Relevance Vector Machine
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and Michael W. Bevan Yunhai Li, Kee Khoon Lee, Sean Walsh, Caroline Smith, Sophie Hadingham, Karim Sorefan, Gavin Cawley
using a Relevance Vector Machine by microarray analysis and promoter classification Arabidopsis Establishing glucose- and ABA-regulated transcription networks in
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Establishing glucose-and ABA-regulated transcription networks in Arabidopsis by microarray analysis and promoter classification using a Relevance Vector Machine
Yunhai Li,1Kee Khoon Lee,3Sean Walsh,2Caroline Smith,1Sophie Hadingham,1 Karim Sorefan,1Gavin Cawley,3and Michael W.Bevan1,4
1Department of Cell and Developmental Biology and2Computational Biology Department,John Innes Centre,Norwich NR47UH, United Kingdom;3The School of Computing Sciences,University of East Anglia,Norwich NR47TJ,United Kingdom
Establishing transcriptional regulatory networks by analysis of gene expression data and promoter sequences shows great promise.We developed a novel promoter classification method using a Relevance Vector Machine(RVM)and Bayesian statistical principles to identify discriminatory features in the promoter sequences of genes that can correctly classify transcriptional responses.The method was applied to microarray data obtained from Arabidopsis seedlings treated with glucose or abscisic acid(ABA).Of those genes showing>2.5-fold changes in expression level,～70%were correctly predicted as being up-or down-regulated(under10-fold cross-validation),based on the presence or absence of a small set of discriminative promoter motifs.Many of these motifs have known regulatory functions in sugar-and ABA-mediated gene expression.One promoter motif that was not known to be involved in glucose-responsive gene expression was identified as the strongest classifier of glucose-up-regulated gene expression.
We show it confers glucose-responsive gene expression in conjunction with another promoter motif,thus validating the classification method.We were able to establish a detailed model of glucose and ABA transcriptional regulatory networks and their interactions,which will help us to understand the mechanisms linking metabolism with growth in Arabidopsis.This study shows that machine learning strategies coupled to Bayesian statistical methods hold significant promise for identifying functionally significant promoter sequences.
[Supplemental material is available online at https://www.wendangku.net/doc/3515305619.html,.The microarray data from this study have been submitted to ArrayExpress under accession no.E-MEXP-475.]
The identification and understanding of transcriptional regula-tory networks and their interactions are a major challenge in biology,as transcriptional mechanisms contribute to the regula-tion of nearly all cellular processes.The time,location,and levels of gene transcripts are known to be specified by combinations of protein interactions with noncoding sequences surrounding genes,and significant progress is being made in defining protein interactions with regulatory motifs on a whole-genome scale.For example,experiments that localize transcription factor binding sites using chromatin immunoprecipitation to the yeast genome sequence have established pathways of gene regulation involving >100of the141known yeast transcription factors(Lee et al. 2002).However,the multitude of transcription factors and the larger genomes of multicellular organisms make direct experi-mental approaches such as this daunting with current technol-ogy.
Computational methods that define relationships between gene expression levels and putative regulatory sequences in up-stream regions of genes are increasingly used to establish ge-nome-scale transcriptional regulatory networks(Smith et al. 2005).By correlating the frequency of occurrence of known pro-moter motifs in coregulated genes,it has been possible to relate promoter motifs with known functions to transcriptional path-ways in yeast(Bussemaker et al.2001).The clustering of genes that are coregulated during the yeast cell cycle according to their functions and alignment of promoter sequences of clustered genes identified promoter motifs with known regulatory func-tions and novel motifs with predicted functions(Tavazoie et al. 1999).This strategy was extended into a systematic approach analyzing a wide range of gene expression patterns in yeast and Caenorhabditis elegans with frequentist statistical methods for identifying promoter DNA elements and combinations of ele-ments that optimally predict gene expression patterns.From this, the expression of a significant proportion of genes was accurately predicted according to promoter sequences(Beer and Tavazoie 2004).Regulatory modules have been defined in yeast based on coregulated gene expression patterns,and promoters in a signifi-cant number of these modules contained a promoter motif that was a known binding site for a coregulated transcription factor (Segal et al.2003).Subsequent testing of these predictions de-fined the functions of several regulatory proteins and established the power of these approaches.
We are interested in elucidating the transcriptional regula-tory mechanisms integrating carbohydrate availability and hor-mone action in the plant Arabidopsis thaliana(Arabidopsis).Wide-spread changes in cell function in response to carbohydrate sta-tus,such as reduced protein synthesis and the mobilization of alternative substrates for energy supply in response to carbohy-
4Corresponding author.
E-mail michael.bevan@https://www.wendangku.net/doc/3515305619.html,;fax01603450025.
Article published online ahead of print.Article and publication date are at https://www.wendangku.net/doc/3515305619.html,/cgi/doi/10.1101/gr.4237406.
Methods
drate starvation,have been predicted based on microarray analy-sis(Price et al.2004;Thimm et al.2004).These experiments also show that the expression of a wide range of genes is regulated by carbohydrates in Arabidopsis and～25%of the genes represented on the8K Affymetrix chip also responded to both light and sugar treatments(Thum et al.2004).Many of these genes encode en-zymes of primary,secondary,and lipid metabolism,and a code-pendent interaction between light-and sugar-responsive gene expression was identified.These transcriptional responses were also interconnected with ABA-and ethylene-mediated gene expression and growth responses.Interactions between glucose-and ABA-response pathways have been established by the isolation of the ABA biosynthetic mutant aba2and the ABA re-sponse mutant abi4in screens for reduced responses of seedlings to high levels of glucose or sucrose(Arenas-Huertero et al.2000; Huijser et al.2000;Laby et al.2000;Rook et al.2001;Cheng et al.2002).
Learning techniques are used in an increasingly wide variety of biological applications such as microarray analysis(Lavine et al.2004),protein homology detection(Jaakkola et al.1999), function prediction based on annotated sequence(Vinayagam et al.2004),and functional predictions based on transcriptional coexpression(Zhang et al.2004).Supervised learning methods construct a decision rule from a training set of known positive and negative examples and algorithms such as Support Vector Machines(SVM)(Boser et al.1992)learn to discriminate between training examples from each category.SVMs have demonstrated both excellent performance in dealing with sparse and noisy data typically generated by biological experimentation and an ability to deal with high-dimensional data in a computation-ally efficient way(Scholkopf et al.2004).Recently SVM applica-tions have also been used to discriminate between promoter and nonpromoter regions of human DNA(Gangal and Sharma 2005),and to resolve promoter sequences and the positions of transcription initiation sites in plant DNA(Shahmuradov et al. 2005).
Here we describe the use of a Relevance Vector Machine (RVM)(Tipping2000)to classify gene expression according to the composition of promoter sequences.The RVM was used with a Bayesian Automatic Relevance Determination(ARD)(MacKay 1994;Neal1994)prior to select a small subset of promoter motifs for its discriminatory rule to optimally distinguish between regu-lated genes.Unlike correlation-based approaches,which consider the significance of individual features,the RVM considers the significance of a feature in the context of the features already selected,which may be useful in considering the effects of com-binations of features on gene expression.This approach has been successfully used to find a small number of genes whose expres-sion is diagnostic for certain cancer types(Li et al.2002).The discriminatory features selected by the RVM classifier included promoter motifs that had known functions in both glucose-and ABA-activated gene expression and revealed that light-responsive promoter motifs were powerful features for classifying promoters controlling glucose down-regulated gene expression.One motif with no established function in glucose-responsive transcrip-tional responses that was the strongest classifier of glucose up-regulated gene expression was shown experimentally to confer glucose-activated gene expression in stable transgenic lines.The successful application of machine learning algorithms for pro-moter sequence analysis using Bayesian statistical principles es-tablished models of transcriptional pathways regulating glucose-and ABA-mediated gene expression and demonstrated that these methods hold promise for establishing transcriptional regulatory networks in Arabidopsis and other organisms.
Results
Transcript profiling reveals that glucose regulates genes
with diverse functions
Affymetrix ATH1Gene Chips were used to identify glucose-and ABA-regulated genes.Seedlings were grown in liquid culture for7 d on low sugar concentrations(0.5%glucose)and constant light to abrogate diurnal responses.Treatments were designed to re-veal transitions in gene expression from a sugar-restricted condi-tion to a sugar-replete state.After7d of growth,the medium was replaced with glucose-free medium for24h,and then glucose or mannitol was added to3%(w/v).Mannitol,a nontoxic nonme-tabolized sugar,was used as an osmotic control in ABA ex-periments to define the interactions between ABA and3% glucose.Seedlings that had developed the first pair of true leaves(stage1.02)(Boyes et al.2001)were sampled at0,2,4,or 6h after addition of glucose,mannitol,glucose+ABA, or mannitol+ABA.The time course was selected to detect proxi-mal events,to minimize transcriptional changes due to acceler-ated growth and development in response to sugars,and to establish the dynamics of glucose-and ABA-mediated gene ex-pression.
Scatterplots(Supplemental Fig.1)show that>99%of the significantly expressed genes(Present)exhibit<2.5-fold variation in signal intensity between two independent chip hybridiza-tions.Up-or down-regulated genes were defined independently for each time point as those with a statistically significant change in treatment/control pairs(Wilcoxon signed-rank test,P<0.005) (Hubbell et al.2002;Liu et al.2002).Genes with expression ratios of glucose/mannitol and glucose/0h of>2.5-fold or<2.5-fold at one time-course point or more were defined as glucose-inducible genes and glucose-repressible genes,respectively.The genes with expression ratios of ABA+mannitol/mannitol,ABA+mannitol/ 0h,ABA+glucose/glucose and ABA+glucose/0h of>2.5-fold or <2.5-fold at one time-course point or more were defined as ABA-inducible and ABA-repressible genes,respectively.The0-h time point was common to all treatments,and time points for the glucose and mannitol treatments were replicated three times and hybridized independently to ATH1arrays to measure changes over time.This scheme provided three experimental replicates of glucose treatment at each time point and nine experimental rep-licates for defining glucose-regulated genes.The ABA treatments provided a minimum of two experimental replicates for defining ABA-regulated genes.Accordingly,983genes were expressed by >2.5-fold in response to3%glucose,769genes were expressed at 2.5-fold lower levels in response to3%glucose(Supplemental Table1),and692and173genes were identified as ABA inducible and ABA repressible with>2.5-fold change,respectively(Supple-mental Table2).To confirm the microarray expression profile analyses,semiquantitative RT-PCR analysis was performed on the RNA samples used for array analysis.Fifty genes exhibiting expression changes in response to glucose were selected and tested two times.Results from15of the selected genes are shown in Supplemental Figure2.Gene expression patterns revealed by RT-PCR exhibited similar dynamics to those seen in array analy-sis,establishing the reliability of the microarray data.
We categorized glucose-and ABA-regulated genes according to their putative functions based on Arabidopsis Gene Ontology
Li et al.
(GO)annotations in GeneSpring lists,the classification of the Munich Information Centre for Protein Sequencing(MIPS)data-base,pathway analysis defined by AraCyc(Mueller et al.2003) and KEGG(Kanehisa2002),and the literature.Table1shows the significance of finding glucose-and ABA-responsive genes in dif-ferent functional categories calculated using the hypergeometric P-value(Tavazoie et al.1999).ABA down-regulated genes were not considered because of the small number of genes in this category.The functional clusters enriched for glucose up-regulated genes include metabolic pathways and cellular pro-cesses associated with enhanced growth,such as amino acid and nucleotide synthesis,sulfur assimilation,and secondary metabo-lism.Genes involved in protein synthesis were significantly en-riched in the glucose up-regulated set,as were protein targeting genes and abiotic stress proteins including chaperonins and heat-shock proteins,demonstrating that glucose-mediated transcrip-tional regulation mediates a coordinated increase in protein syn-thesis and processing.Glucose down-regulated genes were en-riched in functional categories involved in metabolic responses such as amino acid degradation,gluconeogenesis,and glutare-doxins.The regulation of genes involved in trehalose metabolism was highly significant,consistent with the proposed role of tre-halose6P levels in regulating carbon assimilation(Schluepmann et al.2003).Many genes regulating light responses,such as tran-scription factors,light receptors and signaling proteins were also down-regulated in response to glucose,although in general this diverse functional group was not significantly down-regulated as a whole.The most significant categories of genes regulated by ABA in our conditions included abscisic acid metabolism,sec-ondary metabolism,and carbohydrate degradation pathways. Our quantitative analysis is consistent with recent qualitative microarray analysis showing that glucose treatment regulates a broad range of gene functions(Price et al.2004;Thimm et al. 2004).
