Showing papers on "Mutation (genetic algorithm) published in 2017"

Tumor Mutational Burden and Response Rate to PD-1 Inhibition

Abstract: In a survey of the spectrum of mutational burdens in 27 types of cancers, there was a correlation between an increased mutational burden and the response to checkpoint inhibition of PD-1 and PD-L1.

The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies

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.

Genetic CNN

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.

Circulating Tumor DNA Mutation Profiling by Targeted Next Generation Sequencing Provides Guidance for Personalized Treatments in Multiple Cancer Types.

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.

Mutation predicts 40 million years of fly wing evolution

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.

What drives parallel evolution?: How population size and mutational variation contribute to repeated evolution.

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.

Application of mutation operators to flower pollination algorithm

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.

Identification of a High-Frequency Somatic NLRC4 Mutation as a Cause of Autoinflammation by Pluripotent Cell-Based Phenotype Dissection.

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

PERCH: A Unified Framework for Disease Gene Prioritization

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.

Estimating the human mutation rate from autozygous segments reveals population differences in human mutational processes

Abstract: Heterozygous mutations within homozygous sequences descended from a recent common ancestor offer a way to ascertain de novo mutations across multiple generations. Using exome sequences from 3222 British-Pakistani individuals with high parental relatedness, we estimate a mutation rate of 1.45 ± 0.05 × 10−8 per base pair per generation in autosomal coding sequence, with a corresponding non-crossover gene conversion rate of 8.75 ± 0.05 × 10−6 per base pair per generation. This is at the lower end of exome mutation rates previously estimated in parent–offspring trios, suggesting that post-zygotic mutations contribute little to the human germ-line mutation rate. We find frequent recurrence of mutations at polymorphic CpG sites, and an increase in C to T mutations in a 5ʹ CCG 3ʹ to 5ʹ CTG 3ʹ context in the Pakistani population compared to Europeans, suggesting that mutational processes have evolved rapidly between human populations. Estimates of human mutation rates differ substantially based on the approach. Here, the authors present a multi-generational estimate from the autozygous segment in a non-European population that gives insight into the contribution of post-zygotic mutations and population-specific mutational processes.

Mutator genomes decay, despite sustained fitness gains, in a long-term experiment with bacteria.

Abstract: Understanding the extreme variation among bacterial genomes remains an unsolved challenge in evolutionary biology, despite long-standing debate about the relative importance of natural selection, mutation, and random drift. A potentially important confounding factor is the variation in mutation rates between lineages and over evolutionary history, which has been documented in several species. Mutation accumulation experiments have shown that hypermutability can erode genomes over short timescales. These results, however, were obtained under conditions of extremely weak selection, casting doubt on their general relevance. Here, we circumvent this limitation by analyzing genomes from mutator populations that arose during a long-term experiment with Escherichia coli, in which populations have been adaptively evolving for >50,000 generations. We develop an analytical framework to quantify the relative contributions of mutation and selection in shaping genomic characteristics, and we validate it using genomes evolved under regimes of high mutation rates with weak selection (mutation accumulation experiments) and low mutation rates with strong selection (natural isolates). Our results show that, despite sustained adaptive evolution in the long-term experiment, the signature of selection is much weaker than that of mutational biases in mutator genomes. This finding suggests that relatively brief periods of hypermutability can play an outsized role in shaping extant bacterial genomes. Overall, these results highlight the importance of genomic draft, in which strong linkage limits the ability of selection to purge deleterious mutations. These insights are also relevant to other biological systems evolving under strong linkage and high mutation rates, including viruses and cancer cells.

Direct Determination of the Mutation Rate in the Bumblebee Reveals Evidence for Weak Recombination-Associated Mutation and an Approximate Rate Constancy in Insects

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.

State of mutation testing at google

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

Mutational Biases Influence Parallel Adaptation.

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.

The genetic evolution of canine parvovirus - A new perspective.

Abstract: To trace the evolution process of CPV-2, all of the VP2 gene sequences of CPV-2 and FPV (from 1978 to 2015) from GenBank were analyzed in this study. Then, several new ideas regarding CPV-2 evolution were presented. First, the VP2 amino acid 555 and 375 positions of CPV-2 were first ruled out as a universal mutation site in CPV-2a and amino acid 101 position of FPV feature I or T instead of only I in existing rule. Second, the recently confusing nomenclature of CPV-2 variants was substituted with a optional nomenclature that would serve future CPV-2 research. Third, After check the global distribution of variants, CPV-2a is the predominant variant in Asia and CPV-2c is the predominant variant in Europe and Latin America. Fourth, a series of CPV-2-like strains were identified and deduced to evolve from modified live vaccine strains. Finally, three single VP2 mutation (F267Y, Y324I, and T440A) strains were caught concern. Furthermore, these three new VP2 mutation strains may be responsible for vaccine failure, and the strains with VP2 440A may become the novel CPV sub-variant. In conclusion, a summary of all VP2 sequences provides a new perspective regarding CPV-2 evolution and the correlative biological studies needs to be further performed.

