Journal articles on the topic 'RNA-Seq expression levels'
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Sun, Xifang, Shiquan Sun, and Sheng Yang. "An Efficient and Flexible Method for Deconvoluting Bulk RNA-Seq Data with Single-Cell RNA-Seq Data." Cells 8, no. 10 (September 27, 2019): 1161. http://dx.doi.org/10.3390/cells8101161.
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Estimating cell type compositions for complex diseases is an important step to investigate the cellular heterogeneity for understanding disease etiology and potentially facilitate early disease diagnosis and prevention. Here, we developed a computationally statistical method, referring to Multi-Omics Matrix Factorization (MOMF), to estimate the cell-type compositions of bulk RNA sequencing (RNA-seq) data by leveraging cell type-specific gene expression levels from single-cell RNA sequencing (scRNA-seq) data. MOMF not only directly models the count nature of gene expression data, but also effectively accounts for the uncertainty of cell type-specific mean gene expression levels. We demonstrate the benefits of MOMF through three real data applications, i.e., Glioblastomas (GBM), colorectal cancer (CRC) and type II diabetes (T2D) studies. MOMF is able to accurately estimate disease-related cell type proportions, i.e., oligodendrocyte progenitor cells and macrophage cells, which are strongly associated with the survival of GBM and CRC, respectively.
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Mourão, Kira, Nicholas J. Schurch, Radek Lucoszek, Kimon Froussios, Katarzyna MacKinnon, Céline Duc, Gordon Simpson, and Geoffrey J. Barton. "Detection and mitigation of spurious antisense expression with RoSA." F1000Research 8 (June 7, 2019): 819. http://dx.doi.org/10.12688/f1000research.18952.1.
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Antisense transcription is known to have a range of impacts on sense gene expression, including (but not limited to) impeding transcription initiation, disrupting post-transcriptional processes, and enhancing, slowing, or even preventing transcription of the sense gene. Strand-specific RNA-Seq protocols preserve the strand information of the original RNA in the data, and so can be used to identify where antisense transcription may be implicated in regulating gene expression. However, our analysis of 199 strand-specific RNA-Seq experiments reveals that spurious antisense reads are often present in these datasets at levels greater than 1% of sense gene expression levels. Furthermore, these levels can vary substantially even between replicates in the same experiment, potentially disrupting any downstream analysis, if the incorrectly assigned antisense counts dominate the set of genes with high antisense transcription levels. Currently, no tools exist to detect or correct for this spurious antisense signal. Our tool, RoSA (Removal of Spurious Antisense), detects the presence of high levels of spurious antisense read alignments in strand-specific RNA-Seq datasets. It uses incorrectly spliced reads on the antisense strand and/or ERCC spikeins (if present in the data) to calculate both global and gene-specific antisense correction factors. We demonstrate the utility of our tool to filter out spurious antisense transcript counts in an Arabidopsis thaliana RNA-Seq experiment. Availability: RoSA is open source software available under the GPL licence via the Barton Group GitHub page https://github.com/bartongroup.
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Kwon, Taejoon. "Benchmarking Transcriptome Quantification Methods for Duplicated Genes in Xenopus laevis." Cytogenetic and Genome Research 145, no. 3-4 (2015): 253–64. http://dx.doi.org/10.1159/000431386.
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Xenopus is an important model organism for the study of genome duplication in vertebrates. With the full genome sequence of diploid Xenopus tropicalis available, and that of allotetraploid X. laevis close to being finished, we will be able to expand our understanding of how duplicated genes have evolved. One of the key features in the study of the functional consequence of gene duplication is how their expression patterns vary across different conditions, and RNA-seq seems to have enough resolution to discriminate the expression of highly similar duplicated genes. However, most of the current RNA-seq analysis methods were not designed to study samples with duplicate genes such as in X. laevis. Here, various computational methods to quantify gene expression in RNA-seq data were evaluated, using 2 independent X. laevis egg RNA-seq datasets and 2 reference databases for duplicated genes. The fact that RNA-seq can measure expression levels of similar duplicated genes was confirmed, but long paired-end reads are more informative than short single-end reads to discriminate duplicated genes. Also, it was found that bowtie, one of the most popular mappers in RNA-seq analysis, reports significantly smaller numbers of unique hits according to a mapping quality score compared to other mappers tested (BWA, GSNAP, STAR). Calculated from unique hits based on a mapping quality score, both expression levels and the expression ratio of duplicated genes can be estimated consistently among biological replicates, demonstrating that this method can successfully discriminate the expression of each copy of a duplicated gene pair. This comprehensive evaluation will be a useful guideline for studying gene expression of organisms with genome duplication using RNA-seq in the future.
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Jaffe, Andrew E., Ran Tao, Alexis L. Norris, Marc Kealhofer, Abhinav Nellore, Joo Heon Shin, Dewey Kim, et al. "qSVA framework for RNA quality correction in differential expression analysis." Proceedings of the National Academy of Sciences 114, no. 27 (June 20, 2017): 7130–35. http://dx.doi.org/10.1073/pnas.1617384114.
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RNA sequencing (RNA-seq) is a powerful approach for measuring gene expression levels in cells and tissues, but it relies on high-quality RNA. We demonstrate here that statistical adjustment using existing quality measures largely fails to remove the effects of RNA degradation when RNA quality associates with the outcome of interest. Using RNA-seq data from molecular degradation experiments of human primary tissues, we introduce a method—quality surrogate variable analysis (qSVA)—as a framework for estimating and removing the confounding effect of RNA quality in differential expression analysis. We show that this approach results in greatly improved replication rates (>3×) across two large independent postmortem human brain studies of schizophrenia and also removes potential RNA quality biases in earlier published work that compared expression levels of different brain regions and other diagnostic groups. Our approach can therefore improve the interpretation of differential expression analysis of transcriptomic data from human tissue.
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Paşaniuc, Bogdan, Noah Zaitlen, and Eran Halperin. "Accurate Estimation of Expression Levels of Homologous Genes in RNA-seq Experiments." Journal of Computational Biology 18, no. 3 (March 2011): 459–68. http://dx.doi.org/10.1089/cmb.2010.0259.
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Richard, Hugues, Marcel H. Schulz, Marc Sultan, Asja Nürnberger, Sabine Schrinner, Daniela Balzereit, Emilie Dagand, et al. "Prediction of alternative isoforms from exon expression levels in RNA-Seq experiments." Nucleic Acids Research 38, no. 10 (February 11, 2010): e112-e112. http://dx.doi.org/10.1093/nar/gkq041.
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Li, Jun, and Alicia T. Lamere. "DiPhiSeq: robust comparison of expression levels on RNA-Seq data with large sample sizes." Bioinformatics 35, no. 13 (November 19, 2018): 2235–42. http://dx.doi.org/10.1093/bioinformatics/bty952.
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Abstract Motivation In the analysis of RNA-Seq data, detecting differentially expressed (DE) genes has been a hot research area in recent years and many methods have been proposed. DE genes show different average expression levels in different sample groups, and thus can be important biological markers. While generally very successful, these methods need to be further tailored and improved for cancerous data, which often features quite diverse expression in the samples from the cancer group, and this diversity is much larger than that in the control group. Results We propose a statistical method that can detect not only genes that show different average expressions, but also genes that show different diversities of expressions in different groups. These ‘differentially dispersed’ genes can be important clinical markers. Our method uses a redescending penalty on the quasi-likelihood function, and thus has superior robustness against outliers and other noise. Simulations and real data analysis demonstrate that DiPhiSeq outperforms existing methods in the presence of outliers, and identifies unique sets of genes. Availability and implementation DiPhiSeq is publicly available as an R package on CRAN: https://cran.r-project.org/package=DiPhiSeq. Supplementary information Supplementary data are available at Bioinformatics online.
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Ran, Di, Janhavi Moharil, James Lu, Heather Gustafson, Kerry Culm-Merdek, Kristen Strand-Tibbitts, Laura Benjamin, and Marian Navratil. "Platform comparison of HTG EdgeSeq and RNA-Seq for gene expression profiling of tumor tissue specimens." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): 3566. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.3566.
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3566 Background: Clinical biomarker studies are often hindered by the availability of tissue specimens of sufficient quality and quantity. While RNA-Seq is often considered the gold standard for measuring mRNA expression levels in cancer tissue, it typically requires multiple formalin-fixed paraffin-embedded (FFPE) tissue sections to extract a sufficient amount of quality RNA for subsequent gene expression profiling analysis. The HTG EdgeSeq technology is a gene expression profiling platform that combines quantitative nuclease protection assay technology with next-generation sequencing detection. Unlike RNA-Seq, the HTG EdgeSeq technology does not require RNA extraction, and can use small amounts of tissue material, typically several mm2, to generate reproducible gene expression profiles. Methods: This study compares the performance of RNA-Seq and HTG's profiling panel, the HTG EdgeSeq Precision Immuno-Oncology Panel (PIP), which is designed to measure expression levels of 1,392 genes focused on tumor/immune interaction. Approximately 1,200 samples from three tumor indications (gastric cancer, colorectal cancer and ovarian cancer) were tested using both technologies. Results: Up to four FFPE slides were used for RNA extraction to support RNA-Seq testing; out of the 1,202 samples processed, 1,099 generated extracted RNA of sufficient quality and quantity (as measured by RNA concentration, RIN score and %DV200) to proceed to sequencing, which resulted in a pass rate of 91.4% for RNA-Seq. The HTG EdgeSeq PIP panel resulted in a pass rate of 97.3% (samples passing QC metrics) when the same 1,200 samples were tested, and required only a single FFPE section owing to the small sample requirement. The t-SNE (a non-linear dimensionality reduction method) analysis of the common 1,358 genes revealed similar clustering of the three cancer indications between the two methods. Correlations across individual genes by sample resulted in the mean Spearman correlation coefficient of 0.73 (95% confidence interval of 0.61 - 0.80). Additionally, gene-wise comparisons across all samples were also evaluated. Conclusions: These data demonstrate that HTG EdgeSeq gene expression panels can be used as a competitive alternative to RNA-Seq, generating equivalent gene expression results, while offering the added benefits of a small sample size requirement, lack of RNA extraction bias, and fully automated data analysis pipeline.
