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The complete base sequences of yeast and several other microbial genomes have already been determined, and new life science areas based on such data is rapidly expanding. Thus this report takes a life and information science perspective of the technological developments needed to apply genome information to a wide variety of industries. Chapter 1 provides an overview of the genome map as well as sequence and function information at the current level of development of international research on genome analysis and highlights issues confronting the efforts. The information obtained from genome analysis is being compiled in databases which will enable us in the future to simulate diverse biological systems on computers. The analysis of genome structures requires a new system that integrates existing analytical methods and executes them much more quickly on a much greater scale and in a more well organized manner. The methodologies for analyzing genome functions have not been established and need to be further developed and refined. The efforts to analyze gene functions have so far primarily focused on individual genes. The new challenges are to perceive biological manifestations as expressions of entire systems while elucidating how sets of genes interact to control the expression of discrete genes. Chapter 2 reviews the current status of the technologies that analyze and make use of genome information. It also discusses (1) prediction and identification of gene regions in genome sequences, (2) techniques for searching and selecting useful genes, (3) technologies for predicting the expression of gene functions, and (4) technologies that predict gene-product structure and functions. — 1 — On predicting and identifying gene regions in genome sequences, the report discusses the sequences characteristic of eukaryotic gene regions as well as different approaches and pending issues for predicting gene regions using information science methodologies. The report finds that linear models, especially the hidden Markov model, are most suited to genome model constructions. Differences in prokaryote and eukaryote transcription and control mechanisms are compared together with their associated analytical techniques. In the area of techniques used to search for and select useful genes, the report describes fluorescent differential display (FDD), molecular indexing and cDNA microarray technologies which are critical for rapid analysis of gene expression profiles. The report covers gene searching technologies with an examination of the positional cloning method which identifies genes responsible for an expressed trait based on the fact that genes lose intrinsic functions through mutations. Experiments on humans and other primates are limited by ethical and financial considerations. However, important knowledge about useful genes can be obtained using mutants of organisms that are carefully selected for a particular purpose. Discussed here are unique approaches using microorganisms, nematodes, fruit flies and mice to find useful genes. In covering technologies that predict gene-function expression, the report initially reviews methods that reveal transcription-controlling regions for cloning. This is followed by a discussion of methods for analyzing transcription controlling regions. Since gene transcription is controlled by interactions between DNAs and proteins or between proteins, information about many transcription-controlling factors is registered in databases. Many models for simulating the mechanisms for controlling the expression of genes have also been proposed. However, these models are only at the stage of evaluation for usefulness. We still need better models that can be applied to a variety of -ii- complicated processes. Since proteins are synthesized based on gene information, the technologies for predicting gene-product structures and functions are discussed. The two most commonly used methods to analyze the structures of synthesized proteins are examined including nuclear magnetic resonancing applied to proteins in solution and X-ray crystallography which determines protein structures in their crystalline state. To predict the three-dimensional structure of a protein from its amino acid sequence, the 3D-1D alignment method is widely used. The method predicts the initial 3D structure of a protein by comparing the data from the target protein with those from known 3D structures and by selecting the one that fits best. This approach is taken because statistical calculation is impracticable for predicting 3D protein structures that sets of amino acid residues can form. Improving the techniques of gene destruction and expression suppression- useful in clarifying gene functionsis still required. The report reviews the homology and motif search methods which are widely used to predict the function of a protein from its amino acid sequence. It also discusses integrated database systems incorporating data on amino acid sequences, functions including protein sequence motifs as well as 3D protein structures. Turning to the course of future research, Chapter 3 recommends that Japan's industry, government and academic sectors should focus their leading technologies and expertise on developing new technologies, collecting more data and interpretation in order to clarify the inter-gene information networks, a subject which will be of utmost importance when the DNA sequences of major organisms have been determined. It also calls for more research on gene data processing technologies so that inter-gene information networks can be simulated on computers using protein databases on amino acid sequences, structures and functions. -in - Chapter 4 gives examples of how the research project discussed in this report can affect industry and society. The focus is on sectors such as pharmaceuticals, diagnostics, diagnostic and analytical instruments, laboratory equipment, chemicals, food, agriculture, animal husbandry, fishery, electronics, environmental protection, information technologies. The final chapter cites the need to develop technologies for interpreting and using genome information through multidisciplinary cooperation among professionals in genetic biochemistry, computer and information sciences, precision engineering and other fields. 