Apichart Intarapanich

iLOCi: a SNP interaction prioritization technique for detecting epistasis in genome-wide association studies

BackgroundGenome-wide association studies (GWAS) do not provide a full account of the heritability of genetic diseases since gene-gene interactions, also known as epistasis are not considered in single locus GWAS. To address this problem, a considerable number of methods have been developed for identifying disease-associated gene-gene interactions. However, these methods typically fail to identify interacting markers explaining more of the disease heritability over single locus GWAS, since many of the interactions significant for disease are obscured by uninformative marker interactions e.g., linkage disequilibrium (LD).ResultsIn this study, we present a novel SNP interaction prioritization algorithm, named iLOCi (Interacting Loci). This algorithm accounts for marker dependencies separately in case and control groups. Disease-associated interactions are then prioritized according to a novel ranking score calculated from the difference in marker dependencies for every possible pair between case and control groups. The analysis of a typical GWAS dataset can be completed in less than a day on a standard workstation with parallel processing capability. The proposed framework was validated using simulated data and applied to real GWAS datasets using the Wellcome Trust Case Control Consortium (WTCCC) data. The results from simulated data showed the ability of iLOCi to identify various types of gene-gene interactions, especially for high-order interaction. From the WTCCC data, we found that among the top ranked interacting SNP pairs, several mapped to genes previously known to be associated with disease, and interestingly, other previously unreported genes with biologically related roles.ConclusioniLOCi is a powerful tool for uncovering true disease interacting markers and thus can provide a more complete understanding of the genetic basis underlying complex disease. The program is available for download at http://www4a.biotec.or.th/GI/tools/iloci.

An automatic device for detection and classification of malaria parasite species in thick blood film

BackgroundCurrent malaria diagnosis relies primarily on microscopic examination of Giemsa-stained thick and thin blood films. This method requires vigorously trained technicians to efficiently detect and classify the malaria parasite species such as Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) for an appropriate drug administration. However, accurate classification of parasite species is difficult to achieve because of inherent technical limitations and human inconsistency. To improve performance of malaria parasite classification, many researchers have proposed automated malaria detection devices using digital image analysis. These image processing tools, however, focus on detection of parasites on thin blood films, which may not detect the existence of parasites due to the parasite scarcity on the thin blood film. The problem is aggravated with low parasitemia condition. Automated detection and classification of parasites on thick blood films, which contain more numbers of parasite per detection area, would address the previous limitation.ResultsThe prototype of an automatic malaria parasite identification system is equipped with mountable motorized units for controlling the movements of objective lens and microscope stage. This unit was tested for its precision to move objective lens (vertical movement, z-axis) and microscope stage (in x- and y-horizontal movements). The average precision of x-, y- and z-axes movements were 71.481 ± 7.266 μm, 40.009 ± 0.000 μm, and 7.540 ± 0.889 nm, respectively. Classification of parasites on 60 Giemsa-stained thick blood films (40 blood films containing infected red blood cells and 20 control blood films of normal red blood cells) was tested using the image analysis module. By comparing our results with the ones verified by trained malaria microscopists, the prototype detected parasite-positive and parasite-negative blood films at the rate of 95% and 68.5% accuracy, respectively. For classification performance, the thick blood films with Pv parasite was correctly classified with the success rate of 75% while the accuracy of Pf classification was 90%.ConclusionsThis work presents an automatic device for both detection and classification of malaria parasite species on thick blood film. The system is based on digital image analysis and featured with motorized stage units, designed to easily be mounted on most conventional light microscopes used in the endemic areas. The constructed motorized module could control the movements of objective lens and microscope stage at high precision for effective acquisition of quality images for analysis. The analysis program could accurately classify parasite species, into Pf or Pv, based on distribution of chromatin size.

Iterative pruning PCA improves resolution of highly structured populations.

BackgroundNon-random patterns of genetic variation exist among individuals in a population owing to a variety of evolutionary factors. Therefore, populations are structured into genetically distinct subpopulations. As genotypic datasets become ever larger, it is increasingly difficult to correctly estimate the number of subpopulations and assign individuals to them. The computationally efficient non-parametric, chiefly Principal Components Analysis (PCA)-based methods are thus becoming increasingly relied upon for population structure analysis. Current PCA-based methods can accurately detect structure; however, the accuracy in resolving subpopulations and assigning individuals to them is wanting. When subpopulations are closely related to one another, they overlap in PCA space and appear as a conglomerate. This problem is exacerbated when some subpopulations in the dataset are genetically far removed from others. We propose a novel PCA-based framework which addresses this shortcoming.ResultsA novel population structure analysis algorithm called iterative pruning PCA (ipPCA) was developed which assigns individuals to subpopulations and infers the total number of subpopulations present. Genotypic data from simulated and real population datasets with different degrees of structure were analyzed. For datasets with simple structures, the subpopulation assignments of individuals made by ipPCA were largely consistent with the STRUCTURE, BAPS and AWclust algorithms. On the other hand, highly structured populations containing many closely related subpopulations could be accurately resolved only by ipPCA, and not by other methods.ConclusionThe algorithm is computationally efficient and not constrained by the dataset complexity. This systematic subpopulation assignment approach removes the need for prior population labels, which could be advantageous when cryptic stratification is encountered in datasets containing individuals otherwise assumed to belong to a homogenous population.

