ssfinder: high throughput crispr-cas target sites ... · researcharticle ssfinder: high throughput...

Research ArticleSSFinder High Throughput CRISPR-CasTarget Sites Prediction Tool

Santosh Kumar Upadhyay and Shailesh Sharma

National Agri-Food Biotechnology Institute Department of Biotechnology Government of India C-127 Industrial AreaSAS Nagar Phase 8 Mohali Punjab 160071 India

Correspondence should be addressed to Santosh Kumar Upadhyay santoshpandit16gmailcomand Shailesh Sharma shaileshsharmanabiresin

Received 22 February 2014 Revised 31 May 2014 Accepted 12 June 2014 Published 26 June 2014

Academic Editor Phillip E Klebba

Copyright copy 2014 S K Upadhyay and S Sharma This is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited

Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein (Cas) system facilitatestargeted genome editing in organisms Despite high demand of this system finding a reliable tool for the determination of specifictarget sites in large genomic data remained challenging Here we report SSFinder a python script to perform high throughputdetection of specific target sites in large nucleotide datasets The SSFinder is a user-friendly tool compatible with Windows MacOS and Linux operating systems and freely available online

1 Introduction

Genome editing is a very useful technology in the researchareas related to the functional genomics Programmablenucleases like zinc finger nucleases (ZFN) and transcriptionactivator-like effector nucleases (TALEN) are well knowntools for targeted genome editing [1 2] Similarly clusteredregularly interspaced short palindromic repeats (CRISPR)andCRISPR-associated protein (Cas) system enables genomeengineering in animals and plants [3ndash5] This is an RNAguided system which consists of a short guide RNA (gRNA)and a Cas9 protein The gRNA contains 20 nucleotidestargeted DNA sequence (known as spacer) that binds to thecomplementary strand of the target DNAby base-pairing and79 nucleotides conserved sequence to form a specific hairpin-like fold The gRNA interacts with Cas9 protein and formsan active ribonucleoprotein complex The complex binds tothe target sequence by base-pairing and cleaves dsDNA at aspecific position [6] A short conservedmotif ldquoNGGrdquo (knownas a protospacer adjacent motif PAM) at 31015840 downstream ofthe target sequence is also reported as necessary for cleavage[7]

The CRISPR-Cas system is simple in design as comparedto ZFNs and TALENs and highly effective in large and com-plex genome like 17Gb allohexaploid wheat also [5] Someoff-target binding of the system is reported [8] which can beabandoned by selecting the highly specific 12 nucleotide seedsequences in 31015840 region of the spacer [4 9] Besides dsDNAcleavage the CRISPR-Cas system has been recently modifiedfor nicking and repression of gene activity [10] It is reportedas an effective tool for activation of gene expression also [11]Due to these novel features this system is going to play veryimportant role in genome engineering programs Thereforea dedicated tool for determination of specific CRISPR-Castarget sites is an utmost requirement for several researchgroups

Although detection of CRISPR-Cas target site is simplewhich needs direct analysis of DNA sequences for thepresence of specific 23 nucleotides (including ldquoNGGrdquo PAMat 31015840 end) A few tools like CRISPR Design (httpwwwbr-oadinstituteorgmpgcrispr design httpcrisprmitedu)CRISPR Target (httpbioanalysisotagoacnzCRISPRTar-getcrispr analysishtml) and ZiFiT Targeter (httpzifitpar-tnersorgZiFiTChoiceMenuaspx) are recently reported for

Hindawi Publishing CorporationBioMed Research InternationalVolume 2014 Article ID 742482 4 pageshttpdxdoiorg1011552014742482

2 BioMed Research International

the determination of target sites [12] most of these tools arelimited to the analysis of small number or size of sequencesand cannotmodify according to the users need Further theseare web based tools that depend upon internet connectivitySome of the tools like CRISPR design (httpcrisprmitedu)are limited to the model genomes only (See SupplementaryTable 1 in Supplementary Material available online athttpdxdoiorg1011552014742482) Therefore a simpleeasy to edit and high throughput computational tool isrequired for the analysis of large datasets on the localmachine

Here we present SSFinder a freewarewritten in python tofind specific CRISPR-Cas target sites in limited time on evena personal computer It is an organism independent freewareavailable at httpscodegooglecompssfinder under MITlicense

2 Materials and Methods

The SSFinder is a Python script and can execute in Win-dows Mac OS and Linux operating systems It is a lowmemory request tool which enables the finding of specificCRISPR-Cas target sites It can be installed on both personalcomputers and parallel computing system commonly knownas ldquoclusterrdquo dedicated in genome research It only needs acompatible version of Python (22 or higher version) installedin the system