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Kane, Shruti, Himanshu Garg, Neeraja M. Krishnan, Aditya Singh, and Binay Panda. "RNAtor: an Android-based application for biologists to plan RNA sequencing experiments." F1000Research 6 (November 16, 2017): 997. http://dx.doi.org/10.12688/f1000research.11982.2.
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RNA sequencing (RNA-seq) is a powerful technology that allows one to assess the RNA levels in a sample. Analysis of these levels can help in identifying novel transcripts (coding, non-coding and splice variants), understanding transcript structures, and estimating gene/allele expression. Biologists face specific challenges while designing RNA-seq experiments. The nature of these challenges lies in determining the total number of sequenced reads and technical replicates required for detecting marginally differentially expressed transcripts. Despite previous attempts to address these challenges, easily-accessible and biologist-friendly mobile applications do not exist. Thus, we developed RNAtor, a mobile application for Android platforms, to aid biologists in correctly designing their RNA-seq experiments. The recommendations from RNAtor are based on simulations and real data.
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Kubota, Naoto, and Mikita Suyama. "Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits." PLOS Computational Biology 18, no. 8 (August 29, 2022): e1010436. http://dx.doi.org/10.1371/journal.pcbi.1010436.
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Genomic variations are associated with gene expression levels, which are called expression quantitative trait loci (eQTL). Most eQTL may affect the total gene expression levels by regulating transcriptional activities of a specific promoter. However, the direct exploration of genomic loci associated with promoter activities using RNA-seq data has been challenging because eQTL analyses treat the total expression levels estimated by summing those of all isoforms transcribed from distinct promoters. Here we propose a new method for identifying genomic loci associated with promoter activities, called promoter usage quantitative trait loci (puQTL), using conventional RNA-seq data. By leveraging public RNA-seq datasets from the lymphoblastoid cell lines of 438 individuals from the GEUVADIS project, we obtained promoter activity estimates and mapped 2,592 puQTL at the 10% FDR level. The results of puQTL mapping enabled us to interpret the manner in which genomic variations regulate gene expression. We found that 310 puQTL genes (16.1%) were not detected by eQTL analysis, suggesting that our pipeline can identify novel variant–gene associations. Furthermore, we identified genomic loci associated with the activity of “hidden” promoters, which the standard eQTL studies have ignored. We found that most puQTL signals were concordant with at least one genome-wide association study (GWAS) signal, enabling novel interpretations of the molecular mechanisms of complex traits. Our results emphasize the importance of the re-analysis of public RNA-seq datasets to obtain novel insights into gene regulation by genomic variations and their contributions to complex traits.
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LaPaglia, Danielle M., Matthew R. Sapio, Peter D. Burbelo, Jean Thierry-Mieg, Danielle Thierry-Mieg, Stephen J. Raithel, Christopher E. Ramsden, Michael J. Iadarola, and Andrew J. Mannes. "RNA-Seq investigations of human post-mortem trigeminal ganglia." Cephalalgia 38, no. 5 (July 12, 2017): 912–32. http://dx.doi.org/10.1177/0333102417720216.
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Background The trigeminal ganglion contains neurons that relay sensations of pain, touch, pressure, and many other somatosensory modalities to the central nervous system. The ganglion is also a reservoir for latent herpes virus 1 infection. To gain a better understanding of molecular factors contributing to migraine and headache, transcriptome analyses were performed on postmortem human trigeminal ganglia. Methods RNA-Seq measurements of gene expression were conducted on small sub-regions of 16 human trigeminal ganglia. The samples were also characterized for transcripts derived from viral and microbial genomes. Herpes simplex virus 1 (HSV-1) antibodies in blood were measured using the luciferase immunoprecipitation assay. Results Observed molecular heterogeneity could be explained by sampling of anatomically distinct sub-regions of the excised ganglia consistent with neurally-enriched and non-neural, i.e. Schwann cell, enriched subregions. The levels of HSV-1 transcripts detected in trigeminal ganglia correlated with blood levels of HSV-1 antibodies. Multiple migraine susceptibility genes were strongly expressed in neurally-enriched trigeminal samples, while others were expressed in blood vessels. Conclusions These data provide a comprehensive human trigeminal transcriptome and a framework for evaluation of inhomogeneous post-mortem tissues through extensive quality control and refined downstream analyses for RNA-Seq methodologies. Expression profiling of migraine susceptibility genes identified by genetic association appears to emphasize the blood vessel component of the trigeminovascular system. Other genes displayed enriched expression in the trigeminal compared to dorsal root ganglion, and in-depth transcriptomic analysis of the KCNK18 gene underlying familial migraine shows selective neural expression within two specific populations of ganglionic neurons. These data suggest that expression profiling of migraine-associated genes can extend and amplify the underlying neurobiological insights obtained from genetic association studies.
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Eastman, Guillermo, Elizabeth R. Sharlow, John S. Lazo, George S. Bloom, and José R. Sotelo-Silveira. "Transcriptome and Translatome Regulation of Pathogenesis in Alzheimer’s Disease Model Mice." Journal of Alzheimer's Disease 86, no. 1 (March 8, 2022): 365–86. http://dx.doi.org/10.3233/jad-215357.
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Background: Defining cellular mechanisms that drive Alzheimer’s disease (AD) pathogenesis and progression will be aided by studies defining how gene expression patterns change during pre-symptomatic AD and ensuing periods of declining cognition. Previous studies have emphasized changes in transcriptome, but not translatome regulation, leaving the ultimate results of gene expression alterations relatively unexplored in the context of AD. Objective: To identify genes whose expression might be regulated at the transcriptome and translatome levels in AD, we analyzed gene expression in cerebral cortex of two AD model mouse strains, CVN (APPSwDI;NOS2 -/- ) and Tg2576 (APPSw), and their companion wild type (WT) strains at 6 months of age by tandem RNA-Seq and Ribo-Seq (ribosome profiling). Methods: Identical starting pools of bulk RNA were used for RNA-Seq and Ribo-Seq. Differential gene expression analysis was performed at the transcriptome, translatome, and translational efficiency levels. Regulated genes were functionally evaluated by gene ontology tools. Results: Compared to WT mice, AD model mice had similar levels of transcriptome regulation, but differences in translatome regulation. A microglial signature associated with early stages of Aβ accumulation was upregulated at both levels in CVN mice. Although the two mice strains did not share many regulated genes, they showed common regulated pathways related to AβPP metabolism associated with neurotoxicity and neuroprotection. Conclusion: This work represents the first genome-wide study of brain translatome regulation in animal models of AD and provides evidence of a tight and early translatome regulation of gene expression controlling the balance between neuroprotective and neurodegenerative processes in brain.
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Costa, Valerio, Claudia Angelini, Italia De Feis, and Alfredo Ciccodicola. "Uncovering the Complexity of Transcriptomes with RNA-Seq." Journal of Biomedicine and Biotechnology 2010 (2010): 1–19. http://dx.doi.org/10.1155/2010/853916.
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In recent years, the introduction of massively parallel sequencing platforms for Next Generation Sequencing (NGS) protocols, able to simultaneously sequence hundred thousand DNA fragments, dramatically changed the landscape of the genetics studies. RNA-Seq for transcriptome studies, Chip-Seq for DNA-proteins interaction, CNV-Seq for large genome nucleotide variations are only some of the intriguing new applications supported by these innovative platforms. Among them RNA-Seq is perhaps the most complex NGS application. Expression levels of specific genes, differential splicing, allele-specific expression of transcripts can be accurately determined by RNA-Seq experiments to address many biological-related issues. All these attributes are not readily achievable from previously widespread hybridization-based or tag sequence-based approaches. However, the unprecedented level of sensitivity and the large amount of available data produced by NGS platforms provide clear advantages as well as new challenges and issues. This technology brings the great power to make several new biological observations and discoveries, it also requires a considerable effort in the development of new bioinformatics tools to deal with these massive data files. The paper aims to give a survey of the RNA-Seq methodology, particularly focusing on the challenges that this application presents both from a biological and a bioinformatics point of view.
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Qi, Jing, Yang Zhou, Zicen Zhao, and Shuilin Jin. "SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data." PLOS Computational Biology 17, no. 6 (June 17, 2021): e1009118. http://dx.doi.org/10.1371/journal.pcbi.1009118.
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The single-cell RNA sequencing (scRNA-seq) technologies obtain gene expression at single-cell resolution and provide a tool for exploring cell heterogeneity and cell types. As the low amount of extracted mRNA copies per cell, scRNA-seq data exhibit a large number of dropouts, which hinders the downstream analysis of the scRNA-seq data. We propose a statistical method, SDImpute (Single-cell RNA-seq Dropout Imputation), to implement block imputation for dropout events in scRNA-seq data. SDImpute automatically identifies the dropout events based on the gene expression levels and the variations of gene expression across similar cells and similar genes, and it implements block imputation for dropouts by utilizing gene expression unaffected by dropouts from similar cells. In the experiments, the results of the simulated datasets and real datasets suggest that SDImpute is an effective tool to recover the data and preserve the heterogeneity of gene expression across cells. Compared with the state-of-the-art imputation methods, SDImpute improves the accuracy of the downstream analysis including clustering, visualization, and differential expression analysis.
