当前位置：文档库 › RTPCR引物设计方法文献GETPrime a gene- or transcript-specific primer

RTPCR引物设计方法文献GETPrime a gene- or transcript-specific primer

Original article

GETPrime:a gene-or transcript-specific primer database for quantitative real-time PCR

Carine Gubelmann 1,Alexandre Gattiker 2,3,Andreas Massouras 1,Korneel Hens 1,

Fabrice David 2,3,Frederik Decouttere 4,Jacques Rougemont 2,3and Bart Deplancke 1,*

1Institute of Bio-engineering,School of Life Sciences,Laboratory of Systems Biology and Genetics,2Institute of Bio-engineering,School of Life Sciences,Bioinformatics and Biostatistics Core Facility,Ecole Polytechnique Fe ′de ′rale de Lausanne (EPFL),3School of Life Sciences,Swiss Institute of Bioinformatics,Station 15,1015Lausanne,Switzerland and 4Genohm SA,PSE-C site EPFL,1015Lausanne,Switzerland

*Corresponding author:Tel:t41(0)216931821;Fax:t41(0)216939665;Email:bart.deplancke@epfl.ch

Submitted 20December 2010;Revised 20May 2011;Accepted 8August 2011

.............................................................................................................................................................................................................................................................................................The vast majority of genes in humans and other organisms undergo alternative splicing,yet the biological function of splice variants is still very poorly understood in large part because of the lack of simple tools that can map the expression profiles and patterns of these variants with high sensitivity.High-throughput quantitative real-time polymerase chain reaction (qPCR)is an ideal technique to accurately quantify nucleic acid sequences including splice variants.However,currently available primer design programs do not distinguish between splice variants and also differ substantially in overall quality,functionality or throughput mode.Here,we present GETPrime,a primer database supported by a novel platform that uniquely combines and automates several features critical for optimal qPCR primer design.These include the consideration of all gene splice variants to enable either gene-specific (covering the majority of splice variants)or transcript-specific (covering one splice variant)expression profiling,primer specificity validation,automated best primer pair selection ac-cording to strict criteria and graphical visualization of the latter primer pairs within their genomic context.GETPrime primers have been extensively validated experimentally,demonstrating high transcript specificity in complex samples.Thus,the free-access,user-friendly GETPrime database allows fast primer retrieval and visualization for genes or groups of genes of most common model organisms,and is available at http://updepla1srv1.epfl.ch/getprime/.

Database URL:http://deplanckelab.epfl.ch.

or deep sequencing because the latter analysis methods are still less efficient in terms of overall cost and computational expertise required than qPCR for the quantitative detection of gene transcripts,especially those that are lowly ex-

pressed such as many transcription factor (TF)genes (5,6).The choice of suitable primer sets is thereby critical to obtain optimal qPCR results.An ideal qPCR primer design program should at least include the following features:first,given the above mentioned increasing interest in understanding the role of individual gene splice variants (1,7,8),the program needs to take into account all anno-tated splice variants of each gene to enable either gene-(covering the majority of splice variants)or transcript-specific (covering one splice variant)expression profiling;.............................................................................................................................................................................................................................................................................................?The Author(s)2011.Published by Oxford University Press.

This is Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://www.wendangku.net/doc/8015240422.html,/licenses/by-nc/2.5),which permits unrestricted non-commercial use,distribution,and reproduction in any medium,provided the original work is properly cited.Page 1of 12

(page number not for citation

purposes)Database ,Vol.2011,Article ID bar040,doi:10.1093/database/bar040

https://www.wendangku.net/doc/8015240422.html,/Downloaded from

second,at least one primer needs to span exons to avoid amplification of contaminating genomic DNA;third,the specificity of each primer needs to be automatically evalu-ated by similarity search;fourth,no cumbersome post-processing should be required to retrieve the best primer combination and fifth,the location of primers pairs within their genomic context should be visualized for easy,final evaluation by the end user.

In search of qPCR primer design software for a large-scale TF gene expression profiling experiment,we con-sidered several software packages but none of them fulfilled the stipulated requirements as these programs all varied in quality,functionality or throughput mode (Table1).The most popular interface,based on Primer3 (9),named Primer3Plus(10),allows the user to define a variety of possible parameters and options for designing oligonucleotide primers.However,the use of this program is time-consuming because users have to manually process the large number of proposed primers when,for example, verifying primer specificity by BLAST(11).Another pro-gram,RASE(12)generates qPCR primers for the detection and quantitation of specific splicing isoforms,but does not enable the design of gene-specific primers.In addition,its associated web interface only supports low-throughput experiments.Other programs such as PerlPrimer(13), QuantPrime(14),and BatchPrimer3(15)do allow batch primer input,and some databases of qPCR primers includ-ing Quantitative PCR Primer Database(16),RTPrimerDB (17),PrimerBank(18)and qPrimerDepot(19)were de-veloped for high-throughput primer design or retrieval.But again,none of these packages combines and auto-mates all of the important features required to address the increasing demands in qPCR primer design for high-throughput qPCR experiments,especially the requirement to target genes in gene-or transcript-specific fashion with-out post-processing(Table1).

To fill this current void,we developed our own qPCR primer design software,GETPrime.This program was de-signed to generate primers targeting every gene available in the latest Ensembl release,which is used as a reference resource(20).However,to allow fast primer retrieval,we have linked our program to GETPrimedb,a database en-abling fast retrieval via a user-friendly interface of gene-or transcript-specific primers for all Homo sapiens,Mus musculus,Caenorhabditis elegans,Drosophila melanoga-ster and Danio rerio genes in assembled chromosomes annotated in the Ensembl database.

Database construction and development

Primer generation

GETPrime combines several existing tools:the PerlPrimer program(13),Blast(11)and the Ensembl database through a custom perl wrapper,which enables automation of the workflow and decision process for selecting the best primer pairs(see workflow,Figure1).

The first step in the GETPrime pipeline is the selection of a transcript,as an input,from the Ensembl database for

https://www.wendangku.net/doc/8015240422.html,parison between previously established qPCR primer design programs and GETPrime

qPCR primer design Transcript/

sequence

specific

Gene-

specific

(cover the

majority

of or,if

possible,all

transcripts)

Spanning

exons to

avoid

amplification

of contaminating

DNA

Automated

validation

of primer

specificity

No post-

processing

to select

best

primers

Graphical

view

of the

location

within the

genome

Experimental

primer

validation

Interface

for high-

throughput

experiments

Fast

processing

Primer3Plus(10)ˇx x x x x x xˇAutoprime(36)ˇxˇx x xˇxˇPerlPrimer(13)ˇxˇx x xˇxˇPrimer Expressˇx x x x xˇxˇBatchPrimer3(15)ˇx x x x xˇˇˇRASE(12)ˇxˇˇx xˇxˇt/àPrimique(37)ˇxˇt/àˇx xˇˇt/àˇt/àQuantPrime(14)ˇˇt/àˇˇˇxˇˇˇt/àDatabases

[RTPrimerDB(17),

PrimerBank(27),

qPrimerDepot(19)]

ˇxˇˇˇt/àxˇxˇtGETPrimedbˇˇˇˇˇˇˇˇˇt

each target gene of interest.For comprehensiveness,we included all genes featuring a ‘known’or ‘novel’status in the Biomart portal in the GETPrime database (21).Each gene-specific transcript is then selected based on two inde-pendent criteria:(i)its status must be annotated as ‘KNOWN’because we opted not to include ‘novel’tran-scripts in our database as these are not yet validated by species-specific sequencing data (Bert Overduin from Ensembl Project,Personal communication)and (ii)the high-est junction score (Figure 2).In the first round,this score allows the selection of a transcript that contains conserved exon junctions within the alternative splice variants.If the same highest score is obtained by several transcripts,then the transcript with the shortest sequence is selected.Next,this first selected transcript is provided as an input to our modified PerlPrimer program.This program supports primer design for one specific transcript at exon junctions to avoid unspecific amplification due to DNA https://www.wendangku.net/doc/8015240422.html,ing a graphical user interface,PerlPrimer runs Spidey (22)to detect intron/exon boundaries,and searches all possible primer pairs on the input transcript.To enable the generation of primers for a large number of genes,we modified PerlPrimer to use the exon junction coordinates supplied by Ensembl.In addition,in contrast to PerlPrimer,that frequently generates tens of candidate primer pairs without quality scores,GETPrime runs an

extensive Figure 1.Overall primer design pipeline.The overall workflow is depicted.The green box is explained in Figure 2.The pink and purple boxes are explained in more detail in Figures 3and 4,respectively.For more details,please see main

text.

Figure 2.Visualization of the junction score algorithm concept for gene-or transcript-specific primer design.A schematic representation of a gene with three alternative transcripts is depicted.The blue boxes represent exons,the lines represent introns.Each transcript contains a subset of exons A–D and A 0.The junction score (N )constitutes the number of transcripts containing the respective splice junction.To design gene-specific primers,the sum of the junction scores,represented by S ,is calculated for each transcript and the transcript with the highest S -value that contains the junction with the highest N-score is selected (here t 1).Then,the gene-specific primers are preferentially designed so that one of the primers spans the exon junction with the highest N-score within the selected transcript (t 1),as indicated by the dark blue arrows.

