Showing papers on "Mutation (genetic algorithm) published in 2014"

MutationTaster2: mutation prediction for the deep-sequencing age

Abstract: To the Editor: The majority of the gene variants discovered by nextgeneration sequencing (NGS) projects are either intronic or synonymous. These variants are difficult to interpret because their effects on protein expression and function tend to be less obvious than those of missense or nonsense variants. Here we present MutationTaster2 (http://www.mutationtaster.org/), the latest version of our web-based software MutationTaster1, which evaluates the pathogenic potential of DNA sequence alterations. It is designed to predict the functional consequences of not only amino acid substitutions but also intronic and synonymous alterations, short insertion and/or deletion (indel) mutations and variants spanning intron-exon borders. MutationTaster2 includes all publicly available single-nucleotide polymorphisms (SNPs) and indels from the 1000 Genomes Project2 (hereafter referred to as 1000G) as well as known disease variants from ClinVar3 and HGMD Public4. Alterations found more than four times in the homozygous state in 1000G or in HapMap5 are automatically regarded as neutral. Variants marked as pathogenic in ClinVar are automatically predicted to be disease causing, and the disease phenotype is displayed. We have integrated tests for regulatory features, including data from the ENCODE project6 and JASPAR7, and score the evolutionary conservation around DNA variants (Supplementary Methods). To reduce the number of false positive splice-site four barcodes left out). We first immobilized cells on glass surfaces (Supplementary Methods). The DNA probes were hybridized, imaged and then removed by DNase I treatment (88.5% ± 11.0% efficiency (± standard deviation); Supplementary Fig. 2 and Supplementary Note). The remaining signal was photobleached (Supplementary Fig. 3). Even after six hybridizations, mRNAs were observed at 70.9% ± 21.8% of the original intensity (Supplementary Fig. 4). We observed that 77.9% ± 5.6% of the spots that colocalized in the first two hybridizations also colocalized with the third hybridization (Fig. 1b and Supplementary Figs. 5 and 6). We quantified the mRNA abundances by counting the occurrence of corresponding barcodes in the cell (n = 37 cells; Supplementary Figs. 7 and 8). We also show that mRNAs can be stripped and rehybridized efficiently in adherent mammalian cells (Supplementary Figs. 9 and 10). Sequential barcoding has many advantages. First, it scales up quickly; with even two dyes the coding capacity is in principle unlimited. Second, during each hybridization, all available FISH probes against a transcript can be used, thereby increasing the brightness of the FISH signal. Last, barcode readout is robust, enabling full z stacks on native samples. This barcoding scheme is conceptually akin to sequencing transcripts in single cells with FISH. In contrast with the technique used by Ke et al.2, our method takes advantage of the high hybridization efficiency of FISH (>95% of the mRNAs are detected1,3) and the fact that base-pair resolution is usually not needed to uniquely identify a transcript. We note that FISH probes can also be designed to resolve a large number of splice isoforms and single-nucleotide polymorphisms3, as well as chromosome loci4, in single cells. In combination with our previous report of super-resolution FISH1, the sequential barcoding method will enable the transcriptome to be directly imaged at single-cell resolution in complex samples such as brain tissue.

The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine

Abstract: The Human Gene Mutation Database (HGMD®) is a comprehensive collection of germline mutations in nuclear genes that underlie, or are associated with, human inherited disease. By June 2013, the database contained over 141,000 different lesions detected in over 5,700 different genes, with new mutation entries currently accumulating at a rate exceeding 10,000 per annum. HGMD was originally established in 1996 for the scientific study of mutational mechanisms in human genes. However, it has since acquired a much broader utility as a central unified disease-oriented mutation repository utilized by human molecular geneticists, genome scientists, molecular biologists, clinicians and genetic counsellors as well as by those specializing in biopharmaceuticals, bioinformatics and personalized genomics. The public version of HGMD (http://www.hgmd.org) is freely available to registered users from academic institutions/non-profit organizations whilst the subscription version (HGMD Professional) is available to academic, clinical and commercial users under license via BIOBASE GmbH.

A framework for the interpretation of de novo mutation in human disease

Abstract: Mark Daly and colleagues present a statistical framework to evaluate the role of de novo mutations in human disease by calibrating a model of de novo mutation rates at the individual gene level. The mutation probabilities defined by their model and list of constrained genes can be used to help identify genetic variants that have a significant role in disease.

