Abstract: The Human Gene Mutation Database (HGMD®) constitutes a comprehensive collection of published germline mutations in nuclear genes that underlie, or are closely associated with human inherited disease. At the time of writing (March 2017), the database contained in excess of 203,000 different gene lesions identified in over 8000 genes manually curated from over 2600 journals. With new mutation entries currently accumulating at a rate exceeding 17,000 per annum, HGMD represents de facto the central unified gene/disease-oriented repository of heritable mutations causing human genetic disease used worldwide by researchers, clinicians, diagnostic laboratories and genetic counsellors, and is an essential tool for the annotation of next-generation sequencing data. The public version of HGMD (http://www.hgmd.org) is freely available to registered users from academic institutions and non-profit organisations whilst the subscription version (HGMD Professional) is available to academic, clinical and commercial users under license via QIAGEN Inc.

Abstract: The deep convolutional neural network (CNN) is the state-of-the-art solution for large-scale visual recognition. Following some basic principles such as increasing network depth and constructing highway connections, researchers have manually designed a lot of fixed network architectures and verified their effectiveness.,,In this paper, we discuss the possibility of learning deep network structures automatically. Note that the number of possible network structures increases exponentially with the number of layers in the network, which motivates us to adopt the genetic algorithm to efficiently explore this large search space. The core idea is to propose an encoding method to represent each network structure in a fixed-length binary string. The genetic algorithm is initialized by generating a set of randomized individuals. In each generation, we define standard genetic operations, e.g., selection, mutation and crossover, to generate competitive individuals and eliminate weak ones. The competitiveness of each individual is defined as its recognition accuracy, which is obtained via a standalone training process on a reference dataset. We run the genetic process on CIFAR10, a small-scale dataset, demonstrating its ability to find high-quality structures which are little studied before. The learned powerful structures are also transferrable to the ILSVRC2012 dataset for large-scale visual recognition.

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Abstract: Cancer is a disease of complex genetic alterations, and comprehensive genetic diagnosis is beneficial to match each patient to appropriate therapy. However, acquisition of representative tumor samples is invasive and sometimes impossible. Circulating tumor DNA (ctDNA) is a promising tool to use as a non-invasive biomarker for cancer mutation profiling. Here we implemented targeted next generation sequencing (NGS) with a customized gene panel of 382 cancer-relevant genes on 605 ctDNA samples in multiple cancer types. Overall, tumor-specific mutations were identified in 87% of ctDNA samples, with mutation spectra highly concordant with their matched tumor tissues. 71% of patients had at least one clinically-actionable mutation, 76% of which have suggested drugs approved or in clinical trials. In particular, our study reveals a unique mutation spectrum in Chinese lung cancer patients which could be used to guide treatment decisions and monitor drug-resistant mutations. Taken together, our study demonstrated the feasibility of clinically-useful targeted NGS-based ctDNA mutation profiling to guide treatment decisions in cancer.

Abstract: To interpret genetic variants discovered from next-generation sequencing, integration of heterogeneous information is vital for success. This article describes a framework named PERCH (Polymorphism Evaluation, Ranking, and Classification for a Heritable trait), available at http://BJFengLab.org/. It can prioritize disease genes by quantitatively unifying a new deleteriousness measure called BayesDel, an improved assessment of the biological relevance of genes to the disease, a modified linkage analysis, a novel rare-variant association test, and a converted variant call quality score. It supports data that contain various combinations of extended pedigrees, trios, and case-controls, and allows for a reduced penetrance, an elevated phenocopy rate, liability classes, and covariates. BayesDel is more accurate than PolyPhen2, SIFT, FATHMM, LRT, Mutation Taster, Mutation Assessor, PhyloP, GERP++, SiPhy, CADD, MetaLR, and MetaSVM. The overall approach is faster and more powerful than the existing quantitative method pVAAST, as shown by the simulations of challenging situations in finding the missing heritability of a complex disease. This framework can also classify variants of unknown significance (variants of uncertain significance) by quantitatively integrating allele frequencies, deleteriousness, association, and co-segregation. PERCH is a versatile tool for gene prioritization in gene discovery research and variant classification in clinical genetic testing.

