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R.A. Fisher's 1922 paper On the dominance ratio has a strong claim to be the foundation paper for modern population genetics. It greatly influenced subsequent work by Haldane and Wright, and contributed 3 major innovations to the study of evolution at the genetic level. First, the introduction of a general model of selection at a single locus, which showed how variability could be maintained by heterozygote advantage. Second, the use of the branching process approach to show that a beneficial mutation has a substantial chance of loss from the population, even when the population size is extremely large. Third, the invention of the concept of a probability distribution of allele frequency, caused by random sampling of allele frequencies due to finite population size, and the first use of a diffusion equation to investigate the properties of such a distribution. Although Fisher was motivated by an inference that later turned out to lack strong empirical support (a substantial contribution of dominance to quantitative trait variability), and his use of a diffusion equation was marred by a technical mistake, the paper introduced concepts and methods that pervade much subsequent work in population genetics. [ABSTRACT FROM AUTHOR]

Aims: This study was designed to analyse the prevalence of class 1 and class 2 integron integrase genes among antibiotic‐resistant coliform bacteria isolated from paper currency circulating in Pakistan. Methods and Results: A total of 500 individual currency notes were collected from different food vending sites at Lahore, Pakistan. Bacterial population were identified by biochemical and PCR techniques. Antimicrobial susceptibility testing was performed by disc diffusion assay. The highest bacterial population on currency was found from street vendors and butcher shops. Escherichia coli was found to be the most prevalent coliform bacteria followed by Klebsiella sp. and Enterobacter sp. PCR amplification of antimicrobial resistance gene showed the presence of ampC, blaTEM, blaNDM‐1, qnrA, tet(A) and tet(B) genes among coliform isolates. A total of 47 integron integrase bearing strains of coliform bacteria were analysed. Sequence analysis showed the presence of dfrA1‐aadA1, dfrA1, dfrA5, dfrA7, aadA1, aadA4 cassette arrays in class 1 integron and dfrA1‐sat2‐aadA1 in class 2 integrase genes. Conclusion: Circulating currency was heavily contaminated with antimicrobial‐resistant coliform bacteria bearing class 1 and class 2 integron integrase genes. Significance and Impact of the Study: This study describes a potential threat of severe bacterial infections due to improper hand hygiene and community sanitation when dealing with the currency notes. [ABSTRACT FROM AUTHOR]

Celecoxib, a selective cyclooxygenase 2 inhibitor, is known to have anti-inflammatory activity and to induce apoptosis in various types of cancer cells. Here, we examined the molecular mechanism of celecoxib-induced apoptosis in cervical cancer cell lines (HeLa, CaSki and C33A). Screening of a microarray cDNA-chip containing 225 different genes revealed that growth arrest and DNA damage inducible gene (GADD153), a transcription factor involved in apoptosis, showed the strongest differential expression following celecoxib treatment in all three cervical cancer cell lines. Notably, siRNA-induced silencing of GADD153 suppressed celecoxib-induced apoptosis in all the three cell lines, and exogenous expression of GADD153 triggered apoptosis in cervical cancer cells in the absence of other apoptotic stimuli. A luciferase reporter gene assay and mRNA stability tests revealed that expression of GADD153 was regulated at both the transcriptional and post-transcriptional levels following celecoxib treatment. The region between −649 and −249, containing an intact C/EBP-ATF binding site, was required for the basal activity and celecoxib-induced stimulation of GADD153 promoter activity. Also, mRNA stability test showed that celecoxib prolonged the half-life of GADD153 mRNA. In terms of signaling pathway, addition of the NF-κB inhibitor, N-tosyl-l phenylalanyl-chloromethyl ketone (TPCK), had no effect on GADD153 expression levels. Celecoxib treatment induced Bak expression, whereas cell treated with siGADD153 or TPCK showed lower levels of celecoxib-induced Bak up-regulation. These novel findings collectively suggest that GADD153 may play a key role in celecoxib-induced apoptosis in cervical cancer cells by regulating the expression of proapoptotic proteins such as Bak. [ABSTRACT FROM AUTHOR]

An opaque biochemical definition, an insufficient functional characterization, an interpolated database description, and a beautiful 3D structure with a wrong reaction. All these are elements of an exemplar case of misannotation in biological databases and confusion in the scientific literature concerning genes and enzymes acting on ureidoglycolate, an intermediate of purine catabolism. Here we show biochemical evidence for the relocation of genes assigned to EC 3.5.3.19 (ureidoglycolate hydrolase, releasing ammonia), such as allA of Escherichia coli or DAL3 of Saccharomyces cerevisiae, to EC 4.3.2.3 (ureidoglycolate lyase, releasing urea). The EC 3.5.3.19 should be more appropriately named ureidoglycolate amidohydrolase and include genes equivalent to UAH of Arabidopsis thaliana. The distinction between ammonia- or urea-releasing activities from ureidoglycolate is relevant for the understanding of nitrogen metabolism in various organisms and of virulence factors in certain pathogens rather than a nomenclature problem. We trace the original fault in database annotation and provide a rationale for its incorporation and persistence in the scientific literature. Notwithstanding the technological distance, yet not surprising for the constancy of human nature, error categories and mechanisms established in the study of the work of amanuensis monks still apply to the modern curation of biological databases. [ABSTRACT FROM AUTHOR]

Discusses a study on theoretical population genetics. Information on the algorithms used in the study; Background on Melanesian gene trees; Example of an explicit state of a genealogy.

Motivation Large-scale gene expression studies allow gene network construction to uncover associations among genes. To study direct associations among genes, partial correlation-based networks are preferred over marginal correlations. However, FDR control for partial correlation-based network construction is not well-studied. In addition, currently available partial correlation-based methods cannot take existing biological knowledge to help network construction while controlling FDR. Results In this paper, we propose a method called Partial Correlation Graph with Information Incorporation (PCGII). PCGII estimates partial correlations between each pair of genes by regularized node-wise regression that can incorporate prior knowledge while controlling the effects of all other genes. It handles high-dimensional data where the number of genes can be much larger than the sample size and controls FDR at the same time. We compare PCGII with several existing approaches through extensive simulation studies and demonstrate that PCGII has better FDR control and higher power. We apply PCGII to a plant gene expression dataset where it recovers confirmed regulatory relationships and a hub node, as well as several direct associations that shed light on potential functional relationships in the system. We also introduce a method to supplement observed data with a pseudogene to apply PCGII when no prior information is available, which also allows checking FDR control and power for real data analysis. Availability and implementation R package is freely available for download at https://cran.r-project.org/package=PCGII. [ABSTRACT FROM AUTHOR]

Over the coming decade, much more genetic data will enter into the study of economic development. This paper provides an example and emphasizes the uses and misuses of such information. It has assembled for the first time national frequencies of the ACP1 genetic polymorphism and the Interleukin-6 (IL6-174G) and Interleukin-10 (IL10-1082G) cytokines. These three respond over the centuries to ultraviolet radiation and infectious diseases. The study also looks at a national measure of heterozygotic diversity. In particular, it finds that ACP1 frequencies are significantly related to national outcomes ranging from GDP per capita to type and quality of governance, to measures of national "competitiveness," to health, to fertility, to measures of satisfaction with life. These associations do not seem explainable by reverse causation nor by the influence of some of the usual variables in studies of long-run development. Nonetheless, these results do not mean that a few genes have a direct causal effect on world development. The ACP1 *B variable is surely picking up the influences of many genetic and cultural adaptations over evolutionary time in response to ultraviolet exposure and pathogen burdens. This study's findings thus support other research indicating the importance of disease environments in shaping both genetic and sociocultural adaptations that have influence on development outcomes today. The paper concludes with a discussion of what such strong associations mean and do not mean, in hopes of guiding future studies of genes and other deep roots of economic development. [ABSTRACT FROM AUTHOR]