Dynamics of glucose-responsive gene expression
Analysis of gene expression profiles during the first6h after addition of glucose or mannitol showed rapid and transient changes in the expression of many genes.A total of469genes were maximally expressed at the2-h time point,and719and628 genes were maximally expressed at the4-h and6-h time points, respectively(Fig.1A).Nearly42%of the induced genes exhibited overlapping expression at the2-h and4-h time points;54%of the genes were maximally expressed at both the4-h and6-h time points,whereas only32%of the glucose-induced genes had over-lapping expression at the2-h and6-h time points(Fig.1A).Fur-thermore,some genes were specifically induced or repressed by glucose at2h,4h,or6h,respectively(Fig.1A,B).At these three time points,～25%of the induced genes had overlapping expres-sion(Fig.1A),whereas45%of the repressed genes exhibited over-lapping expression(Fig.1B),suggesting that there are more dy-
Table1.Functional categorization of glucose-and ABA-responsive genes
Genes in category Regulated P
v Category n k P?log
10 Glucose up-regulated
Sulfur assimilation62168.2256* Translation152337.3065* Abiotic stress45447 6.3331* Nucleotide synthesis18625 6.1691* Protein targeting52448 5.2264* Secondary metabolism methyl transferases195 3.6209* Lipid transfer proteins154 3.2126 Secondary metabolism flavonoid synthesis939 2.6203 Nucleotide sugar transferases709 2.6128 Ribosomal proteins25830 2.5031 Glucose down-regulated
Amino acid degradation14221 6.9827* Trehalose metabolism226 4.9561* Glutaredoxins408 4.7538* Gluconeogenesis63 4.5454* Storage proteins285 3.2793 Ethylene synthesis1098 2.3114 Pentose phosphate pathway193 2.30036 Lipid degradation1139 2.0985 Ubiquitin conjugation374 1.9157 Light-mediated signaling12811 2.4742 ABA up-regulated
Abscisic acid metabolism66137.5126* Secondary metabolism30728 6.7421* General carbohydrate degradation29922 3.6926* Storage and lipid transfer proteins889 3.1427 Lipid metabolism48528 2.9821 Salicylic acid metabolism173 2.6772 P450and degradation enzymes73937 2.4363 Trehalose metabolism223 2.2472
P-values of<0.05(corrected for multiple comparisons using the Bonferroni correction)in each regulated group are indicated with an asterisk:
Glucose up-regulated,143functional categories,?log
10P
v
=3.445.
Glucose down-regulated,116functional categories,?log
10P
v
=3.365.
ABA up-regulated,113functional categories,?log
10P
v
=3.354.
Glucose-and ABA-transcriptional networks in Arabidopsis
namic changes in the expression of glucose-inducible genes com-pared to glucose-repressible genes.
Quality Threshold (QT)clustering was used to divide glu-cose up-regulated genes into 10clusters of 20or more genes that
shared similar expression dynamics (Fig.1C;Supplemental Table 3).Cluster 11(data not shown)contained the remain-ing genes.Cluster 1comprises 281tran-scripts that have similar expression lev-els at 2-h,4-h,and 6-h time-course points and represents the main expres-sion pattern of glucose-inducible genes.Clusters 2and 7exhibited similar pro-files at the 4-h and 6-h time points,while clusters 3and 8are induced pro-gressively.Genes in clusters 4and 9were induced maximally at the 2-h time point,and then the expression level de-creased.Glucose down-regulated genes were classified into nine groups of 20or more genes that shared a similar expres-sion profile (Fig.1D)and one (cluster 10)(data not shown)including the re-maining genes (Supplemental Table 3).
Clusters 4and 9(Fig.1C),which were maximally expressed 2h after glu-cose treatment,contained a large pro-portion of heat-shock,peptidyl prolyl-transferase,and transcription factor and protein kinase genes.Sixteen genes en-coding heat-shock and DNAJ -like pro-teins (Fig.1E;Supplemental Table 3)were maximally induced by glucose at 2h;10heat-shock genes were maximally up-regulated by glucose at 4h;and only one heat-shock gene was induced by glu-cose at 6h,suggesting that expression of heat-shock proteins is rapidly modu-lated in response to glucose.This sug-gests that transiently increased levels of chaperonin activity are required to pro-cess newly synthesized proteins.Among the most rapidly glucose-repressed genes,found in clusters 1,2,and 9(Fig.1D;Supplemental Table 3),were tran-scription factors regulating light re-sponses.These included genes encoding the trihelix DNA-binding proteins GT1
and GT2,the GATA transcription fac-tor 4,GBF1,and AT1g19000,encoding a 1-repeat MYB protein related to MYBST1,which interact with DNA se-quences in many light-responsive gene
promoters (Lam 1995;Puente et al.1996;Chattopadhyay et al.1998;Smalle et al.1998).Genes encoding the blue-light photoreceptors CRY1and CRY2(Lin et al.1995;Ahmad et al.1998;Kleiner et al.1999),the phytochrome A-specific light signaling component EID1(Buche et al.2000;Dieterle et al.2001),phytochrome kinase substrate 1(PKS1)
(Fankhauser et al.1999),and 6–4photolyase (UVR3),which me-diates light-dependent repair of UV-induced damage products (Jiang et al.1997),were all rapidly and persistently repressed by glucose (Supplemental Table 3).Expression of TOC1,APRR5,
and Figure 1.Expression dynamics of glucose-responsive genes.(A )Venn diagrams showing the number
of genes up-regulated by glucose at 2-h,4-h,and 6-h time course points determined by microarray
analyses.Here,248genes were up-regulated by glucose at all time points;353genes were induced by
glucose at both 2h and 4h;475genes were up-regulated by glucose at both 4h and 6h;and 263
genes were glucose inducible at both 2h and 6h.(B )Venn diagrams showing the number of genes down-regulated by glucose at 2-h,4-h,and 6-h time points determined by microarray analyses.Here,
347genes were down-regulated by glucose at all time-course points;416genes were down-regulated
by glucose at both 2h and 4h;449genes were repressed by glucose at both 4h and 6h;and 368
genes were glucose repressible at both 2h and 6h.(C )The expression profiles of glucose-inducible
genes according to Quality Threshold clustering.Cluster number and time course are indicated.(D )The expression profiles of glucose-repressible genes according to Quality Threshold clustering.Cluster number and time course are indicated.(E )The expression profiles of heat-shock genes,starch me-tabolism,phenylpropanoid biosynthesis,N-,P-,and S-assimilation genes,and amino acid biosynthesis
genes are shown.
Li et al.
APRR7genes belonging to the APRR1/TOC1complex controlling
circadian rhythms (Yamamoto et al.2003)was also rapidly glu-cose-repressed (cluster 1)(Fig.1D;Supplemental Table 3),sug-gesting that carbohydrate levels may in-fluence the central oscillator controlling
circadian rhythms.
Glucose treatment led to a rapid and progressive increase in the expres-sion of genes involved in protein syn-thesis,including 32ribosomal proteins,a putative ribosome recycling factor,and translation initiation and elongation factors,which were predominantly found in clusters 1,2,and 3(Fig.1C;Supplemental Table 3).Genes in cluster 3,which are progressively expressed,tend to encode cell cycle and DNA-replication-related proteins such as a putative CDC21protein (AT2G16440),the DNA-replication licensing factor MCM3homolog,replication factor A (AT5G08020),and MCM5and MCM7(PROLIFERA)(Springer et al.2000;Hold-ing and Springer 2002;Moore et al.2003),which ensure fidelity of DNA rep-lication.Clusters 2and 7(Fig.1C),which were maximally expressed at 4and 6h,were enriched for genes encod-ing metabolic enzymes,ribosomal pro-teins,and transporters (Supplemental Table 3).These included genes encoding a putative glucose-6-phosphate translo-cator (AT1G61800)and genes involved in starch biosynthesis enzymes,(AT1G32900),glucose-1-phosphate ad-enylyltransferase (AT2G21590),and A D P g l u c o s e p y r o p h o s p h o r y l a s e (AT2G21590)—were maximally induced at 4h (Fig.1E).Genes involved in sec-ondary metabolism —such as 4-couma-rate:CoA ligase 3(AT1G65060),flavonol synthase (FLS),putative cinnamoyl CoA reductase (AT2G23910),flavonol 4-sul-fotransferase (AT1G18590),flavanoid 3-hydroxylase (FH3),chalcone synthase (CHS ),and cinnamyl-alcohol dehydro-genase (AT5G19440)—were also maxi-mally induced at 4–6h (Fig.1E).Genes involved in sulfur and ammonium as-similation were up-regulated maximally by glucose between 2h and 4h,and genes involved in amino acid biosynthe-sis were also maximally induced by glu-cose at 4h and 6h (Fig.1E).
Glucose-and ABA-responsive
gene expression
Previous genetic analyses have shown that sugar-and ABA-mediated growth responses are closely interconnected in plants (Zhou et al.1998;Rook et al.
2001).Array analysis revealed >14%of the ABA-inducible genes were also induced by glucose,indicating a substantial overlap between glucose-and ABA-regulated gene expression
(Supple-Figure 2.Glucose-and ABA-coregulated genes.(A )Functional classification of genes induced by both glucose and ABA.(B )Expression patterns of the set of 12genes showing synergistic transcriptional
responses to glucose and ABA.Expression patterns of the two genes encoding large subunits of AGPase
(APL3and AT2g21590)in response to glucose and ABA were indicated with a green line and black line,respectively.(C )Transcriptional responses of APL3:GUS promoter fusions to sugar and ABA in stable
Arabidopsis transformants.Samples were taken from 7-d-old seedlings grown on the following media:10mM glucose +90mM mannitol (Mannitol),100mM glucose (Glucose),10mM glucose +90mM mannitol +0.1μM ABA (Mannitol +ABA),and 100mM glucose +ABA (Glucose +ABA).The fold induction compared with the osmotic control (Mannitol)is given.Error bars represent the standard error from 10independent transformants.(D )Response of the APL3promoter to sugar and ABA in Arabidopsis protoplasts.Protoplasts were made from Col or isi37-d-old seedlings.Protoplasts were cultured in the following media:400mM mannitol,400mM glucose,400mM mannitol +10μM ABA and 400mM glucose +10μM ABA.GUS activity was measured and normalized to Luciferase (Luc)activity expressed from the CaMV 35S promoter.The fold induction compared with the osmotic control of each genotype are given.Error bars represent the standard error of the mean from three samples.
Glucose-and ABA-transcriptional networks in Arabidopsis
mental Table4).Several transcriptional regulators of ABA re-sponses were regulated by glucose.The homeodomain leucine zipper(HD-Zip)proteins in Arabidopsis are involved in ABA regu-lation(Himmelbach et al.2002),and expression of ATHB6is up-regulated by glucose(Supplemental Table1),suggesting that sugars may participate in ABA signaling by regulating the expres-sion of ABA-response regulators.
Ninety-five genes were up-regulated by both glucose and ABA.These genes are involved in stress,defense,and senescence responses,secondary metabolism and cell wall biosynthesis, amino acid metabolism,carbohydrate metabolism,fatty acid and lipid metabolism and transport,transcript regulation,and signal transduction(Fig.2A).More than12%of the genes induced by both glucose and ABA are involved in stress responses,indicating overlapping regulation by glucose and ABA.Expression of key regulators of abiotic stress responses such as CBF3,COR15A,and RD29A were induced by both glucose and ABA(Supplemental Table4).Constitutive expression of CBF3in transgenic Arabidop-sis plants induces expression of target COR(cold-regulated)genes to enhance freezing tolerance in nonacclimated plants(Gilmour et al.2000).Expression of COR15A and RD29A is regulated by CBF3,suggesting that both glucose and ABA may contribute to the regulation of cold stress tolerance.In addition,four genes encoding nonspecific lipid-transfer proteins were induced by both glucose and ABA(Fig.2A),consistent with reports that non-specific lipid-transfer proteins are induced by ABA,wounding, and cold stress(Yubero-Serrano et al.2003).Four genes encoding heat-shock proteins were induced by both glucose and ABA (Supplemental Table4).Finally,several genes involved in fatty acid and lipid metabolism are glucose and ABA inducible,reveal-ing the roles of sugar and ABA in lipid metabolism(Fig.2A; Supplemental Table4).
Thirty-seven genes were identified as glucose-and ABA-corepressed genes,including protein kinases,transcription fac-tors,transporters,and enzymes.Two genes(AT4G36670and AT1G08930)encoding putative sugar transporters are down-regulated by both glucose and ABA.Two genes encoding1-ami-nocyclopropane-1-carboxylate oxidase(AT1G77330)involved in ethylene biosynthesis and putative ethylene-responsive element binding factor(AT5G61590)are repressed by both glucose and ABA,revealing that aspects of ethylene biosynthesis and re-sponses are modulated by both glucose and ABA.Genes regulated by glucose and ABA in opposed ways were also analyzed.Genes involved in ammonium assimilation,such as a putative ammo-nium transporter(AT1G64780),were glucose inducible and ABA repressible,and lysine-ketoglutarate reductase(AT4G33150)ex-hibited a decrease of expression level in glucose treatment and an increase of expression level in ABA treatment(Supplemental Table4),suggesting that nitrogen metabolism may provide dif-ferent compounds for stress and growth responses.Finally,the phosphate transporter gene ATPT2(AT2G38940)is up-regulated by glucose and down-regulated by ABA(Supplemental Table4), suggesting that the sugar-replete state may promote uptake and utilization of the phosphate required for carbon metabolism and ABA may repress this process.
Several examples of the synergistic effects of sugar and ABA on gene expression have been reported.For example,expression of the rice myo-inositol-1-phosphate synthase gene RINO1was induced by both sucrose and ABA treatments,and the combina-tion of both sucrose and ABA resulted in much higher expression levels(Yoshida et al.2002).We defined synergistic interactions as those genes expressed at greater than twofold higher levels in response to glucose+ABA treatment compared to the sum of expression levels observed for glucose and ABA+mannitol treat-ments at two or more points in the time course.A set of12genes was in this class(Fig.2B;Supplemental Table5).These encoded proteins that are involved in lipid metabolism and transport, stress and senescence responses,and starch biosynthesis,such as CER1involved in wax biosynthesis,lipid transfer protein gene4 (LTP4),and two senescence-related genes(SAG29and AT1G22160).Two of the genes encoding large subunits of ADP-glucose pyrophosphorylase,the first step in starch biosynthesis (Fig.2B,C;Supplemental Table5),were synergistically regulated, although the APL3subunit was only synergistically regulated at one time point.The synergistic regulation defined by array analy-sis was confirmed by analysis of APL3?GUS promoter reporter gene expression in transgenic Arabidopsis seedlings(Fig.2C,D). The APL3?GUS gene was3.7-fold and2.9-fold induced by sugar and ABA,respectively,and together they exerted a15.6-fold in-duction(Fig.2C).ABI4has been implicated in regulation of the APL3promoter(Rook et al.2001).To test whether ABI4contrib-uted to the synergistic regulation of the APL3promoter,the APL3 promoter?GUS reporter gene was analyzed by transient expres-sion in Arabidopsis protoplasts.Similar synergistic regulation was seen in protoplasts and stable transformants(Fig.2C,D).Expres-sion of the APL3?GUS construct in isi3protoplasts,which are defective in ABI4activity(Rook et al.2001),showed that ABA and glucose synergism was lost(Fig.2D).This showed that ABI4 is involved in the synergistic responses of the APL3promoter to glucose and ABA.