Abstract: Mutation enables evolution, but the idea that adaptation is also shaped by mutational variation is controversial. Simple evolutionary hypotheses predict such a relationship if the supply of mutations constrains evolution, but it is not clear that constraints exist, and, even if they do, they may be overcome by long-term natural selection. Quantification of the relationship between mutation and phenotypic divergence among species will help to resolve these issues. Here we use precise data on over 50,000 Drosophilid fly wings to demonstrate unexpectedly strong positive relationships between variation produced by mutation, standing genetic variation, and the rate of evolution over the last 40 million years. Our results are inconsistent with simple constraint hypotheses because the rate of evolution is very low relative to what both mutational and standing variation could allow. In principle, the constraint hypothesis could be rescued if the vast majority of mutations are so deleterious that they cannot contribute to evolution, but this also requires the implausible assumption that deleterious mutations have the same pattern of effects as potentially advantageous ones. Our evidence for a strong relationship between mutation and divergence in a slowly evolving structure challenges the existing models of mutation in evolution.

Abstract: Localizing failure-inducing code is essential for software debugging. Manual fault localization can be quite tedious, error-prone, and time-consuming. Therefore, a huge body of research e orts have been dedicated to automated fault localization. Spectrum-based fault localization, the most intensively studied fault localization approach based on test execution information, may have limited effectiveness, since a code element executed by a failed tests may not necessarily have impact on the test outcome and cause the test failure. To bridge the gap, mutation-based fault localization has been proposed to transform the programs under test to check the impact of each code element for better fault localization. However, there are limited studies on the effectiveness of mutation-based fault localization on sufficient number of real bugs. In this paper, we perform an extensive study to compare mutation-based fault localization techniques with various state-of-the-art spectrum-based fault localization techniques on 357 real bugs from the Defects4J benchmark suite. The study results firstly demonstrate the effectiveness of mutation-based fault localization, as well as revealing a number of guidelines for further improving mutation-based fault localization. Based on the learnt guidelines, we further transform test outputs/messages and test code to obtain various mutation information. Then, we propose TraPT, an automated Learning-to-Rank technique to fully explore the obtained mutation information for effective fault localization. The experimental results show that TraPT localizes 65.12% and 94.52% more bugs within Top-1 than state-of-the-art mutation and spectrum based techniques when using the default setting of LIBSVM.

Abstract: Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level where crossover and mutation comes from random variables. The problems of slow and premature convergence to suboptimal solution remain an existing struggle that GA is facing. Due to lower diversity in a population, it becomes challenging to locally exploit the solutions. In order to resolve these issues, the focus is now on reaching equilibrium between the explorative and exploitative features of GA. Therefore, the search process can be prompted to produce suitable GA solutions. This paper begins with an introduction, Section 2 describes the GA exploration and exploitation strategies to locate the optimum solutions. Section 3 and 4 present the lists of some prevalent mutation and crossover operators. This paper concludes that the key issue in developing a GA is to deliver a balance between explorative and exploitative features that complies with the combination of operators in order to produce exceptional performance as a GA as a whole.

Abstract: A new concept based on mutation operators is applied to flower pollination algorithm (FPA).Based on mutation, five new variants of FPA are proposed.Dynamic switch probability is used in all the proposed variants.Benchmarking of Variants with respect to standard FPA.Benchmarking and statistical testing of the best variant with respect to state-of-the-art algorithms. Flower pollination algorithm (FPA) is a recent addition to the field of nature inspired computing. The algorithm has been inspired from the pollination process in flowers and has been applied to a large spectra of optimization problems. But it has certain drawbacks which prevents its applications as a standard algorithm. This paper proposes new variants of FPA employing new mutation operators, dynamic switching and improved local search. A comprehensive comparison of proposed algorithms has been done for different population sizes for optimizing seventeen benchmark problems. The best variant among these is adaptive-Lvy flower pollination algorithm (ALFPA) which has been further compared with the well-known algorithms like artificial bee colony (ABC), differential evolution (DE), firefly algorithm (FA), bat algorithm (BA) and grey wolf optimizer (GWO). Numerical results show that ALFPA gives superior performance for standard benchmark functions. The algorithm has also been subjected to statistical tests and again the performance is better than the other algorithms.

