270805

Hindawi Publishing CorporationBioMed Research InternationalVolume 2013 Article ID 270805 4 pageshttpdxdoiorg1011552013270805

Research ArticleA Guide RNA Sequence Design Platform for the CRISPRCas9System for Model Organism Genomes

Ming Ma1 Adam Y Ye23 Weiguo Zheng4 and Lei Kong2

1 Biomedical Engineering Department College of Engineering Peking University Beijing 100871 China2 Center for Bioinformatics State Key Laboratory of Protein and Plant Gene Research School of Life Sciences Peking UniversityBeijing 100871 China

3National Institute of Biological Sciences 7 Science Park Road Zhongguancun Life Science Park Beijing 102206 China4 Institute of Computer Science and Technology Peking University Beijing 100871 China

Correspondence should be addressed to Lei Kong konglmailcbipkueducn

Received 4 July 2013 Accepted 13 September 2013

Academic Editor Yi Zhao

Copyright copy 2013 Ming Ma et alThis is an open access article distributed under the Creative CommonsAttribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Cas9CRISPR has been reported to efficiently induce targeted gene disruption and homologous recombination in both prokaryoticand eukaryotic cellsThus we developed a Guide RNA Sequence Design Platform for the Cas9CRISPR silencing system for modelorganisms The platform is easy to use for gRNA design with input query sequences It finds potential targets by PAM and ranksthem according to factors including uniqueness SNP RNA secondary structure and AT content The platform allows users toupload and share their experimental results In addition most guide RNA sequences from published papers have been put into ourdatabase

1 Introduction

Gene engineering technology has always been a hot topicin life science research With the development of genemodification technology certain genes can be knocked outor knocked down to a lower level The appearance of zincfinger nuclease (ZFN) and tale nuclease (TALEN) has greatlyaccelerated progress in this field but their efficiency is oftenunpredictable and it is difficult to target selected genes [1ndash8]

Recently Cas9CRISPR has been reported to successfullyinduce targeted gene disruption and homologous recombi-nation in both prokaryotic and eukaryotic cells with higherefficiency compared with ZFN and TALEN [9ndash13] Addition-ally it is easier to design guide sequence and easy to usefor Cas9CRISPR system [10] This novel technology will beof great potential for application in both research field andclinical trials

However there is no available tool for the guide RNAdesign of Cas9CRISPR silencing system Although Mali etal have reported the construction of unique whole humangenome guide RNA library covering more than 40 human

exons [9] they did not provide a tool for researchers to designnovel target sequences for other model organisms

Existed library also did not take into consideration relatedinfluencing factors such as SNP deletion or insertion on thegenome and potential RNA secondary structure Accordingto our current understanding of the gRNAmaturing processthe secondary structure of gRNA is crucial for Cas9-gRNAcomplex [14] The 20 bp guide RNA sequence is used to bindwith target site in genomes If they are mostly involved intoRNA loops the efficiency to bind with target sites wouldbe low Thus this factor should be taken into considerationBesides the interference efficiency is probably closely relatedto the melting temperature of the gRNA-DNA hybrid Arelatively high AT content is negatively correlated with theoff-target effect and thus sequence with extremely low ATpercentage is to some extent not recommended [9]

Thus we developed an online platform for theguide RNA design of the Cas9CRISPR silencing system(httpcas9cbipkueducn) withDNAvariants informationintegrated This tool helps researchers design their candidateguide RNA sequences more easily and provides assistance

2 BioMed Research International

Target sequence

Output candidate targets by finding

Check uniqueness with selected genome sequence

Check SNPindelstatus

Predict final guide RNA secondary structure

PAM (+minus strand)

Figure 1 Streamline of guide RNA design platform Target sequences are searched for the whole genome for uniqueness and then checkSNPindel status The results are output from top to bottom with more unique and fewer SNPindel The entire gRNA secondary structure isalso given as reference

A

B C

D

E

F

Figure 2 Instruction of platform function Overview of platform interface (A)ndash(C) represent functions and database (D) representssenseantisense and position information of output sequences on target sequences (E) represents uniqueness and SNPindel status (F)represents mature gRNA secondary structure

for users to choose better candidates based on preliminaryresults

2 Materials and Methods

Both guide RNA sequences and their corresponding effi-ciency were manually collected from the literature and storedin our database For designing guide RNA we used a Javaframework mainly containing 5 steps and connecting toTomcat web server

