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MSuPDA: A Memory Efficient Algorithm for Sequence Alignment

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Abstract

Space complexity is a million dollar question in DNA sequence alignments. In this regard, memory saving under pushdown automata can help to reduce the occupied spaces in computer memory. Our proposed process is that anchor seed (AS) will be selected from given data set of nucleotide base pairs for local sequence alignment. Quick splitting techniques will separate the AS from all the DNA genome segments. Selected AS will be placed to pushdown automata’s (PDA) input unit. Whole DNA genome segments will be placed into PDA’s stack. AS from input unit will be matched with the DNA genome segments from stack of PDA. Match, mismatch and indel of nucleotides will be popped from the stack under the control unit of pushdown automata. During the POP operation on stack, it will free the memory cell occupied by the nucleotide base pair.

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References

  1. Needleman SB, Wunsch CD (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol 48:443–453

    Article  PubMed  CAS  Google Scholar 

  2. Fleischmann RD, Adams MD, White O, Clayton RA, Kirkness EF, Kerlavage AR (1995) Whole-genome random sequencing and assembly of Haemophilus influenza Rd. Science 269:496–512

    Article  PubMed  CAS  Google Scholar 

  3. Lipman DJ, Pearson WR (1988) Improved tools for biological sequence comparison. Proc Natl Acad Sci USA 85:2444–2448

    Article  PubMed  PubMed Central  Google Scholar 

  4. Notredame C, Higgins DG, Heringa J (2000) T-Coffee: a novel method for fast and accurate multiple sequence alignment. J Mol Biol 302(1):205–217

    Article  PubMed  CAS  Google Scholar 

  5. Do CB, Mahabhashyam MS, Brudno M, Batzoglou S (2005) ProbCons: probabilistic consistency-based multiple sequence alignment. Genome Res 15(2):330–340

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  6. Newberg LA (2008) Memory-efficient dynamic programming backtrace and pairwise local sequence alignment. Bioinformatics 24(16):1772–1778

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  7. Batzoglou S, Pachter L, Mesirov JP, Berger B, Lander ES (2000) Human and mouse gene structure: comparative analysis and application to exon prediction. Genome Res 10:950–958

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  8. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410

    Article  PubMed  CAS  Google Scholar 

  9. Smith TF, Waterman MS (1981) Comparison of bio-sequences. Adv Appl Math 2:482–489

    Article  Google Scholar 

  10. Arratia R, Morris P, Waterman MS (1988) Stochastic scrabbles: a law of large numbers for sequence matching with scores. J Appl Probab 25:106–119

    Article  Google Scholar 

  11. Dembo A, Karlin S (1991) Strong limit theorems of empirical functional for large exceedances of partial sums of id variables. Ann Probab 19:1737–1755

    Article  Google Scholar 

  12. Karlin S, Altschu SF (1993) Applications and statistics for multiple high-scoring segments in molecular sequences. Proc Natl Acad Sci USA 90:5873–5877

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  13. Ning Z, Cox AJ, Mullikin JC (2001) A fast search method for large DNA databases. Genome Res 11:1725–1729

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  14. Watanabe T, Takeda A, Mise K, Okuno T, Suzuki T, Minami N, Imai H (2005) Stage-specific expression of microRNAs during Xenopus, development. FEBS Lett 579:318

    Article  PubMed  CAS  Google Scholar 

  15. Lipman DJ, Pearson WR (1985) Rapid and sensitive protein similarity searches. Science 227:1435–1441

    Article  PubMed  CAS  Google Scholar 

  16. Kent WJ, Sugnet C, Furey T, Roskin K, Pringle T, Zahler A, Haussler D (2002) The human genome browser at UCSC. Genome Res 12:996–1006

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  17. Schwarz DS, Hutvagner G, Du T, Xu Z, Aronin N, Zamore PD (2003) Asymmetry in the assembly of the RNAi enzyme complex. Cell 115:199–208

    Article  PubMed  CAS  Google Scholar 

  18. Khan MI, Kamal MS (2013) RSAM: an integrated algorithm for local sequence alignment. Arch Sci 5:395–412

    Google Scholar 

  19. Weckx S, Favero J, Rademakers R, Claes L, Cruts M, De JP, Van BC, De RP (2005) A novel computational tool for sequence variation discovery. Genome Res 15:436–442

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  20. Ewing B, Green P (1998) Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res 8:186–194

    Article  PubMed  CAS  Google Scholar 

  21. Stephens M, Sloan JS, Robertson PD, Scheet P, Nickerson DA (2006) Automating sequence-based detection and genotyping of SNPs from diploid samples. Nat Genet 38:375–381

    Article  PubMed  CAS  Google Scholar 

  22. Claverie JM, Poirot O, Lopez F (1997) The difficulty of identifying genes in anonymous vertebrate sequences. Comput Chem 21:203–214

    Article  PubMed  CAS  Google Scholar 

  23. Pagani I, Konstantinos L, Jansson J, Chen A, Smirnova T, Bahador N (2012) The Genomes OnLine Database (GOLD) v. 4: status of genomic and meta genomic projects and their associated metadata. Nucleic Acids Res 40:571–579

    Article  Google Scholar 

  24. Yok NG, Rosen GL (2011) Combining gene prediction methods to improve meta genomic gene annotation. BMC Bioinform 12:20

    Article  Google Scholar 

  25. Pati A, Ivanova NN, Mikhailova N, Ovchinnikova G, Hooper SD, Lykidis A (2010) GenePRIMP: a gene prediction improvement pipeline for prokaryotic genomes. Nat Methods 7:455–457

    Article  PubMed  CAS  Google Scholar 

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Correspondence to Linkon Chowdhury.

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Khan, M.I., Kamal, M.S. & Chowdhury, L. MSuPDA: A Memory Efficient Algorithm for Sequence Alignment. Interdiscip Sci Comput Life Sci 8, 84–94 (2016). https://doi.org/10.1007/s12539-015-0275-8

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  • DOI: https://doi.org/10.1007/s12539-015-0275-8

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