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Journal articles on the topic 'Small RNA-Seq'
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1
Hagemann-Jensen, Michael, Ilgar Abdullayev, Rickard Sandberg, and Omid R. Faridani. "Small-seq for single-cell small-RNA sequencing." Nature Protocols 13, no. 10 (September 24, 2018): 2407–24. http://dx.doi.org/10.1038/s41596-018-0049-y.
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Alisoltani, Arghavan, Hossein Fallahi, Behrouz Shiran, Anousheh Alisoltani, and Esmaeil Ebrahimie. "RNA-Seq SSRs and small RNA-Seq SSRs: New approaches in cancer biomarker discovery." Gene 560, no. 1 (April 2015): 34–43. http://dx.doi.org/10.1016/j.gene.2015.01.027.
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Solaguren-Beascoa, Maria, Ana Gámez-Valero, Georgia Escaramís, Marina Herrero-Lorenzo, Ana M. Ortiz, Carla Minguet, Ricardo Gonzalo, Maria Isabel Bravo, Montserrat Costa, and Eulàlia Martí. "Phospho-RNA-Seq Highlights Specific Small RNA Profiles in Plasma Extracellular Vesicles." International Journal of Molecular Sciences 24, no. 14 (July 19, 2023): 11653. http://dx.doi.org/10.3390/ijms241411653.
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Small RNAs (sRNAs) are bioactive molecules that can be detected in biofluids, reflecting physiological and pathological states. In plasma, sRNAs are found within extracellular vesicles (EVs) and in extravesicular compartments, offering potential sources of highly sensitive biomarkers. Deep sequencing strategies to profile sRNAs favor the detection of microRNAs (miRNAs), the best-known class of sRNAs. Phospho-RNA-seq, through the enzymatic treatment of sRNAs with T4 polynucleotide kinase (T4-PNK), has been recently developed to increase the detection of thousands of previously inaccessible RNAs. In this study, we investigated the value of phospho-RNA-seq on both the EVs and extravesicular plasma subfractions. Phospho-RNA-seq increased the proportion of sRNAs used for alignment and highlighted the diversity of the sRNA transcriptome. Unsupervised clustering analysis using sRNA counts matrices correctly classified the EVs and extravesicular samples only in the T4-PNK treated samples, indicating that phospho-RNA-seq stresses the features of sRNAs in each plasma subfraction. Furthermore, T4-PNK treatment emphasized specific miRNA variants differing in the 5′-end (5′-isomiRs) and certain types of tRNA fragments in each plasma fraction. Phospho-RNA-seq increased the number of tissue-specific messenger RNA (mRNA) fragments in the EVs compared with the extravesicular fraction, suggesting that phospho-RNA-seq favors the discovery of tissue-specific sRNAs in EVs. Overall, the present data emphasizes the value of phospho-RNA-seq in uncovering RNA-based biomarkers in EVs.
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Xu, Zhongneng, and Shuichi Asakawa. "Physiological RNA dynamics in RNA-Seq analysis." Briefings in Bioinformatics 20, no. 5 (June 29, 2018): 1725–33. http://dx.doi.org/10.1093/bib/bby045.
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Abstract Physiological RNA dynamics cause problems in transcriptome analysis. Physiological RNA accumulation affects the analysis of RNA quantification, and physiological RNA degradation affects the analysis of the RNA sequence length, feature site and quantification. In the present article, we review the effects of physiological degradation and accumulation of RNA on analysing RNA sequencing data. Physiological RNA accumulation and degradation probably led to such phenomena as incorrect estimations of transcription quantification, differential expressions, co-expressions, RNA decay rates, alternative splicing, boundaries of transcription, novel genes, new single-nucleotide polymorphisms, small RNAs and gene fusion. Thus, the transcriptomic data obtained up to date warrant further scrutiny. New and improved techniques and bioinformatics software are needed to produce accurate data in transcriptome research.
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Zhou, Weiqiang, Zhicheng Ji, Weixiang Fang, and Hongkai Ji. "Global prediction of chromatin accessibility using small-cell-number and single-cell RNA-seq." Nucleic Acids Research 47, no. 19 (August 20, 2019): e121-e121. http://dx.doi.org/10.1093/nar/gkz716.
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Abstract Conventional high-throughput genomic technologies for mapping regulatory element activities in bulk samples such as ChIP-seq, DNase-seq and FAIRE-seq cannot analyze samples with small numbers of cells. The recently developed low-input and single-cell regulome mapping technologies such as ATAC-seq and single-cell ATAC-seq (scATAC-seq) allow analyses of small-cell-number and single-cell samples, but their signals remain highly discrete or noisy. Compared to these regulome mapping technologies, transcriptome profiling by RNA-seq is more widely used. Transcriptome data in single-cell and small-cell-number samples are more continuous and often less noisy. Here, we show that one can globally predict chromatin accessibility and infer regulatory element activities using RNA-seq. Genome-wide chromatin accessibility predicted by RNA-seq from 30 cells can offer better accuracy than ATAC-seq from 500 cells. Predictions based on single-cell RNA-seq (scRNA-seq) can more accurately reconstruct bulk chromatin accessibility than using scATAC-seq. Integrating ATAC-seq with predictions from RNA-seq increases the power and value of both methods. Thus, transcriptome-based prediction provides a new tool for decoding gene regulatory circuitry in samples with limited cell numbers.
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Benesova, Sarka, Mikael Kubista, and Lukas Valihrach. "Small RNA-Sequencing: Approaches and Considerations for miRNA Analysis." Diagnostics 11, no. 6 (May 27, 2021): 964. http://dx.doi.org/10.3390/diagnostics11060964.
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MicroRNAs (miRNAs) are a class of small RNA molecules that have an important regulatory role in multiple physiological and pathological processes. Their disease-specific profiles and presence in biofluids are properties that enable miRNAs to be employed as non-invasive biomarkers. In the past decades, several methods have been developed for miRNA analysis, including small RNA sequencing (RNA-seq). Small RNA-seq enables genome-wide profiling and analysis of known, as well as novel, miRNA variants. Moreover, its high sensitivity allows for profiling of low input samples such as liquid biopsies, which have now found applications in diagnostics and prognostics. Still, due to technical bias and the limited ability to capture the true miRNA representation, its potential remains unfulfilled. The introduction of many new small RNA-seq approaches that tried to minimize this bias, has led to the existence of the many small RNA-seq protocols seen today. Here, we review all current approaches to cDNA library construction used during the small RNA-seq workflow, with particular focus on their implementation in commercially available protocols. We provide an overview of each protocol and discuss their applicability. We also review recent benchmarking studies comparing each protocol’s performance and summarize the major conclusions that can be gathered from their usage. The result documents variable performance of the protocols and highlights their different applications in miRNA research. Taken together, our review provides a comprehensive overview of all the current small RNA-seq approaches, summarizes their strengths and weaknesses, and provides guidelines for their applications in miRNA research.
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Gupta, Vikas, Katharina Markmann, Christian N. S. Pedersen, Jens Stougaard, and Stig U. Andersen. "shortran: a pipeline for small RNA-seq data analysis." Bioinformatics 28, no. 20 (August 22, 2012): 2698–700. http://dx.doi.org/10.1093/bioinformatics/bts496.
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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, Feng, Karolina Elżbieta Kaczor-Urbanowicz, Jie Sun, Blanca Majem, Hsien-Chun Lo, Yong Kim, Kikuye Koyano, et al. "Characterization of Human Salivary Extracellular RNA by Next-generation Sequencing." Clinical Chemistry 64, no. 7 (July 1, 2018): 1085–95. http://dx.doi.org/10.1373/clinchem.2017.285072.