Page 3of 12 at Beijing Normal University on September 18, 2012

https://www.wendangku.net/doc/8015240422.html,/Downloaded from

workflow to select the three best primer pair candidates based on stringent parameters (Table 2)and well-defined criteria which allow primer pair ranking (Figures 1and 3).The first important criterion (Figure 3)is primer specifi-city,which is assessed using BLASTN against the entire genomic DNA and cDNA from all predicted transcripts.To ensure primer specificity in BLAST,we cancelled the default filter,as this allows the detection of spurious alignments even outside biologically relevant genomic regions.This is also the reason why we implemented a low

stringency

Figure 3.Workflow to select the best primer pairs.The selection of the best primer pairs is automated according to these hierarchical criteria.First,each primer pair is blasted and potentially discarded as described in the Figure and in the main text.Then,with the remaining primer pairs,pairs are discarded if at least one primer spans the 50-or 30-UTR.After these two stringent filtering steps,the remaining primers are ranked according to (i)highest transcript coverage,(ii)whether the primers are located within the same exon (not desirable)or not (desirable)and (iii)smallest amplicon size which has shown to be more optimal for qPCR efficiency and experimental variation (35).

Table https://www.wendangku.net/doc/8015240422.html,parison between default and relaxed primer design parameters

qPCR primer quality criteria Default parameters Relaxed parameters

Primer length 19–25bp 19–25bp

Amplicon length 80–200bp 60–300bp

Melting temperature (T m )57–608C 57–608C

áT m ?18C áT m ?28C

Exclude %GC 40–60%only considered 40–60%only considered

GC clamp Two of the three 30-bases of each primer must be a G or a C Two of the three 30-bases of each

primer must be a G or a C

Exon/exon junction primers At least 7bp at the 50-end and 3bp at the 30-end At least 7bp at the 50-end and

3bp at the 30-end

The melting temperature (T m )is calculated in the PerlPrimer program (13)which uses J.SantaLucia’s extensive nearest-neighbor thermodynamic parameters (38,39)and the default salt conditions (1.5mM Mg 2t,200mM oligos,0.2mM dNTPs and 50mM monovalent cations).Bolded text highlights the differences between the default and relaxed primer design parameters.G ?guanine;C ?cytosine.

https://www.wendangku.net/doc/8015240422.html,/Downloaded from

E-value of100,which assures that all possible alignments with primer sequences are detected.If both primers match with at least60%sequence identity,a DNA or cDNA region spanning at most5000bp and the region does not corres-pond to the position of the targeted transcript itself,then the primer pair is discarded because it may wrongly target another gene or a pseudogene.The primer pairs that are similar(at least50%overlap for both primers)to the one just discarded,usually shifted by1to2bases,are also discarded to avoid running too many BLAST searches. The BLAST criterion is especially important to specifically monitor the expression of protein-coding genes from the same family such as,for example,homeodomain TFs.The second criterion is to discard primer pairs that span the 50-or30-untranslated regions(UTRs).Transcript quantifica-tion based on primers targeting these regions can be biased as30-UTRs contain multiple polyadenylated regions and 50-UTRs are frequently absent or truncated if cDNAs are synthesized with an oligo(dT)primer.Other ranking criteria are the number of gene-specific transcripts that are covered (as reflected by the value of N shown in Figure2),the size of the amplicon and whether the primer pair falls within the same exon.

If,at the end of the ranking pipeline,no primer pair has passed the selection,then certain parameters are relaxed until a satisfactory primer pair is obtained.Parameter relaxation is performed within the modified PerlPrimer script and within the best primer pair selection workflow (Figure4).In the modified PerlPrimer script,the first par-ameters that are relaxed are the amplicon length and the melting temperature difference(Table2),followed by the exon/exon junction criteria(allowing<7bp at the50-end and/or<3bp at the30-end)and the requirement to span different exons.In the best primer pair selection workflow, parameters that can be relaxed are the extent of primer specificity and the location of primers in the UTR

regions,

Figure4.Workflow to find at least one suitable primer pair by relaxing the primer design parameters.The circles schematize the

run of the modified PerlPrimer script and the workflow of the best primer selection indicated in Figure1.The relaxation of the

parameters within the modified PerlPrimer script and the allowed options in the selection of the best primer pairs are depicted

in the center and on the right of the circles,respectively.Blue and green circles represent the default parameters and the relaxed

design parameters,respectively(Table2).The arrows symbolize the logical flow.If no primers are found with either set of

parameters,the program reports‘No primers’.

Page5of12

at Beijing Normal University on September 18, 2012

https://www.wendangku.net/doc/8015240422.html,/

Downloaded from

which sometimes constitute the only sites which enable the distinction of two or more transcripts.Primers that are gen-erated under these relaxed conditions are tagged,and the user is informed on which parameters were changed to obtain primers.Genes for which no primers could be found,even after parameter relaxation,are labeled with the statement‘no primers’.

The three top-ranking primer pairs are saved in the output file along with a list of transcripts covered by the primer pair,the sequences of forward and reverse primers, the primer melting temperatures,the start and end primer positions,the Ensembl status of the gene(‘known’or ‘novel’),indication of whether parameters were relaxed to obtain the primer pair,and a link to the graphical view.Finally,potential transcripts not covered by the top ranking primer pair are reanalyzed to design additional primer pairs covering the transcripts from the remaining set.This process is repeated until all predicted transcripts are covered by at least one primer pair.The results of this computational exercise for all annotated genes of the se-lected genomes are stored in a General Feature Format (GFF)file and saved in a MySQL database.

Furthermore,our pipeline has been extended for transcript-specific expression profiling by changing the workflow to select the best primer pairs.Instead of ranking primer pairs according to the highest transcript coverage, the ranking is done according to the smallest transcript coverage.The average number of genes in Ensembl release 61covered by the best ranked gene-or transcript-specific primer pairs for each model organism is listed in Table3. For example,98%of the known Mouse Ensembl genes are covered by a gene-and transcript-specific primer pair.Of them,50%have no associated warning and2%have been obtained by relaxing primer parameters[amplicon length and deviation in the melting temperature(Table2)]without any other type of warning.The overall computing time depended on the number of genes processed and varied per model organism,taking between two days (D.melanogaster)and two weeks(H.sapiens)on our server[Linux system(kernel2.6.18)with48x Intel Xeon 2.67GHz quad CPU with74GB RAM memory].

Database access

Google Web Toolkit was used to generate the web inter-face and to display the MySQL query results directly in the browser.The interface to retrieve primer pairs accepts gene symbols,also defined as‘associated gene names’by the BioMart portal(21),and Ensembl gene or transcript IDs.A choice is available to either find primers covering most of the transcripts of a gene(for maximum coverage with a minimal number of specific primers)or,if possible,to have individual primer pairs specific for each single tran-script for detailed quantification of splice variants.The interface contains different filtering possibilities to obtain the desired primer output.For example,one can select only primers without warnings,or decide to include primers with specific warnings that are tolerated by the individual user(e.g.‘inUTR’).A typical query example featuring visual cues at each processing step has been added to the inter-face to assist users.The final output of the GETPrime inter-face is an Excel file containing primer sequences and parameter properties,as well as hyperlinks directing the user to an in-house browser,based on JBrowse(23), showing the alternative transcripts and primer positions (Figure5).The current GETPrime database is based on the Ensembl release61.A previous version of GETPrime based on Ensembl release50is still accessible to the user on the same web interface.The only differences between the two versions are that,in the current version,novel genes are

Table3.Average number of genes covered by the best ranked primer pair for each species

Species Number of

KNOWN

protein-

coding

genes

(Ensembl v61)Genes

covered

by a primer

pair,n(%)

Primer

pairs

without

warnings

Primer

pairs

design

relaxed

without

other

warnings

Primer

pairs in

UTR

without

other

warnings

Primer

pairs with

spanning

criteria

relaxed

without

other

warnings

Primer

pairs

with both

primers on

separate

exons

without

other

warnings

Primer

pairs

non-specific

without

other

warnings

Primer

pairs

with

other

warnings

Homo sapiens3496034093(97.5)145216439672695899731193151 Mus musculus2944528840(97.9)144256009671832605222072757 Caenorhabditis elegans3823721964(57.4)175104031611528833736793 Drosophila melanogaster1486914368(96.6)8493301427124123593761171 Danio rerio2437024203(99.3)1809148749989912612174792

We calculated the average number of gene-or transcript-specific primer pairs with their principal stringencies.

included and that hyperlinks for each primer pair are sup-ported by distinct browser types.Database validation and application The sensitivity and specificity of the primers generated by GETPrime were validated experimentally.First,as a con-trol experiment,we selected three commonly used,and thus,already published primer pairs,each targeting one of the following control genes:Hprt1,Igfbp4,Tubb2c (see Supplementary Table S1for sequences).Only primer pairs covering exactly the same transcripts as the one generated by GETPrime were selected.Similar to the well-established primer pairs,all three GETPrime primer pairs were of high quality showing high specificity based on melting curve and gel separation analyses as well as good amplification effi-ciency on cDNA synthesized from a qPCR Mouse Reference total RNA (a mean efficiency of 103%with an R 2-value of 0.997,see ‘Materials and Methods’section).In addition,the amount of PCR product generated by each primer pair in pre-adipocyte 3T3-L1cells pre-and post-differentiation induction (‘Materials and Methods’section,D0,D2and D4)was comparable (áC q <1).Next,we evaluated the quality of GETPrime primers by targeting 60TFs in cDNA samples synthesized from the

same mouse reference total RNA (‘Materials and

Methods’section).We chose TFs as they are typically ex-

pressed at lower levels than non-TF-coding genes and are

therefore in general more difficult to detect (24,25),thus rendering this validation assay more stringent than when a number of genes is targeted coding for a diverse set of proteins.In total,45out of the 60tested primer pairs were of high quality as evidenced by their high amplifica-tion efficiency (a mean efficiency of 98.94%with an R 2-value of 0.994)and by their high specificity based on their corresponding dissociation curves.Of the remaining 15primer pairs,one was found to form primer dimers indi-cated by the presence of a clear melting curve peak in the no template and the no reverse transcriptase negative controls,and 14only yielded a low signal likely due to the fact that the corresponding target genes were only lowly abundant both in the Reference RNA as well as the 3T3-L1RNA samples (26)(‘Materials and Methods’section).However,we were able to generate a standard curve for five out of the 14primer pairs because the corresponding TF open reading frame (ORF)clones are available in

our Figure 5.JBrowse-based graphical view of GETPrime primer pairs targeting the Rbbp9mouse gene.The blue boxes on the left are the available tracks that can be dragged in the JBrowse genome view (23).In this example,the transcripts,the gene-specific primers (covering the majority of splice variants if possible)and the transcript-specific primers (covering a single splice variant,when possible)have been dragged into the browser.The upper part of the figure shows tools to zoom,to move to up-or downstream of the genome location,and to enter another chromosome,another position on the chromosome or also an Ensembl ID.Each primer is annotated by its Ensembl ID,its iteration in GETPrime (e.g.à1),its ranking (e.g._3)and its primer type (forward and reverse primers are abbreviated Fwd and Rv,respectively).The blue box for each primer represents the respective alignment to the transcripts and sometimes a thin line between two blue boxes is used to bridge an intron region for primers spanning two exons.The primer pairs in the gene-specific track cover both transcripts.The primer pairs from the first iteration (‘à1’)and the second iteration (‘à2’)in the transcript-specific track are specific to the largest transcript Rbbp9-001and the shortest transcript Rbbp9-002,respectively.

