scholarly journals A 24 h Age Difference Causes Twice as Much Gene Expression Divergence as 100 Generations of Adaptation to a Novel Environment

Genes ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 89 ◽  
Author(s):  
Sheng-Kai Hsu ◽  
Ana Marija Jakšić ◽  
Viola Nolte ◽  
Neda Barghi ◽  
François Mallard ◽  
...  
Keyword(s):  
Gene Expression ◽  
Abiotic Factors ◽  
Transcript Abundance ◽  
Age Difference ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Gene Expression Analyses ◽  
High Throughput Phenotyping ◽  
Influence Gene Expression ◽  
Developmental Staging

Gene expression profiling is one of the most reliable high-throughput phenotyping methods, allowing researchers to quantify the transcript abundance of expressed genes. Because many biotic and abiotic factors influence gene expression, it is recommended to control them as tightly as possible. Here, we show that a 24 h age difference of Drosophila simulans females that were subjected to RNA sequencing (RNA-Seq) five and six days after eclosure resulted in more than 2000 differentially expressed genes. This is twice the number of genes that changed expression during 100 generations of evolution in a novel hot laboratory environment. Importantly, most of the genes differing in expression due to age introduce false positives or negatives if an adaptive gene expression analysis is not controlled for age. Our results indicate that tightly controlled experimental conditions, including precise developmental staging, are needed for reliable gene expression analyses, in particular in an evolutionary framework.

scholarly journals Zea mays RNA-seq estimated transcript abundances are strongly affected by read mapping bias

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shuhua Zhan ◽  
Cortland Griswold ◽  
Lewis Lukens
Keyword(s):  
Gene Expression ◽  
Zea Mays ◽  
Reference Genome ◽  
Transcript Abundance ◽  
Gene Transcript ◽  
Rna Seq ◽  
Individual Genome ◽  
Abundance Estimates ◽  
Mapping Bias ◽  
Quantify Gene Expression

Abstract Background Genetic variation for gene expression is a source of phenotypic variation for natural and agricultural species. The common approach to map and to quantify gene expression from genetically distinct individuals is to assign their RNA-seq reads to a single reference genome. However, RNA-seq reads from alleles dissimilar to this reference genome may fail to map correctly, causing transcript levels to be underestimated. Presently, the extent of this mapping problem is not clear, particularly in highly diverse species. We investigated if mapping bias occurred and if chromosomal features associated with mapping bias. Zea mays presents a model species to assess these questions, given it has genotypically distinct and well-studied genetic lines. Results In Zea mays, the inbred B73 genome is the standard reference genome and template for RNA-seq read assignments. In the absence of mapping bias, B73 and a second inbred line, Mo17, would each have an approximately equal number of regulatory alleles that increase gene expression. Remarkably, Mo17 had 2–4 times fewer such positively acting alleles than did B73 when RNA-seq reads were aligned to the B73 reference genome. Reciprocally, over one-half of the B73 alleles that increased gene expression were not detected when reads were aligned to the Mo17 genome template. Genes at dissimilar chromosomal ends were strongly affected by mapping bias, and genes at more similar pericentromeric regions were less affected. Biased transcript estimates were higher in untranslated regions and lower in splice junctions. Bias occurred across software and alignment parameters. Conclusions Mapping bias very strongly affects gene transcript abundance estimates in maize, and bias varies across chromosomal features. Individual genome or transcriptome templates are likely necessary for accurate transcript estimation across genetically variable individuals in maize and other species.

scholarly journals Transcriptome Analysis of Marchantin Biosynthesis from the Liverwort Marchantia polymorpha

2017 ◽  
Vol 12 (8) ◽  
pp. 1934578X1701200
Author(s):  
Hironobu Takahashi ◽  
Yoshinori Asakawa
Keyword(s):  
Gene Expression ◽  
Transcriptome Analysis ◽  
Transcriptome Sequencing ◽  
Biological Activities ◽  
Digital Gene Expression ◽  
Marchantia Polymorpha ◽  
Rna Seq ◽  
Gene Expression Analyses

Marchantin A, the first characterized macrocyclic bis(bibenzyls) found in the liverwort Marchantia polymorpha shows interesting biological activities such as antifungal, antimicrobial, cytotoxic, antioxidant and muscle relaxing activity. Previously, Zenk et al. reported the phenylpropane/polymalonate pathway in the biosynthesis of the marchantins in M. polymorpha. To clear this pathway, transcriptome sequencing and digital gene expression analyses of M. polymorpha were carried out by using Illumina RNA-seq system.

scholarly journals Using Single Nucleotide Variations in Single-Cell RNA-Seq to Identify Subpopulations and Genotype-phenotype Linkage

2016 ◽  
Author(s):  
Olivier Poirion ◽  
Xun Zhu ◽  
Travers Ching ◽  
Lana X. Garmire
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Transcript Abundance ◽  
Rna Seq ◽  
Linear Modeling ◽  
Modeling Framework ◽  
Single Nucleotide ◽  
Single Nucleotide Variations

