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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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Mittempergher, Lorenza, Iris de Rink, Marja Nieuwland, Ron M. Kerkhoven, Annuska Glas, Rene' Bernards, and Laura van't Veer. "High concordance for MammaPrint 70 genes by RNA next generation sequencing." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): 3065. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.3065.
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3065 Background: The development of new biomarkers often requires fresh frozen (FF) samples. Recently we showed that microarray gene expression data generated from FFPE material are comparable to data extracted from the FF counterpart, including known signatures such as the 70-gene prognosis signature (Mittempergher L et al., 2011). As described by Luo et al (2010) RNA profiling using next generation sequencing (RNA-Seq) is now applicable to archival FFPE specimens. Methods: Technical performance and the comparison between the RNA-Seq 70-gene read-out and the MammaPrint test (Glas et al., 2006) is evaluated in a series of 15 patients (11/15 with matched FFPE/FF material). RNA-Seq was carried out using minor adjustments of the Illumina TruSeq RNA preparation method. RNA sequencing libraries were prepared starting from 100ng of total RNA. Next, the DSN (Duplex-Specific Nuclease) normalization process was used to remove ribosomal RNA and other abundant transcripts (Luo et al, 2010). The libraries were paired-end sequenced on the Illumina HiSeq 2000 instrument with multiplexing of 4 libraries per lane. The resulting sequences were mapped to the human reference genome (build 37) using TopHat 1.3.1(Trapnell et al., 2009). The HTSeq-count tool was used to generate the total number of uniquely mapped reads for each gene. Results: Between 14% and 45% of the total number of reads were assigned to protein-coding genes. The minimum coverage per 1000bp of CDS was 38 reads. The 70 MammaPrint genes were successfully mapped to the RNA-Seq transcripts. We calculated the Pearson correlation coefficient between the centroids of the original good prognosis template (van’t Veer et al., 2002) and the 70-gene read count determined by RNA-Seq of each sample. Predictions based on the 70-gene RNA-Seq data showed a high agreement with the actual MammaPrint test predictions (>90%), irrespective of whether the RNA-seq was performed on FF or FFPE tissue. Conclusions: New generation RNA-sequencing is a feasible technology to assess diagnostic signatures.
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Pommerenke, Claudia, Hans G. Drexler, Sabine A. Denkmann, and Cord C. Uphoff. "Detection of Viruses in Human Cell Lines Applying Next Generation Sequencing." Blood 128, no. 22 (December 2, 2016): 5093. http://dx.doi.org/10.1182/blood.v128.22.5093.5093.
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Abstract It is widely accepted that diverse viral infections can at least contribute to the development of tumor cells in various ways. The viruses can directly alter the eukaryotic genome by DNA integration, alter the gene regulation, or may cause a chronic inflammation. Viral infections can either be lytic causing the production of new viruses or latent with no viral replication. However, in both cases various viral genes are constantly transcribed in the eukaryotic cells. Next generation sequencing (NGS) offers the possibility to capture the whole transcriptome of the cells via RNA-seq including host and viral mRNA. Over the years, we have detected human pathogenic viruses as well as other viruses potentially infecting human cell lines from our cell lines collection applying PCR or RT-PCR. The viruses comprise Epstein-Barr-virus (EBV; human herpesvirus type 4), hepatitis B virus (HBV), hepatitis C virus (HCV), human herpesvirus type 8 (HHV-8), human immunodeficiency virus type 1 and 2 (HIV-1/-2), human papilloma virus (HPV), human T-lymphotropic virus type 1 and 2 (HTLV-1/-2), squirrel monkey retrovirus (SMRV), xenotropic murine leukemia virus (XMLV; including xenotropic murine leukemia virus related virus, XMRV). These data were now compared with the results obtained from the evaluation of RNA-seq and whole exome sequencing (WES) data of the Cancer Cell Lines Encyclopedia (CCLE). We screened 133 RNA seq and 62 WES datasets of the CCLE sequence database for the presence of the previously mentioned viral sequences. NCBI reference complete genome sequences of the respective viruses and the human hg38 genome were used to for the alignment. In these two datasets 118 and 58 cell lines were leukemia/lymphoma cell lines, respectively, comprising the different hematopoietic lineages. Eleven B-cell derived cell lines were concordantly EBV positive in PCR analysis and in RNA-seq. The DOHH-2 cell line exhibited a relatively low number of alignments. This is concordant with our finding applying fluorescent in situ hybridization that this cell line consists of two clones: one infected with EBV and one EBV-free clone. Both clones could be separated by single cell cloning. Comparing RNA-seq and WES, RNA-seq revealed more virus specific reads relative to the total reads (max. 0.5425% vs. max. 0.0026%). Thus, RNA-seq appears to be more sensitive than WES. HHV-8 was concordantly clearly detected by PCR and RNA-seq as well as SMRV in the NAMALWA cell line. To further evaluate the robustness of the virus detection method, we included some viruses not specific for hematopoietic cells, but shown to be positive in distinct cell lines applying PCR: HBV and XMLV. We found complete concordance between PCR and RNA-seq in two liver cell lines (HEP-3B and SNU-886) and - except for the melanoma cell line SK-MEL-1 - XMLV was also detected in PCR positive cell lines by RNA-seq (three hematopoietic cell lines and six non-hematopoietic cell lines). Concerning the SK-MEL-1 cell line, different subcultures of the cell line might have been tested with the two methods, one subculture after heterotransplantation into rodents with PCR assay and the RNA-seq negative one originating from a subclone without previous heterotransplantation. Taken together, only one out of 21 virus positive cell lines were discordant applying RNA-seq. On the other hand, all PCR-negative cell lines were also negative by RNA-seq. Significant background alignments in the range of 0 to 850 reads could be detected only with the retroviruses XMLV, SMRV and HTLV-1 regarding RNA-seq, whereas the positive samples were all above 1x105. The background alignments might be attributed to some homologue sequences to endogenous retroviral elements in the human genome. In summary, RNA-seq can be used as reliable and single-step method to analyze simultaneously a panel of potential virus infections in cell cultures and thereby delivering additional viral information beside host gene expression. Future studies might demonstrate whether non-human mappable reads in RNA-seq data could be used to detect new viruses infecting human cells and being potentially implicated in tumor formation. Disclosures No relevant conflicts of interest to declare.
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Li, Xin, and Shaolei Teng. "RNA Sequencing in Schizophrenia." Bioinformatics and Biology Insights 9s1 (January 2015): BBI.S28992. http://dx.doi.org/10.4137/bbi.s28992.
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Schizophrenia (SCZ) is a serious psychiatric disorder that affects 1% of general population and places a heavy burden worldwide. The underlying genetic mechanism of SCZ remains unknown, but studies indicate that the disease is associated with a global gene expression disturbance across many genes. Next-generation sequencing, particularly of RNA sequencing (RNA-Seq), provides a powerful genome-scale technology to investigate the pathological processes of SCZ. RNA-Seq has been used to analyze the gene expressions and identify the novel splice isoforms and rare transcripts associated with SCZ. This paper provides an overview on the genetics of SCZ, the advantages of RNA-Seq for transcriptome analysis, the accomplishments of RNA-Seq in SCZ cohorts, and the applications of induced pluripotent stem cells and RNA-Seq in SCZ research.
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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.
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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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Ferdous, Tahsin, and Mohammad Ohid Ullah. "An Overview of RNA-seq Data Analysis." Journal of Biology and Life Science 8, no. 2 (August 2, 2017): 57. http://dx.doi.org/10.5296/jbls.v8i2.11255.
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Latest breakthrough in high-throughput DNA sequencing have been launched different arenas for transcriptome analyses, jointly named RNA-seq (RNA-sequencing). It exposes the existence and amount of RNA in a biotic sample at a specific time by utilizing next generation sequencing (NGS). In this review, we aimed to explore the several methods which are applied in analyzing RNA-seq data. We also discussed its importance over microarray data. As establishment of several methods have already taken place to analyze RNA-seq data, therefore, further analysis is very essential to select the best one to avoid false positive outcomes.
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Pisapia, David J., Steven Salvatore, Chantal Pauli, Erika Hissong, Ken Eng, Davide Prandi, Verena-Wilbeth Sailer, et al. "Next-Generation Rapid Autopsies Enable Tumor Evolution Tracking and Generation of Preclinical Models." JCO Precision Oncology, no. 1 (November 2017): 1–13. http://dx.doi.org/10.1200/po.16.00038.