Page 7of 12 at Beijing Normal University on September 18, 2012

https://www.wendangku.net/doc/8015240422.html,/Downloaded from

laboratory.These primer pairs yielded an amplification ef-ficiency mean of98.4%with a mean R2-value of0.995. Thus,50out of51primer pairs that we were able to test, satisfy the qPCR reliability criteria,demonstrating that the vast majority of primers in the GETPrime database are of high quality.

To further evaluate primer specificity,we designed a stringent assay with GETPrime primers which target one TF ORF within a library of TF ORF clones producing hom-ologous proteins,here,either homeodomain-or ZF-C2H2 TFs(‘Materials and Methods’section and Supplementary Table S2).For each of the selected TF targets,we obtained a clear qPCR amplification signal in contrast to when the same TF ORF library was used as qPCR template but without containing the respective target TF ORF(Supplementary Figure S1),indicating that the respective primer pair is highly target-specific.Finally,to evaluate the ability of GETPrime to differentiate between gene-specific tran-scripts,we used3T3-L1cells pre-and post-differentiation induction and chose one relatively straight-forward splicing scenario featuring one gene,Ubtf,that,according to Ensembl release50,has two distinct transcript forms Ubtf_a and Ubtf_b,each representing respectively five and two different splice forms(Figure6A).GETPrime pri-mers were able to differentiate both Ubtf splice forms at the two selected differentiation time points and did so in quantitative fashion in that the sum of the individual tran-script amounts matched the overall gene expression amount(Figure6B).

Discussion

The experimental results demonstrate the power of GETPrime to produce gene-or transcript-specific qPCR pri-mers.The results also show that the generated primers are of high quality and that these primers are able to detect low-abundant transcripts such as those coding for TFs. Moreover,they demonstrate their capacity to specifically recognize targets within a pool of templates coding for highly homologous proteins,as well as their high amplifi-cation efficiency.Thus,given the fact that there are,to our knowledge,no other software and web tools that offer the same set of attributes as the GETPrime platform(Table1), we believe that GETPrime constitutes an important advance of the qPCR primer design field.One other recently de-veloped qPCR primer design software QuantPrime(14) also features gene-specific primer design(i.e.covering the majority of splice variants)as an option.However,this soft-ware offers no straight-forward way to identify which tran-scripts are covered by the gene-specific primers.The user is therefore obliged to blast each primer in the Ensembl data-base to find this information,which is time-consuming,es-pecially if a large set of genes need to be targeted.In addition,it is often impossible to generate primers that cover all gene transcripts.For these genes,multiple primer pairs need to be used to yield a gene-specific read-out.In contrast to QuantPrime,the GETPrime data-base provides all this information in easy-to-retrieve and graphical fashion.Thus,while it is clear that QuantPrime is a powerful primer design program,given its great par-ameter flexibility for large-scale qPCR primer design and user-friendly interface,it appears not to have been de-signed with the a priori aim of yielding gene-or transcript-specific primers.Moreover,we found that QuantPrime processing time significantly increases with increasing gene length and number.GETPrime does not suffer from this drawback as its database interface allows direct primer retrieval.This is similar to other Primer data-bases,like qPrimerDepot(19),PrimerBank(27)and RTPrimerDB(17),which also provide pre-computed qPCR primers.The limitations of these databases,though,are that they do not accommodate primer retrieval in batches and that they list multiple primer pairs per gene.It is then up to the individual user to evaluate each primer pair for experimental suitability,which is cumbersome,in contrast to GETPrime,which ranks all primers according to well-defined criteria.An important GETPrime drawback is that it is so far available only for five commonly used model organisms including humans.However,demands to design primers for other organisms of interest can be accommo-dated or can even be performed by the end user via adjust-ment of the GETPrime Perl script,which is available upon request.Thus,the GETPrime database currently includes primers for H.sapiens,M.musculus,C.elegans,D.melano-gaster and D.rerio genes in assembled chromosomes anno-tated in the Ensembl database release50and61.We thereby plan to update GETPrime as soon as major new Ensembl releases are available.

Materials and Methods

Cell culture

3T3-L1mouse fibroblast cells(28)were cultured in DMEM supplemented with10%fetal bovine serum,with L-glutam-ine2mM and penicillin/streptomycin(1?)in a5%CO2 humidified atmosphere at378C and maintained<80%con-fluence before passaging.Differentiation of3T3-L1cells was induced by exposing two-day post-confluent cells [designated as Day0(D0)]to DMEM containing10%FCS (Bioconcept,Allschwil,Switzerland)supplemented with 1m M dexamethasone,0.5mM3-isobutyl-1-methylxanthine and1m g/ml insulin(Sigma,St Louis,USA).At D2,cells were fed with DMEM containing FCS and1m g/ml insulin and two days later(D4),the media was changed to10%FBS/DMEM. Full differentiation is usually achieved by Days6–8.The D0, D2and D4samples have been used for RNA extraction, cDNA preparation and qPCR.

RNA extraction and cDNA synthesis We have used two different experimental samples.First,to analyze the quality of the primers,we used Agilent’s qPCR Mouse Reference total RNA (Agilent technologies,Santa

Clara,USA).For the other reported experiments,we used

total cellular RNA isolated from 3T3-L1cells using the RNeasy Plus Mini Kit (Qiagen)according to the 0

0.2

0.4

0.6

0.8

1.2

1.41.61.8

D 0D R e

a t

v e

n e

e x

r e

s s

i o n

B A

Figure 6.Graphical view and qPCR results to validate Ubtf -targeting primers covering either all or a subset of Ubtf transcripts.

(A )The ‘Ubtf’primer pair in blue covers all seven transcripts (gene-specific primers)and the red ‘Ubtf_a’and ‘Ubtf_b’primer pairs cover five and two transcripts,respectively (transcript-specific primers).In this example,GETPrime could not find primers differentiating each transcript.(B )The relative gene expression levels before differentiation (D0)and four days after (D4)were normalized to Hprt1and Tubb2c expression levels.‘Ubtf’represents the primer pair covering all seven transcripts,whereas,‘Ubtf_a’and ‘Ubtf_b’are primer pairs specific to a subset of five and two transcripts,respectively.‘Ubtf_a tUbtf_b’represents the sum of relative gene expression of ‘Ubtf_a’and ‘Ubtf_b’.The data indicate that GETPrime can effectively differentiate distinct transcripts,as the sum of the individual transcript amounts matched the overall gene expression amount.

Page 9of 12 at Beijing Normal University on September 18, 2012

https://www.wendangku.net/doc/8015240422.html,/Downloaded from

manufacturers’instructions without DNAse treatment. After extraction,RNA was quantified using a NanoDrop Spectrophotometer1000v3.2.0(NanoDrop Technologies, Detroit,USA).The RNA quality was further determined using a nanodrop(1.8A260/A280 2.2)and by visual in-spection of separated bands on agarose gels.A quantity of1m g of RNA was used for the reverse transcription performed with random primers(Invitrogen)and Superscript III First Strand synthesis Supermix(Invitrogen, Carlsbad,USA)in a total volume of20m l according to sup-plier’s recommendations.The cDNA samples were stored atà208C.

qPCR

qPCR was performed in96-(manual)and384-well plates (robotic)with three technical replicates on the ABI-7900HT Real-Time PCR System(Applied Biosystems,Foster City, USA)using Power SYBR Green Master Mix(Applied Biosystems,Foster City,USA)using standard procedures. Briefly,the standard protocol from Applied Biosystem was used(508C for2min and958C for10min;then 40cycles of958C for15s and608C for1min)and finalized by a dissociation step(958C for15s,608C for15s and a ramp rate of2%to958C).The amount of DNA,primer and Power SYBR Green Master Mix are indicated in the Supplementary Table S1.

qPCR amplification efficiencies were calculated using the qPCR instrument software and were based on the linear regression of five serially diluted samples(a4-fold dilution series).The slope of the standard curve gives the amplifica-tion efficiency by the formula E?10(à1/slope).If the amplifi-cation is100%efficient(percentage expressed by:Eà1), then the amount of PCR product should be doubled per cycle,resulting in an E-value of2.Primers were considered reliable if they featured efficiency values between92%and 108%with a correlation coefficient,R2(i.e.how well the standard curve regression line fits the data),>0.99.Primers were specific in targeting the gene or transcript of interest if qPCR melting curve analysis yielded a single sharp dissoci-ation peak.In rare cases,a specific amplification reaction showed a so-called‘shoulder peak’(29)which occurs within amplicons containing multiple melting domains with vary-ing Guanine-Cytosine(GC)contents.When additional peaks (off-target or primer–dimers)or shoulder peaks were found in the melting curve,the specificity of qPCR products was also assessed by gel separation.A no template(to detect primer dimer formation)and no reverse transcriptase(to exclude DNA amplification)negative controls were also included in the presented qPCR assays.

