Displaying publications 1 - 20 of 63 in total

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  1. Fulltext Floral micromorphology and transcriptome analyses of a fragrant Vandaceous Orchid, Vanda Mimi Palmer, for its fragrance production sites
    Toh C, Mohd-Hairul AR, Ain NM, Namasivayam P, Go R, Abdullah NAP, et al.
    BMC Res Notes, 2017 Nov 02;10(1):554.
    PMID: 29096695 DOI: 10.1186/s13104-017-2872-6
    BACKGROUND: Vanda Mimi Palmer (VMP) is commercially valuable for its strong fragrance but little is known regarding the fragrance production and emission sites on the flowers.

    RESULTS: Olfactory perception detected fragrance only from the petals and sepals. Light and Environmental Scanning Electron microscopy analyses on fresh tissues showed distributions of stomata and trichomes concentrated mostly around the edges. These results paralleled the rich starch deposits and intense neutral red stain, indicating strong fragrance and trichomes as potential main fragrance release sites. Next Generation Sequencing (NGS) transcriptomic data of adaxial and abaxial layers of the tissues showed monoterpene synthase transcripts specifically linalool and ocimene synthases distributed throughout the tissues. qPCR analyses taken at different time points revealed high levels of linalool and ocimene synthases transcripts in the early morning with maximal level at 4.00 am but remained low throughout daylight hours.

    CONCLUSIONS: Knowledge of the VMP floral anatomy and its fragrance production characteristics, which complemented our previous molecular and biochemical data on VMP, provided additional knowledge on how fragrance and flower morphology are closely intertwined. Further investigation on the mechanisms of fragrance biosynthesis and interaction of potential pollinators would elucidate the evolution of the flower morphology to maximize the reproduction success of this plant.

    Matched MeSH terms: Gene Expression Profiling/methods*
  2. Transcriptome dataset of Metroxylon sagu palms from multiple sago plantations in Sarawak
    Pendi FH, Hussain H
    BMC Res Notes, 2024 Sep 05;17(1):251.
    PMID: 39238033 DOI: 10.1186/s13104-024-06924-3
    OBJECTIVE: Sago palm (Metroxylon sagu Rottb.) is one of the most important economic crops abundantly found in Mukah, Sarawak, Malaysia. The robustness of the palm triggered the Sarawak government's selection as one of the state's commodity crops, with the opening of several sago palm plantations. However, stunted (non-trunking) palms were reported in several sago palm plantations despite attaining a maturity period of more than ten years after cultivation. Research targeting this problem has been conducted in various fields, yet information on molecular mechanisms is still scarce. This study aimed to determine the genes responsible for sago palm's normal phenotype (trunking) by attaining leaf transcriptomes from samples of all trunking sago palms from different sago palm plantations.

    DATA DESCRIPTION: The conventional CTAB method was employed in the present investigation to extract total RNA from leaf tissues. Transcriptome sequencing was conducted on the Illumina NovaSeq 6000 platform. Differential expression analysis was performed using the DESeq2 package. A total of 6,119 differentially expressed genes, comprising 4,384 downregulated and 1,735 upregulated genes, were expressed in all three sago palm datasets. The datasets provide insights into the commonly expressed genes among trunking sago palms.

    Matched MeSH terms: Gene Expression Profiling/methods
  3. Fulltext A hybrid distance measure for clustering expressed sequence tags originating from the same gene family
    Ng KH, Ho CK, Phon-Amnuaisuk S
    PLoS One, 2012;7(10):e47216.
    PMID: 23071763 DOI: 10.1371/journal.pone.0047216
    Clustering is a key step in the processing of Expressed Sequence Tags (ESTs). The primary goal of clustering is to put ESTs from the same transcript of a single gene into a unique cluster. Recent EST clustering algorithms mostly adopt the alignment-free distance measures, where they tend to yield acceptable clustering accuracies with reasonable computational time. Despite the fact that these clustering methods work satisfactorily on a majority of the EST datasets, they have a common weakness. They are prone to deliver unsatisfactory clustering results when dealing with ESTs from the genes derived from the same family. The root cause is the distance measures applied on them are not sensitive enough to separate these closely related genes.