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Purpose Patients with cancer who graciously consent for autopsy represent an invaluable resource for the study of cancer biology. To advance the study of tumor evolution, metastases, and resistance to treatment, we developed a next-generation rapid autopsy program integrated within a broader precision medicine clinical trial that interrogates pre- and postmortem tissue samples for patients of all ages and cancer types. Materials and Methods One hundred twenty-three (22%) of 554 patients who consented to the clinical trial also consented for rapid autopsy. This report comprises the first 15 autopsies, including patients with metastatic carcinoma (n = 10), melanoma (n = 1), and glioma (n = 4). Whole-exome sequencing (WES) was performed on frozen autopsy tumor samples from multiple anatomic sites and on non-neoplastic tissue. RNA sequencing (RNA-Seq) was performed on a subset of frozen samples. Tissue was also used for the development of preclinical models, including tumor organoids and patient-derived xenografts. Results Three hundred forty-six frozen samples were procured in total. WES was performed on 113 samples and RNA-Seq on 72 samples. Successful cell strain, tumor organoid, and/or patient-derived xenograft development was achieved in four samples, including an inoperable pediatric glioma. WES data were used to assess clonal evolution and molecular heterogeneity of tumors in individual patients. Mutational profiles of primary tumors and metastases yielded candidate mediators of metastatic spread and organotropism including CUL9 and PIGM in metastatic ependymoma and ANKRD52 in metastatic melanoma to the lung. RNA-Seq data identified novel gene fusion candidates. Conclusion A next-generation sequencing–based autopsy program in conjunction with a premortem precision medicine pipeline for diverse tumors affords a valuable window into clonal evolution, metastasis, and alterations underlying treatment. Moreover, such an autopsy program yields robust preclinical models of disease.
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Ackerman, William E., Irina A. Buhimschi, Guomao Zhao, Taryn Summerfield, Hongwu Jing, and Catalin S. Buhimschi. "478: Next-generation sequencing (RNA-seq) of human term and preterm myometrium." American Journal of Obstetrics and Gynecology 214, no. 1 (January 2016): S262. http://dx.doi.org/10.1016/j.ajog.2015.10.521.
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Han, Yixing, Shouguo Gao, Kathrin Muegge, Wei Zhang, and Bing Zhou. "Advanced Applications of RNA Sequencing and Challenges." Bioinformatics and Biology Insights 9s1 (January 2015): BBI.S28991. http://dx.doi.org/10.4137/bbi.s28991.
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Next-generation sequencing technologies have revolutionarily advanced sequence-based research with the advantages of high-throughput, high-sensitivity, and high-speed. RNA-seq is now being used widely for uncovering multiple facets of transcriptome to facilitate the biological applications. However, the large-scale data analyses associated with RNA-seq harbors challenges. In this study, we present a detailed overview of the applications of this technology and the challenges that need to be addressed, including data preprocessing, differential gene expression analysis, alternative splicing analysis, variants detection and allele-specific expression, pathway analysis, co-expression network analysis, and applications combining various experimental procedures beyond the achievements that have been made. Specifically, we discuss essential principles of computational methods that are required to meet the key challenges of the RNA-seq data analyses, development of various bioinformatics tools, challenges associated with the RNA-seq applications, and examples that represent the advances made so far in the characterization of the transcriptome.
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Orsmark-Pietras, Christina, Henrik Lilljebjörn, Marianne Rissler, Vladimir Lazarevic, Alexandros Arvanitakis, Mats Ehinger, Gunnar Juliusson, and Thoas Fioretos. "Comprehensive Prospective Next Generation Sequencing of Acute Myeloid Leukemia." Blood 126, no. 23 (December 3, 2015): 3830. http://dx.doi.org/10.1182/blood.v126.23.3830.3830.
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Abstract Acute Myeloid Leukemia (AML) is a heterogeneous disease with poor overall five-year survival of less than 30%. Current risk stratification is largely based on cytogenetics, combined with information of the most commonly mutated genes in AML (e.g. NPM1, FLT3, DNMT3A). To improve clinical decision making and to increase our understanding of the mechanisms underlying AML it is essential to gain additional information about the mutational landscape of AML. In this prospective study we perform comprehensive Next Generation Sequencing (NGS) to determine the mutational landscape of AML. Starting from September 2014, bone marrow samples, with matched skin biopsies, were collected from all newly diagnosed samples of AML at Skåne University Hospital, Sweden. So far, almost 40 AML samples have undergone whole-exome sequencing (WES) (100X coverage), targeted AML-gene panel sequencing (>100 genes with recurrent mutations in the TCGA AML data set) (400X), RNA-seq and low pass Whole Genome Sequencing (WGS) (1.5X). Additionally, clinical data such as age, treatment response and survival outcome are collected and samples are also cryopreserved for functional follow-up studies. The targeted AML-panel sequencing allows for high coverage data enabling identification of not only common but also rare variants present in subclones, while WES might reveal genes and pathways not previously associated with AML. Low pass WGS enables the detection of cytogenetic alterations, ranging from larger structural rearrangements to fusion gene detection. RNA-seq also makes the detection of fusion genes possible as well as providing global gene expression data. So far our prospective study has identified 22 recurrently mutated genes (with mutations present in >5% of the reads). Out of these, DNMT3A (34%), NPM1 (29%), TET2 (21%), FLT3 (18%) and RUNX1 (18%) were the most commonly mutated genes. The corresponding mutation frequencies in TCGA AML data set are DNMT3A (26%), NPM1 (27%), TET2 (9%), FLT3 (28%) and RUNX1 (10%). More than 70% of the cases carry combinations of mutations in two up to seven of the genes included in our AML panel. Each patient also carries a private combination of unique exomic variants. RNA-seq data confirmed all clinically known fusion genes and principal component analysis revealed that cases with e.g. NPM1 mutations have a uniform gene expression pattern. Although diagnostics has improved over the last years, information of the most commonly mutated genes has not largely improved risk stratification. A plausible explanation is the clonal complexity in AML and the joint risk combination of common and rare variants. NGS-based methods have greatly improved our possibility to detect genetic alterations and comprehensive NGS of AML has the potential to identify mutational patterns that can further improve diagnostics and risk stratification. Disclosures No relevant conflicts of interest to declare.
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Mayer, Simone, Shokoufeh Khakipoor, Maxim A. Drömer, and Daniel A. Cozetto. "Single-cell RNA-Sequencing in Neuroscience." Neuroforum 25, no. 4 (November 26, 2019): 251–58. http://dx.doi.org/10.1515/nf-2019-0021.
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Summary Technical innovations in the last decade have allowed to sequence transcriptomes of single cells. Single-cell RNA-sequencing (scRNA-seq) has since then opened the window to a deeper understanding of cellular identity and is becoming a widely used method in molecular biology. In neuroscience, scRNA-seq has broad applications, for example in determining cellular diversity in different brain regions and in revealing transcriptomic variations across brain disorders. The method consists of several steps: isolation and lysis of single cells, reverse transcription of RNAs, amplification of cDNAs, and next-generation sequencing. The large datasets can subsequently be analysed using different bioinformatic tools to deduce biological meaning. Current developments aim to integrate scRNA-seq into cellular network analysis through multimodal analysis, spatial localisation and perturbation experiments, in order to understand brain physiology and pathology.
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Gunawan, Asep, Mutasem Ali M. Abuzahra, Kasita Listyarini, Jakaria Jakaria, and Cece Sumantri. "SNP Discovery of Chicken Liver with Divergent Unsaturated Fatty Acid using Next Generation RNA Sequencing." Jurnal Ilmu dan Teknologi Peternakan Tropis 6, no. 1 (January 11, 2019): 100. http://dx.doi.org/10.33772/jitro.v6i1.5807.