In the standard curve analysis,genes having C q-values in at least three dilution series>33were considered as lowly expressed and were excluded from the analysis of calculating the average amplification efficiency.When an ORF clone was available for one of these genes,the primer quality assessment was done on five aliquots of a4-fold dilution series of the clone of interest(starting amount $1pg).

Expression in3T3-L1was quantified using theááC t-method and the data were normalized to Hprt1and Tubb2c expression.The expression of both these genes re-mains the most stable during3T3-L1cell differentiation within a set of six tested candidate reference genes(Actb, Hprt1,Igfbp4,Knab1,Tubb2c,GusB),as found by the nor-malization in geNorm software(30).

In our experiments,primers were retrieved from the GETPrime database based on Ensembl release50with the most stringent parameters if available,or with the slightly less stringent design method‘DesignRelaxed’(Figure4and sequences in Supplementary Table S1).Analysis of the standard curve,dissociation curve and results were done directly by using the software SDS 2.4from Applied Biosystems.To allow qPCR data exchange,RDML files(31) were generated by using qbase PLUS software[http://www https://www.wendangku.net/doc/8015240422.html,,(32)]and are available in the Supplementary Data.

Testing of primer specificity within a family

To validate primer specificity,we first generated two libraries containing80and55ORF clones encoding TFs be-longing to,respectively,the homeodomain and ZF-C2H2 protein families(Supplementary Table S2).TFs from each family were selected based on their phylogenetic rela-tedness as reported in Refs(33)and(34),thus to make primer selection as difficult as possible.Next,we evaluated whether we could specifically target,respectively,seven (Dlx4,Hoxd10,Hoxc10,Pitx2,Barx1,Irx6,Hoxb6)and six randomly selected TF ORFs(Egr2,Zfp148,Zfp354c,Zfp451, Zfp688,Zscan20)within the latter libraries using GETPrime primers.To do this,we generated libraries with and with-out the selected target TF ORFs.

Supplementary Data

Supplementary data are available at Database online.

Acknowledgements

C.G.designed and programmed GETPrime,carried out the primer testing and drafted the manuscript.A.G.designed and programmed GETPrime together with A.M.K.H.super-vised the primer testing and revised the interface. F.

D. loaded the GETPrime primers,designed their alignments in the browser.F.De.tested GETPrime and also designed the graphical user interface.J.R.supervised the project and assisted in the programming of GETPrime.B.D.super-vised the design and the programming of GETPrime,tested the program,revised the interface and drafted the

manuscript.All authors revised and approved the final manuscript.

Funding

The Swiss National Science Foundation;SystemsX.ch;the NCCR program Frontiers in Genetics;Marie Curie International Reintegration Grant from the Seventh Research Framework Programme(to B.D.);Institutional support from the Ecole Polytechnique Fe′de′rale de Lausanne(EPFL).Funding for open access charge:Ecole Polytechnique Fe′de′rale de Lausanne(EPFL).

Conflict of interest.None declared.

References

1.Sultan,M.,Schulz,M.H.,Richard,H.et al.(2008)A global view of

gene activity and alternative splicing by deep sequencing of the human transcriptome.Science,321,956–960.

2.Johnson,J.M.,Castle,J.,Garrett-Engele,P.et al.(2003)Genome-wide

survey of human alternative pre-mRNA splicing with exon junction microarrays.Science,302,2141–2144.

3.Wang,E.T.,Sandberg,R.,Luo,S.et al.(2008)Alternative isoform

regulation in human tissue transcriptomes.Nature,456,470–476.

4.Chen,M.and Manley,J.L.(2009)Mechanisms of alternative splicing

regulation:insights from molecular and genomics approaches.Nat.

Rev.Mol.Cell Biol.,10,741–754.

5.Bookout,A.L.,Jeong,Y.,Downes,M.et al.(2006)Anatomical profil-

ing of nuclear receptor expression reveals a hierarchical transcrip-tional network.Cell,126,789–799.

6.Deplancke,B.,Dupuy,D.,Vidal,M.and Walhout,A.J.(2004)A

gateway-compatible yeast one-hybrid system.Genome Res.,14, 2093–2101.

7.Reece-Hoyes,J.S.,Deplancke,B.,Shingles,J.et al.(2005)A compen-

dium of Caenorhabditis elegans regulatory transcription factors:a resource for mapping transcription regulatory networks.Genome Biol.,6,R110.

8.Taneri,B.,Snyder,B.,Novoradovsky,A.et al.(2004)Alternative spli-

cing of mouse TFs affects their DNA-binding domain architecture and is tissue specific.Genome Biol.,5,R75.

9.Rozen,S.and Skaletsky,H.(2000)Primer3on the WWW for general

users and for biologist programmers.Methods Mol.Biol.,132, 365–386.

10.Untergasser,A.,Nijveen,H.,Rao,X.et al.(2007)Primer3Plus,an

enhanced web interface to Primer3.Nucleic Acids Res.,35, W71–W74.

11.Altschul,S.,Gish,W.,Miller,W.et al.(1990)Basic local alignment

search tool.J.Mol.Biol.,215,403–410.

12.Brosseau,J.P.,Lucier,J.F.,Lapointe,E.et al.(2010)High-throughput

quantification of splicing isoforms.RNA,16,442–449.

13.Marshall,O.J.(2004)PerlPrimer:cross-platform,graphical primer

design for standard,bisulphite and real-time PCR.Bioinformatics, 20,2471–2472.

14.Arvidsson,S.,Kwasniewski,M.,Riano-Pachon,D.et al.(2008)

QuantPrime-a flexible tool for reliable high-throughput primer design for quantitative PCR.BMC Bioinformatics,9,465.15.You,F.M.,Huo,N.,Gu,Y.Q.et al.(2008)BatchPrimer3:a high

throughput web application for PCR and sequencing primer design.BMC Bioinformatics,9,253.

16.National Cancer Institute.Quantitative PCR Primer Database.http://

https://www.wendangku.net/doc/8015240422.html,/cgi-bin/PrimerViewer(6th September2011,date last accessed).

17.Pattyn,F.,Robbrecht,P.,De Paepe,A.et al.(2006)RTPrimerDB:the

real-time PCR primer and probe database,major update2006.Nucl Acids Res.,34,D684–D688.

18.Wang,X.and Seed,B.(2003)A PCR primer bank for quantitative

gene expression analysis.Nucleic Acids Res.,31,e154.

19.Cui,W.,Taub,D.D.and Gardner,K.(2007)qPrimerDepot:a primer

database for quantitative real time PCR.Nucleic Acids Res.,35, D805–D809.

20.Curwen,V.,Eyras,E.,Andrews,T.D.et al.(2004)The Ensembl auto-

matic gene annotation system.Genome Res.,14,942–950.

21.Haider,S.,Ballester,B.,Smedley,D.et al.(2009)BioMart Central

Portal–unified access to biological data.Nucleic Acids Res.,37, W23–W27.

22.Wheelan,S.J.,Church,D.M.and Ostell,J.M.(2001)Spidey:a tool for

mRNA-to-genomic alignments.Genome Res.,11,1952–1957.

23.Skinner,M.E.,Uzilov,A.V.,Stein,L.D.et al.(2009)JBrowse:a

next-generation genome browser.Genome Res.,19,1630–1638. 24.Vaquerizas,J.M.,Kummerfeld,S.K.,Teichmann,S.A.et al.(2009)A

census of human transcription factors:function,expression and evolution.Nat.Rev.Genet.,10,252–263.

25.Deplancke,B.(2009)Experimental advances in the characterization

of metazoan gene regulatory networks.Brief.Funct.Genomic.

Proteomic.,8,12–27.

26.Nielsen,R.,Pedersen,T.A?.s.,Hagenbeek,D.et al.(2008)Genome-

wide profiling of PPARI?3:RXR and RNA polymerase II occupancy reveals temporal activation of distinct metabolic pathways and changes in RXR dimer composition during adipogenesis.Genes Dev.,22,2953–2967.

27.Spandidos,A.,Wang,X.,Wang,H.et al.(2010)PrimerBank:a

resource of human and mouse PCR primer pairs for gene expres-sion detection and quantification.Nucleic Acids Res.,38,D792–9.

28.Green,H.and Kehinde,O.(1975)An established preadipose cell line

and its differentiation in culture.II.Factors affecting the adipose conversion.Cell,5,19–27.

29.D’haene,B.and Hellemans,J.(2010)The importance of quality con-

trol during qPCR data analysis.Int.Drug Disc.,18–24.

30.Vandesompele,J.,De Preter,K.,Pattyn,F.et al.(2002)Accurate nor-

malization of real-time quantitative RT-PCR data by geometric ave-raging of multiple internal control genes.Genome Biol.,3, RESEARCH0034.

31.Lefever,S.,Hellemans,J.,Pattyn,F.et al.(2009)RDML:structured

language and reporting guidelines for real-time quantitative PCR data.Nucleic Acids Res.,37,2065–2069.

32.Hellemans,J.,Mortier,G.,De Paepe,A.et al.(2007)qBase relative

quantification framework and software for management and automated analysis of real-time quantitative PCR data.Genome Biol., 8,R19.

33.Berger,M.F.,Badis,G.,Gehrke,A.R.et al.(2008)Variation in home-

odomain DNA binding revealed by high-resolution analysis of se-quence preferences.Cell,133,1266–1276.