A flow chart showing algorithm of the SSFinder is givenin Figure 1 The DNA sequence is first analyzed for theoccurrence of 23 nucleotide segments (including ldquoNGGrdquoPAM at 31015840 end) These segments are further screened for thepresence of 12 nucleotide seed sequences which are distinctin the input sequence data To further simplify selectedsequences are again classified into four groups on the basis ofthe start and end nucleotides which are (1) GC N

7S11GC

(2)GCN7S11AT (3)ATN

7S11AT and (4)ATN

7S11GC

(N denotes for any nucleotides and S for seed sequences)To use SSFinder users need to download the script or

copy-paste the script in a notepad and save as ldquossfinderpyrdquo inthe directory hosting the Python executable (CPython) Aworking directory containing input file of FASTA formattednucleotide sequences is also required Finally SSFinder canbe executed by using the following command line in Linuxterminal

$python SSFinderpyFor windows user an overview for the execution of

the SSFinder using command prompt is shown in Figure 2User needs to provide an address of working directory filename of input sequences and desired output format (like xlsor txt or csv) when asked by SSFinder The output filewill be saved automatically in the same directory with thefile name ldquoSSFinder-outputrdquo In this file SSFinder providesresults in seven distinct columns consisted of (1) identifier ofthe sequences (2) classification in four groups based on thestart and end nucleotides (3) potential target sites includingldquoNGGrdquo PAM sequence (4) start and (5) end position of targetsites (6) conditionwith specific 12 nucleotide seed sequencesand (7) specific CRISPR-Cas target sites (Table 1) Sequences

Genometranscriptomesequences

Search for (N)20NGG

Present

Present

Search for (N)8(S)12NGG

Use as potential and specificCRISPR-Cas target site

Absent

Absent

Discarded

Discarded

N any nucleotides S seed sequences without duplication in complete genome

Figure 1 A flow chart showing algorithm of the SSFinder forCRISPR-Cas target site prediction

Figure 2 An overview of command prompt for using SSFinder inWindows operating system

from the 7th column can be directly used as spacers in thegRNA designing

3 Results and Discussion

TheSSFinder facilitates prediction of CRISPR-Cas target sitesin small as well as large genomic data It is freeware for theresearchers and can also be used on personal computers ofany configuration Compatibility with operating systems likeWindows Mac OS and Linux makes this tool user-friendlyfor the researcher from nonbioinformatics background aswell

The SSFinder scans aDNA sequence bymoving awindowof 23 nucleotides at the step size of one nucleotide Sliceswith 31015840 ldquoNGGrdquo PAM sequence are selected and analyzedfor the presence of 12 nucleotide seed sequences Since theseed sequence determines the specificity of the CRISPR-Cassystem [4 5 9] the tool ensures that these sequences arenot repeated in the entire input genome data [13] Sequenceshaving distinct seed sequence in entire input sequence fileare displayed in output file as specific target sites Sometimesresearchers need to target the genomic region start andend with specific nucleotide (like ATGC) Therefore theselected slices are further classified into four different motifsto ease the process

To test the performance of SSFinder we used two typesof large datasets (1) multiple sequences with cumulativebig size (27416 Arabidopsis thaliana protein coding genes

BioMed Research International 3

Table 1 An overview of output file given by SSFinder

Identifier Classification CRISPR-target site with ldquoNGGrdquo PAM Position Condition withseed sequences Specific CRISPR-target site

Start End

Sequence 1 A or TN18Aor T ACTTCTTCGTCCAACTTCTTCGG 6 28 A or TN7S11A

or T ACTTCTTCGTCCAACTTCTT

Sequence 1 A or TN18Gor C TTTGCAAGCCTCATCCATTGTGG 386 408 A or TN7S11G

or C TTTGCAAGCCTCATCCATTG

Sequence 1 G or CN18Aor T CCAAAGTTCTATTTGAGCTAAGG 68 90 G or CN7S11A

or T CCAAAGTTCTATTTGAGCTA

Sequence 1 G or CN18Gor C CTAACCGACCTTCAGCTAACAGG 154 176 G or CN7S11G

or C CTAACCGACCTTCAGCTAAC

of sim37 million bases from TAIR10 ftpftparabidopsisorghometairSequencesblast datasetsTAIR10 blastsets) and(2) single sequence of large size (X chromosome of sim22million bases of Drosophila melanogaster accession numberNC 0043543) In the first dataset a total of 1780846 siteswere detected in 27416 genes in which 1618640 sites in 27239genes were specific for the CRISPR-Cas binding The resultwas in agreement with the earlier report [9] In case of seconddataset a total of 1058944 siteswere detected inwhich 977257were specific The analyzed data can be available on requestThe average speed of the analysis was 34 and 40 kilo basesper minute for first and second dataset respectively Theaverage distance between two specific target sites was 229nucleotides in both tested organisms The results indicatedthat the SSFinder is a high throughput tool for CRISPR-Castarget site prediction from large datasets in limited time