Regulatory gene expression
Glucose treatment led to rapid transient increases in the expres-sion of diverse transcription factors including members of the MYB,bZIP,AP2,homeodomain,NAM-like,and heat-shock tran-scription factor protein families.Expression of MYB75/PAP1/AN2 (Borevitz et al.2000;Stracke et al.2001)and the flower pigmen-tation gene ATAN11were rapidly induced by glucose(Supple-mental Table1).AN2and AN11have been well characterized and encode a MYB-domain transcriptional activator and a WD-repeat protein,respectively(de Vetten et al.1997;Quattrocchio et al. 1999).In petunia flowers,AN2and AN11control flower pigmen-tation by stimulating the transcription of anthocyanin biosyn-thetic genes.Overexpression of PAP1also leads to elevated ex-pression of anthocyanin biosynthetic genes(Borevitz et al.2000), suggesting that glucose may promote expression of phenylpro-panoid biosynthetic genes by elevating expression of these MYB transcription factors and ATAN11.The MYB transcript factor gene ATR1,which activates tryptophan gene expression in Ara-bidopsis(Bender and Fink1998),was also up-regulated by glu-cose,suggesting that glucose may increase expression of trypto-phan biosynthetic genes by activating expression of ATR1.Ex-pression of several MADS-box and WRKY-like family members was down-regulated by glucose.The expression of a WRKY class transcription factor(AT5g07100)encoding a protein related to sweet potato SPF1(Kim et al.1997)was reduced in response to glucose.SPF1binds SP8a and SP8b promoter sequences of spora-min and beta-amylase genes expressed in storage roots of sweet potato,and reduced expression of SPF1mRNA levels induced sporamin and beta-amylase expression(Ishiguro and Nakamura 1994).Our analysis suggests that AT5g07100may modulate sugar-regulated gene expression in Arabidopsis by a similar mechanism.
Li et al.
Identification and analysis of promoter motifs
Promoter sequences comprising～1000bp upstream of the pre-dicted ATG initiation codon of all Arabidopsis genes predicted in the TIGR version5annotation(Haas et al.2005)were assembled. Responsive genes were defined as those showing>2.5-fold changes at the2-h,4-h,and6-h time points in response to glu-cose or ABA compared to control treatments.The set of983glu-cose up-regulated promoters was compared with769glucose down-regulated promoters and a set of692ABA up-regulated promoters was compared to a set of647promoters showing no responses to ABA.Matrices of(983+769)promoters regulated by glucose,and381experimentally defined plant transcriptional regulatory sequences established in the PLACE database(Higo et al.1999)were assembled for feature extraction.Matrices of (692+647)ABA-regulated and nonregulated promoters and PLACE elements were also assembled,and features were extracted from both strands of the promoters.Similar matrices were also made with a set of all1024(45)possible5-mers in an unbiased search for promoter motifs.5-mers were chosen because4-mers occurred too frequently to provide discriminatory power,while 6-mers may be too selective.These features served as input into a feature space by the RVM to construct classifiers of gene expres-sion based on either PLACE elements or k-mer sequences.
These classifiers were tested in a10-fold cross-validation procedure that partitioned the data into10disjoint subsets of approximately equal size.A model was then trained using nine segments as the training data and tested on the unused segment. This procedure was repeated10times,each time using a different combination of nine segments to form the training data,such that all10segments were used as test data for a different model. The average test set performance was reasonably stable after10 trials;therefore,a10-fold cross-validation provided a good esti-mate of model performance.
Classification accuracy was displayed in the Receiver-Operator Characteristic(ROC)curves shown in Figure3,A and B.
These show the sensitivity of classification compared to the specificity,or the true-positive rate versus the false-positive rate. The area under the ROC curve shows an optimum classification rate of～74%for both the k-mer and PLACE element features, indicating a robust performance.Only features that were selected in every fold of the cross-validation procedure were selected. These features were then ranked according to the magnitude of their weights over the10-folds of the cross-validation procedure, and the top75%are displayed in Tables2and4.The top-ranked classifiers were seven PLACE elements for glucose up-regulated promoters,seven PLACE elements for glucose down-regulated promoters(Table2),and nine PLACE elements for ABA-up regu-lated genes(Table4).We identified13k-mers as top-ranking classifiers of glucose up-regulated genes and13k-mers as classi-fiers of glucose down-regulated genes(Table3).Some of the k-mer motifs match PLACE elements identified as effective classi-fiers.Three of the highest-ranking k-mer motifs in glucose up-regulated genes had perfect matches to top-ranked PLACE elements:ACCCT matched the TELO-box PLACE element, TAGGT matched the MYB26S PLACE element,and CGGCA matched the E2FBNTRNR PLACE element.A single mismatch of the GGGAG5-mer motif was found in the AMMORESIIUDCR NIA1element.Among the k-mers associated with glucose down-regulated gene expression,GGATA perfectly matched the MYBST1motif and the known sugar-repressible motif(TATCCA) and the OSRAMY3D motif(TATCCAY)(Hwang et al.1998;Lu et al.1998,2002).The GATAA sequence is the IBOXCORE and the GATA factor binding site,TATCT is found in the EVENINGGAT element,and CGTGG is the core of G-box-type motifs such as LRENPCABE.Some k-mer features that were strong classifiers of glucose-regulated genes do not match functionally defined PLACE elements,suggesting that they may have novel functions in sugar regulation.
The hypergeometric probability distribution function was used to assess the enrichment of these motifs in the promoters of genes in various functional categories.Supplemental Table6 shows that many of the motifs were significantly enriched in the promoters of genes found in functional classes involved in glu-cose and ABA responses.These relationships were also consistent with the known functions of these promoter motifs in regulating different cellular functions.
The TELO motif,the top-ranked classifier of glucose-induced genes,was originally identified in promoters of genes encoding components of the translational machinery(Tremou-saygue et al.1999).Consistent with this,our analysis shows it is significantly enriched in the promoters of protein and nucleotide synthesis genes(Supplemental Table6).The BS1EGCCR and MYB26PS motifs have been implicated in the regulation of phen-ylpropanoid biosynthesis genes(Uimari and Strommer1997;La-combe et al.2000),and these were enriched in glucose-regulated carbohydrate metabolism and sulfate-uptake genes.The DRECRTCOREAT motif mediates stress responses(Dubouzet
et Figure3.ROC(Receiver Operating Characteristic)curves of RVM per-formance in classifying glucose-and ABA-regulated genes.(A)The ROC curves of glucose-regulated genes show the proportion of true positives selected by the RVM versus false positives.The performance is shown by the area under the ROC curve.PLACE element features(blue line)and k-mer features(pink line).A random selection is shown by the green line.
(B)The ROC curves of ABA-up-regulated genes show the proportion of true positives selected by the RVM versus false positives.The performance is shown by the area under the ROC curve.PLACE element features(blue line)and k-mer features(pink line).A random selection is shown by the green line.
Glucose-and ABA-transcriptional networks in Arabidopsis
al.2003),and this motif was enriched in the promoters of abiotic stress-related genes.The PLACE elements that were top-ranking classifiers of glucose down-regulated gene expression,such as the I-box,the EVENINGAT,MYBST1,and the G-box-related motif,all have established functions in regulating light-and sugar-related gene expression.For example,the G-box-related element LRENPCABE was previously shown to repress gene expression by sugars (Hwang et al.1998;Lu et al.1998).The MYBST1motif,TATCC,is very similar to the known sugar-repression motifs (TATCCA)and OSRAMY3D (TATCCAY)(Hwang et al.1998;Lu et al.1998,2002),suggesting that TATCC is a core of motifs con-ferring sugar repression.Supplemental Table 6shows these mo-tifs are significantly enriched in the promoters of genes involved in catabolic responses,abiotic stress,and trehalose and jasmo-nate metabolism.
PLACE elements that were strong classifiers of ABA up-regulated promoters (Table 4)were also significantly enriched in classes of genes known to be regulated by ABA,such as stress responses,ABA biosynthesis,carbohydrate breakdown,and phenylpropanoid synthesis (Supplemental Table 6).Many of these PLACE elements have been shown to confer ABA-and stress-responsive gene expression,such as ABARELATERD1,AB AREATRD22,MYB1AT,and DRE2COREZMRAB17(Busk and Pages 1998).Recently these ABRE motifs and the DRE element were also identified as overrepresented sequences in ABA-up-regulated genes (Leonhardt et al.2004).Ten k -mer motifs were top-ranking classifiers of ABA up-regulated promoters (Table 5).ACGTG,the most significant motif,forms the core of ABRE LATERD1,ABREATRD22,and ACGTATBREMOTFA2OSEM;CGTGT is the core of ABREMOTIFAOSOSEM;CGTGG is the core of ABREATRD22;and CGTAC is the core of ABRE3HVA22.
The TELO motif was the best classifier of glucose up-regulated expression.It is required,together with other elements such as the TEF,trap40,and IIa/IIb elements,for high-level ex-pression in actively dividing cells in root meristems (Tremou-saygue et al.1999,2003;Manevski et al.2000).Figure 4A shows that promoters containing the TELO motif are maximally ex-pressed 4h after glucose addition.Inspection of the 222glucose up-regulated promoters containing the TELO motif revealed that all contained the motif CATAAT,which forms the core of the 16-bp TEF motif.Moreover,the performance of classifiers of glu-cose up-regulated expression that included both the TELO motif and all 5-mers was improved by 5-mer motifs AGGGG,GGGCA,CATAA,and ATAAT,which comprise 11of the 16-nt TEF motifs (data not shown).We tested the function of the TELO motif in conferring glucose-responsive gene expression using stable trans-genic lines.Oligonucleotide tetramers of TELO4and TEF4motifs and the combined motif TEF1TELO3,which included one TEF sequence and three TELO sequences,were inserted 5?to a mini-mal ?60CaMV promoter (Fig.4B,C).These promoters were
Table 3.RVM selection of 5-mer motifs in glucose-regulated genes Recognition sequence Number of times picked by RVM
Average weight Up-regulated accct 10 2.4528gggag 10 1.9367agtga 10 1.6673gagaa 10 1.3825attaa 10 1.3428gaata 10 1.1516gaatc 10 1.0956taggt 10 1.0248aatag 100.927aatgt 100.8825cggca 100.8763accgt 100.7836actct
100.7546Down-regulated ggata 10?4.4913gataa 10?3.0703tatct 10?2.0260catcc 10?1.2789aagat 10?1.0549caatg 10?1.014aatcc 10?0.959gatta 10?0.947gactc 10?0.913catcg 10?0.839cacac 10?0.82cgtgg 10?0.773gaccc
10
?0.721
Table 2.RVM selection of PLACE elements in glucose-regulated genes Element ID
Number of times picked by RVM
Average weight Recognition sequence Potential target genes (from Supplemental Table S6)Up-regulated
TELOBOXATEEF1AA110 2.9895aaaccctaa Ribosomal proteins,protein synthesis AMMORESIIUDCRNIA110 1.3346ggwagggt Nucleotide metabolism QARBNEXTA 10 1.1033aacgtgt No significant categories
BS1EGCCR 100.9637agcggg Carbohydrate metabolism enzymes E2FBNTRNR 100.9067gcggcaaa Protein synthesis
MYB26PS
100.8397gttaggtt Carbohydrate metabolism,S uptake DRECRTCOREAT 100.8076rccgac Abiotic stress Down-regulated IBOXCORENT 10?3.3202gataagr Trehalose synthesis IBOXCORE 10?2.1431gataa Abiotic stress
IBOX 10?0.7107gataag MYBST110?3.2140ggata Trehalose synthesis Abiotic stress
Amino acid degradation
LRENPCABE 10?1.1698acgtggca Carbohydrate,lipid and amino acid metabolism
GARE2OSREP110?0.9167taacgta Secondary metabolism
EVENINGAT
10
?0.8441
aaaatatct
Jasmonate synthesis,abiotic stress
Li et al.
fused upstream of the GUS reporter gene,inserted in a binary vector,and used to obtain transgenic Arabidopsis plants.For each construct,～100independent transgenic plants were tested.We observed that the TEF1TELO3promoter specifically conferred glucose-responsive expression of GUS activity in root meristems of transgenic plants (Fig.4D,E,F).These results were consistent with previous studies showing that the TELO motif was required for GUS expression in root meristems and this activation required the TEF element (Tremousaygue et al.1999).Quantitative analy-sis of GUS expression in TEF1TELO3?GUS transgenic plants showed 6.9-fold higher GUS activity in response to glucose com-pared to mannitol treatment (Fig.4G).These results indicated that the TELO motif,the best classifier of glucose-up-regulated promoters,participates in the control of glucose-responsive gene expression in a cooperative manner with the TEF motif.
