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Argonaute CLIP-Seq

Goal: Finding miRNA's targets. Challenge: MiRNA only requires base pairing with 6 nucleotides. Such 6-mers occurs every 4kb Bioinformatics methods predict hundreds or thousands of targets for each miRNA, but overexpression or knockdown studies suggest that only individual miRNA only regulate a s

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Argonaute CLIP-Seq

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    1. Argonaute CLIP-Seq Sung Wook Chi, Julie B. Zang, Aldo Mele & Robert B. Darnell. Nature 2009 July Presented by Xiongying Yuan Institute of Computing Technology http://www.bioinfo.org.cn/~yuanxiongying

    2. Goal: Finding miRNA's targets Challenge: MiRNA only requires base pairing with 6 nucleotides. Such 6-mers occurs every 4kb Bioinformatics methods predict hundreds or thousands of targets for each miRNA, but overexpression or knockdown studies suggest that only individual miRNA only regulate a small number of proteins at modest level.

    3. CLIP: crosslinking immunoprecipitation Basic principle: Use ultraviolet irradiation to covalently crosslink RNA­ protein complexes that are in direct contact (1A) within cells, allowing them to be stringently purified.

    4. CLIP Ago in P13 neocortex Workflow: Ultraviolet-irradiated P13 neocortex. Immunoprecipitated Ago using 2A8 and 7G1-1* radiolabelled RNA with P32, SDS-poly gel electrophoresis

    5. Investigate the two bands (1)? RT-PCR revealed that the lower band contains ~22nt RNA, and upper band >22nt. Solexa sequencing suggests that ~110kDa band corresponding to miRNA and ~130kDa mRNA.

    6. Investigate the two bands (2)? Two questions: Is crosslink fair between mRNA and mRNA? Only 80% of 130kDa bands are mRNA, and only 75% of 110kDa are miRNA. Does this mean 130kDa also contains miRNA, vice versa?

    7. Pre-process:reduce background Solution: Multiple replicates & two different antibodies Random CLIP

    8. Random CLIP Procedure:(For each gene)? Measure abundance using exon array Randomly cleave to Fa fragments Randomly select Tn fragments Count the number of tags in the most overlapped region Repeat 500 times. Retain clusters with empirical P< 0.01

    9. Consistency of the results Correlation with other experiments Explaination: different ages of brain used, different experimental conditions

    10. Basic statistics of Ago-mRNA

    11. Position of Ago-mRNA cluster Fraction is calculated through dividing the number of clusters at each position to the total number of transcripts at that position (blue)? SD was estimated through binomial distribution (light blue)? To determine whether different length of transcripts biased the position of clusters, the position of each cluster was randomly redistributed (red)?

    12. Ago-mRNA footprint Procedure: Search all 6-8-mer motifs within Ago-mRNA clusters. The peak of each cluster was determined by cubic spline interpolation. Results: Ago bound with 45 (-24~22) – 62 (-30~30) nt denoted as average Ago-mRNA footprint

    13. Ago-miRNAs correlate with their seeds in Ago-mRNA

    14. Case study: enrichment of miR-124 seeds in footprint 86% lies in footprint small peaks at 50nt might indicate cooperative secondary binding sites

    15. Ago-miRNA clusters in validated miR-124 targets

    16. Consistency with miR124 overexpressed microarray data Transcripts with Ago-miR-124 clusters show greater tendency to be downregulated at miRNA and protein level Hela does not express miR-124

    17. Ago-miR-124 in 22 validated transcripts

    18. False negative rates and sensitivity Low abundance transcripts are recovered less efficiently Likely to work for low-expressed miRNAs

    19. GO of the targets

    20. Basic data statistics of the targets

    21. A function network involving 3 miRNAs

    22. Advantages over prediction Low false-positive No conservation information needed Tissue-specific Quantification (not the abundance of miRNA, but how many of them are really in function)?

    23. Fields merit investigation 1. Re-assess the items used in prediction methods. Prediction results might be useless, but the models used in prediction helped reveal the function mechansim of miRNA. With the CLIP-Seq data, now it might be the time to re-assess the terms used in prediction models. Like how much the seed sequence has truly contributed to miRNA regulation.

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