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Assessment of Protein Coding Measures. Nucleic Acids Res., Vol. 20, pp. 6441-6450 (1992). 5) Ikemura, T. and Aota, S. Global Variation in G + C Content Along Vertebrate GenomeDNA. J. Mol. Biol., Vol. 203, pp. 1-13 (1988). 6) Kozak, M.An analysis of 5'-noncoding sequences from 699 vertebrate messengerRNAs. Nucleic Acids Res., Vol. 15, pp. 8125-8148 (1987). 7) Lewin, Benjamin. Gene. Oxford University Press and Cell Press, 1994. (# mwt r#fzf#5mj 1996 ) 8) Sazuka,T. and Ohara,O. Sequence features surrounding the translation initiation sitesassigned on the genome sequence of Synechocystis sp. strain PCC 6803 by amino-terminal protein sequencing. DNA Res., Vol. 3, pp. 225-232 (1996). 9) Shapiro, M. B., and P. Senapathy. RNA splice junctions of different classes of eukary otes: Sequence statistics and functional implications in gene expression. Nucleic Acids Res., Vol. 15, pp. 7155-7174 (1987). 10) Sharp, P.A. Speculations on RNA splicing. Cell, Vol. 23, pp. 643-646 (1981). 11) Snyder, E. E. and Stormo, G. D. Identification of Protein Coding Regions in GenomicDNA. J. Mol. Biol., Vol. 248, pp. 1-18 (1995). 12) Wahle and Keller. Annu. Rev. Biochem., 61, 419-440 (1992). 13) Vol. 37, No. 10, pp. 952-954 (1996). —13 — 14) Wahle and Keller. Annu. Rev. Biochem., 61, 419-440 (1992). 15) Yada, T. and Sazuka, T. and Hirosawa, M. Analysis of sequence patterns surroundingthe translation initiation sites on hidden Markov model. DNA Res. (in printing). 1-1-2 (1) &<, z o r^-7»tHj > wn-cv^o tupie#$f. 7#m. )V a 7 A:fOV (Hidden Markov Model:HMM)<7) i. o t°)Vv»6^?i<£> 3 ■oil K - tuple###, f f-7 Wi7 - 7 vt± Efrthetoa4-&#£(7)SStiyEetig u @^ijEM- #+#m: &&#&### gaRm*T=fV-C#9 Rcai%i-&:ef(2) i«$Sg«*r=f'J &. (3) ^iS$tvrv>^0 tztzL, SSto&K - tuple Mr "Cii. r-? (Dtyb r&^£#li-t££:*6e{±. ltl)0^9 art: &#& 6 t(Dt u-cm^ §fit Wtt 45tifcfitMMMU t frta-to ztilzj: 0 . -t^-7eKcr -y<7)^6#"e#aL^±-c(A^f—7rrn-c^ma9^h:#ei?oc -14- $W:, me%7;i/d"vXA(7)j:9^#$% "Cv^v^ -me. ie^-tosm(±. tm^emme^f #%"C#f. 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W/<-t>hlEL<&M'r&&#Bije#LXkt#(±0 1:&&o h%-c^#ibf6ca^''e#&o (krvi'). 342 {\£y H> -^> m% ##^4ooif v h. m#^404^ v h if«Msi i)II Li'tf “71 SEtiWUd'UXA zmim@&#&f6:5#aL-c ^Rm^m^A*7;i/xv XA(/)Z ^ c(DZ9^#^c(i. mf^^T^xvxA (GA)»)#?)#mmG& mv'&zaej; i). GAti. & ri@#:j ##:^'e#^gg^%eAfL#x.& ra%J. & rm^j #&#mc -19- -oz- 3B¥mmm °4-^WSf?t:lWWHa»4-^iOYa-f'l liIMH«^0VO^>K‘ Z-T0 9Z'0S 910 S StiOV 66'0 99'0>t 00 I- tfO ezo d VZ'OA X. 00 LH txyioSoon 9sov ooi. MOV ee'ox.oo'to o c> — ‘ -o 00'o'ti xv zz-oa 91-'01 oot \ ee'oi 00'l ITO© ero <-i] L-l M °??a&-6KUi wwh 1/^1 £C1fAl|:H 4TO2)iiMtox -v 4-4fii® "0 ':)9 3 °9#J.iy3T94#2#a.$BWS& ^-64^4 p-^mc'Tiw&i-i^i ':m- °?4-if? X-^3r4im3 4-M ^ tSEWW '?) £ -^3r £T 2Mf43r X n ^ Z-lffi mW/1-43r§ 42)1^9: O VO °^ T4^^o-a)^^SS^nE2)ffltfl^-'49r?]V9 4-&14-4<( xx °2n4%f $##W? '?)3W#VOW41:?TXfi;:^m#24l#:)miM4.-'4ar '1^^ if 9 ? 7 9"W= 9 ^ 41 #:)% &o ctDh HMM"C(±. ^-7#mt HMM j: 9 4\ &2>®m~mirZ>Mfrm!lris f&bt), #@tvh HMM (I J: *) *TWt1r& dt &V§ -So HMM^f-7 &o #$^^^<7)4:^ mmm*. #Bm*. v h 9-^(NN)#(7)#(7)#$f Tv>6A^$v^ a/fi/>i/ v/<-<7)e!l'e#^f&i9(:, ^ ^ 9 %@(±# 6#mf & AT# HMM C Z&fjm#, #£<*>* r =n; Izmir Zib'Zi h'fi^:(fm'C^&^2:'9^$'mi-K%a#x.'CZV'o HMM(T)##t7 H: d &o a HMM LT, i®?)i:un>f y >y y/<-t^-7(7)m(m: "3V'Til8^i-&o #m(7)HMM(7)#W#&^(±. HMM <7)b^ai;-^^AT#@i-&ac:5t:#m^6&o dfu:j:0. o/fyyyyX-kL DNA#-&@B#&# o*&#<7)g8@(:ai%f 19 x 01-8 (:&& j: 9 (- a ^V y ? xSlBUis V'Ta/f y>(L)^7cg#cmmL. 9 ^yv/<-( 7)Z9 t:a^ y>^^f&d& CA^f&o C(7)j:9^a/f y>y v/i-^#ogG&<7)K^m#e#@t y -21- @ 1-10 o J: 9 ^ « &#%9#acav'T#& a/fy>(L)&&v4iXV>(V) hi'c^#7K#<^7< V%^^rin](:#^o"Cv^(7)^t)^6o C^ZotzA'i y Xx# 3iii [helical wheel] t LX'i&%frb%lbtiX^'Z>i)\ HMM £fflV'£ £ t tz X Y), @# x-XA-x^&E mAM*m^z>tm*mmb¥m^v v^wt Izmir &r- X A-X cDtkmzn 0 1-9 It HMM rmm L /:nly> y t. y b t^itznm Observed from. . above Leuc hr 5 2 Basil Hydrophilic @1-8 n 4 > > v y/<-#^@ @1-9 o^y>y -22- 59 17 @1-10 D4 y > vys'-Zf-yvmmm 01-11 n4 y >y>ri -7 4 m0gM #;vi X ffi 1) Aitken,A.: Identification of Protein Consensus Sequences, Ellis Horwood Series in Biochemistry and Biotechnology, (1990). 2 ) Bairoch, A.: PROSITE: A Dictionary of Protein Sites and Patterns, User’s Manual, in SwissProt Database (1991). 3) Shimozono,S, Shinohara,A.,Shinohara,T.,Miyano,S., Kuhara,S. and Arikawa,S., An Approach to Bioinformatical Knowledge Acquisition, in Hawaii International Conference on System Sciences, (1993). 4 ) Rooman,M.J. and Wodak,S.J., Identification of Predictive Sequence Motifs Limited by Protein Structure Data Base Size, Nature, vol.335, (1) pp. 45-49 (1988). 5 ) Smith, H.O., Annau,T.M. and Chandrasegaran,S. , Finding Sequence Motifs in Groups of Functionally Related Proteins, Natl. Acad. Sci. USA, vol.87, pp.826-830 (1990). 6) /bm# : Z vol.8, no.4,(July 1993), pp.37-48. 7 ) Rissanen, J.: Stochastic Complexity in Statistical Inquiry, World Scientific, Series in Computer Science, vol. 15(1989). 8) Goldberg,D.E. : Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Publishing Company, Inc (1989). 9) Fujiwara,Y., Asogawa,M. and Konagaya,A.:Stochastic Motif Extraction Using Hidden Markov Model, inProc. 2nd Int. Symp. on Intelligent Systems for Molecular Biology (ISMB-94),(1994). -23- (2) yyy^am&miLtmfr fi. ##$tL"C#Tv^o 2ft6<7)imfzf@^J(7)4'[:(i. ##(:-ov'-CI± *&m6o6#K##i-&o iz#>P)^-i)H'' 0 & 4?-X.& ##<&## (DDko'0&6o mTC, DNA(D#^K^J, K^iJ(D?mi#&[:'OV'"Cl8 ^f 60 a. / A @am' & y yf a,E9 J&& c k y v A @d^j^^^& cm#-c&&o C Z CT##Gf(f 6ft&. 