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ABSTRAKRNA sequencing memberikan peluang baru untuk mendeteksi variasi SNP (Single Nucleotide Polymorphism) pada perbedaan jaringan dengan perbedaan fenotipe. Tujuan dari penelitian ini adalah untuk mengkarakterisisasi penemuan SNP terbaru terkait perbedaan asam lemak tak jenuh pada ayam dengan menggunakan RNA sequencing. Sebanyak 6 sampel dipilih dari 62 sampel masing-masing 3 sampel tinggi dan 3 sampel rendah yang merepresentasikan perbedaan fenotip yang kontras terkait asam lemak tak jenuh dianalisis dengan menggunakan RNA Sequensing. Hasil identifikasi SNP memperlihatkan sebanyak 1208 SNP pada sampel tinggi dan rendah setelah disejajarkan dengan genom ayam Gallus gallus (GGA) v4.0. Sekitar 91% dari total SNP yang ditemukan memiliki tingkat polimorfisme yang tinggi pada 5 gen yang ditemukan terkait asam lemak yaitu gen SCD, COL6A2, CYP2J2L4, HSD17B4, dan SLC23A3. Gen SCD, HSD17B4, dan SLC23A3 memiliki jumlah titik mutasi dengan jumlah yang paling tinggi masing-masing berturut-turut 18, 13, dan 12 SNP. Tingkat level signifikan yang tinggi dan peranan dari ketiga gen tersebut yang sangat penting terkait komposisi asam lemak mengindikasikan bahwa gen SCD, HSD17B4, dan SLC23A3 merupakan tiga gen baru dan potensial untuk digunakan sebagai penanda seleksi kandungan asam lemak tak jenuh tinggi. Namun, hasil penelitian ini perlu divalidasi dan dikonfirmasi sebagai potensial kandidat gen dalam jumlah ayam yang lebih besar dan breed yang berbeda.Kata kunci: asam lemak, ayam, RNA-Seq, variasi transkriptomikABSTRACTRNA sequencing (RNA-Seq) reveals new opportunity for identification SNP discovery in different tissues with divergent phenotype. The objective of this study was to characterize SNP profile from divergent unsaturated fatty acids using RNA-Seq. Six liver samples were selected from 62 chicken which classified 3 high and 3 low unsaturated fatty acids were analyzed using RNA-Seq. The SNP identification showed 1208 SNPs in chicken samples and a large number of those corresponded to differences between high and low chicken genome assembly Gallus gallus (GGA) v4.0. Among them, about 91% of genes had multiple polymorphisms within 5 genes (SCD, COL6A2, CYP2J2L4, HSD17B4, and SLC23A3). The SCD, HSD17B4, and SLC23A3 contained the largest number of mutations with 18, 13, and 12 SNPs respectively. Combining the significant level of SNPs and gene function related with fatty acid composition allow us to suggest SCD, SLC23A3, HSD17B4 as the three novel and promising candidate genes for selecting unsaturated fatty acids. However, further validation is required to confirm the effect of these candidate genes in larger chicken populations.Keywords: chicken, fatty acids, RNA-Seq, transcriptome variants
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Terada, Tomoko, Kentaro Shimizu, and Koji Kadota. "Methods for analyzing next-generation sequencing data XV. RNA-seq analysis (Part 3)." Japanese Journal of Lactic Acid Bacteria 31, no. 1 (March 9, 2020): 25–34. http://dx.doi.org/10.4109/jslab.31.25.
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Terada, Tomoko, Kentaro Shimizu, and Koji Kadota. "Methods for analyzing next-generation sequencing data XIV. RNA-seq analysis (Part 2)." Japanese Journal of Lactic Acid Bacteria 30, no. 3 (November 13, 2019): 153–61. http://dx.doi.org/10.4109/jslab.30.153.
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McArt, Darragh G., Philip D. Dunne, Jaine K. Blayney, Manuel Salto-Tellez, Sandra Van Schaeybroeck, Peter W. Hamilton, and Shu-Dong Zhang. "Connectivity Mapping for Candidate Therapeutics Identification Using Next Generation Sequencing RNA-Seq Data." PLoS ONE 8, no. 6 (June 26, 2013): e66902. http://dx.doi.org/10.1371/journal.pone.0066902.
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Radovich, Milan, Ryan Frederick Porter, Madison Conces, Yaman Suleiman, Sunil S. Badve, Kenneth Kesler, Bryan P. Schneider, and Patrick J. Loehrer. "Next-generation sequencing of thymic malignancies." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): 7032. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.7032.
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7032 Background: Thymic malignancies are rare with ~500 cases in the US per year. Apart from standard chemotherapy, treatment options are limited. Further, the challenge of histologic subtyping of these tumors along with an inadequate understanding of the transcriptional biology is a hindrance to the development of targeted therapies. Methods: Thymic malignancies and normal tissues were obtained from the Indiana University Simon Cancer Center. The WHO subtypes of our samples include: (4) type A, (2) A/B, (1) B2, (5) B3, (1) C, and (3) normal tissues. RNA was sequenced on a Life Technologies SOLiD sequencer. For gene expression, reads were mapped to the genome using BioScope and outputs imported into Partek GS. In Partek, statistical comparison of gene expression as well as PCA & clustering analyses were performed. Results: Unsupervised hierarchical clustering of gene expression values revealed 100% concordance between gene expression clusters and WHO subtype. A subsequent unsupervised clustering of 705 pre-miRNAs also showed substantial concordance between clusters and subtype. By analyzing the dendrograms, A & A/B tumors were significantly different from B type tumors, as well as C and normal. A substantial differentiator was a large cluster of overexpression in A&A/B tumors that was nearly absent in the others on chr19q13.42 corresponding to the miR-515 cluster (43 miRNAs). When comparing A & A/B tumors vs. B type tumors, 1334 genes are differentially expressed (258 downregulated) (FDR<5%). 95/258 genes are predicted to be downregulated by the miR-515 cluster including several transcription factors and tumor suppressors. When looking at mutations, we detected no recurrent gene fusions, though we are detecting several point mutations and small insertion deletions. These are being followed up by exome sequencing. Conclusions: Analyses reveal that RNA-seq can be used to accurately subtype Thymic Malignancies and the development of an expression based diagnostic is feasible. Further, these data support that the main differentiator of thymomas may lie in the expression of a single miRNA cluster. In addition, several mutations in key pathways are implicated. Ongoing analyses include: alternative splicing, noncoding RNAs, viral analysis and exome sequencing.
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Costa, Valerio, Maria Assunta Gallo, Francesca Letizia, Marianna Aprile, Amelia Casamassimi, and Alfredo Ciccodicola. "PPARG: Gene Expression Regulation and Next-Generation Sequencing for Unsolved Issues." PPAR Research 2010 (2010): 1–17. http://dx.doi.org/10.1155/2010/409168.
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Peroxisome proliferator-activated receptor gamma (PPARγ) is one of the most extensively studied ligand-inducible transcription factors (TFs), able to modulate its transcriptional activity through conformational changes. It is of particular interest because of its pleiotropic functions: it plays a crucial role in the expression of key genes involved in adipogenesis, lipid and glucid metabolism, atherosclerosis, inflammation, and cancer. Its protein isoforms, the wide number of PPARγtarget genes, ligands, and coregulators contribute to determine the complexity of its function. In addition, the presence of genetic variants is likely to affect expression levels of target genes although the impact ofPPARGgene variations on the expression of target genes is not fully understood. The introduction of massively parallel sequencing platforms—in the Next Generation Sequencing (NGS) era—has revolutionized the way of investigating the genetic causes of inherited diseases. In this context, DNA-Seq for identifying—within both coding and regulatory regions ofPPARGgene—novel nucleotide variations and haplotypes associated to human diseases, ChIP-Seq for defining a PPARγbinding map, and RNA-Seq for unraveling the wide and intricate gene pathways regulated by PPARG, represent incredible steps toward the understanding of PPARγin health and disease.
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Rowley, Jesse W., Andrew J. Oler, Neal D. Tolley, Benjamin N. Hunter, Elizabeth N. Low, David A. Nix, Christian C. Yost, Guy A. Zimmerman, and Andrew S. Weyrich. "Genome-wide RNA-seq analysis of human and mouse platelet transcriptomes." Blood 118, no. 14 (October 6, 2011): e101-e111. http://dx.doi.org/10.1182/blood-2011-03-339705.
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Abstract Inbred mice are a useful tool for studying the in vivo functions of platelets. Nonetheless, the mRNA signature of mouse platelets is not known. Here, we use paired-end next-generation RNA sequencing (RNA-seq) to characterize the polyadenylated transcriptomes of human and mouse platelets. We report that RNA-seq provides unprecedented resolution of mRNAs that are expressed across the entire human and mouse genomes. Transcript expression and abundance are often conserved between the 2 species. Several mRNAs, however, are differentially expressed in human and mouse platelets. Moreover, previously described functional disparities between mouse and human platelets are reflected in differences at the transcript level, including protease activated receptor-1, protease activated receptor-3, platelet activating factor receptor, and factor V. This suggests that RNA-seq is a useful tool for predicting differences in platelet function between mice and humans. Our next-generation sequencing analysis provides new insights into the human and murine platelet transcriptomes. The sequencing dataset will be useful in the design of mouse models of hemostasis and a catalyst for discovery of new functions of platelets. Access to the dataset is found in the “Introduction.”
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Wolfinger, Michael T., Jörg Fallmann, Florian Eggenhofer, and Fabian Amman. "ViennaNGS: A toolbox for building efficient next- generation sequencing analysis pipelines." F1000Research 4 (February 20, 2015): 50. http://dx.doi.org/10.12688/f1000research.6157.1.
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Recent achievements in next-generation sequencing (NGS) technologies lead to a high demand for reuseable software components to easily compile customized analysis workflows for big genomics data. We present ViennaNGS, an integrated collection of Perl modules focused on building efficient pipelines for NGS data processing. It comes with functionality for extracting and converting features from common NGS file formats, computation and evaluation of read mapping statistics, as well as normalization of RNA abundance. Moreover, ViennaNGS provides software components for identification and characterization of splice junctions from RNA-seq data, parsing and condensing sequence motif data, automated construction of Assembly and Track Hubs for the UCSC genome browser, as well as wrapper routines for a set of commonly used NGS command line tools.