CRISPR-mediated direct mutation of cancer genes in the mouse liver

Abstract: The study of cancer genes in mouse models has traditionally relied on genetically-engineered strains made via transgenesis or gene targeting in embryonic stem cells. Here we describe a new method of cancer model generation using the CRISPR/Cas (clustered regularly interspaced short palindromic repeats/CRISPR-associated proteins) system in vivo in wild-type mice. We used hydrodynamic injection to deliver a CRISPR plasmid DNA expressing Cas9 and single guide RNAs (sgRNAs) to the liver that directly target the tumour suppressor genes Pten (ref. 5) and p53 (also known as TP53 and Trp53) (ref. 6), alone and in combination. CRISPR-mediated Pten mutation led to elevated Akt phosphorylation and lipid accumulation in hepatocytes, phenocopying the effects of deletion of the gene using Cre–LoxP technology. Simultaneous targeting of Pten and p53 induced liver tumours that mimicked those caused by Cre–loxP-mediated deletion of Pten and p53. DNA sequencing of liver and tumour tissue revealed insertion or deletion mutations of the tumour suppressor genes, including bi-allelic mutations of both Pten and p53 in tumours. Furthermore, co-injection of Cas9 plasmids harbouring sgRNAs targeting the β-catenin gene and a single-stranded DNA oligonucleotide donor carrying activating point mutations led to the generation of hepatocytes with nuclear localization of β-catenin. This study demonstrates the feasibility of direct mutation of tumour suppressor genes and oncogenes in the liver using the CRISPR/Cas system, which presents a new avenue for rapid development of liver cancer models and functional genomics.

Precise estimates of mutation rate and spectrum in yeast

Abstract: Mutation is the ultimate source of genetic variation. The most direct and unbiased method of studying spontaneous mutations is via mutation accumulation (MA) lines. Until recently, MA experiments were limited by the cost of sequencing and thus provided us with small numbers of mutational events and therefore imprecise estimates of rates and patterns of mutation. We used whole-genome sequencing to identify nearly 1,000 spontaneous mutation events accumulated over ∼311,000 generations in 145 diploid MA lines of the budding yeast Saccharomyces cerevisiae. MA experiments are usually assumed to have negligible levels of selection, but even mild selection will remove strongly deleterious events. We take advantage of such patterns of selection and show that mutation classes such as indels and aneuploidies (especially monosomies) are proportionately much more likely to contribute mutations of large effect. We also provide conservative estimates of indel, aneuploidy, environment-dependent dominant lethal, and recessive lethal mutation rates. To our knowledge, for the first time in yeast MA data, we identified a sufficiently large number of single-nucleotide mutations to measure context-dependent mutation rates and were able to (i) confirm strong AT bias of mutation in yeast driven by high rate of mutations from C/G to T/A and (ii) detect a higher rate of mutation at C/G nucleotides in two specific contexts consistent with cytosine methylation in S. cerevisiae.

Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma.

Abstract: U niversity of California, San Francisco researchers recently reported in Science the mutational profiling of 23 initially low-grade gliomas (LGGs) and associated recurrent tumors and profiled a subset of recurrent tumors in temozolomide-treated patients. Three key findings with potentially important clinical implications for LGG management were demonstrated: (1) LGGs and paired recurrent tumors are highly divergent and often only share a few early mutations, thus partly explaining their differential therapeutic responses; (2) mutant isocitrate dehydrogenase-1 (IDH1) may be critical for LGG formation and is a potential therapeutic target; and (3) temozolomide therapy may contribute to malignant transformation and affect clinical outcome. Johnson et al determined the genetic profiles of LGGs and associated recurrences. Mutations that are shared or exclusive to the initial tumor or recurrences were characterized for each of 23 initial tumors and recurrences found up to 11 years later, and tumor phylogenies were mapped via evolutionary analyses. Overall, the paired tumors shared a significant percentage of mutations exclusive to the initial tumors in 43% of cases. These findings suggest that gliomas and recurrent tumors share early tumorigenic mutational origins but diverge afterward in tumorigenesis. LGG sequencing also revealed that an IDH1 mutation was present and remained unchanged in all paired tumors, highlighting IDH1 as a potentially critical LGG driver mutation. IDH1 mutants produce 2-hydroxyglutarate R-enantiomer, an interesting tumor metabolism product that inhibits histone enzymes and alters gene expression. Recent work highlights a possible LGG-selective therapeutic opportunity because inhibitors of IDH-1 mutant activity selectively reduce tumor growth rate and stimulate glioma differentiation. This work also reported the effects of temozolomide therapy on mutational profiles of recurrent gliomas, especially given that temozolomide use in LGG therapy is controversial. Mutational profiles of paired tumors in 10 temozolomide-treated patients were determined. Recurrent tumors from 6 of the 10 patients exhibited hypermutated phenotypes after temozolomide therapy, carryingmanymoremutations per million base pairs compared with their initial tumors. The hypermutated state is likely caused by the propensity of temozolomide tomutate and compromise DNA mismatch repair pathways. Additionally, the authors characterized the unique hypermutated signature and found significant association with high-grade glioma signaling pathways such as retinoblastoma and protein kinase B–mammalian target of rapamycin signaling (Figure, B). These results suggest that temozolomide therapy may contribute to malignant transformation of LGGs, and further studies are needed to determine whether this alters clinical outcomes.

