Displaying publications 1 - 20 of 21 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. 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*
  5. 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
  6. 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*
  7. 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*
  8. 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
  9. 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*
  10. Transcriptogenomics identification and characterization of RNA editing sites in human primary monocytes using high-depth next generation sequencing data
    Leong WM, Ripen AM, Mirsafian H, Mohamad SB, Merican AF
    Genomics, 2019 07;111(4):899-905.
    PMID: 29885984 DOI: 10.1016/j.ygeno.2018.05.019
    High-depth next generation sequencing data provide valuable insights into the number and distribution of RNA editing events. Here, we report the RNA editing events at cellular level of human primary monocyte using high-depth whole genomic and transcriptomic sequencing data. We identified over a ten thousand putative RNA editing sites and 69% of the sites were A-to-I editing sites. The sites enriched in repetitive sequences and intronic regions. High-depth sequencing datasets revealed that 90% of the canonical sites were edited at lower frequencies (<0.7). Single and multiple human monocytes and brain tissues samples were analyzed through genome sequence independent approach. The later approach was observed to identify more editing sites. Monocytes was observed to contain more C-to-U editing sites compared to brain tissues. Our results establish comparable pipeline that can address current limitations as well as demonstrate the potential for highly sensitive detection of RNA editing events in single cell type.
    Matched MeSH terms: Sequence Analysis, RNA/methods*
  11. Fulltext Small RNA Sequencing across Diverse Biofluids Identifies Optimal Methods for exRNA Isolation
    Srinivasan S, Yeri A, Cheah PS, Chung A, Danielson K, De Hoff P, et al.
    Cell, 2019 04 04;177(2):446-462.e16.
    PMID: 30951671 DOI: 10.1016/j.cell.2019.03.024
    Poor reproducibility within and across studies arising from lack of knowledge regarding the performance of extracellular RNA (exRNA) isolation methods has hindered progress in the exRNA field. A systematic comparison of 10 exRNA isolation methods across 5 biofluids revealed marked differences in the complexity and reproducibility of the resulting small RNA-seq profiles. The relative efficiency with which each method accessed different exRNA carrier subclasses was determined by estimating the proportions of extracellular vesicle (EV)-, ribonucleoprotein (RNP)-, and high-density lipoprotein (HDL)-specific miRNA signatures in each profile. An interactive web-based application (miRDaR) was developed to help investigators select the optimal exRNA isolation method for their studies. miRDar provides comparative statistics for all expressed miRNAs or a selected subset of miRNAs in the desired biofluid for each exRNA isolation method and returns a ranked list of exRNA isolation methods prioritized by complexity, expression level, and reproducibility. These results will improve reproducibility and stimulate further progress in exRNA biomarker development.
    Matched MeSH terms: Sequence Analysis, RNA/methods
  12. 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: Sequence Analysis, RNA/methods
  13. Fulltext RNA-seq and ChIP-seq as Complementary Approaches for Comprehension of Plant Transcriptional Regulatory Mechanism
    Muhammad II, Kong SL, Akmar Abdullah SN, Munusamy U
    Int J Mol Sci, 2019 Dec 25;21(1).
    PMID: 31881735 DOI: 10.3390/ijms21010167
    The availability of data produced from various sequencing platforms offer the possibility to answer complex questions in plant research. However, drawbacks can arise when there are gaps in the information generated, and complementary platforms are essential to obtain more comprehensive data sets relating to specific biological process, such as responses to environmental perturbations in plant systems. The investigation of transcriptional regulation raises different challenges, particularly in associating differentially expressed transcription factors with their downstream responsive genes. In this paper, we discuss the integration of transcriptional factor studies through RNA sequencing (RNA-seq) and Chromatin Immunoprecipitation sequencing (ChIP-seq). We show how the data from ChIP-seq can strengthen information generated from RNA-seq in elucidating gene regulatory mechanisms. In particular, we discuss how integration of ChIP-seq and RNA-seq data can help to unravel transcriptional regulatory networks. This review discusses recent advances in methods for studying transcriptional regulation using these two methods. It also provides guidelines for making choices in selecting specific protocols in RNA-seq pipelines for genome-wide analysis to achieve more detailed characterization of specific transcription regulatory pathways via ChIP-seq.
    Matched MeSH terms: Sequence Analysis, RNA/methods*
  14. Fulltext Comparative transcriptome analysis of leaves and roots in response to sudden increase in salinity in Brassica napus by RNA-seq
    Yong HY, Zou Z, Kok EP, Kwan BH, Chow K, Nasu S, et al.
    Biomed Res Int, 2014;2014:467395.