AbstractDespite its popularity, characterization of subpopulations with transcript abundance is subject to a significant amount of noise. We propose to use effective and expressed nucleotide variations (eeSNVs) from scRNA-seq as alternative features for tumor subpopulation identification. We developed a linear modeling framework, SSrGE, to link eeSNVs associated with gene expression. In all the datasets tested, eeSNVs achieve better accuracies than gene expression for identifying subpopulations. Previously validated cancer-relevant genes are also highly ranked, confirming the significance of the method. Moreover, SSrGE is capable of analyzing coupled DNA-seq and RNA-seq data from the same single cells, demonstrating its value in integrating multi-omics single cell techniques. In summary, SNV features from scRNA-seq data have merits for both subpopulation identification and linkage of genotype-phenotype relationship. The method SSrGE is available at https://github.com/lanagarmire/SSrGE.

ISOFORM ABUNDANCE INFERENCE PROVIDES A MORE ACCURATE ESTIMATION OF GENE EXPRESSION LEVELS IN RNA-SEQ

2010 ◽  
Vol 08 (supp01) ◽  
pp. 177-192 ◽  
Author(s):  
XI WANG ◽  
ZHENGPENG WU ◽  
XUEGONG ZHANG
Keyword(s):  
Gene Expression ◽  
High Throughput Sequencing ◽  
Transcript Abundance ◽  
Accurate Estimation ◽  
Rna Seq ◽  
Estimation Errors ◽  
Expression Levels ◽  
Or Gene ◽  
Read Distribution ◽  
Gene Expression Levels

Due to its unprecedented high-resolution and detailed information, RNA-seq technology based on next-generation high-throughput sequencing significantly boosts the ability to study transcriptomes. The estimation of genes' transcript abundance levels or gene expression levels has always been an important question in research on the transcriptional regulation and gene functions. On the basis of the concept of Reads Per Kilo-base per Million reads (RPKM), taking the union-intersection genes (UI-based) and summing up inferred isoform abundance (isoform-based) are the two current strategies to estimate gene expression levels, but produce different estimations. In this paper, we made the first attempt to compare the two strategies' performances through a series of simulation studies. Our results showed that the isoform-based method gives not only more accurate estimation but also has less uncertainty than the UI-based strategy. If taking into account the non-uniformity of read distribution, the isoform-based method can further reduce estimation errors. We applied both strategies to real RNA-seq datasets of technical replicates, and found that the isoform-based strategy also displays a better performance. For a more accurate estimation of gene expression levels from RNA-seq data, even if the abundance levels of isoforms are not of interest, it is still better to first infer the isoform abundance and sum them up to get the expression level of a gene as a whole.

scholarly journals A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples

2021 ◽  
Author(s):  
Wenpin Hou ◽  
Zhicheng Ji ◽  
Zeyu Chen ◽  
E John Wherry ◽  
Stephanie C Hicks ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Biological Processes ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Computational Framework ◽  
Statistical Framework ◽  
Gene Regulatory ◽  
Multiple Samples ◽  
False Discoveries

Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many computational methods have been developed to infer the pseudo-temporal trajectories of cells within a biological sample, methods that compare pseudo-temporal patterns with multiple samples (or replicates) across different experimental conditions are lacking. Lamian is a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis. It can be used to identify changes in a biological process associated with sample covariates, such as different biological conditions, and also to detect changes in gene expression, cell density, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability, Lamian draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both simulations and real scRNA-seq data, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels, we demonstrate the advantages of Lamian in decoding cellular gene expression programs in continuous biological processes.

scholarly journals Comparison of Microarrays and RNA-Seq for Gene Expression Analyses of Dose-Response Experiments

2013 ◽  
Vol 137 (2) ◽  
pp. 385-403 ◽  
Author(s):  
Michael B. Black ◽  
Bethany B. Parks ◽  
Linda Pluta ◽  
Tzu-Ming Chu ◽  
Bruce C. Allen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dose Response ◽  
Rna Seq ◽  
Gene Expression Analyses

scholarly journals Meta-Analysis of Hypoxic Transcriptomes from Public Databases

Biomedicines ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 10 ◽  
Author(s):  
Hidemasa Bono ◽  
Kiichi Hirota
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Gene Expression Profiles ◽  
Data Driven ◽  
Transcriptome Data ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Oxygen Homeostasis ◽  
Before And After