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Wolfinger, Michael T., Jörg Fallmann, Florian Eggenhofer, and Fabian Amman. "ViennaNGS: A toolbox for building efficient next- generation sequencing analysis pipelines." F1000Research 4 (July 20, 2015): 50. http://dx.doi.org/10.12688/f1000research.6157.2.
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Recent achievements in next-generation sequencing (NGS) technologies lead to a high demand for reuseable software components to easily compile customized analysis workflows for big genomics data. We present ViennaNGS, an integrated collection of Perl modules focused on building efficient pipelines for NGS data processing. It comes with functionality for extracting and converting features from common NGS file formats, computation and evaluation of read mapping statistics, as well as normalization of RNA abundance. Moreover, ViennaNGS provides software components for identification and characterization of splice junctions from RNA-seq data, parsing and condensing sequence motif data, automated construction of Assembly and Track Hubs for the UCSC genome browser, as well as wrapper routines for a set of commonly used NGS command line tools.
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Fan, Xiaoying, Dong Tang, Yuhan Liao, Pidong Li, Yu Zhang, Minxia Wang, Fan Liang, et al. "Single-cell RNA-seq analysis of mouse preimplantation embryos by third-generation sequencing." PLOS Biology 18, no. 12 (December 30, 2020): e3001017. http://dx.doi.org/10.1371/journal.pbio.3001017.
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The development of next generation sequencing (NGS) platform-based single-cell RNA sequencing (scRNA-seq) techniques has tremendously changed biological researches, while there are still many questions that cannot be addressed by them due to their short read lengths. We developed a novel scRNA-seq technology based on third-generation sequencing (TGS) platform (single-cell amplification and sequencing of full-length RNAs by Nanopore platform, SCAN-seq). SCAN-seq exhibited high sensitivity and accuracy comparable to NGS platform-based scRNA-seq methods. Moreover, we captured thousands of unannotated transcripts of diverse types, with high verification rate by reverse transcription PCR (RT-PCR)–coupled Sanger sequencing in mouse embryonic stem cells (mESCs). Then, we used SCAN-seq to analyze the mouse preimplantation embryos. We could clearly distinguish cells at different developmental stages, and a total of 27,250 unannotated transcripts from 9,338 genes were identified, with many of which showed developmental stage-specific expression patterns. Finally, we showed that SCAN-seq exhibited high accuracy on determining allele-specific gene expression patterns within an individual cell. SCAN-seq makes a major breakthrough for single-cell transcriptome analysis field.
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Iles, James C., Richard Njouom, Yacouba Foupouapouognigni, David Bonsall, Rory Bowden, Amy Trebes, Paolo Piazza, et al. "Characterization of Hepatitis C Virus Recombination in Cameroon by Use of Nonspecific Next-Generation Sequencing." Journal of Clinical Microbiology 53, no. 10 (July 22, 2015): 3155–64. http://dx.doi.org/10.1128/jcm.00483-15.
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The importance of recombination in the evolution and genetic diversity of the hepatitis C virus (HCV) is currently uncertain. Only a small number of intergenotypic recombinants have been identified so far, and each has core and envelope genes classified as belonging to genotype 2. Here, we investigated two putative genotype 4/1 recombinants from southern Cameroon using a number of approaches, including standard Sanger sequencing, genotype-specific PCR amplification, and non-HCV-specific Illumina RNA sequencing (RNA-seq). Recombination between genotypes 1 and 4 was confirmed in both samples, and the parental lineages of each recombinant belong to HCV subtypes that are cocirculating at a high prevalence in Cameroon. Using the RNA-seq approach, we obtained a complete genome for one sample, which contained a recombination breakpoint at the E2/P7 gene junction. We developed and applied a new method, called Deep SimPlot, which can be used to visualize and identify viral recombination directly from the short sequence reads created by next-generation sequencing in conjunction with a consensus sequence.
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Dillon, Laura W., Xijun Zhang, Meghali Goswami, Gauthaman Sukumar, Jack Ghannam, Katherine E. Lindblad, Adam Sciambi, et al. "Assessment of Next Generation Sequencing Approaches for the Cytogenetic and Molecular Evaluation of Acute Myeloid Leukemia." Blood 138, Supplement 1 (November 5, 2021): 271. http://dx.doi.org/10.1182/blood-2021-148128.
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Abstract Background: Genomic evaluation of structural and molecular alterations is essential for the classification and risk stratification of patients with acute myeloid leukemia (AML). Cytogenetics (CG) has traditionally been used for the identification of large structural or copy number changes and molecular studies for small sequence changes. Rapid technical advances and decreased sequencing (seq) costs have made whole-genome sequencing (WGS) more feasible and recent efforts have suggested it can match the analytic performance of traditional CG while providing additional prognostic information. However, the use of WGS in the clinic is still in its infancy and questions remain regarding the utility of many factors including seq depth, structural events not identifiable by traditional CG, normal samples, AML enrichment, paired RNA-seq, and single-cell DNA-seq. Methods: Baseline bone marrow (BM) samples from 10 AML patients underwent evaluation using standard clinical CG and targeted next generation seq tests. Additionally, samples were extensively characterized using: 200X WGS (bulk and enriched BM), 50X germline WGS (buccal swab and remission blood), 50X RNA-seq, targeted error-corrected (EC) DNA-seq, and single-cell DNA-seq with oligonucleotide-conjugated antibodies (scDNA-seq+ab). Results: The AML patients analyzed had a mean age of 71 (30-81) and a range of BM blast percentage by clinical flow cytometry (0.4-33%). Standard clinical assessment revealed 14 CG abnormalities and 20 mutations across all patients. 50X WGS of the bulk BM utilizing previously published conditions (PMID:33704937) successfully identified 89% of the alterations. Increasing the WGS depth to 100X recovered 2 additional abnormalities, increasing the capture rate to 94%. Enrichment of AML using the primary cell surface marker prior to WGS achieved a 94% capture rate at only 50X depth, with a depth of only 10X required for the CG abnormalities. Integration of 50X WGS on buccal swab samples reduced the large number of structural variant calls and private mutations without the need for aggressive filtering conditions required for tumor-only samples. However, this also resulted in the removal of a few true mutations due to buccal contamination with immune cells. 50X WGS on remission blood samples from 2 patients mitigated this problem. Next, we sought to determine what additional information WGS could provide that was missed by standard CG (>5Mb) and seq (>5% variant allele fraction, VAF) analyses. Firstly, 3 of the deletions reported by CG were found to be complex translocation events, resulting in different losses and gains than anticipated. Most importantly, decreasing the CNV size threshold (<5Mb) and utilizing SNP data to identify copy neutral loss of heterozygosity (LOH) identified 7 new events in bulk BM and 2 new events in enriched BM. This information provided important prognostic information, including 3 newly identified structural alternations in a patient with a CG-reported normal karyotype, multiple small deletions in critical regions (e.g. within 5q, 7q), and LOH which in combination with molecular data revealed a biallelic loss of important genes (including TP53, EZH2). Targeted EC DNA-seq was used to detect of variants <5% VAF and identified an additional 23 variants beyond clinical capture sequencing. 50X RNA-seq with integrated WGS DNA/RNA calling was able to validate the mutations and translocation fusion genes reported by 50X WGS, while also capturing an additional 4 variants found by EC DNA-seq. Finally, personalized scDNA-seq+ab provided further resolution of each patient's AML through confirmation of new structural and mutational findings, identification of mutations associated with clonal hematopoiesis versus AML, and definition of AML clonal structure (Figure 1). Conclusions: We confirmed that 50X WGS can recapitulate standard clinical CG and mutational profiling in AML patients while providing improved resolution. Deeper seq, AML pre-enrichment, and integrated RNA-seq further aided in identifying low level events. We also demonstrated that WGS can identify additional important structural events missed by CG. Finally, personalized scDNA-seq+ab was able to define clonal architecture important for prognostic and residual disease tracking. Together, these results provide a framework for future integration of WGS into the clinic and personalized patient care. Figure 1 Figure 1. Disclosures Sciambi: Mission Bio Inc.: Current Employment. Durruthy-Durruthy: Mission Bio Inc.: Current Employment. Hourigan: Sellas: Research Funding.
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Nowrousian, Minou. "Next-Generation Sequencing Techniques for Eukaryotic Microorganisms: Sequencing-Based Solutions to Biological Problems." Eukaryotic Cell 9, no. 9 (July 2, 2010): 1300–1310. http://dx.doi.org/10.1128/ec.00123-10.
