Skip to main content

Advertisement

SpringerLink
Log in

A Novel CRISPR-MultiTargeter Multi-agent Reinforcement learning (CMT-MARL) algorithm to identify editable target regions using a Hybrid scoring from multiple similar sequences

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Genome edit is a modern technology to serve mankind. The main idea is derived from RNA mediated Nuclease, which is the CRISPR/Cas9 natural process of the bacterial genome. In this paper, we have developed an algorithm CMT-MARL for finding the multiple editable target site from the similar sequences. Among different types of genes, there are many common regions, which are important concerning the production of proteins or any other biological function in the organisms. Tracing multiple target sites is important for the case of gene duplication, gene fusion, finding mutations from co-expressed genes and transcripts from genes. The complexity to find out common editable targets from similar kind of sequences using brute force method is O(ln), where l is the genome sequence length and n is the number of sequences. If n goes higher then the complexity of the problem reaches to some infeasible computational time. We have applied Reinforcement learning Algorithm using Eligibility Trace and Monte Carlo method to tackle this problem. The time complexity of the algorithm CMT-MARL is O(nl2). Finally we have compared our result set with existing algorithm “CRISPR- MultiTargeter” [1] (http://www.multicrispr.net/) concerning the goodness of editing. We have used the data set from Ensembl BioMart (http://www.ensembl.org). We have run our methodology in Mouse, Rat, Zebrafish, Chicken and Human genes. Finally, we locate the optimal regions for editing diseased or duplicated genes concerning our hybrid score mechanism with all types of biological factors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. http://www.nature.com/news/broad-institute-wins-bitter-battle-over-Crispr-patents-1.21502http://www.nature.com/news/broad-institute-wins-bitter-battle-over-Crispr-patents-1.21502

  2. www.origene.com

  3. https://www.abmgood.com

  4. https://www.google.com/patents/WO2015163733A1?cl=en

  5. http://www.e-crisp.org/E-CRISP/aboutpage.html

  6. www.rgenome.net/

References

  1. Prykhozhij SV, Rajan V, Gaston D, Berman JN (2015) CRISPR Multitargeter: A web tool to find common and unique CRISPR single guide rna targets in a set of similar sequences. PLOS One 10 (9):1–18

    Google Scholar 

  2. Lander ES (2016) The heroes of CRISPR. Cell 164(2):18–28

    Google Scholar 

  3. Lu J, Tong Y, Pan J, Yang Y, Liu Q, Tan X, Zhao S, Li Q, Chen X (2016) A redesigned CRISPR/cas9 system for marker-free genome editing in Plasmodium falciparum. Parasites and Vectors 9(198):1487–1494

    Google Scholar 

  4. Wang T, Weiand JJ, Sabatini DM, Lander ES (2014) Genetic screens in human cells using the CRISPR/cas9 system. Science 343(6166):80–84

    Google Scholar 

  5. Haeussler M, Schonig K, Eckert H, Eschstruth A, Mianné J, Renaud J-B, Schneider-Maunoury S, Shkumatava A, Teboul L, Kent J, Joly JS, Concordet JP (2016) Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol 17(1):148–159

    Google Scholar 

  6. Gagnon JA, Valen E, Thymeand SB, Huang P, Ahkmetova L, Pauli A, Montague TG, Zimmermanand S, Richter C, Schier AF (2014) Efficient mutagenesis by cas9 protein-mediated oligonucleotide insertion and large-scale assessment of single-guide rnas. PLOS One 9(5):1–8

    Google Scholar 

  7. Ran FA, Le C, Yan WX, Scott DA, Jonathan S, Gootenberg A, Kriz J, Zetsche B, Shalem O, XuebingWu KS, Koonin EV, Sharp PA, Zhang F (2015) In vivo genome editing using Staphylococcus aureus cas9. Nature 520(7546):186–191

    Google Scholar 

  8. Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C, Orchard R, Virgin HW, Listgarten J, Root DE (2016) Optimized sgRNA design to maximize activity and minimize offtarget effects of CRISPR-Cas9. Nat Biotechnol 34(2):184–191