2(7)K^J/<^LK3FiJ^6 y y f MkLTkL RNA%y7 4y@B&^##^A6 ft~C V' %> o J^TC, m*^J^#mL^yyy;i/K^J(7)###&(D##MkLT. RNAyY74 RNA%7°7<%Sm(D#^ (M. B. Shapiro and P. Senapathy (Nucl. Asids Res. 15, 1987 7155-7174)) 2<7)^?£'Ct±> E&t69 5'X-/7 4 Xg|M^ 3y X7°7>f XSlS<7)E^Jy-^^S* (20 l&£ "C i± GenBank Release 46 £ fijffl L T i' & X ^M'fUZ^^X, &M2'kizSX 2<7)m$-m*ff^jkLT. s' Po (1) 5'xy7 4 xM<7)xny!t# 5'%y9 4 x@g@:(DX3T(i, rn$ %) o score = 100 (t - mint) / (maxt - mint) -24- mint : (8mmamgB#K9iJ(0&#@#a@(7)/<-t>T-y#'a#) maxt : (8mM<7)@g^Sd^J(D#{&@^#^<7)/<-4:>T-y to 3'%79 ^ 37(±mT(7)^'e#+#$^6o score = 100((tl - 11)/(hi - 11) + (t2 - 12)/(h2 - 12))/2 tl : ^V 11 : i^V < y>V vf-#%<7)10#^rf. 8##^# 12 : hi : < y >V ^f#^(DlomBfr^, h2 : n >-b ±1B (1) (2) (OX 3 7#t#(: Z 3 7CZ 0 , X79 ^ X@M&(D# o b. 7 acto. wm?>/ KL"CV'& ^ > b cisJSt, i z>Jj&\Z'D\'X'&' -25- (D. J. McGeoch (Virus Res. 3, 1985, 271-286)) Ciwjg (4, membrane-translation 9 m^^Ti^-to 1) l££n<£>membrane-translation 9 114@#<7)N • Position 1-11 Hab-5> chaged residue(Lys, His, Arg, Asp, Glu)C# < position 12 ir b TM&'t & 4 b C##T b o CLO^iSC 4 t) ^ 114 Bd^lj fp 113 8d^U (4 position 12 6 $n ^ 6 charged region (CR) £ uncharged region C5K 4 <5 ft/t (1 Sd#|{4CRtfs 17$IJ?£ Wl-St/CH^o £r)o 2) _hBH7 7 4 > h /ieb> Bi^Qco membrane-translation ^ Mtcov'fjy. T<7)SS^MoGtijL^o • CR <7)fflE<7) net charge t4> -lfrb+2 ^'liCiloti^o 4 OJimfflto %#^#(7)aE3FiJ$r#c ^#^"C(4. -2^ + 4 CM LT • 12 - 19 #B OtSliiC54 charged residue /5S^< Mt> • UR7> 12 - 21 #B ^S^KttVtSEa1 !* x non-membrane-translated ? > ^ 7 Si • UR<7) 12 - 30 # B <7)Fe9 <£> 8 H<7) peak hydrophobicity £ UR <7)6$ £ b-tirfc 2 {ATcplot £> membrane-translated ? M £ non-membrane- translated 9 y ^ 9 M £ WFS C 4 < F< g!l "C § £ o 114 b "b 110 iE£!j(96%)/i\ ’’ membrane-translated” -26- W(a,i)t±> position - 1 t + 1 ShBrS'f£<£>£lI -7 -f > / > l'"C(D'eS > h N(a,i) (##i<7)^&a^)%) ft-fficOT < V> bO#l## W(a,i) = ln(N(a,i)/ <^A@gM%N"C#!l&o W(a, - 1) = ln(l / N) if N(a, - 1) = 0 £> o b i) fc> D -b 9 &5MtfralH£{±> (#/E<0) SA-fr?!! b > -?■ tL-?'fu7)position (?) S<£.?>nlH\ S(i) = W(ai - p, i - p) + W(ai - p + 1, i - p + 1) +.... + W(ai + q, i + q) (i-p^i + q^ ’y 4 y Kyips-e^tfit) czo-c, K®ose9iJ iS-x + -v >t&b t S(j) - max[S(i), i=l - p,...,L - q] at^u MfrtmzmmLtzm^, p = -12, q = 2 tZo < ^-x. c a c z i). y gB@ (1995) 2) 9 b V > - DNA (A#) (1986) (3) DNAOga^iJ t*- ? M$$rU LT 730M(GenBank rel. 98> 1996¥l2fl)> * > -27- (PIR rel. 49. 1996^6^) ^ - ?/<-% &:##$fi-cjy 0. DNAK^I^f -15^ "C#h V'9 ^-x Haemophilus influenzae u. Mycoplasma genitalium 2). RIF? >##m4?av ABB #15. ±mm^Ay/A@E^JtGenBank(:#m$^o (:ov'T&. CerevisiaeCK#. 4"#% ^motC. #A. yO^xaXa^m^y/AK^J^^^^fi atoy-##cz i), AKpjki&#:6&w±gG#M&mm#&# D. j: 9 ^ h m? § -l)o H. influenzae b) Mycoplasma genitalium a) Haemophilus influenzae 0.58Mb (1995) 1.83Mb (1995) identified 470 ORFs 1007 identified hypothetical 317 \ 347 mi-12 m@y/Amato m9%5mm%#dsatny-Wf[:j: 20%^f-7/<-X[pmlRm^ >/< ##C#LX(±. 68 %mORFm## A KPJ3 - 6H#mz@%ds. a t o c z ^ x 0%r -28- a. *4=Py-»f;i DNA(Dm&SB^I2f9L. 6&v4i^>/<^®%7 ^DNA66V>(±7< PASTAS BLAST^^t^tt&dk^t^ t^t, dttG ^#&(±#tostm&z g;#to&-Bc&m%A[:m%& SitoSt®&, sut, Smith-Waterman &C <£ %>mmimmWM*m M3 t X «J Mm i-^>8) o cent, PROTEIN h vM 1C^n k PROFILE k^o %¥?>]£&&1rZ>o #to -e t T v»a ##& & w±# wxomm# e & #-& c a % ^ 7 #12 <%# & & M tpffl<7)J§&lzi±0 ZkZo {ltdd-et±MIft0J3&cD T. —^>/<^%(D7 -29- (a)zz3TT-7n' P R 0 T E I N F T Gap Len P 5 -2 -1 0 0 0 0 0 0 -3 -1 R -2 5 0 0 0 0 0 0 0 -3 -1 0 -1 0 5 0 0 0 0 0 0 -3 -1 F 0 0 0 -3 2 0 0 5 -3 -3 -1 I 0 0 0 0 -2 5 0 0 0 -3 -1 L 0 0 0 0 0 0 3 0 0 -3 -1 E 0 0 0 0 5 -2 -2 0 0 -3 -1 -1 -3 P R -2 2 0 10 10 12 11 11 9 F 11 12 1 12 910 9 11 1112 710 14 L •3 4 10 1016 912 1310 17 E 13 1312 17 4 PROTEIN III I P R 0 - F I L E M3 Smith-Waterman mUWmrMM A R N D c E G H I L K M p p S T W Y V B Z X 4 -1 -2 -2 0 -1Q -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0 -2 -1 0 -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 -1 0 -1 -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -3 3 0 -1 -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 -1 -3 -3 4 1 -1 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 -3 -3 -2 Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 0 3 -1 -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2 1 4 -1 G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3 -1 -2 H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3 0 0 -1 I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -1 -3 -1 3 -3 -3 L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 -4 -3 -1 K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2 0 1 -1 M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 5 0 -2 -1 -1 -1 -1 1 -3 -1 F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1 -3 -3 -1 P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2 -2 -1 -2 S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2 0 0 0 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0 -1 -1 0 w -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3 -4 -3 Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1 -3 -2 -1 V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 -3 -2 -1 -2 -1 3 4 -3 0 1 -1 0 -3 -4 0 -3 -3 -2 0 -1 -4 -3 -3 4 1 -1 -1 0 0 1 -3 3 4 -2 0 -3 -3 1 -1 -3 -1 0 -1 -3 -2 -2 1 4 -1 X 0 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 0 0 -2 -1 -1 -1 -1 -1 8 :TZ/V7#>tf7Z/'<7*>ai, Zrf/trfi i s. >m, X:*# El-14 ^37f-^«W (BLOSUM62) El-13 (b) h V 7 ? &o tbfrh-Zo ov h V 7 ^x -30- V HJ 7 ? mzMl&'t & jest’d L Virtue LtJf^x a Tt\ £±<7)#<7)£T<7)X n 7 ffL^K^lCf-vvygrAfL^tODT, #±, &#So X 3T(7)#± Bll-13 (c) PROFILE^##(DEe^V'-C(I(^#"emm^hfL&Z9 KMR"C#m#^tV'@g^"C(±'!-^x=r7(i#(:t&^'e, x LTv^ &o mh, ^<7)CT*et± profile «e i±^^ffl«i**BiaBa9ijiw^jti*v'c:ic^So -^, FASTA"C(i, <%-3<7)Xf #-Xf vYT(±, afm$k<7)^^J (k-tuple) AT^mCf&o -E-LT, WsKfJ &^T(7)k - tuple(Dmm&#^f macmAi-6o ^WSti, -iWA? ^Jjt#T(ik=4-6, T S^If ■? o BLAST?#, ^v^Lt^ m^/<-xt:Mmcm^L"cx37##[ee9ca(:z()mw#%e#mf&o tt, c L^L, BLAST, FASTA, Smith-Waterman *£<7)®?