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Abstract BACKGROUND It was recently discovered that abundant and stable extracellular RNA (exRNA) species exist in bodily fluids. Saliva is an emerging biofluid for biomarker development for noninvasive detection and screening of local and systemic diseases. Use of RNA-Sequencing (RNA-Seq) to profile exRNA is rapidly growing; however, no single preparation and analysis protocol can be used for all biofluids. Specifically, RNA-Seq of saliva is particularly challenging owing to high abundance of bacterial contents and low abundance of salivary exRNA. Given the laborious procedures needed for RNA-Seq library construction, sequencing, data storage, and data analysis, saliva-specific and optimized protocols are essential. METHODS We compared different RNA isolation methods and library construction kits for long and small RNA sequencing. The role of ribosomal RNA (rRNA) depletion also was evaluated. RESULTS The miRNeasy Micro Kit (Qiagen) showed the highest total RNA yield (70.8 ng/mL cell-free saliva) and best small RNA recovery, and the NEBNext library preparation kits resulted in the highest number of detected human genes [5649–6813 at 1 reads per kilobase RNA per million mapped (RPKM)] and small RNAs [482–696 microRNAs (miRNAs) and 190–214 other small RNAs]. The proportion of human RNA-Seq reads was much higher in rRNA-depleted saliva samples (41%) than in samples without rRNA depletion (14%). In addition, the transfer RNA (tRNA)-derived RNA fragments (tRFs), a novel class of small RNAs, were highly abundant in human saliva, specifically tRF-4 (4%) and tRF-5 (15.25%). CONCLUSIONS Our results may help in selection of the best adapted methods of RNA isolation and small and long RNA library constructions for salivary exRNA studies.
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Han, Bo W., Wei Wang, Phillip D. Zamore, and Zhiping Weng. "piPipes: a set of pipelines for piRNA and transposon analysis via small RNA-seq, RNA-seq, degradome- and CAGE-seq, ChIP-seq and genomic DNA sequencing." Bioinformatics 31, no. 4 (October 17, 2014): 593–95. http://dx.doi.org/10.1093/bioinformatics/btu647.
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Hou, Yung-Te, Chia-Chun Wu, Wen-Ting Wang, Wen-Tse Yang, Ying-Hsiu Liao, and Chien-Yu Chen. "Monitoring Cultured Rat Hepatocytes Using RNA-Seq In Vitro." International Journal of Molecular Sciences 24, no. 8 (April 19, 2023): 7534. http://dx.doi.org/10.3390/ijms24087534.
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Compared to other techniques, RNA sequencing (RNA-Seq) has the advantage of having details of the expression abundance of all transcripts in a single run. In this study, we used RNA-Seq to monitor the maturity and dynamic characteristics of in vitro hepatocyte cultures. Hepatocytes, including mature hepatocytes and small hepatocytes, were analyzed in vitro using RNA-Seq and quantitative polymerase chain reaction (qPCR). The results demonstrated that the gene expression profiles measured by RNA-Seq showed a similar trend to the expression profiles measured by qPCR, and can be used to infer the success of in vitro hepatocyte cultures. The results of the differential analysis, which compared mature hepatocytes against small hepatocytes, revealed 836 downregulated and 137 upregulated genes. In addition, the success of the hepatocyte cultures could be explained by the gene list screened from the adopted gene enrichment test. In summary, we demonstrated that RNA-Seq could become an effective method for monitoring the whole transcriptome of hepatocyte cultures and provide a more comprehensive list of factors related to the differentiation of small hepatocytes into mature hepatocytes. This monitoring system not only shows high potential in medical applications but may also be a novel method for the clinical diagnosis of liver-related diseases.
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Raju Paul, Susan, Alexander Bagaev, Ivan Valiev, Vladimir Zyrin, Aleksandr Zaitsev, Daniyar Dyykanov, Katerina Nuzhdina, et al. "Non-small cell lung cancer: Analysis using mass cytometry and next generation sequencing reveals new opportunities for the development of personalized therapies." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e21026-e21026. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e21026.
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e21026 Background: Comprehensive molecular profiling and the use of biomarkers as companion diagnostics have transformed precision medicine for cancer patients. To identify patient-specific tumor microenvironment and biomarker profiles, we assessed the accuracy of our deconvolution algorithm in identifying cellular compositions from whole exome (WES) and whole transcriptome (RNA-seq) sequencing of solid tumors compared with cell populations identified by Mass Cytometry by Time of Flight (CyTOF) in surgically resected tissue from non-small cell lung cancer (NSCLC) patients. Methods: Resected NSCLC tissue was divided for RNA-seq and WES of whole tissue (n = 9) and for generating tissue single cell suspensions through mechanical dissociation and enzymatic digestion (n = 11). Bulk RNA-seq and CyTOF were performed on all cell suspensions. Cellular phenotypes were identified using clustering algorithms in CyTOF and predicted from bulk RNA-seq using our proprietary computational method. Results: Cellular composition reconstructed from RNA-seq correlated with the composition detected by CyTOF (R2= 0.922, n = 7) from cell suspensions. To recover the cell percentage from bulk RNA-seq, a machine learning framework was trained on the cell compendia comprising 7,117 unique cell type RNA-seq profiles. A two-stage hierarchical learning procedure generated a gradient boosting Light GBM model that included training on artificial RNA-seq mixtures of different cell types. With this method, we found that stromal and malignant cells were depleted during single cell suspension preparation, resulting in statistically significant differences in the tumor cell composition reconstructed from solid tissue and single cell suspensions. Immune cell types namely T cells and macrophages were similarly represented in both the bulk tumor tissue and matched single cell suspensions. Transcriptomics revealed a subgroup of patients whose tumors were B-cell-enriched, which was validated in other NSCLC cohorts and was associated with greater CD4+ and CD8+ T cell infiltration and improved clinical outcomes. Conclusions: Since preparation of single cell suspensions leads to the loss of several cellular components, RNA-seq of tumor bulk tissue better describes the molecular and cellular properties of the tumor microenvironment. The combination of RNA-seq and WES of tumor tissue provides a comprehensive profile of cellular composition, suggesting that this combination is ideal for precision medicine applications.
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Richter, Felix. "A broad introduction to RNA-Seq." WikiJournal of Science 4, no. 1 (2021): 4. http://dx.doi.org/10.15347/wjs/2021.004.
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RNA-Seq, named as an abbreviation of "RNA sequencing" and sometimes spelled RNA-seq, RNAseq, or RNASeq, uses next-generation sequencing (NGS) to reveal the presence and quantity of ribonucleic acid (RNA) in a biological sample at a given moment.[1][2] RNA-Seq is used to analyze the continuously changing cellular transcriptome (Figure 1). Specifically, RNA-Seq facilitates the ability to look at alternative gene spliced transcripts, post-transcriptional modifications, gene fusion, mutations/single nucleotide polymorphisms (SNPs) and changes in gene expression over time, or differences in gene expression in different groups or treatments.[3] In addition to messenger RNA (mRNA) transcripts, RNA-Seq can look at different populations of RNA to include total RNA, small RNA, such as microRNA (miRNA), transfer RNA (tRNA), and ribosomal profiling.[4] RNA-Seq can also be used to determine exon/intron boundaries and verify or amend previously annotated 5' and 3' gene boundaries. Recent advances in RNA-Seq include single cell sequencing, in situ sequencing of fixed tissue, and native RNA molecule sequencing with single-molecule real-time sequencing.[5] Prior to RNA-Seq, gene expression studies were done with hybridization-based microarrays. Issues with microarrays include cross-hybridization artifacts, poor quantification of lowly and highly expressed genes, and needing to know the sequence a priori.[6] Because of these technical issues, transcriptomics transitioned to sequencing-based methods. These progressed from Sanger sequencing of Expressed Sequence Tag libraries, to chemical tag-based methods (e.g., serial analysis of gene expression), and finally to the current technology, next-gen sequencing of complementary DNA ( cDNA), notably RNA-Seq.
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Kou, Youwei, Lei Qiao, and Qiang Wang. "RETRACTED ARTICLE: Identification of core miRNA based on small RNA-seq and RNA-seq for colorectal cancer by bioinformatics." Tumor Biology 36, no. 4 (November 21, 2014): 2249–55. http://dx.doi.org/10.1007/s13277-014-2832-x.
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Walsh, P. Sean, Yangyang Hao, Jie Ding, Jianghan Qu, Jonathan Wilde, Ruochen Jiang, Richard T. Kloos, Jing Huang, and Giulia C. Kennedy. "Maximizing Small Biopsy Patient Samples: Unified RNA-Seq Platform Assessment of over 120,000 Patient Biopsies." Journal of Personalized Medicine 13, no. 1 (December 22, 2022): 24. http://dx.doi.org/10.3390/jpm13010024.