Analysis and prediction of protein folding energy changes upon mutation by element specific persistent homology

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.

Universal haplotype-based noninvasive prenatal testing for single gene diseases

Abstract: To detect a fetal mutation inherited from the mother without paternal genetic information, a property of each maternal haplotype can be measured in the cell-free mixture. A separation value between values of the property for the two maternal haplotypes can be compared to thresholds to determine which haplotype is inherited. As measurements of a paternal allele may not be available, embodiments can measure the property at some loci where the fetus is homozygous and some loci where the fetus is heterozygous, but account for such loci where the fetus is heterozygous in the selection of a threshold for determining inheritance of a maternal haplotype. To determine parental haplotypes, direct haplotyping can be performed, and loci within a specified of the mutation can be selected and used in haplotype block for the measurements. Targeted measurements of a region including the mutation using predetermined primer/probes that may be re-used across subjects.

The (1+λ) evolutionary algorithm with self-adjusting mutation rate

Abstract: We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms. Roughly speaking, it consists of creating half the offspring with a mutation rate that is twice the current mutation rate and the other half with half the current rate. The mutation rate is then updated to the rate used in that subpopulation which contains the best offspring.We analyze how the (1 + λ) evolutionary algorithm with this self-adjusting mutation rate optimizes the OneMax test function. We prove that this dynamic version of the (1 + λ) EA finds the optimum in an expected optimization time (number of fitness evaluations) of O(nλ/log λ + n log n). This time is asymptotically smaller than the optimization time of the classic (1 + λ) EA. Previous work shows that this performance is best-possible among all λ-parallel mutation-based unbiased black-box algorithms.This result shows that the new way of adjusting the mutation rate can find optimal dynamic parameter values on the fly. Since our adjustment mechanism is simpler than the ones previously used for adjusting the mutation rate and does not have parameters itself we are optimistic that it will find other applications.

Transforming programs and tests in tandem for fault localization

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.

Crossover and mutation operators of genetic algorithms

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.

A gain-of-function mutation in the cardiac pacemaker HCN4 channel increasing cAMP sensitivity is associated with familial Inappropriate Sinus Tachycardia

Abstract: Aims Inappropriate Sinus Tachycardia (IST), a syndrome characterized by abnormally fast sinus rates and multisystem symptoms, is still poorly understood. Because of the relevance of HCN4 channels to pacemaker activity, we used a candidate-gene approach and screened IST patients for the presence of disease-causing HCN4 mutations. Methods and results Forty-eight IST patients, four of whom of known familial history, were enrolled in the study. We initially identified in one of the patients with familial history the R524Q mutation in HCN4. Investigation extended to the family members showed that the mutation co-segregated with IST-related symptoms. The R524Q mutation is located in the C-linker, a region known to couple cAMP binding to channel activation. The functional relevance of the mutation was investigated in heterologous expression systems by patch-clamp experiments. We found that mutant HCN4 channels were more sensitive to cAMP than wild-type channels, in agreement with increased sensitivity to basal and stimulated adrenergic input and with a faster than normal pacemaker rate. The properties of variant channels indicate therefore that R524Q is a gain-of-function mutation. Increased channel contribution to activity was confirmed by evidence that when spontaneously beating rat newborn myocytes were transfected with R524Q mutant HCN4 channels, they exhibited a faster rate than when transfected with wild-type HCN4 channels. Conclusion This is the first report of a gain-of-function HCN4 mutation associated with IST through increased sensitivity to cAMP-dependent activation.

Genome-Wide Biases in the Rate and Molecular Spectrum of Spontaneous Mutations in Vibrio cholerae and Vibrio fischeri.

Abstract: The vast diversity in nucleotide composition and architecture among bacterial genomes may be partly explained by inherent biases in the rates and spectra of spontaneous mutations. Bacterial genomes with multiple chromosomes are relatively unusual but some are relevant to human health, none more so than the causative agent of cholera, Vibrio cholerae Here, we present the genome-wide mutation spectra in wild-type and mismatch repair (MMR) defective backgrounds of two Vibrio species, the low-%GC squid symbiont V. fischeri and the pathogen V. cholerae, collected under conditions that greatly minimize the efficiency of natural selection. In apparent contrast to their high diversity in nature, both wild-type V. fischeri and V. cholerae have among the lowest rates for base-substitution mutations (bpsms) and insertion-deletion mutations (indels) that have been measured, below 10-3/genome/generation. Vibrio fischeri and V. cholerae have distinct mutation spectra, but both are AT-biased and produce a surprising number of multi-nucleotide indels. Furthermore, the loss of a functional MMR system caused the mutation spectra of these species to converge, implying that the MMR system itself contributes to species-specific mutation patterns. Bpsm and indel rates varied among genome regions, but do not explain the more rapid evolutionary rates of genes on chromosome 2, which likely result from weaker purifying selection. More generally, the very low mutation rates of Vibrio species correlate inversely with their immense population sizes and suggest that selection may not only have maximized replication fidelity but also optimized other polygenic traits relative to the constraints of genetic drift.