RNA-Seq Summit Summary
RNA-Seq is a technology that uses the capabilities of massively parallel sequencing to generate whole transcriptome information at the single transcript level.
The RNA-Seq 2014 summit was an excellent stage to learn about the advantages of RNA-Seq and the transition from microarray or real-time PCR analysis to RNA-Sequencing. This meeting spans two days and each day there were scheduled presentations, group workshops, poster presentations and networking sessions. A total of nineteen talks were delivered covering wide range of topics in RNA-Seq that included sample selection, RNA isolation, evaluating RNA quality and integrity, various methods for library preparation, sequencing, data storage and management, data analysis and interpretation. In addition to the individual presentations, round table discussion sessions were also organized for each day.
In addition a very unique networking session was held on the first day called “speed networking”, yeah you got it right, it was just like speed-dating, only for networking with attendees. The purpose was to enhance the network with the researchers, technology developers and commercial groups with an opportunity to introduce and pass business cards to as many attendees as possible in a short period of time.
In short, this meeting brought together academic and industry leaders to discuss the latest advancements in RNA-Sequencing and evaluated the success and progress of best technologies and software packages. I believe I have benefited from this meeting very much and I am hoping to implement this knowledge to become an expert in RNA-Seq.
Setting the Scene: RNA-Seq Field & New Technologies
Joshua Levin, Broad Institute
Dr. Levin is a research scientist and group leader at the Broad Institute, who has developed and comprehensively evaluated a wide range of assays for RNA sequencing (RNA-Seq) such as targeted, strand-specific, total, and low-input RNA-Seq protocols. These developed assays are being used for a variety of projects such as cancer transcriptomics, single cell studies, and genome annotation.
Dr. Levin opened the summit with his very informative and interesting talk about the basics of RNA-Seq, which he began by demonstrating that in the cell, each, single-stranded RNA is synthesized from one of the two strands of DNA. When RNA is copied back into cDNA for RNA-Seq in the lab, the information about which of the two strands of DNA was copied into RNA can be lost unless special methods are used. Strand-specific RNA-Seq improves on standard RNA-Seq in three ways: accurately identifying antisense transcripts, determining the transcribed strand of non-coding RNAs (e.g. lincRNAs), and demarcating the boundaries of closely situated or overlapping genes.
Further, Dr. Levin stated that non strand-specific RNA sequencing has been the standard method but now strand-specific approaches provide additional valuable information and do not involve that much more work or cost.
Levin also mentioned that along with strand specificity his team also examined other criteria and assessed practical measures like ease of use in the laboratory and in computational analysis. However, looking at all these factors, dUTP turned out to be the one Dr. Levin and his team liked the most and it is their default RNA-Seq method at the Broad right now, but there are some technical challenges that need to be addressed to make the process high-throughput. He commented that researchers at the Broad are addressing this point now to be ready for large sequencing requests as they become more frequent.
Dr. Levin also discussed about the RNase H method that comparatively performed best for chemically fragmented, low-quality RNA. RNase H can even effectively replace oligo (dT)-based methods for standard RNA-seq. Moreover, SMART and NuGEN had distinct strengths for measuring low-quantity RNA. He also pointed that N6-methyladenosine (m6A) is the most ubiquitous mRNA base modification, but little is known about its precise location, temporal dynamics, and regulation. Currently, many methods are available to perform mRNA methylation but function of which is unclear. More importantly full-length mRNA-Seq from single-cell levels of RNA to reveals biological variations in a given population is becoming the method of choice.
Dr. Levin presented evidence that helps biologists to select the most suitable methods and provides a benchmark for future method development.
RNA-Seq vs. Microarrays
Seth Crosby, Washington University School of Medicine
Dr. Crosby presented a comparison of data sets derived from RNA-Seq and microarrays using the same set of samples on both platforms. Those data sets showed a high correlation between gene expression profiles generated by the two platforms. Further, he mentioned that RNA-Seq demonstrated a broader dynamic range than microarrays, because RNA-Seq does not rely on a pre-designed complement sequence detection probe and hence allowed for the detection of more differentially expressed genes with higher fold-change. This comparison also showed that the RNA-Seq avoids technical issues inherent to microarrays like cross-hybridization, non-specific hybridization and no probe saturation in RNA-Seq.
According to Dr. Crosby, in spite of all the available benefits of RNA-Seq, microarrays are still the assay of choice for transcriptional profiling and is cost effective depending upon the objective of the study. He pointed out that RNA-Seq is a relatively new technology, slightly expensive, data storage is more challenging and more importantly it requires special skills to analyze and interpret the data.
However, at the end he conclusively indicated that RNA-Seq would become the predominant tool for transcriptome analysis once everyone understands the benefits of RNA-Seq as the technology continues to improve and the cost continues to drop.
The power of RNA-Seq: What’s Next
Qichao Zho, Boehringer Ingelheim
Dr. Zho delivered a very informative and comparative talk in which he discussed about RNA-Seq in detail. He described that in RNA-Seq the massively parallel sequencing technology is being applied to measure gene expression levels and composition which makes RNA-Seq an extremely powerful tool in quantifying and annotating transcriptomes.
RNA-Seq has the potential to detect and quantify RNAs from all biologically relevant classes, including those with low and moderate abundance. Dr. Zho emphasized that in order to be benefited from this powerful tool there are some steps that can be taken with great considerations besides the technology improvement, such as the experimental design, library preparation (introducing replicates more importantly biological replicates which provides a way to estimate accurately and to evaluate statistically), blocking design which can eliminate the flow cell and lane effects, multiplexed or non multiplexed, read length and single end or paired end. Further, he stated that a thorough and well-designed experiment is a key to a successful project.
Dr. Zho emphasized that sequencing depth or coverage (the number of total mapped sequences per transcript) is a very important characteristic of RNA-seq. Because genes are differentially expressed in each transcriptome the low expressed genes requires relatively large number of sequences to measure. This is a critical step in any experimental design and it should be well considered and discussed.
In RNA-Seq, it is impossible to set a single coverage value for RNA-Seq experiments due to the complex nature of the transcriptome in each sample at any given time. Therefore, it really depends what is needed to accomplish. If only estimating gene expression or to validate variants in medium to highly expressed genes no much depth is required. But for variant calling in lower expressed genes, and to detect rarely expressed genes or to evaluate alternative-splicing patterns a large number of sequences are needed.
Further, he mentioned that at the same sequencing depth the sequencing of both ends of RNA fragments increase the sensitivity and specificity of the detection of the alternative splicing and chimeras compared to single end sequencing. Therefore, paired-end sequencing is more informative. In addition, GC content of the sequence, use of the random hexamer oligoes, 3’and 5’ depletion or bias towards 3’-end due to using oligo dTs introduce biases in the sequencing data which directly affect RNA-Seq analysis. Better analysis strategy and better protocols could avoid all or some of these biases.
Finally, Dr. Zho said that validation of RNA-Seq data has significant importance and it can further validate the biological conclusions from RNA-Seq experiments if validated using different biological replicates from the same populations instead of using same RNA samples used for RNA-Seq experiments.
**EpiGenie offers a huge “Thank You!” to Dr. Shahina B. Maqbool who is a Research Assistant Professor in the Department of Genetics and Director of the Epigenomics Shared Facility (ESF) at Albert Einstein College of Medicine Of Yeshiva University, for kindly providing this conference coverage.**