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Liu, Yun, Yu Tian, Ting Wu, Yun Dai, Weihong Wang, and Guigen Teng. "High Expression and Clinical Significance of Elafin in Colorectal Cancer." Gastroenterology Research and Practice 2019 (June 9, 2019): 1–7. http://dx.doi.org/10.1155/2019/4946824.
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Objective. The decrease of Elafin is associated with several inflammatory diseases. Exogenous Elafin may be a treatment for IBD. Little data has shown the expression of Elafin in patients of colorectal cancer. Here, we tried to explore Elafin expression in human tissues of colorectal cancer. Methods. We examined the protein expression of Elafin in human tissues of adjacent nontumor and colorectal tumor by immunohistochemistry (IHC) or quantitative real-time polymerase chain reaction (qRT-PCR), then analyzed the clinical and RNA-seq data presented in The Cancer Genome Atlas (TCGA) database to confirm the relationship between Elafin levels and colorectal tumor. Results. Of the 88 paired samples, 68 colorectal cancer tissues indicated a high expression of Elafin compared with 52 matched adjacent noncancerous tissues. And the mRNA levels of Elafin in 35 paired tissues showed a similar trend. The RNA-seq and clinical data were available in 438 colorectal cancer tissues and 41 normal tissues in TCGA database. The RNA-seq data showed that Elafin mRNA was upregulated about twofold in colorectal cancer samples as compared to adjacent noncancerous samples (176.42±402.13 vs. 96.75±150.07; P=0.208). No statistically significant correlation was found between the Elafin expression and the age, gender, tumor invasive stage, lymph node metastasis, and distant metastasis both at the protein and mRNA levels. However, the Elafin expression was correlated with clinical stage based on the AJCC guidelines at protein levels but not mRNA levels. Conclusions. Elafin was upregulated in patients of colorectal cancer, resulting to potential limitations for exogenous Elafin treatment.
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WANG, XI, ZHENGPENG WU, and XUEGONG ZHANG. "ISOFORM ABUNDANCE INFERENCE PROVIDES A MORE ACCURATE ESTIMATION OF GENE EXPRESSION LEVELS IN RNA-SEQ." Journal of Bioinformatics and Computational Biology 08, supp01 (December 2010): 177–92. http://dx.doi.org/10.1142/s0219720010005178.
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Due to its unprecedented high-resolution and detailed information, RNA-seq technology based on next-generation high-throughput sequencing significantly boosts the ability to study transcriptomes. The estimation of genes' transcript abundance levels or gene expression levels has always been an important question in research on the transcriptional regulation and gene functions. On the basis of the concept of Reads Per Kilo-base per Million reads (RPKM), taking the union-intersection genes (UI-based) and summing up inferred isoform abundance (isoform-based) are the two current strategies to estimate gene expression levels, but produce different estimations. In this paper, we made the first attempt to compare the two strategies' performances through a series of simulation studies. Our results showed that the isoform-based method gives not only more accurate estimation but also has less uncertainty than the UI-based strategy. If taking into account the non-uniformity of read distribution, the isoform-based method can further reduce estimation errors. We applied both strategies to real RNA-seq datasets of technical replicates, and found that the isoform-based strategy also displays a better performance. For a more accurate estimation of gene expression levels from RNA-seq data, even if the abundance levels of isoforms are not of interest, it is still better to first infer the isoform abundance and sum them up to get the expression level of a gene as a whole.
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Asakage, Masaki, Yoshihiko Usui, Naoya Nezu, Hiroyuki Shimizu, Kinya Tsubota, Kazuhiko Umazume, Naoyuki Yamakawa, et al. "Comprehensive Gene Analysis of IgG4-Related Ophthalmic Disease Using RNA Sequencing." Journal of Clinical Medicine 9, no. 11 (October 27, 2020): 3458. http://dx.doi.org/10.3390/jcm9113458.
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High-throughput RNA sequencing (RNA-seq) uses massive parallel sequencing technology, allowing the unbiased analysis of genome-wide transcription levels and tumor mutation status. Immunoglobulin G4-related ophthalmic disease (IgG4-ROD) is a fibroinflammatory disease characterized by the enlargement of the ocular adnexal tissues. We analyzed RNA expression levels via RNA-seq in the biopsy specimens of three patients diagnosed with IgG4-ROD. Mucosa-associated lymphoid tissue (MALT) lymphoma, reactive lymphoid hyperplasia (RLH), normal lacrimal gland tissue, and adjacent adipose tissue were used as the controls (n = 3 each). RNA-seq was performed using the NextSeq 500 system, and genes with |fold change| ≥ 2 and p < 0.05 relative to the controls were defined as differentially expressed genes (DEGs) in IgG4-ROD. To validate the results of RNA-seq, real-time polymerase chain reaction (PCR) was performed in 30 IgG4-ROD and 30 orbital MALT lymphoma tissue samples. RNA-seq identified 35 up-regulated genes, including matrix metallopeptidase 12 (MMP12) and secreted phosphoprotein 1 (SPP1), in IgG4-ROD tissues when compared to all the controls. Many pathways related to the immune system were included when compared to all the controls. Expressions of MMP12 and SPP1 in IgG4-ROD tissues were confirmed by real-time PCR and immunohistochemistry. In conclusion, we identified novel DEGs, including those associated with extracellular matrix degradation, fibrosis, and inflammation, in IgG4-ROD biopsy specimens. These data provide new insights into molecular pathogenetic mechanisms and may contribute to the development of new biomarkers for diagnosis and molecular targeted drugs.
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Nesline, Mary K., Sarabjot Pabla, Yong Hee Lee, Paul DePietro, Amy Early, Roger Klein, Shengle Zhang, and Jeffrey Conroy. "Abstract 1259: PD-L1 expression by RNA-sequencing and survival from pembrolizumab in non-small cell lung cancer (NSCLC)." Cancer Research 82, no. 12_Supplement (June 15, 2022): 1259. http://dx.doi.org/10.1158/1538-7445.am2022-1259.
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Abstract PURPOSE: The immunohistochemistry companion diagnostic test for pembrolizumab (IHC 22C3 pharmDx) lacks sensitivity, challenging immunotherapy selection for NSCLC patients with lower levels of expression. Unlike IHC 22C3, which restricts assessment of PD-L1 expression to viable tumor cells as a tumor proportion score (% TPS), mRNA next generation sequencing (RNA-seq) measures PD-L1 expression in the tumor microenvironment for both tumor and inflammatory background cells. RNA-seq previously demonstrated concordance with IHC and may be a robust alternative testing method for multiple tumor types. Here, we sought to optimize PD-L1 RNA-seq cutoff values in NSCLC to improve clinical sensitivity. PROCEDURE: NSCLC patients included in the study (n=3,465) were tested for PD-L1 expression by IHC 22C3 and clinically validated RNA-seq, measured as % rank (0-100) relative to a reference population based on normalized reads per million (nRPM). Patients were divided into an RNA-seq cut-off discovery cohort (n=3,168), and a test cohort pembrolizumab treated patients. Principal components analysis (PCA) was used to classify patients based on test results and explore cut-off values in the discovery cohort. Kaplan Meier curves and a Cox proportional hazards regression models assessed overall survival (OS) hazard ratios (HR) for RNA-seq versus standard of care IHC cut-offs in the test cohort. RESULTS: Unsupervised PCA clustering identified three distinct PD-L1 groups separated by combinations of significant over- and under-representation of RNA-seq and IHC result measures from prior testing. The groups were labeled as “low” (rank ≤40), “moderate” (rank 41-73), and “high” (rank ≥74), based on the median RNA-seq rank for each group (+/- 1SD for low and high). Both the low and moderate groups were overrepresented by patients in the PD-L1 IHC low and negative groups. The moderate group was overrepresented by patients with moderately high PD-L1 RNA-seq ranks (median=70), while the low group was overrepresented by patients that were not PD-L1 high by RNA-seq. The high group was overrepresented by patients high for PD-L1 by both IHC and RNA-seq. OS HRs were better for RNA-seq high versus moderate (HR=0.05, CI 0.00-0.63, p=.02), and RNA-seq high versus low (HR=0.16, CI 0.03-0.86, p=.03) groups compared to standard of care IHC 22C3 high versus low groups, (HR=0.21, CI 0.04-1.07, p=.06). Findings were non-significant for the RNA-seq moderate versus low groups, likely due to the limited and disproportionately high number of patients with poor performance status in these groups. CONCLUSIONS: PD-L1 expression by RNA-seq demonstrated improved clinical sensitivity in predicting OS versus standard of care PD-LI IHC in a pembrolizumab treated NSCLC patient cohort. Additional studies are needed to further define cut-offs in the context of performance status, and better understand immune escape mechanisms in the moderate group. Citation Format: Mary K. Nesline, Sarabjot Pabla, Yong Hee Lee, Paul DePietro, Amy Early, Roger Klein, Shengle Zhang, Jeffrey Conroy. PD-L1 expression by RNA-sequencing and survival from pembrolizumab in non-small cell lung cancer (NSCLC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1259.
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Li, Lerong, and Momiao Xiong. "Dynamic Model for RNA-seq Data Analysis." BioMed Research International 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/916352.
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By measuring messenger RNA levels for all genes in a sample, RNA-seq provides an attractive option to characterize the global changes in transcription. RNA-seq is becoming the widely used platform for gene expression profiling. However, real transcription signals in the RNA-seq data are confounded with measurement and sequencing errors and other random biological/technical variation. To extract biologically useful transcription process from the RNA-seq data, we propose to use the second ODE for modeling the RNA-seq data. We use differential principal analysis to develop statistical methods for estimation of location-varying coefficients of the ODE. We validate the accuracy of the ODE model to fit the RNA-seq data by prediction analysis and 5-fold cross validation. To further evaluate the performance of the ODE model for RNA-seq data analysis, we used the location-varying coefficients of the second ODE as features to classify the normal and tumor cells. We demonstrate that even using the ODE model for single gene we can achieve high classification accuracy. We also conduct response analysis to investigate how the transcription process responds to the perturbation of the external signals and identify dozens of genes that are related to cancer.