Epigenetics in the form of DNA methylation and other processes is an established property of genotypes and a focus of empirical research. Yet, there remain fundamental gaps in the evolutionary theory of epigenetics. To support a comprehensive understanding of epigenetics, this paper investigates theoretically the combined effects of deleterious mutation and epimutation with and without inbreeding. Both spontaneous epimutation and paramutation are considered to cover a broader range of epigenetic phenomena. We find that inbreeding generally reduces the amount of segregating deleterious genetic and epigenetic variation at equilibrium, although interestingly inbreeding can also increase the amount of deleterious genetic or epigenetic variation. Furthermore, we also demonstrate that epimutation indirectly can cause increased or decreased deleterious genetic variation at equilibrium relative to classic expectations, which is particularly evident when paramutation is occurring. With the addition of deleterious epimutation, there may be significantly increased purging of deleterious variation in more inbred populations and a significantly increased amount of segregating deleterious variation in more outbred populations, with notable exceptions. [ABSTRACT FROM AUTHOR]

With advances in genomic sequencing technology, a large amount of data is publicly available for the research community to extract meaningful and reliable associations among risk genes and the mechanisms of disease. However, this exponential growth of data is spread in over thousand heterogeneous repositories, represented in multiple formats and with different levels of quality what hinders the differentiation of clinically valid relationships from those that are less well-sustained and that could lead to wrong diagnosis. This paper presents how conceptual models can play a key role to efficiently manage genomic data. These data must be accessible, informative and reliable enough to extract valuable knowledge in the context of the identification of evidence supporting the relationship between DNA variants and disease. The approach presented in this paper provides a solution that help researchers to organize, store and process information focusing only on the data that are relevant and minimizing the impact that the information overload has in clinical and research contexts. A case-study (epilepsy) is also presented, to demonstrate its application in a real context. [ABSTRACT FROM AUTHOR]

Reception of chemical information from the environment is crucial for insects' survival and reproduction. The chemosensory reception mainly occurs by the antennae and mouth parts of the insect, when the stimulus contacts the chemoreceptors located within the sensilla. Chemosensory receptor genes have been well-studied in some social hymenopterans such as ants, honeybees, and wasps. However, although stingless bees are the most representative group of eusocial bees, little is known about their odorant, gustatory, and ionotropic receptor genes. Here, we analyze the transcriptome of the proboscis and antennae of the stingless bee Tetragonisca fiebrigi. We identified and annotated 9 gustatory and 15 ionotropic receptors. Regarding the odorant receptors, we identified 204, and we were able to annotate 161 of them. In addition, we compared the chemosensory receptor genes of T. fiebrigi with those annotated for other species of Hymenoptera. We found that T. fiebrigi showed the largest number of odorant receptors compared with other bees. Genetic expansions were identified in the subfamilies 9-exon, which was also expanded in ants and paper wasps; in G02A, including receptors potentially mediating social behavior; and in GUnC, which has been related to pollen and nectar scent detection. Our study provides the first report of chemosensory receptor genes in T. fiebrigi and represents a resource for future molecular and physiological research in this and other stingless bee species. [ABSTRACT FROM AUTHOR]

Inference of cell–cell communication (CCC) provides valuable information in understanding the mechanisms of many important life processes. With the rise of spatial transcriptomics in recent years, many methods have emerged to predict CCCs using spatial information of cells. However, most existing methods only describe CCCs based on ligand–receptor interactions, but lack the exploration of their upstream/downstream pathways. In this paper, we proposed a new method to infer CCCs, called Intercellular Gene Association Network (IGAN). Specifically, it is for the first time that we can estimate the gene associations/network between two specific single spatially adjacent cells. By using the IGAN method, we can not only infer CCCs in an accurate manner, but also explore the upstream/downstream pathways of ligands/receptors from the network perspective, which are actually exhibited as a new panoramic cell-interaction-pathway graph, and thus provide extensive information for the regulatory mechanisms behind CCCs. In addition, IGAN can measure the CCC activity at single cell/spot resolution, and help to discover the CCC spatial heterogeneity. Interestingly, we found that CCC patterns from IGAN are highly consistent with the spatial microenvironment patterns for each cell type, which further indicated the accuracy of our method. Analyses on several public datasets validated the advantages of IGAN. [ABSTRACT FROM AUTHOR]

Motivation: Recent attempts to account for multiple testing in the analysis of microarray data have focused on controlling the false discovery rate (FDR), which is defined as the expected percentage of the number of false positive genes among the claimed significant genes. As a consequence, the accuracy of the FDR estimators will be important for correctly controlling FDR. Xie et al. found that the standard permutation method of estimating FDR is biased and proposed to delete the predicted differentially expressed (DE) genes in the estimation of FDR for one-sample comparison. However, we notice that the formula of the FDR used in their paper is incorrect. This makes the comparison results reported in their paper unconvincing. Other problems with their method include the biased estimation of FDR caused by over- or under-deletion of DE genes in the estimation of FDR and by the implicit use of an unreasonable estimator of the true proportion of equivalently expressed (EE) genes. Due to the great importance of accurate FDR estimation in microarray data analysis, it is necessary to point out such problems and propose improved methods. Results: Our results confirm that the standard permutation method overestimates the FDR. With the correct FDR formula, we show the method of Xie et al. always gives biased estimation of FDR: it overestimates when the number of claimed significant genes is small, and underestimates when the number of claimed significant genes is large. To overcome these problems, we propose two modifications. The simulation results show that our estimator gives more accurate estimation. Contact: szhang3@unl.edu [ABSTRACT FROM AUTHOR]

The evolution of gene families is complex, involving gene-level evolutionary events such as gene duplication, horizontal gene transfer, and gene loss, and other processes such as incomplete lineage sorting (ILS). Because of this, topological differences often exist between gene trees and species trees. A number of models have been recently developed to explain these discrepancies, the most realistic of which attempts to consider both gene-level events and ILS. When unified in a single model, the interaction between ILS and gene-level events can cause polymorphism in gene copy number, which we refer to as copy number hemiplasy (CNH). In this paper, we extend the Wright–Fisher process to include duplications and losses over several species, and show that the probability of CNH for this process can be significant. We study how well two unified models—multilocus multispecies coalescent (MLMSC), which models CNH, and duplication, loss, and coalescence (DLCoal), which does not—approximate the Wright–Fisher process with duplication and loss. We then study the effect of CNH on gene family evolution by comparing MLMSC and DLCoal. We generate comparable gene trees under both models, showing significant differences in various summary statistics; most importantly, CNH reduces the number of gene copies greatly. If this is not taken into account, the traditional method of estimating duplication rates (by counting the number of gene copies) becomes inaccurate. The simulated gene trees are also used for species tree inference with the summary methods ASTRAL and ASTRAL-Pro, demonstrating that their accuracy, based on CNH-unaware simulations calibrated on real data, may have been overestimated. [ABSTRACT FROM AUTHOR]

In genomics studies, the investigation of gene relationships often brings important biological insights. Currently, the large heterogeneous datasets impose new challenges for statisticians because gene relationships are often local. They change from one sample point to another, may only exist in a subset of the sample, and can be nonlinear or even nonmonotone. Most previous dependence measures do not specifically target local dependence relationships, and the ones that do are computationally costly. In this paper, we explore a state-of-the-art network estimation technique that characterizes gene relationships at the single cell level, under the name of cell-specific gene networks. We first show that averaging the cell-specific gene relationship over a population gives a novel univariate dependence measure, the averaged Local Density Gap (aLDG), that accumulates local dependence and can detect any nonlinear, nonmonotone relationship. Together with a consistent nonparametric estimator, we establish its robustness on both the population and empirical levels. Then, we show that averaging the cell-specific gene relationship over mini-batches determined by some external structure information (eg, spatial or temporal factor) better highlights meaningful local structure change points. We explore the application of aLDG and its minibatch variant in many scenarios, including pairwise gene relationship estimation, bifurcating point detection in cell trajectory, and spatial transcriptomics structure visualization. Both simulations and real data analysis show that aLDG outperforms existing ones. [ABSTRACT FROM AUTHOR]

