scholarly journals Evaluating the impact of sequencing error correction for RNA-seq data with ERCC RNA spike-in controls

2016 ◽  
Author(s):  
Li Tong ◽  
Cheng Yang ◽  
Po-Yen Wu ◽  
May D. Wang
Keyword(s):  
Error Correction ◽  
Sequencing Error ◽  
Rna Seq ◽  
The Impact ◽  
Sequencing Error Correction

scholarly journals Sequencing error correction without a reference genome

2013 ◽  
Vol 14 (1) ◽  
Author(s):  
Julie A Sleep ◽  
Andreas W Schreiber ◽  
Ute Baumann
Keyword(s):  
Error Correction ◽  
Reference Genome ◽  
Sequencing Error ◽  
Sequencing Error Correction

Determinants of Inflation in Vietnam: A VECM Approach

2015 ◽  
Vol 22 (04) ◽  
pp. 26-50
Author(s):  
Ngoc Tran Thi Bich ◽  
Huong Pham Hoang Cam
Keyword(s):  
Interest Rate ◽  
Crude Oil ◽  
Error Correction ◽  
Budget Deficit ◽  
Oil Prices ◽  
Crude Oil Prices ◽  
Domestic Credit ◽  
Short Run ◽  
The Impact

This paper aims to examine the main determinants of inflation in Vietnam during the period from 2002Q1 to 2013Q2. The cointegration theory and the Vector Error Correction Model (VECM) approach are used to examine the impact of domestic credit, interest rate, budget deficit, and crude oil prices on inflation in both long and short terms. The results show that while there are long-term relations among inflation and the others, such factors as oil prices, domestic credit, and interest rate, in the short run, have no impact on fluctuations of inflation. Particularly, the budget deficit itself actually has a short-run impact, but its level is fundamentally weak. The cause of the current inflation is mainly due to public's expectations of the inflation in the last period. Although the error correction, from the long-run relationship, has affected inflation in the short run, the coefficient is small and insignificant. In other words, it means that the speed of the adjustment is very low or near zero. This also implies that once the relationship among inflation, domestic credit, interest rate, budget deficit, and crude oil prices deviate from the long-term trend, it will take the economy a lot of time to return to the equilibrium state.

scholarly journals Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Li Tong ◽  
◽  
Po-Yen Wu ◽  
John H. Phan ◽  
Hamid R. Hassazadeh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Disease Outcome ◽  
Rna Seq ◽  
Next Generation Sequencing Technology ◽  
Normalization Methods ◽  
The Us ◽  
Sequencing Quality ◽  
Improved Accuracy ◽  
The Impact

Abstract To use next-generation sequencing technology such as RNA-seq for medical and health applications, choosing proper analysis methods for biomarker identification remains a critical challenge for most users. The US Food and Drug Administration (FDA) has led the Sequencing Quality Control (SEQC) project to conduct a comprehensive investigation of 278 representative RNA-seq data analysis pipelines consisting of 13 sequence mapping, three quantification, and seven normalization methods. In this article, we focused on the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well as the downstream prediction of disease outcomes. First, we developed and applied three metrics (i.e., accuracy, precision, and reliability) to quantitatively evaluate each pipeline’s performance on gene expression estimation. We then investigated the correlation between the proposed metrics and the downstream prediction performance using two real-world cancer datasets (i.e., SEQC neuroblastoma dataset and the NIH/NCI TCGA lung adenocarcinoma dataset). We found that RNA-seq pipeline components jointly and significantly impacted the accuracy of gene expression estimation, and its impact was extended to the downstream prediction of these cancer outcomes. Specifically, RNA-seq pipelines that produced more accurate, precise, and reliable gene expression estimation tended to perform better in the prediction of disease outcome. In the end, we provided scenarios as guidelines for users to use these three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision, and reliability of gene expression estimation, which lead to the improved downstream gene expression-based prediction of disease outcome.

Does Learners’ Proficiency Level Affect Oral Corrective Feedback Preferences?

