scholarly journals DPAC: a tool for Differential Poly(A) Site usage from poly(A)–targeted RNAseq data

2019 ◽  
Author(s):  
Andrew Routh
Keyword(s):  
Gene Expression ◽  
Alternative Polyadenylation ◽  
Data Mapping ◽  
Library Synthesis ◽  
Sequencing Data ◽  
Synthesis Strategy ◽  
Rnaseq Data ◽  
Differential Gene ◽  
A Site

AbstractPoly(A)-tail targeted RNAseq approaches, such as 3’READS, PAS-seq and Poly(A)-ClickSeq, are becoming popular alternatives to random-primed RNAseq for simplified gene expression analyses as well as to measure changes in poly(A) site usage. We and others have recently demonstrated that these approaches perform similarly to other RNAseq strategies, while saving on the volume of sequencing data required and providing a simpler library synthesis strategy. Here, we present DPAC; a streamlined pipeline for the preprocessing of poly(A)-tail targeted RNAseq data, mapping of poly(A)-sites and poly(A) clustering, and determination of differential poly(A) site usage using DESeq2. Changes in poly(A) site usage is simultaneously used to report differential gene expression, differential terminal exon usage and alternative polyadenylation (APA).

scholarly journals M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data

2019 ◽  
Vol 20 (S24) ◽  
Author(s):  
Yu Zhang ◽  
Changlin Wan ◽  
Pengcheng Wang ◽  
Wennan Chang ◽  
Yan Huo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Model Selection ◽  
Statistical Model ◽  
Single Cell ◽  
Differential Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Sequencing Data ◽  
Differential Gene ◽  
Selection Of

Abstract Background Various statistical models have been developed to model the single cell RNA-seq expression profiles, capture its multimodality, and conduct differential gene expression test. However, for expression data generated by different experimental design and platforms, there is currently lack of capability to determine the most proper statistical model. Results We developed an R package, namely Multi-Modal Model Selection (M3S), for gene-wise selection of the most proper multi-modality statistical model and downstream analysis, useful in a single-cell or large scale bulk tissue transcriptomic data. M3S is featured with (1) gene-wise selection of the most parsimonious model among 11 most commonly utilized ones, that can best fit the expression distribution of the gene, (2) parameter estimation of a selected model, and (3) differential gene expression test based on the selected model. Conclusion A comprehensive evaluation suggested that M3S can accurately capture the multimodality on simulated and real single cell data. An open source package and is available through GitHub at https://github.com/zy26/M3S.

scholarly journals Single-Cell Transcriptome Analysis Reveals Dynamic Cell Populations and Differential Gene Expression Patterns in Control and Aneurysmal Human Aortic Tissue

Circulation ◽  
2020 ◽  
Vol 142 (14) ◽  
pp. 1374-1388
Author(s):  
Yanming Li ◽  
Pingping Ren ◽  
Ashley Dawson ◽  
Hernan G. Vasquez ◽  
Waleed Ageedi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Aortic Wall ◽  
Genome Wide Association ◽  
Aortic Tissue ◽  
Sequencing Data ◽  
Genome Wide ◽  
Single Cell Rna Sequencing ◽  
Differential Gene