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Despite its wide-ranging benefits, whole-transcriptome or RNA exome profiling is challenging to implement in a clinical diagnostic setting. The Unified Assay is a comprehensive workflow wherein exome-enriched RNA-sequencing (RNA-Seq) assays are performed on clinical samples and analyzed by a series of advanced machine learning-based classifiers. Gene expression signatures and rare and/or novel genomic events, including fusions, mitochondrial variants, and loss of heterozygosity were assessed using RNA-Seq data generated from 120,313 clinical samples across three clinical indications (thyroid cancer, lung cancer, and interstitial lung disease). Since its implementation, the data derived from the Unified Assay have allowed significantly more patients to avoid unnecessary diagnostic surgery and have played an important role in guiding follow-up decisions regarding treatment. Collectively, data from the Unified Assay show the utility of RNA-Seq and RNA expression signatures in the clinical laboratory, and their importance to the future of precision medicine.
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Zoephel, Judith, and Lennart Randau. "RNA-Seq analyses reveal CRISPR RNA processing and regulation patterns." Biochemical Society Transactions 41, no. 6 (November 20, 2013): 1459–63. http://dx.doi.org/10.1042/bst20130129.
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In bacteria and archaea, RNA-Seq deep sequencing methodology allows for the detection of abundance and processing sites of the small RNAs that comprise a CRISPR (clustered regularly interspaced short palindromic repeats) RNome. Comparative analyses of these CRISPR RNome sets highlight conserved patterns that include the gradual decline of CRISPR RNA abundance from the leader-proximal to the leader-distal end. In the present review, we discuss exceptions to these patterns that indicate the extensive impact of individual spacer sequences on CRISPR array transcription and RNA maturation. Spacer sequences can contain promoter and terminator elements and can promote the formation of CRISPR RNA–anti-CRISPR RNA duplexes. In addition, potential RNA duplex formation with host tRNA was observed. These factors can influence the functionality of CRISPR–Cas (CRISPR-associated) systems and need to be considered in the design of synthetic CRISPR arrays.
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Lee, Yong Hee, Grace Dy, Paul DePietro, Jeffrey Conroy, Sarabjot Pabla, and Mary Nesline. "65 PD-L1 by RNA next generation sequencing: comparison with PD-L1 IHC 22C3 and association with survival benefit from pembrolizumab with or without chemotherapy in non-small cell lung cancer." Journal for ImmunoTherapy of Cancer 8, Suppl 3 (November 2020): A70. http://dx.doi.org/10.1136/jitc-2020-sitc2020.0065.
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BackgroundPD-L1 immunohistochemistry (IHC) testing is suboptimal for predicting patient clinical benefit for checkpoint inhibition, while PD-L1 liquid biopsy is not clinically validated and lacks sensitivity, underscoring the need to include PD-L1 testing in more robust, tissue-efficient, comprehensive, scalable next generation sequencing (NGS) tests.MethodsTo assess comparability and efficacy of PD-L1 testing by NGS with IHC, we identified NSCLC patients treated by first-line pembrolizumab alone (n=54) or pembrolizumab + chemotherapy (n=49) whose tumors underwent companion diagnostic PD-L1 testing by IHC antibody 22C3 testing (high≥50%; low=1–49%, or negative=0% tissue proportion score), and also by RNA-seq, as part of a comprehensive immune profiling panel. PD-L1 expression by RNA-seq, was measured as a percentile rank, with ≥75 considered ‘high’, and <75 considered ‘not high’, based on comparison to a reference population and normalized to a value of 1–100. All testing was performed in a CLIA certified laboratory prior to treatment initiation (any line) at Roswell Park Comprehensive Cancer Center (June 2017-March 2019, with a minimum of 1 year of follow up). Assay equivalence was assessed by proportion analysis using Fisher exact test comparing IHC versus to RNA-seq, and Bonferroni pairwise post-hoc analysis of IHC (high vs. low, high vs. negative, low vs. negative) with RNA-seq (high vs. not high). A Cox regression model evaluated associations between IHC and RNA-Seq with OS from first dose of pembrolizumab.ResultsMore than 75% of IHC high cases were classified as high by RNA-Seq for both treatment groups (p<0.001). Post-hoc pairwise comparisons showed PD-L1 IHC and RNA-Seq ‘high’ results were significantly associated with each other, and PD-L1 IHC low/negative results were associated with RNA-seq ‘not high’ results. In the pembrolizumab monotherapy group, RNA-seq high was associated with improved survival for pembrolizumab compared to RNA-seq not high status (HR=3.96; CI=1.22–12.87; p=0.02), while PD-L1 IHC high status was not associated with survival benefit in this group (p=0.63). In the pembrolizumab + chemotherapy group, as expected, neither IHC (high versus low), nor RNA-seq (high versus not high) status was associated with survival benefit (p=0.81 and p=0.76, respectively). These findings are consistent with our previous work demonstrating PD-L1 RNA-seq was predictive of CPI response in multiple tumor types.ConclusionsPD-L1 status by RNA-seq and IHC appear to be comparable. Unlike PD-L1 IHC however, PD-L1 RNA-seq high status versus not high status is associated with greater survival benefit, indicating PD-L1 by NGS may have utility for pembrolizumab selection.
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Zayakin, Pawel. "sRNAflow: A Tool for the Analysis of Small RNA-Seq Data." Non-Coding RNA 10, no. 1 (January 17, 2024): 6. http://dx.doi.org/10.3390/ncrna10010006.
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The analysis of small RNA sequencing data across a range of biofluids is a significant research area, given the diversity of RNA types that hold potential diagnostic, prognostic, and predictive value. The intricate task of segregating the complex mixture of small RNAs from both human and other species, including bacteria, fungi, and viruses, poses one of the most formidable challenges in the analysis of small RNA sequencing data, currently lacking satisfactory solutions. This study introduces sRNAflow, a user-friendly bioinformatic tool with a web interface designed for the analysis of small RNAs obtained from biological fluids. Tailored to the unique requirements of such samples, the proposed pipeline addresses various challenges, including filtering potential RNAs from reagents and environment, classifying small RNA types, managing small RNA annotation overlap, conducting differential expression assays, analysing isomiRs, and presenting an approach to identify the sources of small RNAs within samples. sRNAflow also encompasses an alternative alignment-free analysis of RNA-seq data, featuring clustering and initial RNA source identification using BLAST. This comprehensive approach facilitates meaningful comparisons of results between different analytical methods.
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Hackett, Neil R., Marcus W. Butler, Renat Shaykhiev, Jacqueline Salit, Larsson Omberg, Juan L. Rodriguez-Flores, Jason G. Mezey, et al. "RNA-Seq quantification of the human small airway epithelium transcriptome." BMC Genomics 13, no. 1 (2012): 82. http://dx.doi.org/10.1186/1471-2164-13-82.
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Melamed, Sahar, Raya Faigenbaum-Romm, Asaf Peer, Niv Reiss, Omer Shechter, Amir Bar, Yael Altuvia, Liron Argaman, and Hanah Margalit. "Mapping the small RNA interactome in bacteria using RIL-seq." Nature Protocols 13, no. 1 (December 7, 2017): 1–33. http://dx.doi.org/10.1038/nprot.2017.115.
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Kuksa, Pavel P., Alexandre Amlie-Wolf, Živadin Katanić, Otto Valladares, Li-San Wang, and Yuk Yee Leung. "SPAR: small RNA-seq portal for analysis of sequencing experiments." Nucleic Acids Research 46, W1 (May 4, 2018): W36—W42. http://dx.doi.org/10.1093/nar/gky330.
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Nesline, Mary K., Rebecca A. Previs, Grace K. Dy, Lei Deng, Yong Hee Lee, Paul DePietro, Shengle Zhang, et al. "PD-L1 Expression by RNA-Sequencing in Non-Small Cell Lung Cancer: Concordance with Immunohistochemistry and Associations with Pembrolizumab Treatment Outcomes." Cancers 15, no. 19 (September 29, 2023): 4789. http://dx.doi.org/10.3390/cancers15194789.