2016 ◽  
Vol 8 (4) ◽  
pp. 150 ◽  
Author(s):  
Nesa Bandarchian Rashti ◽  
Maryam Danaye Tous
Keyword(s):  
Error Correction ◽  
Corrective Feedback ◽  
Good Deal ◽  
Potential Effect ◽  
Factors Affecting ◽  
Proficiency Level ◽  
Oral Corrective Feedback ◽  
One Step ◽  
Feedback Preferences ◽  
The Impact

<p>For decades now, there has been a good deal of research on factors affecting students’ oral corrective feedback preferences. Although it has been proven that learners’ characteristics such as their verbal intelligence and attitude toward error correction are highly effective in students’ preferred type of oral corrective feedback, the claims regarding the impact of learners’ proficiency level on their choice of oral corrective feedback have yet to be fully substantiated. In order to take this line of research one step forward, it is important to examine the potential effect of students’ level of proficiency in error correction literature. To this end, this paper aims to shed light on lower and higher level learners’ opinion about corrective feedback issues in an EFL context.</p>

scholarly journals Spaceflight Induces Novel Regulatory Responses in Arabidopsis Seedling as Revealed by Combined Proteomic and Transcriptomic Analyses

2020 ◽  
Author(s):  
Colin Peter Singer Kruse ◽  
Alexander D Meyers ◽  
Proma Basu ◽  
Sarahann Hutchinson ◽  
Darron R Luesse ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Wall ◽  
Membrane Proteins ◽  
Plant Growth ◽  
Gene Transcription ◽  
Space Station ◽  
Growth Efficiency ◽  
Rna Seq ◽  
Plastid Gene ◽  
The Impact

Abstract Background: Understanding of gravity sensing and response is critical to long-term human habitation in space and can provide new advantages for terrestrial agriculture. To this end, the altered gene expression profile induced by microgravity has been repeatedly queried by microarray and RNA-seq experiments to understand gravitropism. However, the quantification of altered protein abundance in space has been minimally investigated. Results: Proteomic (iTRAQ-labelled LC-MS/MS) and transcriptomic (RNA-seq) analyses simultaneously quantified protein and transcript differential expression of three-day old, etiolated Arabidopsis thaliana seedlings grown aboard the International Space Station along with their ground control counterparts. Protein extracts were fractionated to isolate soluble and membrane proteins and analyzed to detect differentially phosphorylated peptides. In total, 968 RNAs, 107 soluble proteins, and 103 membrane proteins were identified as differentially expressed. In addition, the proteomic analyses identified 16 differential phosphorylation events. Proteomic data delivered novel insights and simultaneously provided new context to previously made observations of gene expression in microgravity. There is a sweeping shift in post-transcriptional mechanisms of gene regulation including RNA-decapping protein DCP5, the splicing factors GRP7 and GRP8, and AGO4,. These data also indicate AHA2 and FERONIA as well as CESA1 and SHOU4 as central to the cell wall adaptations seen in spaceflight. Patterns of tubulin-a 1, 3,4 and 6 phosphorylation further reveal an interaction of microtubule and redox homeostasis that mirrors osmotic response signaling elements. The absence of gravity also results in a seemingly wasteful dysregulation of plastid gene transcription. Conclusions: The datasets gathered from Arabidopsis seedlings exposed to microgravity revealed marked impacts on post-transcriptional regulation, cell wall synthesis, redox/microtubule dynamics, and plastid gene transcription. The impact of post-transcriptional regulatory alterations represents an unstudied element of the plant microgravity response with the potential to significantly impact plant growth efficiency and beyond. What’s more, addressing the effects of microgravity on AHA2, CESA1, and alpha tubulins has the potential to enhance cytoskeletal organization and cell wall composition, thereby enhancing biomass production and growth in microgravity. Finally, understanding and manipulating the dysregulation of plastid gene transcription has further potential to address the goal of enhancing plant growth in the stressful conditions of microgravity.

scholarly journals Single cell analysis reveals the impact of age and maturation stage on the human oocyte transcriptome