Abstract: Accurate knowledge of the mutation rate provides a base line for inferring expected rates of evolution, for testing evolutionary hypotheses and for estimation of key parameters. Advances in sequencing technology now permit direct estimates of the mutation rate from sequencing of close relatives. Within insects there have been three prior such estimates, two in nonsocial insects (Drosophila: 2.8 × 10-9 per bp per haploid genome per generation; Heliconius: 2.9 × 10-9) and one in a social species, the honeybee (3.4 × 10-9). Might the honeybee's rate be ∼20% higher because it has an exceptionally high recombination rate and recombination may be directly or indirectly mutagenic? To address this possibility, we provide a direct estimate of the mutation rate in the bumblebee (Bombus terrestris), this being a close relative of the honeybee but with a much lower recombination rate. We confirm that the crossover rate of the bumblebee is indeed much lower than honeybees (8.7 cM/Mb vs. 37 cM/Mb). Importantly, we find no significant difference in the mutation rates: we estimate for bumblebees a rate of 3.6 × 10-9 per haploid genome per generation (95% confidence intervals 2.38 × 10-9 and 5.37 × 10-9) which is just 5% higher than the estimate that of honeybees. Both genomes have approximately one new mutation per haploid genome per generation. While we find evidence for a direct coupling between recombination and mutation (also seen in honeybees), the effect is so weak as to leave almost no footprint on any between-species differences. The similarity in mutation rates suggests an approximate constancy of the mutation rate in insects.

Abstract: Parallel evolution is the repeated evolution of the same phenotype or genotype in evolutionarily independent populations. Here, we use evolve-and-resequence experiments with bacteria and yeast to dissect the drivers of parallel evolution at the gene level. A meta-analysis shows that parallel evolution is often rare, but there is a positive relationship between population size and the probability of parallelism. We present a modeling approach to estimate the contributions of mutational and selective heterogeneity across a genome to parallel evolution. We show that, for two experiments, mutation contributes between ∼10 and 45%, respectively, of the variation associated with selection. Parallel evolution cannot, therefore, be interpreted as a phenomenon driven by selection alone; it must also incorporate information on heterogeneity in mutation rates along the genome. More broadly, the work discussed here helps lay the groundwork for a more sophisticated, empirically grounded theory of parallel evolution.

Abstract: Mutation testing assesses test suite efficacy by inserting small faults into programs and measuring the ability of the test suite to detect them It is widely considered the strongest test criterion in terms of finding the most faults and it subsumes a number of other coverage criteria Traditional mutation analysis is computationally prohibitive which hinders its adoption as an industry standard In order to alleviate the computational issues, we present a diff-based probabilistic approach to mutation analysis that drastically reduces the number of mutants by omitting lines of code without statement coverage and lines that are determined to be uninteresting - we dub these arid lines Furthermore, by reducing the number of mutants and carefully selecting only the most interesting ones we make it easier for humans to understand and evaluate the result of mutation analysis We propose a heuristic for judging whether a node is arid or not, conditioned on the programming language We focus on a code-review based approach and consider the effects of surfacing mutation results on developer attention The described system is used by 6,000 engineers in Google on all code changes they author or review, affecting in total more than 13,000 code authors as part of the mandatory code review process The system processes about 30% of all diffs across Google that have statement coverage calculated About 15% of coverage statement calculations fail across Google

Abstract: Objective To elucidate the genetic background of a patient with neonatal-onset multisystem inflammatory disease (NOMID) who does not carry any NLRP3 mutation Methods A Japanese male diagnosed as NOMID was recruited The patient had no NLRP3 mutation even as low frequency mosaicism We performed whole exome sequencing (WES) of the patient and his parents Induced pluripotent stem cells (iPSCs) were established from the fibroblasts of the patient iPSCs were then differentiated into monocytic lineage to evaluate the cytokine profile Results We established multiple iPSC clones from an NOMID patient and incidentally found that the phenotype of monocytes from iPSC clones were heterogeneous, and could be grouped into “diseased” and “normal” phenotype Because each iPSC clone was derived from a single somatic cell, we hypothesized the patient had somatic mosaicism of an IL-1β-related gene WES of both representative iPSC clones and patient's blood identified a novel heterozygous NLRC4 mutation, pT177A (c529A>G), as a specific mutation in “diseased” iPSC clones Knockout of the NLRC4 gene using CRISPR/Cas9 system in a mutant iPSC clone abrogated the pathogenic phenotype Conclusion We concluded the patient as having somatic mosaicism of a novel NLRC4 mutation To our knowledge, this is the first case showing somatic NLRC4 mutation causes autoinflammatory symptoms compatible to NOMID The present study demonstrates the significance of prospective genetic screening combined with iPSC-based phenotypic dissection for individualized diagnoses This article is protected by copyright All rights reserved