4 Conclusion

We report SSFinder a comprehensive tool for the identifica-tion of specific CRISPR-Cas target sites with high reliabilityIt is a freeware easy to edit and low memory demand toolcompatible with many commonly used operating systemsOur tool is very useful in high throughput inhouse screeningapplications of large genomes in limited time This canaccelerate the functional genomics research based on theapplication of CRISPR-Cas system

Availability

The SSFinder is a python script freely available athttpscodegooglecompssfinder

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Authorsrsquo Contribution

Santosh Kumar Upadhyay conceived the idea designed theexperiment analyzed the tool and wrote the paper ShaileshSharma developed the script and performed the experiment

Santosh Kumar Upadhyay and Shailesh Sharma contributedequally to this work

Acknowledgments

Authors are thankful to the National Agri-Food Biotechnol-ogy Institute (NABI) Mohali India for the research facilitySantosh Kumar Upadhyay is thankful to Department ofScience and Technology (DST) Government of India forDST INSPIRE Faculty Fellowship

References

[1] K Chen and C Gao ldquoTALENs customizable molecular DNAscissors for genome engineering of plantsrdquo Journal of Geneticsand Genomics vol 40 no 6 pp 271ndash279 2013

[2] F Zhang M L Maeder E Unger-Wallaced et al ldquoHighfrequency targeted mutagenesis in Arabidopsis thaliana usingzinc finger nucleasesrdquo Proceedings of the National Academy ofSciences of the United States of America vol 107 no 26 pp12028ndash12033 2010

[3] L Cong F A RanD Cox et al ldquoMultiplex genome engineeringusing CRISPRCas systemsrdquo Science vol 339 no 6121 pp 819ndash823 2013

[4] P Mali L Yang K M Esvelt et al ldquoRNA-guided humangenome engineering via Cas9rdquo Science vol 339 no 6121 pp823ndash826 2013

[5] S K Upadhyay J Kumar A Alok and R Tuli ldquoRNA guidedgenome editing for multiple target gene mutations in wheatrdquoG3 Genes Genomes Genetics vol 3 no 12 pp 2233ndash2238 2013

[6] M M Jore M Lundgren E van Duijn et al ldquoStructural basisfor CRISPR RNA-guided DNA recognition by CascaderdquoNatureStructural amp Molecular Biology vol 18 no 5 pp 529ndash536 2011

[7] G Gasiunas R Barrangou P Horvath and V Siksnys ldquoCas9-crRNA ribonucleoprotein complex mediates specific DNAcleavage for adaptive immunity in bacteriardquo Proceedings of theNational Academy of Sciences of the United States of Americavol 109 no 39 pp E2579ndashE2586 2012

[8] Y Fu J A Foden C Khayter et al ldquoHigh-frequency off-targetmutagenesis induced by CRISPR-Cas nucleases in human cellsrdquoNature Biotechnology vol 31 pp 822ndash826 2013

[9] J F Li J E Norville J Aach et al ldquoMultiplex and homologousrecombination-mediated genome editing in Arabidopsis andNicotiana benthamiana using guide RNA and Cas9rdquo NatureBiotechnolog vol 31 no 8 pp 688ndash691 2013


[10] L S QiMH Larson L A Gilbert et al ldquoRepurposingCRISPRas an RNA-120574uided platform for sequence-specific control ofgene expressionrdquo Cell vol 152 no 5 pp 1173ndash1183 2013

[11] D Bikard W Jiang P Samai A Hochschild F Zhang andL A Marraffini ldquoProgrammable repression and activation ofbacterial gene expression using an engineered CRISPR-Cassystemrdquo Nucleic Acids Research vol 41 no 15 pp 7429ndash74372013

[12] A Biswas J N Gagnon S J J Brouns P C Fineran and C MBrown ldquoCRISPRTarget bioinformatic prediction and analysisof crRNA targetsrdquo RNA Biology vol 10 no 5 pp 817ndash827 2013

[13] M Jinek A East A Cheng S Lin E Ma and J DoudnaldquoRNA-programmed genome editing in human cellsrdquo eLife vol2 Article ID e00471 2013

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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International Journal of

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Zoology


Molecular Biology International

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GenomicsInternational Journal of

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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


BioinformaticsAdvances in

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ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Genetics Research International