& & #\ ## -31- x X jesje^auar) r/q^ggo)^^ '£) ouiio;s ^ jap^ug ° Y ^ % 4 gr T :)##^1B#^ ^@4^/ 4 ^ Q $ °8i?^:LTf33mS###m-/3at*:yi##3^ Ar^o*r^@g@/ p@vNa ?iVNd0-4fv/l [i£BB$/ 'Till-A ^ ± L ISV79 VY ? Vox ^ma^gg#/ 33m!llWo> ? '9: p@o)%i °9#a.*#^j_@%#%i#?r?aisvaa vNa '#%&a34W4m3im#:L^;w#m(m#9 \Hj.xisna 'Y#:)^B@VNd ^disna?<$j.< Ex_K3$ qrr^gger t^c^isna "?;:)*# °9^^m'?(r^^#4m?r^QE9 4a 'QEs# ^ccmgmw-^MpavNa K^ri^evNa °9%=)3T9^3@#3?ri42g#/szwTwm:&:?ri4?@a@/ '^n^#:)#/^^:m 'Pi '0.97 ? m ,ifiaT 2(D#m^59#m<7)mf5f (^^v>%385) %&22d'63K\ $d:?#|T#^d'-od:J:XV>^m^27d'619(:, $6Cmmx."C?- iM L/2^X V 57 d'6 17 C#mf6 2 t d*T # d:o DNAK?iJf-?&[f7 < #d:C^$fid:yy A@g ^Ijd^, f-?X&M6d'<7):ttoy- &#-#-6f 1- %c"C, i:]#S$6 6^)h#x.6o #C. 7 X wt I) Fleischmann, R. D. et al. -.Science, 269, 496 — 512 (1995) 2 ) Fraser, C. M. et al. -.Science, 270, 397 — 403 (1995) 3) Needlema, S. B. and Bunsch, C. D. :J. Mol. Biol, 48, 444 — 453 (1970) 4 ) Smith, T. F. and Waterman, M. S. :J. Mol. Biol., 147, 195 — 197 (1981) 5) Pearson, W. R. and Lipman, D. J. :Proc. Natl. Acad. Sci. USA, 85, 2444 — 2448(1988) 6 ) Altschul, S. F. et al. :J. Mol. Biol., 215, 403-410 (1990) 7 ) Brutlag, D. L. et al .-.Computers Chem., 17(2), 203-207 (1993) 8) Luethy, R. and Eisenberg, D. “Sequence Analysis Primer” , pp.78 — 82, Stockton Press (1991) 9 ) Gish, W. and States, D. J. -.Nature Genet., 3, 266 — 272 (1993) 10) Guan, X. and Uberbacher, E. C. -.Comput. Applic. Biosci., 12, 31 — 40 (1996) II) Kasahara, N. et al.: Genome Informatics Workshop 1996, 202 — 203 (1996) 12) Hiraoka, S. and Nagai, K. :Genome Informatics Workshop 1995, 152 — 153 -33- (1995) 13) Snyder, E. E. and Stormo, G. D. :J. Mol. Biol., 248, 1 — 18 (1995) 14) Xu, Y. and Uberbacher, E. C. :Proceedings of Intelligent Systems for Molecular Biology 1996, pp.241 ~ 251 (1996) (4) <7)^&tzA# < 3-f^f T, (l) # @#E#<7)#m. 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E 1-15 C HMM t: j: & y 7 A t y ;P7)##e!l Protein coding region H------H El-15 HMMC Z & y 7 HMM(±^ v b7-7rmm$fi6o 7-K(i^^mL. 4m A 'yStiTv^o HMM"C(±, $ x b 7-7 b *°n 7'-1 tf )W?7 7 - 7 B £ 0 X g?& ^#-ey7A@E3F!i<7)##^f T'v >y$4i-cw&o E 1-15 CO HMM ti> b 7 — 7 l'-t'Dy-(;ii < x 7 - b 7 K X l*lS 7 K>> Xb y'fnyy av'7-4-^ 7 ;v^$S6L> X7- b 7 K>^i5x b 7 yn K>£x:<7)S£b* x b 7 —7^s 7-rV 7 7'#memmLTv^o i8Z(:m*SLTV'&#gB7 K>7)$x b 7-7 b*°n y-[=L 2^7 K><7)mmmm^7-y^ -cv^. )&£> 6B7V-Tp<7)jlSS^(b)(j;^7)illt<7)IFi^SSS:S:SL-rv^o x 7-b 7 K>^ <7)3#M(c)(i%7-b7 p^ggn K>^03##^(d)(i2m7 K>7) — 36 — HMMTkL amm-ribao X 7 - b n K 7 (7)±i,f C Shine-Dalgarno K?'J (D.TSD E^iJ) 22) fcnflf zmmira hmm (^H x .im 1-16 T 1 , I I i ■ 1 E1-16 Shine-Dalgarno @B£iJ <$r MM'i' a HMM ^<7)Tmi:SDKF!l^^X^- j:V'o E a. v-7iNmMit±sd @d?ijth<7M£@iuttas u zat 'f-ymmMb^L^c f'J-y 3 xanDi(±SD8S^iJ4 1 t S^(7DSSlilS(ijHSi"af$E^i3{ia^> -7 y^ESliffiS-rao C(D j: 9 (:. HMM -e i± f- - 7 @B£|J <7)1SfR £ y ^#§©^077 7 )Vb LX^Mt a c t a0 gffiW^yyASS^iJIflRSrE 1-15«HMM^M-C^ao 7 ASe^iJ^Ba GC (:"@A^(G + Crich)#ma AT UWA£'(A + Trich)#m Ei-i7 B#%»y/A##emmfaHMM(7)M — 37 — G + C rich b A + T rich ^ i± A + T rich ffiMfr bG + C rich 7 t i> oimn) "C6&. 0 1-15 # HMM C#f 6 7 1/ - A '>7 ix 7-^T7V?)ffl&iX&f!j£g| 1-18 CC-eii. xr V> AAAWt^^f;^/^ L Deletion Insertion Match Begin AAA End 01-18 7V-Ay7hi5-^at5HMM©W TV'&o ys 4 7^-i/g / HMM^Ii, Viterbi7;vrf V XA24) tm£tiZ>t$MT )V^f') XACi oT, 4x.«b*t ^77 6^##%m# % £? 1) Fickett, J. 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M.: An Introduction to the Applicationof the Theory of Probabilistic Function of a Markov Process to Automatic SpeechRecognition, Bell Syst. Tech. J., Vol. 62, pp. 1035-1074 (1983). (5) GenelD X h <£> Guig G t±> £ 'G fi o JV — — x v X -r A GeneIDem%L^:^o GenelD DNAKm:*HI:L"CV'6)o #XT?7 — 40 — Amc[±^<7)Z9^%T7y^#"C?m^e^9o $f^ixTvyc. ^^7Ky, Ky-y4 i'. 7Xty^-y4i'. &o ^2 XTvy-eii. #^;i/-;i/[:%c"C#lJ:^V>, #$&J:^yy^?#i-&o #3XTv y-eti. #2%Tvye?m$fL^%^<(0J:4rv> 7 xd&W. (xl, x2) #-e&&o #T.yy E<7)# (j:4ry y • x77) ^ritSL> fiv'xn7«i^yy^it^o #4XTvye(i, #u:yyy. o#m± — 41 — • predicted first exons predicted internal exons • predicted last exons E1-19 GenelD fGenelD 9 7i/-A&#c-c&tx %> o GenelDli, mrni^f &x t tt\ $ 7b7-7^6-eblast em GenLang X -y —T^:^<7) Dong t Searls t±> j5C#laX)^# t L-C%x.^GenLangy%TAem%L^:')o GeneLang T*MMMU £ f- 3 A x * -SWeSemi"^0 t±, ffl&nF>, K b. yxtyx —-e&&o mmc(i#*%^#7^A. mm (3XF) *%m# 7 X xllwnx b frb, &a?)y > X 7 XXfpnx b &HtM'tZMl z&^Xi'&o — 42 — GenLangkL GenLang l£3Ci£ DP£fflt'£¥& v a >X • Jv/*>X*^<7)Salzberg bimfeXtm#)'? n^7<>x (DP) 2tu±, ili + VX >X#m) V\ DP 1= j: C0 ^ h6 m-c&ao DPi:z&###(D^-eii. O & 2 b 1: J: c"Cim&#jS^±lf & 2 a i:m%L"Cv^o PAc =##(:<&5mefim&xo ^9 A-eii^v'^ iiu^wj:l-c, ie &T;i/XVXA^g^*#^L"CV'6^o # #%ea LT@g^j&- jM&my >;K;H:/f (^ y/^KSB^J-CT < x m 1) Dong, S. and Searls, D.B. Gene structure prediction by linguistic methods. Genomics, Vol. 23, pp. 540-564 (1994). 2) Guigo, R., Knudsen, S., Drake, N., and Smith, T. Prediction of Gene Structure. Journal of Molecular Biology, No. 226, pp. 141-157 (1992). 3 ) GenelD-Email Server for prediction of Gene Structure, geneid @ darwin.bu.edu <£ 0 X^. 4) gswg. Y S xr- 9 frbfDtomft&MWetM., Vol. 37, No. 10, pp. 935-940 (1996). 5 ) Pereira, F.C.N. and Warren, D.H.D. Definite clause grammars for language analysis. Artif. Intell., Vol. 13, pp. 231-278 (1980). 6 ) Salzberg, S., Chen, X., Henderson, J., and Fasman, K. Finding genes in DNA -43- using decision trees and dynamic programming. ISMB-96, pp. 201-210 (1996). 1-1-3 E#<7) $ >A? H =i - KWiTOJJ y X x A £ <7)M (1) ?y/ - CftG<7) ^^^^#7x7^(1^1)^^311^660 c ftG(7)7X7A^n#C(±3- F#^(7)?#/fUT^<, yat-^#%^-E-(7)#(7)##gg @(:oV'T6?-#LT< ft6#@BSr#ATt'6 6(7)666o &&, C^l6(7)1:#y%T A<7)m&cm^cTi±*a&Am%y%TAm#m^ (##t%:aamAH^mT-z*# #t&^) (7) r^#ft7;i/^0XA#My%TA^)^%t:^i-67/f-yifUf/f%^T'f #^#J (?#8 GeneFinder GeneFinder (Baylor College of Medicine) (± H tz 6 ## £ 6 o /i 7° cr X" 7 A coS t 0 ^G^6o t(7)*<7)FEXIi, &##K(7)^D(7)t'<'0^(7)#%[:^(t&tVd'v-(7) MTxyy/fxggK ^^(:ZD#Gft^#%(7) asm (%3 7) em^fjgij^#<7)emTim^L, 7 7, 7 &o FGENEH & t"C(±, ZtlbCD^? 