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Programmed cell death ligand (PD-L1) expression by immunohistochemistry (IHC) lacks sensitivity for pembrolizumab immunotherapy selection in non-small cell lung cancer (NSCLC), particularly for tumors with low expression. We retrospectively evaluated transcriptomic PD-L1 by mRNA next-generation sequencing (RNA-seq). In an unselected NSCLC patient cohort (n = 3168) tested during standard care (2017–2021), PD-L1 IHC and RNA-seq demonstrated moderate concordance, with 80% agreement overall. Most discordant cases were either low or negative for PD-L1 expression by IHC but high by RNA-seq. RNA-seq accurately discriminated PD-L1 IHC high from low tumors by receiver operator curve (ROC) analysis but could not distinguish PD-L1 IHC low from negative tumors. In a separate pembrolizumab monotherapy cohort (n = 102), NSCLC tumors classified as PD-L1 high versus not high by RNA-seq had significantly improved response, progression-free survival, and overall survival as an individual measure and in combination with IHC high or low status. PD-L1 IHC status (high or low) trended toward but had no significant associations with improved outcomes. Conventional PD-L1 IHC testing has inherent limitations, making it an imperfect reference standard for evaluating novel testing technologies. RNA-seq offers an objective PD-L1 measure that could represent a complementary method to IHC to improve NSCLC patient selection for immunotherapy.
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Engel, Krysta L., Hei-Yong G. Lo, Raeann Goering, Ying Li, Robert C. Spitale, and J. Matthew Taliaferro. "Analysis of subcellular transcriptomes by RNA proximity labeling with Halo-seq." Nucleic Acids Research 50, no. 4 (December 7, 2021): e24-e24. http://dx.doi.org/10.1093/nar/gkab1185.
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Abstract Thousands of RNA species display nonuniform distribution within cells. However, quantification of the spatial patterns adopted by individual RNAs remains difficult, in part by a lack of quantitative tools for subcellular transcriptome analysis. In this study, we describe an RNA proximity labeling method that facilitates the quantification of subcellular RNA populations with high spatial specificity. This method, termed Halo-seq, pairs a light-activatable, radical generating small molecule with highly efficient Click chemistry to efficiently label and purify spatially defined RNA samples. We compared Halo-seq with previously reported similar methods and found that Halo-seq displayed a higher efficiency of RNA labeling, indicating that it is well suited to the investigation of small, precisely localized RNA populations. We then used Halo-seq to quantify nuclear, nucleolar and cytoplasmic transcriptomes, characterize their dynamic nature following perturbation, and identify RNA sequence features associated with their composition. Specifically, we found that RNAs containing AU-rich elements are relatively enriched in the nucleus. This enrichment becomes stronger upon treatment with the nuclear export inhibitor leptomycin B, both expanding the role of HuR in RNA export and generating a comprehensive set of transcripts whose export from the nucleus depends on HuR.
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Jiang, Cissy, Elizabeth Jordan Dreskin, Angelica Olcott, Linda Lingelbach, and Srikanth Perike. "Abstract 5632: Advancing transcriptome insights during RNA-Seq library construction of FFPE samples in a novel workflow." Cancer Research 84, no. 6_Supplement (March 22, 2024): 5632. http://dx.doi.org/10.1158/1538-7445.am2024-5632.
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Abstract Formalin-fixed, paraffin-embedded (FFPE) tumor tissue has significant clinical benefits in oncology research and is considered vital for studying tumor gene expression through RNA sequencing (RNA-Seq). However, RNA extracted from FFPE samples tends to be severely degraded, making it suboptimal for constructing high-quality RNA-Seq libraries. The RNA-Seq workflow can further impair data quality, as traditional library preparation and pre-library ribodepletion workflows may result in the loss of valuable information due to rare transcripts being not captured or washed away. Moreover, small, degraded fragments are a challenging template for ligases, and the resulting adapter addition may be inadequate. Here we assessed a novel workflow employing a proprietary enzyme to overcome the limitations and biases of cDNA synthesis using reverse transcriptase (RT) and ligases. This innovative RNA-Seq library preparation approach, coupled with a post-library ribodepletion strategy, aims to capture a more complete transcriptome profile from RNA extracted from FFPE breast cancer samples, and can also be used for evaluation of gene regulation through intronic and intergenic bases. The study also compared two commercially available kits, RecoverAll and PureLink, for FFPE RNA extraction, evaluating quality metrics such as RNA integrity number (RIN), DV200, recovery of small RNAs during NGS library construction, and transcriptome coverage. A parallel fresh frozen RNA sample, extracted using a phenol-based reagent, served as a benchmark. Our findings demonstrate that the novel workflow yields a richer dataset, encompassing a wider range of transcriptomic elements, including both small and long RNAs from FFPE samples. Performing ribodepletion after the library preparation procedure led to increased detection of small RNAs compared to pre-library ribodepletion. The robust analytical performance of this streamlined library preparation protocol enables its suitability for RNA-Seq analysis of archived FFPE specimens, offering substantial utility for oncology research. Citation Format: Cissy Jiang, Elizabeth Jordan Dreskin, Angelica Olcott, Linda Lingelbach, Srikanth Perike. Advancing transcriptome insights during RNA-Seq library construction of FFPE samples in a novel workflow [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5632.
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Choi, Jungwon, Jungheun Hyun, Jieun Hyun, Jae-Hee Kim, Ji Hyun Lee, and Duhee Bang. "Cost and time-efficient construction of a 3′-end mRNA library from unpurified bulk RNA in a single tube." Experimental & Molecular Medicine 56, no. 2 (February 27, 2024): 453–60. http://dx.doi.org/10.1038/s12276-024-01164-8.
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AbstractThe major drawbacks of RNA sequencing (RNA-seq), a remarkably accurate transcriptome profiling method, is its high cost and poor scalability. Here, we report a highly scalable and cost-effective method for transcriptomics profiling called Bulk transcriptOme profiling of cell Lysate in a single poT (BOLT-seq), which is performed using unpurified bulk 3′-end mRNA in crude cell lysates. During BOLT-seq, RNA/DNA hybrids are directly subjected to tagmentation, and second-strand cDNA synthesis and RNA purification are omitted, allowing libraries to be constructed in 2 h of hands-on time. BOLT-seq was successfully used to cluster small molecule drugs based on their mechanisms of action and intended targets. BOLT-seq competes effectively with alternative library construction and transcriptome profiling methods.
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Smirnov, Alexandre, Konrad U. Förstner, Erik Holmqvist, Andreas Otto, Regina Günster, Dörte Becher, Richard Reinhardt, and Jörg Vogel. "Grad-seq guides the discovery of ProQ as a major small RNA-binding protein." Proceedings of the National Academy of Sciences 113, no. 41 (September 26, 2016): 11591–96. http://dx.doi.org/10.1073/pnas.1609981113.
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The functional annotation of transcriptomes and identification of noncoding RNA (ncRNA) classes has been greatly facilitated by the advent of next-generation RNA sequencing which, by reading the nucleotide order of transcripts, theoretically allows the rapid profiling of all transcripts in a cell. However, primary sequence per se is a poor predictor of function, as ncRNAs dramatically vary in length and structure and often lack identifiable motifs. Therefore, to visualize an informative RNA landscape of organisms with potentially new RNA biology that are emerging from microbiome and environmental studies requires the use of more functionally relevant criteria. One such criterion is the association of RNAs with functionally important cognate RNA-binding proteins. Here we analyze the full ensemble of cellular RNAs using gradient profiling by sequencing (Grad-seq) in the bacterial pathogenSalmonella enterica, partitioning its coding and noncoding transcripts based on their network of RNA–protein interactions. In addition to capturing established RNA classes based on their biochemical profiles, the Grad-seq approach enabled the discovery of an overlooked large collective of structured small RNAs that form stable complexes with the conserved protein ProQ. We show that ProQ is an abundant RNA-binding protein with a wide range of ligands and a global influence onSalmonellagene expression. Given its generic ability to chart a functional RNA landscape irrespective of transcript length and sequence diversity, Grad-seq promises to define functional RNA classes and major RNA-binding proteins in both model species and genetically intractable organisms.
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Kou, Youwei, Lei Qiao, and Qiang Wang. "Retraction Note to: Identification of core miRNA based on small RNA-seq and RNA-seq for colorectal cancer by bioinformatics." Tumor Biology 36, no. 9 (August 17, 2015): 7329. http://dx.doi.org/10.1007/s13277-015-3789-0.
