Sissades Tongsima

National Science and Technology Development Agency

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Chumpol Ngamphiw

National Center for Genetic Engineering and Biotechnology

Anunchai Assawamakin

Mahidol University

Piranit Nik Kantaputra

Chiang Mai University

Philip J. Shaw

National Science and Technology Development Agency

Alisa Wilantho

National Science and Technology Development Agency

Kridsadakorn Chaichoompu

Centogene (Germany)

Worrachet Intachai

Chiang Mai University

Pongsakorn Wangkumhang

National Center for Genetic Engineering and Biotechnology

Apichart Intarapanich

National Science and Technology Development Agency

Edwin H.‐M. Sha

East China Normal University

Cooperative Institutions

Mahidol University

180

National Science and Technology Development Agency

107

National Center for Genetic Engineering and Biotechnology

106

Chulalongkorn University

Siriraj Hospital

Chiang Mai University

Khon Kaen University

Ramathibodi Hospital

King Mongkut's University of Technology Thonburi

Thammasat University

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The Journal of VLSI Signal Processing Systems for Signal Image and Video Technology (2000)

Sissades Tongsima Timothy W. O’Neil Chantana Chantrapornchai Edwin H.‐M. Sha

Graphical model

10.1023/a:1008187622838

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Citations (1)

Draft genome sequence of Arthrospira platensis C1 (PCC9438)

Standards in Genomic Sciences (2012)

Supapon Cheevadhanarak Kalyanee Paithoonrangsarid Peerada Prommeenate Warunee Kaewngam Apiluck Musigkain

Arthrospira platensis is a cyanobacterium that is extensively cultivated outdoors on a large commercial scale for consumption as a food for humans and animals. It can be grown in monoculture under highly alkaline conditions, making it attractive for industrial production. Here we describe the complete genome sequence of A. platensis C1 strain and its annotation. The A. platensis C1 genome contains 6,089,210 bp including 6,108 protein-coding genes and 45 RNA genes, and no plasmids. The genome information has been used for further comparative analysis, particularly of metabolic pathways, photosynthetic efficiency and barriers to gene transfer.

Arthrospira

Spirulina (dietary supplement)

10.4056/sigs.2525955

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Citations (55)

Segmentation Techniques for Bioimages

Elsevier eBooks (2024)

Saowaluck Kaewkamnerd Apichart Intarapanich Sissades Tongsima

10.1016/b978-0-323-95502-7.00102-0

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Osteogenesis imperfecta with ectopic mineralizations in dentin and cementum and a COL1A2 mutation

Journal of Human Genetics (2018)

Piranit Nik Kantaputra Yuddhasert Sirirungruangsarn Worrachet Intachai Chumpol Ngamphiw Sissades Tongsima

Dentinogenesis Imperfecta

10.1038/s10038-018-0448-5

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Roles of Intragenic and Intergenic L1s in Mouse and Human

PLoS ONE (2014)

Chumpol Ngamphiw Sissades Tongsima Apiwat Mutirangura

Long INterspersed Element-1 (LINE-1 or L1) is a retrotransposable element that has shaped the evolution of mammalian genomes. There is increasing evidence that transcriptionally active L1 could have been co-opted through evolution to play various roles including X-inactivation, homologous recombination and gene regulation. Here, we compare putatively active L1 distributions in the mouse with human. L1 density is higher in the mouse except for the Y-chromosome. L1 density is the highest in X-chromosome, implying an X-inactivation role. L1 is more common outside genes (intergenic) except for the Y-chromosome in both species. The structure of mouse L1 is distinguished from human L1 by the presence of a 200 bp repeat in the 5′ UTR of the former. We found that mouse intragenic L1 has significantly higher repeat copy numbers than intergenic L1, suggesting that this is important for control of L1 expression. Furthermore, a significant association between the presence of intragenic L1s and down-regulated genes in early embryogenesis was found in both species. In conclusion, the distribution of L1 in the mouse genome points to biological roles of L1 in mouse similar to human.