Cystic fibrosis (CF), an autosomal recessive disease, is caused by variants in both alleles of the CF transmembrane conductance regulator (CFTR) gene. A new assay based on allele-specific polymerase chain reaction and high-resolution melting analysis was developed for the detection of 18 CF-causing CFTR variants previously identified in Cuba and Latin America. The assay is also useful for zygosity determination of mutated alleles and includes internal controls. The reaction mixtures were normalized and evaluated using blood samples collected on filter paper. The evaluation of analytical parameters demonstrated the specificity and sensitivity of the method to detect the included CFTR variants. Internal and external validations yielded a 100% agreement between the new assay and the used reference tests. This assay can complement CF newborn screening not only in Cuba but also in Latin America. [ABSTRACT FROM AUTHOR]

The paper nautilus or greater argonaut, Argonauta argo , is a species of octopods which is characterized by its pelagic lifestyle and by the presence of a protective spiral-shaped shell-like eggcase in females. To reveal the genomic background of how the species adapted to the pelagic lifestyle and acquired its shell-like eggcase, we sequenced the draft genome of the species. The genome size was 1.1 Gb, which is the smallest among the cephalopods known to date, with the top 215 scaffolds (average length 5,064,479 bp) covering 81% (1.09 Gb) of the total assembly. A total of 26,433 protein-coding genes were predicted from 16,802 assembled scaffolds. From these, we identified nearly intact HOX, Parahox, Wnt clusters, and some gene clusters that could probably be related to the pelagic lifestyle, such as reflectin , tyrosinase , and opsin. The gene models also revealed several homologous genes related to calcified shell formation in Conchiferan mollusks, such as Pif-like, SOD, and TRX. Interestingly, comparative genomics analysis revealed that the homologous genes for such genes were also found in the genome of the shell-less octopus, as well as Nautilus , which has a true outer shell. Therefore, the draft genome sequence of Arg. argo presented here has helped us to gain further insights into the genetic background of the dynamic recruitment and dismissal of genes to form an important, converging extended phenotypic structure such as the shell and the shell-like eggcase. Additionally, it allows us to explore the evolution of from benthic to pelagic lifestyles in cephalopods and octopods. [ABSTRACT FROM AUTHOR]

In recent years, a number of computational approaches have been proposed to effectively integrate multiple heterogeneous biological networks, and have shown impressive performance for inferring gene function. However, the previous methods do not fully represent the critical neighborhood relationship between genes during the feature learning process. Furthermore, it is difficult to accurately estimate the contributions of different views for multi-view integration. In this paper, we propose MGEGFP, a multi-view graph embedding method based on adaptive estimation with Graph Convolutional Network (GCN), to learn high-quality gene representations among multiple interaction networks for function prediction. First, we design a dual-channel GCN encoder to disentangle the view-specific information and the consensus pattern across diverse networks. By the aid of disentangled representations, we develop a multi-gate module to adaptively estimate the contributions of different views during each reconstruction process and make full use of the multiplexity advantages, where a diversity preservation constraint is designed to prevent the over-fitting problem. To validate the effectiveness of our model, we conduct experiments on networks from the STRING database for both yeast and human datasets, and compare the performance with seven state-of-the-art methods in five evaluation metrics. Moreover, the ablation study manifests the important contribution of the designed dual-channel encoder, multi-gate module and the diversity preservation constraint in MGEGFP. The experimental results confirm the superiority of our proposed method and suggest that MGEGFP can be a useful tool for gene function prediction. [ABSTRACT FROM AUTHOR]

The development of single-cell RNA-sequencing (scRNA-seq) technologies has offered insights into complex biological systems at the single-cell resolution. In particular, these techniques facilitate the identifications of genes showing cell-type-specific differential expressions (DE). In this paper, we introduce MARBLES, a novel statistical model for cross-condition DE gene detection from scRNA-seq data. MARBLES employs a Markov Random Field model to borrow information across similar cell types and utilizes cell-type-specific pseudobulk count to account for sample-level variability. Our simulation results showed that MARBLES is more powerful than existing methods to detect DE genes with an appropriate control of false positive rate. Applications of MARBLES to real data identified novel disease-related DE genes and biological pathways from both a single-cell lipopolysaccharide mouse dataset with 24 381 cells and 11 076 genes and a Parkinson's disease human data set with 76 212 cells and 15 891 genes. Overall, MARBLES is a powerful tool to identify cell-type-specific DE genes across conditions from scRNA-seq data. [ABSTRACT FROM AUTHOR]

Preeclampsia is a pregnancy-specific disease that can have serious effects on the health of both mothers and their offspring. Predicting which women will develop preeclampsia in early pregnancy with high accuracy will allow for improved management. The clinical symptoms of preeclampsia are well recognized, however, the precise molecular mechanisms leading to the disorder are poorly understood. This is compounded by the heterogeneous nature of preeclampsia onset, timing and severity. Indeed a multitude of poorly defined causes including genetic components implicates etiologic factors, such as immune maladaptation, placental ischemia and increased oxidative stress. Large datasets generated by microarray and next-generation sequencing have enabled the comprehensive study of preeclampsia at the molecular level. However, computational approaches to simultaneously analyze the preeclampsia transcriptomic and network data and identify clinically relevant information are currently limited. In this paper, we proposed a control theory method to identify potential preeclampsia-associated genes based on both transcriptomic and network data. First, we built a preeclampsia gene regulatory network and analyzed its controllability. We then defined two types of critical preeclampsia-associated genes that play important roles in the constructed preeclampsia-specific network. Benchmarking against differential expression, betweenness centrality and hub analysis we demonstrated that the proposed method may offer novel insights compared with other standard approaches. Next, we investigated subtype specific genes for early and late onset preeclampsia. This control theory approach could contribute to a further understanding of the molecular mechanisms contributing to preeclampsia. [ABSTRACT FROM AUTHOR]

