Displaying publications 1 - 20 of 92 in total

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  1. Fulltext clrDV: a differential variability test for RNA-Seq data based on the skew-normal distribution
    Li H, Khang TF
    PeerJ, 2023;11:e16126.
    PMID: 37790621 DOI: 10.7717/peerj.16126
    BACKGROUND: Pathological conditions may result in certain genes having expression variance that differs markedly from that of the control. Finding such genes from gene expression data can provide invaluable candidates for therapeutic intervention. Under the dominant paradigm for modeling RNA-Seq gene counts using the negative binomial model, tests of differential variability are challenging to develop, owing to dependence of the variance on the mean.

    METHODS: Here, we describe clrDV, a statistical method for detecting genes that show differential variability between two populations. We present the skew-normal distribution for modeling gene-wise null distribution of centered log-ratio transformation of compositional RNA-seq data.

    RESULTS: Simulation results show that clrDV has false discovery rate and probability of Type II error that are on par with or superior to existing methodologies. In addition, its run time is faster than its closest competitors, and remains relatively constant for increasing sample size per group. Analysis of a large neurodegenerative disease RNA-Seq dataset using clrDV successfully recovers multiple gene candidates that have been reported to be associated with Alzheimer's disease.

    Matched MeSH terms: Sequence Analysis, RNA/methods
  2. scGGAN: single-cell RNA-seq imputation by graph-based generative adversarial network
    Huang Z, Wang J, Lu X, Mohd Zain A, Yu G
    Brief Bioinform, 2023 Mar 19;24(2).
    PMID: 36733262 DOI: 10.1093/bib/bbad040
    Single-cell RNA sequencing (scRNA-seq) data are typically with a large number of missing values, which often results in the loss of critical gene signaling information and seriously limit the downstream analysis. Deep learning-based imputation methods often can better handle scRNA-seq data than shallow ones, but most of them do not consider the inherent relations between genes, and the expression of a gene is often regulated by other genes. Therefore, it is essential to impute scRNA-seq data by considering the regional gene-to-gene relations. We propose a novel model (named scGGAN) to impute scRNA-seq data that learns the gene-to-gene relations by Graph Convolutional Networks (GCN) and global scRNA-seq data distribution by Generative Adversarial Networks (GAN). scGGAN first leverages single-cell and bulk genomics data to explore inherent relations between genes and builds a more compact gene relation network to jointly capture the homogeneous and heterogeneous information. Then, it constructs a GCN-based GAN model to integrate the scRNA-seq, gene sequencing data and gene relation network for generating scRNA-seq data, and trains the model through adversarial learning. Finally, it utilizes data generated by the trained GCN-based GAN model to impute scRNA-seq data. Experiments on simulated and real scRNA-seq datasets show that scGGAN can effectively identify dropout events, recover the biologically meaningful expressions, determine subcellular states and types, improve the differential expression analysis and temporal dynamics analysis. Ablation experiments confirm that both the gene relation network and gene sequence data help the imputation of scRNA-seq data.
    Matched MeSH terms: Sequence Analysis, RNA/methods
  3. CircPrime: a web-based platform for design of specific circular RNA primers
    Sharko F, Rbbani G, Siriyappagouder P, Raeymaekers JAM, Galindo-Villegas J, Nedoluzhko A, et al.
    BMC Bioinformatics, 2023 May 19;24(1):205.
    PMID: 37208611 DOI: 10.1186/s12859-023-05331-y
    BACKGROUND: Circular RNAs (circRNAs) are covalently closed-loop RNAs with critical regulatory roles in cells. Tens of thousands of circRNAs have been unveiled due to the recent advances in high throughput RNA sequencing technologies and bioinformatic tools development. At the same time, polymerase chain reaction (PCR) cross-validation for circRNAs predicted by bioinformatic tools remains an essential part of any circRNA study before publication.