    Google Scholar 

  9. Tycko J, Myer VE, Hsu PD (2016) Methods for optimizing CRISPR-cas9 genome editing specificity. Mol Cell 63(3):355–370

    Google Scholar 

  10. Zhu LJ, Holmes BR, Aronin N, Brodsky MH (2014) CRISPR seek: A bioconductor package to identify target-specific guide RNAs for CRISPR-cas9 genome-editing systems. PLOS One 9(9):1–7

    Google Scholar 

  11. Zischewski J, Fischer R, Bortesi L (2017) Detection of on-target and off-target mutations generated by CRISPR/ Cas9 and other sequence-specific nucleases. ScienceDirect 35(1):95–104

    Google Scholar 

  12. Mali P, Esvelt KM, Church GM (2013) Cas9 as a versatile tool for engineering biology. Nature Methods 10(50):957–963

    Google Scholar 

  13. Yang L, Grishin D, Wang G, Aach J, Zhang C-Z, Chari R, Homsy J, Cai X, Zhao Y, Fan J-B, Seidman C, Seidman J, Pu W, Church G (2014) Targeted and genome-wide sequencing reveal single nucleotide variations impacting specificity of Cas9 in human stem cells. Nature Communication 5(5507):1–6

    Google Scholar 

  14. Bae S, Park J, Kim J-S (2014) Cas-OFFinder: a fast and versatile algorithm that searches for potential off-target sites of Cas9 RNA-guided endonucleases. Bioinformatics 30(10):1473–1475

    Google Scholar 

  15. Kim D, Kim S, Kim S, Park J, Kim J-S (2017) Genome-wide target specificities of CRISPR-cas9 nucleases revealed by multiplex Digenome-seq. Nat Biotechnol 35:475–480

    Google Scholar 

  16. Hendel A, Fine EJ, Bao G, Porteus MH (2015) Quantifying on and Off-Target genome editing. Trends Biotechnol 33(2):132–140

    Google Scholar 

  17. Cho SW, Kim S, Kim Y, Kweon J, Kim HS, Bae S, Kim J-S (2014) Analysis of off-target effects of CRISPR/cas-derived RNA-guided endonucleases and nickases. Genome Res 24(1):132–141

    Google Scholar 

  18. Xiao A, Cheng Z, Kong L, Zhu Z, Lin S, Gao G, Zhang B (2014) CasOT: a genome-wide cas9/gRNA off-target searching tool. Bioinformatics 30(8):1180–1182

    Google Scholar 

  19. Ishida K, Gee P, Hotta A (2015) Minimizing off-target mutagenesis risks caused by programmable Nucleases. Molecular Sciences 16(10):24751–71

    Google Scholar 

  20. Sander JD, Joung JK (2014) CRISPR-Cas systems for genome editing, regulation and targeting. Nat Biotechnol 32(4):347–355

    Google Scholar 

  21. Hsu PD, Scott DA, Weinstein JA, Ran F, Konermann S, Agarwala V, Li Y, Fine EJ, Wu X, Shalem O, Cradick TJ, Marraffini LA, Bao G, Zhang F (2013) DNA Targeting specificity of RNA-guided Cas9 nucleases. Nat Biotechnol 31(9):827–832

    Google Scholar 

  22. Naito Y, Hino K, Bono H, Ui-Tei K (2015) CRISPRdirect: software for designing CRISPR/Cas guide RNA with reduced off-target sites. Bioinformatics 31(7):1120–1123

    Google Scholar 

  23. Lin Y, Cradick TJ, Brown MT, Deshmukh H, Ranjan P, Sarode N, Wile BM, Vertino PM, Stewart FJ, Bao G (2014) CRISPR/Cas9 systems have off-target activity with insertions or deletions between target DNA and guide RNA sequences. Nucleic Acids Res 42(11):7473–7485

    Google Scholar 

  24. Zhang XH, Tee LY, Wang X-G, Huang Q-S, Yang SH (2015) Off-target effects in CRISPR/cas9-mediated Genome Engineering, vol 4

  25. Anderson EM, Haupt A, Schiel JA, Chou E, Machado HB, Strezoska Z, Lenger S, McClelland S, Birmingham A, Vermeulen A, van Brabant Smith A (2015) Systematic analysis of CRISPR-cas9 mismatch tolerance reveals lowlevels of off-target activity. J Biotechnol 9(211):56–65