Intergenic region

10.1371/journal.pone.0113434

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Selection of a Minimal Number of Significant Porcine SNPs by an Information Gain and Genetic Algorithm Hybrid Model

arXiv (Cornell University) (2019)

Wanthanee Rathasamuth Kitsuchart Pasupa Sissades Tongsima

A panel of large number of common Single Nucleotide Polymorphisms (SNPs) distributed across an entire porcine genome has been widely used to represent genetic variability of pig. 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 exist 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 process. The proposed feature selection was done by the approach of combining a filter method and a wrapper method--information gain method and genetic algorithm--plus a feature frequency selection step, while classification was done by support vector machine. The approach was able to reduce the number of significant SNPs to 0.86 % of the total number of SNPs in a swine dataset and provided a high classification accuracy of 94.80 %.

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Optimized analysis parameters of variant calling for whole genome-based phylogeny of Mycobacteroides abscessus

Archives of Microbiology (2022)

Orawee Kaewprasert Sissades Tongsima Rick Twee‐Hee Ong Kiatichai Faksri

Subspecies

Tree (set theory)

10.1007/s00203-022-02792-2

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LRP4 mutations, dental anomalies, and oral exostoses

International Journal of Paediatric Dentistry (2023)

Piranit Nik Kantaputra Weena Panichkul Parisri Sillapasorn Ploy Adisornkanj Panita Kitsadayurach

In order to generate a normal set of teeth, fine-tuning of Wnt/β-catenin signaling is required, in which WNT ligands bind to their inhibitors or WNT inhibitors bind to their co-receptors. Lrp4 regulates the number of teeth and their morphology by modulating Wnt/β-catenin signaling as a Wnt/β-catenin activator or inhibitor, depending on its interactions with the partner proteins, such as Sostdc1 and Dkk1.

Dishevelled

LRP5

DKK1

10.1111/ipd.13141

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A diverse ancestrally-matched reference panel increases genotype imputation accuracy in a underrepresented population

Scientific Reports (2023)

John Mauleekoonphairoj Sissades Tongsima Apichai Khongphatthanayothin Sean J. Jurgens Dominic S. Zimmerman

Abstract Variant imputation, a common practice in genome-wide association studies, relies on reference panels to infer unobserved genotypes. Multiple public reference panels are currently available with variations in size, sequencing depth, and represented populations. Currently, limited data exist regarding the performance of public reference panels when used in an imputation of populations underrepresented in the reference panel. Here, we compare the performance of various public reference panels: 1000 Genomes Project, Haplotype Reference Consortium, GenomeAsia 100 K, and the recent Trans-Omics for Precision Medicine (TOPMed) program, when used in an imputation of samples from the Thai population. Genotype yields were assessed, and imputation accuracies were examined by comparison with high-depth whole genome sequencing data of the same sample. We found that imputation using the TOPMed panel yielded the largest number of variants (~ 271 million). Despite being the smallest in size, GenomeAsia 100 K achieved the best imputation accuracy with a median genotype concordance rate of 0.97. For rare variants, GenomeAsia 100 K also offered the best accuracy, although rare variants were less accurately imputable than common variants (30.3% reduction in concordance rates). The high accuracy observed when using GenomeAsia 100 K is likely attributable to the diverse representation of populations genetically similar to the study cohort emphasizing the benefits of sequencing populations classically underrepresented in human genomics.

Imputation (statistics)

Concordance

1000 Genomes Project

Genome-wide Association Study

10.1038/s41598-023-39429-3

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Improved iterative pruning principal component analysis with graph-theoretic hierarchical clustering

Chainarong Amornbunchornvej Tulaya Limpiti Anunchai Assawamakin Apichart Intarapanich Sissades Tongsima

Various unsupervised clustering algorithms have been used to infer population structure in genetic data. The goals are to separate individuals of similar genetic characteristics into clusters and to estimate the number of clusters within each dataset. Among them, a framework called iterative pruning principal component analysis (ipPCA) have been developed. It performs PCA iteratively on subsets of data samples and clusters them using fuzzy c-mean. We believe that the choice of model-based clustering method affects the individual assignments and cluster quality, as well as the estimated number of clusters. Thus, in this paper we introduce a hierarchical tree clustering concept from graph theory, whose performance is independent of cluster shapes, into the ipPCA framework. We also add a PCA-based feature selection technique as a data pre-processing step to reduce data dimension and increase computational efficiency. The resulting algorithm is called HiClust-ipPCA. We illustrate the improved clustering results of the HiClust-ipPCA algorithm using 47-breed bovine and 28-breed sheep datasets.

Pruning

Single-linkage clustering

Hierarchical clustering

10.1109/ecticon.2012.6254120

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