    RESULTS: Here, we present the CircPrime web-based platform, providing a user-friendly solution for DNA primer design and thermocycling conditions for circRNA identification with routine PCR methods.

    CONCLUSIONS: User-friendly CircPrime web platform ( http://circprime.elgene.net/ ) works with outputs of the most popular bioinformatic predictors of circRNAs to design specific circular RNA primers. CircPrime works with circRNA coordinates and any reference genome from the National Center for Biotechnology Information database).

    Matched MeSH terms: Sequence Analysis, RNA/methods
  4. Single-cell RNA sequencing in human lung cancer: Applications, challenges, and pathway towards personalized therapy
    Chong ZX, Ho WY, Yeap SK, Wang ML, Chien Y, Verusingam ND, et al.
    J Chin Med Assoc, 2021 Jun 01;84(6):563-576.
    PMID: 33883467 DOI: 10.1097/JCMA.0000000000000535
    Lung cancer is one of the most prevalent human cancers, and single-cell RNA sequencing (scRNA-seq) has been widely used to study human lung cancer at the cellular, genetic, and molecular level. Even though there are published reviews, which summarized the applications of scRNA-seq in human cancers like breast cancer, there is lack of a comprehensive review, which could effectively highlight the broad use of scRNA-seq in studying lung cancer. This review, therefore, was aimed to summarize the various applications of scRNA-seq in human lung cancer research based on the findings from different published in vitro, in vivo, and clinical studies. The review would first briefly outline the concept and principle of scRNA-seq, followed by the discussion on the applications of scRNA-seq in studying human lung cancer. Finally, the challenges faced when using scRNA-seq to study human lung cancer would be discussed, and the potential applications and challenges of scRNA-seq to facilitate the development of personalized cancer therapy in the future would be explored.
    Matched MeSH terms: Sequence Analysis, RNA*
  5. Fulltext Getting the most out of RNA-seq data analysis
    Khang TF, Lau CY
    PeerJ, 2015;3:e1360.
    PMID: 26539333 DOI: 10.7717/peerj.1360
    Background. A common research goal in transcriptome projects is to find genes that are differentially expressed in different phenotype classes. Biologists might wish to validate such gene candidates experimentally, or use them for downstream systems biology analysis. Producing a coherent differential gene expression analysis from RNA-seq count data requires an understanding of how numerous sources of variation such as the replicate size, the hypothesized biological effect size, and the specific method for making differential expression calls interact. We believe an explicit demonstration of such interactions in real RNA-seq data sets is of practical interest to biologists. Results. Using two large public RNA-seq data sets-one representing strong, and another mild, biological effect size-we simulated different replicate size scenarios, and tested the performance of several commonly-used methods for calling differentially expressed genes in each of them. We found that, when biological effect size was mild, RNA-seq experiments should focus on experimental validation of differentially expressed gene candidates. Importantly, at least triplicates must be used, and the differentially expressed genes should be called using methods with high positive predictive value (PPV), such as NOISeq or GFOLD. In contrast, when biological effect size was strong, differentially expressed genes mined from unreplicated experiments using NOISeq, ASC and GFOLD had between 30 to 50% mean PPV, an increase of more than 30-fold compared to the cases of mild biological effect size. Among methods with good PPV performance, having triplicates or more substantially improved mean PPV to over 90% for GFOLD, 60% for DESeq2, 50% for NOISeq, and 30% for edgeR. At a replicate size of six, we found DESeq2 and edgeR to be reasonable methods for calling differentially expressed genes at systems level analysis, as their PPV and sensitivity trade-off were superior to the other methods'. Conclusion. When biological effect size is weak, systems level investigation is not possible using RNAseq data, and no meaningful result can be obtained in unreplicated experiments. Nonetheless, NOISeq or GFOLD may yield limited numbers of gene candidates with good validation potential, when triplicates or more are available. When biological effect size is strong, NOISeq and GFOLD are effective tools for detecting differentially expressed genes in unreplicated RNA-seq experiments for qPCR validation. When triplicates or more are available, GFOLD is a sharp tool for identifying high confidence differentially expressed genes for targeted qPCR validation; for downstream systems level analysis, combined results from DESeq2 and edgeR are useful.
    Matched MeSH terms: Sequence Analysis, RNA
  6. Fulltext Identification of sRNA mediated responses to nutrient depletion in Burkholderia pseudomallei
    Mohd-Padil H, Damiri N, Sulaiman S, Chai SF, Nathan S, Firdaus-Raih M
    Sci Rep, 2017 12 07;7(1):17173.
    PMID: 29215024 DOI: 10.1038/s41598-017-17356-4