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ABSTRACT Over the past 5 years, large-scale sequencing has been revolutionized by the development of several so-called next-generation sequencing (NGS) technologies. These have drastically increased the number of bases obtained per sequencing run while at the same time decreasing the costs per base. Compared to Sanger sequencing, NGS technologies yield shorter read lengths; however, despite this drawback, they have greatly facilitated genome sequencing, first for prokaryotic genomes and within the last year also for eukaryotic ones. This advance was possible due to a concomitant development of software that allows the de novo assembly of draft genomes from large numbers of short reads. In addition, NGS can be used for metagenomics studies as well as for the detection of sequence variations within individual genomes, e.g., single-nucleotide polymorphisms (SNPs), insertions/deletions (indels), or structural variants. Furthermore, NGS technologies have quickly been adopted for other high-throughput studies that were previously performed mostly by hybridization-based methods like microarrays. This includes the use of NGS for transcriptomics (RNA-seq) or the genome-wide analysis of DNA/protein interactions (ChIP-seq). This review provides an overview of NGS technologies that are currently available and the bioinformatics analyses that are necessary to obtain information from the flood of sequencing data as well as applications of NGS to address biological questions in eukaryotic microorganisms.
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Nannini, Margherita, and Maria A. Pantaleo. "Next generation sequencing (NGS) in oncology: lights and shadows." Cancer Breaking News 4, no. 1 (March 15, 2016): 17–19. http://dx.doi.org/10.19156/cbn.2016.0004.
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Advances in tumor genome sequencing using next generation sequencing (NGS) technologies have facilitated a greater understanding of the genetic abnormalities involved in cancer development and progression, dramatically changing oncology research. There are several different types of NGS technologies. Whole genome sequencing (WGS) determines the sequence of the complete genome, providing information on both coding and non-coding regions and structural variants. However, use is limited by the large volume of data generated, and associated time and resource costs. Whole exome sequencing (WES) determines the sequence of coding regions only, making it faster and cheaper than WGS, and the functional consequences of variants are easier to interpret. However, all variations in non-coding regions are missed. WGS and WES are often used together to maximize detection of variants. A less costly approach is the use of targeted sequencing, which focuses on particular regions of interest, based on their biological relevance. NGS technologies can also be used to sequence RNA, referred to as RNA-Seq. All these NGS technologies, individually or in combination, have a number of potential applications, including identification of biomarkers, and development of diagnostic and therapeutic strategies. However, although advances have been made, there are a number of limitations to be overcome before NGS technologies are routinely applied in both research and clinical practice.
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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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Shigematsu, Megumi, and Yohei Kirino. "Making Invisible RNA Visible: Discriminative Sequencing Methods for RNA Molecules with Specific Terminal Formations." Biomolecules 12, no. 5 (April 20, 2022): 611. http://dx.doi.org/10.3390/biom12050611.
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Next generation sequencing of RNA molecules (RNA-seq) has become a common tool to characterize the expression profiles of RNAs and their regulations in normal physiological processes and diseases. Although increasingly accumulating RNA-seq data are widely available through publicly accessible sites, most of the data for short non-coding RNAs (sncRNAs) have been obtained for microRNA (miRNA) analyses by standard RNA-seq, which only capture the sncRNAs with 5′-phosphate (5′-P) and 3′-hydroxyl (3′-OH) ends. The sncRNAs with other terminal formations such as those with a 5′-hydroxyl end (5′-OH), a 3′-phosphate (3′-P) end, or a 2′,3′-cyclic phosphate end (2′,3′-cP) cannot be efficiently amplified and sequenced by standard RNA-seq. Due to the invisibility in standard RNA-seq data, these non-miRNA-sncRNAs have been a hidden component in the transcriptome. However, as the functional significances of these sncRNAs have become increasingly apparent, specific RNA-seq methods compatible with various terminal formations of sncRNAs have been developed and started shedding light on the previously unrecognized sncRNAs that lack 5′-P/3′-OH ends. In this review, we summarize the expanding world of sncRNAs with various terminal formations and the strategic approaches of specific RNA-seq methods to distinctively characterize their expression profiles.
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Zhao, Ruiying, Yuchen Han, Chan Xiang, Shengnan Chen, Jikai Zhao, Lianying Guo, Anbo Yu, et al. "RNA sequencing effectively identifies gene fusions undetected by DNA sequencing in lung adenocarcinomas." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 3052. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.3052.
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3052 Background: Next-generation sequencing of DNA, which can provide valid information for clinical therapeutic decision-making, has been widely used in the management of lung cancer especially adenocarcinoma. However, due to its technical limitations for detecting certain alterations such as gene rearrangement, the DNA-based sequencing (DNA-seq) may miss the actionable alteration in some cases, who would have benefited from targeted therapy. The study aimed to evaluate the capability of RNA sequencing (RNA-seq) in identifying DNA-seq undetectable gene alterations in lung adenocarcinomas. Methods: A total of 219 lung adenocarcinomas, which had no driver alteration detected by DNA-seq (OncoScreen Plus, Burning Rock Biotech) and had a max AF ≥5%, underwent capture-based RNA-seq using a custom panel (OncoRNA, Burning Rock Biotech) spanning full transcripts of 115 genes commonly involved in cancer genomic rearrangements. Furthermore, an independent cohort of 100 DNA-seq driver–negative lung adenocarcinomas were also subjected to RNA-seq with the same panel. Results: In the discovery cohort, 166/219 samples (75.8%) generated qualified RNA-seq data for subsequent analyses. RNA-seq identified 44 previously undetected alterations (26.5%), including 40 gene fusions (24.1%), 1 MET exon14 skipping variant ( METex14, 0.6%) and 3 other alternative splicing variants (1.8%). Among them, 14 (8.4%) were potential actionable alterations, consisting of METex14 and in-frame fusions containing functional domain of the driver gene (4 ROS1 fusions, 3 BRAF fusions, 2 NRG1 fusions, 2 EGFR fusions, 1 ALK fusion and 1 MET fusion). In the validation cohort, 69/100 samples (69.0%) generated qualified data. RNA-seq identified 22 DNA-seq undetected alterations (31.9%), with 7 of them being potential actionable fusions (10.1%). ROS1 fusion remained as the most common actionable alteration (n = 3), followed by ALK fusion (n = 2), EGFR fusion (n = 1) and MET fusion (n = 1). Further analyses of the two datasets revealed that lacking sufficient coverage spanning the rearrangement breakpoint in the DNA-seq panel mainly accounted for the failure of DNA-seq on detecting these fusions. This can be improved by increasing the corresponding probe coverage in the DNA-seq panel. In addition, complex genomic rearrangement at DNA level and the presence of repetitive sequence in the intronic region spanning or adjacent to the breakpoint might lead to missed calling of canonical fusions by DNA-seq. Conclusions: Targeted RNA-seq can effectively identify genomic rearrangements that are undetectable by DNA-seq and provide lung adenocarcinoma patients with more opportunities for targeted therapy. Therefore, it should be recommended for all patients, in whom DNA-seq fails to detect driver alteration.
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Tang, Jinyang, and Fei Wang. "Detecting differentially expressed genes by smoothing effect of gene length on variance estimation." Journal of Bioinformatics and Computational Biology 13, no. 06 (December 2015): 1542004. http://dx.doi.org/10.1142/s0219720015420044.
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Next-generation sequencing technologies are widely used in genome research, and RNA sequencing (RNA-Seq) is becoming the main application for gene expression profiling. A large number of computational methods have been developed for analyzing differentially expressed (DE) genes in RNA-Seq data. However, most existing algorithms prefer to call long genes as DE. Short DE genes are rarely detected. In this work, we set out to gain insight into the influence of gene length on RNA-Seq data analysis and to figure out the effect of gene length on variance estimation of RNA-Seq read counts, which is important for statistic test to identify DE genes. We proposed a balanced method of hunting for short DE genes with significance by smoothing a gene length factor. Computational experiments indicate that our method performs well. Software available: http://www.iipl.fudan.edu.cn/lenseq/ .
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Gupta, Rashi, Isha Dewan, Richa Bharti, and Alok Bhattacharya. "Differential Expression Analysis for RNA-Seq Data." ISRN Bioinformatics 2012 (September 20, 2012): 1–8. http://dx.doi.org/10.5402/2012/817508.
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RNA-Seq is increasingly being used for gene expression profiling. In this approach, next-generation sequencing (NGS) platforms are used for sequencing. Due to highly parallel nature, millions of reads are generated in a short time and at low cost. Therefore analysis of the data is a major challenge and development of statistical and computational methods is essential for drawing meaningful conclusions from this huge data. In here, we assessed three different types of normalization (transcript parts per million, trimmed mean of M values, quantile normalization) and evaluated if normalized data reduces technical variability across replicates. In addition, we also proposed two novel methods for detecting differentially expressed genes between two biological conditions: (i) likelihood ratio method, and (ii) Bayesian method. Our proposed methods for finding differentially expressed genes were tested on three real datasets. Our methods performed at least as well as, and often better than, the existing methods for analysis of differential expression.