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Liu, Wendao, and Noam Shomron. "Analysis of MicroRNA Regulation and Gene Expression Variability in Single Cell Data." Journal of Personalized Medicine 12, no. 10 (October 21, 2022): 1750. http://dx.doi.org/10.3390/jpm12101750.
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MicroRNAs (miRNAs) regulate gene expression by binding to mRNAs, and thus reduce target gene expression levels and expression variability, also known as ‘noise’. Single-cell RNA sequencing (scRNA-seq) technology has been used to study miRNA and mRNA expression in single cells. To evaluate scRNA-seq as a tool for investigating miRNA regulation, we analyzed datasets with both mRNA and miRNA expression in single-cell format. We found that miRNAs slightly reduce the expression noise of target genes; however, this effect is easily masked by strong technical noise from scRNA-seq. We suggest improvements aimed at reducing technical noise, which can be implemented in experimental design and computational analysis prior to running scRNA-seq. Our study provides useful guidelines for experiments that evaluate the effect of miRNAs on mRNA expression from scRNA-seq.
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Reyes, J. M., and P. J. Ross. "279 CYTOPLASMIC POLYADENYLATION-REGULATED GENE EXPRESSION DURING BOVINE OOCYTE MATURATION." Reproduction, Fertility and Development 27, no. 1 (2015): 228. http://dx.doi.org/10.1071/rdv27n1ab279.
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Using RNA-seq of GV and MII oocytes we have described changes in polyadenylated transcript abundance that occur during in vitro bovine oocyte maturation (Reyes J. M., J. L. Chitwood, and P. J. Ross. 2013. Deciphering regulation of transcript abundance in maturing bovine oocytes. Poster session presented at International Plant & Animal Genome XXI. San Diego, CA). These changes can be attributed to transcript degradation, transcription, or transcript polyadenylation levels. The objectives of the present study were to determine the extent of cytoplasmic polyadenylation (CP) by measuring total and polyadenylated transcript abundance and poly(A) tail length in GV and MII oocytes for 8 (CCNB1, CPEB4, DNMT3B, FBXO43, EZH2, GDF9, PRDX2, and PAIP2) genes selected based on RNA-seq results. Oocytes were obtained by aspiration of abattoir-derived ovaries (GV) and in vitro maturation for 24 h (MII). Four pools of 40 oocytes were collected per stage. Enhanced green fluorescent protein (EGFP) cRNA was spiked into each sample before RNA extraction using the PicoPure RNA Isolation Kit. Extracted RNA was equally divided for cDNA synthesis using either random hexamers or anchored oligo(dT) primers to detect total and polyadenylated transcripts, respectively. Quantitative PCR (qPCR) of target genes, EGFP (exogenous control), and PPIA (endogenous control) was performed in duplicate for each replicate and gene. Relative transcript abundance was calculated using the 2[–ΔΔC(T)] method and statistically analysed using the Student's t-test. Transcript poly(A) tail length was determined for all but 2 genes (DNMT3B and EZH2) at the GV and MII stages using rapid amplification of cDNA ends poly(A) test (RACE-PAT). Two replicates of GV (n = 100) and MII (n = 100) pairs were collected to perform RACE-PAT followed by fragment analysis on a Bioanalyzer DNA 1000 chip. Polyadenylated RNA abundance levels matched those of the RNA-seq study for 7/8 genes using both PPIA and EGFP to normalise qPCR data, demonstrating the validity of RNA-seq results. Furthermore, total transcript levels for 6/8 and 7/8 genes remained unchanged when normalized to an endogenous and exogenous control, respectively. Combined, the results suggest a significant role for CP considering changes in polyadenylated transcript levels occur without changes in total RNA abundance. Also, changes in transcript abundance corresponded to differences in poly(A) tail length at the specific stages as determined by RACE-PAT. In conclusion, CP is the predominant mechanism responsible for changes in transcript abundance during oocyte maturation at least for the majority of the examined genes, though more in-depth studies are required to determine the global extent of CP. This study improved the atlas of CP-regulated genes potentially providing researchers with critical knowledge to improve in silico tools that predict genes regulated by CP based on presence, position, and distribution of motifs within the 3′ untranslated region.
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Kyunai, Yuki M., Mika Sakamoto, Mayuko Koreishi, Yoshio Tsujino, and Ayano Satoh. "Fucosyltransferase 8 (FUT8) and core fucose expression in oxidative stress response." PLOS ONE 18, no. 2 (February 13, 2023): e0281516. http://dx.doi.org/10.1371/journal.pone.0281516.
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GlycoMaple is a new tool to predict glycan structures based on the expression levels of 950 genes encoding glycan biosynthesis-related enzymes and proteins using RNA-seq data. The antioxidant response, protecting cells from oxidative stress, has been focused on because its activation may relieve pathological conditions, such as neurodegenerative diseases. Genes involved in the antioxidant response are defined within the GO:0006979 category, including 441 human genes. Fifteen genes overlap between the glycan biosynthesis-related genes defined by GlycoMaple and the antioxidant response genes defined by GO:0006979, one of which is FUT8. 5-Hydroxy-4-phenyl-butenolide (5H4PB) extracted from Chinese aromatic vinegar induces the expression of a series of antioxidant response genes that protect cells from oxidative stress via activation of the nuclear factor erythroid 2-related factor 2–antioxidant response element pathway. Here, we show that FUT8 is upregulated in both our RNA-seq data set of 5H4PB-treated cells and publicly available RNA-seq data set of cells treated with another antioxidant, sulforaphane. Applying our RNA-seq data set to GlycoMaple led to a prediction of an increase in the core fucose of N-glycan that was confirmed by flow cytometry using a fucose-binding lectin. These results suggest that FUT8 and core fucose expression may increase upon the antioxidant response.
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Wei, Huanhuan, Hui Lu, and Hongyu Zhao. "Inferring Time-Lagged Causality Using the Derivative of Single-Cell Expression." International Journal of Molecular Sciences 23, no. 6 (March 20, 2022): 3348. http://dx.doi.org/10.3390/ijms23063348.
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Many computational methods have been developed to infer causality among genes using cross-sectional gene expression data, such as single-cell RNA sequencing (scRNA-seq) data. However, due to the limitations of scRNA-seq technologies, time-lagged causal relationships may be missed by existing methods. In this work, we propose a method, called causal inference with time-lagged information (CITL), to infer time-lagged causal relationships from scRNA-seq data by assessing the conditional independence between the changing and current expression levels of genes. CITL estimates the changing expression levels of genes by “RNA velocity”. We demonstrate the accuracy and stability of CITL for inferring time-lagged causality on simulation data against other leading approaches. We have applied CITL to real scRNA data and inferred 878 pairs of time-lagged causal relationships. Furthermore, we showed that the number of regulatory relationships identified by CITL was significantly more than that expected by chance. We provide an R package and a command-line tool of CITL for different usage scenarios.
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Mercola, Dan, Farah Rahmatpanah, Anshu Agrawal, Zhenyu (Arthur) Jia, Xiaolin Zi, Michael B. Lilly, and Michael McClelland. "Immune-stimulatory gene expression in stroma cells of African-American prostate cancer tissues." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e16544-e16544. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e16544.
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e16544 Background: Prostate cancer (PCa) of African Americans (AA) has a two-fold higher mortality compared to PCa of European Americans (EA). PCa-adjacent stroma cells from these two populations were examined for differences in their cytokine secretion and gene transcription. Methods: Primary PCa-adjacent stroma cells (carcinoma-associated fibroblasts (CAFs)) and PCa-distant normal fibroblasts of the same gland (dNFs) from 15 AA and 27 EA patients were grown to passage 5. A multiplex immunoassay was then used to measure the secretion of 30 cytokines/chemokines in conditioned medium. In addition, RNA-Seq was performed on four AA and four EA CAF lines, and on PCa-adjacent stroma FFPE tissues of seven AA and five EA patients. Self-identified ethnicity was confirmed using the LASER program and SNPs present in the RNA-Seq. Results: Conditioned media from 42 AA and EA CAFs and dNFs were assayed for 30 immuno-regulatory cytokines/chemokines and growth factors, 20 of which (66%) were secreted at lower levels ( p < 0.05) in AA vs. EA CAFs. Remarkably, dNFs did not show this difference. RNA-Seq analysis of AA vs EA CAFs in culture identified 413 upregulated and 893 downregulated genes (FDR < 0.05, ≥ ±1.5 fold). Several interleukins, interferons, and 120 of 154 differentially transcribed interferon-stimulated genes were downregulated in AA vs. EA CAFs. RNA-Seq of PCa-adjacent stroma from FFPE tissues of seven AA and five EA patients revealed significantly reduced transcription of many immunomodulatory genes in AA stroma, including type I IFN-stimulated genes (ISGs). CIBERSORT analysis of the FFPE RNA-Seq data indicated the presence of significantly less numbers of dendritic cells and higher levels of naïve CD4 Th cells in AA vs. EA stroma. Conclusions: We observed reduced transcription of immunomodulators in CAFs of AA vs. EA, accompanied by profound decreases in the levels of many secreted cytokines and chemokines. These differences may indicate reduced innate and adaptive immune responses in AA PCa, compared to EA PCa. The differences observed here in primary cell lines may permit functional studies of the underlying mechanism(s) of the striking dissimilarity in disease outcome of AA PCa compared to EA PCa
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Schulz, Marcel H., Daniel R. Zerbino, Martin Vingron, and Ewan Birney. "Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels." Bioinformatics 28, no. 8 (February 24, 2012): 1086–92. http://dx.doi.org/10.1093/bioinformatics/bts094.