34.Tadepally,H.D.,Burger,G.and Aubry,M.(2008)Evolution of

C2H2-zinc finger genes and subfamilies in mammals:species-specific duplication and loss of clusters,genes and effector do-mains.BMC Evol.Biol.,8,176.

Page11of12 at Beijing Normal University on September 18, 2012 https://www.wendangku.net/doc/8015240422.html,/ Downloaded from

35.Tichopad,A.,Dzidic,A.and Pfaffl,M.(2003)Improving quantitative

real-time RT-PCR reproducibility by boosting primer-linked amplifi-cation efficiency.Biotechnol.Lett.,24,2053–2056.

36.Wrobel,G.,Kokocinski,F.and Lichter,P.(2004)AutoPrime:selecting

primers for expressed sequences.Genome Biol.,5,P11.

37.Fredslund,J.and Lange,M.(2007)Primique:automatic design of

specific PCR primers for each sequence in a family.BMC Bioinformatics,8,369.38.Allawi,H.T.and SantaLucia,J.Jr.(1997)Thermodynamics and

NMR of internal G.T mismatches in DNA.Biochemistry,36, 10581–10594.

39.SantaLucia,J.Jr.(1998)A unified view of polymer,dumbbell,and

oligonucleotide DNA nearest-neighbor thermodynamics.Proc.Natl https://www.wendangku.net/doc/8015240422.html,A,95,1460–1465.

引物设计的原理与方法

引物设计的原理与方法 This model paper was revised by the Standardization Office on December 10, 2020

PCR引物设计的原理及方法阎振鑫S111666(四川大学生命科学学院细胞生物学成都 610014) 摘要：自20世纪后期发展了PCR技术以来，PCR已经改变了整个生物学研究的进程。而PCR反应的第一步就是设计引物，引物设计的好坏直接关系到PCR的成败。PCR引物设计有许多的原则必须要遵循：引物与引物之间避免形成稳定的二聚体或发夹结构，引物与模板的序列要紧密互补。引物不能在模板的非目的位点引发DNA聚合反应等。另外，引物的设计方法也越来越多，出现了许多专门的设计软件和网站，如：PrimerPremier5.0等。关键词：PCR 引物原理方法 NCBI PrimerPremier5.0 PCR primer design principle and method YanZhenxin (sichuan Univercity, Life science college cell biology chengdu 610014 ) Abstract: When PCR technology was find, PCR has changed all of the program in research of biology. The design of primer is the frist step of PCR. It is relation to the fate of PCR. There are some principals must be obey: dipolymer and hairpin structure must be avoid between different primers. The DNA polymerization reaction should not be triggered at the wrong site. Therefore, there are more and more methods of design primer, include the professional softwares and professional web site. Key word: PCR primer principle NCBI PrimerPremier5.0 聚合酶链式反应(Polymerase chain reaction。PCR)是20世纪后期发展起来的一种体外扩增特异DNA片断的技术。具有快速、简便及高度敏感等优点，能极大地缩短目的基因扩增时间[1]。因此，其一直是生物学者们致力于构建cDNA文库、基因克隆以及表达调控研究的必要前提和基础[2]。PCR的第一步就是引物设计。引物设计的好坏，直接影响了PCR的结果，因此这一步很关键。成功的PCR反应既要高效，又要特异性扩增产物，因此对引物设计提出了较高的要求。引物设计需要注意的地方很多，在大多数情况下，我们都是在知道已知模板序列时进行PCR扩增的。在某些情况比如构建文库的时候也会在不知道模板序列的情况下进行设计。这个时候随机核苷酸序列

引物设计基本方法

Primer 5.0搜索引物： 1.Primer Length我常设置在18－30bp,短了特异性不好，长了没有必要。当然有特殊要求的除外，如加个酶切位点什么的。 2.PCR Product size最好是100－500bp之间，小于100bp的PCR产物琼脂糖凝胶电泳出来，条带很模糊，不好看。至于上限倒也不必要求苛刻。 3.Search parameters还是选Manual吧，Search stringency应选High，GC含量一般是40－60％。其它参数默认就可以了。 4.搜索出来的引物，按Rating排序，逐个送Oligo软件里评估。当然，搜索出的引物，其扩增产物很短，你可以不选择它，或是引物3端≥2个A或T,或引物内部连续的G或C太多,或引物3端≥2个G或C，这样的引物应作为次选，没得选了就选它。对于这样的引物，如果其它各项指标还可以，我喜欢在引物末端去掉一个不满意的或加上一个碱基，看看引物的评估参数有没有变好点。 Oligo 6.0评估引物： 1.在analyze里,Duplex Formation不管是上游引物、下游引物还是上下游引物之间，The most stable 3’-Dimer绝对值应小于4.5kcal/mol, The most stable Dimer overall绝对值一般应小于多少kcal/mol跟PCR退火温度有关，我几次实验感觉在PCR退火温度在65°的时候，The most stable Dimer ove rall 6.7kcal/mol没有问题。 2.Hairpin Formation根据黄金法则 3.False priming sites: Primer的priming efficiency应该是错配地方的4倍左右，更多当然更好。 4.在PCR栏，个人感觉其所显示的optimal annealing temperature数值值得参考。在PCR摸索条件的时候，退火温度为其数值加减2的范围就可以了。 5.Internal stability很重要：我们希望引物的内部稳定性是中间高、两边低的弧形，最起码保证3端不要过于稳定。下图1引物3端过于稳定，很容易导致不适当扩增。△G参照黄金法则，这其实很好理解：把一滴水放到大海里，这滴水就会不停的扩散分布，扩散的越厉害越稳定，所以△G绝对值越大结构越稳定。最后说一句，敢于尝试就会成功。第二贴－－科室工作很多，小医生了，没有办法，所以肯怕不能满足很多战友的要求（qq聊或帮助设计），在此表示抱歉。就楼上的问题我试着回答一下，不一定正确，供参考吧。－－1、两个评价系统不一样，个人感觉oligo评价引物好点，primer出来的引物，我一般按效率排序，再结合退火温度和引物长度，选择引物到oligo测试。这是初步的选择，其实引物到了oligo里，退火温度也不一样。－－2、3端的二聚体应该避免，这个要看你的退火温度决定，一个50°的退火温度肯定和65°对二聚体的影响不一样了，一般来讲尽量控制在－4.5kcal/mol以下（个人观点，很多东西真得还是需要自己摸索）。－－3、个人感觉3端有A无A影响不大，3端有T的没有经验。有T是不是一定不行，个人感觉不见得。软件是评估，法则也不是没有例外，不是1＋1＝2那么确定。－－4、错配和二聚体谁轻谁重，个人觉得“到致命的程度”谁都重要，我也说不好。我设计的时候，尽量两个都不得罪。－－5、GC含量并非不重要，它直接影响引物各端稳定性，3端来两个G或C,稳定性就上去了，粘在模板上很牢。所以我设计的时候，尽量避免这样的情况出现。谈一下我学这个引物设计的过程吧：

引物设计原则

1. 引物的长度一般为15-30 bp，常用的是18-27 bp，但不应大于38，因为过长会导致其延伸温度大于74℃，不适于Taq DNA聚合酶进行反应。 2. 引物序列在模板内应当没有相似性较高，尤其是3’端相似性较高的序列，否则容易导致错配。引物3’端出现3个以上的连续碱基，如GGG或CCC，也会使错误引发机率增加。 3. 引物3’端的末位碱基对Taq酶的DNA合成效率有较大的影响。不同的末位碱基在错配位置导致不同的扩增效率，末位碱基为A的错配效率明显高于其他3个碱基，因此应当避免在引物的3’端使用碱基A。另外，引物二聚体或发夹结构也可能导致PCR反应失败。5’端序列对PCR影响不太大，因此常用来引进修饰位点或标记物。 4. 引物序列的GC含量一般为40-60%，过高或过低都不利于引发反应。上下游引物的GC含量不能相差太大。 5. 引物所对应模板位置序列的Tm值在72℃左右可使复性条件最佳。Tm值的计算有多种方法，如按公式Tm＝4(G+C)＋2(A+T)，在Oligo软件中使用的是最邻近法(the nearest neighbor method)。 6. ΔG值是指DNA双链形成所需的自由能，该值反映了双链结构内部碱基对的相对稳定性。应当选用3’端ΔG值较低（绝对值不超过9），而5’端和中间ΔG值相对较高的引物。引物的3’端的ΔG值过高，容易在错配位点形成双链结构并引发DNA聚合反应。 7. 引物二聚体及发夹结构的能值过高（超过mol）易导致产生引物二聚体带，并且降低引物有效浓度而使PCR反应不能正常进行。 8. 对引物的修饰一般是在5’端增加酶切位点，应根据下一步实验中要插入PCR 产物的载体的相应序列而确定。引物序列应该都是写成5-3方向的， Tm之间的差异最好控制在1度之内，另外我觉得扩增长度大一些比较好，500bp左右。要设计引物首先要找到DNA序列的保守区。同时应预测将要扩增的片段单链是否形成二级结构。如这个区域单链能形成二级结构，就要避开它。如这一段不能形成二级结构，那就可以在这一区域设计引物。

引物设计的11条黄金法则

PCR引物设计的11条黄金法则 1.引物最好在模板cDNA的保守区内设计。DNA序列的保守区是通过物种间相似序列的比较确定的。在NCBI上搜索不同物种的同一基因，通过序列分析软件（比如DNAman）比对（Alignment），各基因相同的序列就是该基因的保守区。 2.引物长度一般在15~30碱基之间。引物长度（primerlength）常用的是18-27bp，但不应大于38，因为过长会导致其延伸温度大于74℃，不适于TaqDNA聚合酶进行反应。 3.引物GC含量在40%~60%之间，Tm值最好接近72℃。 GC含量（composition）过高或过低都不利于引发反应。上下游引物的GC含量不能相差太大。另外，上下游引物的Tm值（meltingtemperature）是寡核苷酸的解链温度，即在一定盐浓度条件下，50%寡核苷酸双链解链的温度。有效启动温度，一般高于Tm值

