The Drosophila genome nexus: a population genomic resource of 623 Drosophila melanogaster genomes, including 197 from a single ancestral range population.

Hundreds of wild-derived Drosophila melanogaster genomes have been published, but rigorous comparisons across data sets are precluded by differences in alignment methodology. The most common approach to reference-based genome assembly is a single round of alignment followed by quality filtering and variant detection. We evaluated variations and extensions of this approach and settled on an assembly strategy that utilizes two alignment programs and incorporates both substitutions and short indels to construct an updated reference for a second round of mapping prior to final variant detection. Utilizing this approach, we reassembled published D. melanogaster population genomic data sets and added unpublished genomes from several sub-Saharan populations. Most notably, we present aligned data from phase 3 of the Drosophila Population Genomics Project (DPGP3), which provides 197 genomes from a single ancestral range population of D. melanogaster (from Zambia). The large sample size, high genetic diversity, and potentially simpler demographic history of the DPGP3 sample will make this a highly valuable resource for fundamental population genetic research. The complete set of assemblies described here, termed the Drosophila Genome Nexus, presently comprises 623 consistently aligned genomes and is publicly available in multiple formats with supporting documentation and bioinformatic tools. This resource will greatly facilitate population genomic analysis in this model species by reducing the methodological differences between data sets

Application of computational intelligence to explore and analyze system architecture and design alternatives

Systems Engineering involves the development or improvement of a system or process from effective need to a final value-added solution. Rapid advances in technology have led to development of sophisticated and complex sensor-enabled, remote, and highly networked cyber-technical systems. These complex modern systems present several challenges for systems engineers including: increased complexity associated with integration and emergent behavior, multiple and competing design metrics, and an expansive design parameter solution space. This research extends the existing knowledge base on multi-objective system design through the creation of a framework to explore and analyze system design alternatives employing computational intelligence. The first research contribution is a hybrid fuzzy-EA model that facilitates the exploration and analysis of possible SoS configurations. The second contribution is a hybrid neural network-EA in which the EA explores, analyzes, and evolves the neural network architecture and weights. The third contribution is a multi-objective EA that examines potential installation (i.e. system) infrastructure repair strategies. The final contribution is the introduction of a hierarchical multi-objective evolutionary algorithm (MOEA) framework with a feedback mechanism to evolve and simultaneously evaluate competing subsystem and system level performance objectives. Systems architects and engineers can utilize the frameworks and approaches developed in this research to more efficiently explore and analyze complex system design alternatives --Abstract, page iv

Genome analysis of a highly virulent serotype 1 strain of streptococcus pneumoniae from West Africa

Streptococcus pneumoniae is a leading cause of pneumonia, meningitis, and bacteremia, estimated to cause 2 million deaths annually. The majority of pneumococcal mortality occurs in developing countries, with serotype 1 a leading cause in these areas. To begin to better understand the larger impact that serotype 1 strains have in developing countries, we characterized virulence and genetic content of PNI0373, a serotype 1 strain from a diseased patient in The Gambia. PNI0373 and another African serotype 1 strain showed high virulence in a mouse intraperitoneal challenge model, with 20% survival at a dose of 1 cfu. The PNI0373 genome sequence was similar in structure to other pneumococci, with the exception of a 100 kb inversion. PNI0373 showed only15 lineage specific CDS when compared to the pan-genome of pneumococcus. However analysis of non-core orthologs of pneumococcal genomes, showed serotype 1 strains to be closely related. Three regions were found to be serotype 1 associated and likely products of horizontal gene transfer. A detailed inventory of known virulence factors showed that some functions associated with colonization were absent, consistent with the observation that carriage of this highly virulent serotype is unusual. The African serotype 1 strains thus appear to be closely related to each other and different from other pneumococci despite similar genetic content

A Genetic Algorithm for Path Generation and its Applications

Abstract-Path generation is an optimization problem mainly performed on grid maps that combines generation of paths with minimization of their cost. Several methods that belong to the class of exhaustive searches are available; however, these methods are only able to obtain a single path as a solution for each iteration of the search. Conversely, while genetic algorithms involving a type of multipoint search methods have been proposed as suitable candidates for this problem with the goal of simultaneously searching for multiple candidate paths, these methods are limited to particular applications, and there are limitations on the types of paths that can be represented. This paper therefore proposes a path generation method that is applicable to more general-purpose applications compared to previous methods based on a new design of the genotype used in the genetic algorithm