    The Burkholderia genus includes many species that are known to survive in diverse environmental conditions including low nutrient environments. One species, Burkholderia pseudomallei is a versatile pathogen that can survive in a wide range of hosts and environmental conditions. In this study, we investigated how a nutrient depleted growth environment evokes sRNA mediated responses by B. pseudomallei. Computationally predicted B. pseudomallei D286 sRNAs were mapped to RNA-sequencing data for cultures grown under two conditions: (1) BHIB as a nutrient rich media reference environment and (2) M9 media as a nutrient depleted stress environment. The sRNAs were further selected to identify potentially cis-encoded systems by investigating their possible interactions with their flanking genes. The mappings of predicted sRNA genes and interactions analysis to their flanking genes identified 12 sRNA candidates that may possibly have cis-acting regulatory roles that are associated to a nutrient depleted growth environment. Our approach can be used for identifying novel sRNA genes and their possible role as cis-mediated regulatory systems.
    Matched MeSH terms: Sequence Analysis, RNA
  7. Fulltext Single-cell RNA sequencing analysis of human Alzheimer's disease brain samples reveals neuronal and glial specific cells differential expression
    Soreq L, Bird H, Mohamed W, Hardy J
    PLoS One, 2023;18(2):e0277630.
    PMID: 36827281 DOI: 10.1371/journal.pone.0277630
    Alzheimer's disease is the most common neurological disease worldwide. Unfortunately, there are currently no effective treatment methods nor early detection methods. Furthermore, the disease underlying molecular mechanisms are poorly understood. Global bulk gene expression profiling suggested that the disease is governed by diverse transcriptional regulatory networks. Thus, to identify distinct transcriptional networks impacted into distinct neuronal populations in Alzheimer, we surveyed gene expression differences in over 25,000 single-nuclei collected from the brains of two Alzheimer's in disease patients in Braak stage I and II and age- and gender-matched controls hippocampal brain samples. APOE status was not measured for this study samples (as well as CERAD and THAL scores). Our bioinformatic analysis identified discrete glial, immune, neuronal and vascular cell populations spanning Alzheimer's disease and controls. Astrocytes and microglia displayed the greatest transcriptomic impacts, with the induction of both shared and distinct gene programs.
    Matched MeSH terms: Sequence Analysis, RNA
  8. The molecular landscape of neurological disorders: insights from single-cell RNA sequencing in neurology and neurosurgery
    Awuah WA, Ahluwalia A, Ghosh S, Roy S, Tan JK, Adebusoye FT, et al.
    Eur J Med Res, 2023 Nov 16;28(1):529.
    PMID: 37974227 DOI: 10.1186/s40001-023-01504-w
    Single-cell ribonucleic acid sequencing (scRNA-seq) has emerged as a transformative technology in neurological and neurosurgical research, revolutionising our comprehension of complex neurological disorders. In brain tumours, scRNA-seq has provided valuable insights into cancer heterogeneity, the tumour microenvironment, treatment resistance, and invasion patterns. It has also elucidated the brain tri-lineage cancer hierarchy and addressed limitations of current models. Neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis have been molecularly subtyped, dysregulated pathways have been identified, and potential therapeutic targets have been revealed using scRNA-seq. In epilepsy, scRNA-seq has explored the cellular and molecular heterogeneity underlying the condition, uncovering unique glial subpopulations and dysregulation of the immune system. ScRNA-seq has characterised distinct cellular constituents and responses to spinal cord injury in spinal cord diseases, as well as provided molecular signatures of various cell types and identified interactions involved in vascular remodelling. Furthermore, scRNA-seq has shed light on the molecular complexities of cerebrovascular diseases, such as stroke, providing insights into specific genes, cell-specific expression patterns, and potential therapeutic interventions. This review highlights the potential of scRNA-seq in guiding precision medicine approaches, identifying clinical biomarkers, and facilitating therapeutic discovery. However, challenges related to data analysis, standardisation, sample acquisition, scalability, and cost-effectiveness need to be addressed. Despite these challenges, scRNA-seq has the potential to transform clinical practice in neurological and neurosurgical research by providing personalised insights and improving patient outcomes.
    Matched MeSH terms: Sequence Analysis, RNA
  9. Fulltext Hepatitis C virus genotyping methods: evaluation of AmpliSens(®) HCV-1/2/3-FRT compared to sequencing method
    Mohamed NA, Rashid ZZ, Wong KK
    J Clin Lab Anal, 2014 May;28(3):224-8.
    PMID: 24478138 DOI: 10.1002/jcla.21670
    BACKGROUND: Hepatitis C virus (HCV) genotyping is important for treatment and epidemiological purposes. The objective of this study was to evaluate the performance of AmpliSens(®) HCV-1/2/3-FRT kit in comparison to sequencing method for genotyping.

