Editing BioMicroCenter:RNAseq (section)

== Comparison of the RNAseq methods ==

The BioMicro Center has done testing in head-to-head competitions of the TruSeq, NuGEN v2 and Clontech kits. These data were presented at AGBT 2012 as part of a poster. The authors were: Avanti Shrikumar, Zachary Banks, Manlin Luo, Ryan Sinapius, Paola Favaretto, Jessica Hurt, Chris Burge, and Stuart S. Levine. Selections of the poster are shown below:

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 |rowspan="2" width=350| '''Summary''' <BR>
Sequencing of the transcriptome (RNAseq) has become an increasingly important tool in the molecular biology toolkit and is rapidly replacing microarrays as the primary method for determining genome-wide expression levels. Several vendors have created pre-packaged kits for creating RNAseq libraries for Illumina sequencing. These kits differ significantly in methodology and in the amounts of input required. Here we provide a head to head test of five different RNAseq kits in a core setting. The kits were evaluated on two experimental samples with similar expression patterns, murine embryonic stem cells and the same cells with a single factor knocked down by RNAi, to determine the sensitivity of each method. Each kit was additionally evaluated across  three different concentrations of RNA input. We found that the different methodologies show different RPKM levels for each transcript and also vary in their technical reproducibility. The different methods resulted in small but largely distinct lists of differentially expressed genes that we compared to genes with known expression changes
 |[[Image:AGBT12_1.png|thumb|center|300px]] <BR>
 |align="center"|
 {|Border=1 style="text-align: center; width:150px;"
  !Kit Type !! Home Made (Burge Lab) !! Illumina TruSeq !! NuGen v1 !! NuGen v2 !! Clontech SMARTer
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  |1ug || X || X ||  ||  ||  
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  |100ng || || X ||  || X ||
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  |10ng ||  || X* || X || X || X
  |-
  |1ng || || || X* || X || X
  |-
  |0.1ng || || || || X* || X
 |}
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|'''Experimental Design:''' ES cells were transfected with siRNA targeting a splicing factor or a control siRNA. The tested splicing factor normally blocks inclusion of specific exons that had been previously identified by RT-PCR. Reduction of the splicing factor’s levels should lead to an increase in the amounts of these specific transcripts. RNA was collected from the cells and analyzed by RNA-seq. Samples were sequenced to a depth of at least 7.5m reads of 40nt length on either a GAIIx or a HiSeq2000
|'''Testing Matrix:''' A single sample of control and splicing factor knockdown samples were serially diluted. Aliquots of the diluted samples were tested against the 5 methods to determine the ability of each kit to identify differentially expressed genes in a biologically challenging situation as well as to identify their sensitivity to different input amounts and quantify the amount of technical variation. * indicates the amount tested was below the minimum recommended input.
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| [[Image:AGBT12_2.png|thumb|center|300px]]
| [[Image:AGBT12_3.png|thumb|center|200px]]
| [[Image:AGBT12_4.png|thumb|center|300px]]
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|'''Fraction of reads aligned:''' Reads aligning once or multiple times to the mouse genome (mm9) are shown. Reads were aligned with Bowtie. NuGen samples show an increase in non-aligned reads at low input amounts.
|'''3’ Bias:''' 
Read densities were calculated along the exonic portions of each transcript. Transcripts were grouped by length and plotted as metagenes with both 5’ and 3’ ends locked. Clear 3’ bias can be observed in the samples processed using the Clontech kit.  
|'''Variation in RPKM score across the transcript.'''(right) The evenness of coverage within the transcript was measured by comparing the read density in largest two exons. Only exons greater than 200nt and genes with PRKMs over 10 were included in this analysis (n = 1,876 genes including 3,676 exons) (left) the average number of exons in the above  data set with very low coverage (<5 reads) is shown. 
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