Identification and expression analysis of ceftriaxone resistance-related genes in Neisseria gonorrhoeae integrating RNA-Seq data and qRT-PCR validation

2019 ◽  
Vol 16 ◽  
pp. 202-209 ◽  
Author(s):  
Yun-hu Zhao ◽  
Xiao-lin Qin ◽  
Jie-yi Yang ◽  
Yi-wen Liao ◽  
Xing-zhong Wu ◽  
...  
Keyword(s):  
Neisseria Gonorrhoeae ◽  
Expression Analysis ◽  
Rna Seq ◽  
Qrt Pcr

NGS-based targeted RNA sequencing for expression analysis of patients with triple-negative breast cancer using a modulized, 96-gene biomarker panel.

2012 ◽  
Vol 30 (30_suppl) ◽  
pp. 56-56
Author(s):  
Byung-In Lee ◽  
Kahuku Oades ◽  
Lien Vo ◽  
Jerry Lee ◽  
Mark Landers ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Sequence Analysis ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Triple Negative ◽  
Dynamic Range ◽  
Rna Seq ◽  
Single Tube ◽  
Qrt Pcr

56 Background: Gene expression profiling has been shown to be effective in analyzing postoperative tumor samples in various cancers. However, in analyzing small specimens such as core biopsies, the limited amount of available material makes multi-gene analyses difficult or impossible. Microarray-based analyses also provide limited dynamic range. We describe the development of targeted RNA-sequencing methodology which combines the power of a universal RNA amplification with NGS for an ultra-deep expression analysis of multiple target genes, enabling <100 ng of sample input for multi-gene analysis in a single tube format. Methods: The gene expression patterns of triple-negative breast cancer FFPE samples were analyzed using a 96-gene breast cancer biomarker panel across three different platforms: Affymetrix Human Gene ST 1.0 microarrays, a pre-developed OncoScore qRT-PCR panel, and targeted RNA-seq. For targeted RNA-seq analysis, the 96-gene panel was amplified using a universal, single-tube “XP-PCR” amplification strategy followed by sequence analysis using the Ion-Torrent Personal Genome Machine. Results: Targeted RNA-seq provided the most sensitivity in terms of detection rates with <100 ng FFPE RNA input and provides unlimited dynamic range with increased sequencing depth. Expression ratio compression issues typically associated with a high number of pre-amplification cycles in standard multiplex-primed methods were not observed here. Low expressing genes, undetectable by qRT-PCR analysis from 1,000 ng input FFPE RNA, were detected and eligible for expression analysis with a significant number of sequencing reads. Alternative transcription/splicing analysis is also possible from sequence analysis of the target transcripts using targeted RNA-seq. Conclusions: By combining universally primed pre-amplification and NGS in multi-gene expression analysis, targeted RNA-seq provides the most sensitive gene expression analysis methodology.

Comprehensive Analysis of Transcriptomics and Metabolomics between the Heads and Tails of Angelica Sinensis: Genes Related to Phenylpropanoid Biosynthesis Pathway

2020 ◽  
Vol 23 ◽  
Author(s):  
Jie Yang ◽  
Chi Zhang ◽  
Wei-Hong Li ◽  
Tian-Er Zhang ◽  
Guang-Zhong Fan ◽  
...  
Keyword(s):  
Ferulic Acid ◽  
Metabolic Regulation ◽  
Comprehensive Analysis ◽  
Angelica Sinensis ◽  
Rna Seq ◽  
Targeted Metabolomics ◽  
Kegg Pathways ◽  
Qrt Pcr ◽  
Phenylpropanoid Biosynthesis ◽  
Combined Strategy