7 'sCDcpfrb, A# (7)X37^#AC^&J:^77(7)m^-drem*t&o #1&, ^ta7—9 — T #/&/<-7 a 7 FEXHB, FGENEHB Sr ftp site TAM VX v>6» GenelD GenelD (Boston University, USA)I±> X ~f y 4 X Sf£<7) X 3 7 in — K 7° t 7 7 -v jv (a-L§SrX37fbt*6(7)) <7)f (7)##T<7) ^SrE-a-tir'C^SJ ■f&o ^(7)a #(:, A--t7 h n7Srfflv»So E^ESTr-^-x b<7)*^n 7-+h — 44 — 1-2 7 K LX (URL) GeneFinder http://dot.imgen.bcm.tmc.edu:9331/gene-fmder/gf.html (FGENEH,etc.) * l GenelD * 2,*3 yi —ty b n y http://www.imim.es/GeneIdentification/Geneid/geneid_input.html GeneMari: *4,*5 71^7 tf)l/ http://amber.biology.gatech.edurgenemark/ GeneModeler *6 GeneParser*7,*8 k b http://beagle.colorado.edu/"eesnyder/GeneParser.html totoins GenViewer *9,*io k h http://www.itba.mi.cnr.it/webgene/ Genie * 11 1 k b http://www-hgc.lbl.gov/projects/genie.html 7)1/37^^ 1/ GenLang * i 2 k b.vTtX.-y 37-73 http://cbil.humgen.upenn.edu/Xdong/geniang_honte.html GRAIL *I3,*14,*15 http://avalon.epm.omI.gov/ S6to5fE$ -> 3 OC1 *16 k b http://www.cs.jhu.edu/salzberg/announce-oc 1 .html Sorfind *17 )V— — 7s k b ftp://iubio.bio.indiana.edu/molbio/ibmpc PROCRUSTES *18 http://www-hto.usc.edu/software/procrustes/ MZEF *19 k b.79X,yO*f XfXf http://clio.cshl.org/genefmder/ *1 Solovyev V.V., Salamov A.A., et aLPredicting internal exons by oligonucleotide composition and discriminant analysis of spliceable open reading frames..Nucleic Acids Res-.Vol.22 No.24,5156-5163(1994) *2 Bronak S., Engelbrechl J., et a/.:Prediction of Human mRNA donor and acceptor sites from the DNA sequence. ,J. Mol. Biol. ,220,49-65(1991) *3 Guigo R., Knudsen S., et al. :Prediction of gene structure.,J. Mol. Biol. ,226,141-157( 1992) *4 Borodovsky M. and Mclninch J.D.,:GeneMark: Parallel gene recognition for both DNA strands.,Computers & Chemistry, 17,123-133(1993) *5 Borodovsky M., Mcininch J.D., et a/.:Detection of new genes in a bacterial genome using Markov models for three gene classes.,Nucleic Acids Res.,Vol.23 No.17,3554-3562(1995) *6 Fields C.A., Soderlund C. A.:gm: a practical tool for automating DNA sequence analysis.,Comput. Applic. Biosci.,6,263-270(1990) *7 Snyder E.E., Stormo G.D.:Identlfication of coding regions in genomic DNA sequences: an application of dynamic programming and neural networks., Nucleic Acids Res. ,21,607-613(1993) *8 Snyder E.E., Stormo G.D.:Identification of protein coding regions in genomic DNA., 3. Mol. Biol.,248,1-1 8(1995) *9 Milanesi L., Kolchanov N., et al.:Guide to human genome computing-chafer 8. Sequence functional inference,249-312 Academic Press limited,(1993) *IOMilanesi L., Kolchanov N.A., er a/.:Genviewer: A computing tool for protein-coding regions prediction in nucleotide sequences. ,In Proceedings of the Second International Conference on Bioinformatics, Supercomputing and Complex Genome Analysis,573-588( 1993) *11 Kulp D., HausslerD., et al.: A generalized hidden Markov model for the recognition of Human genes in DNA.,In Intelligent Systems for Molecular Biology, 134-142(1996) *12 Dong Searls D.B.:Gene structure prediction by linguistic methods. ,23,540-541(1994) * 13 Uberbacher E.C., Mural R.J.: Locating protein-coding regions in Human DNA sequences by a multiple sensor-neural network approach- ,Proc. Natl. Acad. Sci. USA,88, 11261-11265(1991) *14 Uberbacher E.C. , Xu Y., ef a/.:Discovering and understanding genes in Human DNA sequence using GRAIL..Methods Enzymol,266,259-280(1996) *15 Xu Y., Mural R.J., et ^Constructing gene models from accurately-predicted exons: An application of dynamic programming.,Comput. Applic. Biosci.,(1994) *16 Salzberg S.:Locating protein coding regions in Human DNA using a decision tree algorithm. ,!. Comput. Biol. ,3,473-485(1995) *17 Hutchinson G.B., Hayden M.R.:SORFIND: a computer program that predicts exons in vertebrate genomic DNA.,In Proc. 2nd Int. Conf. on Bioinformatics, Supercomputing and Complex Genome Analysis, 513-520(1994) *18 Gelfand M.S., MironovA.A., et a/.:Gene recognition via spliced sequence alignment. J*roc. Natl. Acad. Sci. USA,93,9061-9066(1996) *19 M.Q.Zhang.: ldentification of protein coding regions in the Human genome based on quadratic discriminant analysis. ,(in press) — 45 — Z 9 2 GeneParser j: () Av^Ag#L"C^&o L^'L, y — f-iK ##%D 6^T^V'o GeneMark v 3-yTI^-eil6§tifc GeneMark(iORF t 5th-v v>T^ -Bo Haemophilus influenzae, Methanococcus jannaschii, Mycoplasma genitalium ta $ ii xij $ £ t f A 4i M t> h 0 . GeneModeler GeneModeler (Ceter for Advanced Computing in Molecular and Cellular Biology, USA) (i. C. ORF, %79/f %## &X GC^I* diamino, dinucleotide<7)MSt& £'% X rr tfbL*C^. GeneParser GeneParser (Colorado University, USA)(±#}ili<7)iBn"fb{- —^ —7 ■; 1-7- f&fHV'TV'&o GeneParser 2"C It, GC#"#BUl:m*cf(f &&X.& j: 9 C GeneParser 3(iC GenViewer GenViewer t±> X79 f Xg&{£?)7 3 T, 3-K#r > v 7 :txr;x?) fHifcffV', i^v>x V Genie Genie (UCSC/LBNL, USA){±—flS'ftKtftvfl' 3 7;t(Generalized Hidden -46- Markov Model (GHMM)) ^ nrr-WHMMtli, —0(7)/- Lri'U uWGHMMt'ii—owy - > b n ><7)ii^ii#SLTv^0 y- KR9 -cv'&o y/ff 1996^6^1:ISMB96"C%m%, f&y%TA(%)^c»hux.Z9o GeneLang GeneLang (Pensilvania University, USA) (J ilfci1 ^ tOV £ v > b t$> & jCi# co jU/£ U il alX, Prolog, of i), ;V-;v<7)S’eSEL^o C sntoul±, (Definite Clause Grammar) t LTBSi&SfiTV'So i£P^;K7)X =r rtf1—5Mc-e*ioTi't\ *#U$ftT-E-ft<^)#f@#CJ:^y>(±3-K^T>yf X3Ti:iW( U/io>iiX7'7-f x§Hi©X3T5js±t:^otv^o GRAIL GRAIL (OakRidge National Laboratory,USA)(i> K#MP#J > &o 2 OC1 Oblique classifier (OC1, Johns Hopkins University, USA)T(±, i^/ET^T-ii; V'TV'So #fi<7)ESt#l®C) X n T U^-3 ^3- ##3- K#%?&&d'&mB!lT&o — 47 — Sorfind SORFINDli. 60bpm±(±^fi^GU-AG^tcORF$"*c»^. %794xgB{&(7)X 3 7, 0RFO3-K#T>yt;l/, ^T(7)%3 l±, #X37(D$&#i|g^$:L"C 1^ 77(7)3.9 9 PROCRUSTES PROCRUSTES(UCSC, USA)1±£ < # W t) ft & v ' %m KM LX, oft), L^v>S^iIiJ-e § & vv nmM MZEF MZEF(Michael Zhang’s Exon Finder, Cold Spring Harbor, USA)(±4IF4^ 9 9 9 & #m-ei8bp (79lf K79X(±9bp), ##"C999bp (79 k: K 7 y X U 2,000bp)(7)3. 9 9 9## &#& L, 9 #<7)##(7 9 tf F 7 9 X l± 10 #)e@ -3-C-eW^^ V 9f #$^1/2 Z «)±# v^h^9 9 9T6& 2: (2) af# r(l) 9 9/<9*3-F##?