scholarly journals Finding the active genes in deep RNA-seq gene expression studies

BMC Genomics ◽  
2013 ◽  
Vol 14 (1) ◽  
pp. 778 ◽  
Author(s):  
Traver Hart ◽  
H Komori ◽  
Sarah LaMere ◽  
Katie Podshivalova ◽  
Daniel R Salomon
Keyword(s):  
Gene Expression ◽  
Rna Seq ◽  
Expression Studies ◽  
Gene Expression Studies ◽  
Active Genes

scholarly journals Comparison between Normalised and Unnormalised 454-Sequencing Libraries for Small-Scale RNA-Seq Studies

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Robert Ekblom ◽  
Jon Slate ◽  
Gavin J. Horsburgh ◽  
Tim Birkhead ◽  
Terry Burke
Keyword(s):  
Gene Expression ◽  
454 Sequencing ◽  
Cdna Libraries ◽  
Small Scale ◽  
Rna Seq ◽  
Quantitative Gene Expression ◽  
Expression Studies ◽  
Number Of Genes ◽  
Gene Expression Studies ◽  
Generation Sequencing

Next-generation sequencing of transcriptomes (RNA-Seq) is being used increasingly in studies of nonmodel organisms. Here, we evaluate the effectiveness of normalising cDNA libraries prior to sequencing in a small-scale study of the zebra finch. We find that assemblies produced from normalised libraries had a larger number of contigs but used fewer reads compared to unnormalised libraries. Considerably more genes were also detected using the contigs produced from normalised cDNA, and microsatellite discovery was up to 73% more efficient in these. There was a positive correlation between the detected expression level of genes in normalised and unnormalised cDNA, and there was no difference in the number of genes identified as being differentially expressed between blood and spleen for the normalised and unnormalised libraries. We conclude that normalised cDNA libraries are preferable for many applications of RNA-Seq and that these can also be used in quantitative gene expression studies.

scholarly journals SMART-RDA: A Galaxy Workflow for RNA-Seq Data Analysis

2017 ◽  
Vol 3 (4) ◽  
pp. 186
Author(s):  
Redi Aditama ◽  
Zulfikar Achmad Tanjung ◽  
Widyartini Made Sudania ◽  
Toni Liwang
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Analysis Tool ◽  
Rna Seq ◽  
Expression Studies ◽  
Galaxy Server ◽  
Local Facility ◽  
Computational Workflow ◽  
Gene Expression Studies ◽  
Next Generation Sequencing Ngs

<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>

scholarly journals A comprehensive rat transcriptome built from large scale RNA-seq-based annotation

2020 ◽  
Vol 48 (15) ◽  
pp. 8320-8331
Author(s):  
Xiangjun Ji ◽  
Peng Li ◽  
James C Fuscoe ◽  
Geng Chen ◽  
Wenzhong Xiao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Significant Influence ◽  
Large Scale ◽  
Expression Patterns ◽  
Model Organism ◽  
Rna Seq ◽  
Disease Mechanisms ◽  
Expression Studies ◽  
Gene Expression Studies ◽  
Different Tissues

Abstract The rat is an important model organism in biomedical research for studying human disease mechanisms and treatments, but its annotated transcriptome is far from complete. We constructed a Rat Transcriptome Re-annotation named RTR using RNA-seq data from 320 samples in 11 different organs generated by the SEQC consortium. Totally, there are 52 807 genes and 114 152 transcripts in RTR. Transcribed regions and exons in RTR account for ∼42% and ∼6.5% of the genome, respectively. Of all 73 074 newly annotated transcripts in RTR, 34 213 were annotated as high confident coding transcripts and 24 728 as high confident long noncoding transcripts. Different tissues rather than different stages have a significant influence on the expression patterns of transcripts. We also found that 11 715 genes and 15 852 transcripts were expressed in all 11 tissues and that 849 house-keeping genes expressed different isoforms among tissues. This comprehensive transcriptome is freely available at http://www.unimd.org/rtr/. Our new rat transcriptome provides essential reference for genetics and gene expression studies in rat disease and toxicity models.

