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 Processing of miRNA samples and primary data analysis.

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Presentation on theme: " Processing of miRNA samples and primary data analysis."— Presentation transcript:

1  Processing of miRNA samples and primary data analysis

2 Preparing the starting material

3 Initial evaluation of small RNA sample on Bioanalyzer  Bioanalyzer small RNA chip Mature miRNAs are 16-29 bases (usually 22-23 bases)

4 Library construction

5 Size selection for miRNA inserts (PAGE gel, cut & purify) 80 60 PCR 135 120

6 Sequence on SOLiD  The size-selected, bar-coded libraries are sequenced on the SOLiD 5500.  Reads are from single end, 50 bp.

7 Target Read Counts for miRNA  The vast majority of miRNA-seq reads do map successfully to miRNA (~90%)  Target read counts will be a function of how well resolved low abundance miRNA need to be resolved  Large shift or shifts in abundant miRNAs do not necessitate many reads.  We aimed for about 10 million reads per condition, which was achievable for 9 samples on one multiplexed lane

8 Only a few miRNAs tend to dominate the population # miRNAs Cumulative % of Reads 80% of reads from 30 miRNA; 90% from 54 ~340 miRNAs were described by populations of 1000+ reads across conditions in our experiment

9 Treating Raw miRNA Data  Due to the short length of inserts, trimming of adapter sequence is required.  Due to a high level of redundancy, it’s often advisable to collapse identical reads to speed alignment.  Unique sequences align only once rather than aligning the same sequence thousands of times.  Retain count information for quantitation following alignment.

10 Aligning miRNA reads  Alignment is often performed in two stages  1 st against a prepared reference containing ONLY known miRNA sequences for the appropriate organism (miRBase or elsewhere).  2 nd against the genome for identification of novel small RNA.  Any typical aligner works well for this purpose  Novocraft, Bowtie(1), BWA, etc  Other packages exist that ease this process and identification of novel miRNA such as miRanalyzer.

11 miRanalyzer  Available via command-line or by a webapp (common organisms).  http://bioinfo5.ugr.es/miRanalyzer/miRanalyzer.php

12 Novel miRNA and Quantitation  Novel identified sequences need to be evaluated for the possibility of forming hairpin structures  miRanalyzer does this already, scoring novel alignment regions for the possibility of forming miRNAs  Read count tables are produced for further analysis and comparison  Reads per miRNA  Novel miRNA are only really comparable between experiments in which the same species are observed and are typically kept separately

13 Comparison Between Conditions  Normal RNAseq tools for identifying differential expression from quantitated data tables is the preferred method.  DESeq, edgeR, baySeq, limma, etc  DESeq was utilized on count tables produced from miRanalyzer (and is also a part of the webapp package).  Triplicates from three experimental conditions were compared pairwise for differential expression of miRNA.  p-values for exact test of change between conditions are generated  padj values result from Benjamini-Hochberg multiple testing to determine a FDR (cutoff of 0.1 is typically applied here).  Output varies depending on tool used.

14 Additional tasks  Target Database/Prediction mining of differentially expressed miRNAs  miRbase, miRanda, TarBase (experimental observations), etc  Validation of DE of miRNA and targets  Enrichment analysis


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