scholarly journals Regional Analysis of the Brain Transcriptome in Mice Bred for High and Low Methamphetamine Consumption

2019 ◽  
Vol 9 (7) ◽  
pp. 155 ◽  
Author(s):  
Hitzemann ◽  
Iancu ◽  
Reed ◽  
Baba ◽  
Lockwood ◽  
...  
Keyword(s):  
Large Scale ◽  
Drug Exposure ◽  
Regional Analysis ◽  
Drug Effects ◽  
Network Connectivity ◽  
Transcriptome Profiling ◽  
Rna Seq ◽  
Reward Circuitry ◽  
Brain Transcriptome ◽  
Differential Gene

Transcriptome profiling can broadly characterize drug effects and risk for addiction in the absence of drug exposure. Modern large-scale molecular methods, including RNA-sequencing (RNA-Seq), have been extensively applied to alcohol-related disease traits, but rarely to risk for methamphetamine (MA) addiction. We used RNA-Seq data from selectively bred mice with high or low risk for voluntary MA intake to construct coexpression and cosplicing networks for differential risk. Three brain reward circuitry regions were explored, the nucleus accumbens (NAc), prefrontal cortex (PFC), and ventral midbrain (VMB). With respect to differential gene expression and wiring, the VMB was more strongly affected than either the PFC or NAc. Coexpression network connectivity was higher in the low MA drinking line than in the high MA drinking line in the VMB, oppositely affected in the NAc, and little impacted in the PFC. Gene modules protected from the effects of selection may help to eliminate certain mechanisms from significant involvement in risk for MA intake. One such module was enriched in genes with dopamine-associated annotations. Overall, the data suggest that mitochondrial function and glutamate-mediated synaptic plasticity have key roles in the outcomes of selective breeding for high versus low levels of MA intake.

scholarly journals Advanced Applications of RNA Sequencing and Challenges

2015 ◽  
Vol 9s1 ◽  
pp. BBI.S28991 ◽  
Author(s):  
Yixing Han ◽  
Shouguo Gao ◽  
Kathrin Muegge ◽  
Wei Zhang ◽  
Bing Zhou
Keyword(s):  
High Speed ◽  
Large Scale ◽  
High Sensitivity ◽  
Rna Seq ◽  
Specific Expression ◽  
Sequencing Technologies ◽  
Large Scale Data ◽  
Data Analyses ◽  
Allele Specific ◽  
Differential Gene

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.

scholarly journals Evaluating Methods for Differential Gene Expression And Alternative Splicing Using Internal Synthetic Controls

2020 ◽  
Author(s):  
Sudeep Mehrotra ◽  
Revital Bronstein ◽  
Daniel Navarro-Gomez ◽  
Ayellet V. Segrè ◽  
Eric A. Pierce
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Differential Gene Expression ◽  
Internal Control ◽  
Accurate Determination ◽  
Transcriptome Profiling ◽  
Rna Seq ◽  
Specificity And Sensitivity ◽  
Differential Gene ◽  
Synthetic Controls

AbstractHigh-throughput transcriptome sequencing has become a powerful tool in the study of human diseases. Identification of causal mechanisms may entail analysis of differential gene expression (DGE), differential transcript/isoform expression (DTE) and identification, classification and quantification of alternative splicing (AS) and/or detection of novel AS events. For such a global transcriptome profiling execution of multi-level data analysis methodologies is required. Each level presents its own unique challenges and the questions about their performance remains. In this work we present results from systematic and consistent assessing and comparing a number of widely used methods for detecting DGE, DTE and AS using internal control “spike-in” sequences (Sequins) in RNA-seq data. We demonstrated that inclusion of internal controls in RNA-seq experiments allows accurate determination of lower bounds detection levels, and better assessment of DGE, DTE and AS accuracy and sensitivity. Tools for RNA-seq read alignment and detection of DGE performed reasonably. More efforts are needed to improve specificity and sensitivity of DTE and AS detection. Low expression of isoforms accompanied with sequencing depth does impact sensitivity and specificity of DTE and AS tools.

scholarly journals Stormbow: A Cloud-Based Tool for Reads Mapping and Expression Quantification in Large-Scale RNA-Seq Studies