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Voinsky, Irena, Yazeed Zoabi, Noam Shomron, Moria Harel, Hanoch Cassuto, Joseph Tam, Shannon Rose, et al. "Blood RNA Sequencing Indicates Upregulated BATF2 and LY6E and Downregulated ISG15 and MT2A Expression in Children with Autism Spectrum Disorder." International Journal of Molecular Sciences 23, no. 17 (August 30, 2022): 9843. http://dx.doi.org/10.3390/ijms23179843.
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Mutations in over 100 genes are implicated in autism spectrum disorder (ASD). DNA SNPs, CNVs, and epigenomic modifications also contribute to ASD. Transcriptomics analysis of blood samples may offer clues for pathways dysregulated in ASD. To expand and validate published findings of RNA-sequencing (RNA-seq) studies, we performed RNA-seq of whole blood samples from an Israeli discovery cohort of eight children with ASD compared with nine age- and sex-matched neurotypical children. This revealed 10 genes with differential expression. Using quantitative real-time PCR, we compared RNAs from whole blood samples of 73 Israeli and American children with ASD and 26 matched neurotypical children for the 10 dysregulated genes detected by RNA-seq. This revealed higher expression levels of the pro-inflammatory transcripts BATF2 and LY6E and lower expression levels of the anti-inflammatory transcripts ISG15 and MT2A in the ASD compared to neurotypical children. BATF2 was recently reported as upregulated in blood samples of Japanese adults with ASD. Our findings support an involvement of these genes in ASD phenotypes, independent of age and ethnicity. Upregulation of BATF2 and downregulation of ISG15 and MT2A were reported to reduce cancer risk. Implications of the dysregulated genes for pro-inflammatory phenotypes, immunity, and cancer risk in ASD are discussed.
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Suzuki, Takayuki, Yoko Ono, and Hidemasa Bono. "Comparison of Oxidative and Hypoxic Stress Responsive Genes from Meta-Analysis of Public Transcriptomes." Biomedicines 9, no. 12 (December 3, 2021): 1830. http://dx.doi.org/10.3390/biomedicines9121830.
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Analysis of RNA-sequencing (RNA-seq) data is an effective means to analyze the gene expression levels under specific conditions and discover new biological knowledge. More than 74,000 experimental series with RNA-seq have been stored in public databases as of 20 October 2021. Since this huge amount of expression data accumulated from past studies is a promising source of new biological insights, we focused on a meta-analysis of 1783 runs of RNA-seq data under the conditions of two types of stressors: oxidative stress (OS) and hypoxia. The collected RNA-seq data of OS were organized as the OS dataset to retrieve and analyze differentially expressed genes (DEGs). The OS-induced DEGs were compared with the hypoxia-induced DEGs retrieved from a previous study. The results from the meta-analysis of OS transcriptomes revealed two genes, CRIP1 and CRIP3, which were particularly downregulated, suggesting a relationship between OS and zinc homeostasis. The comparison between meta-analysis of OS and hypoxia showed that several genes were differentially expressed under both stress conditions, and it was inferred that the downregulation of cell cycle-related genes is a mutual biological process in both OS and hypoxia.
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Rahman, Rayees, Nicole Zatorski, Jens Hansen, Yuguang Xiong, J. G. Coen van Hasselt, Eric A. Sobie, Marc R. Birtwistle, Evren U. Azeloglu, Ravi Iyengar, and Avner Schlessinger. "Protein structure–based gene expression signatures." Proceedings of the National Academy of Sciences 118, no. 19 (May 3, 2021): e2014866118. http://dx.doi.org/10.1073/pnas.2014866118.
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Gene expression signatures (GES) connect phenotypes to differential messenger RNA (mRNA) expression of genes, providing a powerful approach to define cellular identity, function, and the effects of perturbations. The use of GES has suffered from vague assessment criteria and limited reproducibility. Because the structure of proteins defines the functional capability of genes, we hypothesized that enrichment of structural features could be a generalizable representation of gene sets. We derive structural gene expression signatures (sGES) using features from multiple levels of protein structure (e.g., domain and fold) encoded by the mRNAs in GES. Comprehensive analyses of data from the Genotype-Tissue Expression Project (GTEx), the all RNA-seq and ChIP-seq sample and signature search (ARCHS4) database, and mRNA expression of drug effects on cardiomyocytes show that sGES are useful for characterizing biological phenomena. sGES enable phenotypic characterization across experimental platforms, facilitates interoperability of expression datasets, and describe drug action on cells.
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Mandric, Igor, Yvette Temate-Tiagueu, Tatiana Shcheglova, Sahar Al Seesi, Alex Zelikovsky, and Ion I. Măndoiu. "Fast bootstrapping-based estimation of confidence intervals of expression levels and differential expression from RNA-Seq data." Bioinformatics 33, no. 20 (June 10, 2017): 3302–4. http://dx.doi.org/10.1093/bioinformatics/btx365.
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Boileau, Etienne, and Christoph Dieterich. "RNA Modification Level Estimation with pulseR." Genes 9, no. 12 (December 10, 2018): 619. http://dx.doi.org/10.3390/genes9120619.
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RNA modifications regulate the complex life of transcripts. An experimental approach called LAIC-seq was developed to characterize modification levels on a transcriptome-wide scale. In this method, the modified and unmodified molecules are separated using antibodies specific for a given RNA modification (e.g., m6A). In essence, the procedure of biochemical separation yields three fractions: Input, eluate, and supernatent, which are subjected to RNA-seq. In this work, we present a bioinformatics workflow, which starts from RNA-seq data to infer gene-specific modification levels by a statistical model on a transcriptome-wide scale. Our workflow centers around the pulseR package, which was originally developed for the analysis of metabolic labeling experiments. We demonstrate how to analyze data without external normalization (i.e., in the absence of spike-ins), given high efficiency of separation, and how, alternatively, scaling factors can be derived from unmodified spike-ins. Importantly, our workflow provides an estimate of uncertainty of modification levels in terms of confidence intervals for model parameters, such as gene expression and RNA modification levels. We also compare alternative model parametrizations, log-odds, or the proportion of the modified molecules and discuss the pros and cons of each representation. In summary, our workflow is a versatile approach to RNA modification level estimation, which is open to any read-count-based experimental approach.
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Liu, Yin, Sujun Chen, Su Wang, Fraser Soares, Martin Fischer, Feilong Meng, Zhou Du, et al. "Transcriptional landscape of the human cell cycle." Proceedings of the National Academy of Sciences 114, no. 13 (March 13, 2017): 3473–78. http://dx.doi.org/10.1073/pnas.1617636114.
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Steady-state gene expression across the cell cycle has been studied extensively. However, transcriptional gene regulation and the dynamics of histone modification at different cell-cycle stages are largely unknown. By applying a combination of global nuclear run-on sequencing (GRO-seq), RNA sequencing (RNA-seq), and histone-modification Chip sequencing (ChIP-seq), we depicted a comprehensive transcriptional landscape at the G0/G1, G1/S, and M phases of breast cancer MCF-7 cells. Importantly, GRO-seq and RNA-seq analysis identified different cell-cycle–regulated genes, suggesting a lag between transcription and steady-state expression during the cell cycle. Interestingly, we identified genes actively transcribed at early M phase that are longer in length and have low expression and are accompanied by a global increase in active histone 3 lysine 4 methylation (H3K4me2) and histone 3 lysine 27 acetylation (H3K27ac) modifications. In addition, we identified 2,440 cell-cycle–regulated enhancer RNAs (eRNAs) that are strongly associated with differential active transcription but not with stable expression levels across the cell cycle. Motif analysis of dynamic eRNAs predicted Kruppel-like factor 4 (KLF4) as a key regulator of G1/S transition, and this identification was validated experimentally. Taken together, our combined analysis characterized the transcriptional and histone-modification profile of the human cell cycle and identified dynamic transcriptional signatures across the cell cycle.
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Jin, Ke, Le Ou-Yang, Xing-Ming Zhao, Hong Yan, and Xiao-Fei Zhang. "scTSSR: gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation." Bioinformatics 36, no. 10 (February 19, 2020): 3131–38. http://dx.doi.org/10.1093/bioinformatics/btaa108.
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Abstract Motivation Single-cell RNA sequencing (scRNA-seq) methods make it possible to reveal gene expression patterns at single-cell resolution. Due to technical defects, dropout events in scRNA-seq will add noise to the gene-cell expression matrix and hinder downstream analysis. Therefore, it is important for recovering the true gene expression levels before carrying out downstream analysis. Results In this article, we develop an imputation method, called scTSSR, to recover gene expression for scRNA-seq. Unlike most existing methods that impute dropout events by borrowing information across only genes or cells, scTSSR simultaneously leverages information from both similar genes and similar cells using a two-side sparse self-representation model. We demonstrate that scTSSR can effectively capture the Gini coefficients of genes and gene-to-gene correlations observed in single-molecule RNA fluorescence in situ hybridization (smRNA FISH). Down-sampling experiments indicate that scTSSR performs better than existing methods in recovering the true gene expression levels. We also show that scTSSR has a competitive performance in differential expression analysis, cell clustering and cell trajectory inference. Availability and implementation The R package is available at https://github.com/Zhangxf-ccnu/scTSSR. Supplementary information Supplementary data are available at Bioinformatics online.