scholarly journals Using RNA-Seq Data to Evaluate Reference Genes Suitable for Gene Expression Studies in Soybean

PLoS ONE ◽  
2015 ◽  
Vol 10 (9) ◽  
pp. e0136343 ◽  
Author(s):  
Aldrin Kay-Yuen Yim ◽  
Johanna Wing-Hang Wong ◽  
Yee-Shan Ku ◽  
Hao Qin ◽  
Ting-Fung Chan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Reference Genes ◽  
Rna Seq ◽  
Expression Studies ◽  
Gene Expression Studies

scholarly journals Validation of reference genes for whole blood gene expression analysis in cord blood of preterm and full-term neonates and peripheral blood of healthy adults

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Kristin Hieronymus ◽  
Benjamin Dorschner ◽  
Felix Schulze ◽  
Neeta L. Vora ◽  
Joel S. Parker ◽  
...  
Keyword(s):  
Gene Expression ◽  
Reference Genes ◽  
Age Groups ◽  
Systematic Bias ◽  
Rna Seq ◽  
Term Neonates ◽  
Blood Samples ◽  
Software Algorithms ◽  
Expression Studies ◽  
Gene Expression Studies

Abstract Background Preterm birth is the leading cause of neonatal morbidity and mortality, but research efforts in neonatology are complicated due to the unavailability of large volume blood samples. Whole blood assays can be used to overcome this problem by performing both functional and gene expression studies using small amounts of blood. Gene expression studies using RT-qPCR estimate mRNA-levels of target genes normalized to reference genes. The goal of this study was to identify and validate stable reference genes applicable to cord blood samples obtained from developing neonates of different gestational age groups as well as to adult peripheral blood samples. Eight reference gene candidates (ACTB, B2M, GAPDH, GUSB, HPRT, PPIB, RPLP0, RPL13) were analyzed using the three published software algorithms Bestkeeper, GeNorm and NormFinder. Results A normalization factor consisting of ACTB and PPIB allows for comparative expression analyses of neonatal samples from different gestational age groups. Normalization factors consisting of GAPDH and PPIB or ACTB and GAPDH are suitable when samples from preterm and full-term neonates and adults are compared. However, all candidate reference genes except RPLP0 exhibited significant intergroup gene expression variance and a higher gene expression towards an older age which resulted in a small but statistically significant systematic bias. Systematic analysis of RNA-seq data revealed new reference gene candidates with potentially superior stability. Conclusions The current study identified suitable normalization factors and proposed the use of the additional single gene RPLP0 to avoid systematic bias. This combination will enable comparative analyses not only between neonates of different gestational ages, but also between neonates and adults, as it facilitates more detailed investigations of developmental gene expression changes. The use of software algorithms did not prevent unintended systematic bias. This generally highlights the need for careful validation of such results to prevent false interpretation of potential age-dependent changes in gene expression. To identify the most stable reference genes in the future, RNA-seq based global approaches are recommended.

scholarly journals Comparison of confound adjustment methods in the construction of gene co-expression networks

2021 ◽  
Author(s):  
Alanna C. Cote ◽  
Hannah E. Young ◽  
Laura M Huckins
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Considerable Effect ◽  
Rna Seq ◽  
Expression Variation ◽  
Adjustment Method ◽  
Large Gene ◽  
Expression Studies ◽  
Gene Expression Studies ◽  
Bulk Tissue

Adjustment for confounding sources of expression variation is an important preprocessing step in large gene expression studies, but the effect of confound adjustment on co-expression network analysis has not been well-characterized. Here, we demonstrate that the choice of confound adjustment method can have a considerable effect on the architecture of the resulting co-expression network. We compare standard and alternative confound adjustment methods and provide recommendations for their use in the construction of gene co-expression networks from bulk tissue RNA-seq datasets.

Sign in / Sign up

Export Citation Format

Share Document