Study Question: Do polymorphisms in the anti-Müllerian hormone (AMH) promoter have an effect on AMH levels in patients with polycystic ovary syndrome (PCOS)?Summary Answer: We have identified a novel AMH promoter polymorphism rs10406324 that is associated with lower serum AMH levels and is suggested to play a role in the mechanism of regulation of AMH gene expression in women.What Is Known Already: Follicle number is positively correlated with serum AMH levels, reflected by elevated AMH levels in women with PCOS. In addition, it is suggested that AMH production per follicle is higher in women with PCOS than in normo-ovulatory women, implying an altered regulation of AMH in PCOS.Study Design, Size, Duration: A discovery cohort of 655 PCOS women of Northern European ancestry and both an internal and external validation PCOS cohort (n = 458 and n = 321, respectively) were included in this study. Summary-level data of an AMH genome-wide association study meta-analysis including 7049 normo-ovulatory women was included as a control cohort. A genetic approach was taken through association analysis and in silico analysis of the associated variants in the AMH promoter. In vitro analysis was performed to investigate the functional mechanisms.Participants/materials, Setting, Methods: All common two-allelic single-nucleotide polymorphisms (SNPs) in the region Chr19:2 245 353-2 250 827 bp (Build 37) were selected for the analysis. Linear regression analyses were performed to determine the association between SNPs in the AMH promoter region and serum AMH levels. For the in silico analysis, the webtools 'HaploReg' v4.1 for ENCODE prediction weight matrices and 'atSNP' were used. In vitro analysis was performed using KK1 cells, a mouse granulosa cell line and COV434 cells, a human granulosa tumor cell line. Cells were transfected with the reference or the variant human AMH promoter reporter construct together with several transcription factors (TFs). Dual-Glo® Luciferase Assay was performed to measure the luciferase activity.Main Results and the Role Of Chance: Polymorphism rs10406324 was significantly associated with serum AMH levels in all three PCOS cohorts. Carriers of the minor allele G had significantly lower log-transformed serum AMH levels compared to non-carriers (P = 8.58 × 10-8, P = 1.35 × 10-3 and P = 1.24 × 10-3, respectively). This result was validated in a subsequent meta-analysis (P = 3.24 × 10-12). Interestingly, rs10406324 was not associated with follicle count, nor with other clinical traits. Also, in normo-ovulatory women, the minor allele of this variant was associated with lower serum AMH levels (P = 1.04 × 10-5). These findings suggest that polymorphism rs10406324 plays a role in the regulation of AMH expression, irrespective of clinical background. In silico analysis suggested a decreased binding affinity of the TFs steroidogenenic factor 1, estrogen-related receptor alpha and glucocorticoid receptor to the minor allele G variant, however in vitro analysis did not show a difference in promoter activity between the A and G allele.Limitations, Reasons For Caution: Functional analyses were performed in a mouse and a human granulosa cell line using an AMH promoter reporter construct. This may have limited assessment of the impact of the polymorphism on higher order chromatin structures. Human granulosa cells generated from induced pluripotent stem cells, combined with gene editing, may provide a method to elucidate the exact mechanism behind the decrease in serum AMH levels in carriers of the -210 G allele. We acknowledge that the lack of follicle number in the external validation and the control cohort is a limitation of the paper. Although we observed that the association between rs10406324 and AMH levels was independent of follicle number in our discovery and internal validation PCOS cohorts, we cannot fully rule out that the observed effects on serum AMH levels are, in part, caused by differences in follicle number.Wider Implications Of the Findings: These results suggest that variations in serum AMH levels are not only caused by differences in follicle number but also by genetic factors. Therefore, the genetic context should be taken into consideration when assessing serum AMH levels in women. This may have clinical consequences when serum AMH levels are used as a marker for the polycystic ovarian morphology phenotype.Study Funding/competing Interest(s): No external funding was used. J.S.E.L. has received consultancy fees from the following companies: Ferring, Roche Diagnostics and Ansh Labs and has received travel reimbursement from Ferring. J.A.V. has received royalties from AMH assays, paid to the institute/lab with no personal financial gain. The other authors declare no competing interests.Trial Registration Number: N/A. [ABSTRACT FROM AUTHOR]

We previously introduced a numerical quantity called the stability (Ps) of an inferred tree and showed that for the tree to be reliable this stability as well as the reliability of the tree, which is usually computed as the bootstrap probability (Pb), must be high. However, if genome duplication occurs in a species, a gene family of the genome also duplicates, and for this reason alone some Ps values can be high in a tree of the duplicated gene families. In addition, the topology of the duplicated gene family can be similar to that of the original gene family if such gene families are identifiable. After genome duplication, however, the gene families are often partially deleted or partially duplicated, and the duplicated gene family may not show the same topology as that of the original family. It is therefore necessary to compute the similarity of the topologies of the duplicated and the original gene families. In this paper, we introduce another quantity called the reproducibility (Pr) for measuring the similarity of the two gene families. To show how to compute the Pr values, we first compute the Pb and Ps values for each of the MHC class II α and β chain gene families, which were apparently generated by genome duplication. We then compute the Pr values for the MHC class II α and β chain gene families. The Pr values for the α and β chain gene families are now low, and this suggests that the diploidization of gene segregation has occurred after the genome duplication. Currently higher animals, defined as animals with complex phenotypic characters, generally have a higher genome size, and this increase in genome size appears to have been caused by genome duplication and diploidization of gene segregation after genome duplication. [ABSTRACT FROM AUTHOR]

Brief summaries describing the function of each gene's product(s) are of great value to the research community, especially when interpreting genome-wide studies that reveal changes to hundreds of genes. However, manually writing such summaries, even for a single species, is a daunting task; for example, the Drosophila melanogaster genome contains almost 14 000 protein-coding genes. One solution is to use computational methods to generate summaries, but this often fails to capture the key functions or express them eloquently. Here, we describe how we solicited help from the research community to generate manually written summaries of D. melanogaster gene function. Based on the data within the FlyBase database, we developed a computational pipeline to identify researchers who have worked extensively on each gene. We e-mailed these researchers to ask them to draft a brief summary of the main function(s) of the gene's product, which we edited for consistency to produce a 'gene snapshot'. This approach yielded 1800 gene snapshot submissions within a 3-month period. We discuss the general utility of this strategy for other databases that capture data from the research literature. Database URL: https://flybase.org/ [ABSTRACT FROM AUTHOR]

Predicting differentially expressed genes (DEGs) from epigenetics signal data is the key to understand how epigenetics controls cell functional heterogeneity by gene regulation. This knowledge can help developing 'epigenetics drugs' for complex diseases like cancers. Most of existing machine learning-based methods suffer defects in prediction accuracy, interpretability or training speed. To address these problems, in this paper, we propose a M ultiple S elf- A ttention model for predicting DEGs on Epi genetic data (Epi-MSA). Epi-MSA first uses convolutional neural networks for neighborhood bins information embedding, and then employs multiple self-attention encoders on different input epigenetics factors data to learn which locations of genes are important for predicting DEGs. Next it trains a soft attention module to pick out which epigenetics factors are significant. The attention mechanism makes the model interpretable, and the pure matrix operation of self-attention enables the model to be parallel calculated and speeds up the training. Experiments on datasets from the Roadmap Epigenome Project and BluePrint Data Analysis Portal (BDAP) show that the performance of Epi-MSA is better than existing competitive methods, and Epi-MSA also has a smaller standard deviation, which shows that Epi-MSA is effective and stable. In addition, Epi-MSA has a good interpretability, this is confirmed by referring its attention weight matrix with existing biological knowledge. [ABSTRACT FROM AUTHOR]

The detection of bacterial antibiotic resistance phenotypes is important when carrying out clinical decisions for patient treatment. Conventional phenotypic testing involves culturing bacteria which requires a significant amount of time and work. Whole-genome sequencing is emerging as a fast alternative to resistance prediction, by considering the presence/absence of certain genes. A lot of research has focused on determining which bacterial genes cause antibiotic resistance and efforts are being made to consolidate these facts in knowledge bases (KBs). KBs are usually manually curated by domain experts to be of the highest quality. However, this limits the pace at which new facts are added. Automated relation extraction of gene-antibiotic resistance relations from the biomedical literature is one solution that can simplify the curation process. This paper reports on the development of a text mining pipeline that takes in English biomedical abstracts and outputs genes that are predicted to cause resistance to antibiotics. To test the generalisability of this pipeline it was then applied to predict genes associated with Helicobacter pylori antibiotic resistance, that are not present in common antibiotic resistance KBs or publications studying H. pylori. These genes would be candidates for further lab-based antibiotic research and inclusion in these KBs. For relation extraction, state-of-the-art deep learning models were used. These models were trained on a newly developed silver corpus which was generated by distant supervision of abstracts using the facts obtained from KBs. The top performing model was superior to a co-occurrence model, achieving a recall of 95%, a precision of 60% and F1-score of 74% on a manually annotated holdout dataset. To our knowledge, this project was the first attempt at developing a complete text mining pipeline that incorporates deep learning models to extract gene-antibiotic resistance relations from the literature. Additional related data can be found at https://github.com/AndreBrincat/Gene-Antibiotic-Resistance-Relation-Extraction [ABSTRACT FROM AUTHOR]