Abstract: CACNA1D encodes the pore-forming α1-subunit of Cav1.3, an L-type voltage-gated Ca2+-channel. Despite the recent discovery of two de novo missense gain-of-function mutations in Cav1.3 in two individuals with autism spectrum disorder (ASD) and intellectual disability CACNA1D has not been considered a prominent ASD-risk gene in large scale genetic analyses, since such studies primarily focus on likely-disruptive genetic variants. Here we report the discovery and characterization of a third de novo missense mutation in CACNA1D (V401L) in a patient with ASD and epilepsy. For the functional characterization we introduced mutation V401L into two major C-terminal long and short Cav1.3 splice variants, expressed wild-type or mutant channel complexes in tsA-201 cells and performed whole-cell patch-clamp recordings. Mutation V401L, localized within the channel's activation gate, significantly enhanced current densities, shifted voltage dependence of activation and inactivation to more negative voltages and reduced channel inactivation in both Cav1.3 splice variants. Altogether, these gating changes are expected to result in enhanced Ca2+-influx through the channel, thus representing a strong gain-of-function phenotype. Additionally, we also found that mutant channels retained full sensitivity towards the clinically available Ca2+ -channel blocker isradipine. Our findings strengthen the evidence for CACNA1D as a novel candidate autism risk gene and encourage experimental therapy with available channel-blockers for this mutation. The additional presence of seizures and neurological abnormalities in our patient define a novel phenotype partially overlapping with symptoms in two individuals with PASNA (congenital primary aldosteronism, seizures and neurological abnormalities) caused by similar Cav1.3 gain-of-function mutations.

Abstract: While mutational biases strongly influence neutral molecular evolution, the role of mutational biases in shaping the course of adaptation is less clear. Here we consider the frequency of transitions relative to transversions among adaptive substitutions. Because mutation rates for transitions are higher than those for transversions, if mutational biases influence the dynamics of adaptation, then transitions should be overrepresented among documented adaptive substitutions. To test this hypothesis, we assembled two sets of data on putatively adaptive amino acid replacements that have occurred in parallel during evolution, either in nature or in the laboratory. We find that the frequency of transitions in these data sets is much higher than would be predicted under a null model where mutation has no effect. Our results are qualitatively similar even if we restrict ourself to changes that have occurred, not merely twice, but three or more times. These results suggest that the course of adaptation is biased by mutation.

Abstract: Motivation Site directed mutagenesis is widely used to understand the structure and function of biomolecules. Computational prediction of mutation impacts on protein stability offers a fast, economical and potentially accurate alternative to laboratory mutagenesis. Most existing methods rely on geometric descriptions, this work introduces a topology based approach to provide an entirely new representation of mutation induced protein stability changes that could not be obtained from conventional techniques. Results Topology based mutation predictor (T-MP) is introduced to dramatically reduce the geometric complexity and number of degrees of freedom of proteins, while element specific persistent homology is proposed to retain essential biological information. The present approach is found to outperform other existing methods in the predictions of globular protein stability changes upon mutation. A Pearson correlation coefficient of 0.82 with an RMSE of 0.92 kcal/mol is obtained on a test set of 350 mutation samples. For the prediction of membrane protein stability changes upon mutation, the proposed topological approach has a 84% higher Pearson correlation coefficient than the current state-of-the-art empirical methods, achieving a Pearson correlation of 0.57 and an RMSE of 1.09 kcal/mol in a 5-fold cross validation on a set of 223 membrane protein mutation samples. Availability and implementation http://weilab.math.msu.edu/TML/TML-MP/. Contact wei@math.msu.edu. Supplementary information Supplementary data are available at Bioinformatics online.

