To run the tutorial please go to the tutorial subfolder.


Introduction:

The user wishes to analyze deep sequencing data mapping to a ~6 kb region on C. elegans chromosome II for known and novel miRNA genes.



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Preliminary files:

cel_cluster.fa:                     a fasta file with the reference genome (this file is in fact a ~6 kb region of the C. elegans chromosome II).

mature_ref_this_species.fa:         a fasta file with the reference miRBase mature miRNAs for the species (C. elegans miRBase v.14 mature miRNAs)

mature_ref_other_species.fa:        a fasta file with the reference miRBase mature miRNAs for related species (C. briggsae and D. melanogaster miRBase v.14 mature miRNAs)

precursors_ref_this_species.fa:     a fasta file with the reference miRBase precursor miRNAs for the species (C. elegans miRBase v.14 precursor miRNAs)

reads.fa:                           a fasta file with the deep sequencing reads.


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Analysis:



Step 1:

build an index of the genome (in this case the ~6 kb region):

bowtie-build cel_cluster.fa cel_cluster



Step 2:

process reads and map them to the genome.

The -c option designates that the input file is a fasta file (for other input formats, see the README file). The -j options removes entries with
non-canonical letters (letters other than a,c,g,t,u,n,A,C,G,T,U,N). The -k option clips adapters. The -l option discards reads shorter than 18 nts.
The -m option collapses the reads. The -p option maps the processed reads against the previously indexed genome (cel_cluster). The -s option
designates the name of the output file of processed reads and the -t option designates the name of the output file of the genome mappings. Last,
-v gives verbose output to the screen.

mapper.pl reads.fa -c -j -k TCGTATGCCGTCTTCTGCTTGT  -l 18 -m -p cel_cluster -s reads_collapsed.fa -t reads_collapsed_vs_genome.arf -v



Step 3:

fast quantitation of reads mapping to known miRBase precursors.

(This step is not required for identification of known and novel miRNAs in the deep sequencing data when using miRDeep2.pl.)

quantifier.pl -p precursors_ref_this_species.fa -m mature_ref_this_species.fa -r reads_collapsed.fa -t cel -y 16_19

The miRNA_expressed.csv gives the read counts of the reference miRNAs in the data in tabular format. The results can also be browsed by opening
expression_16_19.html with an internet browser.



Step 4:

identification of known and novel miRNAs in the deep sequencing data:

miRDeep2.pl reads_collapsed.fa cel_cluster.fa reads_collapsed_vs_genome.arf mature_ref_this_species.fa mature_ref_other_species.fa precursors_ref_this_species.fa -t C.elegans 2> report.log



Step 5:

browse the results.

open the results.html using an internet browser. Notice that cel-miR-37 is predicted twice, since both potential precursors excised from this locus
can fold into hairpins. However, the annotated hairpin scores much higher than the non-annotated one (miRDeep2 score 6.1e+4 vs. -0.2).





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