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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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Kurmyshkina, O. V., A. A. Bogdanova, A. P. Spasova, P. I. Kovchur, and Tatyana O. Volkova. "RNA-SEQ IN THE STUDY OF VIRUS-ASSOCIATED TUMORS: CERVICAL CANCER (REVIEW)." Russian Journal of Oncology 24, no. 1-2 (April 15, 2019): 45–55. http://dx.doi.org/10.18821/1028-9984-2019-24-1-2-45-55.
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For the last few years Next Generation Sequencing technique and its applications has took the leading position in the arsenal of analytical methods that are used for studying the mechanisms of neoplastic progression. Among various experimental opportunities Next Generation Sequencing provides, RNA-sequencing (RNA-Seq) is of great importance as it makes possible unraveling the highest levels of genome expression regulation, which define the molecular phenotype of cells in composition of a tumor. Considerable amount of current studies carried out with the use of RNA-Seq method are designed as pan-cancer integrated research, in which special attention is payed to virus-associated tumors, including papillomavirus-dependent cervical cancer. This review paper summarizes the results of RNA-Seq studies published world-wide within 2017-2019 years and carried out using clinical samples from cervical cancer patients. New facts concerning such hot topics as genomic and transcriptomic instability, neoantigen load, cellular and molecular heterogeneity, tumor epigenetics, antitumor and antiviral immune response, chronic inflammation, immune exhaustion, phenotypic plasticity and tumor cell resistance, are considered. The whole spectrum of issues that are actively discussed in published literature is systematized according to three levels of organization: «molecular», «cellular» and «organismal». The findings reviewed in the paper convincingly illustrate that wide usage of RNA-Seq technology for profiling primary tumors does facilitate moving to a new level of our understanding of the mechanisms of carcinogenesis and emergence of new directions in cancer treatment, namely targeted and immune-therapy.
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Mardis, Elaine R., Li Ding, Peter Westervelt, John S. Welch, Jeffery M. Klco, John F. DiPersio, Richard K. Wilson, and Timothy J. Ley. "Next-Generation Sequencing: A Discovery Tool for Blood Disorders." Blood 120, no. 21 (November 16, 2012): SCI—10—SCI—10. http://dx.doi.org/10.1182/blood.v120.21.sci-10.sci-10.
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Abstract Abstract SCI-10 The advent and evolution of next-generation massively parallel sequencing (MPS) has radically altered our approaches to studying the cancer genome and transcriptome. By using unbiased and comprehensive MPS in the context of clinically annotated samples from leukemia cases, rapid progress has resulted in our understanding of the mutational spectrum of hematopoietic malignancies, the heterogeneity of disease presentation, and the impact of chemotherapy on the cancer genome, among others. The complexity of the transcriptome, while daunting from an analytical standpoint, further reveals the nuances of gene expression changes in leukemia that often cannot be predicted simply by studying the genome. By applying the digital nature of MPS to explore tumor heterogeneity and tumor evolution, we have shown that de novo acute myeloid leukemia (AML) presents either as a mono- or multiclonal disease, and that the relapse presentation in the same patient is an evolved genetic derivation of the de novopresentation, often with novel driver mutations that have been acquired during the course of chemotherapy (1). New data from whole genome sequencing of hematopoietic stem cells in healthy volunteers indicates that somatic mutations largely are acquired during aging and occur randomly, carrying forward in the transformed cell. This baseline is important for the further comparison of AML subtypes, and provides a context for understanding tumor biology. Last, by studying 200 AML cases using whole-genome and exome sequencing, RNA-seq, miRNA-seq and array-based methylation, we have begun an integrated characterization of AML in an effort to inform tumor biology. These studies and the accompanying technologies set the stage for precision treatment of each AML patient according to the additional information provided by the person's integrated “omic” profile. Disclosures: No relevant conflicts of interest to declare.
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Reddy, Anireddy S. N., Jie Huang, Naeem H. Syed, Asa Ben-Hur, Suomeng Dong, and Lianfeng Gu. "Decoding co-/post-transcriptional complexities of plant transcriptomes and epitranscriptome using next-generation sequencing technologies." Biochemical Society Transactions 48, no. 6 (November 16, 2020): 2399–414. http://dx.doi.org/10.1042/bst20190492.
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Next-generation sequencing (NGS) technologies - Illumina RNA-seq, Pacific Biosciences isoform sequencing (PacBio Iso-seq), and Oxford Nanopore direct RNA sequencing (DRS) - have revealed the complexity of plant transcriptomes and their regulation at the co-/post-transcriptional level. Global analysis of mature mRNAs, transcripts from nuclear run-on assays, and nascent chromatin-bound mRNAs using short as well as full-length and single-molecule DRS reads have uncovered potential roles of different forms of RNA polymerase II during the transcription process, and the extent of co-transcriptional pre-mRNA splicing and polyadenylation. These tools have also allowed mapping of transcriptome-wide start sites in cap-containing RNAs, poly(A) site choice, poly(A) tail length, and RNA base modifications. The emerging theme from recent studies is that reprogramming of gene expression in response to developmental cues and stresses at the co-/post-transcriptional level likely plays a crucial role in eliciting appropriate responses for optimal growth and plant survival under adverse conditions. Although the mechanisms by which developmental cues and different stresses regulate co-/post-transcriptional splicing are largely unknown, a few recent studies indicate that the external cues target spliceosomal and splicing regulatory proteins to modulate alternative splicing. In this review, we provide an overview of recent discoveries on the dynamics and complexities of plant transcriptomes, mechanistic insights into splicing regulation, and discuss critical gaps in co-/post-transcriptional research that need to be addressed using diverse genomic and biochemical approaches.
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Aditama, Redi, Zulfikar Achmad Tanjung, Widyartini Made Sudania, and Toni Liwang. "SMART-RDA: A Galaxy Workflow for RNA-Seq Data Analysis." KnE Life Sciences 3, no. 4 (March 27, 2017): 186. http://dx.doi.org/10.18502/kls.v3i4.703.
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<p class="Els-Abstract-text">RNA-seq using the Next Generation Sequencing (NGS) approach is a common technology to analyze large-scale RNA transcript data for gene expression studies. However, an appropriate bioinformatics tool is needed to analyze a large amount of transcriptomes data from RNA-seq experiment. The aim of this study was to construct a system that can be easily applied to analyze RNA-seq data. RNA-seq analysis tool as SMART-RDA was constructed in this study. It is a computational workflow based on Galaxy framework to be used for analyzing RNA-seq raw data into gene expression information. This workflow was adapted from a well-known Tuxedo Protocol for RNA-seq analysis with some modifications. Expression value from each transcriptome was quantitatively stated as Fragments Per Kilobase of exon per Million fragments (FPKM). RNA-seq data of sterile and fertile oil palm (Pisifera) pollens derived from Sequence Read Archive (SRA) NCBI were used to test this workflow in local facility Galaxy server. The results showed that differentially gene expression in pollens might be responsible for sterile and fertile characteristics in palm oil Pisifera.</p><p><strong>Keywords:</strong> FPKM; Galaxy workflow; Gene expression; RNA sequencing.</p>
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Di Bella, Sebastiano, Alessandro La Ferlita, Giovanni Carapezza, Salvatore Alaimo, Antonella Isacchi, Alfredo Ferro, Alfredo Pulvirenti, and Roberta Bosotti. "A benchmarking of pipelines for detecting ncRNAs from RNA-Seq data." Briefings in Bioinformatics 21, no. 6 (December 3, 2019): 1987–98. http://dx.doi.org/10.1093/bib/bbz110.
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Abstract Next-Generation Sequencing (NGS) is a high-throughput technology widely applied to genome sequencing and transcriptome profiling. RNA-Seq uses NGS to reveal RNA identities and quantities in a given sample. However, it produces a huge amount of raw data that need to be preprocessed with fast and effective computational methods. RNA-Seq can look at different populations of RNAs, including ncRNAs. Indeed, in the last few years, several ncRNAs pipelines have been developed for ncRNAs analysis from RNA-Seq experiments. In this paper, we analyze eight recent pipelines (iSmaRT, iSRAP, miARma-Seq, Oasis 2, SPORTS1.0, sRNAnalyzer, sRNApipe, sRNA workbench) which allows the analysis not only of single specific classes of ncRNAs but also of more than one ncRNA classes. Our systematic performance evaluation aims at guiding users to select the appropriate pipeline for processing each ncRNA class, focusing on three key points: (i) accuracy in ncRNAs identification, (ii) accuracy in read count estimation and (iii) deployment and ease of use.
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Youssefian, Leila, Amir Hossein Saeidian, Fahimeh Palizban, Atefeh Bagherieh, Fahimeh Abdollahimajd, Soheila Sotoudeh, Nikoo Mozafari, et al. "Whole-Transcriptome Analysis by RNA Sequencing for Genetic Diagnosis of Mendelian Skin Disorders in the Context of Consanguinity." Clinical Chemistry 67, no. 6 (May 10, 2021): 876–88. http://dx.doi.org/10.1093/clinchem/hvab042.
