Dumitru Brinza

Single cell genome amplification accelerates identification of the apratoxin biosynthetic pathway from a complex microbial assemblage.

Filamentous marine cyanobacteria are extraordinarily rich sources of structurally novel, biomedically relevant natural products. To understand their biosynthetic origins as well as produce increased supplies and analog molecules, access to the clustered biosynthetic genes that encode for the assembly enzymes is necessary. Complicating these efforts is the universal presence of heterotrophic bacteria in the cell wall and sheath material of cyanobacteria obtained from the environment and those grown in uni-cyanobacterial culture. Moreover, the high similarity in genetic elements across disparate secondary metabolite biosynthetic pathways renders imprecise current gene cluster targeting strategies and contributes sequence complexity resulting in partial genome coverage. Thus, it was necessary to use a dual-method approach of single-cell genomic sequencing based on multiple displacement amplification (MDA) and metagenomic library screening. Here, we report the identification of the putative apratoxin. A biosynthetic gene cluster, a potent cancer cell cytotoxin with promise for medicinal applications. The roughly 58 kb biosynthetic gene cluster is composed of 12 open reading frames and has a type I modular mixed polyketide synthase/nonribosomal peptide synthetase (PKS/NRPS) organization and features loading and off-loading domain architecture never previously described. Moreover, this work represents the first successful isolation of a complete biosynthetic gene cluster from Lyngbya bouillonii, a tropical marine cyanobacterium renowned for its production of diverse bioactive secondary metabolites.

2SNP: scalable phasing based on 2-SNP haplotypes

2SNP software package implements a new very fast scalable algorithm for haplotype inference based on genotype statistics collected only for pairs of SNPs. This software can be used for comparatively accurate phasing of large number of long genome sequences, e.g. obtained from DNA arrays. As an input 2SNP takes genotype matrix and outputs the corresponding haplotype matrix. On datasets across 79 regions from HapMap 2SNP is several orders of magnitude faster than GERBIL and PHASE while matching them in quality measured by the number of correctly phased genotypes, single-site and switching errors. For example, 2SNP requires 41 s on Pentium 4 2 Ghz processor to phase 30 genotypes with 1381 SNPs (ENm010.7p15:2 data from HapMap) versus GERBIL and PHASE requiring more than a week and admitting no less errors than 2SNP.

DEEPS: Deterministic Energy-Efficient Protocol for Sensor networks

Energy consumption in monitoring and communication protocols for wireless sensor networks became one of the most important performance objective. We assume a commonly accepted sensor network model in which sensors can interchange idle and active modes both for monitoring and communicating. We introduce a reliability requirement for distributed target-monitoring protocols and prove that previously considered protocols (P. Berman et al., 2004) are reliable. In this paper we propose a new deterministic energy-efficient protocol for sensor networks (DEEPS) aimed at prolonging the lifetime. We prove that DEEPS is reliable and compare DEEPS with several known target-monitoring protocols in NS2 environment using LEACH (W. Heizelman et al., 2002) protocol for data delivery to the base. We implemented the full-fledged simulation of the monitoring protocols on NS2 combined with LEACH as a communication protocol, and performed extensive experimental study of several protocols showing almost 2 times increase in the lifetime for DEEPS over known protocols

Combinatorial Search Methods for Multi-SNP Disease Association

Recent improvements in the accessibility of high-throughput genotyping have brought a deal of attention to genome-wide association studies for common complex diseases. Although, such diseases can be caused by multi-loci interactions, locus-by-locus studies are prevailing. Recently, two-loci analysis has been shown promising (Marchini et al, 2005), and multi-loci analysis is expected to find even deeper disease-associated interactions. Unfortunately, an exhaustive search among all possible corresponding multi-markers can be unfeasible even for small number of SNPs let alone the complete genome. In this paper we first propose to extract informative (indexing) SNPs that can be used for reconstructing of all SNPs almost without loss (He and Zelikovsky, 2006). In the reduced set of SNPs, we then propose to apply a novel combinatorial method for finding disease-associated multi-SNP combinations (MSCs). Our experimental study shows that the proposed methods are able to find MSCs whose disease association is statistically significant even after multiple testing adjustment. For (Daly et al, 2001) data we found a few unphased MSCs associated with Crohns disease with multiple testing adjusted p-value below 0.05 while no single SNP or pair of SNPs show any significant association. For (Ueda et al, 2003) data we found a few new unphased and phased MSCs associated with autoimmune disorder

2SNP: Scalable Phasing Method for Trios and Unrelated Individuals

Emerging microarray technologies allow affordable typing of very long genome sequences. A key challenge in analyzing such a huge amount of data is scalable and accurate computational inferring of haplotypes (that is, splitting of each genotype into a pair of corresponding haplotypes). In this paper, we first phase genotypes consisting only of two SNPs using genotypes frequencies adjusted to the random mating model and then extend the phasing of two-SNP genotypes to the phasing of complete genotypes using maximum spanning trees. The runtime of the proposed 2SNP algorithm is O(nm(n + logm)), where n and m are the numbers of genotypes and SNPs, respectively, and it can handle genotypes spanning the entire chromosomes in a matter of hours. On data sets across 23 chromosomal regions from HapMap [11], 2SNP is several orders of magnitude faster than GERBIL and PHASE when matching them in quality measured by the number of correctly phased genotypes, single-site, and switching errors. For example, the 2SNP software phases the entire chromosome (l05 SNPs from HapMap) for 30 individuals in 2 hours with an average switching error of 7.7 percent. We have also enhanced the 2SNP algorithm to phase family trio data and compared it with four other well-known phasing methods on simulated data from [15]. 2SNP is much faster than all of them while losing in quality only to PHASE. 2SNP software is publicly available at http://alla.cs.gsu.edu/~software/2SNP.