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Botella, Leticia, and Thomas Jung. "Multiple Viral Infections Detected in Phytophthora condilina by Total and Small RNA Sequencing." Viruses 13, no. 4 (April 4, 2021): 620. http://dx.doi.org/10.3390/v13040620.
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Marine oomycetes have recently been shown to be concurrently infected by (−)ssRNA viruses of the order Bunyavirales. In this work, even higher virus variability was found in a single isolate of Phytophthora condilina, a recently described member of Phytophthora phylogenetic Clade 6a, which was isolated from brackish estuarine waters in southern Portugal. Using total and small RNA-seq the full RdRp of 13 different potential novel bunya-like viruses and two complete toti-like viruses were detected. All these viruses were successfully confirmed by reverse transcription polymerase chain reaction (RT-PCR) using total RNA as template, but complementarily one of the toti-like and five of the bunya-like viruses were confirmed when dsRNA was purified for RT-PCR. In our study, total RNA-seq was by far more efficient for de novo assembling of the virus sequencing but small RNA-seq showed higher read numbers for most viruses. Two main populations of small RNAs (21 nts and 25 nts-long) were identified, which were in accordance with other Phytophthora species. To the best of our knowledge, this is the first study using small RNA sequencing to identify viruses in Phytophthora spp.
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Ager-Wick, Eirill, Christiaan V. Henkel, Trude M. Haug, and Finn-Arne Weltzien. "Using normalization to resolve RNA-Seq biases caused by amplification from minimal input." Physiological Genomics 46, no. 21 (November 1, 2014): 808–20. http://dx.doi.org/10.1152/physiolgenomics.00196.2013.
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RNA-Seq has become a widely used method to study transcriptomes, and it is now possible to perform RNA-Seq on almost any sample. Nevertheless, samples obtained from small cell populations are particularly challenging, as biases associated with low amounts of input RNA can have strong and detrimental effects on downstream analyses. Here we compare different methods to normalize RNA-Seq data obtained from minimal input material. Using RNA from isolated medaka pituitary cells, we have amplified material from six samples before sequencing. Both synthetic and real data are used to evaluate different normalization methods to obtain a robust and reliable pipeline for analysis of RNA-Seq data from samples with very limited input material. The analysis outlined here shows that quantile normalization outperforms other more commonly used normalization procedures when using amplified RNA as input and will benefit researchers employing low amounts of RNA in similar experiments.
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Wang, Fang, Yu Sun, Jishou Ruan, Rui Chen, Xin Chen, Chengjie Chen, Jan F. Kreuze, ZhangJun Fei, Xiao Zhu, and Shan Gao. "Using Small RNA Deep Sequencing Data to Detect Human Viruses." BioMed Research International 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/2596782.
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Small RNA sequencing (sRNA-seq) can be used to detect viruses in infected hosts without the necessity to have any prior knowledge or specialized sample preparation. The sRNA-seq method was initially used for viral detection and identification in plants and then in invertebrates and fungi. However, it is still controversial to use sRNA-seq in the detection of mammalian or human viruses. In this study, we used 931 sRNA-seq runs of data from the NCBI SRA database to detect and identify viruses in human cells or tissues, particularly from some clinical samples. Six viruses including HPV-18, HBV, HCV, HIV-1, SMRV, and EBV were detected from 36 runs of data. Four viruses were consistent with the annotations from the previous studies. HIV-1 was found in clinical samples without the HIV-positive reports, and SMRV was found in Diffuse Large B-Cell Lymphoma cells for the first time. In conclusion, these results suggest the sRNA-seq can be used to detect viruses in mammals and humans.
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Lambert, Marine, Abderrahim Benmoussa, and Patrick Provost. "Small Non-Coding RNAs Derived From Eukaryotic Ribosomal RNA." Non-Coding RNA 5, no. 1 (February 4, 2019): 16. http://dx.doi.org/10.3390/ncrna5010016.
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The advent of RNA-sequencing (RNA-Seq) technologies has markedly improved our knowledge and expanded the compendium of small non-coding RNAs, most of which derive from the processing of longer RNA precursors. In this review article, we will present a nonexhaustive list of referenced small non-coding RNAs (ncRNAs) derived from eukaryotic ribosomal RNA (rRNA), called rRNA fragments (rRFs). We will focus on the rRFs that are experimentally verified, and discuss their origin, length, structure, biogenesis, association with known regulatory proteins, and potential role(s) as regulator of gene expression. This relatively new class of ncRNAs remained poorly investigated and underappreciated until recently, due mainly to the a priori exclusion of rRNA sequences—because of their overabundance—from RNA-Seq datasets. The situation surrounding rRFs resembles that of microRNAs (miRNAs), which used to be readily discarded from further analyses, for more than five decades, because no one could believe that RNA of such a short length could bear biological significance. As if we had not yet learned our lesson not to restrain our investigative, scientific mind from challenging widely accepted beliefs or dogmas, and from looking for the hidden treasures in the most unexpected places.
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Diendorfer, Andreas, Kseniya Khamina, Marianne Pultar, and Matthias Hackl. "miND (miRNA NGS Discovery pipeline): a small RNA-seq analysis pipeline and report generator for microRNA biomarker discovery studies." F1000Research 11 (February 24, 2022): 233. http://dx.doi.org/10.12688/f1000research.94159.1.
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In contrast to traditional methods like real-time polymerase chain reaction, next-generation sequencing (NGS), and especially small RNA-seq, enables the untargeted investigation of the whole small RNAome, including microRNAs (miRNAs) but also a multitude of other RNA species. With the promising application of small RNAs as biofluid-based biomarkers, small RNA-seq is the method of choice for an initial discovery study. However, the presentation of specific quality aspects of small RNA-seq data varies significantly between laboratories and is lacking a common (minimal) standard. The miRNA NGS Discovery pipeline (miND) aims to bridge the gap between wet lab scientist and bioinformatics with an easy to setup configuration sheet and an automatically generated comprehensive report that contains all essential qualitative and quantitative results that should be reported. Besides the standard steps like preprocessing, mapping, visualization, and quantification of reads, the pipeline also incorporates differential expression analysis when given the appropriate information regarding sample groups. Although miND has a focus on miRNAs, other RNA species like tRNAs, piRNA, snRNA, or snoRNA are included and mapping statistics are available for further analysis. miND has been developed and tested on a multitude of data sets with various RNA sources (tissue, plasma, extracellular vesicles, urine, etc.) and different species. miND is a Snakemake based pipeline and thus incorporates all advantages using a flexible workflow management system. Reference databases are downloaded, prepared and built with an included (but separate) workflow and thus can easily be updated to the most recent version but also stored for reproducibility. In conclusion, the miND pipeline aims to streamline the bioinformatics processing of small RNA-seq data by standardizing the processing from raw data to a final, comprehensive and reproducible report.
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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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Hilmi, Marc, Lucile Armenoult, Mira Ayadi, and Rémy Nicolle. "Whole-Transcriptome Profiling on Small FFPE Samples: Which Sequencing Kit Should Be Used?" Current Issues in Molecular Biology 44, no. 5 (May 13, 2022): 2186–93. http://dx.doi.org/10.3390/cimb44050148.
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RNA sequencing (RNA-Seq) appears as a great tool with huge clinical potential, particularly in oncology. However, sufficient sample size is often a limiting factor and the vast majority of samples from patients with cancer are formalin-fixed paraffin-embedded (FFPE). To date, several sequencing kits are proposed for FFPE samples yet no comparison on low quantities were performed. To select the most reliable, cost-effective, and relevant RNA-Seq approach, we applied five FFPE-compatible kits (based on 3′ capture, exome-capture and ribodepletion approaches) using 8 ng to 400 ng of FFPE-derived RNA and compared them to Nanostring on FFPE samples and to a reference PolyA (Truseq) approach on flash-frozen samples of the same tumors. We compared gene expression correlations and reproducibility. The Smarter Pico V3 ribodepletion approach appeared systematically the most comparable to Nanostring and Truseq (p < 0.001) and was a highly reproducible technique. In comparison with exome-capture and 3′ kits, the Smarter appeared more comparable to Truseq (p < 0.001). Overall, our results suggest that the Smarter is the most robust RNA-Seq technique to study small FFPE samples and 3′ Lexogen presents an interesting quality–price ratio for samples with less limiting quantities.