    METHODS: A total of 17 samples collected from December 2009 to January 2011 were analyzed. Reverse transcriptase polymerase chain reaction (PCR) was performed, followed by sequencing technique. Results were analyzed based on sequence information in GenBank. A second genotyping method (AmpliSens(®) HCV-1/2/3-FRT) was done, which differentiates HCV genotypes by means of real-time hybridization-fluorescence detection.

    RESULTS: From 17 samples, four were untypeable by AmpliSens(®) HCV-1/2/3-FRT. Eleven of 13 (84.6%) results showed concordant genotypes. A specimen that was determined as genotype 3a by sequencing was genotype 1 by the AmpliSens(®) HCV-1/2/3-FRT. Another specimen that was genotype 1 by sequencing was identified as genotype 3 by AmpliSens(®) HCV-1/2/3-FRT.

    CONCLUSION: HCV genotyping with AmpliSens(®) HCV-1/2/3-FRT using real-time PCR method provides a much simpler and more feasible workflow with shorter time compared to sequencing method. There was good concordance compared to sequencing method. However, more evaluation studies would be required to show statistical significance, and to troubleshoot discordant results. AmpliSens(®) HCV-1/2/3-FRT does differentiate between genotype but not until subtype level.

    Matched MeSH terms: Sequence Analysis, RNA
  10. Optimization of illumina truseq targeted RNA Expression (TREx) library quality
    Nadiah Abu, Noraini Nordin, Noorjahan Banu Alitheen, Nadiah Abu, Sheau Wei Tan, Swee Keong Yeap, et al.
    Sains Malaysiana, 2018;47:303-308.
    RNA-seq has become an essential tool in molecular research. Nevertheless, application of RNA-seq was limited by cost and technical difficulties. Illumina has introduced the cost effective and ease to handle Truseq Targeted RNA Sequencing. In this study, we present the requirements and the optimization procedure for this Truseq Targeted RNA sequencing on cell line. Total RNA was recommended as starting materials but it required optimization including additional purification step and adjusting the AMPure beads ratio to eliminate unwanted contaminants. This can be resolved by using PolyA-enriched mRNA as starting material. TREx is a useful assay to evaluate gene expression. Quality library of TREx can be prepared by adding multiple washing steps or changing input sample to mRNA.
    Matched MeSH terms: Sequence Analysis, RNA
  11. Fulltext Exploring the current landscape of single-cell RNA sequencing applications in gastric cancer research
    Awuah WA, Roy S, Tan JK, Adebusoye FT, Qiang Z, Ferreira T, et al.
    J Cell Mol Med, 2024 Apr;28(7):e18159.
    PMID: 38494861 DOI: 10.1111/jcmm.18159
    Gastric cancer (GC) represents a major global health burden and is responsible for a significant number of cancer-related fatalities. Its complex nature, characterized by heterogeneity and aggressive behaviour, poses considerable challenges for effective diagnosis and treatment. Single-cell RNA sequencing (scRNA-seq) has emerged as an important technique, offering unprecedented precision and depth in gene expression profiling at the cellular level. By facilitating the identification of distinct cell populations, rare cells and dynamic transcriptional changes within GC, scRNA-seq has yielded valuable insights into tumour progression and potential therapeutic targets. Moreover, this technology has significantly improved our comprehension of the tumour microenvironment (TME) and its intricate interplay with immune cells, thereby opening avenues for targeted therapeutic strategies. Nonetheless, certain obstacles, including tumour heterogeneity and technical limitations, persist in the field. Current endeavours are dedicated to refining protocols and computational tools to surmount these challenges. In this narrative review, we explore the significance of scRNA-seq in GC, emphasizing its advantages, challenges and potential applications in unravelling tumour heterogeneity and identifying promising therapeutic targets. Additionally, we discuss recent developments, ongoing efforts to overcome these challenges, and future prospects. Although further enhancements are required, scRNA-seq has already provided valuable insights into GC and holds promise for advancing biomedical research and clinical practice.
    Matched MeSH terms: Sequence Analysis, RNA
  12. Transcriptome analysis and molecular characterization of novel small RNAs in Mycobacterium tuberculosis Lineage 1