Discussion
Dynamic transcriptional responses to glucose
Glucose and ABA treatments lead to rapid dynamic changes in gene expression in Arabidopsis seedlings.Quantitative analysis of gene function and clustering of gene expression dynamics iden-tified patterns of coregulation of classes of genes that revealed large-scale changes in cell function in response to glucose and ABA.Among the most rapid transient transcriptional responses to glucose involved the up-regulation of genes encoding heat-shock and DNAJ-like chaperonin proteins.Genes encoding com-ponents of protein synthesis were also rapidly induced,but their expression persisted,suggesting a temporal control the cellular machinery for protein synthesis that involves rapid initial syn-thesis of chaperonins for stabilizing newly synthesized proteins and longer-term expression of components involved in protein synthesis.Transcription factors and protein kinase genes were among the most rapidly modulated by glucose.Rapidly up-regulated genes in these classes included those encoding tran-scription factors regulating biosynthetic pathways such as MYB75/PAP1,ATR1,MYB28,and JAF13.This is consistent with these transcription factors mediating subsequent more persistent expression of many genes encoding enzymes,transporters,and other proteins involved in the reprogramming of biosynthetic and catabolic pathways.This is supported by the identification of cognate transcription-factor-binding sites as strong classifiers of glucose up-regulated expression of these classes of genes (see be-low).Among the rapidly induced and persistently expressed genes were those functioning in the cell cycle,cell division,DNA replication and recombination,and in growth.These rapid re-sponses,which occur before any significant growth or develop-ment,suggest that glucose-mediated transcriptional responses directly orchestrate cell division and growth.One of the most striking responses to glucose was the rapid and persistent down-regulation of transcription factors regulating light responses and regulators of the circadian clock.Longer-term cellular responses to high sugar include suppression of photogene expression (Jang et al.1997),and our analysis suggests a mechanism involving the rapid down-regulation of transcription factors conferring light-responsive expression of photogenes.This proposed mechanism is supported by the identification of cognate promoter elements that are strong classifiers of glucose down-regulated expression (see below).How these major changes in gene expression are regulated remains to be elucidated.A large number of genes were coregulated by glucose and ABA,including key regulators of ABA action such as ATHB6(Himmelbach et al.2002)and a diverse set of genes involved in signal transduction and transcription,stress responses,and metabolism.Furthermore,several genes involved in ethylene-mediated gene expression were also coregulated by ABA and glucose,identifying regulatory points for three-way in-teractions between these growth regulators (Yanagisawa et al.2003;Price et al.2004).
Regulatory mechanisms
Our application of machine learning methods for promoter clas-sification linked known transcription factors and their cognate binding sites into a model of glucose-and ABA-mediated gene expression and revealed new glucose-mediated transcriptional control mechanisms.The TELO promoter motif was identified by the RVM as the strongest classifier of glucose up-regulated gene expression.It was found in >200of the 983glucose-up-regulated genes and was significantly enriched in the promoters of genes encoding components of protein and nucleotide synthesis path-ways (Supplemental Table 6).The TELO motif and the associated
Table 5.RVM selection of 5-mer motifs in ABA-regulated genes Recognition sequence Number of times picked by RVM
Average weight acgtg 10 4.5791cgtgt 10 3.8186cgtgg 10 1.9653cgtac 10 1.8358ccgac 10 1.7705cacac 10 1.7430gaaca 10 1.7009atatc 10 1.4722gatac 10 1.1721ccatc
10
1.0937
Table 4.RVM selection of place elements in ABA-regulated genes Element ID Number of times picked by RVM
Average weight Recognition sequence Potential target genes (from Supplemental Table S6)ABRELATERD1
10 4.4946acgtg Abiotic stress,phenylpropanoid and ABA metabolism,carbohydrate breakdown ABREATRD22
10 3.0406ryacgtggyr Abiotic stress
ACGTABREMOTIFA2OSEM 10 2.6213acgtgkc Phenylpropanoid metabolism
DRE2COREZMRAB1710 2.0471accgac Abiotic stress,raffinose metabolism ACGTATERD110 1.7750acgt Phenylpropanoid metabolism MYB1AT
10 1.6179waacca DPBFCOREDCDC310 1.3682acacnng ABREMOTIFAOSOSEM 10 1.3261tacgtgtc Abiotic stress
SGBFGMGMAUX28
10
1.2677
tccacgtgtc
Glucose-and ABA-transcriptional networks in Arabidopsis
TEF motif conferred increased gene expression in response to glucose,thus establishing a new role for this element and vali-dating the feature extraction and classification strategy.The TELO motif,together with the adjacent TEF sequence in the eEF1A promoter,was previously shown to direct high-level ex-pression in rapidly cycling primordia (Tremousaygue et al.1999).Recently,the TELO motif was shown to be overrepresented in the promoters of genes up-regulated during axillary bud outgrowth in Arabidopsis ,such as ribosomal protein and cell cycle genes (Tatematsu et al.2005).Together these data demonstrate a key role for the TELO motif in regulating the expression of genes in response to growth stimuli such as glucose and decapitation.The MYB26S and BS1EGCCR motifs,which are enriched in genes involved in carbohydrate metabolism and sulfur uptake (Supplemental Table 6),were previously shown to regulate genes in the phenylpropanoid pathway (Uimari and Strommer 1997;Lacombe et al.2000).The E2FBNTRNR motif is enriched in protein synthesis genes,consistent with experimental evi-dence (Chaboute et al.2000),and the AMMORESIIUDCRNIA1motif involved in the transcriptional control of the ni-trate reductase gene (Loppes and Radoux 2001)was enriched in nucleotide me-tabolism genes.This model proposes that glucose may either regulate the transcription of genes encoding tran-scription factors that then activate these classes of genes,or glucose promotes the activity of transcription factors by post-transcriptional mechanisms.The cyclo-heximide dependence of glucose up-regulated expression (Price et al.2004)is consistent with the former mechanism.
Several examples of possible regula-tory chains (Yu et al.2003)involved in glucose-down-regulated gene expression were evident from the promoter features described in Tables 2and 3.Four motifs involved in conferring light regulation (Puente et al.1996),the I-box core mo-tif,the GATA motif,light regulatory mo-tifs related to the evening element,and a G-box-related element were all top-weighted classifiers of glucose-down-regulated gene expression (Table 2).GBF1binds the G-box and confers light regulation,and the down-regulation of GBF1in response to glucose suggests that glucose-down-regulates light-re-sponsive gene expression by reducing
expression of GBF1(Supplemental Table 1).Glucose down-regulates the expres-sion of GATA4expression (Supplemen-tal Table 1),which encodes a GATA tran-scription factor.This binds the se-quences GGATA and GATAA (Puente et
al.1996),the top-weighted k -mer mo-tifs for classifying glucose-down-regu-lated expression and establishes another
putative regulatory chain.Glucose also down-regulates the expression of
AT1G19000(Supplemental Table 1)encoding a 1repeat MYB protein related to MYBST1.This transcription factor binds to the
GGATA motif and I-box-related sequences (Lu et al.2002),which are also top-weighted classifiers of glucose down-regulated ex-pression.This suggests another transcriptional regulatory chain contributing to glucose-mediated transcriptional repression of light-regulated genes.Expression of genes encoding the trihelix proteins GT1and GT2,which confer light activation (Lam 1995),was also reduced by glucose treatment (Supplemental Table 1),but their cognate GT promoter elements were not selected as classifiers by the RVM.This analysis provides potential mecha-nisms linking glucose-and light-mediated gene expression sug-gested by earlier analyses (Thum et al.2004).The promoter of the Amy3D ?-amylase gene contains a TATCCA-and a G-box-related motif required for repression by sugars or induction by sugar starvation (Hwang et al.1998;Lu
et
Figure 4.The TELO motif confers glucose-mediated transcriptional regulation.(A )Expression pat-terns of the glucose-up-regulated genes with promoters containing the TELO motif.(B )Sequences of the TELO4,TEF4,and TEF1TELO3motifs.(C )Constructs containing the TELO4,TEF4,and TEF1TELO3
motifs in a ?60CaMV ?GUS reporter vector are shown.An oligonucleotide tetramer of TELO (TELO4)
and TEF (TEF4)motifs and a combined motif (TEF1TELO3)containing one TEF sequence and three TELO sequences were inserted upstream of the ?60CaMV ?GUS reporter construct.(D,E,F )Histo-chemical analysis of GUS activity of TEF1TELO3?GUS transgenic plants in response to 3%glucose (D ),3%mannitol (E ),and water (F )for 12h.GUS activities in lateral root primordia are shown.(G )GUS
activity of TEF4?GUS ,TELO4?GUS ,and TEF1TELO3?GUS transgenic plants.Protoplasts made from
7-d-old TEF4?GUS ,TELO4?GUS ,and TEF1TELO3?GUS transgenic plants were cultured in 400mM
glucose or 400mM mannitol for 48h before GUS activity was measured.Error bars represent the standard error of the mean from five samples.These transgenic lines were assayed at least three times.Li et al.
al.1998,2002;Toyofuku et al.1998).Three rice MYB proteins (OsMybS1,OsMybS2,and OsMybS3)bind to the TATCCA ele-ment and mediate these sugar responses.The expression of two Arabidopsis genes(AT1G19000and AT5G47390)encoding MYB proteins with high overall similarity to OsMybS2and OsMybS3is glucose repressible(Supplemental Table1),and the TATCCA-related motif(TATCC)is a strong classifier of glucose down-regulated gene expression.This suggests a third regulatory chain in which these Arabidopsis MYB proteins mediate glucose down-regulated transcription through the TATCC element.
Several cis-acting promoter elements confer ABA-responsive gene expression.These include the ABA-responsive element (ABRE)(Marcotte Jr.et al.1989),coupling elements(Shen et al. 1996),and recognition sites for MYB and MYC classes of tran-scription factors(Iwasaki et al.1995;Abe et al.1997).Our RVM analyses of ABA-responsive promoters identified ABRE-like mo-tifs,recognition sequences for the ATMYB2transcription factor, a G-box-related motif and DRE-related motifs as top-weighted classifiers of ABA-induced genes.These motifs were enriched in the promoters of genes encoding proteins involved in stress re-sponses,secondary metabolism,and hormone metabolism (Table4;Supplemental Table6).Our RVM classification is con-sistent with recently reported analysis of motif frequencies in ABA-regulated genes,which identified ABRE and DRE motifs as overrepresented(Leonhardt et al.2004).The expression of genes encoding ABF3,DREB1A,DREB1B,DREB1C,and DREB2A tran-scription factors,which mediate ABA-responsive gene expression through ABRE-and DRE-related motifs,respectively,was induced by ABA,suggesting a regulator chain model in which these tran-scription factors mediate ABA responsiveness through the motifs identified as strong classifiers of ABA-regulated expression.Simi-larly,expression of ATMYB2is up-regulated by ABA(Supplemen-tal Table2).It has been shown to function as a transcriptional activator in ABA-inducible gene expression under drought stress in plants(Abe et al.2003)and its recognition motif(WAACCA) was a strong classifier of ABA-up-regulated promoters(Table4). The DRE-related motif(ACCGAC)conferred glucose-,ABA-, drought-,high salt-,and cold-responsive gene expression(Busk et al.1997;Kizis and Pages2002;Dubouzet et al.2003).Its cog-nate transcription factor DREB1A/CBF3was also transcription-ally up-regulated by both glucose and ABA,suggesting a regulator chain model for glucose and ABA regulation of stress-responsive and other target genes.
Promoter analysis
A variety of approaches have been taken to establish regulatory networks based on whole-genome analysis of gene expression levels.Many of these use frequentist probabilistic methods to identify overrepresented sequence motifs associated with expres-sion profiles(Beer and Tavazoie2004),which can then be used to infer relationships between motifs and gene expression patterns. Our analysis of promoter sequences uses an RVM classifier to give an estimate of the probability that a gene is up-or down-regulated based on promoter sequence features.The advantage of the RVM(Tipping2001)with a Bayesian Automatic Relevance Determination(MacKay1994;Neal1994)prior is that it selects a small subset of promoter motifs for its discriminatory rule that optimally distinguish between regulated genes.The RVM also has the useful property that no parameters are set,such as the threshold of significance of a feature,since the entire model is generated automatically from the data.It also considers the sig-nificance of a feature in the context of the features already se-lected.This makes the application especially suitable for biologi-cal problems with many variables of unknown significance that may influence each other.The RVM correctly predicted the up-or down-regulation of～70%of the1752promoters in the glucose regulon and692promoters in the ABA-up regulon.This success is similar to that achieved in a recent study(Beer and Tavazoie2004),which correctly predicted the expression pat-terns of73%of2587yeast genes in255conditions using pro-babilistic methods.Our analysis also shows that there are other features affecting gene expression that are not captured by PLACE elements or5-mer sequences within1kb of the initia-tion codon of Arabidopsis genes.These“missing”features prob-ably include combinatorial effects and protein–protein interac-tions.
The promoter sequences selected by the RVM strategy were validated by demonstrating that the TELO motif,which was the top-weighted classifier of glucose-up-regulated gene expression, conferred glucose-mediated expression in conjunction with the TEF motif.Furthermore,other promoter motifs selected as top-weighted classifiers had established functions in glucose-and ABA-mediated gene regulation.The transcriptional coregulation of transcription factors and promoters containing cognate pro-moter elements selected by the RVM provides further validation of the classification strategy and permitted regulatory networks to be established.