Estimation of the spontaneous mutation rate per nucleotide site in a Drosophila melanogaster full-sib family

Abstract: We employed deep genome sequencing of two parents and 12 of their offspring to estimate the mutation rate per site per generation in a full-sib family of Drosophila melanogaster recently sampled from a natural population. Sites that were homozygous for the same allele in the parents and heterozygous in one or more offspring were categorized as candidate mutations and subjected to detailed analysis. In 1.23 × 10(9) callable sites from 12 individuals, we confirmed six single nucleotide mutations. We estimated the false negative rate in the experiment by generating synthetic mutations using the empirical distributions of numbers of nonreference bases at heterozygous sites in the offspring. The proportion of synthetic mutations at callable sites that we failed to detect was <1%, implying that the false negative rate was extremely low. Our estimate of the point mutation rate is 2.8 × 10(-9) (95% confidence interval = 1.0 × 10(-9) - 6.1 × 10(-9)) per site per generation, which is at the low end of the range of previous estimates, and suggests an effective population size for the species of ∼1.4 × 10(6). At one site, point mutations were present in two individuals, indicating that there had been a premeiotic mutation cluster, although surprisingly one individual had a G→A transition and the other a G→T transversion, possibly associated with error-prone mismatch repair. We also detected three short deletion mutations and no insertions, giving a deletion mutation rate of 1.2 × 10(-9) (95% confidence interval = 0.7 × 10(-9) - 11 × 10(-9)).

Analysing mutation schemes for real-parameter genetic algorithms

Abstract: Mutation is an important operator in genetic algorithms GAs, as it ensures maintenance of diversity in evolving populations of GAs. Real-parameter GAs RGAs handle real-valued variables directly without going to a binary string representation of variables. Although RGAs were first suggested in early '90s, the mutation operator is still implemented variable-wise - in a manner that is independent to each variable. In this paper, we investigate the effect of five different mutation schemes for RGAs using two different mutation operators - polynomial and Gaussian mutation operators. Based on extensive simulation studies, it is observed that a mutation clock implementation is computationally quick and also efficient in finding a solution close to the optimum on four different problems used in this study for both mutation operators. Moreover, parametric studies with their associated parameters reveal suitable working ranges of the parameters. Interestingly, both mutation operators with their respective optimal parameter settings are found to possess a similar inherent probability of offspring creation, a matter that is believed to be the reason for their superior working. This study signifies that the long suggested mutation clock operator should be considered as a valuable mutation operator for RGAs.

Using next-generation sequencing to isolate mutant genes from forward genetic screens

Abstract: The long-lasting success of forward genetic screens relies on the simple molecular basis of the characterized phenotypes, which are typically caused by mutations in single genes. Mapping the location of causal mutations using genetic crosses has traditionally been a complex, multistep procedure, but next-generation sequencing now allows the rapid identification of causal mutations at single-nucleotide resolution even in complex genetic backgrounds. Recent advances of this mapping-by-sequencing approach include methods that are independent of reference genome sequences, genetic crosses and any kind of linkage information, which make forward genetics amenable for species that have not been considered for forward genetic screens so far.