    Matched MeSH terms: Gene Expression Profiling/methods*
  4. In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
    Ealam Selvan M, Lim KS, Teo CH, Lim YY
    J Vis Exp, 2022 Oct 21.
    PMID: 36342167 DOI: 10.3791/64565
    Circular RNAs (circRNAs) are a class of non-coding RNAs that are formed via back-splicing. These circRNAs are predominantly studied for their roles as regulators of various biological processes. Notably, emerging evidence demonstrates that host circRNAs can be differentially expressed (DE) upon infection with pathogens (e.g., influenza and coronaviruses), suggesting a role for circRNAs in regulating host innate immune responses. However, investigations on the role of circRNAs during pathogenic infections are limited by the knowledge and skills required to carry out the necessary bioinformatic analysis to identify DE circRNAs from RNA sequencing (RNA-seq) data. Bioinformatics prediction and identification of circRNAs is crucial before any verification, and functional studies using costly and time-consuming wet-lab techniques. To solve this issue, a step-by-step protocol of in silico prediction and characterization of circRNAs using RNA-seq data is provided in this manuscript. The protocol can be divided into four steps: 1) Prediction and quantification of DE circRNAs via the CIRIquant pipeline; 2) Annotation via circBase and characterization of DE circRNAs; 3) CircRNA-miRNA interaction prediction through Circr pipeline; 4) functional enrichment analysis of circRNA parental genes using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). This pipeline will be useful in driving future in vitro and in vivo research to further unravel the role of circRNAs in host-pathogen interactions.
    Matched MeSH terms: Gene Expression Profiling/methods
  5. Fulltext A review of feature extraction software for microarray gene expression data
    Tan CS, Ting WS, Mohamad MS, Chan WH, Deris S, Shah ZA
    Biomed Res Int, 2014;2014:213656.
    PMID: 25250315 DOI: 10.1155/2014/213656
    When gene expression data are too large to be processed, they are transformed into a reduced representation set of genes. Transforming large-scale gene expression data into a set of genes is called feature extraction. If the genes extracted are carefully chosen, this gene set can extract the relevant information from the large-scale gene expression data, allowing further analysis by using this reduced representation instead of the full size data. In this paper, we review numerous software applications that can be used for feature extraction. The software reviewed is mainly for Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), and Local Linear Embedding (LLE). A summary and sources of the software are provided in the last section for each feature extraction method.
    Matched MeSH terms: Gene Expression Profiling/methods*
  6. Fulltext Biases in small RNA deep sequencing data
    Raabe CA, Tang TH, Brosius J, Rozhdestvensky TS
    Nucleic Acids Res, 2014 Feb;42(3):1414-26.
    PMID: 24198247 DOI: 10.1093/nar/gkt1021
    High-throughput RNA sequencing (RNA-seq) is considered a powerful tool for novel gene discovery and fine-tuned transcriptional profiling. The digital nature of RNA-seq is also believed to simplify meta-analysis and to reduce background noise associated with hybridization-based approaches. The development of multiplex sequencing enables efficient and economic parallel analysis of gene expression. In addition, RNA-seq is of particular value when low RNA expression or modest changes between samples are monitored. However, recent data uncovered severe bias in the sequencing of small non-protein coding RNA (small RNA-seq or sRNA-seq), such that the expression levels of some RNAs appeared to be artificially enhanced and others diminished or even undetectable. The use of different adapters and barcodes during ligation as well as complex RNA structures and modifications drastically influence cDNA synthesis efficacies and exemplify sources of bias in deep sequencing. In addition, variable specific RNA G/C-content is associated with unequal polymerase chain reaction amplification efficiencies. Given the central importance of RNA-seq to molecular biology and personalized medicine, we review recent findings that challenge small non-protein coding RNA-seq data and suggest approaches and precautions to overcome or minimize bias.