In the first step the program would find any candidatesequences based on the N

20NGG sequence pattern principle

where NGG represents PAM sequence by utilizing Javaregular expressionmatching In the second step the programwould put all the candidate sequences to a fasta file and runbowtie 0129 to check if they could be mapped on selected

model organismrsquos genome uniquely [15] The parameters forbowtie were ldquo-f -v 1 -k 10 -l 16 ndashSrdquo as ldquo-f rdquo told bowtie the inputwas fasta file ldquo-v 1rdquo for only allowing at most one mismatchldquo-k 10rdquo reporting up to 10 good alignments ldquo-l 16rdquo settingseed length to 16 and ldquo-Srdquo outputting sam format As thelength of target region was only 23 bp the default seed length28 for bowtie was not proper for this job so we adjusted itto 16 We thought the number of mismatches might largelyaffect effectiveness and this step mainly focused on checkingthe mapping uniqueness so we just looked for hits with atmost one mismatch and output at most 10 hits The mappingresult would be parsed in Java and then in the third stepwould call tabix 025 to find out any overlapped SNPs orindels as reported in dbSNP135 [16ndash18] if the target genomewas humanhg19ThedbSNP135 vcf filewas downloaded fromGATK bundle In the fourth step it would predict RNA

BioMed Research International 3

Table 1 Analyze of reported targets in human cells in this platform

Target genes Guide RNA sequences Mapping and SNP bp inloops AT Efficiency Methods Reference

HumanPVALB ATTGGGTGTTCAGGGCAGAG

1 places matched on genomechr2237196884-37196906(+) with 1SNPs rs12483924 (2 bp to 31015840 end)

6 45 650

SurveyorCong et al2013 [10]Human

PVALB GTGGCGAGAGGGGCCGAGAT

1 places matched on genomechr2237196866-37196888(+) with 3

SNPs rs3484 (18 bp to 31015840 end)rs181855770 (10 bp to 31015840 end)rs9607383 (9 bp to 31015840 end)

9 30 ND

HumanPVALB GGGGCCGAGATTGGGTGTTC


rs9607383 (18 bp to 31015840 end)

9 35 ND

HumanAAVS1 GGGGCCACTAGGGACAGGAT

1 places matched on genomechr1955627117-55627139(minus) with 0

SNPs8 35 807

HRMali et al2013 [9]Human

AAVS1 GTCCCCTCCACCCCACAGTG

2 places matched ongenomechr1955627136-55627158(minus) with 0 SNP

schr4108975634-108975656(+) with1 SNPs rs115503552 (7 bp to 31015840 end)

7 30 326

HumanVEGFA GGGTGGGGGGAGTTTGCTCC

1 places matched on genomechr643737291-43737313(minus) with 1SNPs rs12210204 (1 bp to 31015840 end)

11 30 26

T7EI assayFu et al2013 [21]

HumanVEGFA GACCCCCTCCACCCCGCCTC


SNPs4 20 50

HumanVEGFA GGTGAGTGAGTGTGTGCGTG


SNPs12 40 4940

lowastND represents not detectable Italic font represents low efficient gRNAs within the same gene group

secondary structures for those candidate gRNA sequences bycalling Vienna RNAfold 207 with default parameters [19]In the last step the program rearranged all the informationfor the designed gRNA and formatted it to better-lookingHTMLTheATand the distance of the variants to the 31015840 endof the target region were also calculated The output gRNAswere sorted by both number of mapping hits and numberof overlapping SNPs The time consumption for this pipelinewas mainly on running bowtie and sometimes tabix whenthere existed many target sequences and was roughly aboutthree seconds for one query sequence

3 Results and Discussion

Multiple gene sequences are allowed for batch gRNAdesign and the streamline of this platform is shown inFigure 1 The results contain genomic loci information ofgRNAs and SNPINDEL inside them This would helpresearchers choose a more unique target candidate and avoidSNPinsertiondeletion Moreover this platform evaluates

all candidates based on their RNA secondary structureand AT content allowing users to choose better candidates(Figure 2)

