RNA-seq differential expression studies: more sequence, or more replication?
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE51403 | GEO | 2014/01/07
SECONDARY ACCESSION(S): PRJNA222975
REPOSITORIES: GEO
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Project description:Motivation: RNA-seq is replacing microarrays as the primary tool for gene expression studies. Many RNA-seq studies have used insufficient biological replicates, resulting in low statistical power and inefficient use of sequencing resources. Results: We show the explicit trade-off between more biological replicates and deeper sequencing in increasing power to detect differentially expressed (DE) genes. In the human cell line MCF-7, adding more sequencing depth after 10M reads gives diminishing returns on power to detect DE genes, while adding biological replicates improves power significantly regardless of sequencing depth. We also propose a cost-effectiveness metric for guiding the design of large scale RNA-seq DE studies. Our analysis showed that sequencing less reads and perform more biological replication is an effective strategy to increase power and accuracy in large scale differential expression RNA-seq studies, and provided new insights into efficient experiment design of RNA-seq studies Treatment (10nM E2 treatment for 24h) and control MCF7 cells are both replicated 7 times, and collected for mRNA-seq. Reads are then subsampled for statistical analysis.
2014-01-07 | E-GEOD-51403 | biostudies-arrayexpress
Project description:Recent advances in RNA sequencing (RNA-Seq) have enabled the discovery of novel transcriptomic variations that are not possible with traditional microarray-based methods. Tissue and cell specific transcriptome changes during pathophysiological stress, in disease cases versus controls and in response to therapies are of particular interest to investigators studying cardiometabolic diseases. Thus, knowledge on the relationships between sequencing depth and detection of transcriptomic variation is needed for designing RNA-Seq experiments and for interpreting results of analyses. Using deeply sequenced RNA-Seq data derived from adipose of a healthy individual before and after systemic administration of endotoxin (LPS), we investigated the sequencing depths needed for studies of gene expression and alternative splicing (AS). We found that to detect expressed genes and AS events, ~100 million (M) filtered reads were needed. However, the requirement on sequencing depth for the detection of LPS modulated differential expression (DE) and differential alternative splicing (DAS) was much higher. To detect 80% of events, ~300M filtered reads were needed for DE analysis whereas at least 400M filtered reads were necessary for detecting DAS. Although the majority of expressed genes and AS events can be detected with modest sequencing depths (~100M filtered reads), the estimated gene expression levels and exon/intron inclusion levels were less accurate. We report the first study that evaluates the relationship between RNA-Seq depth and the ability to detect DE and DAS in human adipose. Our results suggest that a much higher sequencing depth is needed to reliably identify DAS events than for DE genes. Random sampling the RNA-seq data in different depth for gene and alternative-splicing analysis
2013-07-12 | E-GEOD-46323 | biostudies-arrayexpress
Project description:Background The combination of sodium bisulfite treatment with highly-parallel sequencing is a common method for quantifying DNA methylation across the genome. The power to detect between-group differences in DNA methylation using bisulfite-sequencing approaches is influenced by both experimental (e.g. read depth, missing data and sample size) and biological (e.g. mean level of DNA methylation and difference between groups) parameters. There is, however, no consensus about the optimal thresholds for filtering bisulfite sequencing data with implications for the reproducibility of findings in epigenetic epidemiology. Results We used a large reduced representation bisulfite sequencing (RRBS) dataset to assess the distribution of read depth across DNA methylation sites and the extent of missing data. To investigate how various study variables influence power to identify DNA methylation differences between groups, we developed a framework for simulating bisulfite sequencing data. As expected, sequencing read depth, group size, and the magnitude of DNA methylation difference between groups all impacted upon statistical power. The influence on power was not dependent on one specific parameter, but reflected the combination of study-specific variables. As a resource to the community, we have developed a tool, POWEREDBiSeq, which utilizes our simulation framework to predict study-specific power for the identification of DNAm differences between groups, taking into account user-defined read depth filtering parameters and the minimum sample size per group. Conclusions Our data-driven approach highlights the importance of filtering bisulfite-sequencing data by minimum read depth and illustrates how the choice of threshold is influenced by the specific study design and the expected differences between groups being compared. The POWEREDBiSeq tool can help users identify the level of data filtering needed to optimize power and aims to improve the reproducibility of bisulfite sequencing studies.