    Matched MeSH terms: Gene Expression Profiling/methods*
  7. Multi-stage filtering for improving confidence level and determining dominant clusters in clustering algorithms of gene expression data
    Kasim S, Deris S, Othman RM
    Comput Biol Med, 2013 Sep;43(9):1120-33.
    PMID: 23930805 DOI: 10.1016/j.compbiomed.2013.05.011
    A drastic improvement in the analysis of gene expression has lead to new discoveries in bioinformatics research. In order to analyse the gene expression data, fuzzy clustering algorithms are widely used. However, the resulting analyses from these specific types of algorithms may lead to confusion in hypotheses with regard to the suggestion of dominant function for genes of interest. Besides that, the current fuzzy clustering algorithms do not conduct a thorough analysis of genes with low membership values. Therefore, we present a novel computational framework called the "multi-stage filtering-Clustering Functional Annotation" (msf-CluFA) for clustering gene expression data. The framework consists of four components: fuzzy c-means clustering (msf-CluFA-0), achieving dominant cluster (msf-CluFA-1), improving confidence level (msf-CluFA-2) and combination of msf-CluFA-0, msf-CluFA-1 and msf-CluFA-2 (msf-CluFA-3). By employing double filtering in msf-CluFA-1 and apriori algorithms in msf-CluFA-2, our new framework is capable of determining the dominant clusters and improving the confidence level of genes with lower membership values by means of which the unknown genes can be predicted.
    Matched MeSH terms: Gene Expression Profiling/methods*
  8. Fulltext GnRH neuron type-specific transcriptome analysis by laser captured single-cell microarray in the medaka
    Moriya S, Ogawa S, Parhar IS
    Biochem Biophys Res Commun, 2013 Jun 14;435(4):562-6.
    PMID: 23669040 DOI: 10.1016/j.bbrc.2013.05.004
    Most vertebrates possess at least two gonadotropin-releasing hormone (GnRH) neuron types. To understand the physiological significance of the multiple GnRH systems in the brain, we examined three GnRH neuron type-specific transcriptomes using single-cell microarray analyses in the medaka (Oryzias latipes). A microarray profile of the three GnRH neuron types revealed five genes that are uniquely expressed in specific GnRH neuron types. GnRH1 neurons expressed three genes that are homologous to functionally characterised genes, GnRH2 neurons uniquely expressed one unnamed gene, and GnRH3 neurons uniquely expressed one known gene. These genes may be involved in the modulation or maintenance of each GnRH neuron type.
    Matched MeSH terms: Gene Expression Profiling/methods*
  9. Fulltext Cellular transcripts of chicken brain tissues in response to H5N1 and Newcastle disease virus infection
    Balasubramaniam VR, Wai TH, Omar AR, Othman I, Hassan SS
    Virol J, 2012;9:53.
    PMID: 22361110 DOI: 10.1186/1743-422X-9-53
    Highly-pathogenic avian influenza (HPAI) H5N1 and Newcastle disease (ND) viruses are the two most important poultry viruses in the world, with the ability to cause classic central nervous system dysfunction in poultry and migratory birds. To elucidate the mechanisms of neurovirulence caused by these viruses, a preliminary study was design to analyze host's cellular responses during infections of these viruses.
    Matched MeSH terms: Gene Expression Profiling/methods
  10. Fulltext The inference of breast cancer metastasis through gene regulatory networks
    Ahmad FK, Deris S, Othman NH
    J Biomed Inform, 2012 Apr;45(2):350-62.