#|9XTAj C^V'-cm^L^^miyXTAiim^L'C IfOfSSiM ?z h $%, 7079 ^^#^, j: 0 6nT#'C&&o Cft3'f#Lw yXfA /5 t L"t(±, R. Guigo -48- Xff-f #5#J (?^8^ 9^) Guigo&kL cm±A-3Tv^####<7) ‘complete gene ’Bfi^J (Itn^ K 6#-% 3 K > $ T<7)i"^ Exon Nucleotide Level Exon Level Tool Loci actual predict Sn Sp ACC AC cc Sn Sp ME WE GRAIL2 188 571 529 0.65 0.87 0.88 0.59 0.62 0.42 0.46 0.19 0.12 GRAIL2(gap) 188 571 513 0.54 0.66 0.81 0.46 0.50 0.49 0.63 0.24 0.14 GenLang 185 568 685 0.47 0.65 0.77 0.32 0.35 0.36 0.30 0.35 0.37 FEXHB 188 571 762 0.71 0.78 0.88 0.63 0.63 0.47 0.35 0.20 0.37 FGENEHB 188 571 492 0.57 0.84 0.85 0.53 0.55 0.36 0.42 0.35 0.14 FEXH 188 571 650 0.73 0.77 0.85 0.64 0.65 0.55 0.48 0.15 0.26 FGENEH 188 571 557 0.63 0.78 0.84 0.54 0.57 0.54 0.55 0.19 0.16 HEXON 188 571 486 0.46 0.74 0.77 0.36 0.42 0.42 0.49 0.29 0.17 GenelD 188 571 645 0.60 0.73 0.85 0.51 0.53 0.43 0.38 0.29 0.32 GeneParser2 188 571 624 0.61 0.71 0.83 0.54 0.54 0.36 0.33 0.23 0.29 GeneParserS 186 582 588 0.76 0.82 0.90 0.71 0.71 0.51 0.49 0.15 0.19 M^>± $-7cc#?miy XT AT## v^f^. U-tirJT xr; t>V' 49- &o (Specificity) Sp(±. 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CCTD^-C^ yy^-^y < y l±V'x.f. 21 &#{&Lt±?. z c is % fit ;m & (7) f-emm-e # & & „ m±<7)± 9 I-cDNA-fishingt±, 9 >^y®*3- Y'-$ &^#(7)cDNA -#mc#PCR&m'"c -52- ##C##f6o 2fi6(D4]'C^m^cDNA'r&&2 2:^^ cDNA(7)5'^Z^3'%e^ n —— > y-f h RACE & (Rapid Amplification of cDNA Ends) <£>—® "Cab & h zm^fz PCR K£Z> CDNA <7)S#M<7)M n £E 1-20 C^Ltc mRNA (polyA RNA) ^ 1st Strand Synthesis y- AWv%iWVAVAPATUVVWAW/WIV- XULUli-1 r-r :-:_v.;-iJi^.—--.-r-iKli'nTiT;r=i-y ^ 2nd Strand Synthesis cDNA — ’ =^:%3 ^ Adaptor Ligation 1 PCR Full Length r_==------1------cDNA ~ * !f El-20 7 ^7°7-e#t)W^^mcDNA(Om# z-f. muh S VMS& ^ mRNA(*° V AttiO RNA)£i6ffiL. t')=r( dT)79^ '7-&m'TcDNA&-&#L, y-va *\m-tz> 0 c^cDNA(Dm^#e#ma c-c. mpsem v -(gspi &£td±GSP2)i TV?97 4 v-(APl)£fflv>TPCR£lT p - tlzX 0, TSEfclfiJ —(GSP1)cDNA<%3’ffl/$C ±E* IrJv *tz 7 y A v-(GSP2) emv'tiecacDNA^ 9 C GSP1 t GSP2 ^nefi±Ei3 Z rrFEKftfi-r & £ 7 K LX 5’ fc 3’-RACE # £ 9 (cEltL-Cti < ChCZ^T. 3’* £ V5-RACE «M^n f ? hem-a'LT7--;p$iir^^ $^i:^# -53- ##(08 V' PCR 9 6 a o (2) hybridization % f i/f f- xmmmMc2a%we#muy. a DNA & a V4i RNA e#m& a a ^ * I), < #BU t^63KM$frCv\&a#T6ao #6ftm%^6^)(±y^7/\ 4 7" V V 4 -tf— 7 a 7 (Southern Hybridization) "7% V > DNABrlt h n -tr;vn-xE^ hi:$5%:U FylSS$ *l£#£<7)SSE?iJ DNA Brlt^yo-yh Ly/^yv y/f-tf-y a 7$-±^#, t- b9^ty77/f V'T#mi-a^&"e6ao 7V y^f-tf-y 3 7^#(:(±#/(r^/ V y t -t: - y 3 7 & ifvHtMy & a 0 t fcSJBffl b L*Ct±, 7 4 7 if-?V 7 r 4 7 7\ T7 7 7 7 7 y -v )V/\4 7"V y 4 -tf— y a 7 4\ SBH(Sequice by Hybridization) 5’6\ FISH(Fluorescence in situ Hybridization)^ 7 < v>/:? n — 7 jjHb LTt±> tf^-f-7 - 7 tfy 7co|g^- J^DIG(Digoxygenine)i:'? ‘<7)i^#^:fflV'7t:t) <7)&av4iFiTC#(om^#m^mv^6%^m'e&ao $6s. la^yii^myvx *y$&*?$k% /f yv y/f-e-y a 7Bm — 54 — 1) Chenchik, A, Moqadam, F. and Siebert, P. (1995) CLONTECHniques 9, 9-12. 2) Don, R.H., Cox, P.T., Wainwright, B.J., Baker, K. and Mattick J.S. (1996) Nucl. Acids Res. 24,4008. 3 ) Hecker, K.H. and Roux, K.H. (199 6) BioTechniques 20, 478-485. 4 ) St.John, T.P. and Davis, R.W. (1979) Cell 16, 443-452. 5 ) Drmanac, R., Rabat, I., Brukner, I. and Crkvenjakov, R. (1989) Genomics 4, 114. 6) Dramanac, R. Rabat, I. and Crkvenjakov, R. (1991) J. Biomol. Struct. Dyn. 8,1085. 7) Scholler, P. Karger, A.E., Meier-Ewert, S., Rehrach, H., Delius, H. and Hoheisel, J.D. (1995) Nucl. Acids Res. 23, 3842-3849. 8 ) Regalado A. (1996) Technology Strategies 24-30 1-1-5 "1-1-2 (4) tr* ####, #CBftv(Hidden Markov Model: D.THMM)<^f#fi14 XAO&A HMM N'it, Viterbi 7 )V n" 'j XA u t Uf l£fi& WjffJStl@i(X (Dynamic Programming: DlTDP)(7)-aM!t7^rf0 XA t HMMKZ&YJ ifti )i &Viterbi7)Vrf V XA<7)pViterbi7XV XA? HMM $ 7 1 Xi hmm k j: o rry mi^hmm^ ? 17-7 (NeuralNetwork:mTNN)^€'^i:^&o - 55 - ^ Ltx Kulp biziz,-mt HMM(Generalized Hidden Markov Model: W'L. 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Fujiwara Ct Yada £> <£>#££ !±. —#%tHMM $ v St. C4iG<7)#&TW:. yvAgm^c^u &t)6^7") - i/ 3 >4^#^4 7-9 —y 3 >4^gg &d*T#&V'o f; X A £ m%-f x m 1) Levinson, S. E., Rabiner, L. R. and Sondhi, M. M.: An Introduction to the Application of the Theory of Probabilistic Function of a Markov Process to Automatic Speech Recognition, Bell Syst. Tech. J., Vol. 62, pp. 1035-1074 (1983). 2 ) Krogh, A, Mian, I. S. and Haussler, D.: A Hidden Markov Model that Finds Gene in E.coli DNA, Nucleic Acids Res., Vol. 22, pp. 4768-4778 (1994). 3 ) Yada, T. and Hirosawa, M.: Gene Recognition in Cyanobacterium Genomic Sequence Data Using the Hidden Markov Model, Proc. of the 4th Int. Conf. on Intelligent Systems for Molecular Biology, Menlo Park, Calif.: AAAI Press., pp. 252-260 (1996). 4) Kulp, D., Haussler, D., Reese, M. G. and Beckman, F. H.: A generalized hidden Markov model for the recognition of human genes in DNA, Proc. of the 4th Int. Conf. on Intelligent Systems for Molecular Biology, Menlo Park, Calif.: AAAI Press., pp. 134-142 (1996). — 57 — 5 ) Lewin, B.: Genes, Oxford University Press, 5th edition (1994). 6) Takami, J. and Sagayama, S.: Automatic Generation of the Hidden Markov Network by Successive State Splitting, Proc. ofICASSP, pp. 2-5-13 (1991). 7) Krogh, A., Brown, M., Mian, I. S., Sj” olander, K. and Haussler, D.: Hidden Markov Models in Computational Biology, Applications to Protein Modeling, J. Mol. Biol., Vol. 235, pp. 1501-1531 (1994). 8) Fujiwara, Y., Saogawa, M. and Konagaya, A.: Stochastic Motif Extraction Using Hidden Markov Model, Proc. of the 3rd Int. Conf. on Intelligent System for Molecular Biology, pp. 121-129 (1994). 9) E;n#A, &&DNA Vol. 37, pp. HIT- 1129 (1996). 10) Asai, K, Yada, T. and Ito, K.: Finding Genes by Hidden Markov Models with a Protein Motif Dictionary, Proc. of Genome Informatics Workshop VII, pp. 88-97 (1996). 11) Gribskov, M. and Veretnik, S.: Identification of Sequence Patterns with Profile Anal- ysis, Methods in Enzymology, Vol. 266, Academic Press, pp. 198-211 (1996). 