Abstract: Identifying regions of the genome that are depleted of mutations can distinguish potentially deleterious variants. Short tandem repeats (STRs), also known as microsatellites, are among the largest contributors of de novo mutations in humans. However, per-locus studies of STR mutations have been limited to highly ascertained panels of several dozen loci. Here we harnessed bioinformatics tools and a novel analytical framework to estimate mutation parameters for each STR in the human genome by correlating STR genotypes with local sequence heterozygosity. We applied our method to obtain robust estimates of the impact of local sequence features on mutation parameters and used these estimates to create a framework for measuring constraint at STRs by comparing observed versus expected mutation rates. Constraint scores identified known pathogenic variants with early-onset effects. Our metric will provide a valuable tool for prioritizing pathogenic STRs in medical genetics studies.

Abstract: Diseases often run in families. These disease are frequently linked to changes in DNA that are passed down through generations. Close family members may share these disease-causing mutations; so may distant relatives who inherited the same mutation from a common ancestor long ago. Geneticists use a method called linkage mapping to trace a disease found in multiple members of a family over generations to genetic changes in a shared ancestor. This allows scientists to pinpoint the exact place in the genome the disease-causing mutation occurred. Using computer algorithms, scientists can apply the same technique to identify mutations that distant relatives inherited from a common ancestor. Belbin et al. used this computational technique to identify a mutation that may cause unusually short stature or bone and joint problems in up to 2% of people of Puerto Rican decent. In the experiments, the genomes of about 32,000 New Yorkers who have volunteered to participate in the BioMe Biobank and their health records were used to search for genetic changes linked to extremely short stature. The search revealed that people who inherited two copies of this mutation from their parents were likely to be extremely short or to have bone and joint problems. People who inherited one copy had an increased likelihood of joint or bone problems. This mutation affects a gene responsible for making a form of protein called collagen that is important for bone growth. The analysis suggests the mutation first arose in a Native American ancestor living in Puerto Rico around the time that European colonization began. The mutation had previously been linked to a disorder called Steel syndrome that was thought to be rare. Belbin et al. showed this condition is actually fairly common in people whose ancestors recently came from Puerto Rico, but may often go undiagnosed by their physicians. The experiments emphasize the importance of including diverse populations in genetic studies, as studies of people of predominantly European descent would likely have missed the link between this disease and mutation.

Abstract: We reinvestigate a fundamental question: How effective is crossover in genetic algorithms in combining building blocks of good solutions? Although this has been discussed controversially for decades, we are still lacking a rigorous and intuitive answer. We provide such answers for royal road functions and OneMax, where every bit is a building block. For the latter, we show that using crossover makes every + i¾ genetic algorithm at least twice as fast as the fastest evolutionary algorithm using only standard bit mutation, up to small-order terms and for moderatei¾ andi¾ . Crossover is beneficial because it can capitalize on mutations that have both beneficial and disruptive effects on building blocks: crossover is able to repair the disruptive effects of mutation in later generations. Compared to mutation-based evolutionary algorithms, this makes multibit mutations more useful. Introducing crossover changes the optimal mutation rate on OneMax from to . This holds both for uniform crossover and k-point crossover. Experiments and statistical tests confirm that our findings apply to a broad class of building block functions.

Abstract: Background Haemophilia B is caused by genetic aberrations in the F9 gene. The majority of these are non-synonymous mutations that alter the primary structure of blood coagulation factor IX (FIX). However, a synonymous mutation c.459G>A (Val107Val) was clinically reported to result in mild haemophilia B (FIX coagulant activity 15%–20% of normal). The F9 mRNA of these patients showed no skipping or retention of introns and/or change in mRNA levels, suggesting that mRNA integrity does not contribute to the origin of the disease in affected individuals. The aim of this study is to elucidate the molecular mechanisms that can explain disease manifestations in patients with this synonymous mutation. Methods We analyse the molecular mechanisms underlying the FIX deficiency through in silico analysis and reproducing the c.459G>A (Val107Val) mutation in stable cell lines. Conformation and non-conformation sensitive antibodies, limited trypsin digestion, activity assays for FIX, interaction with other proteins and post-translation modifications were used to evaluate the biophysical and biochemical consequences of the synonymous mutation. Results The Val107Val synonymous mutation in F9 was found to significantly diminish FIX expression. Our results suggest that this mutation slows FIX translation and affects its conformation resulting in decreased extracellular protein level. The altered conformation did not change the specific activity of the mutated protein. Conclusions The pathogenic basis for one synonymous mutation (Val107Val) in the F9 gene associated with haemophilia B was determined. A mechanistic understanding of this synonymous variant yields potential for guiding and developing future therapeutic treatments.