    PMID: 22179053 DOI: 10.1016/j.jbi.2011.11.015
    Understanding the mechanisms of gene regulation during breast cancer is one of the most difficult problems among oncologists because this regulation is likely comprised of complex genetic interactions. Given this complexity, a computational study using the Bayesian network technique has been employed to construct a gene regulatory network from microarray data. Although the Bayesian network has been notified as a prominent method to infer gene regulatory processes, learning the Bayesian network structure is NP hard and computationally intricate. Therefore, we propose a novel inference method based on low-order conditional independence that extends to the case of the Bayesian network to deal with a large number of genes and an insufficient sample size. This method has been evaluated and compared with full-order conditional independence and different prognostic indices on a publicly available breast cancer data set. Our results suggest that the low-order conditional independence method will be able to handle a large number of genes in a small sample size with the least mean square error. In addition, this proposed method performs significantly better than other methods, including the full-order conditional independence and the St. Gallen consensus criteria. The proposed method achieved an area under the ROC curve of 0.79203, whereas the full-order conditional independence and the St. Gallen consensus criteria obtained 0.76438 and 0.73810, respectively. Furthermore, our empirical evaluation using the low-order conditional independence method has demonstrated a promising relationship between six gene regulators and two regulated genes and will be further investigated as potential breast cancer metastasis prognostic markers.
    Matched MeSH terms: Gene Expression Profiling/methods
  11. A modified binary particle swarm optimization for selecting the small subset of informative genes from gene expression data
    Mohamad MS, Omatu S, Deris S, Yoshioka M
    IEEE Trans Inf Technol Biomed, 2011 Nov;15(6):813-22.
    PMID: 21914573 DOI: 10.1109/TITB.2011.2167756
    Gene expression data are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. In order to select a small subset of informative genes from the data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset. Thus, we propose an improved (modified) binary particle swarm optimization to select the small subset of informative genes that is relevant for the cancer classification. In this proposed method, we introduce particles' speed for giving the rate at which a particle changes its position, and we propose a rule for updating particle's positions. By performing experiments on ten different gene expression datasets, we have found that the performance of the proposed method is superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared to BPSO.
    Matched MeSH terms: Gene Expression Profiling/methods*
  12. Using fuzzy association rule mining in cancer classification
    Mahmoodian H, Hamiruce Marhaban M, Abdulrahim R, Rosli R, Saripan I
    Australas Phys Eng Sci Med, 2011 Apr;34(1):41-54.
    PMID: 21327594 DOI: 10.1007/s13246-011-0054-8
    The classification of the cancer tumors based on gene expression profiles has been extensively studied in numbers of studies. A wide variety of cancer datasets have been implemented by the various methods of gene selection and classification to identify the behavior of the genes in tumors and find the relationships between them and outcome of diseases. Interpretability of the model, which is developed by fuzzy rules and linguistic variables in this study, has been rarely considered. In addition, creating a fuzzy classifier with high performance in classification that uses a subset of significant genes which have been selected by different types of gene selection methods is another goal of this study. A new algorithm has been developed to identify the fuzzy rules and significant genes based on fuzzy association rule mining. At first, different subset of genes which have been selected by different methods, were used to generate primary fuzzy classifiers separately and then proposed algorithm was implemented to mix the genes which have been associated in the primary classifiers and generate a new classifier. The results show that fuzzy classifier can classify the tumors with high performance while presenting the relationships between the genes by linguistic variables.
    Matched MeSH terms: Gene Expression Profiling/methods*
  13. Fulltext A Hybrid One-Way ANOVA Approach for the Robust and Efficient Estimation of Differential Gene Expression with Multiple Patterns
    Mollah MM, Jamal R, Mokhtar NM, Harun R, Mollah MN
    PLoS One, 2015;10(9):e0138810.
    PMID: 26413858 DOI: 10.1371/journal.pone.0138810
    Identifying genes that are differentially expressed (DE) between two or more conditions with multiple patterns of expression is one of the primary objectives of gene expression data analysis. Several statistical approaches, including one-way analysis of variance (ANOVA), are used to identify DE genes. However, most of these methods provide misleading results for two or more conditions with multiple patterns of expression in the presence of outlying genes. In this paper, an attempt is made to develop a hybrid one-way ANOVA approach that unifies the robustness and efficiency of estimation using the minimum β-divergence method to overcome some problems that arise in the existing robust methods for both small- and large-sample cases with multiple patterns of expression.