Study of large and highly stratified population datasets by combining iterative pruning principal component analysis and structure.

BackgroundThe ever increasing sizes of population genetic datasets pose great challenges for population structure analysis. The Tracy-Widom (TW) statistical test is widely used for detecting structure. However, it has not been adequately investigated whether the TW statistic is susceptible to type I error, especially in large, complex datasets. Non-parametric, Principal Component Analysis (PCA) based methods for resolving structure have been developed which rely on the TW test. Although PCA-based methods can resolve structure, they cannot infer ancestry. Model-based methods are still needed for ancestry analysis, but they are not suitable for large datasets. We propose a new structure analysis framework for large datasets. This includes a new heuristic for detecting structure and incorporation of the structure patterns inferred by a PCA method to complement STRUCTURE analysis.ResultsA new heuristic called EigenDev for detecting population structure is presented. When tested on simulated data, this heuristic is robust to sample size. In contrast, the TW statistic was found to be susceptible to type I error, especially for large population samples. EigenDev is thus better-suited for analysis of large datasets containing many individuals, in which spurious patterns are likely to exist and could be incorrectly interpreted as population stratification. EigenDev was applied to the iterative pruning PCA (ipPCA) method, which resolves the underlying subpopulations. This subpopulation information was used to supervise STRUCTURE analysis to infer patterns of ancestry at an unprecedented level of resolution. To validate the new approach, a bovine and a large human genetic dataset (3945 individuals) were analyzed. We found new ancestry patterns consistent with the subpopulations resolved by ipPCA.ConclusionsThe EigenDev heuristic is robust to sampling and is thus superior for detecting structure in large datasets. The application of EigenDev to the ipPCA algorithm improves the estimation of the number of subpopulations and the individual assignment accuracy, especially for very large and complex datasets. Furthermore, we have demonstrated that the structure resolved by this approach complements parametric analysis, allowing a much more comprehensive account of population structure. The new version of the ipPCA software with EigenDev incorporated can be downloaded from http://www4a.biotec.or.th/GI/tools/ippca.

Detection and classification device for malaria parasites in thick-blood films

In Thailand, malaria diagnosis still relies primarily on microscopic examination of Giemsa-stained thick and thin blood films. However, the method requires vigorously trained technicians to correctly identify the disease, and is known to be error-prone due to human fatigue. The limited number of such technicians further reduces the effectiveness of the attempt to control malaria. Thus, this project aims to develop an automated system to identify and analyze parasite species on thick blood films by image analysis techniques. The system comprises two main components: (1) Image acquisition unit and (2) Image analysis module. In our work, we have developed an image acquisition system that can be easily mounted on most conventional light microscopes. It automatically controls the movement of microscope stage in 3-directional planes. The vertical adjustment (focusing) can be made in a nanometer range (7–9 nm). Images are acquired with a digital camera that is installed at the top of microscope. The captured images are analyzed by our image analysis software which utilizes the state-of-the-art algorithms to detect and identify malaria parasites.

Automatic DNA Diagnosis for 1D Gel Electrophoresis Images using Bio-image Processing Technique