Background:: In Traditional Chinese Medicine (TCM), the heads and tails of Angelica sinensis (Oliv.) Diels (AS) is used in treating different diseases due to their different pharmaceutical efficacies. The underline mechanisms, however, have not been fully explored. Objective:: Novel mechanisms responsible for the discrepant activities between AS heads and tails were explored by a combined strategy of transcriptomes and metabolomics. Method:: Six pairs of the heads and tails of AS roots were collected in Min County, China. Total RNA and metabolites, which were used for RNA-seq and untargeted metabolomics analysis, were respectively isolated from each AS sample (0.1 g) by Trizol and methanol reagent. Subsequently, differentially expressed genes (DEGs) and discrepant pharmaceutical metabolites were identified for comparing AS heads and tails. Key DEGs and metabolites were quantified by qRT-PCR and targeted metabolomics experiment. Results:: Comprehensive analysis of transcriptomes and metabolomics results suggested that five KEGG pathways with significant differences included 57 DEGs. Especially, fourteen DEGs and six key metabolites were relation to the metabolic regulation of Phenylpropanoid biosynthesis (PB) pathway. Results of qRT-PCR and targeted metabolomics indicated that higher levels of expression of crucial genes in PB pathway, such as PAL, CAD, COMT and peroxidase in the tail of AS were positively correlated with levels of ferulic acid-related metabolites. The average content of ferulic acid in tails (569.58162.39 nmol/g) was higher than those in the heads (168.73  67.30 nmol/g) (P˂0.01); Caffeic acid in tails (3.82  0.88 nmol/g) vs heads (1.37  0.41 nmol/g) (P˂0.01), and Cinnamic acid in tails (0.24  0.09 nmol/g) vs heads (0.14  0.02 nmol/g) (P˂0.05). Conclusion:: Our work demonstrated that overexpressed genes and accumulated metabolites derived from PB pathway might be responsible for the discrepant pharmaceutical efficacies between AS heads and tails.

scholarly journals Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Zeeshan Ahmed ◽  
Eduard Gibert Renart ◽  
Saman Zeeshan ◽  
XinQi Dong
Keyword(s):  
Data Analysis ◽  
Patient Care ◽  
Expression Analysis ◽  
High Throughput ◽  
Gene Annotation ◽  
Next Generation Sequencing Data ◽  
Rna Seq ◽  
Sequencing Data ◽  
Complex Disorders ◽  
Transcriptomics Data

Abstract Background Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists. Results In this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients’ transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer’s disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases. Conclusions We emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.

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 Identification and Expression Analysis of the Genes Involved in the Raffinose Family Oligosaccharides Pathway of Phaseolus vulgaris and Glycine max

Plants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1465
Author(s):  
Ramon de Koning ◽  
Raphaël Kiekens ◽  
Mary Esther Muyoka Toili ◽  
Geert Angenon
Keyword(s):  
Common Bean ◽  
Seed Development ◽  
Expression Analysis ◽  
De Novo ◽  
Expression Patterns ◽  
Gene Families ◽  
Rna Seq ◽  
Raffinose Family Oligosaccharides ◽  
Specific Expression ◽  
Raffinose Synthase

Raffinose family oligosaccharides (RFO) play an important role in plants but are also considered to be antinutritional factors. A profound understanding of the galactinol and RFO biosynthetic gene families and the expression patterns of the individual genes is a prerequisite for the sustainable reduction of the RFO content in the seeds, without compromising normal plant development and functioning. In this paper, an overview of the annotation and genetic structure of all galactinol- and RFO biosynthesis genes is given for soybean and common bean. In common bean, three galactinol synthase genes, two raffinose synthase genes and one stachyose synthase gene were identified for the first time. To discover the expression patterns of these genes in different tissues, two expression atlases have been created through re-analysis of publicly available RNA-seq data. De novo expression analysis through an RNA-seq study during seed development of three varieties of common bean gave more insight into the expression patterns of these genes during the seed development. The results of the expression analysis suggest that different classes of galactinol- and RFO synthase genes have tissue-specific expression patterns in soybean and common bean. With the obtained knowledge, important galactinol- and RFO synthase genes that specifically play a key role in the accumulation of RFOs in the seeds are identified. These candidate genes may play a pivotal role in reducing the RFO content in the seeds of important legumes which could improve the nutritional quality of these beans and would solve the discomforts associated with their consumption.

scholarly journals Transcriptome Analysis of Responses to Dengue Virus 2 Infection in Aedes albopictus (Skuse) C6/36 Cells

