Shapiro - Senapathy Algorithm

From HandWiki
The different kinds of splicing mutations in genes. Mutations within the splicing regions of genes can lead to a defective transcript and protein. Depending on where exactly the mutation occurs and which "cryptic" splice site near the original site is chosen for splicing, the specific defect in the transcript and protein will vary. Frequently, splicing mutations will lead to exon skipping, intron inclusion, exon extension/truncation, and premature termination in the resulting transcript. The various defects in the transcript will in turn result in different kinds of disruption in the amino acid sequence of the protein.

Gene regulation is the main genetic program through which an organism controls its normal functions. Thus, any error in this program caused by mutations will alter the normal state and lead to disease. RNA splicing is increasingly realized to be at the center of gene regulation in eukaryotic organisms, including all animals and plants. In this context, Dr. Periannan Senapathy has pioneered research in the biology of RNA splicing, including understanding of why genes are split, what are splice junction sequences, and why exons are very short and introns are very long.[1][2][3] Based on these findings, he has provided an algorithm (known as Shapiro & Senapathy algorithm, S&S) for predicting the splice sites, exons and genes in animals and plants .[4][5] This algorithm has the ability to discover disease-causing mutations in splice junctions in cancerous and non-cancerous diseases that is being used in major research institutions around the world. The S&S algorithm has been cited in ~3,000 publications on finding splicing mutations in thousands of diseases including many different forms of cancer, non-cancer diseases and plants.

By using the S&S algorithm, mutations and genes that cause different forms of cancer, including, for example, breast cancer,[6] [7][8] ovarian cancer,[9] [10] [11] colorectal cancer,[12][13][14] leukemia,[15][16] head and neck cancers,[17][18][19] prostate cancer,[20][21] retinoblastoma,[22][23] squamous cell carcinoma [24] and gastrointestinal cancer[25][26] have been discovered. In addition, other diseases such as diabetes,[27] hypertension,[28][29] marfan syndrome,[30][31][32] cystic fibrosis,[33][34][35] cardiac diseases,[36][37][38] eye disorders,[39] familial hypercholesterolemia,[28] Parkinson disease,[34] have also been uncovered. S&S has also been used in identifying mutations in genes involved in immunodeficiency diseases[40][41][42] and in adverse drug reactions.[43][44] Furthermore, S&S has been implemented for finding splice sites and mutations in several tools such as Human Splice Finder,[45] SROOGLE,[46] Splice-site Analyzer Tool,[47] and dbass (Ensmbl).[48]

Applying the S&S technology platform in modern clinical genomics research will advance diagnosis and treatment of human diseases. In addition to its application in thousands of studies involving a variety of diseases, it has been used in finding mutations in drug metabolizing genes that cause adverse reactions. S&S algorithm has also been used in many studies in agricultural plants[49][50][51] and animals.[52][53][54] Using his split gene theory and S&S algorithm, Senapathy's team has developed analytical platforms and several database resources[55][56][57][58] dedicated to the analysis of split genes, splice junctions, and mutations in several genomes including human, animals and plants. Furthermore, based on the statistics of splice signal sequences, Senapathy has developed a functional genome-wide fingerprinting method.[59]

As the mechanism of splicing is inherently complex, the identification of splicing mutations that cause disease is also difficult. The structure of the eukaryotic split genes is highly complex compared to the simple structure of bacterial genes. The reason for this difference is a major question in eukaryotic biology, as it involves the question of how the extremely complex eukaryotic genes could have evolved from the simple genes of prokaryotes. Senapathy has formulated a theory based on his Random-sequence Origin of Split Genes model (ROSG) to explain why the genes of eukaryotes are split into short exon and long intron sequences.[1][2][3] His research has shown that split genes can easily occur within random DNA sequence whereas contiguous genes of bacteria are extremely improbable to occur. These findings show that eukaryotic genes could have originated from prebiotic genetic sequences, and possibly gave rise to eukaryotic genomes. Senapathy has also shown that splice signal sequences that enable the spliceosome to recognize the splicing junctions originated from the stop-codon ends of Open Reading Frames (ORFs) in random sequence.[2]

Studies in evolution of eukaryotic genes and genomes involve the origin of exons, introns and splice junctions, as all eukaryotic genes are split into many exons separated by introns, whereas prokaryotic genes are not. The exons are very short and introns are very long in large genomes such as the human (~3.2 billion bases). Genomes of many invertebrates are also very large such as that of sea urchin (~one billion bases) and contain many introns in their genes. However, the genomes of some animals and plants are relatively small such as those of sea squirt (Ciona intestinalis – ~115 million bases) and Arabidopsis thaliana (~120 million bases). The genes in the genomes of these organisms are also split into exons and introns, and exhibit basically the same splice junction sequences. The S&S algorithm is being used in researching the evolution of numerous animal and plant genomes.