The major mutation framework: efficient and scalable mutation analysis for Java

Abstract: Mutation analysis seeds artificial faults (mutants) into a pro- gram and evaluates testing techniques by measuring how well they detect those mutants. Mutation analysis is well- established in software engineering research but hardly used in practice due to inherent scalability problems and the lack of proper tool support. In response to those challenges, this paper presents Major, a framework for mutation analysis and fault seeding. Major provides a compiler-integrated mu- tator and a mutation analyzer for JUnit tests. Major implements a large set of optimizations to enable efficient and scalable mutation analysis of large software sys- tems. It has already been applied to programs with more than 200,000 lines of code and 150,000 mutants. Moreover, Major features its own domain specific language and is de- signed to be highly configurable to support fundamental re- search in software engineering. Due to its efficiency and flexibility, the Major mutation framework is suitable for the application of mutation analysis in research and practice. It is publicly available at http://mutation-testing.org.

Establishing Theoretical Minimal Sets of Mutants

Abstract: Mutation analysis generates tests that distinguish variations, or mutants, of an artifact from the original. Mutation analysis is widely considered to be a powerful approach to testing, and hence is often used to evaluate other test criteria in terms of mutation score, which is the fraction of mutants that are killed by a test set. But mutation analysis is also known to provide large numbers of redundant mutants, and these mutants can inflate the mutation score. While mutation approaches broadly characterized as reduced mutation try to eliminate redundant mutants, the literature lacks a theoretical result that articulates just how many mutants are needed in any given situation. Hence, there is, at present, no way to characterize the contribution of, for example, a particular approach to reduced mutation with respect to any theoretical minimal set of mutants. This paper's contribution is to provide such a theoretical foundation for mutant set minimization. The central theoretical result of the paper shows how to minimize efficiently mutant sets with respect to a set of test cases. We evaluate our method with a widely-used benchmark.

Admixture and the organization of genetic diversity in a butterfly species complex revealed through common and rare genetic variants.

Abstract: Detailed information about the geographic distribution of genetic and genomic variation is necessary to better understand the organization and structure of biological diversity. In particular, spatial isolation within species and hybridization between them can blur species boundaries and create evolutionary relationships that are inconsistent with a strictly bifurcating tree model. Here, we analyse genome-wide DNA sequence and genetic ancestry variation in Lycaeides butterflies to quantify the effects of admixture and spatial isolation on how biological diversity is organized in this group. We document geographically widespread and pervasive historical admixture, with more restricted recent hybridization. This includes evidence supporting previously known and unknown instances of admixture. The genome composition of admixed individuals varies much more among than within populations, and tree- and genetic ancestry-based analyses indicate that multiple distinct admixed lineages or populations exist. We find that most genetic variants in Lycaeides are rare (minor allele frequency <0.5%). Because the spatial and taxonomic distributions of alleles reflect demographic and selective processes since mutation, rare alleles, which are presumably younger than common alleles, were spatially and taxonomically restricted compared with common variants. Thus, we show patterns of genetic variation in this group are multifaceted, and we argue that this complexity challenges simplistic notions concerning the organization of biological diversity into discrete, easily delineated and hierarchically structured entities.

Overcoming the Equivalent Mutant Problem: A Systematic Literature Review and a Comparative Experiment of Second Order Mutation

Abstract: Context. The equivalent mutant problem (EMP) is one of the crucial problems in mutation testing widely studied over decades. Objectives. The objectives are: to present a systematic literature review (SLR) in the field of EMP; to identify, classify and improve the existing, or implement new, methods which try to overcome EMP and evaluate them. Method. We performed SLR based on the search of digital libraries. We implemented four second order mutation (SOM) strategies, in addition to first order mutation (FOM), and compared them from different perspectives. Results. Our SLR identified 17 relevant techniques (in 22 articles) and three categories of techniques: detecting (DEM); suggesting (SEM); and avoiding equivalent mutant generation (AEMG). The experiment indicated that SOM in general and JudyDiffOp strategy in particular provide the best results in the following areas: total number of mutants generated; the association between the type of mutation strategy and whether the generated mutants were equivalent or not; the number of not killed mutants; mutation testing time; time needed for manual classification. Conclusions . The results in the DEM category are still far from perfect. Thus, the SEM and AEMG categories have been developed. The JudyDiffOp algorithm achieved good results in many areas.

The population genetics of evolutionary rescue.