Deciphering microbiota functions is crucial to predict ecosystem sustainability in response to global change. High-throughput sequencing at the individual or community level has revolutionized our understanding of microbial ecology, leading to the big data era and improving our ability to link microbial diversity with microbial functions. Recent advances in bioinformatics have been key for developing functional prediction tools based on DNA metabarcoding data and using taxonomic gene information. This cheaper approach in every aspect serves as an alternative to shotgun sequencing. Although these tools are increasingly used by ecologists, an objective evaluation of their modularity, portability, and robustness is lacking. Here, we reviewed 100 scientific papers on functional inference and ecological trait assignment to rank the advantages, specificities, and drawbacks of these tools, using a scientific benchmarking. To date, inference tools have been mainly devoted to bacterial functions, and ecological trait assignment tools, to fungal functions. A major limitation is the lack of reference genomes—compared with the human microbiota—especially for complex ecosystems such as soils. Finally, we explore applied research prospects. These tools are promising and already provide relevant information on ecosystem functioning, but standardized indicators and corresponding repositories are still lacking that would enable them to be used for operational diagnosis. [ABSTRACT FROM AUTHOR]

Background: Spermatozoa acquire their motility and fertilizing abilities during their maturation through the epididymis. This process is controlled by epididymal epithelial cells that possess features adapted to sense and respond to their surrounding environment and to communicate with spermatozoa. During the past decade, new intercellular communication processes have been discovered, including the secretion and transport of molecules from the epithelium to spermatozoa via extracellular vesicles (EVs), as well as sensing of the intraluminal milieu by cellular extensions.Objective and Rationale: This review addresses recent findings regarding epididymal epithelial cell features and interactions between spermatozoa and the epididymal epithelium as well as epigenetic modifications undergone by spermatozoa during transit through the epididymal microenvironment.Search Methods: A systematic search was conducted in Pubmed with the keyword 'epididymis'. Results were filtered on original research articles published from 2009 to 2021 and written in the English language. One hundred fifteen original articles presenting recent advancements on the epididymis contribution to sperm maturation were selected. Some additional papers cited in the primary reference were also included. A special focus was given to higher mammalian species, particularly rodents, bovines and humans, that are the most studied in this field.Outcomes: This review provides novel insights into the contribution of epididymal epithelium and EVs to post-testicular sperm maturation. First, new immune cell populations have been described in the epididymis, where they are proposed to play a role in protecting the environment surrounding sperm against infections or autoimmune responses. Second, novel epididymal cell extensions, including dendrites, axopodia and primary cilia, have been identified as sensors of the environment surrounding sperm. Third, new functions have been outlined for epididymal EVs, which modify the sperm epigenetic profile and participate in transgenerational epigenetic inheritance of paternal traits.Wider Implications: Although the majority of these findings result from studies in rodents, this fundamental research will ultimately improve our knowledge of human reproductive physiopathologies. Recent discoveries linking sperm epigenetic modifications with paternal environmental exposure and progeny outcome further stress the importance of advancing fundamental research on the epididymis. From this, new therapeutic options for infertile couples and better counseling strategies may arise to increase positive health outcomes in children conceived either naturally or with ART. [ABSTRACT FROM AUTHOR]

Study Question: Which genes are confidently linked to human monogenic male infertility?Summary Answer: Our systematic literature search and clinical validity assessment reveals that a total of 78 genes are currently confidently linked to 92 human male infertility phenotypes.What Is Known Already: The discovery of novel male infertility genes is rapidly accelerating with the availability of next-generating sequencing methods, but the quality of evidence for gene-disease relationships varies greatly. In order to improve genetic research, diagnostics and counseling, there is a need for an evidence-based overview of the currently known genes.Study Design, Size, Duration: We performed a systematic literature search and evidence assessment for all publications in Pubmed until December 2018 covering genetic causes of male infertility and/or defective male genitourinary development.Participants/materials, Setting, Methods: Two independent reviewers conducted the literature search and included papers on the monogenic causes of human male infertility and excluded papers on genetic association or risk factors, karyotype anomalies and/or copy number variations affecting multiple genes. Next, the quality and the extent of all evidence supporting selected genes was weighed by a standardized scoring method and used to determine the clinical validity of each gene-disease relationship as expressed by the following six categories: no evidence, limited, moderate, strong, definitive or unable to classify.Main Results and the Role Of Chance: From a total of 23 526 records, we included 1337 publications about monogenic causes of male infertility leading to a list of 521 gene-disease relationships. The clinical validity of these gene-disease relationships varied widely and ranged from definitive (n = 38) to strong (n = 22), moderate (n = 32), limited (n = 93) or no evidence (n = 160). A total of 176 gene-disease relationships could not be classified because our scoring method was not suitable.Large Scale Data: Not applicable.Limitations, Reasons For Caution: Our literature search was limited to Pubmed.Wider Implications Of the Findings: The comprehensive overview will aid researchers and clinicians in the field to establish gene lists for diagnostic screening using validated gene-disease criteria and help to identify gaps in our knowledge of male infertility. For future studies, the authors discuss the relevant and important international guidelines regarding research related to gene discovery and provide specific recommendations for the field of male infertility.Study Funding/competing Interest(s): This work was supported by a VICI grant from The Netherlands Organization for Scientific Research (918-15-667 to J.A.V.), the Royal Society, and Wolfson Foundation (WM160091 to J.A.V.) as well as an investigator award in science from the Wellcome Trust (209451 to J.A.V.).Prospero Registration Number: None. [ABSTRACT FROM AUTHOR]

Research on the genomics of adaptation is rapidly changing. In the last few decades, progress in this area has been driven by methodological advances, not only in the way increasingly large amounts of molecular data are generated (e.g. with high‐throughput sequencing), but also in the way these data are analysed. This includes a growing appreciation and quantitative treatment of covariation among units within the same data type (e.g. genes) or across data types (e.g. genes and phenotypes). The development and adoption of more and more integrative tools have resulted in richer and more interesting empirical work. This special issue – comprising methodological, empirical, and review papers – aims to capture a 'snapshot' of this rapidly evolving field. We discuss in particular three important themes in the study of adaptation: the genetic architecture of adaptive variation, protein‐coding and regulatory changes, and parallel evolution. We highlight how more traditional key themes in the study of genetic architecture (e.g. the number of loci underlying adaptive traits and the distribution of their effects) are now being complemented by other factors (e.g. how patterns of linkage and number of loci interact to affect the ability to adapt). Similarly, apart from addressing the relative importance of protein‐coding and regulatory changes, we now have the tools to look in‐depth at specific types of regulatory variation to gain a clearer picture of regulatory networks. Finally, parallel evolution has always been central to the study of adaptation, but now we are often able to address the question of whether – and to what extent – parallelism at the organismal or phenotypic level is matched by parallelism at the genetic level. Perhaps most importantly, we can now determine what mechanisms are driving parallelism (or lack thereof) across levels of biological organization. All these recent methodological developments open up new directions for future studies of adaptive changes across traits, levels of biological organization, demographic contexts and time scales. [ABSTRACT FROM AUTHOR]

Echinoderm embryos and larvae are prominent experimental model systems for studying developmental mechanisms. High-quality, assembled, annotated genome sequences are now available for several echinoderm species, including representatives from most classes. The increased availability of these data necessitates the development of a nomenclature that assigns universally interpretable gene symbols to echinoderm genes to facilitate cross-species comparisons of gene functions, both within echinoderms and across other phyla. This paper describes the implementation of an improved set of echinoderm gene nomenclature guidelines that both communicates meaningful orthology information in protein-coding gene symbols and names and establishes continuity with nomenclatures developed for major vertebrate model organisms, including humans. Differences between the echinoderm gene nomenclature guidelines and vertebrate guidelines are examined and explained. This nomenclature incorporates novel solutions to allow for several types of orthologous relationships, including the single echinoderm genes with multiple vertebrate co-orthologs that result from whole-genome-duplication events. The current version of the Echinoderm Gene Nomenclature Guidelines can be found at https://www.echinobase.org/gene/static/geneNomenclature.jsp Database URL https://www.echinobase.org/ [ABSTRACT FROM AUTHOR]