5~10℃。若按公式Tm=4（G+C）+2（A+T）估计引物的Tm值，则有效引物的Tm为55~80℃，其Tm值最好接近72℃以使复性条件最佳。 4.引物3′端要避开密码子的第3位。如扩增编码区域，引物3′端不要终止于密码子的第3位，因密码子的第3位易发生简并，会影响扩增的特异性与效率。 5.引物3′端不能选择A，最好选择T。引物3′端错配时，不同碱基引发效率存在着很大的差异，当末位的碱基为A时，即使在错配的情况下，也能有引发链的合成，而当末位链为T 时，错配的引发效率大大降低，G、C错配的引发效率介于A、T之间，所以3′端最好选择T。 6.碱基要随机分布。引物序列在模板内应当没有相似性较高，尤其是3’端相似性较高的序列，否则容易导致错误引发（Falsepriming）。降低引物与模板相似性的一种方法是，引物中四种碱基的分布最好是随机的，不要有聚嘌呤或聚嘧啶的存在。尤其3′端

引物设计的原理和程序

1 引物的设计以及初步筛选引物的设计与初步筛选基本上通过一些分子生物学软件和相关网站来完成的，目前运用软件Primer Premier 5 或美国 whitehead 生物医学研究所基因组研究中心在因特网上提供的一款免费在线PCR引物设计程序 Primer 3来设计引物，再用软件Oligo 6进行引物评估，就可以初步获得一组比较满意的引物。但是对于初学者来说，运用软件和程序来设计引物好象无从着手，其实只要我们掌握了引物设计的基本原则和注意事项，所有问题便迎刃而解。因为无论是软件还是程序，都是以这些基本原则和注意事项为默认标准来进行引物设计的。所以，我们在进行引物设计的时候大可不必在软件和程序的参数上花费过多的时间来思考，如果没有特殊要求我们完全可以把一些参数设为默认值。下面我们主要讨论一下引物设计的原则和注意事项。 ①引物的长度一般为15-30 bp，最好在18~24 bp，因为太短易形成错配(False pr iming) 降低特异性，而太长也会降低特异性，并且降低产量[21。 ②引物在模板内最好具有单一性，也就是说在模板内部没有错配。特别是3’端，一定要避免连续4个以上的碱基互补错配。 ③引物序列的GC 含量最好在40％一60％，且上下游引物序列GC含量的差异不要太大，3’端最后5个碱基最好不要富含GC，特别是连续3个的G或C。 ④DNA双链形成所需的自由能AG，应该以5’端向3’端递减，3’端AG最好不要高于9．0 keaf mol[31。 ⑤避免形成稳定的引物二聚体(Dimer and Cross DimeO 和发夹结构(Hairpin)，AG 高于4．5 keal／mol时易引发上述两种结构的产生。 ⑥引物所在的模板区域应该位于外显子区，最好跨越一个内含子区，这样便于对扩增出来的片段进行功能鉴定和表型分析。 ⑦如果以DNA为模板设计引物，产物长度在100—600 bp比较理想。而以mRNA为模板设计引物时，产物长度在150—300 bp比较理想。 ⑧5’ 端对PCR影响不太大，可以引进修饰位点和标记物[2]。只要掌握了以上原则和注意事项，我们可以在软件和程序设计的一组引物中筛选出几对我们需要的目标引物。Primer Premier 5和Oligo 6可以在https://www.wendangku.net/doc/8015240422.html,/soft/下载，primer3的主页位置在h ttp://https://www.wendangku.net/doc/8015240422.html,。 2 引物的二次筛选引物的二次筛选是指在初次筛选出的几对引物中进一步筛选出适合我们进行特异、高效PCR扩增的那对引物。本步应注意以下两点，一是得到的一系列引物分别在Genebank 中进行回检。也就是把每条引物在比对工具(https://www.wendangku.net/doc/8015240422.html,/blast/) 的bl astnr中进行同源性检索，弃掉与基因组其它部分同源性比较高的引物，也就是有可能形成错配的引物。一般连续10 bp以上的同源有可能形成比较稳定的错配，特别是引物的3’端应避免连续5-6 bp的同源。二是以mRNA为模板设计引物时要先利用生物信息学的知识大致判断外显子与内含子的剪接位点(例如https://www.wendangku.net/doc/8015240422.html,/GENESCAN.html的GENESCA N工具或者GeneParser软，然后弃掉正好位于剪接位点的引物。

microRNA反转和定量引物设计原理、实验方法

microRNA的引物设计以ssc-miR-222-3p为例设计引物，其成熟体序列为：AGCTACATCTGGCTACTGGGTCT 反向引物：每个反向引物的都带有一段固定的序列，可以形成一个茎环，固定的序列为：5，-CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAG-3，在这个序列后加上八个碱基，这八个碱基是ssc-miR-222-3p从后面数八个碱基的反向互补序列，就是CTGGGTCT的反向互补：AGACCCAG ，最后得到的反向引物的序列为 5，-CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAG AGACCCAG -3，正向引物：每个正向引物也带有一段固定的序列，固定的序列为：ACACTCCAGCTGGG 在这个序列后加上于成熟体除后面六个外剩下的碱基序列，成熟体除掉后面六个碱基后序列为：AGCTACATCTGGCTACT 5，-ACACTCCAGCTGGG AGCTACATCTGGCTACT -3， URP：统一反向引物，也是一段固定的序列，TGGTGTCGTGGAGTCG U6引物F-CTCGCTTCGGCAGCACA ，R-AACGCTTCACGAATTTGCGT 使用方法： 1逆转的引物：所有要做的miRNA反向引物的混合，每个10微升 2PCR的引物：50微升的体系，30微升的水，10微升的正向引物，10微升的URP

2．hsa-miR-124（hsmq-0032 引物）推荐退火温度：60℃ hsa-miR-124 扩增曲线示意图hsa-miR-124 融解曲线示意图 3．hsa-miR-125b(hsmq-0034 引物) 推荐退火温度：60℃ hsa-miR-125b 扩增曲线示意图hsa-miR-125b 融解曲线示意图

引物设计步骤与要点

引物设计step by step 1、在NCBI上搜索到目的基因，找到该基因的mRNA，在CDS选项中，找到编码区所在位置，在下面的origin中，Copy该编码序列作为软件查询序列的候选对象。 2、用Primer Premier5搜索引物 ①打开Primer Premier5，点击File-New-DNA sequence, 出现输入序列窗口，Copy目的序列在输入框内（选择As），此窗口内，序列也可以直接翻译成蛋白。点击Primer，进入引物窗口。 ②此窗口可以链接到“引物搜索”、“引物编辑”以及“搜索结果”选项，点击Search按钮，进入引物搜索框，选择“PCR primers”，“Pairs”，设定搜索区域和引物长度和产物长度。在Search Parameters里面，可以设定相应参数。一般若无特殊需要，参数选择默认即可，但产物长度可以适当变化，因为100～200bp的产物电泳跑得较散，所以可以选择300～500bp. ③点击OK，软件即开始自动搜索引物，搜索完成后，会自动跳出结果窗口，搜索结果默认按照评分（Rating）排序，点击其中任一个搜索结果，可以在“引物窗口”中，显示出该引物的综合情况，包括上游引物和下游引物的序列和位置，引物的各种信息等。 ④对于引物的序列，可以简单查看一下，避免出现下列情况：3’不要出现连续的3个碱基相连的情况，比如GGG或CCC，否则容易引起错配。此窗口中需要着重查看的包括：Tm 应该在55～70度之间，GC％应该在45％～55％间，上游引物和下游引物的Tm值最好不要相差太多，大概在2度以下较好。该窗口的最下面列出了两条引物的二级结构信息，包括，发卡，二聚体，引物间交叉二聚体和错误引发位置。若按钮显示为红色，表示存在该二级结构，点击该红色按钮，即可看到相应二级结构位置图示。最理想的引物，应该都不存在这些二级结构，即这几个按钮都显示为“None”为好。但有时很难找到各个条件都满足的引物，所以要求可以适当放宽，比如引物存在错配的话，可以就具体情况考察该错配的效率如何，是否会明显影响产物。对于引物具体详细的评价需要借助于Oligo来完成，Oligo自身虽然带有引物搜索功能，但其搜索出的引物质量感觉不如Primer5. ⑤在Primer5窗口中，若觉得某一对引物合适，可以在搜索结果窗口中，点击该引物，然后在菜单栏，选择File-Print-Current pair，使用PDF虚拟打印机，即可转换为Pdf文档，里面有该引物的详细信息。 3、用Oligo验证评估引物 ①在Oligo软件界面，File菜单下，选择Open，定位到目的cDNA序列（在primer中，该序列已经被保存为Seq文件），会跳出来两个窗口，分别为Internal Stability（Delta G）窗口和Tm窗口。在Tm窗口中，点击最左下角的按钮，会出来引物定位对话框，输入候选的上游引物序列位置（Primer5已经给出）即可，而引物长度可以通过点击Change－Current oligo length来改变。定位后，点击Tm窗口的Upper按钮，确定上游引物，同样方法定位下游引物位置，点击Lower按钮，确定下游引物。引物确定后，即可以充分利用Analyze 菜单中各种强大的引物分析功能了。