2021-03-20 | GSE169234 | GEO
Project description:Background The combination of sodium bisulfite treatment with highly-parallel sequencing is a common method for quantifying DNA methylation across the genome. The power to detect between-group differences in DNA methylation using bisulfite-sequencing approaches is influenced by both experimental (e.g. read depth, missing data and sample size) and biological (e.g. mean level of DNA methylation and difference between groups) parameters. There is, however, no consensus about the optimal thresholds for filtering bisulfite sequencing data with implications for the reproducibility of findings in epigenetic epidemiology. Results We used a large reduced representation bisulfite sequencing (RRBS) dataset to assess the distribution of read depth across DNA methylation sites and the extent of missing data. To investigate how various study variables influence power to identify DNA methylation differences between groups, we developed a framework for simulating bisulfite sequencing data. As expected, sequencing read depth, group size, and the magnitude of DNA methylation difference between groups all impacted upon statistical power. The influence on power was not dependent on one specific parameter, but reflected the combination of study-specific variables. As a resource to the community, we have developed a tool, POWEREDBiSeq, which utilizes our simulation framework to predict study-specific power for the identification of DNAm differences between groups, taking into account user-defined read depth filtering parameters and the minimum sample size per group. Conclusions Our data-driven approach highlights the importance of filtering bisulfite-sequencing data by minimum read depth and illustrates how the choice of threshold is influenced by the specific study design and the expected differences between groups being compared. The POWEREDBiSeq tool can help users identify the level of data filtering needed to optimize power and aims to improve the reproducibility of bisulfite sequencing studies.
2021-03-20 | GSE169218 | GEO
Project description:A large number of computational methods have been recently developed for analyzing differential gene expression (DE) in RNA-seq data. We report on a comprehensive evaluation of the commonly used DE methods using the SEQC benchmark data set and data from ENCODE project. We evaluated a number of key features including: normalization, accuracy of DE detection and DE analysis when one condition has no detectable expression. We found significant differences among the methods. Furthermore, computational methods designed for DE detection from expression array data perform comparably to methods customized for RNA-seq. Most importantly, our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth. The Sequencing Quality Control Consortium generated two datasets from two reference RNA samples in order to evaluate transcriptome profiling by next-generation sequencing technology. Each sample contains one of the reference RNA source and a set of synthetic RNAs from the External RNA Control Consortium (ERCC) at known concentrations. Group A contains 5 replicates of the Strategene Universal Human Reference RNA (UHRR), which is composed of total RNA from 10 human cell lines, with 2% by volume of ERCC mix 1. Group B includes 5 replicate samples of the Ambion Human Brain Reference RNA (HBRR) with 2% by volume of ERCC mix 2. The ERCC spike-in control is a mixture of 92 synthetic polyadenylated oligonucleotides of 250-2000 nucleotides long that are meant to resemble human transcripts.
2013-08-20 | E-GEOD-49712 | biostudies-arrayexpress
Project description:A large number of computational methods have been recently developed for analyzing differential gene expression (DE) in RNA-seq data. We report on a comprehensive evaluation of the commonly used DE methods using the SEQC benchmark data set and data from ENCODE project. We evaluated a number of key features including: normalization, accuracy of DE detection and DE analysis when one condition has no detectable expression. We found significant differences among the methods. Furthermore, computational methods designed for DE detection from expression array data perform comparably to methods customized for RNA-seq. Most importantly, our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.