The sparse feature selection of our RVM provides a compu-tationally efficient way of dealing with the wide range of vari-ables commonly encountered in biology and is suitable for bi-ologists to apply,as the classification rule is built automatically without any statistical assumptions.Bayesian statistical methods such as we have used also provide more realistic probability mod-els based on these large data sets(Eddy2004).Our work reveals that these approaches have significant promise in classifying pro-moter functions according to their sequence and establishing transcriptional regulatory networks.
Methods
Plant material,growth condition,and time course
Arabidopsis thaliana seedlings(ecotype Columbia-0)were grown in liquid culture for7d on MS medium containing0.5%glucose in constant light.After7d of growth,the medium was replaced with glucose-free medium for24h,and then seedlings were treated with3%glucose,3%mannitol,3%glucose+10μM ABA or3%mannitol+10μM ABA,and sampled at0,2,4, or6h after treatment.Three independent sets of cultures grown in3%glucose and3%mannitol were sampled for RNA isolation.
RNA preparation,cRNA synthesis,and microarray hybridization
Total RNA was extracted from the treated Arabidopsis seedlings using an RNeasy Plant Mini Kit(Qiagen)according to the kit manual.Affymetrix Gene Chip array expression profiling was carried out at the John Innes Genome Lab(http://www. https://www.wendangku.net/doc/3515305619.html,)according to Affymetrix Expression Analysis Technical Manual II(Affymetrix Manual II;http://www. https://www.wendangku.net/doc/3515305619.html,/support/technical/manuals.affx).Further infor-mation on processing microarray data and clustering is provided in the Supplemental material.
Glucose-and ABA-transcriptional networks in Arabidopsis
Machine learning methods
The Relevance Vector Machine(RVM)(Tipping2001)was se-lected as the most appropriate technique for learning to distin-guish between up-and down-regulated genes according to the sequence composition of their promoter regions.A MATLAB implementation of the RVM is available from http://www. https://www.wendangku.net/doc/3515305619.html,.
Assume that our data set,D,is comprised of?coregulated genes
D=??x?i,t i??i=1?,x?i∈?d,t i∈??1,+1?
where x?i represents a set of features describing the i-th training pattern,in this case k-mers representing putative promoter pro-tein-binding sites,and t i indicates whether the i-th gene is up-regulated(t i=+1)or down-regulated or nonregulated(t i=?1). The Relevance Vector Machine,in a statistical pattern recogni-tion setting,essentially implements a familiar logistic regression model,
p?t|x??≈
1
1+exp??f?x???
where f?x??=?i=1??i x i+?o
However,a Bayesian training algorithm was used,with an Auto-matic Relevance Determination(ARD)(MacKay1994;Neal1994) prior over the vector of model parameters,??={?0,?1,?2,…,??}. The advantage of this approach was that the model was able to determine a small set of the most discriminatory features to form its decision rule.In this application it chooses,from a large set of arbitrary motifs,a small number of motifs that“optimally”dis-tinguish between differentially regulated genes.A more extensive explanation is provided in the Supplemental material,and the method is available as a Web service for Arabidopsis promoter analysis from https://www.wendangku.net/doc/3515305619.html,/～gcc/cbl/bred/,using the TIGR version5annotation(Haas et al.2005).
Calculating enrichment in functional categories
To ascribe functions to genes represented on the ATH1chip, Gene Ontology(GO)annotations were integrated within Gene-Spring6.1(Silicon Genetics,Redwood City,CA)as“GeneLists.”This was achieved by converting the Gene Ontology graph struc-tures as exported from DAG-Edit(GO flat-file format,http:// https://www.wendangku.net/doc/3515305619.html,/)into a file-system-based data structure, where vertices are represented by directories.A list of Arabidopsis genes annotated to each GO term was prepared from the TIGR version5XML files(ftp://https://www.wendangku.net/doc/3515305619.html,/pub/data/a_thaliana/ath1/ PSEUDOCHROMOSOMES/),and each list was stored in Gene-Spring XML format within the appropriate directory.We classi-fied sugar-regulated genes according to their putative functions based on Arabidopsis Gene Ontology(GO)annotations in Gene-Spring lists,the classification of the Munich Information Centre for Protein Sequencing(MIPS)database,pathway analysis de-fined by AraCyc(Mueller et al.2003)and KEGG(Kanehisa2002), and the literature.
We calculated the P-value of the enrichment of regulated genes and promoter elements in functional categories using the hypergeometric cumulative distribution function(Tavazoie et al. 1999).Values were expressed as?log10of P,where at least x genes in category of size k were regulated.k was determined from gene annotations as described above.The total number of genes on the array(M)was21,000,and the total numbers of regulated genes were glucose up-regulated genes(N=983),glucose down-regulated(N=769),and ABA up-regulated(N=692).The Bon-ferroni Correction was used to establish the significance of mul-tiple comparisons of functional categories.Functional categories containing fewer than five genes were not considered for statis-tical reasons,and larger and heterogeneous functional groups were also not included in the analysis.
Construction of synthetic promoter motifs,Arabidopsis transformation,and?-glucuronidase(GUS)assays
Promoter motifs were synthesized,annealed into double-stranded DNA oligomers,cloned into a minimal promoter-reporter cassette,and transformed into Arabidopsis as described in the Supplemental material.Transformants were selected and assayed as described in the Supplemental material. Acknowledgments
We thank Georg Harberer and Klaus Mayer(MIPS,GSF,Munich) for an initial version of the promoter database,James Hadfield of the John Innes Genome Laboratory for advice on RNA isolation and Affymetrix array processing,and members of the Bevan group for advice.This work was supported by BBSRC Exploiting Genomics Grants EGM16126and EGM16128to M.W.B.and G.C.,respectively,and EC grant QLRT-1999-00351(PlaNET)to M.W.B.
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ps中各个工具的作用