    Jumat MI, Chin KL
    World J Microbiol Biotechnol, 2024 Jul 25;40(9):279.
    PMID: 39048776 DOI: 10.1007/s11274-024-04089-6
    Mycobacterium tuberculosis (Mtb), the tuberculosis-causing agent, exhibits diverse genetic lineages, with known links to virulence. While genomic and transcriptomic variations between modern and ancient Mtb lineages have been explored, the role of small non-coding RNA (sRNA) in post-translational gene regulation remains largely uncharted. In this study, Mtb Lineage 1 (L1) Sabahan strains (n = 3) underwent sRNA sequencing, revealing 351 sRNAs, including 23 known sRNAs and 328 novel ones identified using ANNOgesic. Thirteen sRNAs were selected based on the best average cut-off value of 300, with RT-qPCR revealing significant expression differences for sRNA 1 (p = 0.0132) and sRNA 29 (p = 0.0012) between Mtb L1 and other lineages (L2 and L4, n = 3) (p > 0.05). Further characterization using RACE (rapid amplification of cDNA ends), followed by target prediction with TargetRNA3 unveils that sRNA 1 (55 base pairs) targets Rv0506, Rv0697, and Rv3590c, and sRNA 29 (86 base pairs) targets Rv33859c, Rv3345c, Rv0755c, Rv0107c, Rv1817, Rv2950c, Rv1181, Rv3610c, and Rv3296. Functional characterization with Mycobrowser reveals these targets involved in regulating intermediary metabolism and respiration, cell wall and cell processes, lipid metabolism, information pathways, and PE/PPE. In summary, two novel sRNAs, sRNA 1 and sRNA 29, exhibited differential expression between L1 and other lineages, with predicted roles in essential Mtb functions. These findings offer insights into Mtb regulatory mechanisms, holding promise for the development of improved tuberculosis treatment strategies in the future.
    Matched MeSH terms: Sequence Analysis, RNA
  13. 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: Sequence Analysis, RNA/methods*
  14. Fulltext The rubber tree genome shows expansion of gene family associated with rubber biosynthesis
    Lau NS, Makita Y, Kawashima M, Taylor TD, Kondo S, Othman AS, et al.
    Sci Rep, 2016 06 24;6:28594.
    PMID: 27339202 DOI: 10.1038/srep28594
    Hevea brasiliensis Muell. Arg, a member of the family Euphorbiaceae, is the sole natural resource exploited for commercial production of high-quality natural rubber. The properties of natural rubber latex are almost irreplaceable by synthetic counterparts for many industrial applications. A paucity of knowledge on the molecular mechanisms of rubber biosynthesis in high yield traits still persists. Here we report the comprehensive genome-wide analysis of the widely planted H. brasiliensis clone, RRIM 600. The genome was assembled based on ~155-fold combined coverage with Illumina and PacBio sequence data and has a total length of 1.55 Gb with 72.5% comprising repetitive DNA sequences. A total of 84,440 high-confidence protein-coding genes were predicted. Comparative genomic analysis revealed strong synteny between H. brasiliensis and other Euphorbiaceae genomes. Our data suggest that H. brasiliensis's capacity to produce high levels of latex can be attributed to the expansion of rubber biosynthesis-related genes in its genome and the high expression of these genes in latex. Using cap analysis gene expression data, we illustrate the tissue-specific transcription profiles of rubber biosynthesis-related genes, revealing alternative means of transcriptional regulation. Our study adds to the understanding of H. brasiliensis biology and provides valuable genomic resources for future agronomic-related improvement of the rubber tree.
    Matched MeSH terms: Sequence Analysis, RNA/methods
  15. Paper-based sample-to-answer molecular diagnostic platform for point-of-care diagnostics
    Choi JR, Tang R, Wang S, Wan Abas WA, Pingguan-Murphy B, Xu F
    Biosens Bioelectron, 2015 Dec 15;74:427-39.
    PMID: 26164488 DOI: 10.1016/j.bios.2015.06.065