2013-08-20 | GSE49712 | GEO
Project description:Data analysis is a critical part of quantitative proteomics studies in interpreting biological questions. Numerous computational tools including protein quantification, imputation, and differential expression (DE) analysis were generated in the past decade. However, searching optimized tools is still an unsolved issue. Moreover, due to the rapid development of RNA-Seq technology, a vast number of DE analysis methods are created. Applying these newly developed RNA-Seq-oriented tools to proteomics data is still a question that needs to be addressed. In order to benchmark these analysis methods, a proteomics dataset constituted the proteins derived from human, yeast, and drosophila with different ratios were generated. Based on this dataset, DE analysis tools (including array-based and RNA-Seq based), imputation algorithms, and protein quantification methods were compared and benchmarked. This study provided useful information on analyzing quantitative proteomics datasets. All the methods used in this study were integrated into Perseus which are available at https://www.maxquant.org/perseus.
2022-11-16 | PXD029891 | JPOST Repository
Project description:RNA sequencing (RNA-seq) is a widely used method for quantifying RNA levels across the environmental, biological and medical sciences. The accuracy of the output from an RNA-seq experiment is known to vary due to sequencing biases or errors in quantification. These have the potential to lead to false calls of differential expression (DE), and hence, to affect the accuracy of the biological inference. A proposed solution to reduce the number of false positives and increase confidence in the quality of results from such experiments is to increase the number of biological replicates. In addition, more recent suggestions are to create additional technical replicates within biological replicates (i.e. to split samples across sequencing lanes). The optimal strategy for analysing and normalizing such data, and for maximising accuracy as a function of cost, biological and technical replication is important to understand, yet currently unclear. The aim of this study was to test the effect of technical replication and sample splitting on the overall outcome of gene expression profiling for RNA-seq data.
2018-12-15 | GSE85806 | GEO
Project description:Recent advances in RNA sequencing (RNA-Seq) have enabled the discovery of novel transcriptomic variations that are not possible with traditional microarray-based methods. Tissue and cell specific transcriptome changes during pathophysiological stress, in disease cases versus controls and in response to therapies are of particular interest to investigators studying cardiometabolic diseases. Thus, knowledge on the relationships between sequencing depth and detection of transcriptomic variation is needed for designing RNA-Seq experiments and for interpreting results of analyses. Using deeply sequenced RNA-Seq data derived from adipose of a healthy individual before and after systemic administration of endotoxin (LPS), we investigated the sequencing depths needed for studies of gene expression and alternative splicing (AS). We found that to detect expressed genes and AS events, ~100 million (M) filtered reads were needed. However, the requirement on sequencing depth for the detection of LPS modulated differential expression (DE) and differential alternative splicing (DAS) was much higher. To detect 80% of events, ~300M filtered reads were needed for DE analysis whereas at least 400M filtered reads were necessary for detecting DAS. Although the majority of expressed genes and AS events can be detected with modest sequencing depths (~100M filtered reads), the estimated gene expression levels and exon/intron inclusion levels were less accurate. We report the first study that evaluates the relationship between RNA-Seq depth and the ability to detect DE and DAS in human adipose. Our results suggest that a much higher sequencing depth is needed to reliably identify DAS events than for DE genes.
2013-07-12 | GSE46323 | GEO
Project description:This project will chart with high-resolution the genetic pathways that give rise to defined clinical phenotypes related to cocaine addiction. We will utilize the power of the hybrid mouse diversity panel (HMDP), combined with high quality behavioral phenotyping and massive-scale RNA sequencing, to trace the interconnections from DNA to RNA to clinical trait and provide layered information on the mechanisms of cocaine abuse. The HMDP consists of 100 inbred and recombinant inbred strains and has a wide array of meiotic breakpoints. The panel has been densely genotyped with more than 200,000 single nucleotide polymorphisms (SNPs), allowing very fine mapping of quantitative trait loci (QTLs). Further, the HMDP is genetically stable and renewable and can be assayed for multiple phenotypes, yielding cumulative biological information. We will expand our previous HMDP-based studies of cocaine abuse-related traits by adding massive-scale RNA sequencing (RNA-Seq), in order to detail the genetic control of the pathways to addiction.
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