Ps中各个工具的作用 1、移动工具，可以对PHOTOSHOP里的图层进行移动图层。 2、矩形选择工具，可以对图像选一个矩形的选择范围，一般对规则的选择用多。 3、单列选择工具，可以对图像在垂直方向选择一列像素，一般对比较细微的选择用。 4、裁切工具，可以对图像进行剪裁，前裁选择后一般出现八个节点框，用户用鼠标对着节点进行缩放，用鼠标对着框外可以对选择框进行旋转，用鼠标对着选择框双击或打回车键即可以结束裁切。 5、套索工具，可任意按住鼠标不放并拖动进行选择一个不规则的选择范围，一般对于一些马虎的选择可用。 6、多边形套索工具，可用鼠标在图像上某点定一点，然后进行多线选中要选择的范围，没有圆弧的图像勾边可以用这个工具，但不能勾出弧度。 7、磁性套索工具，这个工具似乎有磁力一样，不须按鼠标左键而直接移动鼠标，在工具头处会出现自动跟踪的线，这条线总是走向颜色与颜色边界处，边界越明显磁力越强，将首尾连接后可完成选择，一般用于颜色与颜色差别比较大的图像选择。 8、魔棒工具，用鼠标对图像中某颜色单击一下对图像颜色进行选择，选择的颜色范围要求是相同的颜色，其相同程度可对魔棒工具双击，在屏幕右上角上容差值处调整容差度，数值越大，表示魔棒所选择的颜色差别大，反之，颜色差别小。 9、喷枪工具，主要用来对图像上色，上色的压力可由右上角的选项调整压力，上色的大小可由右边的画笔处选择自已所须的笔头大小，上色的颜色可由右边的色板或颜色处选择所须的颜色。 10、画笔工具，同喷枪工具基本上一样，也是用来对图像进行上色，只不过笔头的蒙边比喷枪稍少一些。 11、铅笔工具，主要是模拟平时画画所用的铅笔一样，选用这工具后，在图像内按住鼠标左键不放并拖动，即可以进行画线，它与喷枪、画笔不同之处是所画出的线条没有蒙边。笔头可以在右边的画笔中选取。 12、图案图章工具，它也是用来复制图像，但与橡皮图章有些不同，它前提要求先用矩形选择一范围，再在"编辑"菜单中点取"定义图案"命令，然后再选合适的笔头，再在图像中进和行复制图案。 13、历史记录画笔工具，主要作用是对图像进行恢复图像最近保存或打开图像的
ps基本工具介绍初学者必看解析
广军影视2015-11-15 初学者必看 工具介绍 1、移动工具：可以对PS里的图层进行移动。 2、 矩形选框工具：可以对图像选择一个矩形的选择范围 单列选框工具：可以在图像或图层中绘制出1个像素高的横线或竖线区域，主要用于修复图像中丢失的像素。 椭圆选框工具：可以对图片选择一个椭圆或正圆的选择范围。【椭圆变正圆：按着shift 画圆为正圆；按shift+alt是从中心点出发往外画正圆】 3、【取消选区：ctrl+d 或菜单栏【选择】--取消选择】 套索工具：可以用来选区不规则形状的图像【在图像适当的位置单机并按住鼠标左键，拖曳鼠标绘制出需要的选区，松开鼠标左键，选区会自动封闭】 有羽化50所以看到的效果为圆选区） 属性栏红框：为选择方式选项【相加、相减、交叉】。 黄框：用于设定边缘的羽化程度。 白框：用于清除选区边缘的锯齿。 多边形套索工具：可以用来选取不规则的多边形图像(属性与套锁工具相同)【没有圆弧的图像沟边可以用这个工具，但不能勾出弧度】 【使用套索工具选区时，按enter键封闭选区。按ESC键取消选区，按delete键，删除上一个单击建立的选区点。】
磁性套索工具：可以用来选取不规则的并与背景反差大的图像【不须按鼠标而直接移动鼠标，在工具头处会出现自动跟踪的线，这条线总是走向颜色与颜色边界处，边界越明显磁力越强，将首尾相接后可完成选择】 属性：“宽度”选项用于设定套索检测检测范围，磁性套索工具将在这个范围内选取反差最大的边缘。“对比度”选项用于设定选取边缘的灵敏度，数值越大，则要求边缘与背景的反差越大。“频率”选项用于设定选区点的速率，数值越大，标记速率越快，标记点越多。 频率57 频率71 对比度10% 对比度50% 4、 魔棒工具：可以用来选取图像中的某一点，并将与这一点颜色相同或相近的点自动融入选区中。【直接在图像上单击就会出现选区】
ps工具作用介绍大全
ps工具作用介绍大全 位图：又称光栅图，一般用于照片品质的图像处理，是由许多像小方块一样的"像素"组成的图形。由其位置与颜色值表示，能表现出颜色阴影的变化。在PHOTOSHOP主要用于处理位图 矢量图：通常无法提供生成照片的图像物性，一般用于工程持术绘图。如灯光的质量效果很难在一幅矢量图表现出来。 分辩率：每单位长度上的像素叫做图像的分辩率，简单讲即是电脑的图像给读者自己观看的清晰与模糊，分辩率有很多种。如屏幕分辩率，扫描仪的分辩率，打印分辩率。 图像尺寸与图像大小及分辩率的关系：如图像尺寸大，分辩率大，文件较大，所占内存大，电脑处理速度会慢，相反，任意一个因素减少，处理速度都会加快。 通道：在PHOTOSHOP中，通道是指色彩的范围，一般情况下，一种基本色为一个通道。如RGB颜色，R为红色，所以R通道的范围为红色，G为绿色，B为蓝色。 图层：在PHOTOSHOP中，一般都是多是用到多个图层制作每一层好象是一张透明纸，叠放在一起就是一个完整的图像。对每一图层进行修改处理，对其它的图层不含造成任何的影响。 图像的色彩模式： 1）RGB彩色模式：又叫加色模式，是屏幕显示的最佳颜色，由红、绿、蓝三种颜色组成，每一种颜色可以有0-255的亮度变化。 2）CMYK彩色模式：由青色Cyan、洋红色Magenta、禁用语言Yellow。而K取的是black最后一个字母，之所以不取首字母，是为了避免与蓝色(Blue)混淆，又叫减色模式。一般打印输出及印刷都是这种模式，所以打印图片一般都采用CMYK模式。 3）HSB彩色模式：是将色彩分解为色调，饱和度及亮度通过调整色调，饱和度及亮度得到颜色和变化。4）Lab彩色模式：这种模式通过一个光强和两个色调来描述一个色调叫a，另一个色调叫b。它主要影响着色调的明暗。一般RGB转换成CMYK都先经Lab的转换。 5）索引颜色：这种颜色下图像像素用一个字节表示它最多包含有256色的色表储存并索引其所用的颜色，它图像质量不高，占空间较少。 6）灰度模式：即只用黑色和白色显示图像，像素0值为黑色，像素255为白色。 7）位图模式：像素不是由字节表示，而是由二进制表示，即黑色和白色由二进制表示，从而占磁盘空间最小。 ___________工____________具_____________用_____________法_____________ 移动工具，可以对PHOTOSHOP里的图层进行移动图层。 矩形选择工具，可以对图像选一个矩形的选择范围，一般对规则的选择用多。 单列选择工具，可以对图像在垂直方向选择一列像素，一般对比较细微的选择用。 裁切工具，可以对图像进行剪裁，前裁选择后一般出现八个节点框，用户用鼠标对着节点进行缩放，用鼠标对着框外可以对选择框进行旋转，用鼠标对着选择框双击或打回车键即可以结束裁切。 套索工具，可任意按住鼠标不放并拖动进行选择一个不规则的选择范围，一般对于一些马虎的选择可用。
PS基本用法工具介绍
PS基本用法工具介绍 它是由Adobe公司开发的图形处理系列软件之一，主要应用于在图像处理、广告设计的一个电脑软件。最先它只是在Apple机（MAC）上使用，后来也开发出了forwindow的版本。 一、基本的概念。 位图：又称光栅图，一般用于照片品质的图像处理，是由许多像小方块一样的"像素"组成的图形。由其位置与颜色值表示，能表现出颜色阴影的变化。 在PHOTOSHOP主要用于处理位图。 矢量图：通常无法提供生成照片的图像物性，一般用于工程持术绘图。如灯光的质量效果很难在一幅矢量图表现出来。 分辩率：每单位长度上的像素叫做图像的分辩率，简单讲即是电脑的图像给读者自己观看的清晰与模糊，分辩率有很多种。如屏幕分辩率，扫描仪的分辩率，打印分辩率。 图像尺寸与图像大小及分辩率的关系：如图像尺寸大，分辩率大，文件较大，所占内存大，电脑处理速度会慢，相反，任意一个因素减少，处理速度都会加快。 通道：在PHOTOSHOP中，通道是指色彩的范围，一般情况下，一种基本色为一个通道。如RGB颜色，R为红色，所以R通道的范围为红色，G为绿色，B为蓝色。 图层：在PHOTOSHOP中，一般都是多是用到多个图层制作每一层好象是一张透明纸，叠放在一起就是一个完整的图像。对每一图层进行修改处理，对其它的图层不含造成任何的影响。 二、图像的色彩模式 1）RGB彩色模式：又叫加色模式，是屏幕显示的最佳颜色，由红、绿、蓝三种颜色组成，每一种颜色可以有0-255的亮度变化。 2）、CMYK彩色模式：由品蓝，品红，品黄和黄色组成，又叫减色模式。 一般打印输出及印刷都是这种模式，所以打印图片一般都采用CMYK模式。 3）、HSB彩色模式：是将色彩分解为色调，饱和度及亮度通过调整色调，饱和度及亮度得到颜色和变化。 4）、Lab彩色模式：这种模式通过一个光强和两个色调来描述一个色调叫a，另一个色调叫b。它主要影响着色调的明暗。一般RGB转换成CMYK 都先经Lab的转换。 5）、索引颜色：这种颜色下图像像素用一个字节表示它最多包含有256色的色表储存并索引其所用的颜色，它图像质量不高，占空间较少。 6）、灰度模式：即只用黑色和白色显示图像，像素0值为黑色，像素255为白色。
PS工具作用介绍 及 快捷键大全
基本术语了解： 位图：又称光栅图，一般用于照片品质的图像处理，是由许多像小方块一样的"像素"组成的图形。由其位置与颜色值表示，能表现出颜色阴影的变化。在PHOTOSHOP主要用于处理位图 矢量图：通常无法提供生成照片的图像物性，一般用于工程持术绘图。如灯光的质量效果很难在一幅矢量图表现出来。 分辩率：每单位长度上的像素叫做图像的分辩率，简单讲即是电脑的图像给读者自己观看的清晰与模糊，分辩率有很多种。如屏幕分辩率，扫描仪的分辩率，打印分辩率。 图像尺寸与图像大小及分辩率的关系：如图像尺寸大，分辩率大，文件较大，所占内存大，电脑处理速度会慢，相反，任意一个因素减少，处理速度都会加快。 通道：在PHOTOSHOP中，通道是指色彩的范围，一般情况下，一种基本色为一个通道。如RGB颜色，R为红色，所以R通道的范围为红色，G为绿色，B为蓝色。 图层：在PHOTOSHOP中，一般都是多是用到多个图层制作每一层好象是一张透明纸，叠放在一起就是一个完整的图像。对每一图层进行修改处理，对其它的图层不含造成任何的影响。 图像的色彩模式： 1）RGB彩色模式：又叫加色模式，是屏幕显示的最佳颜色，由红、绿、蓝三种颜色组成，每一种颜色可以有0-255的亮度变化。 2）CMYK彩色模式：由青色Cyan、洋红色Magenta、禁用语言Yellow。
而K取的是black最后一个字母，之所以不取首字母，是为了避免与蓝色(Blue)混淆，又叫减色模式。一般打印输出及印刷都是这种模式，所以打印图片一般都采用CMYK模式。 3）HSB彩色模式：是将色彩分解为色调，饱和度及亮度通过调整色调，饱和度及亮度得到颜色和变化。 4）Lab彩色模式：这种模式通过一个光强和两个色调来描述一个色调叫a，另一个色调叫b。它主要影响着色调的明暗。一般RGB转换成CMYK 都先经Lab的转换。 5）索引颜色：这种颜色下图像像素用一个字节表示它最多包含有256色的色表储存并索引其所用的颜色，它图像质量不高，占空间较少。 6）灰度模式：即只用黑色和白色显示图像，像素0值为黑色，像素255为白色。 7）位图模式：像素不是由字节表示，而是由二进制表示，即黑色和白色由二进制表示，从而占磁盘空间最小。 工具用途： 移动工具，可以对PHOTOSHOP里的图层进行移动图层。 矩形选择工具，可以对图像选一个矩形的选择范围，一般对规则的选择用多。 单列选择工具，可以对图像在垂直方向选择一列像素，一般对比较细微的选择用。 裁切工具，可以对图像进行剪裁，前裁选择后一般出现八个节点框，用户用鼠标对着节点进行缩放，用鼠标对着框外可以对选择框进行旋转，用鼠标对着选择框双击或打回车键即可以结束裁切。 套索工具，可任意按住鼠标不放并拖动进行选择一个不规则的选择范围，一般对于一些马虎的选择可用。
Photoshop基本操作介绍(图文介绍)
第一课：工具的使用 一、Photoshop 简介： Adobe 公司出品的Photoshop 是目前最广泛的图像处理软件，常用于广告、艺术、平面设计等创作。也广泛用于网页设计和三维效果图的后期处理，对于业余图像爱好者，也可将自己的照片扫描到计算机，做出精美的效果。总之，Photoshop 是一个功能强大、用途广泛的软件，总能做出惊心动魄的作品。 二、认识工具栏 1、 选框工具 ：用于选取需要的区域 ----选择一个像素的横向区域 属性栏： 注：按shift 键+框选，可画出正方形或正圆形区域 2、移动工具： -----用于移动图层或选区里的图像 3、套索工具： ----用于套索出选区 ----用于套索出多边形选区 ----可根据颜色的区别而自动产生套索选区 4、魔术棒工具： ----根据颜色相似原理，选择颜色相近的区域。 注：“容差”，定义可抹除的颜色范围，高容差会抹除范围更广的像素。 5、修复工具： 且是 ----类似于“仿制图工具”，但有智能修复功能。 选区相减
----用于大面积的修复 一新 ----用采样点的颜色替换原图像的颜色 注：Alt+鼠标单击，可拾取采样点。 6、仿制图章工具----仿制图章工具从图像中取样，然后您可将样本应用到其它图像或同一图像的其它部分。 ----仿制图章工具从图像中取样，然后将样本应用到其它图像或同 一图像的其它部分（按Alt键，拾取采样点）。 ----可先自定义一个图案，然后把图案复制到图像的其它区域或其它图像上。 三、小技巧： ①、取消选区：【Ctrl＋D】 ②、反选选区：【Shif+F7】 ③、复位调板：窗口—工作区—复位调板位置。 ④、ctrl+[+、-]=图像的缩放 ⑤空格键：抓手工具 ⑥Atl+Delete = 用前景色填充 Ctrl+Delete = 用背景色填充 第二课：工具的使用二 一、工具栏 二、小技巧 1、自由变换工具：【Ctrl 、使用框选工具的时候，按【
PS CC 面及工具介绍
工具栏： 1.移动工具，可以对PHOTOSHOP里的图层进行移动图层。 2.矩形选框工具，可以对图像选一个矩形的选择范围，一般对规则的选择用多。 3.椭圆选框工具，可以对图像选一个矩形的选择范围，一般对规则的选择用多。 4.单行选框工具，可以对图像在水平方向选择一行像素，一般对比较细微的选择用。 5.单列选框工具，可以对图像在垂直方向选择一列像素，一般对比较细微的选择用。 6.?套索工具，可任意按住鼠标不放并拖动进行选择一个不规则的选择范围，一般对于一些马虎的选择可用。 7.?多边形套索工具，可用鼠标在图像上某点定一点，然后进行多线选中要选择的范围，没有圆弧的图像勾边可以用这个工具，但不能勾出弧线，所勾出的选择区域都是由多条线组成的 8.磁性套索工具，这个工具似乎有磁力一样，不须按鼠标左键而直接移动鼠标，在工具头处会出现自动跟踪的线，这条线总是走向颜色与颜色边界处，边界越明显磁力越强，将首尾连接后可完成选择，一般用于颜色与颜色差别比较大的图像选择。 9.魔棒工具，用鼠标对图像中某颜色单击一下对图像颜色进行选择，选择的颜色范围要求是相同的颜色，其相同程度可对魔棒工具双击，在辅助工具栏上容差值处调整容差度，数值越大，表示魔棒所选择的颜色差别大，反之，颜色差别小。 10.快速选择工具，选择快速选择工具后我们可以调节工具的大
