scholarly journals DBtRend: A Web-Server of tRNA Expression Profiles from Small RNA Sequencing Data in Humans

Genes ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1576
Author(s):  
Jin-Ok Lee ◽  
Minho Lee ◽  
Yeun-Jun Chung
Keyword(s):  
Small Rna ◽  
Molecular Subtype ◽  
Expression Profiles ◽  
Web Server ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Microrna Sequencing ◽  
User Friendly

Transfer RNA (tRNA), a key component of the translation machinery, plays critical roles in stress conditions and various diseases. While knowledge regarding the importance of tRNA function is increasing, its biological roles are still not well understood. There is currently no comprehensive database or web server providing the expression landscape of tRNAs across a variety of human tissues and diseases. Here, we constructed a user-friendly and interactive database, DBtRend, which provides a profile of mature tRNA expression across various biological conditions by reanalyzing the small RNA or microRNA sequencing data from the Cancer Genome Atlas (TCGA) and NCBI’s Gene Expression Omnibus (GEO) in humans. Users can explore not only the expression values of mature individual tRNAs in the human genome, but also those of isodecoders and isoacceptors based on our specific pipelines. DBtRend provides the expressed patterns of tRNAs, the differentially expressed tRNAs in different biological conditions, and the information of samples or patients, tissue types, and molecular subtype of cancers. The database is expected to help researchers interested in functional discoveries of tRNAs.

scholarly journals Characterizing the Cancer-Associated Microbiome with Small RNA Sequencing Data

2019 ◽  
Author(s):  
Wei-Hao Lee ◽  
Kai-Pu Chen ◽  
Kai Wang ◽  
Hsuan-Cheng Huang ◽  
Hsueh-Fen Juan
Keyword(s):  
Small Rna ◽  
The Cancer Genome Atlas ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Microbial Composition ◽  
Rna Sequences ◽  
Colon And Rectal Cancer ◽  
Microrna Sequencing ◽  
Cancer Genome Atlas ◽  
Inflammatory Immune Response

AbstractThe microbiome is recognized as a quasi-organ in the human body. In particular, the gut microbiome is correlated with immune function, metabolism, and tumorigenesis. When dysbiosis of the microbiome occurs, this variation may contribute to alterations in the microenvironment, potentially inducing an inflammatory immune response and providing a niche for neoplastic growth. However, there is limited evidence regarding the correlation and interaction between the microbiome and tumorigenesis. By utilizing microRNA sequencing data of patients with colon and rectal cancer from The Cancer Genome Atlas, we designed a novel analytical process to extract non-human small RNA sequences and align them with the microbial genome to obtain a comprehensive view of the cancer-associated microbiome. In the present study, we identified > 1000 genera among 630 colorectal samples and clustered these samples into three distinctive colorectal enterotypes. Each cluster has its own distinctive microbial composition and interactions. Furthermore, we found 12 genera from these clusters that are associated with cancer stages and revealed their putative functions. Our results indicate that the proposed analytical approach can effectively determine the cancer-associated microbiome. It may be readily applied to explore other types of cancer, in which specimens of the microbiome are difficult to collect.

scholarly journals Evaluating gastroenteropancreatic neuroendocrine tumors through microRNA sequencing

2019 ◽  
Vol 26 (1) ◽  
pp. 47-57 ◽  
Author(s):  
Nicole Panarelli ◽  
Kathrin Tyryshkin ◽  
Justin Jong Mun Wong ◽  
Adrianna Majewski ◽  
Xiaojing Yang ◽  
...  
Keyword(s):  
Neuroendocrine Tumors ◽  
Small Rna ◽  
Mirna Expression ◽  
Expression Profiles ◽  
Machine Learning Techniques ◽  
Disease Stage ◽  
Small Rna Sequencing ◽  
Gastroenteropancreatic Neuroendocrine Tumors ◽  
Mirna Expression Profiles ◽  
Microrna Sequencing

Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) can be challenging to evaluate histologically. MicroRNAs (miRNAs) are small RNA molecules that often are excellent biomarkers due to their abundance, cell-type and disease stage specificity and stability. To evaluate miRNAs as adjunct tissue markers for classifying and grading well-differentiated GEP-NETs, we generated and compared miRNA expression profiles from four pathological types of GEP-NETs. Using quantitative barcoded small RNA sequencing and state-of-the-art sequence annotation, we generated comprehensive miRNA expression profiles from archived pancreatic, ileal, appendiceal and rectal NETs. Following data preprocessing, we randomly assigned sample profiles to discovery (80%) and validation (20%) sets prior to data mining using machine-learning techniques. High expression analyses indicated that miR-375 was the most abundant individual miRNA and miRNA cistron in all samples. Leveraging prior knowledge that GEP-NET behavior is influenced by embryonic derivation, we developed a dual-layer hierarchical classifier for differentiating GEP-NET types. In the first layer, our classifier discriminated midgut (ileum, appendix) from non-midgut (rectum, pancreas) NETs based on miR-615 and -92b expression. In the second layer, our classifier discriminated ileal from appendiceal NETs based on miR-125b, -192 and -149 expression, and rectal from pancreatic NETs based on miR-429 and -487b expression. Our classifier achieved overall accuracies of 98.5% and 94.4% in discovery and validation sets, respectively. We also found provisional evidence that low- and intermediate-grade pancreatic NETs can be discriminated based on miR-328 expression. GEP-NETs can be reliably classified and potentially graded using a limited panel of miRNA markers, complementing morphological and immunohistochemistry-based approaches to histologic evaluation.

scholarly journals Comprehensive analysis of human small RNA sequencing data provides insights into expression profiles and miRNA editing

RNA Biology ◽  
2014 ◽  
Vol 11 (11) ◽  
pp. 1375-1385 ◽  
Author(s):  
Jing Gong ◽  
Yuliang Wu ◽  
Xiantong Zhang ◽  
Yifang Liao ◽  
Vusumuzi Leroy Sibanda ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Small Rna ◽  
Expression Profiles ◽  
Comprehensive Analysis ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Mirna Editing

scholarly journals Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments

2020 ◽  
Vol 13 (S8) ◽  
Author(s):  
Nicolas Borisov ◽  
Maxim Sorokin ◽  
Victor Tkachev ◽  
Andrew Garazha ◽  
Anton Buzdin
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Low Grade ◽  
Sequencing Data ◽  
Kidney Tumors ◽  
Pediatric Leukemia ◽  
Adult Leukemia

Abstract Background Machine learning (ML) methods still have limited applicability in personalized oncology due to low numbers of available clinically annotated molecular profiles. This doesn’t allow sufficient training of ML classifiers that could be used for improving molecular diagnostics. Methods We reviewed published datasets of high throughput gene expression profiles corresponding to cancer patients with known responses on chemotherapy treatments. We browsed Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Tumor Alterations Relevant for GEnomics-driven Therapy (TARGET) repositories. Results We identified data collections suitable to build ML models for predicting responses on certain chemotherapeutic schemes. We identified 26 datasets, ranging from 41 till 508 cases per dataset. All the datasets identified were checked for ML applicability and robustness with leave-one-out cross validation. Twenty-three datasets were found suitable for using ML that had balanced numbers of treatment responder and non-responder cases. Conclusions We collected a database of gene expression profiles associated with clinical responses on chemotherapy for 2786 individual cancer cases. Among them seven datasets included RNA sequencing data (for 645 cases) and the others – microarray expression profiles. The cases represented breast cancer, lung cancer, low-grade glioma, endothelial carcinoma, multiple myeloma, adult leukemia, pediatric leukemia and kidney tumors. Chemotherapeutics included taxanes, bortezomib, vincristine, trastuzumab, letrozole, tipifarnib, temozolomide, busulfan and cyclophosphamide.

scholarly journals miRAuto: An automated user-friendly MicroRNA prediction tool utilizing plant small RNA sequencing data

2013 ◽  
Vol 35 (4) ◽  
pp. 342-347 ◽  
Author(s):  
Jeongsoo Lee ◽  
Dong-in Kim ◽  
June Hyun Park ◽  
Ik-Young Choi ◽  
Chanseok Shin
Keyword(s):  
Rna Sequencing ◽  
Small Rna ◽  
Small Rna Sequencing ◽  
Prediction Tool ◽  
Sequencing Data ◽  
User Friendly ◽  
Plant Small Rna

scholarly journals Metavisitor, a suite of Galaxy tools for simple and rapid detection and discovery of viruses in deep sequence data

2016 ◽  
Author(s):  
Guillaume Carissimo ◽  
Marius van den Beek ◽  
Juliana Pegoraro ◽  
Kenneth D Vernick ◽  
Christophe Antoniewski
Keyword(s):  
Small Rna ◽  
De Novo ◽  
Sequence Data ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Viral Genomes ◽  
Bioinformatic Tools ◽  
The Galaxy ◽  
Galaxy Server ◽  
User Friendly