To date, research on inflammatory bowel disease (IBD, encompassing Crohn's disease and ulcerative colitis), a chronic complex disorder, has generated a large amount of data scattered across published literature (1 06 333) listed in PubMed on 14 October 2020, and no dedicated database currently exists that catalogues information on genes associated with IBD. We aimed to manually curate 289 genes that are experimentally validated to be linked with IBD and its known phenotypes. Furthermore, we have developed an integrated platform providing information about different aspects of these genes by incorporating several resources and an extensive text-mined knowledgebase. The curated IBD database (IBDDB) allows the selective display of collated 34 subject-specific concepts (listed as columns) exportable through a user-friendly IBDDB portal. The information embedded in concepts was acquired via text-mining of PubMed (manually cleaned and curated), accompanied by data-mining from varied resources. The user can also explore different biomedical entities and their co-occurrence with other entities (about one million) from 11 curated dictionaries in the indexed PubMed records. This functionality permits the user to generate and cross-examine a new hypothesis that is otherwise not easy to comprehend by just reading the published abstracts and papers. Users can download required information using various file formats and can display information in the form of networks. To our knowledge, no curated database of IBD-related genes is available so far. IBDDB is free for academic users and can be accessed at https://www.cbrc.kaust.edu.sa/ibd/. [ABSTRACT FROM AUTHOR]

Clustering is central to many data-driven bioinformatics research and serves a powerful computational method. In particular, clustering helps at analyzing unstructured and high-dimensional data in the form of sequences, expressions, texts and images. Further, clustering is used to gain insights into biological processes in the genomics level, e.g. clustering of gene expressions provides insights on the natural structure inherent in the data, understanding gene functions, cellular processes, subtypes of cells and understanding gene regulations. Subsequently, clustering approaches, including hierarchical, centroid-based, distribution-based, density-based and self-organizing maps, have long been studied and used in classical machine learning settings. In contrast, deep learning (DL)-based representation and feature learning for clustering have not been reviewed and employed extensively. Since the quality of clustering is not only dependent on the distribution of data points but also on the learned representation, deep neural networks can be effective means to transform mappings from a high-dimensional data space into a lower-dimensional feature space, leading to improved clustering results. In this paper, we review state-of-the-art DL-based approaches for cluster analysis that are based on representation learning, which we hope to be useful, particularly for bioinformatics research. Further, we explore in detail the training procedures of DL-based clustering algorithms, point out different clustering quality metrics and evaluate several DL-based approaches on three bioinformatics use cases, including bioimaging, cancer genomics and biomedical text mining. We believe this review and the evaluation results will provide valuable insights and serve a starting point for researchers wanting to apply DL-based unsupervised methods to solve emerging bioinformatics research problems. [ABSTRACT FROM AUTHOR]

Motivation The evolution of complexity is one of the most fascinating and challenging problems in modern biology, and tracing the evolution of complex traits is an open problem. In bacteria, operons and gene blocks provide a model of tractable evolutionary complexity at the genomic level. Gene blocks are structures of co-located genes with related functions, and operons are gene blocks whose genes are co-transcribed on a single mRNA molecule. The genes in operons and gene blocks typically work together in the same system or molecular complex. Previously, we proposed a method that explains the evolution of orthologous gene blocks (orthoblocks) as a combination of a small set of events that take place in vertical evolution from common ancestors. A heuristic method was proposed to solve this problem. However, no study was done to identify the complexity of the problem. Results Here, we establish that finding the homologous gene block problem is NP-hard and APX-hard. We have developed a greedy algorithm that runs in polynomial time and guarantees an O (ln ⁡ n) approximation. In addition, we formalize our problem as an integer linear program problem and solve it using the PuLP package and the standard CPLEX algorithm. Our exploration of several candidate operons reveals that our new method provides more optimal results than the results from the heuristic approach, and is significantly faster. Availability and implementation The software and data accompanying this paper are available under the GPLv3 and CC0 license respectively on: https://github.com/nguyenngochuy91/Relevant-Operon. [ABSTRACT FROM AUTHOR]

For a diallelic genetic marker locus, tests like the parental-asymmetry test (PAT) are simple and powerful for detecting parent-of-origin effects. However, these approaches are applicable only to qualitative traits and thus are currently not suitable for quantitative traits. In this paper, the authors propose a novel class of PAT-type parent-of-origin effects tests for quantitative traits in families with both parents and an arbitrary number of children, which is denoted by Q-PAT(c) for some constant c. The authors further develop Q-1-PAT(c) for detection of parent-of-origin effects when information is available on only 1 parent in each family. The authors suggest the Q-C-PAT(c) test for combining families with data on both parental genotypes and families with data on only 1 parental genotype. Simulation studies show that the proposed tests control the empirical type I error rates well under the null hypothesis of no parent-of-origin effects. Power comparison also demonstrates that the proposed methods are more powerful than the existing likelihood ratio test. Although normality is commonly assumed in methods for studying quantitative traits, the tests proposed in this paper do not make any assumption about the distribution of the quantitative trait. [ABSTRACT FROM PUBLISHER]

Rice (Oryza sativa L.) is one of the most important cereal that has fed the world over a longer period. Before green revolution, cultivated rice is believed to have consisted of thousands of landraces each adapted to its specific climatic conditions by surviving against different abiotic and biotic selection pressure. However, owing to the low yield, photo-period sensitivity, late maturity and sensitivity to lodging of these landraces grown world-wide, serious concerns of impending global food crisis was felt during the 1960s because of (i) unprecedented increase of the population and (ii) concomitant decline in the cultivable land. Fortunately, high-yielding varieties developed through the introgression of the semi-dwarf1 gene (popularly known as sd1) during the 1960s led to significant increments in the food grain production that averted the apprehensions of nearing famine. This historical achievement having deep impact in the global agriculture is popularly referred as 'Green Revolution.' In this paper, we reviewed, its genetics as well as molecular regulations, evolutionary relationship with orthologous genes from other cereals as well as pseudo-cereals and attempted to provide an up-to-date information about its introgression to different rice cultivars of the world. [ABSTRACT FROM AUTHOR]

Biologists very often use enrichment methods based on statistical hypothesis tests to identify gene properties that are significantly over-represented in a given set of genes of interest, by comparison with a 'background' set of genes. These enrichment methods, although based on rigorous statistical foundations, are not always the best single option to identify patterns in biological data. In many cases, one can also use classification algorithms from the machine-learning field. Unlike enrichment methods, classification algorithms are designed to maximize measures of predictive performance and are capable of analysing combinations of gene properties, instead of one property at a time. In practice, however, the majority of studies use either enrichment or classification methods (rather than both), and there is a lack of literature discussing the pros and cons of both types of method. The goal of this paper is to compare and contrast enrichment and classification methods, offering two contributions. First, we discuss the (to some extent complementary) advantages and disadvantages of both types of methods for identifying gene properties that discriminate between gene classes. Second, we provide a set of high-level recommendations for using enrichment and classification methods. Overall, by highlighting the strengths and the weaknesses of both types of methods we argue that both should be used in bioinformatics analyses. [ABSTRACT FROM AUTHOR]