Recently Jiang et al report that only the first six base pairsnear PAM are of great importance for recognition efficiencyin bacteria [20] It is unknown whether or not this is still thecase for eukaryotic or even mammalian cells We will keepupdating our algorithm to rank candidate gRNAs

We conducted a validation by using those reported resultsin our platform on factors such as uniqueness SNP andbase in loops (Table 1 italic font represents low efficienttargets)Themore unique with fewer SNPs and base in loopsgenerally the gRNA is more efficient For the given genePVALB the first target sequence is 50 more efficient thanthe rest two since the first has 0 SNP while the rest have 3 or2 SNPsThefirst target sequence has fewer base pairs involvedin RNA secondary structure loops allowing it to bind morewith target genome while the rest two both have 9 base pairsin loops For the given gene AAVS1 the first target is morethan twofold efficient than the other since the other one has


an off-target site in genomes For the given gene VEGFAthe first one is about half efficient with the rest two since ithas 1 SNP while the rest have none

AT content is crucial factor as those previously men-tioned since evidence is not clear Thus we list it here as aconsideration for users

4 Conclusions

Our platform is an easy-to-use software to identify poten-tial efficient gRNA sites within given sequences for modelorganisms avoiding off-target effects and SNPsThis platformalso allows users to search existing guide RNAprotospacersequences and share their results We have manuallyextracted most reported gRNAprotospacer sequences intoour database for reference and will expand it with newlypublished work

Disclosure

The online platform database and document are available athttpcas9cbipkueducn

Authorsrsquo Contribution

Ming Ma and Adam Y Ye contributed equally to this workMing Ma conceived the idea and Adam Y Ye Weiguo Zhengconducted programming and website construction Lei Kongsupervised the whole job and give guidance MingMa AdamY Ye and Lei Kong drafted the paper

References

[1] J C Miller M C Holmes J Wang et al ldquoAn improved zinc-finger nuclease architecture for highly specific genome editingrdquoNature Biotechnology vol 25 no 7 pp 778ndash785 2007

[2] J D Sander E J Dahlborg M J Goodwin et al ldquoSelection-free zinc-finger-nuclease engineering by context-dependentassembly (CoDA)rdquoNatureMethods vol 8 no 1 pp 67ndash69 2011

[3] M Christian T Cermak E L Doyle et al ldquoTargeting DNAdouble-strand breaks with TAL effector nucleasesrdquo Geneticsvol 186 no 2 pp 756ndash761 2010

[4] J C Miller S Tan G Qiao et al ldquoA TALE nuclease architecturefor efficient genome editingrdquo Nature Biotechnology vol 29 no2 pp 143ndash148 2011

[5] D Reyon S Q Tsai C Khayter J A Foden J D Sander andJ K Joung ldquoFLASH assembly of TALENs for high-throughputgenome editingrdquo Nature Biotechnology vol 30 no 5 pp 460ndash465 2012

[6] L Tesson CUsal SMenoret et al ldquoKnockout rats generated byembryo microinjection of TALENsrdquo Nature Biotechnology vol29 no 8 pp 695ndash696 2011

[7] C Tong G Huang C Ashton H Wu H Yan and Q L YingldquoRapid and cost-effective gene targeting in rat embryonic stemcells by TALENsrdquo Journal of Genetics and Genomics vol 39 no6 pp 275ndash280 2012

[8] JD Sander L Cade CKhayter et al ldquoTargeted gene disruptionin somatic zebrafish cells using engineered TALENsrdquo NatureBiotechnology vol 29 no 8 pp 697ndash698 2011

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

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

[11] M Villion and S Moineau ldquoThe double-edged sword ofCRISPR-Cas systemsrdquo Cell Research vol 23 no 1 pp 15ndash172013

[12] H Wang H Yang C S Shivalila et al ldquoOne-step generationof mice carrying mutations in multiple genes by CRISPRCas-mediated genome engineeringrdquoCell vol 153 no 4 pp 910ndash9182013

[13] N Chang C Sun L Gao et al ldquoGenome editing with RNA-guided Cas9 nuclease in zebrafish embryosrdquo Cell Research vol23 no 4 pp 465ndash472 2013

[14] K S Makarova D H Haft R Barrangou et al ldquoEvolutionand classification of the CRISPR-Cas systemsrdquo Nature ReviewsMicrobiology vol 9 no 6 pp 467ndash477 2011