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Shanrong Zhao ◽  
Kurt Prenger ◽  
Lance Smith
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Scale Up ◽  
Local Environment ◽  
Transcriptome Profiling ◽  
Cost Effective ◽  
Rna Seq ◽  
Practical Challenge ◽  
Amazon Web Services ◽  
Computational Resources

RNA-Seq is becoming a promising replacement to microarrays in transcriptome profiling and differential gene expression study. Technical improvements have decreased sequencing costs and, as a result, the size and number of RNA-Seq datasets have increased rapidly. However, the increasing volume of data from large-scale RNA-Seq studies poses a practical challenge for data analysis in a local environment. To meet this challenge, we developed Stormbow, a cloud-based software package, to process large volumes of RNA-Seq data in parallel. The performance of Stormbow has been tested by practically applying it to analyse 178 RNA-Seq samples in the cloud. In our test, it took 6 to 8 hours to process an RNA-Seq sample with 100 million reads, and the average cost was $3.50 per sample. Utilizing Amazon Web Services as the infrastructure for Stormbow allows us to easily scale up to handle large datasets with on-demand computational resources. Stormbow is a scalable, cost effective, and open-source based tool for large-scale RNA-Seq data analysis. Stormbow can be freely downloaded and can be used out of box to process Illumina RNA-Seq datasets.

scholarly journals IRIS-DGE: An integrated RNA-seq data analysis and interpretation system for differential gene expression

2018 ◽  
Author(s):  
Brandon Monier ◽  
Adam McDermaid ◽  
Jing Zhao ◽  
Anne Fennell ◽  
Qin Ma
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Supplementary Information ◽  
Analysis Tool ◽  
Rna Seq ◽  
Differential Gene ◽  
User Friendly

AbstractMotivationNext-Generation Sequencing has made available much more large-scale genomic and transcriptomic data. Studies with RNA-sequencing (RNA-seq) data typically involve generation of gene expression profiles that can be further analyzed, many times involving differential gene expression (DGE). This process enables comparison across samples of two or more factor levels. A recurring issue with DGE analyses is the complicated nature of the comparisons to be made, in which a variety of factor combinations, pairwise comparisons, and main or blocked main effects need to be tested.ResultsHere we present a tool called IRIS-DGE, which is a server-based DGE analysis tool developed using Shiny. It provides a straightforward, user-friendly platform for performing comprehensive DGE analysis, and crucial analyses that help design hypotheses and to determine key genomic features. IRIS-DGE integrates the three most commonly used R-based DGE tools to determine differentially expressed genes (DEGs) and includes numerous methods for performing preliminary analysis on user-provided gene expression information. Additionally, this tool integrates a variety of visualizations, in a highly interactive manner, for improved interpretation of preliminary and DGE analyses.AvailabilityIRIS-DGE is freely available at http://bmbl.sdstate.edu/IRIS/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals CDCP: a visualization and analyzing platform for single-cell datasets

2021 ◽  
Author(s):  
Yuejiao Li ◽  
Tao Yang ◽  
Tingting Lai ◽  
Lijin You ◽  
Fan Yang ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Rna Seq ◽  
Rapid Accumulation ◽  
Unique Approach ◽  
Functional States ◽  
User Friendly ◽  
Human Primate

Advances in single-cell sequencing technology provide a unique approach to characterize the heterogeneity and distinctive functional states at single-cell resolution, leading to rapid accumulation of large-scale single-cell datasets. A big challenge undertaken by research community especially bench scientists is how to simplify the way of retrieving, processing and analyzing the huge number of datasets. Towards this end, we developed Cell-omics Data Coordinate Platform (CDCP),a platform that aims to share and integrate comprehensive single-cell datasets, and to provide a network analysis toolkit for personalized analysis. CDCP contains single-cell RNA-seq and ATAC-seq datasets of 474,572 cells from 6,459 samples in species covering humans, non-human primate models and other animals. It allows querying and visualization of interested datasets and the expression profile of distinct genes in different cell clusters and cell types. Besides, this platform provides an analysis pipeline for non-bioinformatician experimental scientists to address questions not focused by the submitters of the datasets. In summary, CDCP provides a user-friendly interface for researchers to explore, visualize, analyze, download and submit published single-cell datasets and it will be a valuable resource for investigators to explore the global transcriptome profiling at single-cell level.

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