Motivation Gene set enrichment analysis has been shown to be effective in identifying relevant biological pathways underlying complex diseases. Existing approaches lack the ability to quantify the enrichment levels accurately, hence preventing the enrichment information to be further utilized in both upstream and downstream analyses. A modernized and rigorous approach for gene set enrichment analysis that emphasizes both hypothesis testing and enrichment estimation is much needed. Results We propose a novel computational method, Bayesian Analysis of Gene Set Enrichment (BAGSE), for gene set enrichment analysis. BAGSE is built on a Bayesian hierarchical model and fully accounts for the uncertainty embedded in the association evidence of individual genes. We adopt an empirical Bayes inference framework to fit the proposed hierarchical model by implementing an efficient EM algorithm. Through simulation studies, we illustrate that BAGSE yields accurate enrichment quantification while achieving similar power as the state-of-the-art methods. Further simulation studies show that BAGSE can effectively utilize the enrichment information to improve the power in gene discovery. Finally, we demonstrate the application of BAGSE in analyzing real data from a differential expression experiment and a transcriptome-wide association study. Our results indicate that the proposed statistical framework is effective in aiding the discovery of potentially causal pathways and gene networks. Availability and implementation BAGSE is implemented using the C++ programing language and is freely available from https://github.com/xqwen/bagse/. Simulated and real data used in this paper are also available at the Github repository for reproducibility purposes. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Almost all life on earth is facing environmental change, and understanding how populations will respond to these changes is of urgent importance. One factor that is known to affect the speed by which a population can evolve when faced with changes in the environment is strong sexual selection. This increases the adaptive capacity of a population by increasing reproductive skew toward well-adapted (usually) males who will, on average, be best able to compete for matings. This effect could potentially be disrupted when males pursue alternative reproductive tactics (ARTs), whereby males within a species exhibit qualitatively different behaviors in their pursuit of matings. ARTs are diverse, but one common class is those expressed through condition-dependent polyphenism such that high-quality, well-adapted males compete aggressively for mates and low-quality, poorly adapted males attempt to acquire matings via other, nonaggressive behaviors. Here, using an individual-based modeling approach, we consider the possible impacts of ARTs on adaptation and evolutionary rescue. When the ART is simultaneous, meaning that low-quality males not only engage in contests but also pursue other tactics, adaptive capacity is reduced and evolutionary rescue, where a population avoids extinction by adapting to a changing environment, becomes less likely. This is because the use of the ART allows low-quality males to contribute more maladaptive genes to the population than would happen otherwise. When the ART is fixed, however, such that low-quality males will only use the alternative tactic and do not engage in contests, we find the opposite: adaptation happens more quickly and evolutionary rescue when the environment changes is more likely. This surprising effect is caused by an increase in the mating success of the highest quality males who face many fewer competitors in this scenario—counterintuitively, the presence of males pursuing the ART increases reproductive skew toward those males in the best condition. [ABSTRACT FROM AUTHOR]

The article offers a perspective on variants of unknown significance (VUS) in precision oncology and their impact on treatment choice. It presents clinical cases of patients with pancreatic cancer, a family history of cancer, and germline or somatic VUS, to illustrate the challenges of VUS in clinical practice. It considers pathogenic, germline mutations as loss-of-function mutations in tumor suppressors and presents data regarding pathogenic TET3 mutations and VUS.

Motivation Gene clusters, defined as a set of genes encoding functionally related proteins, are abundant in eukaryotic genomes. Despite the increasing availability of chromosome-level genomes, the comprehensive analysis of gene family evolution remains largely unexplored, particularly for large and highly dynamic gene families or those including very recent family members. These challenges stem from limitations in genome assembly contiguity, particularly in repetitive regions such as large gene clusters. Recent advancements in sequencing technology, such as long reads and chromatin contact mapping, hold promise in addressing these challenges. Results To facilitate the identification, analysis, and visualization of physically clustered gene family members within chromosome-level genomes, we introduce GALEON, a user-friendly bioinformatic tool. GALEON identifies gene clusters by studying the spatial distribution of pairwise physical distances among gene family members along with the genome-wide gene density. The pipeline also enables the simultaneous analysis and comparison of two gene families and allows the exploration of the relationship between physical and evolutionary distances. This tool offers a novel approach for studying the origin and evolution of gene families. Availability and implementation GALEON is freely available from https://www.ub.edu/softevol/galeon and https://github.com/molevol-ub/galeon [ABSTRACT FROM AUTHOR]

Recent years have seen a dramatic increase in the number of canine genome assemblies available. Duplications are an important source of evolutionary novelty and are also prone to misassembly. We explored the duplication content of nine canine genome assemblies using both genome self-alignment and read-depth approaches. We find that 8.58% of the genome is duplicated in the canFam4 assembly, derived from the German Shepherd Dog Mischka, including 90.15% of unplaced contigs. Highlighting the continued difficulty in properly assembling duplications, less than half of read-depth and assembly alignment duplications overlap, but the mCanLor1.2 Greenland wolf assembly shows greater concordance. Further study shows the presence of multiple segments that have alignments to four or more duplicate copies. These high-recurrence duplications correspond to gene retrocopies. We identified 3,892 candidate retrocopies from 1,316 parental genes in the canFam4 assembly and find that ∼8.82% of duplicated base pairs involve a retrocopy, confirming this mechanism as a major driver of gene duplication in canines. Similar patterns are found across eight other recent canine genome assemblies, with metrics supporting a greater quality of the PacBio HiFi mCanLor1.2 assembly. Comparison between the wolf and other canine assemblies found that 92% of retrocopy insertions are shared between assemblies. By calculating the number of generations since genome divergence, we estimate that new retrocopy insertions appear, on average, in 1 out of 3,514 births. Our analyses illustrate the impact of retrogene formation on canine genomes and highlight the variable representation of duplicated sequences among recently completed canine assemblies. [ABSTRACT FROM AUTHOR]

Motivation As the most abundant mammalian mRNA methylation, N 6-methyladenosine (m6A) exists in >25% of human mRNAs and is involved in regulating many different aspects of mRNA metabolism, stem cell differentiation and diseases like cancer. However, our current knowledge about dynamic changes of m6A levels and how the change of m6A levels for a specific gene can play a role in certain biological processes like stem cell differentiation and diseases like cancer is largely elusive. Results To address this, we propose in this paper FunDMDeep-m6A a novel pipeline for identifying context-specific (e.g. disease versus normal, differentiated cells versus stem cells or gene knockdown cells versus wild-type cells) m6A-mediated functional genes. FunDMDeep-m6A includes, at the first step, DMDeep-m6A a novel method based on a deep learning model and a statistical test for identifying differential m6A methylation (DmM) sites from MeRIP-Seq data at a single-base resolution. FunDMDeep-m6A then identifies and prioritizes functional DmM genes (FDmMGenes) by combing the DmM genes (DmMGenes) with differential expression analysis using a network-based method. This proposed network method includes a novel m6A-signaling bridge (MSB) score to quantify the functional significance of DmMGenes by assessing functional interaction of DmMGenes with their signaling pathways using a heat diffusion process in protein-protein interaction (PPI) networks. The test results on 4 context-specific MeRIP-Seq datasets showed that FunDMDeep-m6A can identify more context-specific and functionally significant FDmMGenes than m6A-Driver. The functional enrichment analysis of these genes revealed that m6A targets key genes of many important context-related biological processes including embryonic development, stem cell differentiation, transcription, translation, cell death, cell proliferation and cancer-related pathways. These results demonstrate the power of FunDMDeep-m6A for elucidating m6A regulatory functions and its roles in biological processes and diseases. Availability and implementation The R-package for DMDeep-m6A is freely available from https://github.com/NWPU-903PR/DMDeepm6A1.0. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Addressing the origin of axial-patterning machinery is essential for understanding the evolution of animal form. Historically, sponges, a lineage that branched off early in animal evolution, were thought to lack Hox and ParaHox genes, suggesting that these critical axial-patterning genes arose after sponges diverged. However, a recent study has challenged this long-held doctrine by claiming to identify ParaHox genes (Cdx family) in two calcareous sponge species, Sycon ciliatum and Leucosolenia complicata. We reanalyzed the main data sets in this paper and analyzed an additional data set that expanded the number of bilaterians represented and removed outgroup homeodomains. As in the previous study, our Neighbor-Joining analyses of the original data sets recovered a clade that included sponge and Cdx genes, whereas Bayesian analyses placed these sponge genes within the NKL subclass of homeodomains. Unlike the original study, only one of our two maximum-likelihood analyses was congruent with Cdx genes in sponges. Our analyses of our additional data set led to the sponge genes consistently being placed within the NKL subclass of homeodomains regardless of method or model. Our results show more support for these sponge genes belonging to the NKL subclass, and therefore imply that Hox and ParaHox genes arose after Porifera diverged from the rest of animals. [ABSTRACT FROM AUTHOR]