[15] B Langmead C Trapnell M Pop and S L Salzberg ldquoUltrafastand memory-efficient alignment of short DNA sequences tothe human genomerdquo Genome Biology vol 10 no 3 article R252009

[16] H Li ldquoTabix fast retrieval of sequence features from genericTAB-delimited filesrdquo Bioinformatics vol 27 no 5 pp 718ndash7192011

[17] S T SherryM-HWardMKholodov et al ldquoDbSNP theNCBIdatabase of genetic variationrdquo Nucleic Acids Research vol 29no 1 pp 308ndash311 2001

[18] A McKenna M Hanna E Banks et al ldquoThe genome analysistoolkit a MapReduce framework for analyzing next-generationDNA sequencing datardquo Genome Research vol 20 no 9 pp1297ndash1303 2010

[19] R Lorenz S H Bernhart C Honer Zu Siederdissen et alldquoViennaRNA package 20rdquo Algorithms for Molecular Biologyvol 6 no 1 article 26 2011

[20] W Jiang D Bikard D Cox F Zhang and L A MarraffinildquoRNA-guided editing of bacterial genomes using CRISPR-Cassystemsrdquo Nature Biotechnology vol 31 no 3 pp 233ndash239 2013

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

Submit your manuscripts athttpwwwhindawicom

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Genetics Research International


Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational



Enzyme Research



Microbiology


Target sequence

Output candidate targets by finding

Check uniqueness with selected genome sequence

Check SNPindelstatus

Predict final guide RNA secondary structure

PAM (+minus strand)

Figure 1 Streamline of guide RNA design platform Target sequences are searched for the whole genome for uniqueness and then checkSNPindel status The results are output from top to bottom with more unique and fewer SNPindel The entire gRNA secondary structure isalso given as reference

A

B C

D

E

F

Figure 2 Instruction of platform function Overview of platform interface (A)ndash(C) represent functions and database (D) representssenseantisense and position information of output sequences on target sequences (E) represents uniqueness and SNPindel status (F)represents mature gRNA secondary structure

for users to choose better candidates based on preliminaryresults

2 Materials and Methods

Both guide RNA sequences and their corresponding effi-ciency were manually collected from the literature and storedin our database For designing guide RNA we used a Javaframework mainly containing 5 steps and connecting toTomcat web server

In the first step the program would find any candidatesequences based on the N

20NGG sequence pattern principle

where NGG represents PAM sequence by utilizing Javaregular expressionmatching In the second step the programwould put all the candidate sequences to a fasta file and runbowtie 0129 to check if they could be mapped on selected

model organismrsquos genome uniquely [15] The parameters forbowtie were ldquo-f -v 1 -k 10 -l 16 ndashSrdquo as ldquo-f rdquo told bowtie the inputwas fasta file ldquo-v 1rdquo for only allowing at most one mismatchldquo-k 10rdquo reporting up to 10 good alignments ldquo-l 16rdquo settingseed length to 16 and ldquo-Srdquo outputting sam format As thelength of target region was only 23 bp the default seed length28 for bowtie was not proper for this job so we adjusted itto 16 We thought the number of mismatches might largelyaffect effectiveness and this step mainly focused on checkingthe mapping uniqueness so we just looked for hits with atmost one mismatch and output at most 10 hits The mappingresult would be parsed in Java and then in the third stepwould call tabix 025 to find out any overlapped SNPs orindels as reported in dbSNP135 [16ndash18] if the target genomewas humanhg19ThedbSNP135 vcf filewas downloaded fromGATK bundle In the fourth step it would predict RNA






6 45 650





9 30 ND



rs9607383 (18 bp to 31015840 end)

9 35 ND



SNPs8 35 807





7 30 326



11 30 26




SNPs4 20 50



SNPs12 40 4940











4 Conclusions


Disclosure




References





























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology






6 45 650





9 30 ND



rs9607383 (18 bp to 31015840 end)

9 35 ND



SNPs8 35 807





7 30 326



11 30 26




SNPs4 20 50



SNPs12 40 4940











4 Conclusions


Disclosure




References





























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology




4 Conclusions


Disclosure




References





























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology








Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

Documents

270805