User:Rgocs/RNA-test2

RNA-Seq

(redirect from WTSS, Whole Transcriptome Shotgun Sequencing, mRNA Sequencing, mRNA-Seq)

Also called "Whole Transcriptome Shotgun Sequencing" ("WTSS"), and dubbed "a revolutionary tool for transcriptomics", refers to the use of high throughput sequencing technologies to sequence cDNA in order to get information about a sample's RNA content, a technique that is quickly becoming invaluable in the study of diseases like cancer. Thanks to the deep coverage and base level resolution provided by next-generation sequencing instruments, RNA-Seq provides researchers with efficient ways, for example, to measure how different alleles of a gene are expressed, detect post-transcriptional mutations, identify gene fusions.

Introduction
The introduction of Next-Generation Sequencing, or High-Throughput Sequencing, technologies opened new doors into the field of DNA sequencing, however as understanding of these technologies becomes more widespread and new tools are being developed, so are new innovative ways of applying these technologies being created.

Given High-Throughput Sequencing technologies' low requirements of nucleotide sequence product, together with its deep coverage and base-scale resolution, its use has expanded to the field of transcriptomics. Transcriptomics is an area of research characterizing the RNA transcribed from a particular genome under investigation. Although transcriptomes are more dynamic relative to genomic DNA, these molecules provide direct access to gene regulation and protein information. Sequencing these transcriptomes is not a new idea, methods have been previously developed to directly determina cDNA sequences mostly based around traditional (and more expensive) Sanger sequencing, some of the methodologies in existance include Serial analysis of gene expression (SAGE), cap analysis of gene expression (CAGE) and massively parallel signature sequencing (MPSS).

Transcriptome Sequencing (RNA-seq) can be done with a variety of platforms. For example, recent applications include using the Illumina (company) Genome Analyzer platform to sequence mammalian transcriptomes, ABI Solid Sequencing to profile stem cell transcriptomes or Life Science's 454 Sequencing to discover SNPs in maize through its transcriptome. Even though each platform has its technical individualities, the information gathered from each is of the same nature.

RNA Poly(A) Library
Creation of a library can change from platform to platform in high throughput sequencing, where each platform has several kits designed to build different types of libraries and adapting the resulting sequences to the specific requirements of their instruments.

However, due to the nature of the template being analyzed, ie RNA, there are commonalities within each technology. Frequently in mRNA analysis the 3' polyadenylated (poly(A)) tail is targeted in order to ensure that coding RNA is separated from non-coding RNA. This can be accomplished simply with poly (T) oligos covalently attached to a given substrate. Presently many studies utilize magnetic beads for this step (Invitrogen, MACS mRNA Isolation kit).

Certain studies have shown that non-poly(A) RNA can yield important non-coding RNA gene discovery and therefore, selecting only for poly (A) RNA molecules significantly reduces this efficiency (Morin, 2008). Since ribosomal RNA represents over 90% of the RNA within a given cell, studies have shown that its removal via probe hybridization assists this process of transcriptome coverage. (Invitrogen, RiboMinus Human/Mouse Transcriptome Isolation kit)

Due to the 5' bias of random priming and secondary structures influencing primer binding sites, hydrolysis of RNA into 200-300 nucleotides prior to reverse transcription theoretically and practically reduces both problems. Once the cDNA is synthesized it can be further fragmented to reach the desired read length as specified in table 1. Finally, the template is now ready for the desired sequencing method.

The Protocol Online website provides a list of several protocols relating to mRNA isolation.

Next generation sequencing
High-throughput sequencing technologies generate millions of short reads from library of sequences, the most used technologies and some of their characteristics are shown in the following table

Table 1. Comparing metrics and performance of next-generation DNA sequencers

Transcriptome alignment
Due to the small size of the short reads (for Illumina Genome Analyzer this can be around 42 bases) de novo assembly may be difficult (though some software does exist: Velvet_(algorithm)), as there cannot be large overlaps between each read needed to easily reconstruct the original sequences, and the deep coverage makes the computing power to track all of the possible alignments prohibitibed. This can be somewhat overcome by having larger sequences obtained from the same sample using other techniques as Sanger Sequencing, and using these larger reads as a "skeleton" or a "template" to help assemble reads in difficult regions (e.g. regions with repetitive sequences).

The recommended approach is that of aligning the millions of reads to a "reference Genome" (wiki link). There are many tools available for aligning Genomic reads to a reference Genome (http://en.wikipedia.org/wiki/List_of_sequence_alignment_software), however, special attention is needed when alignment of a transcriptome to a genome, mainly when dealing with genes having intronic regions.