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Virolog y


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Volume 2014

Stem CellsInternational



Enzyme Research



Microbiology


the determination of target sites [12] most of these tools arelimited to the analysis of small number or size of sequencesand cannotmodify according to the users need Further theseare web based tools that depend upon internet connectivitySome of the tools like CRISPR design (httpcrisprmitedu)are limited to the model genomes only (See SupplementaryTable 1 in Supplementary Material available online athttpdxdoiorg1011552014742482) Therefore a simpleeasy to edit and high throughput computational tool isrequired for the analysis of large datasets on the localmachine

Here we present SSFinder a freewarewritten in python tofind specific CRISPR-Cas target sites in limited time on evena personal computer It is an organism independent freewareavailable at httpscodegooglecompssfinder under MITlicense

2 Materials and Methods

The SSFinder is a Python script and can execute in Win-dows Mac OS and Linux operating systems It is a lowmemory request tool which enables the finding of specificCRISPR-Cas target sites It can be installed on both personalcomputers and parallel computing system commonly knownas ldquoclusterrdquo dedicated in genome research It only needs acompatible version of Python (22 or higher version) installedin the system

A flow chart showing algorithm of the SSFinder is givenin Figure 1 The DNA sequence is first analyzed for theoccurrence of 23 nucleotide segments (including ldquoNGGrdquoPAM at 31015840 end) These segments are further screened for thepresence of 12 nucleotide seed sequences which are distinctin the input sequence data To further simplify selectedsequences are again classified into four groups on the basis ofthe start and end nucleotides which are (1) GC N

7S11GC

(2)GCN7S11AT (3)ATN

7S11AT and (4)ATN

7S11GC

(N denotes for any nucleotides and S for seed sequences)To use SSFinder users need to download the script or

copy-paste the script in a notepad and save as ldquossfinderpyrdquo inthe directory hosting the Python executable (CPython) Aworking directory containing input file of FASTA formattednucleotide sequences is also required Finally SSFinder canbe executed by using the following command line in Linuxterminal

$python SSFinderpyFor windows user an overview for the execution of

the SSFinder using command prompt is shown in Figure 2User needs to provide an address of working directory filename of input sequences and desired output format (like xlsor txt or csv) when asked by SSFinder The output filewill be saved automatically in the same directory with thefile name ldquoSSFinder-outputrdquo In this file SSFinder providesresults in seven distinct columns consisted of (1) identifier ofthe sequences (2) classification in four groups based on thestart and end nucleotides (3) potential target sites includingldquoNGGrdquo PAM sequence (4) start and (5) end position of targetsites (6) conditionwith specific 12 nucleotide seed sequencesand (7) specific CRISPR-Cas target sites (Table 1) Sequences

Genometranscriptomesequences

Search for (N)20NGG

Present

Present

Search for (N)8(S)12NGG

Use as potential and specificCRISPR-Cas target site

Absent

Absent

Discarded

Discarded

N any nucleotides S seed sequences without duplication in complete genome

Figure 1 A flow chart showing algorithm of the SSFinder forCRISPR-Cas target site prediction

Figure 2 An overview of command prompt for using SSFinder inWindows operating system

from the 7th column can be directly used as spacers in thegRNA designing

3 Results and Discussion

TheSSFinder facilitates prediction of CRISPR-Cas target sitesin small as well as large genomic data It is freeware for theresearchers and can also be used on personal computers ofany configuration Compatibility with operating systems likeWindows Mac OS and Linux makes this tool user-friendlyfor the researcher from nonbioinformatics background aswell

The SSFinder scans aDNA sequence bymoving awindowof 23 nucleotides at the step size of one nucleotide Sliceswith 31015840 ldquoNGGrdquo PAM sequence are selected and analyzedfor the presence of 12 nucleotide seed sequences Since theseed sequence determines the specificity of the CRISPR-Cassystem [4 5 9] the tool ensures that these sequences arenot repeated in the entire input genome data [13] Sequenceshaving distinct seed sequence in entire input sequence fileare displayed in output file as specific target sites Sometimesresearchers need to target the genomic region start andend with specific nucleotide (like ATGC) Therefore theselected slices are further classified into four different motifsto ease the process

To test the performance of SSFinder we used two typesof large datasets (1) multiple sequences with cumulativebig size (27416 Arabidopsis thaliana protein coding genes




Start End










4 Conclusion


Availability







Acknowledgments


References






















Volume 2014

Zoology





Volume 2014


















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology




Start End










4 Conclusion


Availability







Acknowledgments


References






















Volume 2014

Zoology





Volume 2014


















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology













Volume 2014

Zoology





Volume 2014


















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

ssfinder: high throughput crispr-cas target sites ... · researcharticle ssfinder: high throughput...

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