References

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  2. ↑ 2.0 2.1 2.2 Senapathy, P. (1 February 1988). Possible evolution of splice-junction signals in eukaryotic genes from stop codons. 85. pp. 1129–1133. doi:10.1073/pnas.85.4.1129. PMID 3422483. 
  3. ↑ 3.0 3.1 Senapathy, P. (2 June 1995). "Introns and the origin of protein-coding genes" (in en). Science 268 (5215): 1366–1367. doi:10.1126/science.7761858. ISSN 0036-8075. PMID 7761858. http://science.sciencemag.org/content/268/5215/1366. 
  4. ↑ Shapiro, M. B.; Senapathy, P. (11 September 1987). RNA splice junctions of different classes of eukaryotes: sequence statistics and functional implications in gene expression. 15. pp. 7155–7174. doi:10.1093/nar/15.17.7155. PMID 3658675. 
  5. ↑ Senapathy, P.; Shapiro, M. B.; Harris, N. L. (1 January 1990). Splice junctions, branch point sites, and exons: sequence statistics, identification, and applications to genome project. 183. pp. 252–278. doi:10.1016/0076-6879(90)83018-5. PMID 2314278. 
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  52. ↑ Koo, Taeyoung; Popplewell, Linda; Athanasopoulos, Takis; Dickson, George (5 November 2013). "Triple Trans-Splicing Adeno-Associated Virus Vectors Capable of Transferring the Coding Sequence for Full-Length Dystrophin Protein into Dystrophic Mice". pp. 98–108. doi:10.1089/hum.2013.164. http://online.liebertpub.com/doi/abs/10.1089/hum.2013.164. 
  53. ↑ Meher, Prabina Kumar; Sahu, Tanmaya Kumar; Rao, A. R.; Wahi, S. D. (1 January 2016). "Identification of donor splice sites using support vector machine: a computational approach based on positional, compositional and dependency features". Algorithms for Molecular Biology 11: 16. doi:10.1186/s13015-016-0078-4. ISSN 1748-7188. https://almob.biomedcentral.com/articles/10.1186/s13015-016-0078-4. 
  54. ↑ Itoh, Hitomi; Washio, Takanori; Tomita, Masaru (1 July 2004). "Computational comparative analyses of alternative splicing regulation using full-length cDNA of various eukaryotes" (in en). RNA 10 (7): 1005–1018. doi:10.1261/rna.5221604. ISSN 1355-8382. http://rnajournal.cshlp.org/content/10/7/1005.full. 
  55. ↑ Bhasi, Ashwini; Philip, Philge; Manikandan, Vinu; Senapathy, Periannan (1 January 2009). "ExDom: an integrated database for comparative analysis of the exon–intron structures of protein domains in eukaryotes" (in en). Nucleic Acids Research 37 (suppl 1): D703–D711. doi:10.1093/nar/gkn746. ISSN 0305-1048. PMID 18984624. PMC 2686582. http://nar.oxfordjournals.org/content/37/suppl_1/D703.full. 
  56. ↑ Bhasi, Ashwini; Philip, Philge; Sreedharan, Vipin T.; Senapathy, Periannan (1 July 2009). AspAlt: A tool for inter-database, inter-genomic and user-specific comparative analysis of alternative transcription and alternative splicing in 46 eukaryotes. 94. pp. 48–54. doi:10.1016/j.ygeno.2009.02.006. PMID 19285128. 
  57. ↑ Bhasi, Ashwini; Pandey, Ram Vinay; Utharasamy, Suriya Prabha; Senapathy, Periannan (15 July 2007). EuSplice: a unified resource for the analysis of splice signals and alternative splicing in eukaryotic genes. 23. pp. 1815–1823. doi:10.1093/bioinformatics/btm084. PMID 17344236. 
  58. ↑ Bhasi, Ashwini; Senalik, Doug; Simon, Philipp W; Kumar, Brajendra; Manikandan, Vinu; Philip, Philge; Senapathy, Periannan (6 August 2010). "RoBuST: an integrated genomics resource for the root and bulb crop families Apiaceae and Alliaceae". BMC Plant Biology 10: 161. doi:10.1186/1471-2229-10-161. ISSN 1471-2229. PMID 20691054. 
  59. ↑ Senapathy, Periannan; Bhasi, Ashwini; Mattox, Jeffrey; Dhandapany, Perundurai S.; Sadayappan, Sakthivel (16 June 2010). "Targeted Genome-Wide Enrichment of Functional Regions". PLOS ONE 5 (6): e11138. doi:10.1371/journal.pone.0011138. ISSN 1932-6203. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0011138. 




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