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Keever, Abigail L., Kathryn M. Collins, Rachel A. Clark, Amber L. Framstad, and Jason W. Ashley. "RANK signaling in osteoclast precursors results in a more permissive epigenetic landscape and sexually divergent patterns of gene expression." PeerJ 11 (February 9, 2023): e14814. http://dx.doi.org/10.7717/peerj.14814.
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Background Sex is an important risk factor in the development of osteoporosis and other bone loss disorders, with women often demonstrating greater susceptibility than men. While variation in sex steroids, such as estradiol, accounts for much of the risk, there are likely additional non-endocrine factors at transcriptional and epigenetic levels that result in a higher rate of bone loss in women. Identification of these factors could improve risk assessment and therapies to preserve and improve bone health. Methods Osteoclast precursors were isolated male and female C57Bl/6 mice and cultured with either MCSF alone or MCSF and RANKL. Following the culture period RNA was isolated for RNA sequencing and DNA was isolated for tagmentation and ATAC sequencing. RNA-Seq and ATAC-seq were evaluated via pathway analysis to identify sex- and RANKL-differential transcription and chromatin accessibility. Results Osteoclasts demonstrated significant alterations in gene expression compared to macrophages with both shared and differential pathways between the sexes. Transcriptional pathways differentially regulated between male and female cells were associated with immunological functions with evidence of greater sensitivity in male macrophages and female osteoclasts. ATAC-Seq revealed a large increase in chromatin accessibility following RANKL treatment with few alterations attributable to sex. Comparison of RNA-Seq and ATAC-seq data revealed few common pathways suggesting that many of the transcriptional changes of osteoclastogenesis occur independently of chromatin remodeling.
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Dohn, Ryan, Bingqing Xie, Rebecca Back, Alan Selewa, Heather Eckart, Reeta Prusty Rao, and Anindita Basu. "mDrop-Seq: Massively Parallel Single-Cell RNA-Seq of Saccharomyces cerevisiae and Candida albicans." Vaccines 10, no. 1 (December 27, 2021): 30. http://dx.doi.org/10.3390/vaccines10010030.
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Advances in high-throughput single-cell RNA sequencing (scRNA-seq) have been limited by technical challenges such as tough cell walls and low RNA quantity that prevent transcriptomic profiling of microbial species at throughput. We present microbial Drop-seq or mDrop-seq, a high-throughput scRNA-seq technique that is demonstrated on two yeast species, Saccharomyces cerevisiae, a popular model organism, and Candida albicans, a common opportunistic pathogen. We benchmarked mDrop-seq for sensitivity and specificity and used it to profile 35,109 S. cerevisiae cells to detect variation in mRNA levels between them. As a proof of concept, we quantified expression differences in heat shock S. cerevisiae using mDrop-seq. We detected differential activation of stress response genes within a seemingly homogenous population of S. cerevisiae under heat shock. We also applied mDrop-seq to C. albicans cells, a polymorphic and clinically relevant species of yeast with a thicker cell wall compared to S. cerevisiae. Single-cell transcriptomes in 39,705 C. albicans cells were characterized using mDrop-seq under different conditions, including exposure to fluconazole, a common anti-fungal drug. We noted differential regulation in stress response and drug target pathways between C. albicans cells, changes in cell cycle patterns and marked increases in histone activity when treated with fluconazole. We demonstrate mDrop-seq to be an affordable and scalable technique that can quantify the variability in gene expression in different yeast species. We hope that mDrop-seq will lead to a better understanding of genetic variation in pathogens in response to stimuli and find immediate applications in investigating drug resistance, infection outcome and developing new drugs and treatment strategies.
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Galinousky, Dmitry, and Tsimafei Padvitski. "Analysis of public RNA-seq data in studies of flax fiber biogenesis." EuroBiotech Journal 1, no. 2 (May 9, 2017): 177–79. http://dx.doi.org/10.24190/issn2564-615x/2017/02.10.
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Abstract In this work we used publicly available raw RNA-seq data to elucidate mechanisms of flax fiber biogenesis by measuring expression of cell-wall related genes (cellulose synthase, cellulose synthase-like and chitinase-like genes) in stem of flax (Linum usitatissimum cv. Bethune). Using public RNA-sequence data we have quantified and characterised the expression of the specific cell-wall genes in the top, middle and bottom parts of the Bethune flax stem. The most prominent findings are: Secondary cell-wall cellulose synthase (CesA) genes are expressed differentially in phloem and xylem in all parts of Bethune stem, in contrast with primary cell-wall cellulose synthase genes. Total expression level of cellulose synthase-like (Csl) genes is tissue invariant (although, CslG and CslE are differentially expressed) and smaller than the total expression of cellulose synthase genes. The CslD2D3 subgroup dominates total expression of CslD genes in both xylem and phloem. Expression levels of all expressed chitinase-like (Ctl) genes are tissue dependent in all parts of stem. Total expression level of chitinase-like genes is much higher than expression of cellulose synthase and cellulose synthase-like genes in both tissues.
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Singhal, Sandeep K., Sarmad Al-Marsoummi, Emilie E. Vomhof-DeKrey, Bo Lauckner, Trysten Beyer, and Marc D. Basson. "Schlafen 12 Slows TNBC Tumor Growth, Induces Luminal Markers, and Predicts Favorable Survival." Cancers 15, no. 2 (January 7, 2023): 402. http://dx.doi.org/10.3390/cancers15020402.
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The Schlafen 12 (SLFN12) protein regulates triple-negative breast cancer (TNBC) growth, differentiation, and proliferation. SLFN12 mRNA expression strongly correlates with TNBC patient survival. We sought to explore SLFN12 overexpression effects on in vivo human TNBC tumor xenograft growth and performed RNA-seq on xenografts to investigate related SLFN12 pathways. Stable SLFN12 overexpression reduced tumorigenesis, increased tumor latency, and reduced tumor volume. RNA-seq showed that SLFN12 overexpressing xenografts had higher luminal markers levels, suggesting that TNBC cells switched from an undifferentiated basal phenotype to a more differentiated, less aggressive luminal phenotype. SLFN12-overexpressing xenografts increased less aggressive BC markers, HER2 receptors ERBB2 and EGFR expression, which are not detectable by immunostaining in TNBC. Two cancer progression pathways, the NAD signaling pathway and the superpathway of cholesterol biosynthesis, were downregulated with SLFN12 overexpression. RNA-seq identified gene signatures associated with SLFN12 overexpression. Higher gene signature levels indicated good survival when tested on four independent BC datasets. These signatures behaved differently in African Americans than in Caucasian Americans, indicating a possible biological difference between these races that could contribute to the worse survival observed in African Americans with BC. These results suggest an increased SLFN12 expression modulates TNBC aggressiveness through a gene signature that could offer new treatment targets.
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Ng, Christopher J., Alice Liu, Sujatha Venkataraman, Katrina J. Ashworth, Christopher D. Baker, Rebecca O’Rourke, Rajeev Vibhakar, Kenneth L. Jones, and Jorge Di Paola. "Single-cell transcriptional analysis of human endothelial colony-forming cells from patients with low VWF levels." Blood 139, no. 14 (April 7, 2022): 2240–51. http://dx.doi.org/10.1182/blood.2021010683.
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Abstract von Willebrand factor (VWF) plays a key role in normal hemostasis, and deficiencies of VWF lead to clinically significant bleeding. We sought to identify novel modifiers of VWF levels in endothelial colony-forming cells (ECFCs) using single-cell RNA sequencing (scRNA-seq). ECFCs were isolated from patients with low VWF levels (plasma VWF antigen levels between 30 and 50 IU/dL) and from healthy controls. Human umbilical vein endothelial cells were used as an additional control cell line. Cells were characterized for their Weibel Palade body (WPB) content and VWF release. scRNA-seq of all cell lines was performed to evaluate for gene expression heterogeneity and for candidate modifiers of VWF regulation. Candidate modifiers identified by scRNA-seq were further characterized with small-interfering RNA (siRNA) experiments to evaluate for effects on VWF. We observed that ECFCs derived from patients with low VWF demonstrated alterations in baseline WPB metrics and exhibit impaired VWF release. scRNA-seq analyses of these endothelial cells revealed overall decreased VWF transcription, mosaicism of VWF expression, and genes that are differentially expressed in low VWF ECFCs and control endothelial cells (control ECs). An siRNA screen of potential VWF modifiers provided further evidence of regulatory candidates, and 1 such candidate, FLI1, alters the transcriptional activity of VWF. In conclusion, ECFCs from individuals with low VWF demonstrate alterations in their baseline VWF packaging and release compared with control ECs. scRNA-seq revealed alterations in VWF transcription, and siRNA screening identified multiple candidate regulators of VWF.
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Wen, Wuwu, Haimeng Fang, Lingqi Yue, Muhammad Khalil-Ur-Rehman, Yiqi Huang, Zhaoxuan Du, Guoshun Yang, and Yanshuai Xu. "RNA-Seq Based Transcriptomic Analysis of Bud Sport Skin Color in Grape Berries." Horticulturae 9, no. 2 (February 15, 2023): 260. http://dx.doi.org/10.3390/horticulturae9020260.
