This is what makes for such huge saving of sequence reads in in Expression Profiling sequencing. Simply by choosing expression profiling libraries instead of the ones for whole transcriptome, you can save about 10X of sequencing capacity which can, instead, contribute to higher multiplexing capability which allows more samples to be compared in parallel.
For this you need a dedicated library prep which is designed to sequence only the representative position of each transcript. When mapped to the reference and successfully identified, such expression profiling library will allow you to sequence 10 times more samples than the whole transcriptome library. Therefore, we developed a protocol which starts reverse transcription from the poly A tail of each transcript followed by random priming for the second strand synthesis. Most importantly, only one fragment is generated from a transcript, enabling simple counting of NGS reads for fast evaluation of gene expression values.
The advantages are numerous. Such approach increases multiplexing capability which significantly drops the price per sample, enabling high-throughput screening. Also, short read with single-end sequencing is enough for the purpose which saves sequencing cost dramatically. So, before you start the RNA-Seq project, it is very important to define the objective of your project, whether to see the entire transcriptome of your samples including splice variants and de novo assembly of transcripts, or to get the gene expression profiles and perform massive screening of diverse samples.
For details about this library prep, please refer to the product information page or Nature Methods publication. This difference in methodology for managing lower sample numbers might explain the abrupt shift to high recall with reduced precision. Sample number's impact on performance. Precision and recall, averaged over the 10 iterations at a given sample number and read depth, split by read depth columns and sample number rows.
Given the somewhat surprising result that sample numbers below six had severely reduced performance, we next sought to assess how widely used low sample numbers were in recent RNA-Seq studies. When this survey was repeated and restricted to studies of human samples, the average sample numbers were slightly higher, with about half of the studies falling at or below six samples per group Fig. This suggests that while some authors of human studies recognize the increased variability inherit to clinical samples and increase sample size accordingly, the performance characteristics of many human studies would be improved with increased sample numbers.
Given these results, caution should be exercised in interpreting many recent RNA-Seq studies that may conform to common experimental design approaches, but that may be underpowered for RNA-Seq analysis. Additionally, this highlights the necessity of benchmarking RNA-Seq tool performance using datasets most similar to those that the methods will be applied to, to better define best practices for study design and analysis. Literature survey of RNA-Seq experiment sample numbers. Violin plots of sample numbers used in studies containing RNA-Seq differential gene expression analysis, either from all species a or limited to primary human samples b.
Individual dots represent average sample number used in each study. Grey dashed line represents six samples. In our initial study examining the performance of workflows, we observed that the number of genes called significant by a workflow heavily influenced the recall and precision, with a strong correlation between recall and the number of genes identified as significant, and an inverse relationship between precision and the number of significant genes [ 7 ].
As such, we hypothesized that changes in the number of genes identified as significant would be correlated with the degradation of performance at lower sample numbers and read depths. As predicted, we observe a strong relationship between recall and number of genes called significant, with the number of genes called significant tending to increase as sample number increased, with a commensurate increase in recall Fig. Surprisingly, and in contrast to our previous observations across workflows [ 7 ], the converse was not true for precision Fig.
While the trend that higher numbers of genes called significant tended to have lower precision remained true, this effect was much less pronounced. Interestingly, the precision across workflows tended to decrease at the highest sample numbers. Notably, workflows employing NOISeqBIO at three and four samples called the highest number of significant genes of any workflows, which likely accounts for the relatively high recall with poor precision displayed by these workflows at low sample numbers.
Significant gene number's impact on performance. Average recall a or average precision b versus the average number of genes identified as significant. Dots represent values for individual workflows read aligner, expression modeler, and differential expression tool at a given sample number and read depth, averaged over the ten sample combination iterations run at each given sample number and read depth.
Bars represent standard deviation. Colors represent sample number. Red line represents linear regression for plotted data. Of the workflows examined, all performed well at higher read depths and sample numbers, and the choice of workflows at these parameters should be largely influenced by the tolerance of a specific application for type I versus type II error, as we concluded previously [ 7 ].
However, caution should be used at lower read depths and sample numbers, as performance is variable and highly dependent on the choice of differential expression tool, with much smaller impact from read aligner and expression modeler. These results also give insight into the read depth and sample number required for robust results when designing RNA-Seq experiments involving clinical samples, which exhibit more genetic and pre-analytical heterogeneity than typical in vitro study designs.