    Nucleic acid testing (NAT), as a molecular diagnostic technique, including nucleic acid extraction, amplification and detection, plays a fundamental role in medical diagnosis for timely medical treatment. However, current NAT technologies require relatively high-end instrumentation, skilled personnel, and are time-consuming. These drawbacks mean conventional NAT becomes impractical in many resource-limited disease-endemic settings, leading to an urgent need to develop a fast and portable NAT diagnostic tool. Paper-based devices are typically robust, cost-effective and user-friendly, holding a great potential for NAT at the point of care. In view of the escalating demand for the low cost diagnostic devices, we highlight the beneficial use of paper as a platform for NAT, the current state of its development, and the existing challenges preventing its widespread use. We suggest a strategy involving integrating all three steps of NAT into one single paper-based sample-to-answer diagnostic device for rapid medical diagnostics in the near future.
    Matched MeSH terms: Sequence Analysis, RNA/instrumentation*
  16. Transcriptomics-based analysis using RNA-Seq of the coconut (Cocos nucifera) leaf in response to yellow decline phytoplasma infection
    Nejat N, Cahill DM, Vadamalai G, Ziemann M, Rookes J, Naderali N
    Mol Genet Genomics, 2015 Oct;290(5):1899-910.
    PMID: 25893418 DOI: 10.1007/s00438-015-1046-2
    Invasive phytoplasmas wreak havoc on coconut palms worldwide, leading to high loss of income, food insecurity and extreme poverty of farmers in producing countries. Phytoplasmas as strictly biotrophic insect-transmitted bacterial pathogens instigate distinct changes in developmental processes and defence responses of the infected plants and manipulate plants to their own advantage; however, little is known about the cellular and molecular mechanisms underlying host-phytoplasma interactions. Further, phytoplasma-mediated transcriptional alterations in coconut palm genes have not yet been identified. This study evaluated the whole transcriptome profiles of naturally infected leaves of Cocos nucifera ecotype Malayan Red Dwarf in response to yellow decline phytoplasma from group 16SrXIV, using RNA-Seq technique. Transcriptomics-based analysis reported here identified genes involved in coconut innate immunity. The number of down-regulated genes in response to phytoplasma infection exceeded the number of genes up-regulated. Of the 39,873 differentially expressed unigenes, 21,860 unigenes were suppressed and 18,013 were induced following infection. Comparative analysis revealed that genes associated with defence signalling against biotic stimuli were significantly overexpressed in phytoplasma-infected leaves versus healthy coconut leaves. Genes involving cell rescue and defence, cellular transport, oxidative stress, hormone stimulus and metabolism, photosynthesis reduction, transcription and biosynthesis of secondary metabolites were differentially represented. Our transcriptome analysis unveiled a core set of genes associated with defence of coconut in response to phytoplasma attack, although several novel defence response candidate genes with unknown function have also been identified. This study constitutes valuable sequence resource for uncovering the resistance genes and/or susceptibility genes which can be used as genetic tools in disease resistance breeding.
    Matched MeSH terms: Sequence Analysis, RNA*
  17. Fulltext Gene co-expression networks from RNA sequencing of dairy cattle identifies genes and pathways affecting feed efficiency
    Salleh SM, Mazzoni G, Løvendahl P, Kadarmideen HN
    BMC Bioinformatics, 2018 Dec 17;19(1):513.
    PMID: 30558534 DOI: 10.1186/s12859-018-2553-z
    BACKGROUND: Selection for feed efficiency is crucial for overall profitability and sustainability in dairy cattle production. Key regulator genes and genetic markers derived from co-expression networks underlying feed efficiency could be included in the genomic selection of the best cows. The present study identified co-expression networks associated with high and low feed efficiency and their regulator genes in Danish Holstein and Jersey cows. RNA-sequencing data from Holstein and Jersey cows with high and low residual feed intake (RFI) and treated with two diets (low and high concentrate) were used. Approximately 26 million and 25 million pair reads were mapped to bovine reference genome for Jersey and Holstein breed, respectively. Subsequently, the gene count expressions data were analysed using a Weighted Gene Co-expression Network Analysis (WGCNA) approach. Functional enrichment analysis from Ingenuity® Pathway Analysis (IPA®), ClueGO application and STRING of these modules was performed to identify relevant biological pathways and regulatory genes.