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Hardigan, Andrew A., Brian S. Roberts, Dianna E. Moore, Ryne C. Ramaker, Angela L. Jones, and Richard M. Myers. "CRISPR/Cas9-targeted removal of unwanted sequences from small-RNA sequencing libraries." Nucleic Acids Research 47, no. 14 (June 5, 2019): e84-e84. http://dx.doi.org/10.1093/nar/gkz425.
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Abstract In small RNA (smRNA) sequencing studies, highly abundant molecules such as adapter dimer products and tissue-specific microRNAs (miRNAs) inhibit accurate quantification of lowly expressed species. We previously developed a method to selectively deplete highly abundant miRNAs. However, this method does not deplete adapter dimer ligation products that, unless removed by gel-separation, comprise most of the library. Here, we have adapted and modified recently described methods for CRISPR/Cas9–based Depletion of Abundant Species by Hybridization (‘DASH’) to smRNA-seq, which we have termed miRNA and Adapter Dimer—DASH (MAD-DASH). In MAD-DASH, Cas9 is complexed with single guide RNAs (sgRNAs) targeting adapter dimer ligation products, alongside highly expressed tissue-specific smRNAs, for cleavage in vitro. This process dramatically reduces adapter dimer and targeted smRNA sequences, can be multiplexed, shows minimal off-target effects, improves the quantification of lowly expressed miRNAs from human plasma and tissue derived RNA, and obviates the need for gel-separation, greatly increasing sample throughput. Additionally, the method is fully customizable to other smRNA-seq preparation methods. Like depletion of ribosomal RNA for mRNA-seq and mitochondrial DNA for ATAC-seq, our method allows for greater proportional read-depth of non-targeted sequences.
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Ferre, Adriana, Lucía Santiago, José Francisco Sánchez-Herrero, Olga López-Rodrigo, Josvany Sánchez-Curbelo, Lauro Sumoy, Lluís Bassas, and Sara Larriba. "3′IsomiR Species Composition Affects Reliable Quantification of miRNA/isomiR Variants by Poly(A) RT-qPCR: Impact on Small RNA-Seq Profiling Validation." International Journal of Molecular Sciences 24, no. 20 (October 21, 2023): 15436. http://dx.doi.org/10.3390/ijms242015436.
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Small RNA-sequencing (small RNA-seq) has revealed the presence of small RNA-naturally occurring variants such as microRNA (miRNA) isoforms or isomiRs. Due to their small size and the sequence similarity among miRNA isoforms, their validation by RT-qPCR is challenging. We previously identified two miR-31-5p isomiRs—the canonical and a 3′isomiR variant (3′ G addition)—which were differentially expressed between individuals with azoospermia of different origin. Here, we sought to determine the discriminatory capacity between these two closely-related miRNA isoforms of three alternative poly(A) based-RT-qPCR strategies in both synthetic and real biological context. We found that these poly(A) RT-qPCR strategies exhibit a significant cross-reactivity between these miR-31-5p isomiRs which differ by a single nucleotide, compromising the reliable quantification of individual miRNA isoforms. Fortunately, in the biological context, given that the two miRNA variants show changes in the same direction, RT-qPCR results were consistent with the findings of small RNA-seq study. We suggest that miRNA selection for RT-qPCR validation should be performed with care, prioritizing those canonical miRNAs that, in small RNA-seq, show parallel/homogeneous expression behavior with their most prevalent isomiRs, to avoid confounding RT-qPCR-based results. This is suggested as the current best strategy for robust biomarker selection to develop clinically useful tests.
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Wang, Jing, Hua-Chang Chen, Quanhu Sheng, T. Renee Dawson, Robert J. Coffey, James G. Patton, Alissa M. Weaver, Yu Shyr, and Qi Liu. "Systematic Assessment of Small RNA Profiling in Human Extracellular Vesicles." Cancers 15, no. 13 (June 30, 2023): 3446. http://dx.doi.org/10.3390/cancers15133446.
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Motivation: Extracellular vesicles (EVs) are produced and released by most cells and are now recognized to play a role in intercellular communication through the delivery of molecular cargo, including proteins, lipids, and RNA. Small RNA sequencing (small RNA-seq) has been widely used to characterize the small RNA content in EVs. However, there is a lack of a systematic assessment of the quality, technical biases, RNA composition, and RNA biotypes enrichment for small RNA profiling of EVs across cell types, biofluids, and conditions. Methods: We collected and reanalyzed small RNA-seq datasets for 2756 samples from 83 studies involving 55 with EVs only and 28 with both EVs and matched donor cells. We assessed their quality by the total number of reads after adapter trimming, the overall alignment rate to the host and non-host genomes, and the proportional abundance of total small RNA and specific biotypes, such as miRNA, tRNA, rRNA, and Y RNA. Results: We found that EV extraction methods varied in their reproducibility in isolating small RNAs, with effects on small RNA composition. Comparing proportional abundances of RNA biotypes between EVs and matched donor cells, we discovered that rRNA and tRNA fragments were relatively enriched, but miRNAs and snoRNA were depleted in EVs. Except for the export of eight miRNAs being context-independent, the selective release of most miRNAs into EVs was study-specific. Conclusion: This work guides quality control and the selection of EV isolation methods and enhances the interpretation of small RNA contents and preferential loading in EVs.
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Tang, Chong, Yeming Xie, Mei Guo, and Wei Yan. "AASRA: an anchor alignment-based small RNA annotation pipeline†." Biology of Reproduction 105, no. 1 (March 31, 2021): 267–77. http://dx.doi.org/10.1093/biolre/ioab062.
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Abstract Small noncoding RNAs deep sequencing (sncRNA-Seq) has become a routine for sncRNA detection and quantification. However, the software packages currently available for sncRNA annotation can neither recognize sncRNA variants in the sequencing reads, nor annotate all known sncRNA simultaneously. Here, we report a novel anchor alignment-based small RNA annotation (AASRA) software package (https://github.com/biogramming/AASRA). AASRA represents an all-in-one sncRNA annotation pipeline, which allows for high-speed, simultaneous annotation of all known sncRNA species with the capability to distinguish mature from precursor microRNAs, and to identify novel sncRNA variants in the sncRNA-Seq sequencing reads.
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Yu, Hao, Li Yang, Ka-On Lam, Jian-Yue Jin, Chen Hu, and Feng-Ming Spring Kong. "Deep learning survival model on transcriptomes level in patients with non-small cell lung cancer." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): e20518-e20518. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e20518.
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e20518 Background: Non-small cell lung cancer (NSCLC) is associated with poor prognosis. Global gene expression profiling with overall survival (OS) may help improving individualize survival. In this study, we identify biological important gene clusters and studied their prognostic abilities for OS by deep learning method. Methods: Using GEO genomics data repository, we identified 196 NSCLC patients (trainset: GSE37745) and 181 NSCLC patients (testset: GSE50081) with clinical information and long-term follow-up. In both cohorts, expression profiling was performed on RNA from tumor tissues using Affymetrix microarrays HG-U133-Plus2; and normalized using the Robust Multiarray Averaging (RMA). We established deep learning survival models through neural network extension of the Cox regression model for predicting OS, which were developed by 5-folds cross-validation in GSE37745 and independently validated in GSE50081. Significant RNA-seq and clinical variables were multiple inputs. Concordance index (CI) was evaluated and compared with multivariable Cox regression. Then we conducted Uniform Manifold Approximation and Projection (UMAP) using weights in hidden layer of the model for clustering the important RNA-seq and then performed enrichment analysis though GO/KEGG for revealing biological progresses. Results: Total 1039 RNA-seq levels were found significant with OS ( P < 0.05) by Cox proportional hazard model adjusted by clinical variables (age, gender, cancer stage, histology) in trainset. The deep learning survival model with 20 most significant RNA-seq and clinical variables had best average performances as CI = 0.74±0.04 in trainset (GSE37745) and CI = 0.68±0.06 in testset (GSE50081) in 10 iterations, better than multivariable Cox regression ( P < 0.05). The deep learning survival model with all significant RNA-seq were also established and the weights in the hidden layer were clustered by UMAP into 5 positive and 5 negative clusters. The clusters were enriched, such as in positive clusters, negative regulation of RNA metabolic process, negative regulation of RNA biosynthetic process and positive regulation of protein modification process were top three significant biological processes for shorten survival; while in negative clusters, DNA metabolic process, positive regulation of phosphate metabolic process and positive regulation of RNA metabolic process were the top three for prolonged survival. Conclusions: In this study, the deep learning survival algorithm was established for survival prediction based on a transcriptome level in patients with NSCLC. Given the models’ robustness and better performances, our study would be useful at predicting and applying more biological information for survival.
