RNA-Seq Analysis. August Strand Genomics, Inc All rights reserved.

Transcription

1 RNA-Seq Analysis August 2014 Strand Genomics, Inc All rights reserved.

2 Contents Introduction... 3 Sample import... 3 Quantification... 4 Novel exon... 5 Differential expression Differential Splicing

3 RNA-Seq Analysis in Strand NGS Introduction For this demo, we created a RNA-Seq experiment based on the data from the paper Detection of single nucleotide variations in expressed exons of the human genome using RNA-Seq. Nucleic Acids Res; 37(16) (2009), by Chepelev I, Wei G, Tang Q and Zhao K. This experiment was conducted to find differences between CD4+ T cells from a healthy donor and Jurkat T cells, a line of T lymphocytic cells established from the peripheral blood of a 14 year old boy with T cell leukemia. At this point, we assume that Strand NGS is installed and that the steps listed in the Getting Started tutorial have been completed. Sample import The samples were aligned against the hg19 assembly. You can create the appropriate RNA-Seq analysis experiment with UCSC annotations. Other meta data selected for this experiment is shown in figure 1. A new experiment can be done either from the Project menu or from the icon in the tool bar (fourth from the left). This would launch the new experiment wizard. Figure 1: RNA-Seq experiment creation While loading samples, make sure to change the names to CD4 and Jurkat as shown below. 3

4 Figure 2: Renaming the samples It would be good to see how many reads are there in each of the samples. We can get this information by double-clicking the All Aligned Reads list. Both samples contain around 4.5 million reads. Since it is non-directional RNA-Seq, there should be an approximately equal number of +ve and -ve aligned reads. The spreadsheet in the reads list inspector contains this information as well. Quantification Quantification is an important step in a RNA-Seq experiment as it helps us in collating the raw counts of the gene to arrive at a normalized expression value. This step can be accessed from the Quantification tab in the workflow navigator. We can run quantification with the default options. At the quantification step, the user is given an option to turn on novel gene detection, choose either RPKM/DESeq/TMM as the normalization option and also the baselining options. More information about these methods can be found in the Strand NGS reference manual. 4

5 Figure 3: Quantification Workflow The navigator after quantification will look like this: Figure 4: Post-quantification navigator To aid further analysis, four gene lists are output at the end of this analysis (one containing the list of new genes found if any, one containing a list of known genes for which new partitions have been identified, one containing a list of known genes which are untouched by novel discoveries and a union of all 3 lists). Novel exon Expressed regions that do not overlap exons (of the chosen transcript model) are shown in the Novel 5

6 Detection Report. It is important that we prioritize these regions. In general, regions with decent conservation sufficient number of reads can be considered to be of higher confidence. Finding a conservation threshold The median conservation value of the known exons could be a reasonable threshold. We first need to create only the exonic regions (Utilities -> Find Genic Parts). Figure 5: Exonic Regions We can then annotate these regions with the conservation track as shown in figure 6. Then we need to create a subset of this with only chr1 exons as shown in figure 6 (using Utilities Region List Operations and the Chromosome filter) (24543 regions) Then the median conservation value can be found through the Summary statistics of the newly created region list (0.78). 6

7 Figure 6: Annotate with conservation value 7

8 Figure 7: Exonic Regions from chr1 Find the total number of reads aligned against the novel regions The novel regions already indicate the number of reads aligning against them in each sample. We need to add up these two numbers by creating a new column in Region List Operation. We can then use the filters to retain only those which have conservation at least 0.8, total reads at least 10, and are neither ambiguous nor low RPKM. 8

9 Figure 6: Invoking the new column creator 9

10 Figure 7: Filtering the novel regions We can also look at other attributes like location (internal, upstream, downstream), nature (extension, stand-alone) etc. Known gene quantification results We can look at the normalized values associated with the genes (View -> Normalized Signal values). Also isoform quantification is a part of the same quantification step and hence one can also examine the raw counts, drilled-down to the level of individual exonic regions. 10

11 Figure 8: Raw counts table We can also launch a variety of plots on these normalized values from the tool bar. Figure 9: Icon Bar Figure 10: Scatter Plot and Box Whisker Plot for Normalized values 11

12 Differential expression Before we can continue with differential expression, we need to classify the samples. This can be done using the Experimental Setup -> Experiment Grouping workflow link where we can specify the parameters for the samples. Figure 11: Sample parameters We can then create an interpretation based on this parameter from Experimental Setup -> Create Interpretation. Use Quantification -> Filter by Expression to retain only those genes which have at least 20 reads mapping to them in any one of the two conditions. 12

13 Figure 12: Filter by expression Only 1541 (of 40k) genes have this property. Fold change analysis (Expression Analysis Fold Change) can be used to find the subset having more than 2 fold change difference etc. 13

14 Figure 13: Fold change analysis Genes with more than 2 fold change, can be saved and GO analysis (Results Interpretation GO Analysis) can be used on the resulting entity list to find processes/pathways which are different between the two cell lines. Differential Splicing Differential splicing analysis can be run on the 1541 entities with reasonable number of reads in at least one sample (Expression Analysis Differential Splicing). This is based on the EM algorithm which computes the proportion of a gene s count that can be ascribed to a particular transcript. If the proportion for a particular transcript changes substantially across conditions, the gene is said to be differentially spliced Some of the genes which are differentially spliced could have the major contributor be an isoform that is not in the transcript model. The novel isoform could either be a combination of known exons, or it might involve novel exons. As a first step, we can try to ignore the genes which have novel exons and which are differentially spliced. We can save this list by clicking the Create entity list from selection button after selecting the blue area. 14

15 Figure 14: Genes which have novel exons and are differentially spliced We can then launch the gene view on the 219 gene set (Right-click menu) and view each of the genes and all the tabs in the gene view to get familiar. The gene LDLRAP1 is a good example. We can check only the partition densities and guess which transcripts are contributing to the gene expression in each sample. Check the transcript proportion tab to confirm (or re-guess). This is a very brief overview of the RNA-Seq experiment in Strand NGS. For more details or clarifications, please revert back (sales@strandngs.com or support@strandngs.com) and we will address your queries. 15