    Google Scholar 

  26. Ran F, Hsu PD, Lin C-Y, Gootenberg JS, Konermann S, Trevino A, Scott DA, Inoue A, Matoba S, Yi Z, Zhang F (2013) Double nicking by RNA-guided CRISPR Cas9 for enhanced genome editing specificity. Cell 154(6):1380–1397

    Google Scholar 

  27. Bae S, Kweon J, Kim HS, Kim J-S (2014) Microhomology-based choice of Cas9 nuclease target sites. Nature Method 11(7):705–706

    Google Scholar 

  28. Long HK, King HW, Patient RK, Odom DT, Klose RJ (2016) Protection of CpG islands from DNA methylation is DNA-encoded and evolutionarily conserved. Nucleic Acids Research 04(14):6693–6706

    Google Scholar 

  29. Wu X, Kriz AJ, Sharpu PA (2014) Target specificity of the CRISPR-cas9 system. Quant Biol 2(2):59–70

    Google Scholar 

  30. Doench JG, Hartenian E, Graham DB, Tothova Z, Hegde M, Smith I, Sullender M, Ebert BL, Xavier RJ, Root DE (2014) Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation. Nat Biotechnol 32(12):1262–1267

    Google Scholar 

  31. Sutton RS, Barto AG (1998) Reinforcement learning: An introduction (Adaptive computation and machine learning series). The MIT Press, Cambridge

    Google Scholar 

  32. Rajasekaran S, Dinh H (2011) A speedup technique for (l, d)-motif finding algorithms. BMC Research Notes 4(54):1–7

    Google Scholar 

  33. Hwang WY, Fu Y, Reyon D, Maeder ML, Tsai SQ, Sander JD, Peterson RT, Yeh J-R, Joung JK (2013) Efficient genome editing in zebrafish using a crispr-cas system. Nat Biotechnol 31 (3):227–229

    Google Scholar 

  34. Naito Y, Hino K, Bono H, Ui-Tei K (2015) Crisprdirect: software for designing crispr/cas guide rna with reduced off-target sites. Bioinformatics 31(7):1120–1123

    Google Scholar 

  35. Montague TG, Cruz JM, Gagnon JA, Church GM, Valen Eivind (2014) Chopchop: a crispr/cas9 and talen web tool for genome editing. Nucleic Acids Res, 42 JULY

  36. Heigwer F, Kerr G, Boutros M (February 2014) E-crisp: fast crispr target site identification. Nat Methods 11(2)

  37. Xie S, Zhang C, Shen B, Huang X, Zhang Y (2014) Sgrnacas9: A software package for designing crispr sgrna and evaluating potential off-target cleavage sites. PLOS ONE 9(6):1–9

    Google Scholar 

  38. Doench JG, Hartenian E, Graham DB, Tothova Z, Hegde M, Smith I, Sullender M, Ebert BL, Xavier RJ, Root DE (2014) Rational design of highly active sgrnas for crispr-cas9-mediated gene inactivation. Nat Biotechnol 32(12):1262–1267

    Google Scholar 

  39. Zhu LJ, Holmes BR, Aronin N, Brodsky MH (2014) Crisprseek: a bioconductor package to identify target-specific guide rnas for crispr-cas9 genome-editing systems. PloS one 9(9):09

    Google Scholar 

  40. Lee S-T, Wiemels JL (2017) Protospacer Adjacent Motif-Induced Allostery Activates CRISPR-cas9. J Am Chem Soc 139(1):16028–16031

    Google Scholar 

  41. Muramoto T, Iriki H, Watanabe j, Kawata T (2019) Recent Advances in CRISPR/cas9-mediated Genome Editing in Dictyostelium. Cells 8(1):1–13

    Google Scholar 

  42. Martin F, Sánchez-hernández S, Gutiérrez-Guerrero A, Pinedo-Gomez J, Benabdellah K (2016) Biased and unbiased methods for the detection of off-target cleavage by CRISPR/cas9: An overview. Int J Mol Sci 17(9):1507–1515

    Google Scholar 

  43. Lim DHK, Maher ER (2010) DNA Methylation:a form of epigenetic control of gene expression. Obstet Gynaecol 12(1):37–42

    Google Scholar 

  44. Illingworth RS, Bird AP (2009) CpG islands- A Rough Guide. Federation of European Biochemical Societies 583(11):1713–20,