Conversely, tool performance—particularly recall and the commensurate number of genes called significant—rapidly declined as sample number per group decreased, with changes apparent even by eight samples. If sample number is constrained, caution must be exercised in choosing a differential expression tool, as performance is more variable. These findings represent a departure from current practices used in many studies, which tend to follow more traditional experimental designs employing fewer replicates.
From RNA-seq reads to differential expression results. Genome Biol. BioMed Central. Provart NJ, editor. PLoS One. Public Libr Sci; ;9:e A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae. Comparison of software packages for detecting differential expression in RNA-seq studies. Brief Bioinform. A benchmark for RNA-seq quantification pipelines. Article Google Scholar.
A benchmarking of workflows for detecting differential splicing and differential expression at isoform level in human RNA-seq studies. Empirical assessment of analysis workflows for differential expression analysis of human samples using RNA-Seq. BMC Bioinformatics. RNA-seq differential expression studies: more sequence or more replication?
Transcriptome landscape of human primary monocytes at different sequencing depth. Power analysis and sample size estimation for RNA-Seq differential expression. Curr Protoc Hum Genet.
Differential expression in RNA-seq: a matter of depth. Genome Res. Liu Z, editor. Public Libr Sci; ;8:e Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. A comparative study of techniques for differential expression analysis on RNA-Seq data. Comparison of normalization and differential expression analyses using RNA-Seq data from individual Drosophila melanogaster.
BMC Genomics. How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? Cold Spring Harbor Laboratory Press. Scotty: a web tool for designing RNA-Seq experiments to measure differential gene expression. Oxford University Press. Bi R, Liu P. Sample size calculation while controlling false discovery rate for differential expression analysis with RNA-sequencing experiments.
R package; Calculating sample size estimates for RNA sequencing data. J Comput Biol. General power and sample size calculations for high-dimensional genomic data. Poplawski A, Binder H.
Feasibility of sample size calculation for RNA-seq studies. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments.
RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Transcript profiling of CDpositive monocytes reveals a unique molecular fingerprint. Eur J Immunol. Gene expression profiling reveals the defining features of the classical, intermediate, and nonclassical human monocyte subsets. Comparison of gene expression profiles between human and mouse monocyte subsets.
RMTA can easily process thousands of RNA-seq datasets with features that include automated read quality analysis, filters for lowly expressed transcripts, and read counting for differential expression analysis. RMTA is containerized using Docker for easy deployment within any compute environment [cloud, local, or high-performance computing HPC ] and is available as two apps in CyVerse's Discovery Environment, one for normal use and one specifically designed for introducing undergraduates and high school to RNA-seq analysis.
RMTA is designed to be useful for data scientists, of any skill level, interested in rapidly and reproducibly analyzing their large RNA-seq data sets. RNA-sequencing RNA-seq provides scientists with the ability to monitor genome-wide transcription across numerous cells or tissues and between experimental conditions in a rapid and affordable manner.
Data generated from RNA-sequencing are incredibly powerful for differential gene expression analysis Mortazavi et al. In addition to generating and examining novel RNA-seq data, scientists are re-examining the hundreds of thousands of publicly available archived datasets to make novel discoveries Lachmann et al. SRA run information associated with transcriptomic analyses was downloaded and sorted by year deposited.
Thousands of experiments deposited, per year, is shown with the black line on the right y-axis. Alignment-based processing of these massive volumes of RNA-seq data typically involves two computationally intensive steps: mapping reads against a reference genome and transcript assembly.
Reference genome based read mapping is performed using splice-aware algorithms such as STAR Dobin et al. The computational cost associated with mapping reads is dependent on the size of the genome and the number of reads to be mapped but typically takes hours to days on a standard lab server. The mapped reads are then used to assemble transcripts using programs such as StringTie or Cufflinks.
Transcript assembly is less computationally intensive than read mapping but can still require several hours to complete. In addition to the computational requirements, both of these steps require substantial data storage resources and technical skills in transferring and manipulating large files, further increasing the technological burden for the researcher.
Successful assembly of RNA-seq data is insufficient to achieve the ultimate experimental goal: extraction of meaningful data. Data extraction usually involves differential expression analyses, isoform analysis, or novel gene identification. Each of these analyses requires different input file types and the use of different applications—each with their own intricacies surrounding installation, use, and preference for a Linux environment. In addition, preparing data files and then organizing them into the appropriate file structure for these next steps rapidly becomes tedious when performed on hundreds to thousands of files.
Thus, despite the wealth of computing resources, extracting meaningful knowledge from RNA-seq data is still a non-trivial task.
Cloud-computing cyber-infrastructure platforms such as CyVerse Merchant et al. In contrast to fee-based services such as the Cancer Genomics Cloud Lau et al.