Abstract: Evolutionary rescue occurs when a population that is threatened with extinction by an environmental change adapts to the change sufficiently rapidly to survive. Here we extend the mathematical theory of evolutionary rescue. In particular, we model evolutionary rescue to a sudden environmental change when adaptation involves evolution at a single locus. We consider adaptation using either new mutations or alleles from the standing genetic variation that begin rare. We obtain several results: i) the total probability of evolutionary rescue from either new mutation or standing variation; ii) the conditions under which rescue is more likely to involve a new mutation versus an allele from the standing genetic variation; iii) a mathematical description of the U-shaped curve of total population size through time, conditional on rescue; and iv) the time until the average population size begins to rebound as well as the minimal expected population size experienced by a rescued population. Our analysis requires taking into account a subtle population-genetic effect (familiar from the theory of genetic hitchhiking) that involves “oversampling” of those lucky alleles that ultimately sweep to high frequency. Our results are relevant to conservation biology, experimental microbial evolution, and medicine (e.g., the dynamics of antibiotic resistance).

Familias 3 – Extensions and new functionality

Abstract: In relationship testing the aim is to determine the most probable pedigree structure given genetic marker data for a set of persons. Disaster Victim Identification (DVI) based on DNA data from pres ...

A novel improved accelerated particle swarm optimization algorithm for global numerical optimization

Abstract: Purpose – Meta-heuristic algorithms are efficient in achieving the optimal solution for engineering problems. Hybridization of different algorithms may enhance the quality of the solutions and improve the efficiency of the algorithms. The purpose of this paper is to propose a novel, robust hybrid meta-heuristic optimization approach by adding differential evolution (DE) mutation operator to the accelerated particle swarm optimization (APSO) algorithm to solve numerical optimization problems. Design/methodology/approach – The improvement includes the addition of DE mutation operator to the APSO updating equations so as to speed up convergence. Findings – A new optimization method is proposed by introducing DE-type mutation into APSO, and the hybrid algorithm is called differential evolution accelerated particle swarm optimization (DPSO). The difference between DPSO and APSO is that the mutation operator is employed to fine-tune the newly generated solution for each particle, rather than random walks used i...

Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations

Abstract: Massively parallel sequencing studies have led to the identification of a large number of mutations present in a minority of cancers of a given site. Hence, methods to identify the likely pathogenic mutations that are worth exploring experimentally and clinically are required. We sought to compare the performance of 15 mutation effect prediction algorithms and their agreement. As a hypothesis-generating aim, we sought to define whether combinations of prediction algorithms would improve the functional effect predictions of specific mutations. Literature and database mining of single nucleotide variants (SNVs) affecting 15 cancer genes was performed to identify mutations supported by functional evidence or hereditary disease association to be classified either as non-neutral (n = 849) or neutral (n = 140) with respect to their impact on protein function. These SNVs were employed to test the performance of 15 mutation effect prediction algorithms. The accuracy of the prediction algorithms varies considerably. Although all algorithms perform consistently well in terms of positive predictive value, their negative predictive value varies substantially. Cancer-specific mutation effect predictors display no-to-almost perfect agreement in their predictions of these SNVs, whereas the non-cancer-specific predictors showed no-to-moderate agreement. Combinations of predictors modestly improve accuracy and significantly improve negative predictive values. The information provided by mutation effect predictors is not equivalent. No algorithm is able to predict sufficiently accurately SNVs that should be taken forward for experimental or clinical testing. Combining algorithms aggregates orthogonal information and may result in improvements in the negative predictive value of mutation effect predictions.

BRAFV600E Mutation and Its Association with Clinicopathological Features of Colorectal Cancer: A Systematic Review and Meta-Analysis

Abstract: Background Colorectal cancer (CRC) is a heterogeneous disease with multiple underlying causative genetic mutations. The B-type Raf proto-oncogene (BRAF) plays an important role in the mitogen-activated protein kinase (MAPK) signaling cascade during CRC. The presence of BRAFV600E mutation can determine the response of a tumor to chemotherapy. However, the association between the BRAFV600E mutation and the clinicopathological features of CRC remains controversial. We performed a systematic review and meta-analysis to estimate the effect of BRAFV600E mutation on the clinicopathological characteristics of CRC. Methods We identified studies that examined the effect of BRAFV600E mutation on CRC within the PubMed, ISI Science Citation Index, and Embase databases. The effect of BRAFV600E on outcome parameters was estimated by odds ratios (ORs) with 95% confidence intervals (CIs) for each study using a fixed effects or random effects model. Results 25 studies with a total of 11,955 CRC patients met inclusion criteria. The rate of BRAFV600 was 10.8% (1288/11955). The BRAFV600E mutation in CRC was associated with advanced TNM stage, poor differentiation, mucinous histology, microsatellite instability (MSI), CpG island methylator phenotype (CIMP). This mutation was also associated with female gender, older age, proximal colon, and mutL homolog 1 (MLH1) methylation. Conclusions This meta-analysis demonstrated that BRAFV600E mutation was significantly correlated with adverse pathological features of CRC and distinct clinical characteristics. These data suggest that BRAFV600E mutation could be used to supplement standard clinical and pathological staging for the better management of individual CRC patients, and could be considered as a poor prognostic marker for CRC.