LAMP技术原理和引物设计

LAMP原理及引物设计与实例．LAMP引物的设计 LAMP引物的设计主要是针对靶基因的六个不同的区域，基于靶基因3' 端的F3c、F2c和Flc区以及5' 端的Bl、B2和B3区等6个不同的位点设计4种引物。 FIP(Forward Inner Primer)：上游内部引物，由F2区和F1C区域组成，F2区与靶基因3’端的F2c区域互补，F1C区与靶基因5' 端的Flc区域序列相同。 F3引物：上游外部引物(Forward Outer Primer)，由F3区组成，并与靶基因的F3c区域互补。 BIP引物：下游内部引物(Backward Inner Primer )，由B1C和B2区域组成，B2区与靶基因3' 端的B2c区域互补，B1C域与靶基因5' 端的Blc区域序列相同. B3引物：下游外部引物(Backward Outer Primer )，由B3区域组成，和靶基因的B3c区域互补。 2．扩增原理 60-65℃是双链DNA复性及延伸的中间温度，DNA在65℃左右处于动态平衡状态。因此，DNA在此温度下合成是可能的。利用4种特异引物依靠一种高活性链置换DNA聚合酶。使得链置换DNA合成在不停地自我循环。扩增分两个阶段。第1阶段为起始阶段，任何一个引物向双链DNA的互补部位进行碱基配对延伸时，另一条链就会解离，变成单链。上游内部引物FIP的F2序列首先与模板F2c结合（如图B所示），在链置换型DNA聚合酶的作用下向前延伸启动链置换合成。外部引物F3与模板F3c结合并延伸，置换出完整的FIP连接的互补单链（如图C所示）。FIP上的F1c与此单链上的Fl 为互补结构。自我碱基配对形成环状结构（如图C所示）。以此链为模板。下游引物BIP与B3先后启动类似于FIP和F3的合成，形成哑铃状结构的单链。迅速以3' 末端的Fl区段为起点。以自身为模板，进行DNA合成延伸形成茎环状结构。该结构是LAMP基因扩增循环的起始结构。第2阶段是扩增循环阶段。以茎环状结构为模板，FIP与茎环的F2c区结合。开始链置换合成，解离出的单链核酸上也会形成环状结构。迅速以3’末端的B1区段为起点，以自身为模板。进行DNA合成延伸及链置换．形成长短不一的2条新茎环状结构的DNA，BIP引物上的B2与其杂交。启动新一轮扩增。且产物DNA长度增加一倍。在反应体系中添加2条环状引物LF和LB，它们也分别与茎环状结构结合启动链置换合成，周而复始。扩增的最后产物是具有不同个数茎环结构、不同长度DNA的混合物。且产物DNA为扩增靶序列的交替反向重复序列。 https://www.wendangku.net/doc/8015240422.html,MP的特点 LAMP与以往的核酸扩增方法相比具有如下优点： (1)操作简单，LAMP核酸扩增是在等温条件下进行，对于中小医院只需要水浴锅即可，产

甲基化引物探针设计方法

本文叙述了一种用于甲基化分析的探针法定量PCR的引物和探针设计方法，目前用于甲基化检测的引物探针设计工具非常多，都有使用成功的案例，经过初步多方尝试，本文中叙述的为本人认为较为靠谱的方法。Oligo7的优势在于专业，参数详尽且可自由设置，模块化设计，学会后使用便利。专业的活就是要专业的用专业的工具干。

首先是进行序列转换，有较多的在线工具和联机软件都可实现，这里使用https://www.wendangku.net/doc/8015240422.html,/methprimer/，较为简单直观。

直接将目标序列放入如上图的编辑框中，此也可直接用于相关引物的设计，不过本人没使用过，因为不能设计探针。submit后就有转化后的序列信息，如下图：以上详细标记了CpG位置和非CpG位置的C，可直接复制到Word标注使用，下面就可以使用Oligo7利用上边的序列设计引物和探针了，如果是设计非甲基化引物探针，则使用原始序列。

关于引物和探针的一些主要参数，主要参考invtrogen的建议： Primer设计的基本原则： a)引物长度一般在18-35mer。 b)G-C含量控制在40-60%左右。 c)避免近3’端有酶切位点或发夹结构。 d)如果可能避免在3’端最后5个碱基有2个以上的G或C。 e)如果可能避免在3’端最后1个碱基为A。 f)避免连续相同碱基的出现，特别是要避免GGGG或更多G出现。 g)退火温度Tm控制在58-60C左右。 h)如果是设计点突变引物，突变点应尽可能在引物的中间。 T aqMan 探针设计的基本原则： a)T aqMan 探针位置尽可能靠近扩增引物（扩增产物50-150bp），但不能与引物重叠。 b)长度一般为18-40mer 。 c)G-C含量控制在40-80%左右。 d)避免连续相同碱基的出现，特别是要避免GGGG或更多G出现。 e)在引物的5’端避免使用G。 f)选用比较多的碱基C。 g)退火温度Tm控制在68-70℃左右。另：目标变异碱基最好在3’末端或3’末端-1位置，保证扩增特异性，对于甲基化，则最好是C。

引物设计的原理与方法

PCR引物设计的原理及方法阎振鑫S111666(四川大学生命科学学院细胞生物学成都610014) 摘要：自20世纪后期发展了PCR技术以来，PCR已经改变了整个生物学研究的进程。而PCR反应的第一步就是设计引物，引物设计的好坏直接关系到PCR的成败。PCR引物设计有许多的原则必须要遵循：引物与引物之间避免形成稳定的二聚体或发夹结构，引物与模板的序列要紧密互补。引物不能在模板的非目的位点引发DNA聚合反应等。另外，引物的设计方法也越来越多，出现了许多专门的设计软件和网站，如：PrimerPremier5.0等。关键词：PCR 引物原理方法NCBI PrimerPremier5.0 PCR primer design principle and method YanZhenxin (sichuan Univercity, Life science college cell biology chengdu 610014 ) Abstract: When PCR technology was find, PCR has changed all of the program in research of biology. The design of primer is the frist step of PCR. It is relation to the fate of PCR. There are some principals must be obey: dipolymer and hairpin structure must be avoid between different primers. The DNA polymerization reaction should not be triggered at the wrong site. Therefore, there are more and more methods of design primer, include the professional softwares and professional web site. Key word: PCR primer principle NCBI PrimerPremier5.0 聚合酶链式反应(Polymerase chain reaction。PCR)是20世纪后期发展起来的一种体外扩增特异DNA片断的技术。具有快速、简便及高度敏感等优点，能极大地缩短目的基因扩增时间[1]。因此，其一直是生物学者们致力于构建cDNA文库、基因克隆以及表达调控研究的必要前提和基础[2]。PCR的第一步就是引物设计。引物设计的好坏，直接影响了PCR的结果，因此这一步很关键。成功的PCR反应既要高效，又要特异性扩增产物，因此对引物设计提出了较高的要求。引物设计需要注意的地方很多，在大多数情况下，我们都是在知道已知模板序列时进行PCR扩增的。在某些情况比如构建文库的时候也会在不知道模板序列的情况下进行设计。这个时候随机核苷酸序列就与模板不是完全匹配。我们通常指的设计引物都是在已知模板序列的情况下进行。设计的目的是在两个目标间取得平衡：扩增特异性和扩增效率。

引物设计的原理与方法

引物设计的原理与方法 The latest revision on November 22, 2020

PCR引物设计原理及原则

PCR引物设计原理及原则 PCR引物设计原理PCR引物设计的目的是为了找到一对合适的核苷酸片段，使其能有效地扩增模板DNA序列。因此，引物的优劣直接关系 PCR引物设计原理 PCR引物设计的目的是为了找到一对合适的核苷酸片段，使其能有效地扩增模板DNA序列。因此，引物的优劣直接关系到PCR的特异性与成功与否。要设计引物首先要找到DNA序列的保守区。同时应预测将要扩增的片段单链是否形成二级结构。如这个区域单链能形成二级结构，就要避开它。如这一段不能形成二级结构，那就可以在这一区域设计引物。现在可以在这一保守区域里设计一对引物。一般引物长度为15~30碱基，扩增片段长度为100~600碱基对。让我们先看看P1引物。一般引物序列中G+C含量一般为40%~60%。而且四种碱基的分布最好随机。不要有聚嘌呤或聚嘧啶存在。否则P1引物设计的就不合理。应重新寻找区域设计引物。同时引物之间也不能有互补性，一般一对引物间不应多于4个连续碱基的互补。引物确定以后，可以对引物进行必要的修饰，例如可以在引物的5′端加酶切位点序列；标记生物素、荧光素、地高辛等，这对扩增的特异性影响不大。但3′端绝对不能进行任何修饰，因为引物的延伸是从3′端开始的。这里还需提醒的是3′端不要终止于密码子的第3位，因为密码子第3位易发生简并，会影响扩增的特异性与效率。 PCR引物的设计原则： ①引物应用核酸系列保守区内设计并具有特异性。 ②产物不能形成二级结构。 ③引物长度一般在15~30碱基之间。

④G+C含量在40%~60%之间。 ⑤碱基要随机分布。 ⑥引物自身不能有连续4个碱基的互补。 ⑦引物之间不能有连续4个碱基的互补。 ⑧引物5′端可以修饰。 ⑨引物3′端不可修饰。 ⑩引物3′端要避开密码子的第3位。 PCR引物设计的目的是找到一对合适的核苷酸片段，使其能有效地扩增模板DNA序列。如前述，引物的优劣直接关系到PCR的特异性与成功与否。对引物的设计不可能有一种包罗万象的规则确保PCR的成功，但遵循某些原则，则有助于引物的设计。 1.引物的特异性引物与非特异扩增序列的同源性不要超过70%或有连续8个互补碱基同源。 2.避开产物的二级结构区某些引物无效的主要原因是引物重复区DNA二级结构的影响，选择扩增片段时最好避开二级结构区域。用有关计算机软件可以预测估计mRNA的稳定二级结构，有助于选择模板。实验表明，待扩区域自由能（△G°）小于 58.6lkJ/mol时，扩增往往不能成功。若不能避开这一区域时，用7-deaza-2′-脱氧GTP取代dGTP对扩增的成功是有帮助的。 3.长度寡核苷酸引物长度为15~30bp，一般为20~27mer。引物的有效长度：Ln=2（G+C）+（A+T+，Ln值不能大于38，因为>38时，最适延伸温度会超过Taq DNA聚合酶的最适温度（74℃),不能保证产物的特异性。4.G+C含量