Viruses ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 343
Author(s):  
Manjin Li ◽  
Dan Xing ◽  
Duo Su ◽  
Di Wang ◽  
Heting Gao ◽  
...  
Keyword(s):  
Dengue Virus ◽  
Aedes Albopictus ◽  
Bioinformatics Analysis ◽  
Interaction Mechanism ◽  
Transcriptional Analysis ◽  
Functional Verification ◽  
Mosquito Vector ◽  
Rna Seq ◽  
Sequencing Data ◽  
Qrt Pcr

Dengue virus (DENV), a member of the Flavivirus genus of the Flaviviridae family, can cause dengue fever (DF) and more serious diseases and thus imposes a heavy burden worldwide. As the main vector of DENV, mosquitoes are a serious hazard. After infection, they induce a complex host–pathogen interaction mechanism. Our goal is to further study the interaction mechanism of viruses in homologous, sensitive, and repeatable C6/36 cell vectors. Transcriptome sequencing (RNA-Seq) technology was applied to the host transcript profiles of C6/36 cells infected with DENV2. Then, bioinformatics analysis was used to identify significant differentially expressed genes and the associated biological processes. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was performed to verify the sequencing data. A total of 1239 DEGs were found by transcriptional analysis of Aedes albopictus C6/36 cells that were infected and uninfected with dengue virus, among which 1133 were upregulated and 106 were downregulated. Further bioinformatics analysis showed that the upregulated DEGs were significantly enriched in signaling pathways such as the MAPK, Hippo, FoxO, Wnt, mTOR, and Notch; metabolic pathways and cellular physiological processes such as autophagy, endocytosis, and apoptosis. Downregulated DEGs were mainly enriched in DNA replication, pyrimidine metabolism, and repair pathways, including BER, NER, and MMR. The qRT-PCR results showed that the concordance between the RNA-Seq and RT-qPCR data was very high (92.3%). The results of this study provide more information about DENV2 infection of C6/36 cells at the transcriptome level, laying a foundation for further research on mosquito vector–virus interactions. These data provide candidate antiviral genes that can be used for further functional verification in the future.

Application of qRT-PCR and RNA-Seq analysis for the identification of housekeeping genes useful for normalization of gene expression values during Striga hermonthica development

2012 ◽  
Vol 40 (4) ◽  
pp. 3395-3407 ◽  
Author(s):  
M. Fernández-Aparicio ◽  
K. Huang ◽  
E. K. Wafula ◽  
L. A. Honaas ◽  
N. J. Wickett ◽  
...  
Keyword(s):  
Gene Expression ◽  
Housekeeping Genes ◽  
Striga Hermonthica ◽  
Rna Seq ◽  
Qrt Pcr

scholarly journals The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Xueyi Dong ◽  
Luyi Tian ◽  
Quentin Gouil ◽  
Hasaru Kariyawasam ◽  
Shian Su ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Transcriptomic Analysis ◽  
Statistical Testing ◽  
Rna Seq ◽  
Sequencing Data ◽  
Short Read ◽  
Sequencing Platform ◽  
Long Read

Abstract Application of Oxford Nanopore Technologies’ long-read sequencing platform to transcriptomic analysis is increasing in popularity. However, such analysis can be challenging due to the high sequence error and small library sizes, which decreases quantification accuracy and reduces power for statistical testing. Here, we report the analysis of two nanopore RNA-seq datasets with the goal of obtaining gene- and isoform-level differential expression information. A dataset of synthetic, spliced, spike-in RNAs (‘sequins’) as well as a mouse neural stem cell dataset from samples with a null mutation of the epigenetic regulator Smchd1 was analysed using a mix of long-read specific tools for preprocessing together with established short-read RNA-seq methods for downstream analysis. We used limma-voom to perform differential gene expression analysis, and the novel FLAMES pipeline to perform isoform identification and quantification, followed by DRIMSeq and limma-diffSplice (with stageR) to perform differential transcript usage analysis. We compared results from the sequins dataset to the ground truth, and results of the mouse dataset to a previous short-read study on equivalent samples. Overall, our work shows that transcriptomic analysis of long-read nanopore data using long-read specific preprocessing methods together with short-read differential expression methods and software that are already in wide use can yield meaningful results.

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