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module load boost/1.45.0 module load bowtie module load tophat module load cufflinks |
QuestionExercise: If I wanted to provide a trancript annotation file (gtf file) in cufflinks command, what would I add to the command?
i. If I wanted cufflinks to not assemble any novel transcripts outside of what is in the gtf file
ii. If I wanted cufflinks to assemble novel as well as annotated transcripts?
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cuffdiff [options] <merged.gtf> <sample1_rep1.bam,sample1_rep2.bam> <sample2_rep1.bam,sample2_rep2.bam> |
QuestionExercise: What does cuffdiff -b do?
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For d), see the file "cuffdiff.sh" and "cuffdiff.sh.log" for the input, command line, and output. Then, find the cuffdiff output (either by understanding cuffdiff.sh or by looking) and by looking at it and/or reading the documentation find the isoforms and genes that are differentially expressed. Note that cuffdiff has performed a statistical test on the expression values between our two biological groups. It reports the FPKM expression levels for each group, the log2(group 1 FPKM/ group 2 FPKM), and a p-value measure of statistical confidence, among many other helpful
data items.
25 pointsExercise: Find the number of genes that are differentially expressed at the gene level and the number of genes that are differentially expressed in isoform level. Find the number of genes that are differentially expressed in one (gene/isoform) level, but not in the other. This can be done with a python or perl script, or with a one-line linux command.
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cat isoform_exp.diff | awk '{print $10 "\t" $4}' | sort -n -r | head
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Using cummeRbund to inspect differential expression data.
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module load R
R
>source("http://bioconductor.org/biocLite.R")
>biocLite("cummeRbund")
>library(cummeRbund)
>cuff_data <- readCuf?inks('diff_out')
>csScatter(genes(cuff_data), 'C1', 'C2')
>gene_diff_data <- diffData(genes(cuff_data))
>sig_gene_data <- subset(gene_diff_data, (signi?cant = = 'yes'))
>nrow(sig_gene_data)
>isoform_diff_data <-diffData(isoforms(cuff_data), 'C1', 'C2')
>sig_isoform_data <- subset(isoform_diff_data, (significant == 'yes'))
>nrow(sig_isoform_data))
>mygene1 <- getGene(cuff_data,'regucalcin')
>expressionBarplot(mygene)
>expressionBarplot(isoforms(mygene))
>mygene2 <- getGene(cuff_data, 'Rala')
>expressionBarplot(mygene2)
>expressionBarplot(isoforms(mygene))
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