PCR引物设计过程

PCR引物设计过程（一）设计引物前应的准备工作： 1．准备载体图谱，大致准备把片断插在那个部分 2．对片断进行酶切分析，确定一下那些酶切位点不能用3．准备一本所买公司的酶的商品目录，便于查酶的各种数据及两种酶是否可以配用（二）引物的结构：5’—保护碱基+酶切位点+引物配对区—3’1．两个酶切位点 2．酶切位点的保护碱基 3．5’端保护碱基 4．3’端保护碱基 5．引物配对区（三）设计引物所要考虑的问题 1．酶切位点两个酶切位点应是载体上的，所连接片断上没有这两个位点，且距离不能太近，否则往往导致两个酶都切不好。因此，两个酶切位点要紧挨在一起，只能切一个，除非恰好是与上面两个酶在一起的酶切位点，最好隔四个核苷酸。且不能有碱基的交叉，比如AGATCTTAAG，这样的位点比较难切。2．酶的选择

最好使用双酶切效率高的，但两个酶切点最好不要是同尾酶（切下来的残基不要互补），否则效果相当于单酶切，最好使用具有共同buffer，且较常用的酶（如hind3，bamh1，ecor1等），这样可以省钱。 3．Tm的计算。 Tm是由互补的DNA区域决定的，而不互补的区域对DNA 的溶解是没有作用的。因此，对于引物的Tm，只有和模板互补的区域对Tm才有贡献。计算Tm时，只计算互补的区域（除非你的酶切位点也与模板互补）。设计引物的时候，先不管5'端的修饰序列，把互补区的Tm控制在55度以上（我喜欢控制在58以上，具体根据PCR的具体情况，对于困难的PCR，需要适当提高Tm），再加上酶切位点和保护碱基，这样的引物通常都是可用的，即使有小的问题，也可以挽回。 Tm温度高的引物就比较容易克服3’发卡、二聚体及3'非特异结合等问题。简单的计算公式可以用2+4的公式。若你计算的Tm值达到了快90 ，不包括酶切位点。引物公司给你发的单子是包括酶切位点的。自己可以再估计一下。如你设计了带酶切位点的引物，总长分别为29、33个碱基，去掉酶切位点和保护碱基，分别为17、21个碱基。引物公司给的单子是70多度，实际用的只有50度，用55度扩的结果也差不多。

PCR引物设计基本思路

PCR引物设计基本思路 1.根据实验需要，确定需要扩增的DNA序列，并知道其CDS区序列（编码结构基因区, 即从起始密码子区至终止密码子区）ncbi网站查询 RBS 149..153 /gene="eryF" CDS 158..1372 /gene="eryF" 1 ggatcccgat cgtgtcggag g aa gaggcc a agtcgcgccg ccc cgaccag ctgctggtgc 61 tgccctggat ctaccgcgac gggttcgtcg aacgcgagca ggagttcctc gctggcggcg 121 gaaagctgat cttcccccta ccccgactgg aagtcgt atg acgaccgttc ccgatctcga 181 aagcgactcc ttccacgtcg actggtaccg cacctacgcc gagctgcgcg agaccgcgcc 241 ggtgacgccg gtgcgcttcc tcggccagga cgcgtggctg gtcaccggct acgacgaggc 301 gaaggccgcg ctgagcgacc tgcgcctgag cagcgacccg aagaagaagt acccgggcgt 361 ggaggtcgag ttcccggcat acctcggttt ccccgaggac gtgcggaact acttcgccac 421 caacatgggc accagcgacc cgccgaccca cacccggctg cgcaagctgg tgtcgcagga 481 gttcaccgtc cgccgcgtgg aggcgatgcg gccccgcgtc gagcagatca ccgcggagct 541 gctcgacgag gtgggcgact ccggcgtggt cgacatcgtc gaccgcttcg cccacccgct 601 gcccatcaag gtcatctgcg agctgctcgg cgtcgacgag aagtaccgcg gggagttcgg 661 gcggtggagc tcggagatcc tggtcatgga cccggagcgg gccgaacagc gcgggcaggc 721 ggccagggag gtcgtcaact tcatcctcga cctggtcgag cgccgccgca ccgagcccgg 781 cgacgacctg ctgtccgcgc tgatcagggt ccaggacgac gatgacggtc ggctcagcgc 841 cgacgagctg acctccatcg cgctggtgct gctgctggcc ggtttcgagg cgtcggtgag 901 cctcatcggg atcggcacct acctgctgct cacccacccg gaccagctcg cgctggtgcg 961 gcgggacccg tcggcgctgc ccaacgccgt cgaggagatc ctgcgctaca tcgctccgcc 1021 ggagaccacc acgcgcttcg ccgcggagga ggtggagatc ggcggtgtcg cgatccccca 1081 gtacagcacg gtgctggtcg cgaacggcgc ggccaaccgc gacccgaagc agttcccgga 1141 cccccaccgc ttcgacgtca cccgcgacac ccgcggccac ctgtcgttcg ggcagggcat 1201 ccacttctgc atgggccggc cgctggccaa gctggagggc gaggtggcgc tgcgggcgct 1261 gttcggccgc ttccccgctc tgtcgctggg aatcgacgcc gacgacgtgg tgtggcggcg 1321 ttcgctgctg ctgcggggca tcgaccacct accggtgcgg ctcgacgga t ga gcacctgg 1381 ctgcggcggt tcggtcctcc cgtcgagcac cgggcgcggc tggtgtgctt cccgcacgcg 1441 ggagccgcgg ccgactccta cctcgacctc gcgcgcgcct tggcgcccga gatcgacgtg 1501 cacgccgtgc agtacccggg gcgccaggac cgccgcgacg aggagcccct gggcaccgcc 1561 ggcgagatcg ccgacgaggt ggccgccgtg ctgcgcgcgt cgggcggcga cggcccgttc 1621 gccctg ttcg ggcacagcat g ggcgcg ttg atcgcctacg agacggcgcg caggctcgaa 1681 cgcgagcccg gcggcgggcc gctgcggctg ttcgtgtccg ggcagaccgc cccgcgcgtg 1741 cacgagcgcc gcaccgacct gcccggcgac gacggtctgg tggacgagct gcgccggctc 1801 ggcaccagcg aggcggcgct ggccgacgag gccctgctcg ccatgtcgct gccggtgctg 1861 cgcgccgact accgcgtgct gcgctcctac gcctgggcgg acggaccacc gctgcgggcc 1921 ggcatcaccg cgctgtgcgg cgacgccgac ccgctgaccg cgaccgggga cgccgagcgc

引物设计的标准操作规程

引物设计的标准操作规程（编号：004） 1、软件使用 1.1 推荐软件：Primer Premier 5.0 1.2 优点：操作简单、显示各种参数改变和二聚体、异二聚体、发夹结构等。 1.3 本地同类软件：DNAClub；Oligo 6.22；Vector NTI Suit；Dnasis；Omiga；Dnastar；DNAMAN (Lynnon Biosoft, Quebec, Canada)。 1.4 网上同类软件：Primer3、JaMBW（European Molecular Biology Laboratory of Heidelberg 开发）。http://210.7 2.11.60网站已引进并调试好这两种软件。 2、推荐操作引物搜索：Primer Premier 5.0、引物评价：Oligo 6.22 3、引物设计的原则首先引物要跟模板紧密结合，其次引物与引物之间不能有稳定的二聚体或发夹结构存在，再次引物不能在别的非目的位点引起DNA聚合反应（即错配）。围绕这几条基本原则，设计引物需要考虑诸多因素，如引物长度（primer length）、产物长度（product length）、序列Tm值(melting temperature)、ΔG值(internal stability)、引物二聚体及发夹结构（duplex formation and hairpin）、错误引发位点（false priming site）、引物及产物GC 含量（composition），有时还要对引物进行修饰，如增加限制酶切点，引进突变等。以使用Oligo 软件分析设计引物为例，笔者总结出以下的要点：3.1 引物的长度一般为18-25bp，引物长度过长会导致延伸温度过高，从而影响DNA聚合酶的效率，上下游引物长度差别最好不要大于3bp。 3.2 引物最好在模板DNA的保守区内设计。 DNA序列的保守区是通过物种间相似序列的比较确定的。可在NCBI上搜索不同物种的同一基因，通过序列分析软件（比如DNAman）比对（Alignment），各基因相同的序列就是该基因的保守区。 3.3 引物3′端不能选择A，最好选择T。引物3′端错配时，不同碱基引发效率存在着很大的差异，当末位的碱基为A时，即使在错配的情况下，也能有引发链的合成，而当末位链为T时，错配的引发效率大大降低，G、C错配的引发效率介于A、T之间，所以3′端最好选择T。 3.4 引物的GC含量一般为40-60%。上下游引物的GC含量不能相差太大。Tm值以接近72℃为宜，上下游引物Tm值同样不应相差过大。 3.5 引物3′端要避开密码子的第3位。如扩增编码区域，引物3′端不要终止于密码子的第3位，因密码子的第3位易发生简并，会影响扩增的特异性与效率。 14