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Unravelling the hidden diversity of cave mycobiota in Thailand’s Satun Geopark
(2023-12-01)
Suetrong, Satinee
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Preedanon, Sita
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Kobmoo, Noppol
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Srihom, Charisa
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Somrithipol, Sayanh
Karst caves are distinctive ecosystems that have limited nutrients, darkness, low to moderate temperatures, and high moisture levels, which allow for a diverse range of fungal communities to thrive. Despite their significance, little is understood about the fungi found in karst caves in Thailand. In 2019, we studied the cultured mycobiota from five substrate types (air, water, rock, soil/sediment, and organic debris) in two karst caves (Le Stegodon and Phu Pha Phet Caves) of the Satun UNESCO Global Geopark, southern Thailand. A cumulative count of 829 distinct fungal morphological types was identified, encompassing 319 fungal culturable were observed. Based on preliminary analyses of the internal transcribed spacer (ITS) sequence using BLAST searches, the most common phylum among the fungal morphotypes was Ascomycota, harboring 282 species in 91 genera, 93.4% of which were distributed in the classes Eurotiomycetes, Sordariomycetes, and Dothideomycetes. The most common fungal genera identified in the two karst caves were Aspergillus, Penicillium, Cladosporium, Talaromyces, Xylaria, and Trichoderma, with 45, 41, 24, 14, 14, and 6 species identified, respectively. Discovering fungi in Thai karst caves highlights the extensive fungal diversity in the Satun UNESCO Global Geopark, implying undiscovered species, and emphasizing the need for comprehensive investigations in other unexplored Thai karst caves.
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Discovery of significant porcine SNPs for swine breed identification by a hybrid of information gain, genetic algorithm, and frequency feature selection technique
(2020-05-26)
Pasupa, Kitsuchart
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Rathasamuth, Wanthanee
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Tongsima, Sissades
Background: The number of porcine Single Nucleotide Polymorphisms (SNPs) used in genetic association studies is very large, suitable for statistical testing. However, in breed classification problem, one needs to have a much smaller porcine-classifying SNPs (PCSNPs) set that could accurately classify pigs into different breeds. This study attempted to find such PCSNPs by using several combinations of feature selection and classification methods. We experimented with different combinations of feature selection methods including information gain, conventional as well as modified genetic algorithms, and our developed frequency feature selection method in combination with a common classification method, Support Vector Machine, to evaluate the method's performance. Experiments were conducted on a comprehensive data set containing SNPs from native pigs from America, Europe, Africa, and Asia including Chinese breeds, Vietnamese breeds, and hybrid breeds from Thailand. Results: The best combination of feature selection methods - information gain, modified genetic algorithm, and frequency feature selection hybrid - was able to reduce the number of possible PCSNPs to only 1.62% (164 PCSNPs) of the total number of SNPs (10,210 SNPs) while maintaining a high classification accuracy (95.12%). Moreover, the near-identical performance of this PCSNPs set to those of bigger data sets as well as even the entire data set. Moreover, most PCSNPs were well-matched to a set of 94 genes in the PANTHER pathway, conforming to a suggestion by the Porcine Genomic Sequencing Initiative. Conclusions: The best hybrid method truly provided a sufficiently small number of porcine SNPs that accurately classified swine breeds.
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Counting and classification of malarial parasite from giemsa-stained thin film images
(2020-01-01)
Kudisthalert, Wasu
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Pasupa, Kitsuchart
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Tongsima, Sissades
Malaria is a life-threatening disease causing by an infection of the protozoan parasite Plasmodium. Plasmodium falciparum is the deadliest and most common human infected parasites hosted by anopheles mosquito vector. To cure a malaria infected patient and prevent further spreading, malaria diagnosis using microscopy to visualize Giemsa-stained parasites is commonly done. The microscopy diagnosis is somewhat time consuming and requires well-trained malaria experts to interpret what they see under the microscope. To address this limitation, an automated malaria infected diagnosis is needed. This work proposed a computer-aided automated diagnosis system that can perform remote field diagnosis with high accuracy while requiring less computational demands. The proposed framework consists of two main parts that are red blood cell counting and parasite life-cycle stage classification. The counting process is performed by computer vision techniques, namely Hough transform. Different machine learning techniques, i.e., Multilayer Perceptron, Linear Discriminant Analysis, Support Vector Machine, and Weighted Similarity Extreme Learning Machine, are employed in the classification task. We also demonstrated that combining hand-crafted and deep-learned features can enhance the overall performance of the framework. The experimental results showed that the proposed methods could correctly count and classify at 97.94% and 98.12% accuracy, respectively. The overall proposal system can achieve at 96.18% accuracy. This is achieved by WELM in conjunction with deep-learned (AlexNet_FC7) and the hand-crafted (color) features.