12) Bairoch, A.: PROSITE: A Dictionary of Sites and Patterns in Protein, Nucleic Acids Res., Vol. 20, pp. 2013-2018 (1992). 1-2 S# £ W ^ o C> mRNA ti 1 9 ® £ 3 - K LtA'S (f / h V - v (CAP#1&) &ZCf3'*%<7)#200bp?)7f-;i'm(:Z&## mM%<7)AUG3K -58- (SDK^!l, %#^#mRNA(7)^VA A^#mLTtU^(dT)@^il:*9 A^ &'&## L"C mRNA U ^V-A RNA ^ k'f)#?)RNA)0' 6^#fa C hM#"C& 0 . yy e%#L-CcDNAe-e-Rlci-&^^(:#m$fi-C^ &o AWtL^^ca^6im#(D%#^Mv^2Z:(±^»r#T6&^ 1 #mmRNA#fC#% ?ii RNA ^ v y 9 - -tf c z ioobp@&(o^» v -kmc&^amgm -E-cc^mimaf^^LTRNA^v ^9--tfZ:m:#%$)av4±m#%[: RNA^V y 9--fe*!i^§&f£<7)r fma-a-fa c z: o , &#&#& z mmAgmv'f %##-&(: t a#^mv^acaM#BT&ao 2. 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T ^'-S t b "Cafe-So C (Dt^'uiz & „ cDNA microarray %t)-E-9"C66o AffyrnetrWimf y7##mmi20 < %#(7)7f h 07/974 -R#T(±-f v/±C0.3 < 9 & L< (i0.2 < RT#h^c"Cj3i), 4"# $ ^ (=$ < <7)oligonucleotide& 9o x m I) Liang, P., et al., Science, 1992. 257: P 967*971 2 ) Liang, P., et al., Cancer Res., 1992. 52: P 6966*6968 3 ) Donohue, P. J., et al., J. Biol. Chem., 1994. 269 (11): 8604*8609 4) Donohue, P. J., et al., J. Biol. Chem., 1995. 270 (17): 10351*10357 5 ) Miniati, D. N., et al., Cancer Lett., 1996. 104: P 137*144 6 ) Nakagama, H., et al., SfiM ## BE, 1996. 41 (5): P 580*584 7) =p 196-200 s) wmei, rpcR#o###*j =p 114-123 9 ) Kato, K, Nucleic Acids Res., 1995. 23 (18): P 3685*3690 10) Kato, K, Nucleic Acids Res., 1996. 24 (2): P 394*395 II) Strezoska, Z. et al., Proc.Natl.Acad.Sci.USA, 1991, 88: P 10089*10093 12) Maskos, U. et al., Nucleic Acids Res., 1993, 21:P 2267-2268 13) Drmanac, R. et al., Science, 1993, 260:P 1649*1652 14) Kozal, M. J., et al., Nature Medicine, 1996. 2: P 753*759 15) Kreiner, T., American Laboratory, 1996. March 16) Schena, M. et al., Sciense, 1995, 270:P 467*470 17) Schena, M., BioEssays, 1996, 18:P 427*431 18) Schena, M. et al., Proc.Natl.Acad.Sci.USA, 1996, 93:P 10614*10619 19) Shalon, D., et al., Genome Res., 1996, 6:P 639*645 2-1 -4 45^074-nmvt -66- -^mwfum^&Tyn yoff-Ah(±AT(7)%mma^)-m^mf^o 7aTt-Ae#mcL -c <7)ga mk#c z a 7 /f - ##m±#a& i% /< ^ T(D##cjN- L -c, @ yaft-A^# W'L. mm^#me#mL^mamT (1) WHWmrn# a. sa*-^5ctt»ac»as mam%^7cm%#(mmi±0'FarreUt: j: 0 y;i/±c%^'y h^L-c# tf b tl%>o 1. 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ExPASy ittp://expasy,hcuge.ch/ Links to other databases federated * University of Geneva 3rfift?#ffiHool, software##^ SWISS-2DPAGE iltp://cxpasy.hcuge.ch/ch2d-lop.html University of Geneva Links to other databases federated Z-DF. h 3^(7)138 HSC-2DPAGE htlp:/7www.harefield.nthames.nhs.uk/nhli/protein/ Harcfield Hospital (UK) Human heart, endothelial cells, rat heart, dog heart federated chamber-specific, disease HEART-2DPAGE http://www.chemi6.fu-berlin.de/user/pleiss/dhzb.html German Heart Institute Berlin Human heart (ventricle, atrium) federated (dilated cardiomyopathy)-specific proteins HP-2DPAGE http:www.mdc-berlin.de/~emu/heart/ Max Delbroek Center Human heart (ventricle) federated WSf — ^ t" <£ proleinf&Sservice PDD http://www-lmmb.ncifrcrf.gov/PDD/ NIMH, NCI Human body fluids in disease states federated t tern CSH QUEST http://siva.cshl.org/ Cold Spring Harbor Laboratory Yeast, rat fibroblast cell lines, mouse embryos partially federated 2-DE based protein database?)## MitoDat http://www-lmmb.ncitcrf.gov.80/mitoDat/ LMMB.NCl mitochondria partially federated m i t ochondr i a^BE# jftfS T" # ft? PMG Argonne http://www anl.gov/CMB/PMG/ Argonne National Laboratory Mouse liver, human breast cell, Pyrococcus furiosus partially federated s.e. i; i z mmitnmm Cambridge 2D PAGE http://sunspot.bioc.eam.ac.uk/ University of Cambridge Links to other databases partially federated entry point YEAST 2D-PAGE http://yeast-2dpaee.gmn.gu.se/ University of Goeteborg (Sweden) a number of species of yeast, S.cerevisiae focused partially federated S. pombe, C. albicans, R. glutinisIS YPD http://www.proteome com/YPDhome.html Proteome, Inc. S.cerevisiae partially federated yeast proteins^# 1-Mf"^database Institut de Biochemie et Gen clique YPN http://www.ibgc.a-bordeaux2.fr/YPM/ S.cerevisiae partially federated 322 spots?)|s|5£ Cellulaires (France) rat liver, mouse liver, human liver, plasma, com, 2-DE maps http://www.LSBC.COM/2dmaps/pattems.htm Large Scale Biology Corporation partially federated IEF/NEPHGE 2-D pattern wheat, rabbit psoas muscle Maize Genome http://moulcm.moulon.inra.fr/imgd/ Institut National de la Recherche maize partially federated maizelcMi'h relational database Database Agronomique (France) 2D-PAGE Protein http://tyr.cmb.ki.se/ Karolinska Institute Drosophila melanogasler: major body parts partially federated Drosophila^Z-fi& database Databse Biobasel2-D PAGE Human keratinocytes, transitional cell carcinomas, human skin, cancer cell# http://biobase.dk/cgi-bin/celis University of Aarhus (Denmark) partially federated Database bladder squamous carcinoma, urine, MRC-5 fibroblast, 2-DE database mouse kidney University of Michigan Medical EC02DBASE http://pcsf.brcf.med.umich.edu/eco2dbase/ E. cali partially federated E. coli proteins# MHfl Center *ExPASyL Z 62-DEy — (###&. hypertext cross-referenced =t S-ftilT'— 9 7, ^<07 & -b^^) & Acti. LA: t> 0 ho X ffi 1) Kahn, P. :Sde%ce, 270, 369-370 (1995) 2) Wilkins, M R., Sanchez, J.C., Gooley, A.A., Appel, R.D., Humphery-Smith, Hochstrasser, D.F. and Williams, K.L. :Biotechnol. Genet. Eng. Rev., 13, 19- 50 (1996) 3) Wilkins, M.R., Sanchez, J.C., Williams, K.L. and Hochstrasser, D.F. ■.Electrophoresis, 17, 830-838 (1996) 4 ) O’Farrell, P.H. :J. Biol. Chem., 250, 4007-4021 (1975) 5 ) Dunn, M.J. and Corbett, J. M. -.Methods Enzymol, 271, 177-203 (1996) 6) O’Farrell, P.Z., Goodman, H.M. and O’Farrell, P.H. -.Cell, 12, 1133-1142 (1977) 7) Bjellqvist, B., Ek, K, Righetti, P.G., Gianazza, E., Goerg, A., Westermeier, R. and Postel, W. .J. Biochem. Biophys. Methods, 6, 317-339 (1982) 8 ) Goerg, A., Postel, W., Guenther, S. -.Electrophoresis, 9, 531-546 (1988) 9) Bjellqvist, B., Pasquali, Ch., Ravier, F., Sanchez, J.