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The most common bud sport trait in grapevines is the change in color of grape berry skin, and the color of grapes is mainly developed by the composition and accumulation of anthocyanins. Many studies have shown that MYBA is a key gene regulates the initiation of bud sport color and anthocyanin synthesis in grape peels. In the current study, we used berry skins of ‘Italia’, ‘Benitaka’, ‘Muscat of Alexandria’, ‘Flame Muscat’, ‘Rosario Bianco’, ‘Rosario Rosso’, and ‘Red Rosario’ at the véraison stage (10 weeks post-flowering and 11 weeks post-flowering) as research materials. The relative expressions of genes related to grape berry bud sport skin color were evaluated utilizing RNA-Seq technology. The results revealed that the expressions of the VvMYBA1/A2 gene in the three red grape varieties at the véraison stage were higher than in the three white grape varieties. The VvMYBA1/A2 gene is known to be associated with UFGT in the anthocyanin synthesis pathway. According to the results, VvMYBA1/A2 gene expression could also be associated with the expression of LDOX. In addition, a single gene (gene ID: Vitvi19g01871) displayed the highest expressions in all the samples at the véraison stage for the six varieties. The expression of this gene was much higher in the three green varieties compared to the three red ones. GO molecular function annotation identified it as a putative metallothionein-like protein with the ability to regulate the binding of copper ions to zinc ions and the role of maintaining the internal stable state of copper ions at the cellular level. High expression levels of this screened gene may play an important role in bud sport color of grape berry skin at the véraison stage.
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Lee, Bradford W., Virender B. Kumar, Pooja Biswas, Audrey C. Ko, Ramzi M. Alameddine, David B. Granet, Radha Ayyagari, Don O. Kikkawa, and Bobby S. Korn. "Transcriptome Analysis of Orbital Adipose Tissue in Active Thyroid Eye Disease Using Next Generation RNA Sequencing Technology." Open Ophthalmology Journal 12, no. 1 (April 16, 2018): 41–52. http://dx.doi.org/10.2174/1874364101812010041.
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Objective: This study utilized Next Generation Sequencing (NGS) to identify differentially expressed transcripts in orbital adipose tissue from patients with active Thyroid Eye Disease (TED) versus healthy controls. Method: This prospective, case-control study enrolled three patients with severe, active thyroid eye disease undergoing orbital decompression, and three healthy controls undergoing routine eyelid surgery with removal of orbital fat. RNA Sequencing (RNA-Seq) was performed on freshly obtained orbital adipose tissue from study patients to analyze the transcriptome. Bioinformatics analysis was performed to determine pathways and processes enriched for the differential expression profile. Quantitative Reverse Transcriptase-Polymerase Chain Reaction (qRT-PCR) was performed to validate the differential expression of selected genes identified by RNA-Seq. Results: RNA-Seq identified 328 differentially expressed genes associated with active thyroid eye disease, many of which were responsible for mediating inflammation, cytokine signaling, adipogenesis, IGF-1 signaling, and glycosaminoglycan binding. The IL-5 and chemokine signaling pathways were highly enriched, and very-low-density-lipoprotein receptor activity and statin medications were implicated as having a potential role in TED. Conclusion: This study is the first to use RNA-Seq technology to elucidate differential gene expression associated with active, severe TED. This study suggests a transcriptional basis for the role of statins in modulating differentially expressed genes that mediate the pathogenesis of thyroid eye disease. Furthermore, the identification of genes with altered levels of expression in active, severe TED may inform the molecular pathways central to this clinical phenotype and guide the development of novel therapeutic agents.
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Rao, Guodong, Jianguo Zhang, Xiaoxia Liu, Xue Li, and Chenhe Wang. "Combined Metabolome and Transcriptome Profiling Reveal Optimal Harvest Strategy Model Based on Different Production Purposes in Olive." Foods 10, no. 2 (February 7, 2021): 360. http://dx.doi.org/10.3390/foods10020360.
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Olive oil has been favored as high-quality edible oil because it contains balanced fatty acids (FAs) and high levels of minor components. The contents of FAs and minor components are variable in olive fruits of different color at harvest time, which render it difficult to determine the optimal harvest strategy for olive oil producing. Here, we combined metabolome, Pacbio Iso-seq, and Illumina RNA-seq transcriptome to investigate the association between metabolites and gene expression of olive fruits at harvest time. A total of 34 FAs, 12 minor components, and 181 other metabolites (including organic acids, polyols, amino acids, and sugars) were identified in this study. Moreover, we proposed optimal olive harvesting strategy models based on different production purposes. In addition, we used the combined Pacbio Iso-seq and Illumina RNA-seq gene expression data to identify genes related to the biosynthetic pathways of hydroxytyrosol and oleuropein. These data lay the foundation for future investigations of olive fruit metabolism and gene expression patterns, and provide a method to obtain olive harvesting strategies for different production purposes.
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Lin, Zhen, Guorong Xu, Nan Deng, Christopher Taylor, Dongxiao Zhu, and Erik K. Flemington. "Quantitative and Qualitative RNA-Seq-Based Evaluation of Epstein-Barr Virus Transcription in Type I Latency Burkitt's Lymphoma Cells." Journal of Virology 84, no. 24 (October 13, 2010): 13053–58. http://dx.doi.org/10.1128/jvi.01521-10.
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ABSTRACT RNA-seq provides a rich source of transcriptome information with high qualitative and quantitative value. Here, we provide a pipeline for Epstein-Barr virus (EBV) transcriptome analysis using RNA-seq and we apply it to two type I latency cell lines, Mutu I and Akata. This analysis revealed substantial average expression levels of many lytic genes in predominantly latent cell populations. The lytic transcripts BHLF1 and LF3 were expressed at levels greater than those for 98% of all cellular polyadenylated transcripts. Exon junction mapping accurately identified the Qp-derived EBNA1 splicing pattern, lytic gene splicing, and a complex splicing pattern within the BamHI A region.
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Lee, Kwanuk, Dario Leister, and Tatjana Kleine. "Arabidopsis Mitochondrial Transcription Termination Factor mTERF2 Promotes Splicing of Group IIB Introns." Cells 10, no. 2 (February 3, 2021): 315. http://dx.doi.org/10.3390/cells10020315.
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Plastid gene expression (PGE) is essential for chloroplast biogenesis and function and, hence, for plant development. However, many aspects of PGE remain obscure due to the complexity of the process. A hallmark of nuclear-organellar coordination of gene expression is the emergence of nucleus-encoded protein families, including nucleic-acid binding proteins, during the evolution of the green plant lineage. One of these is the mitochondrial transcription termination factor (mTERF) family, the members of which regulate various steps in gene expression in chloroplasts and/or mitochondria. Here, we describe the molecular function of the chloroplast-localized mTERF2 in Arabidopsis thaliana. The complete loss of mTERF2 function results in embryo lethality, whereas directed, microRNA (amiR)-mediated knockdown of MTERF2 is associated with perturbed plant development and reduced chlorophyll content. Moreover, photosynthesis is impaired in amiR-mterf2 plants, as indicated by reduced levels of photosystem subunits, although the levels of the corresponding messenger RNAs are not affected. RNA immunoprecipitation followed by RNA sequencing (RIP-Seq) experiments, combined with whole-genome RNA-Seq, RNA gel-blot, and quantitative RT-PCR analyses, revealed that mTERF2 is required for the splicing of the group IIB introns of ycf3 (intron 1) and rps12.
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Ross, P. J., and J. L. Chitwood. "139 TRANSCRIPTOME ANALYSIS OF SINGLE BOVINE EMBRYOS BY RNA-Seq." Reproduction, Fertility and Development 24, no. 1 (2012): 182. http://dx.doi.org/10.1071/rdv24n1ab139.
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Transcriptome sequencing by high-throughput technologies provides global gene expression levels as well as gene structure information. Moreover, analysis of samples from single individuals allows the detection of allele-specific expression. We investigated the possibility of RNA-Seq analysis using single bovine blastocysts. Embryos were in vitro produced using abattoir-derived, in vitro- matured oocytes, TALP-based fertilization and KSOM embryo culture medium. On Day 7 of culture, 5 expanded blastocysts were collected and stored in RNA extraction buffer at –80°C. Total RNA was extracted from each individual embryo using the Arcturus PicoPure RNA isolation kit including DNAse treatment. Approximately 1.5 ng of high-quality total RNA was obtained per embryo. The RNA was amplified using the SPIA-based Ovation RNA-Seq kit (NuGen, San Carlos, CA, USA). After amplification, 6 μg of cDNA was obtained and directly used for library construction with the NuGen Encore NGS Library I kit. Libraries were submitted to the University of California Davis Genome Center for 40-bp single read sequencing on an Illumina GAIIx apparatus. Data analysis was performed using CLC Genomics Workbench. On average, 38 094 173 good-quality reads were produced from each sample. Removing 9 bp from the 5′ end of the sequences greatly improved read alignment. Mapping of trimmed reads to BTAU 4.0 allowing up to 2 mismatches per read resulted in 88.9 ± 0.3% of reads aligning to the genome. Using an SNP discovery algorithm, a total of 31 993 unique SNP were detected with an average of 12 530 ± 496 SNP per sample, 50% of which were heterozygous. Of the total, 45, 21, 14 and 9% were common to at least 2, 3, 4 and 5 samples, respectively. Allelic expression imbalance, defined as 75% of reads corresponding to one allele of a heterozygous SNP with coverage ≥50, was observed in 22% of SNP among those common to at least 2 samples. Mapping the reads to the transcriptome resulted in 71.8 ± 0.4% of reads aligning to genes present in Ensembl. Among those mapping to RefSeq transcripts, 64.4% corresponded to exons, 7.2% to exon-exon boundaries and 0.3% to exon-intron boundaries, with the remainder mapping to introns. An average of 9746 ± 122 genes with RPKM greater than 0.3 were detected in each sample, with 7982 genes expressed commonly among all 5 embryos. The correlation for RPKM between sample pairs was between 0.978 and 0.993. Genes known to be almost exclusively expressed by pre-implantation embryos were present, including OCT4, NANOG and CDX2 among others. Gene ontology analysis of gene groups, divided into quintiles by level of expression, indicated that the most highly expressed genes are enriched in ribosomal proteins and oxidative phosphorylation. Genes with medium-high levels of expression were enriched in structural components including organelles and the cytoskeleton. Genes at the medium level of expression represented nuclear and chromatin proteins, whereas medium-low and low were related to regulation of transcription and DNA metabolism. We conclude that RNA-Seq from a single bovine blastocyst is possible and represents a powerful tool for understanding the biology and pathologies of pre-implantation embryo development.