    Matched MeSH terms: Gene Expression Profiling/methods*
  14. Fulltext Barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel in situ analyses
    Liu S, Punthambaker S, Iyer EPR, Ferrante T, Goodwin D, Fürth D, et al.
    Nucleic Acids Res, 2021 06 04;49(10):e58.
    PMID: 33693773 DOI: 10.1093/nar/gkab120
    We present barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel insitu analyses (BOLORAMIS), a reverse transcription-free method for spatially-resolved, targeted, in situ RNA identification of single or multiple targets. BOLORAMIS was demonstrated on a range of cell types and human cerebral organoids. Singleplex experiments to detect coding and non-coding RNAs in human iPSCs showed a stem-cell signature pattern. Specificity of BOLORAMIS was found to be 92% as illustrated by a clear distinction between human and mouse housekeeping genes in a co-culture system, as well as by recapitulation of subcellular localization of lncRNA MALAT1. Sensitivity of BOLORAMIS was quantified by comparing with single molecule FISH experiments and found to be 11%, 12% and 35% for GAPDH, TFRC and POLR2A, respectively. To demonstrate BOLORAMIS for multiplexed gene analysis, we targeted 96 mRNAs within a co-culture of iNGN neurons and HMC3 human microglial cells. We used fluorescence in situ sequencing to detect error-robust 8-base barcodes associated with each of these genes. We then used this data to uncover the spatial relationship among cells and transcripts by performing single-cell clustering and gene-gene proximity analyses. We anticipate the BOLORAMIS technology for in situ RNA detection to find applications in basic and translational research.
    Matched MeSH terms: Gene Expression Profiling/methods*
  15. Construction of a microRNA-mRNA Regulatory Network in De Novo Cytogenetically Normal Acute Myeloid Leukemia Patients
    Esa E, Hashim AK, Mohamed EHM, Zakaria Z, Abu Hassan AN, Mat Yusoff Y, et al.
    Genet Test Mol Biomarkers, 2021 Mar;25(3):199-210.
    PMID: 33734890 DOI: 10.1089/gtmb.2020.0182
    Background: The association between dysregulated microRNAs (miRNAs) and acute myeloid leukemia (AML) is well known. However, our understanding of the regulatory role of miRNAs in the cytogenetically normal AML (CN-AML) subtype pathway is still poor. The current study integrated miRNA and mRNA profiles to explore novel miRNA-mRNA interactions that affect the regulatory patterns of de novo CN-AML. Methods: We utilized a multiplexed nanoString nCounter platform to profile both miRNAs and mRNAs using similar sets of patient samples (n = 24). Correlations were assessed, and an miRNA-mRNA network was constructed. The underlying biological functions of the mRNAs were predicted by gene enrichment. Finally, the interacting pairs were assessed using TargetScan and microT-CDS. We identified 637 significant negative correlations (false discovery rate <0.05). Results: Network analysis revealed a cluster of 12 miRNAs representing the majority of mRNA targets. Within the cluster, five miRNAs (miR-495-3p, miR-185-5p, let-7i-5p, miR-409-3p, and miR-127-3p) were posited to play a pivotal role in the regulation of CN-AML, as they are associated with the negative regulation of myeloid leukocyte differentiation, negative regulation of myeloid cell differentiation, and positive regulation of hematopoiesis. Conclusion: Three novel interactions in CN-AML were predicted as let-7i-5p:HOXA9, miR-495-3p:PIK3R1, and miR-495-3p:CDK6 may be responsible for regulating myeloid cell differentiation in CN-AML.