    PMID: 25177691 DOI: 10.1155/2014/467395
    Amphidiploid species in the Brassicaceae family, such as Brassica napus, are more tolerant to environmental stress than their diploid ancestors.A relatively salt tolerant B. napus line, N119, identified in our previous study, was used. N119 maintained lower Na(+) content, and Na(+)/K(+) and Na(+)/Ca(2+) ratios in the leaves than a susceptible line. The transcriptome profiles of both the leaves and the roots 1 h and 12 h after stress were investigated. De novo assembly of individual transcriptome followed by sequence clustering yielded 161,537 nonredundant sequences. A total of 14,719 transcripts were differentially expressed in either organs at either time points. GO and KO enrichment analyses indicated that the same 49 GO terms and seven KO terms were, respectively, overrepresented in upregulated transcripts in both organs at 1 h after stress. Certain overrepresented GO term of genes upregulated at 1 h after stress in the leaves became overrepresented in genes downregulated at 12 h. A total of 582 transcription factors and 438 transporter genes were differentially regulated in both organs in response to salt shock. The transcriptome depicting gene network in the leaves and the roots regulated by salt shock provides valuable information on salt resistance genes for future application to crop improvement.
    Matched MeSH terms: Sequence Analysis, RNA/methods
  15. Identifying IGH disease clones for MRD monitoring in childhood B-cell acute lymphoblastic leukemia using RNA-Seq
    Li Z, Jiang N, Lim EH, Chin WHN, Lu Y, Chiew KH, et al.
    Leukemia, 2020 09;34(9):2418-2429.
    PMID: 32099036 DOI: 10.1038/s41375-020-0774-4
    Identifying patient-specific clonal IGH/TCR junctional sequences is critical for minimal residual disease (MRD) monitoring. Conventionally these junctional sequences are identified using laborious Sanger sequencing of excised heteroduplex bands. We found that the IGH is highly expressed in our diagnostic B-cell acute lymphoblastic leukemia (B-ALL) samples using RNA-Seq. Therefore, we used RNA-Seq to identify IGH disease clone sequences in 258 childhood B-ALL samples for MRD monitoring. The amount of background IGH rearrangements uncovered by RNA-Seq followed the Zipf's law with IGH disease clones easily identified as outliers. Four hundred and ninety-seven IGH disease clones (median 2, range 0-7 clones/patient) are identified in 90.3% of patients. High hyperdiploid patients have the most IGH disease clones (median 3) while DUX4 subtype has the least (median 1) due to the rearrangements involving the IGH locus. In all, 90.8% of IGH disease clones found by Sanger sequencing are also identified by RNA-Seq. In addition, RNA-Seq identified 43% more IGH disease clones. In 69 patients lacking sensitive IGH targets, targeted NGS IGH MRD showed high correlation (R = 0.93; P = 1.3 × 10-14), better relapse prediction than conventional RQ-PCR MRD using non-IGH targets. In conclusion, RNA-Seq can identify patient-specific clonal IGH junctional sequences for MRD monitoring, adding to its usefulness for molecular diagnosis in childhood B-ALL.
    Matched MeSH terms: Sequence Analysis, RNA/methods*
  16. Fulltext CORNAS: coverage-dependent RNA-Seq analysis of gene expression data without biological replicates
    Low JZB, Khang TF, Tammi MT
    BMC Bioinformatics, 2017 12 28;18(Suppl 16):575.
    PMID: 29297307 DOI: 10.1186/s12859-017-1974-4
    BACKGROUND: In current statistical methods for calling differentially expressed genes in RNA-Seq experiments, the assumption is that an adjusted observed gene count represents an unknown true gene count. This adjustment usually consists of a normalization step to account for heterogeneous sample library sizes, and then the resulting normalized gene counts are used as input for parametric or non-parametric differential gene expression tests. A distribution of true gene counts, each with a different probability, can result in the same observed gene count. Importantly, sequencing coverage information is currently not explicitly incorporated into any of the statistical models used for RNA-Seq analysis.

    RESULTS: We developed a fast Bayesian method which uses the sequencing coverage information determined from the concentration of an RNA sample to estimate the posterior distribution of a true gene count. Our method has better or comparable performance compared to NOISeq and GFOLD, according to the results from simulations and experiments with real unreplicated data. We incorporated a previously unused sequencing coverage parameter into a procedure for differential gene expression analysis with RNA-Seq data.

    CONCLUSIONS: Our results suggest that our method can be used to overcome analytical bottlenecks in experiments with limited number of replicates and low sequencing coverage. The method is implemented in CORNAS (Coverage-dependent RNA-Seq), and is available at https://github.com/joel-lzb/CORNAS .

    Matched MeSH terms: Sequence Analysis, RNA/methods*
  17. Fulltext Long non-coding RNA expression in primary human monocytes
    Mirsafian H, Manda SS, Mitchell CJ, Sreenivasamurthy S, Ripen AM, Mohamad SB, et al.
    Genomics, 2016 07;108(1):37-45.
    PMID: 26778813 DOI: 10.1016/j.ygeno.2016.01.002
    Long non-coding RNAs (lncRNAs) have been shown to possess a wide range of functions in both cellular and developmental processes including cancers. Although some of the lncRNAs have been implicated in the regulation of the immune response, the exact function of the large majority of lncRNAs still remains unknown. In this study, we characterized the lncRNAs in human primary monocytes, an essential component of the innate immune system. We performed RNA sequencing of monocytes from four individuals and combined our data with eleven other publicly available datasets. Our analysis led to identification of ~8000 lncRNAs of which >1000 have not been previously reported in monocytes. PCR-based validation of a subset of the identified novel long intergenic noncoding RNAs (lincRNAs) revealed distinct expression patterns. Our study provides a landscape of lncRNAs in monocytes, which could facilitate future experimental studies to characterize the functions of these molecules in the innate immune system.