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Abstract Background Among the approximately 8000 Mendelian disorders, >1000 have cutaneous manifestations. In many of these conditions, the underlying mutated genes have been identified by DNA-based techniques which, however, can overlook certain types of mutations, such as exonic-synonymous and deep-intronic sequence variants. Whole-transcriptome sequencing by RNA sequencing (RNA-seq) can identify such mutations and provide information about their consequences. Methods We analyzed the whole transcriptome of 40 families with different types of Mendelian skin disorders with extensive genetic heterogeneity. The RNA-seq data were examined for variant detection and prioritization, pathogenicity confirmation, RNA expression profiling, and genome-wide homozygosity mapping in the case of consanguineous families. Among the families examined, RNA-seq was able to provide information complementary to DNA-based analyses for exonic and intronic sequence variants with aberrant splicing. In addition, we tested the possibility of using RNA-seq as the first-tier strategy for unbiased genome-wide mutation screening without information from DNA analysis. Results We found pathogenic mutations in 35 families (88%) with RNA-seq in combination with other next-generation sequencing methods, and we successfully prioritized variants and found the culprit genes. In addition, as a novel concept, we propose a pipeline that increases the yield of variant calling from RNA-seq by concurrent use of genome and transcriptome references in parallel. Conclusions Our results suggest that “clinical RNA-seq” could serve as a primary approach for mutation detection in inherited diseases, particularly in consanguineous families, provided that tissues and cells expressing the relevant genes are available for analysis.
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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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Mekso, Mekibib Million, and Tileye Feyissa. "RNA-Seq as an Effective Tool for Modern Transcriptomics, A Review-based Study." Journal of Applied Research in Plant Sciences 3, no. 02 (July 30, 2022): 236–41. http://dx.doi.org/10.38211/joarps.2022.3.2.29.
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Transcriptome analysis is a useful method for identification and understanding genes. Finding genes that are differentially expressed between conditions is a crucial aspect of transcriptomics. The discovery of RNA seq has been revolutionized next-generation sequencing technology. The fact that RNA sequencing does not requires gene probes and provides a precise measure of gene expression over a much wider range proved its credibility over other common techniques. The expressed gene profile and transcriptome data are stored in a database and could be accessed freely. During RNA seq short read mapping to the reference transcriptome (the set of all known transcript RNA sequences for a species) or genome in the database, a variety of database search tools and alignment methods become visible. There are a variety of applications that help align short reads generated by fragment sequencing. The study of expressed genes is aided by quantifying reads that align to the reference genome or transcriptome. RNA sequencing gives crucial information regarding alternative splicing and gene isoforms, in addition to differential gene expression.
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Zhou, Shuang, Min Gan, Jianyu Zhu, Xinxing Liu, and Guanzhou Qiu. "Assessment of Bioleaching Microbial Community Structure and Function Based on Next-Generation Sequencing Technologies." Minerals 8, no. 12 (December 17, 2018): 596. http://dx.doi.org/10.3390/min8120596.
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It is widely known that bioleaching microorganisms have to cope with the complex extreme environment in which microbial ecology relating to community structure and function varies across environmental types. However, analyses of microbial ecology of bioleaching bacteria is still a challenge. To address this challenge, numerous technologies have been developed. In recent years, high-throughput sequencing technologies enabling comprehensive sequencing analysis of cellular RNA and DNA within the reach of most laboratories have been added to the toolbox of microbial ecology. The next-generation sequencing technology allowing processing DNA sequences can produce available draft genomic sequences of more bioleaching bacteria, which provides the opportunity to predict models of genetic and metabolic potential of bioleaching bacteria and ultimately deepens our understanding of bioleaching microorganism. High-throughput sequencing that focuses on targeted phylogenetic marker 16S rRNA has been effectively applied to characterize the community diversity in an ore leaching environment. RNA-seq, another application of high-throughput sequencing to profile RNA, can be for both mapping and quantifying transcriptome and has demonstrated a high efficiency in quantifying the changing expression level of each transcript under different conditions. It has been demonstrated as a powerful tool for dissecting the relationship between genotype and phenotype, leading to interpreting functional elements of the genome and revealing molecular mechanisms of adaption. This review aims to describe the high-throughput sequencing approach for bioleaching environmental microorganisms, particularly focusing on its application associated with challenges.
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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.
Full textAbstract:
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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Cheng, Clarence Y., Wipapat Kladwang, Joseph D. Yesselman, and Rhiju Das. "RNA structure inference through chemical mapping after accidental or intentional mutations." Proceedings of the National Academy of Sciences 114, no. 37 (August 29, 2017): 9876–81. http://dx.doi.org/10.1073/pnas.1619897114.
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Despite the critical roles RNA structures play in regulating gene expression, sequencing-based methods for experimentally determining RNA base pairs have remained inaccurate. Here, we describe a multidimensional chemical-mapping method called “mutate-and-map read out through next-generation sequencing” (M2-seq) that takes advantage of sparsely mutated nucleotides to induce structural perturbations at partner nucleotides and then detects these events through dimethyl sulfate (DMS) probing and mutational profiling. In special cases, fortuitous errors introduced during DNA template preparation and RNA transcription are sufficient to give M2-seq helix signatures; these signals were previously overlooked or mistaken for correlated double-DMS events. When mutations are enhanced through error-prone PCR, in vitro M2-seq experimentally resolves 33 of 68 helices in diverse structured RNAs including ribozyme domains, riboswitch aptamers, and viral RNA domains with a single false positive. These inferences do not require energy minimization algorithms and can be made by either direct visual inspection or by a neural-network–inspired algorithm called M2-net. Measurements on the P4–P6 domain of the Tetrahymena group I ribozyme embedded in Xenopus egg extract demonstrate the ability of M2-seq to detect RNA helices in a complex biological environment.
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Sheng, Zizhang, Chen Huang, Runxia Liu, Yicheng Guo, Zhiguang Ran, Feng Li, and Dan Wang. "Next-Generation Sequencing Analysis of Cellular Response to Influenza B Virus Infection." Viruses 12, no. 4 (March 31, 2020): 383. http://dx.doi.org/10.3390/v12040383.
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Influenza B virus (IBV) is a respiratory pathogen that infects humans and causes seasonal influenza epidemics. However, cellular response to IBV infection in humans and mechanisms of host-mediated restriction of IBV replication are not thoroughly understood. In this study, we used next-generation sequencing (NGS) to perform transcriptome profiling of IBV-infected human lung epithelial A549 cells at 0, 6, 12, and 24 h post infection (hpi) and characterized the cellular gene expression dynamics. We observed that more than 4000 host genes were differentially regulated during the study period, which included up regulation of genes encoding proteins, having a role in the innate antiviral immune responses, immune activation, cellular metabolism, autophagy, and apoptosis, as well as down regulation of genes involved in mitosis and cell proliferation. Further analysis of RNA-Seq data coupled with RT-qPCR validation collectively showed that double-strand RNA recognition pathways, including retinoic acid-inducible gene I (RIG-I) and Toll-like receptor 3 (TLR3), were substantially activated following IBV infection. Taken together, these results provide important initial insights into the intimate interaction between IBV and lung epithelial cells, which can be further explored towards elucidation of the cellular mechanisms in restriction or elimination of IBV infections in humans.
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Son, Keunhong, Sungryul Yu, Wonseok Shin, Kyudong Han, and Keunsoo Kang. "A Simple Guideline to Assess the Characteristics of RNA-Seq Data." BioMed Research International 2018 (November 4, 2018): 1–9. http://dx.doi.org/10.1155/2018/2906292.
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Next-generation sequencing (NGS) techniques have been used to generate various molecular maps including genomes, epigenomes, and transcriptomes. Transcriptomes from a given cell population can be profiled via RNA-seq. However, there is no simple way to assess the characteristics of RNA-seq data systematically. In this study, we provide a simple method that can intuitively evaluate RNA-seq data using two different principal component analysis (PCA) plots. The gene expression PCA plot provides insights into the association between samples, while the transcript integrity number (TIN) score plot provides a quality map of given RNA-seq data. With this approach, we found that RNA-seq datasets deposited in public repositories often contain a few low-quality RNA-seq data that can lead to misinterpretations. The effect of sampling errors for differentially expressed gene (DEG) analysis was evaluated with ten RNA-seq data from invasive ductal carcinoma tissues and three RNA-seq data from adjacent normal tissues taken from a Korean breast cancer patient. The evaluation demonstrated that sampling errors, which select samples that do not represent a given population, can lead to different interpretations when conducting the DEG analysis. Therefore, the proposed approach can be used to avoid sampling errors prior to RNA-seq data analysis.