    Matched MeSH terms: Gene Expression Profiling/methods
  16. Fulltext De novo sequencing, assembly and analysis of eight different transcriptomes from the Malayan pangolin
    Mohamed Yusoff A, Tan TK, Hari R, Koepfli KP, Wee WY, Antunes A, et al.
    Sci Rep, 2016 09 13;6:28199.
    PMID: 27618997 DOI: 10.1038/srep28199
    Pangolins are scale-covered mammals, containing eight endangered species. Maintaining pangolins in captivity is a significant challenge, in part because little is known about their genetics. Here we provide the first large-scale sequencing of the critically endangered Manis javanica transcriptomes from eight different organs using Illumina HiSeq technology, yielding ~75 Giga bases and 89,754 unigenes. We found some unigenes involved in the insect hormone biosynthesis pathway and also 747 lipids metabolism-related unigenes that may be insightful to understand the lipid metabolism system in pangolins. Comparative analysis between M. javanica and other mammals revealed many pangolin-specific genes significantly over-represented in stress-related processes, cell proliferation and external stimulus, probably reflecting the traits and adaptations of the analyzed pregnant female M. javanica. Our study provides an invaluable resource for future functional works that may be highly relevant for the conservation of pangolins.
    Matched MeSH terms: Gene Expression Profiling/methods*
  17. High-Throughput RT-qPCR for the Analysis of Circulating MicroRNAs
    Tan GW, Tan LP
    Methods Mol Biol, 2017;1580:7-19.
    PMID: 28439823 DOI: 10.1007/978-1-4939-6866-4_2
    Reverse transcription followed by real-time or quantitative polymerase chain reaction (RT-qPCR) is the gold standard for validation of results from transcriptomic profiling studies such as microarray and RNA sequencing. The current need for most studies, especially biomarker studies, is to evaluate the expression levels or fold changes of many transcripts in a large number of samples. With conventional low to medium throughput qPCR platforms, many qPCR plates would have to be run and a significant amount of RNA input per sample will be required to complete the experiments. This is particularly challenging when the size of study material (small biopsy, laser capture microdissected cells, biofluid, etc.), time, and resources are limited. A sensitive and high-throughput qPCR platform is therefore optimal for the evaluation of many transcripts in a large number of samples because the time needed to complete the entire experiment is shortened and the usage of lab consumables as well as RNA input per sample are low. Here, the methods of high-throughput RT-qPCR for the analysis of circulating microRNAs are described. Two distinctive qPCR chemistries (probe-based and intercalating dye-based) can be applied using the methods described here.
    Matched MeSH terms: Gene Expression Profiling/methods
  18. Fulltext Transcriptome landscape of human primary monocytes at different sequencing depth
    Mirsafian H, Ripen AM, Leong WM, Manaharan T, Mohamad SB, Merican AF
    Genomics, 2017 Oct;109(5-6):463-470.
    PMID: 28733102 DOI: 10.1016/j.ygeno.2017.07.003
    Differential gene and transcript expression pattern of human primary monocytes from healthy young subjects were profiled under different sequencing depths (50M, 100M, and 200M reads). The raw data consisted of 1.3 billion reads generated from RNA sequencing (RNA-Seq) experiments. A total of 17,657 genes and 75,392 transcripts were obtained at sequencing depth of 200M. Total splice junction reads showed an even more significant increase. Comparative analysis of the expression patterns of immune-related genes revealed a total of 217 differentially expressed (DE) protein-coding genes and 50 DE novel transcripts, in which 40 DE protein-coding genes were related to the immune system. At higher sequencing depth, more genes, known and novel transcripts were identified and larger proportion of reads were allowed to map across splice junctions. The results also showed that increase in sequencing depth has no effect on the sequence alignment.
    Matched MeSH terms: Gene Expression Profiling/methods*
  19. Identification and expression profiling of genes governing lignin biosynthesis in Casuarina equisetifolia L
    Vikashini B, Shanthi A, Ghosh Dasgupta M
    Gene, 2018 Nov 15;676:37-46.