Abstract: Congenital hyperinsulinaemic hypoglycaemia (HH) can occur in isolation or it may present as part of a wider syndrome. For approximately 40%-50% of individuals with this condition, sequence analysis of the known HH genes identifies a causative mutation. Identifying the underlying genetic aetiology in the remaining cases is important as a genetic diagnosis will inform on recurrence risk, may guide medical management and will provide valuable insights into β-cell physiology. We sequenced the exome of a child with persistent diazoxide-responsive HH, mild aortic insufficiency, severe hypotonia, and developmental delay as well as the unaffected parents. This analysis identified a de novo mutation, p.G403D, in the proband's CACNA1D gene. CACNA1D encodes the main L-type voltage-gated calcium channel in the pancreatic β-cell, a key component of the insulin secretion pathway. The p.G403D mutation had been reported previously as an activating mutation in an individual with primary hyper-aldosteronism, neuromuscular abnormalities, and transient hypoglycaemia. Sequence analysis of the CACNA1D gene in 60 further cases with HH did not identify a pathogenic mutation. Identification of an activating CACNA1D mutation in a second patient with congenital HH confirms the aetiological role of CACNA1D mutations in this disorder. A genetic diagnosis is important as treatment with a calcium channel blocker may be an option for the medical management of this patient.

Abstract: Precise estimates of the single-nucleotide mutation rate and its variability are essential to the study of human genome evolution and genetic diseases. Here we use ~36 million singleton variants observed in 3,716 whole-genome sequences to characterize the heterogeneity of germline mutation rates across the genome. Adjacent-nucleotide context is the strongest predictor of mutability, with mutation rates varying by >650-fold depending on the identity of three bases upstream or downstream of the mutated site. Histone modifications, replication timing, recombination rate, and other local genomic features further modify mutability; magnitude and direction of this modification varies with the sequence context. Compared to estimates based on common variants used in previous approaches, singleton-based estimates provide a more accurate prediction of the mutation patterns seen in an independent dataset of ~46,000 de novo mutations; and incorporating the effects of genomic features further improves the prediction. The effects of sequence contexts, genomic features, and their interactions reported here capture the most refined portrait to date of the germline mutation patterns in humans.

Abstract: The precise clinicopathologic significance of myeloid differentiation primary response gene (MYD88) L265P mutation in diffuse large B-cell lymphomas (DLBCLs) remains elusive. To investigate the frequency and clinicopathologic significance of the MYD88 L265P mutation in DLBCLs, we conducted a meta-analysis of 40 published studies on 2736 DLBCL patients. We collected relevant published research findings identified using the PubMed and Embase databases. The effect sizes of outcome parameters were calculated using a random-effects model. In this meta-analysis, the MYD88 L265P mutation in DLBCL showed a significant difference according to tumor sites. The overall incidence of the MYD88 L265P mutation in DLBCLs, excluding the central nervous system and testicular DLBCLs, was 16.5%. Notably, the MYD88 L265P mutation rates of CNS and testicular DLBCL patients were 60% and 77%, respectively. Interestingly, the MYD88 L265P mutation was more frequently detected in activated B-cell-like (ABC) or non-germinal center B-cell-like (GCB) than GCB subtype (OR = 3.414, p < 0.001). The MYD88 L265P mutation was significantly associated with old age and poor overall survival, but not with sex and clinical stage. This pooled analysis demonstrates that the MYD88 L265P mutation is significantly associated with the tumor sites and molecular subtypes in DLBCL patients.