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Ganguly, Payal, Bradley Toghill, and Shelly Pathak. "Aging, Bone Marrow and Next-Generation Sequencing (NGS): Recent Advances and Future Perspectives." International Journal of Molecular Sciences 22, no. 22 (November 12, 2021): 12225. http://dx.doi.org/10.3390/ijms222212225.
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The aging of bone marrow (BM) remains a very imperative and alluring subject, with an ever-increasing interest among fellow scientists. A considerable amount of progress has been made in this field with the established ‘hallmarks of aging’ and continued efforts to investigate the age-related changes observed within the BM. Inflammaging is considered as a low-grade state of inflammation associated with aging, and whilst the possible mechanisms by which aging occurs are now largely understood, the processes leading to the underlying changes within aged BM remain elusive. The ability to identify these changes and detect such alterations at the genetic level are key to broadening the knowledgebase of aging BM. Next-generation sequencing (NGS) is an important molecular-level application presenting the ability to not only determine genomic base changes but provide transcriptional profiling (RNA-seq), as well as a high-throughput analysis of DNA–protein interactions (ChIP-seq). Utilising NGS to explore the genetic alterations occurring over the aging process within alterative cell types facilitates the comprehension of the molecular and cellular changes influencing the dynamics of aging BM. Thus, this review prospects the current landscape of BM aging and explores how NGS technology is currently being applied within this ever-expanding field of research.
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Satake, Noriko, Sheila Thampi, Clifford Tepper, Astra Chang, Ping Zhou, Bridget McLaughlin, Jeannine McGee, and Jan A. Nolta. "Validation of a Human Acute Lymphoblastic Leukemia Mouse Model Using Next Generation RNA Sequencing,." Blood 118, no. 21 (November 18, 2011): 3576. http://dx.doi.org/10.1182/blood.v118.21.3576.3576.
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Abstract Abstract 3576 Primary leukemia cells are known to be difficult to culture in vitro. Therefore, mouse xenograft models are commonly used to study human leukemia biology and to develop new therapeutics. Leukemia can be maintained and expanded through serial transplantations. Although many mouse models with different types of leukemia, mouse strains, and inoculation methods, have been used, there are very few studies validating the models. We have established acute lymphoblastic leukemia (ALL) mouse models using primary ALL samples and NOD/SCID/IL2Rg null (NSG) mice. In order to increase engraftment efficiency, we transplanted leukemia cells into healthy adult mice via intra-bone marrow (BM) injection (1 to 100 million cells per mouse to tibia bilaterally). To our knowledge, the model with primary ALL samples in NSG mice via intra-BM injection is novel. A total of 9 samples, 2 T cell ALL and 7 precursor B (preB) ALL, were transplanted into cohorts of 3 to 8 mice each. Of these, 1 T cell ALL and 6 preB ALL samples engrafted and 1 T cell and 1 preB ALL sample failed to engraft. Leukemia engraftment was observed between 10 to 33 weeks after transplantation. In each cohort, each mouse developed leukemia at approximately the same time after leukemia transplantation. Necropsy showed significant hepatosplenomegaly and white BM in all mice, indicating leukemia infiltration. Cells were harvested from BM, spleen and other leukemia-infiltrated organs, and confirmed to be human origin by flow cytometry using anti-HLA-ABC and anti- human CD45 antibodies. Nearly 100% of BM was replaced with human leukemia cells. One T cell and 1 preB ALL sample have been serially transplanted using the same method as the primary transplantation to quaternary and tertiary generations, respectively. We observed that the time to develop leukemia became shorter with each transplantation: 16 weeks for the first transplantation and 10 weeks for the tertiary transplantation in a T cell ALL sample. Some mice transplanted with a preB ALL sample developed chloroma. Interestingly, chloroma development was consistently observed with this sample, which was transferred through serial transplantation although the patient did not develop chloroma. Morphology and immunophenotype were similar to the original leukemia. There were some changes in CD expression patterns; however, immunophenotyping was consistent with the original leukemia. To help understand phenotype progression in transplanted leukemia samples, we are currently comparing the transcriptome profiles of leukemia samples obtained from the patient, primary and quaternary mice for T cell ALL, and primary and tertiary mice for preB ALL samples (preB ALL patient sample not available). Next-generation sequencing (NGS)-based RNA-Sequencing (RNA-Seq) is in progress for this analysis. In addition to obtaining digital expression profiles, and differential gene expression, RNA-Seq analysis will reveal the complete repertoire of splice variants, point mutations, and fusion transcripts in the ALL samples. Complete results of these analyses for our mouse models with both T cell and preB ALL will be presented. Thus, this technique will provide the most detailed transcriptomic analysis and no validation studies have yet been reported on ALL samples sequentially engrafted NSG mice using RNA-Seq. Disclosures: No relevant conflicts of interest to declare.
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De Wispelaere, Koenraad, and Kathleen Freson. "The Analysis of the Human Megakaryocyte and Platelet Coding Transcriptome in Healthy and Diseased Subjects." International Journal of Molecular Sciences 23, no. 14 (July 11, 2022): 7647. http://dx.doi.org/10.3390/ijms23147647.
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Platelets are generated and released into the bloodstream from their precursor cells, megakaryocytes that reside in the bone marrow. Though platelets have no nucleus or DNA, they contain a full transcriptome that, during platelet formation, is transported from the megakaryocyte to the platelet. It has been described that transcripts in platelets can be translated into proteins that influence platelet response. The platelet transcriptome is highly dynamic and has been extensively studied using microarrays and, more recently, RNA sequencing (RNA-seq) in relation to diverse conditions (inflammation, obesity, cancer, pathogens and others). In this review, we focus on bulk and single-cell RNA-seq studies that have aimed to characterize the coding transcriptome of healthy megakaryocytes and platelets in humans. It has been noted that bulk RNA-seq has limitations when studying in vitro-generated megakaryocyte cultures that are highly heterogeneous, while single-cell RNA-seq has not yet been applied to platelets due to their very limited RNA content. Next, we illustrate how these methods can be applied in the field of inherited platelet disorders for gene discovery and for unraveling novel disease mechanisms using RNA from platelets and megakaryocytes and rare disease bioinformatics. Next, future perspectives are discussed on how this field of coding transcriptomics can be integrated with other next-generation technologies to decipher unexplained inherited platelet disorders in a multiomics approach.
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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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Raju Paul, Susan, Nikita Kotlov, Viktor Svekolkin, Felix Frenkel, Nava Almog, Maria Tsiper, Krystle Nomie, et al. "Immune functional portraits of head and neck cancer using next generation sequencing." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): 6561. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.6561.
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6561 Background: The addition of biomarkers as companion diagnostics and Next Generation Sequencing (NGS) have dramatically increased therapeutic efficacy and have aided precision medicine development. The unique genomic profile and tumor microenvironment (TME) composition of each patient can be ascertained through NGS. Using TCGA and Geo datasets, we characterized head and neck cancers (HNC) according to the cellular and functional state of their TME and conducted a pilot validation study using prospectively collected HNC tumors. Methods: To stratify the TME of HNC tumors into molecular functional portraits, we analyzed the sequencing data of 1,486 HNC tumor samples and 143 controls (normal, oral leukoplakia) from TCGA and GEO data sets. For the prospective pilot study, resected tissue from oropharyngeal carcinomas independent of HPV status were processed for whole exome (WES) and RNA-seq (n = 6; HPV-positive = 1). Results: To characterize the cellular composition and functional state of HNC tumors and their TMEs, we created 26 separate molecular signatures related to functional processes such as immune checkpoint inhibition, immune infiltration, immunosuppression, and stromal activities represented by angiogenesis and mesenchymal stromal cells. Unsupervised clustering of these signatures delineated tumors into 4 types: immune infiltration with increased stromal signatures (type A), immune infiltration with decreased stromal signatures (type B), no immune infiltration with increased stromal signature (type C), and no immune infiltration and decreased stromal signatures (type D). Most HPV-positive tumors were type B (p = 1e-27) and associated with increased survival compared to the HPV-negative tumors (types C and D; p = 3e-05). Type B HPV-positive tumors had reduced FAT1 and TP53 mutations, whereas type B HPV-negative tumors had increased caspase 8 mutations/loss. In the validation cohort, actionable mutations were found in PI3KCA and TSC2 in types A and B HPV-negative tumors. Moreover, while the HPV-positive tumor was classified as type C, we identified a caspase 8 homozygous deletion and absence of FAT1 and TP53 mutations, supporting the TCGA and GEO analysis. Conclusions: Exome and transcriptome analyses with cellular deconvolution from bulk RNA-seq enrich tumor characterization by including major TME components, providing a comprehensive biomarker profile for precision therapy and clinical decision making. Our prospective analysis identified TME parameters comparable with the large datasets and revealed targetable genomic alterations.