2020 ◽  
Author(s):  
Silvia Llonch ◽  
Montserrat Barragán ◽  
Paula Nieto ◽  
Anna Mallol ◽  
Marc Elosua-Bayes ◽  
...  
Keyword(s):  
Single Cell ◽  
Chromosome Segregation ◽  
Maternal Age ◽  
Maturation Stage ◽  
P Value ◽  
Rna Seq ◽  
Transcript Levels ◽  
Human Oocytes ◽  
The Impact

AbstractStudy questionTo which degree does maternal age affect the transcriptome of human oocytes at the germinal vesicle (GV) stage or at metaphase II after maturation in vitro (IVM-MII)?Summary answerWhile the oocytes’ transcriptome is predominantly determined by maturation stage, transcript levels of genes related to chromosome segregation, mitochondria and RNA processing are affected by age after in vitro maturation of denuded oocytes.What is known alreadyFemale fertility is inversely correlated with maternal age due to both a depletion of the oocyte pool and a reduction in oocyte developmental competence. Few studies have addressed the effect of maternal age on the human mature oocyte (MII) transcriptome, which is established during oocyte growth and maturation, and the pathways involved remain unclear. Here, we characterize and compare the transcriptomes of a large cohort of fully grown GV and IVM-MII oocytes from women of varying reproductive age.Study design, size, durationIn this prospective molecular study, 37 women were recruited from May 2018 to June 2019. The mean age was 28.8 years (SD=7.7, range 18-43). A total of 72 oocytes were included in the study at GV stage after ovarian stimulation, and analyzed as GV (n=40) and in vitro matured oocytes (IVM-MII; n=32).Participants/materials, setting, methodsDenuded oocytes were included either as GV at the time of ovum pick-up or as IVM-MII after in vitro maturation for 30 hours in G2™ medium, and processed for transcriptomic analysis by single-cell RNA-seq using the Smart-seq2 technology. Cluster and maturation stage marker analysis were performed using the Seurat R package. Genes with an average fold change greater than 2 and a p-value < 0.01 were considered maturation stage markers. A Pearson correlation test was used to identify genes whose expression levels changed progressively with age. Those genes presenting a correlation value (R) >= |0.3| and a p-value < 0.05 were considered significant.Main results and the role of chanceFirst, by exploration of the RNA-seq data using tSNE dimensionality reduction, we identified two clusters of cells reflecting the oocyte maturation stage (GV and IVM-MII) with 4,445 and 324 putative marker genes, respectively. Next we identified genes, for which RNA levels either progressively increased or decreased with age. This analysis was performed independently for GV and IVM-MII oocytes. Our results indicate that the transcriptome is more affected by age in IVM-MII oocytes (1,219 genes) than in GV oocytes (596 genes). In particular, we found that genes involved in chromosome segregation and RNA splicing significantly increase in transcript levels with age, while genes related to mitochondrial activity present lower transcript levels with age. Gene regulatory network analysis revealed potential upstream master regulator functions for genes whose transcript levels present positive (GPBP1, RLF, SON, TTF1) or negative (BNC1, THRB) correlation with age.Limitations, reasons for cautionIVM-MII oocytes used in this study were obtained after in vitro maturation of denuded GV oocytes, therefore, their transcriptome might not be fully representative of in vivo matured MII oocytes.The Smart-seq2 methodology used in this study detects polyadenylated transcripts only and we could therefore not assess non-polyadenylated transcripts.Wider implications of the findingsOur analysis suggests that advanced maternal age does not globally affect the oocyte transcriptome at GV or IVM-MII stages. Nonetheless, hundreds of genes displayed altered transcript levels with age, particularly in IVM-MII oocytes. Especially affected by age were genes related to chromosome segregation and mitochondrial function, pathways known to be involved in oocyte ageing. Our study thereby suggests that misregulation of chromosome segregation and mitochondrial pathways also at the RNA-level might contribute to the age-related quality decline in human oocytes.Study funding/competing interest(s)This study was funded by the AXA research fund, the European commission, intramural funding of Clinica EUGIN, the Spanish Ministry of Science, Innovation and Universities, the Catalan Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) and by contributions of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership and to the “Centro de Excelencia Severo Ochoa”.The authors have no conflict of interest to declare.

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