The contribution of deleterious germline mutations in BRCA1, BRCA2 and the mismatch repair genes to ovarian cancer in the population

Abstract: The aim of this study was to estimate the contribution of deleterious mutations in BRCA1, BRCA2, MLH1, MSH2, MSH6 and PMS2 to invasive epithelial ovarian cancer (EOC) in the population. The coding sequence and splice site boundaries of all six genes were amplified in germline DNA from 2240 invasive EOC cases and 1535 controls. Barcoded fragment libraries were sequenced using the Illumina GAII or HiSeq and sequence data for each subject de-multiplexed prior to interpretation. GATK and Annovar were used for variant detection and annotation. After quality control 2222 cases (99.2%) and 1528 controls (99.5%) were included in the final analysis. We identified 193 EOC cases (8.7%) carrying a deleterious mutation in at least one gene compared with 10 controls (0.65%). Mutations were most frequent in BRCA1 and BRCA2, with 84 EOC cases (3.8%) carrying a BRCA1 mutation and 94 EOC cases (4.2%) carrying a BRCA2 mutation. The combined BRCA1 and BRCA2 mutation prevalence was 11% in high-grade serous disease. Seventeen EOC cases carried a mutation in a mismatch repair gene, including 10 MSH6 mutation carriers (0.45%) and 4 MSH2 mutation carriers (0.18%). At least 1 in 10 women with high-grade serous EOC has a BRCA1 or BRCA2 mutation. The development of next generation sequencing technologies enables rapid mutation screening for multiple susceptibility genes at once, suggesting that routine clinical testing of all incidence cases should be considered.

Differential evolution enhanced with multiobjective sorting-based mutation operators.

Abstract: Differential evolution (DE) is a simple and powerful population-based evolutionary algorithm. The salient feature of DE lies in its mutation mechanism. Generally, the parents in the mutation operator of DE are randomly selected from the population. Hence, all vectors are equally likely to be selected as parents without selective pressure at all. Additionally, the diversity information is always ignored. In order to fully exploit the fitness and diversity information of the population, this paper presents a DE framework with multiobjective sorting-based mutation operator. In the proposed mutation operator, individuals in the current population are firstly sorted according to their fitness and diversity contribution by nondominated sorting. Then parents in the mutation operators are proportionally selected according to their rankings based on fitness and diversity, thus, the promising individuals with better fitness and diversity have more opportunity to be selected as parents. Since fitness and diversity information is simultaneously considered for parent selection, a good balance between exploration and exploitation can be achieved. The proposed operator is applied to original DE algorithms, as well as several advanced DE variants. Experimental results on 48 benchmark functions and 12 real-world application problems show that the proposed operator is an effective approach to enhance the performance of most DE algorithms studied.

Parkinson disease-associated mutation R1441H in LRRK2 prolongs the "active state" of its GTPase domain.

Abstract: Mutation in leucine-rich-repeat kinase 2 (LRRK2) is a common cause of Parkinson disease (PD). A disease-causing point mutation R1441H/G/C in the GTPase domain of LRRK2 leads to overactivation of its kinase domain. However, the mechanism by which this mutation alters the normal function of its GTPase domain [Ras of complex proteins (Roc)] remains unclear. Here, we report the effects of R1441H mutation (RocR1441H) on the structure and activity of Roc. We show that Roc forms a stable monomeric conformation in solution that is catalytically active, thus demonstrating that LRRK2 is a bona fide self-contained GTPase. We further show that the R1441H mutation causes a twofold reduction in GTPase activity without affecting the structure, thermal stability, and GDP-binding affinity of Roc. However, the mutation causes a twofold increase in GTP-binding affinity of Roc, thus suggesting that the PD-causing mutation R1441H traps Roc in a more persistently activated state by increasing its affinity for GTP and, at the same time, compromising its GTP hydrolysis.

