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Whole-genome approach to highly specific siRNA design by CRM

Whole-genome approach to highly specific siRNA design by CRM (Comprehensive Redundancy Minimizer) Algorith m. Tariq Alsheddi, Leonid Vasin & Ancha Baranova George Mason University USA. Presented by Prof. Vikas Chandhoke. siRNA. Introduction:

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Whole-genome approach to highly specific siRNA design by CRM

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  1. Whole-genome approach to highly specific siRNA design by CRM (Comprehensive Redundancy Minimizer) Algorithm Tariq Alsheddi, Leonid Vasin & Ancha Baranova George Mason University USA Presented by Prof. Vikas Chandhoke

  2. siRNA Introduction: RNA interference (RNAi) : use of double-stranded RNA to target specific mRNAs for degradation, thereby silencing gene expression. short, interfering (si) RNAs are commonly used as a tool to analyze the functions of the genes. DNA mRNA Protein

  3. Major Challenge in RNAi technology:Off-targeting by Cross-hybridization82% of the respondents to the survey have said that off-target effect requires major improvement.(Genome technology 46 : Sep. 2004) Gene 1 A G C A A A G C G A G C G C G T A A T G C G A C G T G siRNA T C G C T C G C G C A T T A C G C T G C Gene 2 C C T A CA G C G C G T A A T G C G A C G Gene 3 CA G C G A G C G C G T A A T GT A G T G Gene 4 A G T T AA G C G C G T A A T G C G AA T Gene 5 A A G C G A G C G C G T AAC A C T G T C

  4. Good: siRNA siRNA gene gene Off-target: Off – targeting leads to the misinterpreted experiments or to the failures (or side-effects) in the siRNA based therapy

  5. Common sense of the work with siRNA • Don’t assume that your siRNA is working exclusively through silencing • its intended target and nothing else. Assume off-target action until you’ve proven otherwise. Peter S. Linsley, Vice President of Research at Rosetta Infarmatics

  6. Methods dealing with off-target hybridization of siRNA are mostly based on BLAST or Smith-Waterman algorithm. BLAST may sacrifices some sensitivity to gain speed. Different BLAST search parameters may yield very different results. The Smith–Waterman local alignment algorithm (exhaustive search of homologies) may return accurate answers but is very time-consuming to execute.

  7. Both BLAST and Smith-Waterman algorithms are executed as de novo searches of matches that your pre-designed mRNA will off-target That means that you design your siRNA first, then you run siRNA against transcriptome with the hope that your siRNA will not be rejected If siRNA is rejected, you need to start form scratch

  8. BOTTOM-UP approach to siRNA design siRNA that will efficiently silence your gene All possible siRNA with minimal off-target Complete human transcriptome

  9. Bottom – up method (simple): Human genome (and transcriptome) sequence is known Retrieve sequences of all human genes one by one Find all the places within human genes that are represented by unique sequences with length N (e.g. N = 14) Mask all non-unique sequences

  10. siRNA with minimal off-target effects are selected by elimination

  11. CRM (Comprehensive Redundancy Minimizer)Algorithm All human mRNA are retrieved from UNIGENE (one longest mRNA per cluster) These mRNA targets are parsed into overlapping kernels of length N to create a kernel set specific for each target (target ID is preserved in the database);

  12. All redundant kernels • that are present more than once in the kernel set • are removed from the set Each kernel is concatenated with its suffix according to the sequence of the original mRNA target. L-length (N+X) siRNA candidate sequences are created for every gene L ( siRNA length) = N (kernel length) + X (suffix length)

  13. Gene Id. Target Suffix P gi=31560634 AGTACAGCTTGTTG CGCTCTG 73gi=31560634 GTACAGCTTGTTGC GCTCTGA 74gi=31560634 TACAGCTTGTTGCG CTCTGAA 75gi=31560634 ACAGCTTGTTGCGC TCTGAAT 76gi=31560634 CAGCTTGTTGCGCT CTGAATA 77gi=31560634 AGCTTGTTGCGCTC TGAATAT 78gi=31560634 GCTTGTTGCGCTCT GAATATA 79gi=31560634 CTTGTTGCGCTCTG AATATAT 80gi=31560634 TTGTTGCGCTCTGA ATATATT 81 When all the overlapping kernels with corresponding suffixes are present in the table, siRNA candidate with length L (L = N+X) is truly non-redundant (will not produce off target hybridization with other genes by its kernel with length N) siRNAs with kernel N = 14 are better than ones with N = 15 etc

  14. Download Ref-Seq Data Flow Generate all possible ‘N’ length sequences for each gene. • 2 3 16 AA AC AG TT Redundant Sort on the sequences, compare, remove redundant and save unique Unique Sort the unique sequences on gene id, starting position respectively, compare, remove sequences that do not have the next (21-n) present in these files. Apply the suggested siRNA designing rules siRNA

  15. MOUSE Percentage covered MOUSE siRNAs per gene mouse HUMAN Percentage covered HUMAN siRNAs per gene

  16. Transcriptome minimization step aimed at removal of the hypothetical genes (19.2% of human transcriptome)proven unnecessary – gains are little)

  17. To evaluate the potential for CRM application in siRNA design: • A number of siRNAs sequences used in various published experiments were run through CRM database. • The presence of overlapping kernel targets in siRNA sequences was analyzed at the level of uniqueness N = 15 nt • (even not very deep). • 17 paper describing 25 siRNA sequences • used for characterization of phenotypic effects • of the corresponding genes were studied, • only 2 (two) sequences were shown • to produce no significant off-target effects

  18. In some cases indicated effects may potentially cloud scientific conclusions • Experiments with siRNA prove that Histone Acetyl Transferase Tip60 (HTATIP) • is putatively involved in the p53 response, • but its siRNA non-specifically targets Cyclin M4, • which plays a role in cell cycle regulation [Legube G, et al 2004].

  19. In Semizarov’ dataset the predicted degree of siRNA “uniqueness” directly correlates with its efficiency of gene silencing Semizarov et al., 2004 (Nucleic Acid Research) worst Measured effects of 5 different siRNAs specific for gene RB1 best

  20. In Semizarov’ dataset the predicted degree of siRNA “uniqueness” directly correlates with its efficiency of gene silencing BEST siRNA (suppressing gene expression by 100%) were not found in the off-target lists at all WORST

  21. Public interface that allow everybody to search for the siRNA candidates with minimized off-target effects http://129.174.194.243 Database can be searched with human and mouse gene names

  22. Typical CRM output (IPO4 – importin 4) siRNA with kernel N = 14

  23. Authors are grateful to Dr. Matthias Trust for providing HuSiDa database Truss et al., 2005)) Acknowledgement: The algorithm filed to the US Patent and Trademark Office with the help of Dr. R.Lebovitz

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