小，（工具大的话选择的快一些，小的话可以更精准）。根据处理的图片选择快速选择工具的大小。如果多选或者少选可以添加选区，从选区减去。 11.裁切工具，可以对图像进行剪裁，前裁选择后一般出现八个节点框，用户用鼠标对着节点进行缩放，用鼠标对着框外可以对选择框进行旋转，用鼠标对着选择框双击或打回车键即可以结束裁切。 12.吸管工具，主要用来吸取图像中某一种颜色，并将其变为前景色，一般用于要用到相同的颜色时候，在色板上又难以达到相同的可能，宜用该工具。用鼠标对着该颜色单击一下即可吸取。 13.画笔工具，用来对图像进行上色，主要用来对图像上色，上色的压力可由右键的选项调整压力，上色的大小可由右边的画笔处选择自已所须的笔头大小，上色的颜色可由右边的色板或颜色处选择所须的颜色。 14.铅笔工具，主要是模拟平时画画所用的铅笔一样，选用这工具后，在图像内按住鼠标左键不放并拖动，即可以进行画线，它与画笔不同之处是所画出的线条没有蒙边。笔头可以在右边的画笔中选取。 15.仿制图章工具，主要用来对图像的修复用多，亦可以理解为局部复制。先按住Alt键，再用鼠标在图像中需要复制或要修复取样点处单击一左键，再在右边的画笔处选取一个合适的笔头，就可以在图像中修复图像。 16.?图案图章工具，它也是用来复制图像，但与橡皮图章有些不同，它前提要求先用矩形选择一范围，再在”编辑”菜单中点取”定义图案”命令，然后再选合适的笔头，再在图像中进和行复制图案。 17.?历史记录画笔工具，主要作用是对图像进行恢复图像最近保存或打开图像的原来的面貌，如果对打开的图像操作后没有保存，使
Photoshop工具箱中各工具的名称及功能介绍
Photoshop工具箱中各工具的名称及功能介绍： 一、选择工具组 1、矩形选框工具：选择该工具可以在图像中创建矩形选区。按住“Shift”键拖动光标，可创建出正方形选区。 2、移动工具：移动选区的图像部分，如果没有建立选区，则移动的是整幅图像。 3、套索工具：用这个工具可以建立自由形状的选区。 4、魔棒工具：这个工具自动地以颜色近似度作为选择的依据，适合选择大面积颜色相近的区域。如果想选定不相邻的区域，按住“Shift”键对其它想要增加的部分单击，得以扩大选区。 5、裁切工具：可用来切割图像，选择使用该工具后，先在图像中建立一个矩形选区，然后通过选区边框上的控制句柄（边线上的小方块）来调整选区的大小，按下“Enter”键，选择区域以外的图像将被切掉，同时Photoshop会自动将选区内的图像建立一个新文件。按“Esc”键可以取消操作。使用该工具时，光标会变成按钮上的图标样子。 6、切片工具：可以在Photoshop6.0中切割图片并输出和将切割好的图片转移至ImageReady重进行更多的操作。 二、着色编缉工具组： 1、喷枪工具：用来绘制非常柔和的手绘线。 2、画笔工具：用来绘制比较柔和的线条。 3、橡皮图章工具：这是自由复制图像的工具。选择该工具后，按住“Alt”键单击图像某一处，然后在图像的其他地方单击鼠标左键，即可将刚才光标所在处的图像复制到该处。如果按住鼠标左键不放拖动光标，则可将复制的区域扩
大，在光标的旁边会有一个十字光标，用来指示你所复制的原图像的部位。（注意：可以在同是地打开的几个图像之间进行这种自由复制。） 4、历史记录画笔工具：使用该工具时，按住鼠标左键，在图像上拖动，光标所过之处，可将图像恢复到打开时的状态。当你对图像进行了多次编辑后，使用它能够将图像的某一部分一次恢复到初始状态。 5、橡皮擦工具：能把图层擦为透明，如果是在背景层上使用此工具，则擦为背景色。 6、模糊工具：用来减少相邻像素间的对比度，使图像变模糊。使用该工具时，按住鼠标左键拖动光标在图像上涂抹，可以减弱图像中过于生硬的颜色过渡和边缘。 7、减淡工具：拖动此工具可以增加光标经过之处图像的亮度。 三、专用工具组： 1、渐变工具：用逐渐过渡的色彩填充一个选择区域，如果没有建立选区，则填充整幅图像。 2、油漆桶工具：用前景颜色填充选择区域。 3、直接选择工具：用来调整路径上锚点的位置的工具。使用时光标变成箭头样。 4、文字工具：用来向图像中输入文字。 5、钢笔工具：路径勾点工具，勾画出首尾相接的路径。（注意：路径并不是图像的一部分，它是独立于图像存在的，这点与选区不同。利用路径可以建立复杂的选区或绘制复杂的图形，还可以对路径灵活地进行修改和编辑，并可以在路径与选区之间进行切换。） 6、矩形工具：选择此工具，拖到光标可画出矩形。 7、吸管工具：将所取位置的点的颜色作为前景色，如同时按住“Alt”键，则选取背景色。使用时，光标会变成按钮上标示的图标样。
ps中各种工具的介绍
1、选框工具共有4种包括【矩形选框工具】、【椭圆选框工具】、 【单行选框工具】和【单列选框工具】。它们的功能十分相似，但也有各自不同的特长。 矩形选框工具 使用【矩形选框工具】可以方便的在图像中制作出长宽随意的矩形选区。 操作时，只要在图像窗口中按下鼠标左键同时移动鼠标， 拖动到合适的大小松开鼠标即可建立一个简单的矩形选区了。 椭圆选框工具 使用【椭圆选框工具】可以在图像中制作出半径随意的椭圆形选区。 它的使用方法和工具选项栏的设置与【矩形选框工具】的大致相同。 单行选框工具：使用【单行选框工具】可以在图像中制作出1个像素高的单行选区. 单列选框工具：与【单行选框工具】类似，使用【单列选框工具】可以在 图像中制作出1个像素宽的单列选区。 2、套索工具:快捷键:L 套索工具也是一种经常用到的制作选区的工具， 可以用来制作折线轮廓的选区或者徒手绘画不规则的选区轮廓。 套索工具共有3种，包括：套索工具、多边形套索工具、磁性套索工具。 套索工具 使用【套索工具】，我们可以用鼠标在图像中徒手描绘， 制作出轮廓随意的选区。通常用它来勾勒一些形状不规则的图像边缘。 多边形套索工具 【多边形套索工具】可以帮助我们在图像中制作折线轮廓的多边形选区。 使用时，先将鼠标移到图像中点击以确定折线的起点， 然后再陆续点击其它折点来确定每一条折线的位置。 最后当折线回到起点时，光标下会出现一个小圆圈， 表示选择区域已经封闭，这时再单击鼠标即可完成操作。 3、魔棒工具:快捷键:W 【魔棒工具】是Photoshop中一个有趣的工具， 它可以帮助大家方便的制作一些轮廓复杂的选区，这为我们了节省大量的精力。 该工具可以把图像中连续或者不连续的颜色相近的区域作为选区的范围， 以选择颜色相同或相近的色块。魔棒工具使用起来很简单， 只要用鼠标在图像中点击一下即可完成操作。 【魔棒工具】的选项栏中包括：选择方式、容差、消除锯齿、连续的和用于所有图层 ⑴选择方式：使用方法和原理与【矩形选框工具】提到的一样，这里就不再介绍了。 ⑵容差：用来控制【魔棒工具】在识别各像素色值差异时的容差范围。 可以输入0～255之间的数值，取值越大容差的范围越大；相反取值越小容差的范围越小。
ps的工作界面的介绍
ps的工作界面的介绍（界面介绍） 界面的组成 photoshop的界面是由以下6个部分组成的。 标题栏 标题栏左边显示photoshop的标志和软件名称。右边三个图标分别是最小化、最大化和关闭按钮。菜单栏 photoshop菜单栏包括文件、编辑、图像等9个菜单。 工具属性栏 主要用来显示工具箱中所选用工具的一些延展的选项。选择不同的工具时出现的相应选项也是不同的，具体内容在工具箱介绍中详细讲解。 工具箱 对图像的修饰以及绘图等工具，都从工具箱中调用。几乎每种工具都有相应键盘快捷键，工具箱很想画家的画箱。 调板窗 用来存放不常用的调板。调板在其中只显示名称，点击后才出现整个调板，这样可以有效利用空间。防止调板过多挤占了图像的空间。 浮动调板（调板区） 用来安放制作需要的各种常用的调板。也可以称为浮动面板或面板。 其余的区域称为工作区，用来显示制作中的图像。Photoshop可以同时打开多幅图像进行制作，图像之间还可以互相传送数据。 除了菜单的位置不可变动外，其余各部分都是可以自由移动的，我们可以根据自己的喜好去安排界面。并且调板在移动过程中有自动对齐其他调板的功能，这可以让界面看上去比较整洁。 ★.标题栏 标题栏左边显示软件标志和软件名称，通过标题栏可以确认软件的版本。
标题栏右边是最小化，最大化和关闭按钮。 ★.菜单栏 菜单栏是Photoshop CS2的重要组成部分，和其他应用程序一样，Photoshop CS2根据图像处理的各种要求，将所有的功能命令分类后，分别放在9个菜单中，如下图所示，在其中几乎可以实现Photoshop的全部功能。 ★.工具属性栏 在默认状态下，Photoshop CS2中的工具属性栏位于菜单栏的下方，在其中可详细设置所选工具的各种属性。选择不同的工具或者进行不同的操作时，其属性栏中的内容会随之变化。 ★.工具箱 用phtoshop处理图像，首先要熟悉工具的使用。工具面板如下图。 根据工具的作用和特性，可分为： 1.选择与切割类； 2.编辑类； 3.矢量与文字类； 4.辅助工具，四大类,中间用分隔栏分开. 此外我们发现在一部分工具图标的右下角有个黑色的小箭头，这表示这里是一个相类似的工具的集合，用鼠标按下一个工具的按钮不放稍停一下，就会展开下级菜单，显示该集合的全部工具。
PS基本工具详解
【PS基本工具详 解】 01.选框工具---快捷键【M】 01-1.问：如何快速切换选框工具列表？ 答：按住键盘“ALT”键，鼠标左击选框工具。 01-2.问：圆形选框或矩形选框如何画出正圆形或正方形选区？ 答：按住键盘“shift”键，鼠标左键在画布拖动即可。 01-3.问：如何精确设定选区的大小？ 答：在选框工具状态下，属性栏中样式一栏，选择固定比例或者固定大小，然后在其后的宽高设置中输入固定数值，再点击画布即可。 01-4.问：选框工具的属性栏中，羽化是干嘛的？ 答：羽化就是对选区边缘进行模糊处理，使其内外衔接有个自然的过渡。在创建选区之前设置好羽化数值，创建后就能看到效果。 01-5.问：矩形选框的属性栏中，消除锯齿项为什么是灰色不能用的？ 答：因为矩形选框都是直线的，所以不存在锯齿。其选项只有在椭圆选框状态下才可以勾选。 01-6.问：单行选框和单列选框是干嘛的？ 答：这两个工具是为了方便选择一个像素的行和列而设置的，多用于线条绘制。 01-7.问：为什么用了单行单列选框在画布上操作，画布却不显示选区？填充也看不见？ 答：这是因为画布大小设置过大，一像素的选区或者填充后的内容与画布的比例过大，所以显示不了，这时只要放大画布就能显示了。 01-8.问：如何重复制作单列或单行选区？
答：按住键盘“SHIFT”键，在画布不同位置上左键点击即可。 01-9.问：选框工具做好的选区大小不适合，能不能自由更改选区大小？ 答：当然可以。点击“选择-变换选区”就能自由变换选区的大小，调整合适后，确认即可。 01-10.问：如何退出选框工具创建的虚线选区？ 答：快捷键" Ctrl+D "即可。 02.选择工具---快捷键【V】 02-1.问：什么是选择工具？ 答：选择工具，顾名思义是用来移动一个图层或者移动选中的内容。 02-2.问：选择工具如何快速复制？ 答：在选择工具状态下，按住键盘"ALT"键，就可以实现快速复制图层或者选中内容。 02-3.问：当图层内容很多的时候，如何用选择工具快速选择想要的画面而不是每次都要点选图层才能够做移动编辑操作？ 答：选择“选择工具”，勾选属性栏的“自动选择”，就能够快速选中鼠标点选的画面。 03.套索工具---快捷键【L】 03-1.问：用套索工具做出选区抠出来的图有很多的锯齿怎么办？ 答：可以在使用套索之前，在属性栏的“羽化”项里设置好羽化数值，再使用套索，就能平滑边缘锯齿。 03-2.问：用套索创建选区后有多余的选区部分或者没有选到的部分，要怎么处理？ 答：套索工具状态下，按住键盘“ALT”键来减去多余的选区，按住键盘“SHIFT”键来添加不够的选区。 03-3.问：用磁性套索创建选区的时候不好控制，老是产生错误的偏离的边界点，怎么办？ 答：使用磁性套索工具时，可以结合DELETE删除错误的边界点再单击重新创建。 03-4.问：磁性套索工具属性栏中的宽度、边对比度和频率有什么作用？ 答：1>宽度。数值框可以输入0-40之间的数值，对于某一给定的数值，
photoshop(基本功能介绍)
PS面板介绍大全 “色板”面板：该面板用于保存经常用的颜色。单击相应的色块，该颜色就会内指定为前景色。 “通道”面板该面板用于管理颜色信息或者利用通道指定的选区。主要用于创建Alpha通道及有效管理颜色通道。 “图层”面板：在合成若干个图像时使用该面板。该面板提供图层的创建和删除功能，并且可以设置图像的不透明度、图层蒙版等。 “信息”面板：该面板以数值形式显示图像信息。将鼠标的光标移动到图层上，就会显示相关信息。 “颜色”面板：用于设置背景色和前景色。颜色可通过拖动滑块指定，也可以通过输入相应颜色值指定。 “样式”面板：用于制作立体图标。只要单击鼠标即可制作出一个用特性的图像。 “直方图”面板：在该面板中可以看到图像的所有色调的分布情况，图像的色调分为最亮的区域（高光）、中间区域（中间色调）和暗淡区域（暗调）三部分。 “字符样式”面板：在该面板中可以对文字进行字体、符号、文字间距特殊效果的设置，字符样式仅作用于选定的字符。 1.“3D”面板：可以为图像制作出立体可见的效果。选择3D图层后，“3D”面板中会显示与之关联的3D文件组件，面板的顶部列出了文件中的场景、网络、材质和光源，面板底部显示了在面板顶部选择的3D组件的相关选项。
2.“动作“面板：利用该面板可以一次完成多个操作过程。记录操作顺序后，在其他图像上可以一次性应用整个过程。 3.“导航器”面板：通过放大或缩小图像来查找指定区域。利用视图框便于搜索大图像。 4.“测量记录”面板：可以为记录中的列重新排序，删除行或列，或者将记录中的数据导出到逗号分隔的文件中。 5.“段落”面板：利用该面板可以设置与文本段落相关选项。可调整行间距，增加缩进或减少缩进等。 6.“调整”面板：该面板用于对图像进行破坏性的调整。 7.“仿制源”面板：具有用于仿制图章工具或修复画笔工具的选项。可以设置5个不同的样本源并快速选择所需要改为不同的样本源时重新取样。 8.“字符”面板：在编辑或修改文本是提供相关功能的面板。可设置的主要选项有文字大小和间距、颜色、字间距等。 9.“动画”面板：利用该面板便于进行动作操作。 10.“路径”面板：用于将选区转换为路径，或者将路径转换为选区。利用该面板可以应用各种路径相关功能。 11.“历史记录”面板：该面板用于恢复操作过程，将图像操作过程按顺序记录下来。 12.“工具预设”面板：在该面板中可保持常哦那个的工具。可以将相同的工具保存为不同的设置，由此刻提高操作效率。 photoshop里各工具的用法，用途 1、选框工具:快捷键：M 选框工具共有4种包括【矩形选框工具】、【椭圆选框工具】、【单行选框工具】和【单列选框工具】。它们的功能十分相似，但也有各自不同的特长。矩形选框工具使用【矩形选框工具】可以方便的在