Hypoxia is the insufficiency of oxygen in the cell, and hypoxia-inducible factors (HIFs) are central regulators of oxygen homeostasis. In order to obtain functional insights into the hypoxic response in a data-driven way, we attempted a meta-analysis of the RNA-seq data from the hypoxic transcriptomes archived in public databases. In view of methodological variability of archived data in the databases, we first manually curated RNA-seq data from appropriate pairs of transcriptomes before and after hypoxic stress. These included 128 human and 52 murine transcriptome pairs. We classified the results of experiments for each gene into three categories: upregulated, downregulated, and unchanged. Hypoxic transcriptomes were then compared between humans and mice to identify common hypoxia-responsive genes. In addition, meta-analyzed hypoxic transcriptome data were integrated with public ChIP-seq data on the known human HIFs, HIF-1 and HIF-2, to provide insights into hypoxia-responsive pathways involving direct transcription factor binding. This study provides a useful resource for hypoxia research. It also demonstrates the potential of a meta-analysis approach to public gene expression databases for selecting candidate genes from gene expression profiles generated under various experimental conditions.

scholarly journals Cross-species Transcriptomic Comparison of In Vitro and In Vivo Mammalian Neural Cells

2015 ◽  
Vol 9 ◽  
pp. BBI.S33124 ◽  
Author(s):  
Peter R. LoVerso ◽  
Christopher M. Wachter ◽  
Feng Cui
Keyword(s):  
Gene Expression ◽  
Transcript Abundance ◽  
Cell Types ◽  
Mammalian Brain ◽  
Neural Cells ◽  
Rna Seq ◽  
Commercial Sources ◽  
And Function

The mammalian brain is characterized by distinct classes of cells that differ in morphology, structure, signaling, and function. Dysregulation of gene expression in these cell populations leads to various neurological disorders. Neural cells often need to be acutely purified from animal brains for research, which requires complicated procedure and specific expertise. Primary culture of these cells in vitro is a viable alternative, but the differences in gene expression of cells grown in vitro and in vivo remain unclear. Here, we cultured three major neural cell classes of rat brain (ie, neurons, astrocytes, and oligodendrocyte precursor cells [OPCs]) obtained from commercial sources. We measured transcript abundance of these cell types by RNA sequencing (RNA-seq) and compared with their counterparts acutely purified from mouse brains. Cross-species RNA-seq data analysis revealed hundreds of genes that are differentially expressed between the cultured and acutely purified cells. Astrocytes have more such genes compared to neurons and OPCs, indicating that signaling pathways are greatly perturbed in cultured astrocytes. This dataset provides a powerful resource to demonstrate the similarities and differences of biological processes in mammalian neural cells grown in vitro and in vivo at the molecular level.

scholarly journals Discrete Distributional Differential Expression (D3E) - A Tool for Gene Expression Analysis of Single-cell RNA-seq Data

2015 ◽  
Author(s):  
Mihails Delmans ◽  
Martin Hemberg
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Burst Size ◽  
Synthetic Data ◽  
Driving Mechanism ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Distributional Method ◽  
Average Burst Size ◽  
Differential Gene

The advent of high throughput RNA-seq at the single-cell level has opened up new opportunities to elucidate the heterogeneity of gene expression. One of the most widespread applications of RNA-seq is to identify genes which are differentially expressed (DE) between two experimental conditions. Here, we present a discrete, distributional method for differential gene expression (D3E), a novel algorithm specifically designed for single-cell RNA-seq data. We use synthetic data to evaluate D3E, demonstrating that it can detect changes in expression, even when the mean level remains unchanged. D3E is based on an analytically tractable stochastic model, and thus it provides additional biological insights by quantifying biologically meaningful properties, such as the average burst size and frequency. We use D3E to investigate experimental data, and with the help of the underlying model, we directly test hypotheses about the driving mechanism behind changes in gene expression.

scholarly journals Multiplexed single-cell RNA-seq via transient barcoding for simultaneous expression profiling of various drug perturbations

2019 ◽  
Vol 5 (5) ◽  
pp. eaav2249 ◽  
Author(s):  
Dongju Shin ◽  
Wookjae Lee ◽  
Ji Hyun Lee ◽  
Duhee Bang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cost Effective ◽  
Specific Gene ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Cost Effective Method ◽  
Treatment Experiment ◽  
Single Cell Profiling ◽  
Multiple Samples

The development of high-throughput single-cell RNA sequencing (scRNA-seq) has enabled access to information about gene expression in individual cells and insights into new biological areas. Although the interest in scRNA-seq has rapidly grown in recent years, the existing methods are plagued by many challenges when performing scRNA-seq on multiple samples. To simultaneously analyze multiple samples with scRNA-seq, we developed a universal sample barcoding method through transient transfection with short barcode oligonucleotides. By conducting a species-mixing experiment, we have validated the accuracy of our method and confirmed the ability to identify multiplets and negatives. Samples from a 48-plex drug treatment experiment were pooled and analyzed by a single run of Drop-Seq. This revealed unique transcriptome responses for each drug and target-specific gene expression signatures at the single-cell level. Our cost-effective method is widely applicable for the single-cell profiling of multiple experimental conditions, enabling the widespread adoption of scRNA-seq for various applications.

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