AbstractWe present user-friendly and adaptable software to provide biologists, clinical researchers and possibly diagnostic clinicians with the ability to robustly detect and reconstruct viral genomes from complex deep sequence datasets. A set of modular bioinformatic tools and workflows was implemented as the Metavisitor package in the Galaxy framework. Using the graphical Galaxy workflow editor, users with minimal computational skills can use existing Metavisitor workflows or adapt them to suit specific needs by adding or modifying analysis modules. Metavisitor can be used on our Mississippi server, or can be installed on any Galaxy server instance and a pre-configured Metavisitor server image is provided. Metavisitor works with DNA, RNA or small RNA sequencing data over a range of read lengths and can use a combination of de novo and guided approaches to assemble genomes from sequencing reads. We show that the software has the potential for quick diagnosis as well as discovery of viruses from a vast array of organisms. Importantly, we provide here executable Metavisitor use cases, which increase the accessibility and transparency of the software, ultimately enabling biologists or clinicians to focus on biological or medical questions.

scholarly journals Evaluation of Commercially Available Exosomal Isolation Kits from Human Plasma

2019 ◽  
Author(s):  
Yuzhe Sun ◽  
Hefu Zhen ◽  
Mei Guo ◽  
Jingyu Ye ◽  
Zhili Liu ◽  
...  
Keyword(s):  
Data Quality ◽  
Human Plasma ◽  
Small Rna ◽  
Profile Analysis ◽  
Biological Fluids ◽  
Small Rna Sequencing ◽  
Plasma Samples ◽  
Sequencing Data ◽  
Isolation Methods ◽  
User Friendly

AbstractExosomes are cell-derived lipid bilayer particles which are abundant in biological fluids. Exosome is an emerging source of biomarkers to diagnose various human diseases. Sequencing based exosomal studies could provide a comprehensive view of exosomal RNA and protein. To extracted these inclusions, exosomes should be isolated from the plasma first. Several exosome isolation methods were introduced since the discover of exosome. To promote the clinical application of exosomal inclusions, different isolation methods should be compared. We isolated exosomes from human plasma by using user-friendly and commercially available kits, SBI ExoQuick and QIAGEN exoRNeasy. Subsequently, small RNA sequencing was performed with two groups of isolated exosome samples and one group of plasma samples. No fundamental differences of exRNA yield between SC and EQ were found. In RNA profile analysis, the small RNA aligned reads, miRNA pattern, sample clustering varied as a result of methodological differences. Small RNA isolated by ExoQuick presented better data quality and RNA profile than exoRNeasy. This study compared sRNA sequencing data generated from two exosome isolation kits, it provides a reference for future small RNA studies and biomarker prediction in human plasma exosome.

scholarly journals Upgrading the Repertoire of miRNAs in Gastric Adenocarcinoma to Provide a New Resource for Biomarker Discovery

2019 ◽  
Vol 20 (22) ◽  
pp. 5697 ◽  
Author(s):  
Michelle E. Pewarchuk ◽  
Mateus C. Barros-Filho ◽  
Brenda C. Minatel ◽  
David E. Cohn ◽  
Florian Guisier ◽  
...  
Keyword(s):  
Gastric Adenocarcinoma ◽  
Biomarker Discovery ◽  
The Cancer Genome Atlas ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Specific Expression ◽  
Novel Mirna ◽  
Cancer Genome Atlas ◽  
Context Specific ◽  
Independent Cohort

Recent studies have uncovered microRNAs (miRNAs) that have been overlooked in early genomic explorations, which show remarkable tissue- and context-specific expression. Here, we aim to identify and characterize previously unannotated miRNAs expressed in gastric adenocarcinoma (GA). Raw small RNA-sequencing data were analyzed using the miRMaster platform to predict and quantify previously unannotated miRNAs. A discovery cohort of 475 gastric samples (434 GA and 41 adjacent nonmalignant samples), collected by The Cancer Genome Atlas (TCGA), were evaluated. Candidate miRNAs were similarly assessed in an independent cohort of 25 gastric samples. We discovered 170 previously unannotated miRNA candidates expressed in gastric tissues. The expression of these novel miRNAs was highly specific to the gastric samples, 143 of which were significantly deregulated between tumor and nonmalignant contexts (p-adjusted < 0.05; fold change > 1.5). Multivariate survival analyses showed that the combined expression of one previously annotated miRNA and two novel miRNA candidates was significantly predictive of patient outcome. Further, the expression of these three miRNAs was able to stratify patients into three distinct prognostic groups (p = 0.00003). These novel miRNAs were also present in the independent cohort (43 sequences detected in both cohorts). Our findings uncover novel miRNA transcripts in gastric tissues that may have implications in the biology and management of gastric adenocarcinoma.