An Improved Parent-Centric Mutation With Normalized Neighborhoods for Inducing Niching Behavior in Differential Evolution

Abstract: In real life, we often need to find multiple optimally sustainable solutions of an optimization problem Evolutionary multimodal optimization algorithms can be very helpful in such cases They detect and maintain multiple optimal solutions during the run by incorporating specialized niching operations in their actual framework Differential evolution (DE) is a powerful evolutionary algorithm (EA) well-known for its ability and efficiency as a single peak global optimizer for continuous spaces This article suggests a niching scheme integrated with DE for achieving a stable and efficient niching behavior by combining the newly proposed parent-centric mutation operator with synchronous crowding replacement rule The proposed approach is designed by considering the difficulties associated with the problem dependent niching parameters (like niche radius) and does not make use of such control parameter The mutation operator helps to maintain the population diversity at an optimum level by using well-defined local neighborhoods Based on a comparative study involving 13 well-known state-of-the-art niching EAs tested on an extensive collection of benchmarks, we observe a consistent statistical superiority enjoyed by our proposed niching algorithm

Refined histopathological predictors of BRCA1 and BRCA2 mutation status: a large-scale analysis of breast cancer characteristics from the BCAC, CIMBA, and ENIGMA consortia

Abstract: The distribution of histopathological features of invasive breast tumors in BRCA1 or BRCA2 germline mutation carriers differs from that of individuals with no known mutation. Histopathological features thus have utility for mutation prediction, including statistical modeling to assess pathogenicity of BRCA1 or BRCA2 variants of uncertain clinical significance. We analyzed large pathology datasets accrued by the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) and the Breast Cancer Association Consortium (BCAC) to reassess histopathological predictors of BRCA1 and BRCA2 mutation status, and provide robust likelihood ratio (LR) estimates for statistical modeling. Selection criteria for study/center inclusion were estrogen receptor (ER) status or grade data available for invasive breast cancer diagnosed younger than 70 years. The dataset included 4,477 BRCA1 mutation carriers, 2,565 BRCA2 mutation carriers, and 47,565 BCAC breast cancer cases. Country-stratified estimates of the likelihood of mutation status by histopathological markers were derived using a Mantel-Haenszel approach. ER-positive phenotype negatively predicted BRCA1 mutation status, irrespective of grade (LRs from 0.08 to 0.90). ER-negative grade 3 histopathology was more predictive of positive BRCA1 mutation status in women 50 years or older (LR = 4.13 (3.70 to 4.62)) versus younger than 50 years (LR = 3.16 (2.96 to 3.37)). For BRCA2, ER-positive grade 3 phenotype modestly predicted positive mutation status irrespective of age (LR = 1.7-fold), whereas ER-negative grade 3 features modestly predicted positive mutation status at 50 years or older (LR = 1.54 (1.27 to 1.88)). Triple-negative tumor status was highly predictive of BRCA1 mutation status for women younger than 50 years (LR = 3.73 (3.43 to 4.05)) and 50 years or older (LR = 4.41 (3.86 to 5.04)), and modestly predictive of positive BRCA2 mutation status in women 50 years or older (LR = 1.79 (1.42 to 2.24)). These results refine likelihood-ratio estimates for predicting BRCA1 and BRCA2 mutation status by using commonly measured histopathological features. Age at diagnosis is an important variable for most analyses, and grade is more informative than ER status for BRCA2 mutation carrier prediction. The estimates will improve BRCA1 and BRCA2 variant classification and inform patient mutation testing and clinical management.

The arrival of the frequent: how bias in genotype-phenotype maps can steer populations to local optima.