    RESULTS: WGCNA identified two groups of co-expressed genes (modules) significantly associated with RFI and one module significantly associated with diet. In Holstein cows, the salmon module with module trait relationship (MTR) = 0.7 and the top upstream regulators ATP7B were involved in cholesterol biosynthesis, steroid biosynthesis, lipid biosynthesis and fatty acid metabolism. The magenta module has been significantly associated (MTR = 0.51) with the treatment diet involved in the triglyceride homeostasis. In Jersey cows, the lightsteelblue1 (MTR = - 0.57) module controlled by IFNG and IL10RA was involved in the positive regulation of interferon-gamma production, lymphocyte differentiation, natural killer cell-mediated cytotoxicity and primary immunodeficiency.

    CONCLUSION: The present study provides new information on the biological functions in liver that are potentially involved in controlling feed efficiency. The hub genes and upstream regulators (ATP7b, IFNG and IL10RA) involved in these functions are potential candidate genes for the development of new biomarkers. However, the hub genes, upstream regulators and pathways involved in the co-expressed networks were different in both breeds. Hence, additional studies are required to investigate and confirm these findings prior to their use as candidate genes.

    Matched MeSH terms: Sequence Analysis, RNA/methods*
  18. 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: Sequence Analysis, RNA/methods*
  19. 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: Sequence Analysis, RNA/methods
  20. 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: Sequence Analysis, RNA/methods*
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