Background: Ascending thoracic aortic aneurysm (ATAA) is caused by the progressive weakening and dilatation of the aortic wall and can lead to aortic dissection, rupture, and other life-threatening complications. To improve our understanding of ATAA pathogenesis, we aimed to comprehensively characterize the cellular composition of the ascending aortic wall and to identify molecular alterations in each cell population of human ATAA tissues. Methods: We performed single-cell RNA sequencing analysis of ascending aortic tissues from 11 study participants, including 8 patients with ATAA (4 women and 4 men) and 3 control subjects (2 women and 1 man). Cells extracted from aortic tissue were analyzed and categorized with single-cell RNA sequencing data to perform cluster identification. ATAA-related changes were then examined by comparing the proportions of each cell type and the gene expression profiles between ATAA and control tissues. We also examined which genes may be critical for ATAA by performing the integrative analysis of our single-cell RNA sequencing data with publicly available data from genome-wide association studies. Results: We identified 11 major cell types in human ascending aortic tissue; the high-resolution reclustering of these cells further divided them into 40 subtypes. Multiple subtypes were observed for smooth muscle cells, macrophages, and T lymphocytes, suggesting that these cells have multiple functional populations in the aortic wall. In general, ATAA tissues had fewer nonimmune cells and more immune cells, especially T lymphocytes, than control tissues did. Differential gene expression data suggested the presence of extensive mitochondrial dysfunction in ATAA tissues. In addition, integrative analysis of our single-cell RNA sequencing data with public genome-wide association study data and promoter capture Hi-C data suggested that the erythroblast transformation-specific related gene( ERG ) exerts an important role in maintaining normal aortic wall function. Conclusions: Our study provides a comprehensive evaluation of the cellular composition of the ascending aortic wall and reveals how the gene expression landscape is altered in human ATAA tissue. The information from this study makes important contributions to our understanding of ATAA formation and progression.

scholarly journals SPARTA: Simple Program for Automated reference-based bacterial RNA-seq Transcriptome Analysis

2015 ◽  
Author(s):  
Benjamin K Johnson ◽  
Matthew B Scholz ◽  
Tracy K Teal ◽  
Robert B Abramovitch
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Quality Analysis ◽  
Rna Seq ◽  
Sequencing Data ◽  
Data Set ◽  
Bacterial Rna ◽  
Analysis Workflow ◽  
Differential Gene ◽  
Reference Counting

Summary: SPARTA is a reference-based bacterial RNA-seq analysis workflow application for single-end Illumina reads. SPARTA is turnkey software that simplifies the process of analyzing RNA-seq data sets, making bacterial RNA-seq analysis a routine process that can be undertaken on a personal computer or in the classroom. The easy-to-install, complete workflow processes whole transcriptome shotgun sequencing data files by trimming reads and removing adapters, mapping reads to a reference, counting gene features, calculating differential gene expression, and, importantly, checking for potential batch effects within the data set. SPARTA outputs quality analysis reports, gene feature counts and differential gene expression tables and scatterplots. The workflow is implemented in Python for file management and sequential execution of each analysis step and is available for Mac OS X, Microsoft Windows, and Linux. To promote the use of SPARTA as a teaching platform, a web-based tutorial is available explaining how RNA-seq data are processed and analyzed by the software. Availability and Implementation: Tutorial and workflow can be found at sparta.readthedocs.org. Teaching materials are located at sparta-teaching.readthedocs.org. Source code can be downloaded at www.github.com/abramovitchMSU/, implemented in Python and supported on Mac OS X, Linux, and MS Windows. Contact: Robert B. Abramovitch ([email protected]) Supplemental Information: Supplementary data are available online

scholarly journals Authentication of Differential Gene Expression in Oral Squamous Cell Carcinoma using Machine Learning Applications

2020 ◽  
Author(s):  
Rian Pratama ◽  
Jae Joon Hwang ◽  
Ji Hye Lee ◽  
Giltae Song ◽  
Hae Ryoun Park
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Squamous Cell Carcinoma ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Rna Sequencing ◽  
Differential Gene Expression ◽  
Sequencing Data ◽  
Machine Learning Applications ◽  
Differential Gene