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Selection of a minimal number of significant porcine snps by an information gain and genetic algorithm hybrid model
(2019-01-01)
Rathasamuth, Wanthanee
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Pasupa, Kitsuchart
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Tongsima, Sissades
A panel of a large number of common Single Nucleotide Polymorphisms (SNPs) distributed across an entire porcine genome has been widely used to represent genetic variability of pigs. With the advent of SNP-array technology, a genome-wide genetic profile of a specimen can be easily observed. Among the large number of such variations, there exists a much smaller subset of the SNP panel that could equally be used to correctly identify the corresponding breed. This work presents a SNP selection heuristic that can still be used effectively in the breed classification. The features were selected by combining a filter method and a wrapper method-information gain method and genetic algorithm-plus a feature frequency selection step, while classification used a support vector machine. We were able to reduce the number of significant SNPs to 0.86 % of the total number of SNPs in a swine dataset with 94.80 % classification accuracy.
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Selection of SNP Subsets for Severity of Beta-thalassaemia Classification Problem
(2018-12-10)
Thamwiwatthana, Ek
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Pasupa, Kitsuchart
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Tongsima, Sissades
Single-nucleotide polymorphisms (SNPs) are important genetic variables that are very popular in Genome-wide association study at the present time. They are often used in studies related to genetic disorders. A distinctive trait of SNPs is that there are a lot of them since they are variables originated from various positions in a DNA sequence. Unfortunately, the number of samples investigated are usually far fewer than the number of SNPs and so an over-fitting often occurs when one wants to construct a predictive model for classifying a sample into a case or a control. This study investigated a dataset on beta-thalassemia, a common genetic disorder widely found in Thai population. The data in the set are divided into two groups: severe and mild groups. The aims of the study were to develop and evaluate methods for screening and ranking SNPs related to this disorder. The screening methods tested were Chi-squared test (χ <sup>2</sup> ), Information Gain, and Gradient Boosting (GB). The SNPs that were screened in and selected were then used to construct a predictive model for classifying a sample to be either a severe or mild case. The model construction methods tested were Support Vector Machine (SVM), GB, and Naïve Bayes. Several combinations of a screening method and a model construction method were evaluated, and the evaluation results show that the best combination was χ <sup>2</sup> -SVM which used the number of selected SNPs of 10.
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PCA-based informative SNP selection for analyzing population structure
(2016-12-19)
Limpiti, Tulaya
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Intarapanich, Apichart
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Tongsima, Sissades
Phenotypic differences among individuals of the same species are the result of a set of genetic variations which can be observed in the DNA sequence. To conduct a population genetic study, a high throughput genotyping platform such as Single Nucleotide Polymorphism (SNP) array is popularly used to obtain a large set of SNPs for each individual. However, analyzing today's genotypic data can be computationally expensive due to its large size and complexity. Faulty substructure may also be detected if the data is noisy from redundant or non-informative SNPs. Considerable efforts have been done to extract a smaller informative SNP subset that still represents the same intrinsic structure of populations within a data set as the full panel of SNPs. This work describes a foundation of a PCA-based informative marker selection technique. The proposed technique is simple and efficient. It improves upon another spectral analysis technique called PCA-correlated SNPs. A new informativeness score based on a basis function expansion of the SNP variation patterns across individuals is introduced. Such score is computed for each SNP to select a subset of SNPs with the best scores. Using a bovine data set, we demonstrate that our technique is superior to the PCAcorrelated SNPs method, which requires accurate rank estimation to perform well. In contrast, our method is robust to the assumed rank of the data. High data representation accuracy is also achieved after a significant reduction of the number of SNPs while retaining information about the underlying population structure from the original data.