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Bridges, Mary C., Joyce Nair-Menon, and Antonis Kourtidis. "2041 The cell-cell adhesion component PLEKHA7 regulates the pro-tumorigenic MIR17HG long non-coding RNA in colon epithelial cells." Journal of Clinical and Translational Science 2, S1 (June 2018): 30. http://dx.doi.org/10.1017/cts.2018.129.
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OBJECTIVES/SPECIFIC AIMS: The goal of this study is to test the hypothesis that the adherens junctions of colon epithelial cells regulate lncRNAs levels and function via the microprocessor and RISC complexes to suppress expression of pro-tumorigenic markers and aberrant cell behavior. METHODS/STUDY POPULATION: To test this hypothesis, we used colon epithelial cancer cell lines. We performed RNA-seq following knockdown of PLEKHA7, a key component of the adherens junctions, to identify changes in lncRNA expression and downstream mRNA levels. We confirmed junctional localization of affected lncRNAs from the RNA-seq and those that we found in our preliminary study by using in situ hybridization (ISH). RESULTS/ANTICIPATED RESULTS: RNA-seq identified junction-associated lncRNAs whose expression levels are regulated by PLEKHA7. The top upregulated lncRNA upon PLEKHA7 depletion was MIR17HG, an oncogenic host transcript of a cluster of miRNAs. These mature miRNAs also co-precipitate with PLEKHA7. PLEKHA7 knockdown results in increased levels of MIR17HG, but only a subset of its hosted miRNAs (miR-19a,b). Notably, miR-19a and mir-19b are highly upregulated in colon cancer. Our data suggest that 2 PLEKHA7-associated miRNAs, miR-203a and miR-372, mediate suppression MIR17HG. Re-expression of PLEKHA7 in aggressive colon cancer cells that lack PLEKHA7 suppressed expression of MIR17HG, as well as anchorage independent growth of these cells. DISCUSSION/SIGNIFICANCE OF IMPACT: Our data point towards a novel mechanism of lncRNA regulation that tethers epithelial tissue integrity with pro-tumorigenic cell transformation. Reducing elevated MIR17HG levels, is a potential therapeutic approach to suppress the tumorigenic behavior of cells that have lost their junctional integrity and homeostasis. identify a network of miRNA-mRNA-lncRNA interactions that could be exploited for further mechanistic studies, as well as for diagnostic and therapeutic purposes in the future.
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Mondal, Pronoy Kanti, Udit Surya Saha, and Indranil Mukhopadhyay. "PseudoGA: cell pseudotime reconstruction based on genetic algorithm." Nucleic Acids Research 49, no. 14 (July 9, 2021): 7909–24. http://dx.doi.org/10.1093/nar/gkab457.
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Abstract Dynamic regulation of gene expression is often governed by progression through transient cell states. Bulk RNA-seq analysis can only detect average change in expression levels and is unable to identify this dynamics. Single cell RNA-seq presents an unprecedented opportunity that helps in placing the cells on a hypothetical time trajectory that reflects gradual transition of their transcriptomes. This continuum trajectory or ‘pseudotime’, may reveal the developmental pathway and provide us with information on dynamic transcriptomic changes and other biological processes. Existing approaches to build pseudotime heavily depend on reducing huge dimension to extremely low dimensional subspaces and may lead to loss of information. We propose PseudoGA, a genetic algorithm based approach to order cells assuming that gene expressions vary according to a smooth curve along the pseudotime trajectory. We observe superior accuracy of our method in simulated as well as benchmarking real datasets. Generality of the assumption behind PseudoGA and no dependence on dimensionality reduction technique make it a robust choice for pseudotime estimation from single cell transcriptome data. PseudoGA is also time efficient when applied to a large single cell RNA-seq data and adaptable to parallel computing. R code for PseudoGA is freely available at https://github.com/indranillab/pseudoga.
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Wu, Han, and Yu Zhu. "Deconvolution of base pair level RNA-Seq read counts for quantification of transcript expression levels." Annals of Applied Statistics 10, no. 3 (September 2016): 1195–216. http://dx.doi.org/10.1214/16-aoas906.
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Rehman, Atta Ur, Abdur Rashid, and Ijaz Anwar. "Single Cell RNA Sequencing (scRNA-Seq) as an Emerging Technology in Cancer Research." Proceedings of the Pakistan Academy of Sciences: B. Life and Environmental Sciences 58, no. 3 (January 17, 2022): 19–28. http://dx.doi.org/10.53560/ppasb(58-3)663.
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RNA sequencing (RNA-seq) has revolutionized basic biomedical research by studying the transcriptome at high resolution, and thus it has been proved to be very successful for understanding the molecular mechanisms of cancers. For example, RNA-seq has facilitated a comprehensive and multidimensional mapping of the key genomic changes that lead to various types of cancers. Nevertheless, the heterogeneous nature of cancer tissues has always been a problem. To overcome this challenge, single-cell RNA sequencing (scRNA-seq) has emerged as the most powerful tool to characterize cancer tissues by enhancing our knowledge of transcriptome at a single-cell resolution. In addition to disentangling the heterogeneity problem, scRNA-seq has other applications such as determining the molecular mechanisms of cellular differentiation, characterizing gene expression levels, and determining rare cell types found within cancer tissue. scRNA-Seq is used, as an emerging diagnostic tool, in tertiary healthcare settings with diverse clinical applications. Thus, the utility of scRNA-Seq in a healthcare system not only provides compelling evidence about understanding cancer biology but also points towards the development of therapeutic options in the future. The purpose of this review is to educate readers about the applications of scRNA-seq in cancer research in a wider context..
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Teixeira, Andreia Sofia, Francisco Fernandes, and Alexandre P. Francisco. "SpliceTAPyR — An Efficient Method for Transcriptome Alignment." International Journal of Foundations of Computer Science 29, no. 08 (December 2018): 1297–310. http://dx.doi.org/10.1142/s0129054118430049.
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RNA-Seq is a Next-Generation Sequencing (NGS) protocol for sequencing the messenger RNA in a cell and generates millions of short sequence fragments, reads, in a single run. These reads can be used to measure levels of gene expression and to identify novel splice variants of genes. One of the critical steps in an RNA-Seq experiment is mapping NGS reads to the reference genome. Because RNA-Seq reads can span over more than one exon in the genome, this task is challenging. In the last decade, tools for RNA-Seq alignment have emerged, but most of them run in two phases. First, the pipeline only maps reads that have a direct match in the reference, and the remaining are set aside as initially unmapped reads. Then, they use heuristics based approaches, clustering or even annotations, to decide where to align the later. This work presents an efficient computational solution for the problem of transcriptome alignment, named SpliceTAPyR. It identifies signals of splice junctions and relies on compressed full-text indexing methods and succinct data structures to efficiently align RNA-Seq reads in a single phase. This way it achieves the same or better accuracy than other tools while using considerably less memory and time to the most competitive tools.
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Lee, J. J., H. Y. Kang, W.-I. Lee, S. Y. Cho, Y. J. Kim, and H. J. Lee. "Efflux pump gene expression study using RNA-seq in multidrug-resistant TB." International Journal of Tuberculosis and Lung Disease 25, no. 12 (December 1, 2021): 974–81. http://dx.doi.org/10.5588/ijtld.21.0117.
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BACKGROUND: The mechanism underlying kanamycin (KM) resistance in Mycobacterium tuberculosis is not well understood, although efflux pump proteins are thought to play a role. This study used RNA-seq data to investigate changes in the expression levels of efflux pump genes following exposure to KM.METHODS: RNA expression of efflux pump and regulatory genes following exposure to different concentrations of KM (minimum inhibitory concentration MIC 25 and MIC50) in rrs wild-type strain and rrs A1401G mutated strain were compared with the control group.RESULTS: The selected strains had differential RNA expression patterns. Among the 71 putative efflux pump and regulatory genes, 46 had significant fold changes, and 12 genes (Rv0842, Rv1146, Rv1258c, Rv1473, Rv1686c, Rv1687c, Rv1877, Rv2038c, Rv3065, Rv3197a, Rv3728 and Rv3789) that were overexpressed following exposure to KM were thought to contribute to drug resistance. Rv3197A (whiB7) showed a distinct fold change based on the concentration of KM.CONCLUSION: The significant changes in the expression of the efflux pump and regulatory genes following exposure to KM may provide insights into the identification of a new resistance mechanism.
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Hia, Nazifa Tasnim, and Sumon Ahmed. "Automatic cell type annotation using supervised classification: A systematic literature review." Systematic Literature Review and Meta-Analysis Journal 3, no. 3 (October 21, 2022): 99–108. http://dx.doi.org/10.54480/slrm.v3i3.45.
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Single-cell sequencing gives us the opportunity to analyze cells on an individual level rather than at a population level. There are different types of sequencing based on the stage and portion of the cell from where the data are collected. Among those Single Cell RNA seq is most widely used and most application of cell type annotation has been on Single-cell RNA seq data. Tools have been developed for automatic cell type annotation as manual annotation of cell type is time-consuming and partially subjective. There are mainly three strategies to associate cell type with gene expression profiles of single cell by using marker genes databases, correlating expression data, transferring levels by supervised classification. In this SLR, we present a comprehensive evaluation of the available tools and the underlying approaches to perform automated cell type annotations on scRNA-seq data.