    PMID: 30201104 DOI: 10.1016/j.gene.2018.07.012
    Casuarina equisetifolia L. is an important multi-purpose, fast growing and widely planted tree species native to tropical and subtropical coastlines of Australia, Southeast Asia, Malaysia, Melanesia, Polynesia and New Caledonia. It is a nitrogen-fixing tree mainly used for charcoal making, construction poles, landscaping, timber, pulp, firewood, windbreaks, shelterbelts, soil erosion and sand dune stabilization. Casuarina wood is presently used for paper and pulp production. Raw material with reduced lignin is highly preferred to increase the pulp yield. Hence, understanding the molecular regulation of wood formation in this tree species is vital for selecting industrially suitable phenotypes for breeding programs. The lignin biosynthetic pathway has been extensively studied in tree species like Eucalypts, poplars, pines, Picea, Betula and Acacia sp. However, studies on wood formation at molecular level is presently lacking in casuarinas. Hence, in the present study, the transcriptome of the developing secondary tissues of 15 years old Casuarina equiseitfolia subsp. equisetifolia was sequenced, de novo assembled, annotated and mapped to functional pathways. Transcriptome sequencing generated a total of 26,985 transcripts mapped to 31 pathways. Mining of the annotated data identified nine genes involved in lignin biosynthesis pathway and relative expression of the transcripts in four tissues including scale-like leaves, needle-like brachlets, wood and root were documented. The expression of CeCCR1 and CeF5H were found to be significantly high in wood tissues, while maximum expression of CeHCT was documented in stem. Additionally, CeTUBA and CeH2A were identified as the most stable reference transcript for normalization of qRT-PCR data in C. equisetifolia. The present study is the first wood genomic resource in C. equisetifolia, which will be valuable for functional genomics research in this genus.
    Matched MeSH terms: Gene Expression Profiling/methods
  20. Fulltext On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data
    Ng GYL, Tan SC, Ong CS
    PLoS One, 2023;18(10):e0292961.
    PMID: 37856458 DOI: 10.1371/journal.pone.0292961
    Cell type identification is one of the fundamental tasks in single-cell RNA sequencing (scRNA-seq) studies. It is a key step to facilitate downstream interpretations such as differential expression, trajectory inference, etc. scRNA-seq data contains technical variations that could affect the interpretation of the cell types. Therefore, gene selection, also known as feature selection in data science, plays an important role in selecting informative genes for scRNA-seq cell type identification. Generally speaking, feature selection methods are categorized into filter-, wrapper-, and embedded-based approaches. From the existing literature, methods from filter- and embedded-based approaches are widely applied in scRNA-seq gene selection tasks. The wrapper-based method that gives promising results in other fields has yet been extensively utilized for selecting gene features from scRNA-seq data; in addition, most of the existing wrapper methods used in this field are clustering instead of classification-based. With a large number of annotated data available today, this study applied a classification-based approach as an alternative to the clustering-based wrapper method. In our work, a quantum-inspired differential evolution (QDE) wrapped with a classification method was introduced to select a subset of genes from twelve well-known scRNA-seq transcriptomic datasets to identify cell types. In particular, the QDE was combined with different machine-learning (ML) classifiers namely logistic regression, decision tree, support vector machine (SVM) with linear and radial basis function kernels, as well as extreme learning machine. The linear SVM wrapped with QDE, namely QDE-SVM, was chosen by referring to the feature selection results from the experiment. QDE-SVM showed a superior cell type classification performance among QDE wrapping with other ML classifiers as well as the recent wrapper methods (i.e., FSCAM, SSD-LAHC, MA-HS, and BSF). QDE-SVM achieved an average accuracy of 0.9559, while the other wrapper methods achieved average accuracies in the range of 0.8292 to 0.8872.
    Matched MeSH terms: Gene Expression Profiling/methods
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