BackgroundDNA gel electrophoresis is a molecular biology technique for separating different sizes of DNA fragments. Applications of DNA gel electrophoresis include DNA fingerprinting (genetic diagnosis), size estimation of DNA, and DNA separation for Southern blotting. Accurate interpretation of DNA banding patterns from electrophoretic images can be laborious and error prone when a large number of bands are interrogated manually. Although many bio-imaging techniques have been proposed, none of them can fully automate the typing of DNA owing to the complexities of migration patterns typically obtained.ResultsWe developed an image-processing tool that automatically calls genotypes from DNA gel electrophoresis images. The image processing workflow comprises three main steps: 1) lane segmentation, 2) extraction of DNA bands and 3) band genotyping classification. The tool was originally intended to facilitate large-scale genotyping analysis of sugarcane cultivars. We tested the proposed tool on 10 gel images (433 cultivars) obtained from polyacrylamide gel electrophoresis (PAGE) of PCR amplicons for detecting intron length polymorphisms (ILP) on one locus of the sugarcanes. These gel images demonstrated many challenges in automated lane/band segmentation in image processing including lane distortion, band deformity, high degree of noise in the background, and bands that are very close together (doublets). Using the proposed bio-imaging workflow, lanes and DNA bands contained within are properly segmented, even for adjacent bands with aberrant migration that cannot be separated by conventional techniques. The software, called GELect, automatically performs genotype calling on each lane by comparing with an all-banding reference, which was created by clustering the existing bands into the non-redundant set of reference bands. The automated genotype calling results were verified by independent manual typing by molecular biologists.ConclusionsThis work presents an automated genotyping tool from DNA gel electrophoresis images, called GELect, which was written in Java and made available through the imageJ framework. With a novel automated image processing workflow, the tool can accurately segment lanes from a gel matrix, intelligently extract distorted and even doublet bands that are difficult to identify by existing image processing tools. Consequently, genotyping from DNA gel electrophoresis can be performed automatically allowing users to efficiently conduct large scale DNA fingerprinting via DNA gel electrophoresis. The software is freely available from http://www.biotec.or.th/gi/tools/gelect.

Effect of Antenna Patterns on Narrowband MIMO Capacity

In this paper, a model for MIMO capacity calculation with array element radiation patterns is proposed. The proposed model considers effect of radiation pattern, channel impulse responses and elements correlation separately. The effects of both directional and omni-directional antennas to the MIMO capacity are investigated. For the directional antenna case, horizontal polarized Dipole and Yagi-uda antennas are employed in the array. The omni-directional antennas case is considered to be an ideal MIMO capacity. We have shown that directional antennas reduce the narrowband MIMO capacity when the angle of arrival (AOA) is uniformly distributed. When the directional antennas with proper alignment to the mean AOA are used in a scenario where the AOA is Laplacian distribution, the capacity is increased. However, if the antenna alignment is off from the mean AOA, then the capacity is decreased.

Chromosome classification for metaphase selection

Identification of good metaphase spread is an important step in chromosome analysis for genetic disorder detection. In this paper, we propose a rule for chromosome classification to identify good metaphase spreads. The chromosome shapes were classified into four main classes. The first and the second classes refer to individual chromosomes with straight and bended shapes, respectively. The third class is characterized as those chromosomes with overlapping bodies and the forth class is for the non-chromosomal artifacts. Good metaphase spreads should largely contain the first and the second classes while the number of the third class should be kept minimal. Several image parameters were examined and used for creating rule-based classification. The threshold value for each parameter is determined using statistical model. We observed that the empirical probability density function of the parameters can be represented by Gaussian model and, hence, the threshold value can be easily determined. The proposed rules can efficiently and accurately classify the individual chromosome with > 90% accuracy.

Iterative PCA for population structure analysis

An extension of principal component analysis called ipPCA has been proposed earlier for analyzing structure in genetic data. This non-parametric framework iteratively classifies individuals into subpopulations. However, it is prone to false positives when dealing with large datasets and mixed-type genetic markers. We address these shortcomings by introducing a unified encoding scheme and suggesting a new terminating criterion for ipPCA. To validate the improvements, simulated datasets as well as real bovine and large human genetic datasets are analyzed. It is observed that the estimation of the number of subpopulations and the individual assignment accuracy have been improved. Furthermore, the structure resolved by this approach can be used to identify subset of individuals for further parametric population structure analysis.

Wireless Sensor Network: Application to Vehicular Traffic

In this paper we are reporting our current development of wireless sensor network to effectively monitor vehicular traffic. A simple star configuration that consists of a server node communicating with a number of sensor nodes is proposed because of its low complexity, and easy and quick deployment, maintenance and relocation. Our system consists of the sensor, processor, and RF transceiver. We choose the magneto-resistive sensor to detect vehicles as it yields high accuracy with small size. The sensor yields important vehicle informations such as vehicle count, speed, and classification. The network topology is a simple star network. Two Medium Access Communication Protocols (MAC) are analyzed and can be automatically switched based on two different traffic scenarios. An antenna design is shown to fit with a small sensor node. Experiments show that the proposed system yields good data processing results. The classification of vehicles is very promising for major types of vehicles: motorcycle, small vehicle, and bus. RF communications is employed that cable installation can be avoided. Protocol frame formats are provided for both RF communications and RS232. This protocol is very simple and can be easily extended when new sensors or new data types are available.