    Matched MeSH terms: Sequence Analysis, RNA/methods*
  18. Transcriptome-wide identification and characterization of the Rab GTPase family in mango
    Lawson T, Lycett GW, Mayes S, Ho WK, Chin CF
    Mol Biol Rep, 2020 Jun;47(6):4183-4197.
    PMID: 32444976 DOI: 10.1007/s11033-020-05519-y
    The Rab GTPase family plays a vital role in several plant physiological processes including fruit ripening. Fruit softening during ripening involves trafficking of cell wall polymers and enzymes between cellular compartments. Mango, an economically important fruit crop, is known for its delicious taste, exotic flavour and nutritional value. So far, there is a paucity of information on the mango Rab GTPase family. In this study, 23 genes encoding Rab proteins were identified in mango by a comprehensive in silico approach. Sequence alignment and similarity tree analysis with the model plant Arabidopsis as a reference enabled the bona fide assignment of the deduced mango proteins to classify into eight subfamilies. Expression analysis by RNA-Sequencing (RNA-Seq) showed that the Rab genes were differentially expressed in ripe and unripe mangoes suggesting the involvement of vesicle trafficking during ripening. Interaction analysis showed that the proteins involved in vesicle trafficking and cell wall softening were interconnected providing further evidence of the involvement of the Rab GTPases in fruit softening. Correlation analyses showed a significant relationship between the expression level of the RabA3 and RabA4 genes and fruit firmness at the unripe stage of the mango varieties suggesting that the differences in gene expression level might be associated with the contrasting firmness of these varieties. This study will not only provide new insights into the complexity of the ripening-regulated molecular mechanism but also facilitate the identification of potential Rab GTPases to address excessive fruit softening.
    Matched MeSH terms: Sequence Analysis, RNA/methods
  19. Fulltext RNA sequencing analysis of human podocytes reveals glucocorticoid regulated gene networks targeting non-immune pathways
    Jiang L, Hindmarch CC, Rogers M, Campbell C, Waterfall C, Coghill J, et al.
    Sci Rep, 2016 10 24;6:35671.
    PMID: 27774996 DOI: 10.1038/srep35671
    Glucocorticoids are steroids that reduce inflammation and are used as immunosuppressive drugs for many diseases. They are also the mainstay for the treatment of minimal change nephropathy (MCN), which is characterised by an absence of inflammation. Their mechanisms of action remain elusive. Evidence suggests that immunomodulatory drugs can directly act on glomerular epithelial cells or 'podocytes', the cell type which is the main target of injury in MCN. To understand the nature of glucocorticoid effects on non-immune cell functions, we generated RNA sequencing data from human podocyte cell lines and identified the genes that are significantly regulated in dexamethasone-treated podocytes compared to vehicle-treated cells. The upregulated genes are of functional relevance to cytoskeleton-related processes, whereas the downregulated genes mostly encode pro-inflammatory cytokines and growth factors. We observed a tendency for dexamethasone-upregulated genes to be downregulated in MCN patients. Integrative analysis revealed gene networks composed of critical signaling pathways that are likely targeted by dexamethasone in podocytes.
    Matched MeSH terms: Sequence Analysis, RNA/methods
  20. In silico 'fishing' using known small regulatory RNA (sRNA) candidates as the decoy from Escherichia coli, Salmonella typhi and Salmonella typhimurium manifested 14 novel sRNA candidates in the orthologous region of Proteus mirabilis
    KishanRaj S, Sumitha S, Siventhiran B, Thiviyaa O, Sathasivam KV, Xavier R, et al.
    Mol Biol Rep, 2018 Dec;45(6):2333-2343.
    PMID: 30284142 DOI: 10.1007/s11033-018-4397-z
    Proteus mirabilis, a gram-negative bacterium of the family Enterobacteriaceae, is a leading cause of urinary tract infection (UTI) with rapid development of multi-drug resistance. Identification of small regulatory RNAs (sRNAs), which belongs to a class of RNAs that do not translate into a protein, could permit the comprehension of the regulatory roles this molecules play in mediating pathogenesis and multi-drug resistance of the organism. In this study, comparative sRNA analysis across three different members of Enterobacteriaceae (Escherichia coli, Salmonella typhi and Salmonella typhimurium) was carried out to identify the sRNA homologs in P. mirabilis. A total of 232 sRNA genes that were reported in E. coli, S. typhi and S. typhimurium were subjected to comparative analysis against P. mirabilis HI4320 genome. We report the detection of 14 sRNA candidates, conserved in the orthologous regions of P. mirabilis, that are not included in Rfam database. Northern-blot analysis was carried out for selected three sRNA candidates from the current investigation and three known sRNA from Rfam of P. mirabilis. The expression pattern of the six sRNA candidates shows that they are growth stage-dependant. To the best of our knowledge, this is the first report on the identification of sRNA candidates in P. mirabilis.
    Matched MeSH terms: Sequence Analysis, RNA/methods
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