading to human and mouse from their last common ancestor. Using a randomization procedure, we show that genes containing the homeobox domain show less change in intron content than expected, given the number of insertion and deletion events within their introns. Conclusions Our results suggest selection for gene expression precision or the existence of additional development-associated genes for which transcriptional delay is functionally significant.

Heterogeneity in sample composition is an inherent issue in many gene expression studies and, in many cases, should be taken into account in the downstream analysis to enable correct interpretation of the underlying biological processes. Typical examples are infectious diseases or immunology-related studies using blood samples, where, for example, the proportions of lymphocyte sub-populations are expected to vary between cases and controls. Nonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, notably in bioinformatics where its ability to extract meaningful information from high-dimensional data such as gene expression microarrays has been demonstrated. Very recently, it has been applied to biomarker discovery and gene expression deconvolution in heterogeneous tissue samples. Being essentially unsupervised, standard NMF methods are not guaranteed to find components corresponding to the cell types of interest in the sample, which may jeopardize the correct estimation of cell proportions. We have investigated the use of prior knowledge, in the form of a set of marker genes, to improve gene expression deconvolution with NMF algorithms. We found that this improves the consistency with which both cell type proportions and cell type gene expression signatures are estimated. The proposed method was tested on a microarray dataset consisting of pure cell types mixed in known proportions. Pearson correlation coefficients between true and estimated cell type proportions improved substantially (typically from about 0.5 to approximately 0.8) with the semi-supervised (marker-guided) versions of commonly used NMF algorithms. Furthermore known marker genes associated with each cell type were assigned to the correct cell type more frequently for the guided versions. We conclude that the use of marker genes improves the accuracy of gene expression deconvolution using NMF and suggest modifications to how the marker gene information is used that may lead to further improvements.

The evolutionary origin of stem cell pluripotency is an unresolved question. In mammals, pluripotency is limited to early embryos and is induced and maintained by a small number of key transcription factors, of which the POU domain protein Oct4 is considered central. Clonal invertebrates, by contrast, possess pluripotent stem cells throughout their life, but the molecular mechanisms that control their pluripotency are poorly defined. To address this problem, we analyzed the expression pattern and function of Polynem (Pln), a POU domain gene from the marine cnidarian Hydractinia echinata. We show that Pln is expressed in the embryo and adult stem cells of the animal and that ectopic expression in epithelial cells induces stem cell neoplasms and loss of epithelial tissue. Neoplasm cells downregulated the transgene but expressed the endogenous Pln gene and also Nanos, Vasa, Piwi and Myc, which are all known cnidarian stem cell markers. Retinoic acid treatment caused downregulation of Pln and the differentiation of neoplasm cells to neurosensory and epithelial cells. Pln downregulation by RNAi led to differentiation. Collectively, our results suggest an ancient role of POU proteins as key regulators of animal stem cells.