Abstract: Genotype-phenotype (GP) maps specify how the random mutations that change genotypes generate variation by altering phenotypes, which, in turn, can trigger selection. Many GP maps share the following general properties: 1) The total number of genotypes is much larger than the number of selectable phenotypes; 2) Neutral exploration changes the variation that is accessible to the population; 3) The distribution of phenotype frequencies , with the number of genotypes mapping onto phenotype , is highly biased: the majority of genotypes map to only a small minority of the phenotypes. Here we explore how these properties affect the evolutionary dynamics of haploid Wright-Fisher models that are coupled to a random GP map or to a more complex RNA sequence to secondary structure map. For both maps the probability of a mutation leading to a phenotype scales to first order as , although for the RNA map there are further correlations as well. By using mean-field theory, supported by computer simulations, we show that the discovery time of a phenotype similarly scales to first order as for a wide range of population sizes and mutation rates in both the monomorphic and polymorphic regimes. These differences in the rate at which variation arises can vary over many orders of magnitude. Phenotypic variation with a larger is therefore be much more likely to arise than variation with a small . We show, using the RNA model, that frequent phenotypes (with larger ) can fix in a population even when alternative, but less frequent, phenotypes with much higher fitness are potentially accessible. In other words, if the fittest never ‘arrive’ on the timescales of evolutionary change, then they can't fix. We call this highly non-ergodic effect the ‘arrival of the frequent’.

VarWalker: personalized mutation network analysis of putative cancer genes from next-generation sequencing data.

Abstract: A major challenge in interpreting the large volume of mutation data identified by next-generation sequencing (NGS) is to distinguish driver mutations from neutral passenger mutations to facilitate the identification of targetable genes and new drugs. Current approaches are primarily based on mutation frequencies of single-genes, which lack the power to detect infrequently mutated driver genes and ignore functional interconnection and regulation among cancer genes. We propose a novel mutation network method, VarWalker, to prioritize driver genes in large scale cancer mutation data. VarWalker fits generalized additive models for each sample based on sample-specific mutation profiles and builds on the joint frequency of both mutation genes and their close interactors. These interactors are selected and optimized using the Random Walk with Restart algorithm in a protein-protein interaction network. We applied the method in >300 tumor genomes in two large-scale NGS benchmark datasets: 183 lung adenocarcinoma samples and 121 melanoma samples. In each cancer, we derived a consensus mutation subnetwork containing significantly enriched consensus cancer genes and cancer-related functional pathways. These cancer-specific mutation networks were then validated using independent datasets for each cancer. Importantly, VarWalker prioritizes well-known, infrequently mutated genes, which are shown to interact with highly recurrently mutated genes yet have been ignored by conventional single-gene-based approaches. Utilizing VarWalker, we demonstrated that network-assisted approaches can be effectively adapted to facilitate the detection of cancer driver genes in NGS data.

A genetic algorithm analysis towards optimization solutions

Abstract: In today’s world, an optimal and intelligent problem solving approaches are required in every field, regardless of simple or complex problems. Researches and developers are trying to make machines and software's more efficient and intelligent. This is where the Artificial Intelligence plays its role in developing efficient and optimal searching algorithm solutions. Genetic algorithm is one of most pervasive and advanced developed heuristic search technique in AI. Genetic algorithm (GA) is developed to find the most optimized solution for a given problem based on inheritance, mutation, selection and some other techniques. It was proved that genetic algorithms are the most powerful unbiased optimization techniques for sampling a large solution space. In this paper, we have used GA for the image optimization and Knapsack Problems, which are commonly found in a real world scenario. Furthermore, a research based on a tool that uses Genetic Algorithm, called the GA Playground is done to demonstrate the capability of solving the Knapsack Problem with the fitness function and a case study on how images can be reproduced using the optimal parameters. Lastly, a few methods such as the Hash Table and the Taguchi Method are suggested to improve the performance of the Genetic Algorithm.

A mutation burst during the acute phase of Helicobacter pylori infection in humans and rhesus macaques

Abstract: The evolution rate and genetic changes that occur during chronic infection with Helicobacter pylori have been analysed, but little is known about the genomic changes during the initial, acute bacterial infection phase. Here we analyse the rate and pattern of genome evolution in H. pylori from the genomes of two input strains isolated from human volunteers with asymptomatic infection, and the genomes of two output strains collected 20 and 44 days after re-infection. Similarly, we analyse genome evolution in bacteria from the genome sequences of input and output strains sequentially taken after experimental infection of a rhesus macaque. The estimated mutation rate reveals a mutation burst during the acute infection phase that is over 10 times faster than the mutation rate during chronic infection, and orders of magnitude faster than mutation rates in any other bacteria. The elevated frequency of mutations in outer membrane protein genes suggests that the mutation burst facilitates rapid host adaptation of the bacteria.