CyVerse and Galaxy also offer graphical user interface GUI platforms which allow researchers with minimal programming experience to easily deploy and handle large volumes of jobs in parallel. Thus, these computational resources make large dataset analysis and re-analysis feasible in a reasonable time-frame and cost-effective way. RMTA is easy to use and incorporates features that move beyond the standard RNA-seq workflow, allowing data scientists to focus their time on downstream analyses.
For users with access and familiarity with high-performance computing HPC command-line operations, RMTA is packaged as a Docker container for one-step installation Table 1. In contrast to other containerized RNA-seq analysis tools Folarin et al. Beyond read mapping and assembly, RMTA has a number of additional features that automate onerous data transformation and quality control steps, thus producing outputs that can be directly used for differential expression analysis or novel gene identification.
In addition, the output from RMTA may be rapidly integrated in downstream transcriptomic data visualization platforms to help researchers extract meaningful knowledge. RMTA is both straightforward to install and use, and is meant to be used by both advanced and novice data scientists in their examination of their RNA-seq data.
In this section we provide an overview of RMTA, its different features, and its deployment options. This BAM file is then automatically used as input for StringTie, where it, along with the reference genome annotation, is used to assemble transcripts. Several optional features are included, such as the ability to perform quality control on RNA-sequencing RNA-seq data with FastQC, filtering of lowly expressed transcripts, and removal of duplicate reads Bowtie 2 only.
Output is listed, and are ready for downstream analyses such as those shown. As an alternative to genome-guided read mapping and transcript assembly, RMTA also allows for read alignment directly to a transcriptome using the quasi-aligner and transcript abundance quantifier Salmon Patro et al.
Salmon maps reads to the provided transcript assembly and then counts the number of reads associated with each transcript, generating an output file quant. The utilization of Salmon is only appropriate when the user is wanting to rapidly test for differential expression and cannot facilitate the identification of novel genes or data visualization in a genome browser.
The primary difference is how the user plans on launching jobs and providing the necessary input data to the OSG. When launched directly from within the OSG through a user's personal account, the user must provide access to all necessary data e. When jobs are submitted via the Discovery Environment, it automatically prepares the information needed to run the job and submits it to the OSG via HTCondor Thain et al.
Once the job is launched OSG-RMTA uses the information provided by the Discovery Environment to retrieve input files, process the data, and upload the results back to the Data Store, allowing the user to submit and walk away. For local or cloud-based computing, a Dockerized version of RMTA identical to that used in the Discovery Environment is available for use inside a Docker command line environment.
Several additional features have been included in the RMTA workflow to facilitate data discovery and quality control. When the Bowtie option is selected, HISAT2 and StringTie are both removed from the workflow, but the additional option to remove duplicate reads important for population level analyses becomes available. Poor quality RNA-seq reads, particularly at the 5' or 3' ends as a result of adaptor contamination or a drop in sequencing quality, can lead to a significant population of unmapped reads.
To help the user identify issues resulting from poor read mapping rates, the quality control tool FastQC Andrews, is available as an additional option in the RMTA workflow for both genome or transcriptome-guided read mapping approaches. FastQC provides the user with both an overview of potential issues with their data, as well as summary graphs highlighting issues such as per base sequence quality and Kmer content. If issues are detected at the 5' or 3' of sequencing reads, RMTA includes additional options for specifically trimming bases off of either end during the next analysis.
Sequencing reads of overall poor quality will simply not be mapped and therefore do not need to be trimmed, but will still be highlighted in the FastQC results. RMTA is also designed to aid in the identification of novel genes such as long non-coding RNAs from genome-guided transcriptome assemblies.
In general 5 M mapped reads is a good bare minimum for a differential gene expression DGE analysis in human. In many cases 5 M — 15 M mapped reads are sufficient. You will be able to get a good snapshot of highly expressed genes. A higher sequencing depth generates more informational reads, which increases the statistical power to detect differential expression also among genes with lower expression levels. For that reason, many published human RNA-Seq experiments have been sequenced with a sequencing depth between 20 M - 50 M reads per sample.
This gives a more global view on gene expression and also some information for alternative splicing analysis. There are also cases where significantly less e. For planning an RNA-seq experiment it is important to consider that a rising number of biological replicates number of examined samples also increases the statistical power of differential gene expression detection - a compromise should be made between sequencing depth and biological replication.
In an DGE methodology experiment Liu et al. An important step of any NGS experiment is the initial planning.
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