A Combinatorial Method for Predicting Genetic Susceptibility to Complex Diseases

Recent improvements in the accessibility of high-throughput genotyping have brought a great deal of attention to disease association and susceptibility studies. This paper explores possibility of applying combinatorial methods to disease susceptibility prediction. The proposed combinatorial methods as well as standard statistical methods are applied to publicly available genotype data on Crohns disease and autoimmune disorders for predicting susceptibility to these diseases. The quality of susceptibility prediction algorithm is assessed using leave-one-out and leave-many-out tests - the disease status of one or several individuals is predicted and compared to the their actual disease status which is initially made unknown to the algorithm. The best prediction rate achieved by the proposed algorithms is 77.78% for Crohns disease and 64.99% for autoimmune disorders, respectively

Combinatorial methods for disease association search and susceptibility prediction

Accessibility of high-throughput genotyping technology makes possible genome-wide association studies for common complex diseases. When dealing with common diseases, it is necessary to search and analyze multiple independent causes resulted from interactions of multiple genes scattered over the entire genome. This becomes computationally challenging since interaction even of pairs gene variations require checking more than 1012 possibilities genome-wide. This paper first explores the problem of searching for the most disease-associated and the most disease-resistant multi-gene interactions for a given population sample of diseased and non-diseased individuals. A proposed fast complimentary greedy search finds multi-SNP combinations with non-trivially high association on real data. Exploiting the developed methods for searching associated risk and resistance factors, the paper addresses the disease susceptibility prediction problem. We first propose a relevant optimum clustering formulation and the model-fitting algorithm transforming clustering algorithms into susceptibility prediction algorithms. For three available real data sets (Crohns disease (Daly et al, 2001), autoimmune disorder (Ueda et al, 2003), and tick-borne encephalitis (Barkash et al, 2006)), the accuracies of the prediction based on the combinatorial search (respectively, 84%, 83%, and 89%) are higher by 15% compared to the accuracies of the best previously known methods. The prediction based on the complimentary greedy search almost matches the best accuracy but is much more scalable.

Genotype susceptibility and integrated risk factors for complex diseases

Recent improvements in the accessibility of high- throughput genotyping have brought a great deal of attention to disease association and susceptibility studies. This paper explores possibility of applying discrete optimization methods to predict the genotype susceptibility for complex disease. The proposed combinatorial methods have been applied to publicly available genotype data on Crohns disease and autoimmune disorders for predicting susceptibility to these diseases. The result of predicted status can be also viewed as an integrated risk factor. The quality of susceptibility prediction algorithm has been assessed using leave-one-out and leave-many-out tests and shown to be statistically significant based on randomization tests.The best prediction rate achieved by the prediction algorithms is 69.5% for Crohns disease and 63.9% for autoimmune disorder. The risk rate of the corresponding integrated risk factor is 2.23 for Crohns disease and 1.73 for autoimmune disorder.

Design and validation of methods searching for risk factors in genotype case-control studies.

Accessibility of high-throughput genotyping technology allows genome-wide association studies for common complex diseases. This paper addresses two challenges commonly facing such studies: (i) searching an enormous amount of possible gene interactions and (ii) finding reproducible associations. These challenges have been traditionally addressed in statistics while here we apply computational approaches--optimization and cross-validation. A complex risk factor is modeled as a subset of single nucleotide polymorphisms (SNPs) with specified alleles and the optimization formulation asks for the one with the maximum odds ratio. To measure and compare ability of search methods to find reproducible risk factors, we propose to apply a cross-validation scheme usually used for prediction validation. We have applied and cross-validated known search methods with proposed enhancements on real case-control studies for several diseases (Crohns disease, autoimmune disorder, tick-borne encephalitis, lung cancer, and rheumatoid arthritis). Proposed methods are compared favorably to the exhaustive search: they are faster, find more frequently statistically significant risk factors, and have significantly higher leave-half-out cross-validation rate.

Association testing by haplotype-sharing methods applicable to whole-genome analysis

We propose two new haplotype-sharing methods for identifying disease loci: the haplotype sharing statistic (HSS), which compares length of shared haplotypes between cases and controls, and the CROSS test, which tests whether a case and a control haplotype show less sharing than two random haplotypes. The significance of the HSS is determined using a variance estimate from the theory of U-statistics, whereas the significance of the CROSS test is estimated from a sequential randomization procedure. Both methods are fast and hence practical, even for whole-genome screens with high marker densities. We analyzed data sets of Problems 2 and 3 of Genetic Analysis Workshop 15 and compared HSS and CROSS to conventional association methods. Problem 2 provided a data set of 2300 single-nucleotide polymorphisms (SNPs) in a 10-Mb region of chromosome 18q, which had shown linkage evidence for rheumatoid arthritis. The CROSS test detected a significant association at approximately position 4407 kb. This was supported by single-marker association and HSS. The CROSS test outperformed them both with respect to significance level and signal-to-noise ratio. A 20-kb candidate region could be identified. Problem 3 provided a simulated 10 k SNP data set covering the whole genome. Three known candidate regions for rheumatoid arthritis were detected. Again, the CROSS test gave the most significant results. Furthermore, both the HSS and the CROSS showed better fine-mapping accuracy than straightforward haplotype association. In conclusion, haplotype sharing methods, particularly the CROSS test, show great promise for identifying disease gene loci.