A comparison of methods for haplotype inference

This study presents some of the available methods for haplotype reconstruction and evaluates the accuracy and efficiency of three different software programs that utilize these methods. The analysis is performed on the QTLMAS XII common dataset, which is publicly available. The program LinkPHASE 5+, rule-based software, considers pedigree information (deduction and linkage) only. HiddenPHASE is a likelihood-based software, which takes into account molecular information (linkage disequilibrium). The DualPHASE software combines both of the above mentioned methods. We will see how usage of different available sources of information as well as the shape of the data affects the haplotype inference

WFABC: a Wright-Fisher ABC-based approach for inferring effective population sizes and selection coefficients from time-sampled data

With novel developments in sequencing technologies, time-sampled data are becoming more available and accessible. Naturally, there have been efforts in parallel to infer population genetic parameters from these datasets. Here, we compare and analyze four recent approaches based on the Wright-Fisher model for inferring selection coefficients (s) given effective population size (Ne), with simulated temporal datasets. Furthermore, we demonstrate the advantage of a recently proposed ABC-based method that is able to correctly infer genome-wide average Ne from time-serial data, which is then set as a prior for inferring per-site selection coefficients accurately and precisely. We implement this ABC method in a new software and apply it to a classical time-serial dataset of the medionigra genotype in the moth Panaxia dominula. We show that a recessive lethal model is the best explanation for the observed variation in allele frequency by implementing an estimator of the dominance ratio (h)

Genomic landscape and evolution of metastatic chromophobe renal cell carcinoma

Chromophobe renal cell carcinoma (chRCC) typically shows ~7 chromosome losses (1, 2, 6, 10, 13, 17, and 21) and ~31 exonic somatic mutations, yet carries ~5%-10% metastatic incidence. Since extensive chromosomal losses can generate proteotoxic stress and compromise cellular proliferation, it is intriguing how chRCC, a tumor with extensive chromosome losses and a low number of somatic mutations, can develop lethal metastases. Genomic features distinguishing metastatic from nonmetastatic chRCC are unknown. An integrated approach, including whole-genome sequencing (WGS), targeted ultradeep cancer gene sequencing, and chromosome analyses (FACETS, OncoScan, and FISH), was performed on 79 chRCC patients including 38 metastatic (M-chRCC) cases. We demonstrate that TP53 mutations (58%), PTEN mutations (24%), and imbalanced chromosome duplication (ICD, duplication of ≥ 3 chromosomes) (25%) were enriched in M-chRCC. Reconstruction of the subclonal composition of paired primary-metastatic chRCC tumors supports the role of TP53, PTEN, and ICD in metastatic evolution. Finally, the presence of these 3 genomic features in primary tumors of both The Cancer Genome Atlas kidney chromophobe (KICH) (n = 64) and M-chRCC (n = 35) cohorts was associated with worse survival. In summary, our study provides genomic insights into the metastatic progression of chRCC and identifies TP53 mutations, PTEN mutations, and ICD as high-risk features

Copy number alterations in pancreatic cancer identify recurrent PAK4 amplification

Pancreatic cancer is one of the most lethal of all cancers. The median survival is 6 months, and less than 5% of those diagnosed survive 5-years. Recurrent genetic deletions and amplifications in 73 pancreatic adenocarcinomas, the largest sample set analyzed to date for pancreatic cancer, were defined using comparative genomic hybridization The recurrent genetic alterations identified target a number of previously well-characterized genes, as well as regions that contain possible new oncogenes and tumor suppressor genes. We have focused on chromosome 19q13, a region frequently found amplified in pancreatic cancer, and demonstrate how boundaries of common regions of mutation can be mapped, and how a gene, in this case PAK4 amplified on chromosome19q13, can be functionally validated. We show that although the PAK4 gene is not activated by mutation in cell lines with gene amplification, an oncogenic form of the KRAS2 gene is present in these cells, and oncogenic KRAS2 can activate PAK4. In fact in the three samples we identified with PAK4 gene amplification, the KRAS2 gene was activated and genomically amplified. The kinase activity of the PAK4 protein is significantly higher in cells with genomic amplification as compared to cells without amplification. Our study demonstrates the utility of analyzing copy number data in a large set of neoplasms to identify genes involved in cancer. We have generated a useful dataset which will be particularly useful for the pancreatic cancer community as efforts are undertaken to sequence the pancreatic cancer genome

Predicting selective drug targets in cancer through metabolic networks

The authors develop a genome-scale model of cancer metabolism and use it to predict genes that are essential for cancer cell growth. An array of target combinations are then identified that could potentially provide novel selective treatments for specific cancers