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Michuda, Jackson, Ben Ho Park, Amy Lauren Cummings, Siddhartha Devarakonda, Bert O'Neil, Sumaiya Islam, Jerod Parsons, et al. "Use of clinical RNA-sequencing in the detection of actionable fusions compared to DNA-sequencing alone." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): 3077. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.3077.
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3077 Background: While targeted DNA-seq can detect clinically actionable fusions in tumor tissue samples, technical and analytical challenges may give rise to false negatives. RNA-based, whole-exome sequencing provides a complementary method for fusion detection, and may improve the identification of actionable variants. In this study, we quantify this benefit using a large, real-world clinical dataset to assess actionable fusions detected from RNA in conjunction with DNA profiling. Methods: Using the Tempus Research Database, we retrospectively analyzed a de-identified dataset of ̃80K samples (77.4K patients) profiled with the Tempus xT assay (both DNA-seq with fusion detection in 21 genes and whole exome capture RNA-seq). Only patients that had successful RNA- and DNA-seq were included. Fusions were detected using the Tempus bioinformatic and clinical workflow. Candidate fusions were filtered based on read support thresholds, fusion annotation ( i.e., breakpoints, reading frame, conserved domains), and manual review. OncoKB was used to select fusion alterations in levels 1 and 2 and to identify those indication-matched to targeted therapies. Results: We identified 2118 level 1 and 2 fusion events across 1945 patients across 20 different cancer types. Most fusions were observed in non-small cell lung cancer (NSCLC) (25%) and biliary cancer (9%) samples. Of the 2118 fusion events, 29.1% (616) were detected only through RNA-seq while 4.8% (101) of the events were identifiable only through DNA-seq. Notably, 69.4% of fusions in low-grade glioma and 58.2% in sarcomas were detected only by RNA-seq. When evaluating specific gene fusion events, RNA-seq consistently improved the detection of fusions compared to DNA-seq alone (Table) across all cancer types. A total of 1106 fusions were classified as targetable by OncoKB indication-matched therapies with 19% (214) of these identifiable through RNA-seq alone, 5% (54) by DNA-seq alone, and 76% (838) identifiable through RNA- and DNA-seq. Overall, fusions identified through RNA-seq alone led to a 24% increase in the number of patients who were eligible to receive matched therapies (214 / 892). This included imatinib for patients with CML/BLCL (69.8%), crizotinib for NSCLC (40.3%) and entrectinib for NTRK and ROS1 fusions (32.5%). Conclusions: The addition of RNA-seq to DNA-seq significantly increased the detection of fusion events and ability to match patients to targeted therapies. Results support consideration of combined RNA-DNA-seq for standard-of-care fusion calling. [Table: see text]
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Michuda, Jackson, Ben Ho Park, Amy Lauren Cummings, Siddhartha Devarakonda, Bert O'Neil, Sumaiya Islam, Jerod Parsons, et al. "Use of clinical RNA-sequencing in the detection of actionable fusions compared to DNA-sequencing alone." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): 3077. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.3077.
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3077 Background: While targeted DNA-seq can detect clinically actionable fusions in tumor tissue samples, technical and analytical challenges may give rise to false negatives. RNA-based, whole-exome sequencing provides a complementary method for fusion detection, and may improve the identification of actionable variants. In this study, we quantify this benefit using a large, real-world clinical dataset to assess actionable fusions detected from RNA in conjunction with DNA profiling. Methods: Using the Tempus Research Database, we retrospectively analyzed a de-identified dataset of ̃80K samples (77.4K patients) profiled with the Tempus xT assay (both DNA-seq with fusion detection in 21 genes and whole exome capture RNA-seq). Only patients that had successful RNA- and DNA-seq were included. Fusions were detected using the Tempus bioinformatic and clinical workflow. Candidate fusions were filtered based on read support thresholds, fusion annotation ( i.e., breakpoints, reading frame, conserved domains), and manual review. OncoKB was used to select fusion alterations in levels 1 and 2 and to identify those indication-matched to targeted therapies. Results: We identified 2118 level 1 and 2 fusion events across 1945 patients across 20 different cancer types. Most fusions were observed in non-small cell lung cancer (NSCLC) (25%) and biliary cancer (9%) samples. Of the 2118 fusion events, 29.1% (616) were detected only through RNA-seq while 4.8% (101) of the events were identifiable only through DNA-seq. Notably, 69.4% of fusions in low-grade glioma and 58.2% in sarcomas were detected only by RNA-seq. When evaluating specific gene fusion events, RNA-seq consistently improved the detection of fusions compared to DNA-seq alone (Table) across all cancer types. A total of 1106 fusions were classified as targetable by OncoKB indication-matched therapies with 19% (214) of these identifiable through RNA-seq alone, 5% (54) by DNA-seq alone, and 76% (838) identifiable through RNA- and DNA-seq. Overall, fusions identified through RNA-seq alone led to a 24% increase in the number of patients who were eligible to receive matched therapies (214 / 892). This included imatinib for patients with CML/BLCL (69.8%), crizotinib for NSCLC (40.3%) and entrectinib for NTRK and ROS1 fusions (32.5%). Conclusions: The addition of RNA-seq to DNA-seq significantly increased the detection of fusion events and ability to match patients to targeted therapies. Results support consideration of combined RNA-DNA-seq for standard-of-care fusion calling. [Table: see text]
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Hu, Jennifer F., Daniel Yim, Duanduan Ma, Sabrina M. Huber, Nick Davis, Jo Marie Bacusmo, Sidney Vermeulen, et al. "Quantitative mapping of the cellular small RNA landscape with AQRNA-seq." Nature Biotechnology 39, no. 8 (April 15, 2021): 978–88. http://dx.doi.org/10.1038/s41587-021-00874-y.
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Cheng, Wei-Chung, I.-Fang Chung, Tse-Shun Huang, Shih-Ting Chang, Hsing-Jen Sun, Cheng-Fong Tsai, Muh-Lii Liang, Tai-Tong Wong, and Hsei-Wei Wang. "YM500: a small RNA sequencing (smRNA-seq) database for microRNA research." Nucleic Acids Research 41, no. D1 (November 29, 2012): D285—D294. http://dx.doi.org/10.1093/nar/gks1238.
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Bai, Baoyan, Srinivasan Yegnasubramanian, Sarah J. Wheelan, and Marikki Laiho. "RNA-Seq of the Nucleolus Reveals Abundant SNORD44-Derived Small RNAs." PLoS ONE 9, no. 9 (September 9, 2014): e107519. http://dx.doi.org/10.1371/journal.pone.0107519.
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Huiskes, Fabries G., Esther E. Creemers, and Bianca J. J. M. Brundel. "Dissecting the Molecular Mechanisms Driving Electropathology in Atrial Fibrillation: Deployment of RNA Sequencing and Transcriptomic Analyses." Cells 12, no. 18 (September 9, 2023): 2242. http://dx.doi.org/10.3390/cells12182242.
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Despite many efforts to treat atrial fibrillation (AF), the most common progressive and age-related cardiac tachyarrhythmia in the Western world, the efficacy is still suboptimal. A plausible reason for this is that current treatments are not directed at underlying molecular root causes that drive electrical conduction disorders and AF (i.e., electropathology). Insights into AF-induced transcriptomic alterations may aid in a deeper understanding of electropathology. Specifically, RNA sequencing (RNA-seq) facilitates transcriptomic analyses and discovery of differences in gene expression profiles between patient groups. In the last decade, various RNA-seq studies have been conducted in atrial tissue samples of patients with AF versus controls in sinus rhythm. Identified differentially expressed molecular pathways so far include pathways related to mechanotransduction, ECM remodeling, ion channel signaling, and structural tissue organization through developmental and inflammatory signaling pathways. In this review, we provide an overview of the available human AF RNA-seq studies and highlight the molecular pathways identified. Additionally, a comparison is made between human RNA-seq findings with findings from experimental AF model systems and we discuss contrasting findings. Finally, we elaborate on new exciting RNA-seq approaches, including single-nucleotide variants, spatial transcriptomics and profiling of different populations of total RNA, small RNA and long non-coding RNA.