Abstract Background: Recently, the possibility of tumour classification based on genetic data has been investigated. However, genetic datasets are difficult to handle because of their massive size and complexity of manipulation. In the present study, we examined the diagnostic performance of machine learning applications using imaging-based classifications of oral squamous cell carcinoma (OSCC) gene sets.Methods: RNA sequencing data from SCC tissues from various sites, including oral, non-oral head and neck, oesophageal, and cervical regions, were downloaded from The Cancer Genome Atlas (TCGA). The feature genes were extracted through a convolutional neural network (CNN) and machine learning, and the performance of each analysis was compared.Results: The ability of the machine learning analysis to classify OSCC tumours was excellent. However, the tool exhibited poorer performance in discriminating histopathologically dissimilar cancers derived from the same type of tissue than in differentiating cancers of the same histopathologic type with different tissue origins, revealing that the differential gene expression pattern is a more important factor than the histopathologic features for differentiating cancer types.Conclusion: The CNN-based diagnostic model and the visualisation methods using RNA sequencing data were useful for correctly categorising OSCC. The analysis showed differentially expressed genes in multiwise comparisons of various types of SCCs, such as KCNA10, FOSL2, and PRDM16, and extracted leader genes from pairwise comparisons were FGF20, DLC1, and ZNF705D.

High-throughput global transcriptional profiling to identify the STAT3 signaling pathway as a potential biomarker for immune checkpoint inhibitor resistance in metastatic/advanced urothelial carcinoma.

2021 ◽  
Vol 39 (6_suppl) ◽  
pp. 474-474
Author(s):  
Yung Lyou ◽  
Tanya B. Dorff ◽  
Sumanta K. Pal ◽  
Yate-Ching Yuan
Keyword(s):  
Gene Expression ◽  
Clinical Trial ◽  
Urothelial Carcinoma ◽  
Immune Checkpoint ◽  
Clinical Benefit ◽  
Differentially Expressed ◽  
Sequencing Data ◽  
Stat3 Signaling ◽  
Potential Biomarker ◽  
Differential Gene

474 Background: Advanced/metastatic urothelial carcinoma (UC) is a significant public health burden with median overall survival of 15 months. Although, immune checkpoint inhibitors (ICI) have provided an additional second line treatment option, only 15-40% of patients will respond. There has been much effort in determining the mechanisms of immunotherapy resistance and predictive biomarkers to further improve these treatments. Methods: Pre-treatment genomic sequencing data derived from FFPE samples from the IMVIGOR210 clinical trial (n=298) was accessed for analysis. Briefly it was a single arm phase II clinical trial where advanced/metastatic UC patients refractory to platinum chemotherapy treatment received the ICI atezolizumab. This study has been published with detailed methods (PMID: 28950298). The raw sequencing data was pre-processed using standard QC measures and aligned to the human reference genome (hg38). The resulting outputs were then normalized and processed to generate the gene level counts for differential gene expression (DGE). We did DGE analysis comparing patients who had clinical benefit (CR, PR, SD) vs non-clinical benefit (ie. PD) to atezolizumab. The list of differentially expressed genes were then analyzed using various gene ontology, pathway and systems biology tools (IPA, Enrichr, and X2Kweb). Further subset analysis was done using gene-gene correlations (ie. PD-L1 and STAT3) and clinicopathologic features (eg. gender, race, smoking history). Results: Among the 298 patients in this study, there were 25 with CR, 43 with PR, 63 with SD, and 167 with PD based on clinical response to atezolizumab. Subgroup analysis for CR vs PD patients found that approximately 847 genes were differentially expressed with statistical significance (p ≤ 0.05). IPA analysis for this list of differentially expressed genes found among the top signaling pathways were “primary immunodeficiency” and “sirtuin signaling”. Further subset analysis of 39 genes (p ≤ 0.01) enriched in PD patients using Enrichr and X2kweb found that there was an overrepresentation of STAT3 signaling genes (hypergeometric p-val 6.32x104). Conclusions: Our results found that when the transcriptional profiles of CR vs PD there was differential gene expression in STAT3, primary immunodeficiency, and sirtuin signaling pathways. Of note it has been reported that STAT3 signaling can modulate immune activity and its expression is correlated with poor prognosis in urothelial carcinoma patients. These results warrant a larger study to see if STAT3 signaling is a potential biomarker for ICI resistance. If validated this may indicate that the STAT3 pathway is a potential therapeutic target to overcome ICI resistance and improve the efficacy of these agents.

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