scholarly journals Comprehensive analyses of competing endogenous RNA networks reveal potential biomarkers for predicting hepatocellular carcinoma recurrence

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ping Yan ◽  
Zuotian Huang ◽  
Tong Mou ◽  
Yunhai Luo ◽  
Yanyao Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Clinical Information ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
High Rate ◽  
Competing Endogenous Rna ◽  
Tissue Samples ◽  
Potential Biomarkers

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common and deadly malignant tumors, with a high rate of recurrence worldwide. This study aimed to investigate the mechanism underlying the progression of HCC and to identify recurrence-related biomarkers. Methods We first analyzed 132 HCC patients with paired tumor and adjacent normal tissue samples from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). The expression profiles and clinical information of 372 HCC patients from The Cancer Genome Atlas (TCGA) database were next analyzed to further validate the DEGs, construct competing endogenous RNA (ceRNA) networks and discover the prognostic genes associated with recurrence. Finally, several recurrence-related genes were evaluated in two external cohorts, consisting of fifty-two and forty-nine HCC patients, respectively. Results With the comprehensive strategies of data mining, two potential interactive ceRNA networks were constructed based on the competitive relationships of the ceRNA hypothesis. The ‘upregulated’ ceRNA network consists of 6 upregulated lncRNAs, 3 downregulated miRNAs and 5 upregulated mRNAs, and the ‘downregulated’ network includes 4 downregulated lncRNAs, 12 upregulated miRNAs and 67 downregulated mRNAs. Survival analysis of the genes in the ceRNA networks demonstrated that 20 mRNAs were significantly associated with recurrence-free survival (RFS). Based on the prognostic mRNAs, a four-gene signature (ADH4, DNASE1L3, HGFAC and MELK) was established with the least absolute shrinkage and selection operator (LASSO) algorithm to predict the RFS of HCC patients, the performance of which was evaluated by receiver operating characteristic curves. The signature was also validated in two external cohort and displayed effective discrimination and prediction for the RFS of HCC patients. Conclusions In conclusion, the present study elucidated the underlying mechanisms of tumorigenesis and progression, provided two visualized ceRNA networks and successfully identified several potential biomarkers for HCC recurrence prediction and targeted therapies.

scholarly journals Extensive Analysis of miRNA Trimming and Tailing Indicates that AGO1 Has a Complex Role in miRNA Turnover

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 267
Author(s):  
Axel J. Giudicatti ◽  
Ariel H. Tomassi ◽  
Pablo A. Manavella ◽  
Agustin L. Arce
Keyword(s):  
Small Rna ◽  
Stress Responses ◽  
Cellular Localization ◽  
Mirna Biogenesis ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Extensive Analysis ◽  
Small Regulatory Rnas ◽  
Regulatory Rnas ◽  
Argonaute 1

MicroRNAs are small regulatory RNAs involved in several processes in plants ranging from development and stress responses to defense against pathogens. In order to accomplish their molecular functions, miRNAs are methylated and loaded into one ARGONAUTE (AGO) protein, commonly known as AGO1, to stabilize and protect the molecule and to assemble a functional RNA-induced silencing complex (RISC). A specific machinery controls miRNA turnover to ensure the silencing release of targeted-genes in given circumstances. The trimming and tailing of miRNAs are fundamental modifications related to their turnover and, hence, to their action. In order to gain a better understanding of these modifications, we analyzed Arabidopsis thaliana small RNA sequencing data from a diversity of mutants, related to miRNA biogenesis, action, and turnover, and from different cellular fractions and immunoprecipitations. Besides confirming the effects of known players in these pathways, we found increased trimming and tailing in miRNA biogenesis mutants. More importantly, our analysis allowed us to reveal the importance of ARGONAUTE 1 (AGO1) loading, slicing activity, and cellular localization in trimming and tailing of miRNAs.

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