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INJclust: Iterative neighbor-joining tree clustering framework for inferring population structure
(2014-09-01)
Limpiti, Tulaya
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Amornbunchornvej, Chainarong
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Intarapanich, Apichart
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Assawamakin, Anunchai
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Tongsima, Sissades
Understanding genetic differences among populations is one of the most important issues in population genetics. Genetic variations, e.g., single nucleotide polymorphisms, are used to characterize commonality and difference of individuals from various populations. This paper presents an efficient graph-based clustering framework which operates iteratively on the Neighbor-Joining (NJ) tree called the iNJclust algorithm. The framework uses well-known genetic measurements, namely the allele-sharing distance, the neighbor-joining tree, and the fixation index. The behavior of the fixation index is utilized in the algorithm's stopping criterion. The algorithm provides an estimated number of populations, individual assignments, and relationships between populations as outputs. The clustering result is reported in the form of a binary tree, whose terminal nodes represent the final inferred populations and the tree structure preserves the genetic relationships among them. The clustering performance and the robustness of the proposed algorithm are tested extensively using simulated and real data sets from bovine, sheep, and human populations. The result indicates that the number of populations within each data set is reasonably estimated, the individual assignment is robust, and the structure of the inferred population tree corresponds to the intrinsic relationships among populations within the data.
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Performance analysis & improvement of SNPHAP on Multi-core CPUs
(2013-09-02)
Ranokphanuwat, Ratthaslip
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Kittitornkun, Surin
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Tongsima, Sissades
In this paper, we attempt to analyse this highly computational problem by parallelizing a haplotype inference algorithm, called SNPHAP. The analysis is based on both the orignal (sequential) algorithm and its corresponding run time complexity in Big-O notations. Then, we improve its performance using OpenMP 3.0 and test on a 4-core Intel Core i7-2600 (Hyper-Threading), an 8-core Intel XeonE5405, an 8-core Intel Xeon E5520 (Hyper-Threading) and a 32-core AMD Opteron 8356 Linux machines. The achievements in terms of maximum speedups are 260%, 316%, 410% and 488%, respectively. The factors that affect the speedup of SNPHAP are the specific parallelized code fraction, the suitable OpenMP constructs, the number of physical cores, the sizes of cache memories within/among CPU cores, the clock frequency and finally the memory technology. © 2013 IEEE.
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Analysing and optimizing snphap using radix-2 computation and openmp
(2013-07-12)
Ranokphanuwat, Ratthaslip
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Rattanatranurak, Apisit
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Kittitornkun, Surin
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Tongsima, Sissades
In this paper, the run time complexity of SNPHAP, which is a haplotype inference tool, is extensively examined. The analysis is based on our previous work in terms of profiling and run-time complexity function. To reduce the run time complexity and enhance its performance, a Radix-2 computation and OpenMP multithreading are applied. The optimized results are compared with both original and compiler optimized versions on an AMD A6-3650 Linux machine. Due to the Radix-2 technique, the complexity is drastically reduced. In addition, the theoretical Speedup is consistent with the experimental one. Furthermore, up to 1,303% Speedup is achievable as a result of OpenMP multithreading.
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Optimizing and multithreading SNPHAP on a multi-core APU with OpenCL
(2012-09-24)
Rattanatranurak, Apisit
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Kittitornkun, Surin
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Tongsima, Sissades
In this paper, we have optimized and multithreaded SNPHAP, a bioinformatics program, with OpenCL to reduce the computation time and thus accelerate the execution. Our method is called Radix Comparison algorithm running in sequential and parallel (multithreading). Based on the recent multi-core AMD A6-3650 APU (Accelerated Processing Unit), the achieveable Speedups of Sequential Radix and Parallel Radix SNPHAP compared with the original SNPHAP are 260% and 271%, respectively. © 2012 IEEE.

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