PS基本工具介绍
选框工具:快捷键：M 1、选框工具共有4种包括【矩形选框工具】、【椭圆选框工具】、【单行选框工具】和【单列选框工具】。它们的功能十分相似，但也有各自不同的特长。矩形选框工具 使用【矩形选框工具】可以方便的在图像中制作出长宽随意的矩形选区。操作时，只要在图像窗口中按下鼠标左键同时移动鼠标，拖动到合适的大小松开鼠标即可建立一个简单的矩形选区了。 椭圆选框工具 使用【椭圆选框工具】可以在图像中制作出半径随意的椭圆形选区。 它的使用方法和工具选项栏的设置与【矩形选框工具】的大致相同。 单行选框工具：使用【单行选框工具】可以在图像中制作出1个像素高的单行选区. 单列选框工具：与【单行选框工具】类似，使用【单列选框工具】可以在图像中制作出1个像素宽的单列选区。 2、套索工具:快捷键 套索工具也是一种经常用到的制作选区的工具， 可以用来制作折线轮廓的选区或者徒手绘画不规则的选区轮廓。 套索工具共有3种，包括：套索工具、多边形套索工具、磁性套索工具。 套索工具 使用【套索工具】，我们可以用鼠标在图像中徒手描绘， 制作出轮廓随意的选区。通常用它来勾勒一些形状不规则的图像边缘。 多边形套索工具 【多边形套索工具】可以帮助我们在图像中制作折线轮廓的多边形选区。 使用时，先将鼠标移到图像中点击以确定折线的起点， 然后再陆续点击其它折点来确定每一条折线的位置。 最后当折线回到起点时，光标下会出现一个小圆圈， 表示选择区域已经封闭，这时再单击鼠标即可完成操作。 3、魔棒工具:快捷键:W 【魔棒工具】是Photoshop中一个有趣的工具， 它可以帮助大家方便的制作一些轮廓复杂的选区，这为我们了节省大量的精力。该工具可以把图像中连续或者不连续的颜色相近的区域作为选区的范围， 以选择颜色相同或相近的色块。魔棒工具使用起来很简单， 只要用鼠标在图像中点击一下即可完成操作。 【魔棒工具】的选项栏中包括：选择方式、容差、消除锯齿、连续的和用于所有
PS基础工具全面介绍
PS基础工具全面介绍！！！ 位图：又称光栅图，一般用于照片品质的图像处理，是由许多像小方块一样的"像素"组成的图形。由其位置与颜色值表示，能表现出颜色阴影的变化。在PHOTOSHOP主要用于处理位图 矢量图：通常无法提供生成照片的图像物性，一般用于工程持术绘图。如灯光的质量效果很难在一幅矢量图表现出来。 分辩率：每单位长度上的像素叫做图像的分辩率，简单讲即是电脑的图像给读者自己观看的清晰与模糊，分辩率有很多种。如屏幕分辩率，扫描仪的分辩率，打印分辩率。 图像尺寸与图像大小及分辩率的关系：如图像尺寸大，分辩率大，文件较大，所占内存大，电脑处理速度会慢，相反，任意一个因素减少，处理速度都会加快。 通道：在PHOTOSHOP中，通道是指色彩的范围，一般情况下，一种基本色为一个通道。如RGB颜色，R为红色，所以R通道的范围为红色，G为绿色，B为蓝色。 图层：在PHOTOSHOP中，一般都是多是用到多个图层制作每一层好象是一张透明纸，叠放在一起就是一个完整的图像。对每一图层进行修改处理，对其它的图层不含造成任何的影响。 图像的色彩模式： 1）RGB彩色模式：又叫加色模式，是屏幕显示的最佳颜色，由红、绿、蓝三种颜色组成，每一种颜色可以有0-255的亮度变化。 2）CMYK彩色模式：由青色Cyan、洋红色Magenta、禁用语言Yellow。而K取的是black最后一个字母，之所以不取首字母，是为了避免与蓝色(Blue)混淆，又叫减色模式。一般打印输出及印刷都是这种模式，所以打印图片一般都采用CMYK模式。 3）HSB彩色模式：是将色彩分解为色调，饱和度及亮度通过调整色调，饱和度及亮度得到颜色和变化。 4）Lab彩色模式：这种模式通过一个光强和两个色调来描述一个色调叫a，另一个色调叫b。它主要影响着色调的明暗。一般RGB转换成CMYK 都先经Lab的转换。 5）索引颜色：这种颜色下图像像素用一个字节表示它最多包含有256色的色表储存并索引其所用的颜色，它图像质量不高，占空间较少。6）灰度模式：即只用黑色和白色显示图像，像素0值为黑色，像素255为白色。 7）位图模式：像素不是由字节表示，而是由二进制表示，即黑色和白色由二进制表示，从而占磁盘空间最小。 ___________工____________具_____________用_____________法_____________ 移动工具，可以对PHOTOSHOP里的图层进行移动图层。 矩形选择工具，可以对图像选一个矩形的选择范围，一般对规则的选择用多。 单列选择工具，可以对图像在垂直方向选择一列像素，一般对比较细微的选择用。 裁切工具，可以对图像进行剪裁，前裁选择后一般出现八个节点框，用户用鼠标对着节点进行缩放，用鼠标对着框外可以对选择框进行旋转，用鼠标对着选择框双击或打回车键即可以结束裁切。 套索工具，可任意按住鼠标不放并拖动进行选择一个不规则的选择范围，一般对于一些马虎的选择可用。 多边形套索工具，可用鼠标在图像上某点定一点，然后进行多线选中要选择的范围，没有圆弧的图像勾边可以用这个工具，但不能勾出弧度 磁性套索工具，这个工具似乎有磁力一样，不须按鼠标左键而直接移动鼠标，在工具头处会出现自动跟踪的线，这条线总是走向颜色与颜色边界处，边界越明显磁力越强，将首尾连接后可完成选择，一般用于颜色与颜色差别比较大的图像选择。 魔棒工具，用鼠标对图像中某颜色单击一下对图像颜色进行选择，选择的颜色范围要求是相同的颜色，其相同程度可对魔棒工具双击，在屏幕右上角上容差值处调整容差度，数值越大，表示魔棒所选择的颜色差别大，反之，颜色差别小。 喷枪工具，主要用来对图像上色，上色的压力可由右上角的选项调整压力，上色的大小可由右边的画笔处选择自已所须的笔头大小，上色的颜
PS工具介绍
Photoshop工具介绍 一、矩形选择/椭圆形选择（选取物体、限制编辑范围） （1）按Shift，由一边向另一边画正方形或正圆 （2）按Alt，由中心向两侧画对称的形状 （3）按Shift+Alt：由中心向外画正方形或正圆 （4）按Shift+Ctrl+I：反选 （5）取消选择: 按Ctrl+D或在空白处单击 羽化:使填充的颜色边界产生柔和虚化效果 二、移动工具： 移动已选择的图像，没选区的将移动整幅画面 三、套索工具 套索：按住鼠标拖动绘制出任意的选择范围 多边形套索：由直线连成选择范围 磁性套索：沿颜色的边界进行选择 四、魔棒工具：根据图像中颜色的相似度来选取图形 容差：值越小，选取的颜色范围越小，反之，范围大 五、裁切工具：裁切画面，删除不需要的图像 六、切片工具 七、 1、修复画笔：修复图像中的缺陷，并能使修复的结果自然溶入周围图像 方法：按ALT键取样，到目标点拖动 2、修补工具：可以从图像的其它区域或使用图案来修补当前选中的区域 源：将源图像选区拖至目标区，则源区域图像将被目标区域的图像覆盖目标：将选定区域作为目标区，用其覆盖共他区域 图案：用图案覆盖选定的区域 3、颜色替换工具：用于修改红眼 八、 1、画笔：用前景色在画布上绘画，模仿现实生活中的毛笔进行绘画， 创建柔和的彩色线条 +shift：画连接的直线不透明度：决定颜色的深浅 2、铅笔：用于创建硬边界的线条 自动抹掉：前景色、背景色相互转换 参数： 1、直径：笔刷的大小 2、角度：笔刷的旋转角度 3、圆度：控制笔刷的长短轴比例，以制作扁形笔刷 4、硬度：控制笔刷边界的柔和程度，值越小，越柔和 5、间距：两笔之间的距离
PS“计算工具”的功能与用法详解
PS“计算工具”的功能与用法详解（网摘) 初学Photoshop的同学，最早接触“计算”工具，应该是网上大堆大堆介绍通道磨皮法那几个步骤，“选择反差适中的绿色通道，然后高反差保留，然后计算，然后再高反差保留，然后再计算，然后再再高反差保留，然后再计算……”。为什么计算过程中我们选择的是绿通道副本到绿通道副本的“强光”混合模式而不是选择“变暗”或者其他的混合模式呢？我们只是按部就班记住了这些过程，然后得出了想要的效果，但为什么计算工具有如此美妙之处呢？其实，通道磨皮法是通道作为选区功能的一次彻底的应用，它通过计算工具选取出人物皮肤需要提亮美化的部分，“计算”是通道的一个选择手段或者工具。“计算”和“应用图像”以及“图层混合模式”既有关联又有区别，要了解计算的作用，并且熟练为自己所用，就需要对通道、图层混合有一个初步的认识。今天我们就来通过实例来剖析一下Photoshop的“计算”工具有些什么功能，是如何工作的。 第一、“计算”工具和“通道”紧密关联 它在通道中运算产生，形成新的ALPHA专用通道，ALPHA通道，又是为我们所需要的选取部分。通道就是选区，实际上，“计算”也是通道“选区”作用的一个产生工具而已。 第二、“应用图像”和“计算”的区别 “应用图像”是直接作用于本图层，是不可逆的，而“计算”是在通道中形成新的待选区域，是待选或备用的。
第三、“计算”和“图层混合模式”紧密相连 要了解图层混合模式，必须从色彩开始系统了解PS的基础应用，图层混合模式的应用是中级阶段PS学习应该熟练掌握和认识的，后面附件中有各个混合模式的比例或者公式。 第四、“图层混合”、“应用图像”与“计算” “图层混合”是图层与图层之间以某种模式进行某种比例的混合，“图层混合”是层与层之间发生关系。 “应用图像”是某一图层内通道到通道(包括RGB通道或者ALPHA通道)采用“图层混合”的混合模式进行直接作用——实质上是把通道(包括RGB通道或者ALPHA通道)看成为图层与图层的一种混合，只是这种混合的主题是图层内的单一通道或者RGB通道，是通道到通道发生作用而直接产生结果于单一图层，“应用图像”的结果是单一图层发生了改变。 “计算”的结果，既不像图层与图层混合那样产生图层混合的视觉上的变化，又不像“应用图像”那样让单一图层发生变化，“计算”工具实质是通道与通道间，采用“图层混合”的模式进行混合，产生新的选区，这个选区是为下一步操作所需要的。 明白了以上几点之后，下面我们通过实例了解计算是如何做出我们需要的选区，这是掌握“计算”法最最关键的一点。我们拿到一张准备PS的图片，要养成没处理之前，就要想到我们将会处理什么样子或者怎样去处理。而这里的关键，就是我们充分了解图层混合这样的光和色的比例关系。这是初级迈向高级的应用的一个坎。了解这个坎，借助“图层混合”，“应用图像”，“计算”这几个工具，飞跃这个坎。 附：图层混合模式的应用 图层混合模式可以将两个图层的色彩值紧密结合在一起，从而创造出大量的效果。 混合模式在Photoshop应用中非常广泛，大多数绘画工具或编辑调整工具都可以使用混合模式，所以正确、灵活使用各种混合模式，可以为图像的效果锦上添花。 单击图层混合模式的下拉组合框，将弹出25种混合模式命令的下拉列表菜单，选择不同的混合模式命令，就可以创建不同的混合效果;图层的混合模式是用于控制上下图层的混合效果，在设置混合效果时还需设置图层的不透明度，以下介绍混合模式选项说明的不透明度在100%的前提下。 正常：该选项可以使上方图层完全遮住下方图层。 溶解：如果上方图层具有柔和的关透明边缘，选择该项则可以创建像素点状效果。 变暗：两个图层中较暗的颜色将作为混合的颜色保留，比混合色亮的像素将被替换，而比混合色暗像素保持不变。 正片叠底：整体效果显示由上方图层和下方图层的像素值中较暗的像素合成的图像效果，任意颜色与黑色重叠时将产生黑色，任意颜色和白色重叠时颜色则保持不变。 颜色加深：选择该项将降低上方图层中除黑色外的其他区域的对比度，使图像的对比度下降，产生下方图层透过上方图层的投影效
PS基本用法工具介绍
P S基本用法工具介绍 Document serial number【UU89WT-UU98YT-UU8CB-UUUT-UUT108】
PS基本用法工具介绍 它是由Adobe公司开发的图形处理系列软件之一，主要应用于在图像处理、广告设计的一个电脑软件。最先它只是在Apple机（MAC）上使用，后来也开发出了forwindow的版本。 一、基本的概念。 位图：又称光栅图，一般用于照片品质的图像处理，是由许多像小方块一样的"像素"组成的图形。由其位置与颜色值表示，能表现出颜色阴影的变化。在PHOTOSHOP主要用于处理位图。 矢量图：通常无法提供生成照片的图像物性，一般用于工程持术绘图。如灯光的质量效果很难在一幅矢量图表现出来。 分辩率：每单位长度上的像素叫做图像的分辩率，简单讲即是电脑的图像给读者自己观看的清晰与模糊，分辩率有很多种。如屏幕分辩率，扫描仪的分辩率，打印分辩率。 图像尺寸与图像大小及分辩率的关系：如图像尺寸大，分辩率大，文件较大，所占内存大，电脑处理速度会慢，相反，任意一个因素减少，处理速度都会加快。 通道：在PHOTOSHOP中，通道是指色彩的范围，一般情况下，一种基本色为一个通道。如RGB颜色，R为红色，所以R通道的范围为红色，G为绿色，B为蓝色。 图层：在PHOTOSHOP中，一般都是多是用到多个图层制作每一层好象是一张透明纸，叠放在一起就是一个完整的图像。对每一图层进行修改处理，对其它的图层不含造成任何的影响。
二、图像的色彩模式 1）RGB彩色模式：又叫加色模式，是屏幕显示的最佳颜色，由红、绿、蓝三种颜色组成，每一种颜色可以有0-255的亮度变化。 2）、CMYK彩色模式：由品蓝，品红，品黄和黄色组成，又叫减色模式。一般打印输出及印刷都是这种模式，所以打印图片一般都采用CMYK模式。 3）、HSB彩色模式：是将色彩分解为色调，饱和度及亮度通过调整色调，饱和度及亮度得到颜色和变化。 4）、Lab彩色模式：这种模式通过一个光强和两个色调来描述一个色调叫a，另一个色调叫b。它主要影响着色调的明暗。一般RGB转换成CMYK都先经Lab的转换。 5）、索引颜色：这种颜色下图像像素用一个字节表示它最多包含有256色的色表储存并索引其所用的颜色，它图像质量不高，占空间较少。 6）、灰度模式：即只用黑色和白色显示图像，像素0值为黑色，像素255为白色。 7）、位图模式：像素不是由字节表示，而是由二进制表示，即黑色和白色由二进制表示，从而占磁盘空间最小。 三、工具介绍移动工具：可以对PHOTOSHOP里的图层进行移动图层。矩形选择工具：可以对图像选一个矩形的选择范围，一般对规则的选择用多。