As discused above, the sequence libraries created extracting mRNA using its poly(A) tail, which is added to the mRNA molecule post-transcriptionally and thus splicing has taken place. Therefore, the created library and the short reads obtained cannot come from intronic sequences, when trying to align these short reads to a reference Genome, only short reads aligning entirely inside exonic regions will be matched, short reads coming from exon-exon junction regions will not be aligned.

A possible way to work around for this is to try to align the unaligned short reads using a proxy genome generated with known exonic sequences [reference]. This need not cover whole exons, only enough so that the short reads can match on both sides of the exon-exon junction with minimum overlap.

[Final version of Transcriptome alignment figure. Some short reads that are in an exon-exon junction will be split when alighning to the reference genome]

Gene Expression
The characterization of gene expression in cells via meassurement of mRNA levels has long been of interest to researchers. Even though it has been shown that due to other post transcriptional gene regulation events (such as RNA interference) there is not a strong correlation between the abundance of mRNA and the related proteins, meassuring mRNA concentration levels is still a useful tool in determining how the transcriptional machinery of the cell is affected in the presence of external signals (e.g. drug treatment), or how do cells differ between a healthy state and a disease state.

Microarray approach
Prior to RNA-Seq, DNA microarrays were unchallenged as the experiment of choice for transcriptome analysis. Although many exciting experiments are still using microarrays with exciting results, where the amount of time to retrieve results for a given sample is shorter in time, intrinsic experimental limitations of microarrays seem to make RNA-Seq the method of choice. One important limitation, amongst others, is a pre-requisite for sequence information in order to detect and therefore evaluate transcripts (Marioni, 2008)

Coverage as meassure of expression
Expression can be deduced via RNA-Seq to the extent at which a sequence is retrieved. Transcriptome studies in Yeast (Nagalakshmi, 2008) show that in this experimental setting, a four-fold coverage is required for amplicons to be classified and characterized as an expressed gene. When the transcriptome is fragmented prior to cDNA synthesis, the number of reads corresponding to the particular exon normalized by its length in vivo yields gene expression levels which correlate with those obtained through qPCR.

Single Nucleotide Variation (SNP) Discovery
Transcriptome single nucleotide variation has been analyzed in maize on the Roche 454 sequencing platform. In this study researchers were able to conservatively obtain almost 5000 valid SNPs covering more than 2400 maize genes. This impressive transcriptome analysis is currently being applied to cancer research and microbiology which could lead to new forms of medicine.

Coverage
Coverage/depth can affect mutations seen, everything is expression-centric, so an allele might not be seen either because it is not in the genome, or because it is not being expressed.

At the same time, RNA-seq can give additional information than just the existance of an heterozygous gene, it can also help in estimating the proportion of expression of each allele.

In association studies, genotypes are associated to disease and expression levels can also be associated with disease. Using RNA-seq, we can a meassure of how these two relate, this is: in what relation are each of the alleles being expressed.

Germline vs Expressed alleles
The only way to be absolutely sure of the individual's mutations is to compare the transcriptome sequences to the germline DNA sequence. This enables the distinction of homozygous genes vs skewed expression of one of the alleles, it can also provide information about genes that were not expressed in the transcriptomic experiment.

Post-transcriptional SNVs
Having the matching Genomic and Transcriptomic sequences of an individual can also help in detecting post-transcriptional edits, if genome-wise the individual is homozygous for a gene, but the gene's transcript has a different allele, then a post-transcriptional modification event is determined.

mRNA mutations are generally not considered as a representative source of functional variation in cells, mainly due to the fact that these mutations disappear with the mRNA molecule, however the fact that efficient DNA correction mechanisms do not apply to RNA molecules can cause them to appear more often. This has been proposed as the source of prion diseases, also known as TES or transmissible spongiform encephalopathies.

Fusion Gene Detection
In any short that fails to align to the reference sequences is then

[Final version of Gene Fusion detection image follows]

Caveats
The information gathered when sequencing a sample's transcriptome in this way has many of the same limitations as other RNA expression analysis pipelines. Mainly, the information gathered is:

a) Tissue specific: Gene expression is not uniform throughout an organism's cells, it is strongly dependant on the tissue type being meassured [need reference].

b) Time dependant: During a cell's lifetime gene expression changes

Because of this, care must be taken when drawing conclusions from the sequencing experiment, as some of the information gathered might not be representative of the individual itself.

An example of this would be when doing Mutation discovery [anchor] as the mutations discovered are more precisely the mutations being expressed, this is: observing an homozygote location to a non-reference allele in an organism does not necessarily mean that that is the individual's genotype, it could just mean that the gene copy with the reference allele is not being expressed in that tissue and/or at the time snapshot the sample was aquired.

References

(marioni2008) RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays http://genome.cshlp.org/content/early/2008/06/11/gr.079558.108.abstract?ck=nck