    Google Scholar 

  45. Lee S-T, Wiemels JL (2016) Genome-wide CpG island methylation and intergenic demethylation propensities vary among different tumor sites. Nucleic Acids Res 44(3):1105–1117

    Google Scholar 

  46. Alexander J, Findlay GM, Kircher M, Shendure J (2019) Concurrent genome and epigenome editing by CRISPR-mediated sequence replacement. BMC Biol 17(90):1–13

    Google Scholar 

  47. Moon SB, Kim DY, Ko J-H, Kim Y-S (2019) Recent advances in the CRISPR genome editingtool set. Exp Mol Med 17(90):1–13

    Google Scholar 

  48. Syding LA, Nickl P, Kasparek P, Sedlacek Ra (2020) CRISPR/Cas9 Epigenome Editing Potential for Rare Imprinting diseases: a review. Cells 9(993):2–11

    Google Scholar 

  49. Josephs EA, Kocak DD, Fitzgibbon CJ, McMenemy J, Gersbach CA, Marszalek PE (2015) Structure and specificity of the RNA-guided endonuclease Cas9 during DNA interrogation, target binding and cleavage. Nucleic Acids Res 43(18):8924–8941

    Google Scholar 

  50. Moreno-Mateos MA, Vejnar CE, Beaudoin J-D, Fernandez JP, Mis EK, Khokha MK, Giraldez AJ (2015) CRISPRscan: designing highly efficient sgRNAs for CRISPR/cas9 targeting in vivo. Nat Methods 12(10):982–988

    Google Scholar 

  51. Sutton RS, Barto AG (2012) Reinforcemnet learning: An introduction, 2nd. The MIT Press, Cambridge

    Google Scholar 

  52. Sehgal N, Sylves ME, Walker SE, Sahoo A, Chow J, Cullen PJ, Berry JO (2018) Crispr gene editing in yeast: an experimental protocol for an upper-Division undergraduate laboratory course. Biochem mol biol educ, 46(6) NOV

  53. Sentmanat MF, Peters ST, Florian CP, Connelly JP, Pruett-Miller SM (2018) A survey of validation Strategies for RISPR-Cas9 Editing Nature 8(888)

  54. Pieczynski JN, Deets A, McDuffee A, Lynn Kee H (2019) An undergraduate laboratory experience using CRISPR-cas9 technology to deactivate green fluorescent protein expression in Escherichia coli. Biochem Mol Biol Educ, 47(2) MAR

  55. Van Vu T, Thi Hai Doan D, Kim J, Sung YW, Thi Tran M, Song YJ, Das S, Kim J-Y (2021) Crispr/cas-based precision genome editing via microhomology-mediated end joining. Plant Biotechnol J 19(2):230–239

    Google Scholar 

  56. Taghbalout A, Du M, Jillette N, Rosikiewicz W, Rath A, Heinen CD, Li S, Cheng AW (2019) Enhanced CRISPR-based DNA demethylation by Casilio-ME-mediated RNA-guided coupling of methylcytosine oxidation and DNA repair pathways, Nature communications, 10(4296) MAR

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Heritage Institute of Techhnology, Kolkata, India

    Susobhan Baidya

  2. Department of Computer Science & Engineering, University of Calcutta, Kolkata, India

    Sankhayan Choudhury

  3. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

    Rajat Kumar De

Authors
  1. Susobhan Baidya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sankhayan Choudhury
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rajat Kumar De
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Susobhan Baidya.

Additional information

Availability

We have shared our program in terms executable file in the Github with all instructions. The link is given below https://github.com/saheb80/Myfiles.git. The shared repository contain a zip file, named dist.rar. Users will get a readme document in the zip file. They can easily go through the instructions and use the method using the JAR file. Furthermore user can also access the original source code from the project.rar file.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baidya, S., Choudhury, S. & De, R.K. A Novel CRISPR-MultiTargeter Multi-agent Reinforcement learning (CMT-MARL) algorithm to identify editable target regions using a Hybrid scoring from multiple similar sequences. Appl Intell 53, 9562–9579 (2023). https://doi.org/10.1007/s10489-022-03871-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03871-z

Keywords

Search

Navigation