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Lama, Lodoe, Jose Cobo, Diego Buenaventura, and Kevin Ryan. "Small RNA-seq: The RNA 5’-end adapter ligation problem and how to circumvent it." Journal of Biological Methods 6, no. 1 (February 20, 2019): 108. http://dx.doi.org/10.14440/jbm.2019.269.
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Shahjaman, Md, Habiba Akter, Md Mamunur Rashid, Md Ibnul Asifuzzaman, Md Bipul Hossen, and Md Rezanur Rahman. "Robust and efficient identification of biomarkers from RNA-Seq data using median control chart." F1000Research 8 (January 3, 2019): 7. http://dx.doi.org/10.12688/f1000research.17351.1.
Full textAbstract:
Background: One of the main goals of RNA-seq data analysis is identification of biomarkers that are differentially expressed (DE) across two or more experimental conditions. RNA-seq uses next generation sequencing technology and it has many advantages over microarrays. Numerous statistical methods have already been developed for identification the biomarkers from RNA-seq data. Most of these methods were based on either Poisson distribution or negative binomial distribution. However, efficient biomarker identification from discrete RNA-seq data is hampered by existing methods when the datasets contain outliers or extreme observations. Specially, the performance of these methods becomes more severe when the data come from a small number of samples in the presence of outliers. Therefore, in this study, an attempt is made to propose an outlier detection and modification approach for RNA-seq data to overcome the aforesaid problems of traditional methods. We make our proposed method facilitate in RNA-seq data by transforming the read count data into continuous data. Methods: We use median control chart to detect and modify the outlying observation in a log-transformed RNA-seq dataset. To investigate the performance of the proposed method in absence and presence of outliers, we employ the five popular biomarker selection methods (edgeR, edgeR_robust, DEseq, DEseq2 and limma) both in simulated and real datasets. Results: The simulation results strongly suggest that the performance of the proposed method improved in the presence of outliers. The proposed method also detected an additional 18 outlying DE genes from a real mouse RNA-seq dataset that were not detected by traditional methods. Using the KEGG pathway and gene ontology analysis results we reveal that these genes may be biomarkers, which require validation in a wet lab. Conclusions: Our proposal is to apply the proposed method for biomarker identification from other RNA-seq data.
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Sun, Qingchao, Ruixue Liu, Haiping Zhang, Liang Zong, Xiaoliang Jing, Long Ma, Jie Li, and Liwei Zhang. "Fascin actin-bundling protein 1 regulates non-small cell lung cancer progression by influencing the transcription and splicing of tumorigenesis-related genes." PeerJ 11 (December 5, 2023): e16526. http://dx.doi.org/10.7717/peerj.16526.
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Background High mortality rates are prevalent among patients with non-small-cell lung cancer (NSCLC), and effective therapeutic targets are key prognostic factors. Fascin actin-bundling protein 1 (FSCN1) promotes NSCLC; however, its role as an RNA-binding protein in NSCLC remains unexplored. Therefore, we aimed to explore FSCN1 expression and function in A549 cells. Method We screened for alternative-splicing events and differentially expressed genes (DEGs) after FSCN1 silence via RNA-sequencing (RNA-seq). FSCN1 immunoprecipitation followed by RNA-seq were used to identify target genes whose mRNA expression and pre-mRNA alternative-splicing levels might be influenced by FSCN1. Results Silencing FSCN1 in A549 cells affected malignant phenotypes; it inhibited proliferation, migration, and invasion, and promoted apoptosis. RNA-seq analysis revealed 2,851 DEGs and 3,057 alternatively spliced genes. Gene ontology-based functional enrichment analysis showed that downregulated DEGs and alternatively splicing genes were enriched for the cell-cycle. FSCN1 promoted the alternative splicing of cell-cycle-related mRNAs involved in tumorigenesis (i.e., BCCIP, DLGAP5, PRC1, RECQL5, WTAP, and SGO1). Combined analysis of FSCN1 RNA-binding targets and RNA-seq data suggested that FSCN1 might affect ACTG1, KRT7, and PDE3A expression by modulating the pre-mRNA alternative-splicing levels of NME4, NCOR2, and EEF1D, that were bound to long non-coding RNA transcripts (RNASNHG20, NEAT1, NSD2, and FTH1), which were highly abundant. Overall, extensive transcriptome analysis of gene alternative splicing and expression levels was performed in cells transfected with FSCN1 short-interfering RNA. Our data provide global insights into the regulatory mechanisms associated with the roles of FSCN1 and its target genes in lung cancer.
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Li, Dongmei, Martin S. Zand, Timothy D. Dye, Maciej L. Goniewicz, Irfan Rahman, and Zidian Xie. "An evaluation of RNA-seq differential analysis methods." PLOS ONE 17, no. 9 (September 16, 2022): e0264246. http://dx.doi.org/10.1371/journal.pone.0264246.
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RNA-seq is a high-throughput sequencing technology widely used for gene transcript discovery and quantification under different biological or biomedical conditions. A fundamental research question in most RNA-seq experiments is the identification of differentially expressed genes among experimental conditions or sample groups. Numerous statistical methods for RNA-seq differential analysis have been proposed since the emergence of the RNA-seq assay. To evaluate popular differential analysis methods used in the open source R and Bioconductor packages, we conducted multiple simulation studies to compare the performance of eight RNA-seq differential analysis methods used in RNA-seq data analysis (edgeR, DESeq, DESeq2, baySeq, EBSeq, NOISeq, SAMSeq, Voom). The comparisons were across different scenarios with either equal or unequal library sizes, different distribution assumptions and sample sizes. We measured performance using false discovery rate (FDR) control, power, and stability. No significant differences were observed for FDR control, power, or stability across methods, whether with equal or unequal library sizes. For RNA-seq count data with negative binomial distribution, when sample size is 3 in each group, EBSeq performed better than the other methods as indicated by FDR control, power, and stability. When sample sizes increase to 6 or 12 in each group, DESeq2 performed slightly better than other methods. All methods have improved performance when sample size increases to 12 in each group except DESeq. For RNA-seq count data with log-normal distribution, both DESeq and DESeq2 methods performed better than other methods in terms of FDR control, power, and stability across all sample sizes. Real RNA-seq experimental data were also used to compare the total number of discoveries and stability of discoveries for each method. For RNA-seq data analysis, the EBSeq method is recommended for studies with sample size as small as 3 in each group, and the DESeq2 method is recommended for sample size of 6 or higher in each group when the data follow the negative binomial distribution. Both DESeq and DESeq2 methods are recommended when the data follow the log-normal distribution.
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Audemard, Eric Olivier, Patrick Gendron, Albert Feghaly, Vincent-Philippe Lavallée, Josée Hébert, Guy Sauvageau, and Sébastien Lemieux. "Targeted variant detection using unaligned RNA-Seq reads." Life Science Alliance 2, no. 4 (August 2019): e201900336. http://dx.doi.org/10.26508/lsa.201900336.
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Mutations identified in acute myeloid leukemia patients are useful for prognosis and for selecting targeted therapies. Detection of such mutations using next-generation sequencing data requires a computationally intensive read mapping step followed by several variant calling methods. Targeted mutation identification drastically shifts the usual tradeoff between accuracy and performance by concentrating all computations over a small portion of sequence space. Here, we present km, an efficient approach leveraging k-mer decomposition of reads to identify targeted mutations. Our approach is versatile, as it can detect single-base mutations, several types of insertions and deletions, as well as fusions. We used two independent cohorts (The Cancer Genome Atlas and Leucegene) to show that mutation detection by km is fast, accurate, and mainly limited by sequencing depth. Therefore, km allows the establishment of fast diagnostics from next-generation sequencing data and could be suitable for clinical applications.