Motivation RNA quantification experiments result in compositional data, however usual methods for compositional data analysis [additive log ratio (alr), centered log ratio (clr), isometric log ratio (ilr)] do not apply easily and give results difficult to interpret. To handle this, a method based on disjoint subgraphs in a graph whose nodes are the quantified RNAs is proposed. Edges in the graph are defined by lack of change in ratios of the corresponding RNAs between conditions. Results The methods is suited for qRT-PCR and RNA-Seq data analyses, and leads to easy-to-interpret, graphical results and the identification of set of genes that share a similar behavior when the studied condition changes. For qRT-PCR data, it has better statistical properties than the common Δ Δ C q method. Availability and implementation Construction of all pairwise ratio analysis P -values matrix, and conversion into a graph was implemented in an R package, named SARP.compo. It is freely available for download on the CRAN repository. Example R script using the package are provided as Supplementary Material ; the R package includes the data needed. One of these scripts reproduces the Figure 2 of this paper. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Complex diseases cannot be understood only on the basis of single gene, single mRNA transcript or single protein but the effect of their collaborations. The combination consequence in molecular level can be captured by the alterations of metabolites. With the rapidly developing of biomedical instruments and analytical platforms, a large number of metabolite signatures of complex diseases were identified and documented in the literature. Biologists' hardship in the face of this large amount of papers recorded metabolic signatures of experiments' results calls for an automated data repository. Therefore, we developed MetSigDis aiming to provide a comprehensive resource of metabolite alterations in various diseases. MetSigDis is freely available at http://www.bio-annotation.cn/MetSigDis/. By reviewing hundreds of publications, we collected 6849 curated relationships between 2420 metabolites and 129 diseases across eight species involving Homo sapiens and model organisms. All of these relationships were used in constructing a metabolite disease network (MDN). This network displayed scale-free characteristics according to the degree distribution (power-law distribution with R 2 = 0.909), and the subnetwork of MDN for interesting diseases and their related metabolites can be visualized in the Web. The common alterations of metabolites reflect the metabolic similarity of diseases, which is measured using Jaccard index. We observed that metabolite-based similar diseases are inclined to share semantic associations of Disease Ontology. A human disease network was then built, where a node represents a disease, and an edge indicates similarity of pair-wise diseases. The network validated the observation that linked diseases based on metabolites should have more overlapped genes. [ABSTRACT FROM AUTHOR]

The X chromosome is found twice as often in females as males. This has led to an intuition that X‐linked genes for traits experiencing sexually antagonistic selection should tend to evolve toward the female optimum. However, this intuition has never been formally examined. In this paper, I present a simple mathematical model and ask whether the X chromosome is indeed biased toward effecting female‐optimal phenotypes. Counter to the intuition, I find that the exact opposite bias exists; the X chromosome is revealed to be a welcome spot for mutations that benefit males at the expense of females. Not only do male‐beneficial alleles have an easier time of invading and spreading through a population, but they also achieve higher equilibrium frequencies than comparable female‐beneficial alleles. The X chromosome is therefore expected over evolutionary time to nudge phenotypes closer to the male optimum. Consequently, the X chromosome should find itself engaged in perpetual intragenomic conflicts with the autosomes and the mitochondria over developmental outcomes. The X chromosome's male bias and the intragenomic conflicts that ensue bear on the evolution of gene regulation, speciation, and our concept of organismality. [ABSTRACT FROM AUTHOR]

Motivation Given multi-platform genome data with prior knowledge of functional gene sets, how can we extract interpretable latent relationships between patients and genes? More specifically, how can we devise a tensor factorization method which produces an interpretable gene factor matrix based on functional gene set information while maintaining the decomposition quality and speed? Results We propose GIFT, a G uided and I nterpretable F actorization for T ensors. GIFT provides interpretable factor matrices by encoding prior knowledge as a regularization term in its objective function. We apply GIFT to the PanCan12 dataset (TCGA multi-platform genome data) and compare the performance with P-Tucker, our baseline method without prior knowledge constraint, and Silenced-TF, our naive interpretable method. Results show that GIFT produces interpretable factorizations with high scalability and accuracy. Furthermore, we demonstrate how results of GIFT can be used to reveal significant relations between (cancer, gene sets, genes) and validate the findings based on literature evidence. Availability and implementation The code and datasets used in the paper are available at https://github.com/leesael/GIFT. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Introduction: Human genetic research has succeeded in definitively identifying multiple genetic variants associated with risk for nicotine dependence and heavy smoking. To build on these advances, and to aid in reducing the prevalence of smoking and its consequent health harms, the next frontier is to identify genetic predictors of successful smoking cessation and also of the efficacy of smoking cessation treatments ("pharmacogenomics"). More broadly, additional biomarkers that can be quantified from biosamples also promise to aid "Precision Medicine" and the personalization of treatment, both pharmacological and behavioral.Aims and Methods: To motivate ongoing and future efforts, here we review several compelling genetic and biomarker findings related to smoking cessation and treatment.Results: These Key results involve genetic variants in the nicotinic receptor subunit gene CHRNA5, variants in the nicotine metabolism gene CYP2A6, and the nicotine metabolite ratio. We also summarize reports of epigenetic changes related to smoking behavior.Conclusions: The results to date demonstrate the value and utility of data generated from biosamples in clinical treatment trial settings. This article cross-references a companion paper in this issue that provides practical guidance on how to incorporate biosample collection into a planned clinical trial and discusses avenues for harmonizing data and fostering consortium-based, collaborative research on the pharmacogenomics of smoking cessation.Implications: Evidence is emerging that certain genotypes and biomarkers are associated with smoking cessation success and efficacy of smoking cessation treatments. We review key findings that open potential avenues for personalizing smoking cessation treatment according to an individual's genetic or metabolic profile. These results provide important incentive for smoking cessation researchers to collect biosamples and perform genotyping in research studies and clinical trials. [ABSTRACT FROM AUTHOR]

There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances statistical power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated type I error in the corresponding tests can occur. In this paper, we extend the empirical Bayes (EB) approach previously developed for multiplicative interaction, which trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of the relative excess risk due to interaction is derived, and the corresponding Wald test is proposed with a general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides a gain in power compared with the standard logistic regression analysis and better control of type I error when compared with the analysis assuming gene-environment independence. We illustrate themethods with data from the Ovarian Cancer Association Consortium. [ABSTRACT FROM AUTHOR]

Lay Summary: An evolutionary mechanism of aging was hypothesized 60 years ago to be the genetic trade-off between early life fitness and late life mortality. Genetic evidence supporting this hypothesis was unavailable then, but has accumulated recently. These tradeoffs, known as antagonistic pleiotropy, are common, perhaps ubiquitous. George Williams' 1957 paper developed the antagonistic pleiotropy hypothesis of aging, which had previously been hinted at by Peter Medawar. Antagonistic pleiotropy, as it applies to aging, hypothesizes that animals possess genes that enhance fitness early in life but diminish it in later life and that such genes can be favored by natural selection because selection is stronger early in life even as they cause the aging phenotype to emerge. No genes of the sort hypothesized by Williams were known 60 years ago, but modern molecular biology has now discovered hundreds of genes that, when their activity is enhanced, suppressed, or turned off, lengthen life and enhance health under laboratory conditions. Does this provide strong support for Williams' hypothesis? What are the implications of Williams' hypothesis for the modern goal of medically intervening to enhance and prolong human health? Here we briefly review the current state of knowledge on antagonistic pleiotropy both under wild and laboratory conditions. Overall, whenever antagonistic pleiotropy effects have been seriously investigated, they have been found. However, not all trade-offs are directly between reproduction and longevity as is often assumed. The discovery that antagonistic pleiotropy is common if not ubiquitous implies that a number of molecular mechanisms of aging may be widely shared among organisms and that these mechanisms of aging can be potentially alleviated by targeted interventions. [ABSTRACT FROM AUTHOR]