scholarly journals Heterogeneous distribution of mRNAs within flight muscle fibers, and implications for function

2020 ◽  
Author(s):  
Aditya Parekh ◽  
Kunal Chakraborty ◽  
Devam J Purohit ◽  
Shaik Naseer Pasha ◽  
R. Sowdhamini ◽  
...  
Keyword(s):  
Proteolytic Enzymes ◽  
Fiber Type ◽  
Mrna Localization ◽  
Rna Seq ◽  
Molecular Processes ◽  
Heterogeneous Distribution ◽  
Protein Requirements ◽  
Differential Gene ◽  
Fiber Type Distribution ◽  
Muscle Homeostasis

AbstractMuscle heterogeneity has been explored in terms of fiber-type distribution, structural organisation, and differences at their junctions with neurons and tendons. We amplify on such observation to additionally suggest that muscle syncytia have nonuniform protein requirements along their length, deployed for developmental and functional uses. An exploration of regionalized proteins or their mRNA across muscle syncytia has not been done. We investigated mRNA localization in regions of Drosophila melanogaster dorsal longitudinal muscle (DLM) syncytia over their entire transcriptome. Dissection of muscle regions, their RNA-seq and stringent Differential Gene Expression analysis indeed reveals statistically significant regionalization of nearly a hundred mRNA over the length of DLMs. Functions of over half of these genes require experimental verification. A preponderance of mRNA coding for catabolic and proteolytic enzymes is conspicuous among transcripts enriched in the posterior of DLMs. Our findings provide a foundation for exploring molecular processes that contribute to syncytial maturation and muscle homeostasis in a spatially non-homogenous manner.

scholarly journals Best practices on the differential expression analysis of multi-species RNA-seq

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Matthew Chung ◽  
Vincent M. Bruno ◽  
David A. Rasko ◽  
Christina A. Cuomo ◽  
José F. Muñoz ◽  
...  
Keyword(s):  
Best Practices ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Single Species ◽  
Rna Seq ◽  
Species Analysis ◽  
Differential Gene ◽  
Multiple Species ◽  
Downstream Analysis

AbstractAdvances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

scholarly journals Skeletal muscle fiber size and fiber type distribution in human cancer: Effects of weight loss and relationship to physical function

2016 ◽  
Vol 35 (6) ◽  
pp. 1359-1365 ◽  
Author(s):  
Michael J. Toth ◽  
Damien M. Callahan ◽  
Mark S. Miller ◽  
Timothy W. Tourville ◽  
Sarah B. Hackett ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Weight Loss ◽  
Physical Function ◽  
Muscle Fiber ◽  
Human Cancer ◽  
Fiber Type ◽  
Skeletal Muscle Fiber ◽  
Type Distribution ◽  
Fiber Size ◽  
Fiber Type Distribution

scholarly journals Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 952 ◽  
Author(s):  
Michael I. Love ◽  
Charlotte Soneson ◽  
Rob Patro
Keyword(s):  
Software Package ◽  
Gene Expression Analysis ◽  
Real Data ◽  
Transcript Level ◽  
Bioinformatic Analysis ◽  
Rna Seq ◽  
Statistical Framework ◽  
Gene Level ◽  
Show Evidence ◽  
Differential Gene

Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.

scholarly journals Differential Gene Expression by RNA-Seq Analysis of the Primo Vessel in the Rabbit Lymph

2019 ◽  
Vol 12 (1) ◽  
pp. 11-19 ◽  
Author(s):  
Jun-Young Shin ◽  
Sang-Heon Choi ◽  
Da-Woon Choi ◽  
Ye-Jin An ◽  
Jae-Hyuk Seo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Rna Seq ◽  
Differential Gene

scholarly journals Angelman Syndrome and Angelman-like Syndromes Share the Same Calcium-Related Gene Signatures

2021 ◽  
Vol 22 (18) ◽  
pp. 9870
Author(s):  
Julia Panov ◽  
Hanoch Kaphzan
Keyword(s):  
Angelman Syndrome ◽  
Neurodevelopmental Disorders ◽  
Clinical Presentation ◽  
Rna Seq ◽  
Related Gene ◽  
Bioinformatics Analyses ◽  
Gene Signatures ◽  
Signature Genes ◽  
Gene Expression Analyses ◽  
Differential Gene

Angelman-like syndromes are a group of neurodevelopmental disorders that entail clinical presentation similar to Angelman Syndrome (AS). In our previous study, we showed that calcium signaling is disrupted in AS, and we identified calcium-target and calcium-regulating gene signatures that are able to differentiate between AS and their controls in different models. In the herein study, we evaluated these sets of calcium-target and calcium-regulating genes as signatures of AS-like and non-AS-like syndromes. We collected a number of RNA-seq datasets of various AS-like and non-AS-like syndromes and performed Principle Component Analysis (PCA) separately on the two sets of signature genes to visualize the distribution of samples on the PC1–PC2 plane. In addition to the evaluation of calcium signature genes, we performed differential gene expression analyses to identify calcium-related genes dysregulated in each of the studied syndromes. These analyses showed that the calcium-target and calcium-regulating signatures differentiate well between AS-like syndromes and their controls. However, in spite of the fact that many of the non-AS-like syndromes have multiple differentially expressed calcium-related genes, the calcium signatures were not efficient classifiers for non-AS-like neurodevelopmental disorders. These results show that features based on clinical presentation are reflected in signatures derived from bioinformatics analyses and suggest the use of bioinformatics as a tool for classification.

scholarly journals Identification of Key Genes Affecting Porcine Fat Deposition Based on Co-Expression Network Analysis of Weighted Genes

2021 ◽  
Author(s):  
Kai Xing ◽  
Huatao Liu ◽  
Fengxia Zhang ◽  
Yibing Liu ◽  
Yong Shi ◽  
...  
Keyword(s):  
Network Analysis ◽  
Production Efficiency ◽  
Fat Deposition ◽  
Rna Seq ◽  
Genetic Studies ◽  
Gene Expression Matrix ◽  
Differential Gene ◽  
Key Genes ◽  
Strong Candidate ◽  
Molecular Regulatory Mechanisms

Abstract Background: Fat deposition is an important economic consideration for pig production. The amount of fat deposition in pigs seriously affects production efficiency, quality, and reproductive performance, while also affecting consumers' choice of pork. Weighted gene co-expression network analysis (WGCNA) has been shown to be effective in pig genetic studies. Therefore, this study aimed to identify modules that co-express genes associated with fat deposition in pigs (Songliao black and Landrace breeds) with extreme levels of backfat (high and low), and to identify the central genes in each of these modules. Results: We used RNA-seq of different pig tissues to construct a gene expression matrix consisting of 12 862 genes from 36 samples. Eleven co-expression modules were identified using WGCNA; the number of genes in these modules ranged from 39 to 3363. We found four co-expression modules were significantly correlated with backfat thickness. A total of 14 genes ( RAD9A , IGF2R , SCAP , TCAP , DGAT1 , GPS2 , IGF1 , MAPK8 , FABP , FABP5 , LEPR , UCP3 , APOF , and FASN ) were found to be related to fat deposition. Conclusions: RAD9A , TCAP , GPS2 , and APOF were found to be the key genes in the four modules according to the degree of gene connectivity. Combining the results of differential gene analysis, APOF was proposed as a strong candidate gene for body size traits. This study explores the key genes that regulate porcine fat deposition and lays the foundation for further research into the molecular regulatory mechanisms behind porcine fat deposition.

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