-Ch. and Hochstrasser, D.F. -.Electrophoresis, 14, 1357-1365 (1993) 10) SWISS-2DPAGE Homepage (http://expasy.hcuge . ch/ch2d-top.html) Technical information on 2-D PAGE 11) Miller, M.J. “Advances in Electrophoresis ” Vol.3, p. 181, VCH (1989) 12) Dunn, M.J. “Microcomputers in Biochemistry :A Practical Approach ” p. 215 IRL Press (1992) 13) Gillece, B.L. and Stults, J.T. -.Methods Enzymol., 271, 427-448 (1996) 14) Hillenkamp, F. and Karas, M. -.Anal. Chem., 60, 2299-2301 (1988) 15) Fenn, J.B., Mann, M., Meng, C.K., Wong, S.F. and Whitehouse, C.M. -.Science, 246, 64-71 (1989) 16) Billed, T.M. and Stults, J.T. -Anal. Chem., 65, 1709-1716 (1993) 17) Henzel, W.J., Billed, T.M., Stults, J.T., Wong, S.C., Grimley, C. and -71- Watanabe, C. \Proc. Nat. Acad. Sci. 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V — — > 57"'t"62 h C j: 0 x homeotic gene Antp j: 0 ^ 6 strain Lx Antp tc X 0 negative &BU ^fs^fiJfPT^itLH1^ homeotic gene salm"C&-5> L.b ^MBMLfb 31)0 ##(7)^(tobacco, potato, ArabidopsisltcisivxT & b-glucuronidase £ l/*° — ? — ^ 2) $K^af knockout $fi"C 6« Hh (3v7x tri> homeotic gene productx steroid receptor superfamily CMi" h knockout M~t %>&Msb L T (±#1W L (± differential hybridization ?£35) ;5sSiB $ tiX^'tztih #H1 subtraction^^) (i%Am^%^i'TM#$fix PCR l#$M^#A$fiAL b t- X h suppression subtractive hybridization &37' §l$|‘/n 7 'f — ;!/##%#L L’Clil) DNA sequencing ##L j: %> BodyMapvi 38) > SAGE(Serial Analysis of Gene Expression^ 39 \ 2) DNA Writ### L Z & -88- Differential Display i£40,41)> Molecular Indexing v£42)> SAFE(Selectively Amplified restriction-Fragment Electropherogram)?i 43\ RLCS(Restriction Landmark cDNA Scanning)?^ 44) > 3)hybridization#$r 6 Sequence Fingerprinting High-density cDNA filter hybridization &46) > Microarrayed cDNA chip ’}£47)> High-density oligonucleotide array 48) . 4 ) 7'nft-^ throughput f-^^-%2:m#L^cDNAKr Saccharomyces cerevisiae J U tiT jo D 49) > Is^BU v YPDy'-?'<-X(D*T=fV-#m[: (148#) f #E#afaL"CM&$^^m8K(±156#. ^(7)^38 # (24%) 6,ooo#2:m&$fi-ca D. ^ 3oo#am#<)ft&<7)T, mm mi-nknockoutm^wtmm^vy j x 0 1) Maniatis, T., Fritsch, E.F. and Sambrook, J. “Molecular Cloning. 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B — 92 — mf&z tc DNA^-e-#^>/^#af^mmi-&DNA (i-mcmbp -+bpo , „ DNA^^ ^#:(7)^ $ &CBE&K7)DNA$g'&#? <7) < . tb a.y/ fi-ctz ^ i±# g-e(±^^o at, -ecc^f&K^$(i#af<7)## 6 z h#mg"C6&c (i) t # x. ^ (7)±m#i&<7)Tm(:^ (7)##%# e t *#$IJ#l#%<7)##^^#f&ZaM#&o Z(7)^, @4 ^-misf z & ca o-c. %i-&za^"c#& (Bis-Do -f&tdx ^<7)mm^T(7)##&]E#(:#;#i-6zaM#'C6&o L^L, $^c. m%iie%(:j:^-c^fL^(7)DNA&#i-&mme < mg#; aumdt&^a & &„ (2) y;^y 7 &. y^5/7hm'\ 7 7h7U>T'&^^^(T)#8iJ^^m^^2:L»v^&^6, m -93- 5’ Reporter Gene h 133-1 M|6]3) £fflv>£k<7)^Se@7°7X*>£,S4\ S^E»E5) ^AFMCAtomic Force Microsicopy)"'^k\ @4r(D^ m&. mm. y;i/y7h&T(±. #$m(:DNA&#mi-&t&. #m^&hL"C(±32P#(=Z&% L^'L. y^S/7hmcmv'6fL&DNAKfM-(±m^l00bp#KmTt DNA«rM-msp<7)^mmmme#mi-&2 2:(imLv^ %^-c. &c a##L<. $ 6c$6^w#af&c 6 2 2:^ r#. V'6ma 2: #x. 6. y;]/y 7 b^(i. #*m:#m$fi2:DNABf#-& ? &6%. y;i/y7b&t:^»6^)(±. frb, gBDNA'>-^>-y--&&v>t±^tf 7 V-r WSiE;?;kS6$IB7) %£\ 6S y^i/7 b&(±M%^f|:T'C^7;<^@2:6D# — 94 — /<> KiM£ 9 £912 Tv h^Ml.2 x 10 17 mol) 8) <9DN &oTv & 2 a ^ G. L-c^+^^ (:w#-e&6 k#x.6*i&0 y^77 bm[±m#'CK^I##%DNAie'&'^ 7? h7V > W'L, m# 2<7)z^ trf / u 7 b u-c «&##&&&h#;l6fLao ^ ffi 1) Gamer, M.M. and Revzin, A. (1989) BioThechniques 7, 346-355. 2 ) Galas, D. and Schmitz, A. (1978) Nucleic Acids Res. 5, 3157-3170. 3) Checovich, W.J., Bolger, RE. and Burke, T. (1995) Nature 375, 254-256. 4) Davis, R.D., Edwards, PR, Watts, H.J., Lowe, C.R, Buckle, P.E., Yeung, D., Kinning, T. and Pollard-Knight, D.B. (1994) Techniques in Protein Chemistry V, 285-292. 5) Griffith, J.D., Makhov, A., Zawel, L. Reinberg, D. (1995) J. Mol. Biol. 246, 576-584. 6) Allison, D.P., Kerper, P.S., Doktycz, M.J., Spain, J.A., Modrich, P., Larimer, F.W., Thundat, T. and Warmach, R.J. (1996) Proc. Natl. Acad. Sci. USA 93, 8826-8829. 7) Kambara, H. and Takahashi, S. (1993) Nature 361, 565 8) Mclndoe, R.A., Hood, L. and Bumgarner, R.E. (1996) Electrophoresis 17, 652-658. 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Ofgmb $: ## C L T K (gi3-3)o /\/fyovp&^) mm-e^ao se-e-Be^iemmi-aftt'^cm^^c^t'TDNA-BDL^^ga Mm:%m$-±tAD-/<-hf-gam-&#a#*&'&&#x.6o tL^fa^^lf, iiem%CDNA-BD2:AD^#^@e2:'e -&&^LTim#i#CiB'&f &chc&&(7)TV;K-?-#fsf &f5%ibi-&#;%&6"3o — 99 — ^ Yeast GAL4 Protein (DNA-Bp[ "C UAS ) Promoter} Reporter Gene (lacZ or His3) Transcription Idna -bd I 1 UAS ) Promoter Reporter Gene ( /acZ or His3) j" cDNA Library GAL4 Reporter Gene (lacZ or His2) Target Sequence T Transcription XX y Promoter Reporter Gene (lacZ or His3) |" A UAL4tC£& ALf. DNA-BD, DNAffnn^^ UAS, GAL4SS&E51J (Upstream Activation Sequence ) B • )sa 70 v bp. Target Sequence i:g-&T Sit E3-3 7 > • /'>f 7"V -7 (@3-4)o bSffl m$DNA^e^$s^$ij#iaf- vt ? u z t ^7> 7 $:3- Kf 1993^m%(:2^$- f'Jffl LA Wang 6 7- - yu 7 R#&#cT#;W(:m*&frCV'ao ggG-15) ^^3-1 £MT & Og fi It 1ST- —100 — "C § & 0 Activation HI 7- “ smaa 7 Rb TNFR HIV p53 c-Jun Ras Grb2 Max SNF1 t reverse FAS/APOl /-K 3-4 C dna UAS (**D<7)) ad Gag > - • bd ) Sequence /W j Promoter Promoter V — DNA-BD; ) two 7"V YtiS6SJ-Ca56@'6'i:cDNA7-i' 9/79U - Transcription X; Reporter Reporter -101- $> hybrid -7 OAMDNA**#: TRAP-1 ibA'KAKSSfLfcTarget Protein Sosl T-anrigen c-Fos Raf MORT1 Cyclophilin MM SNF4 h-r-aa Gene Gene -^^.^sn^aa 7*0 1 phosphatase, ( ( dna lacZ l&cZ system A, v - or or bdz B ttii His3 His3 Type x UAS: ) ) ja -75 Z0»AD/YtWK*L 1 ” Protein GAL4S$-£g2?IJ ') — b h'b7.V'i 30R 23 22 20 24 21 25 19 18 17 16 - ri-"Cv^ 16) 0 S iii) Mx.1^ L-cd^, &&maa^-e# coawmmmLv'cas &Z ad:4?&< &V';K &fr£dfr £ mm L £ii{5^<7) W#t±liM[6] ic & 0 DNA-S 6 J: ^'S ti-§ 6 o V y'r-'J ■ Vy x m 1) Wang, M. M. and Reed, R. 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(1996) FEES Lett. 390, 327-333. 3-2 "j v 7 - o h%-#"#-&RNA#o * 7--tfnci o suspense signal sequence 1 Rf DNA^'f(7)#^#iSl##K)C#ISf#fHi' Z>0 > 9 — ^ "j h 'SrS BT T 9 *b X iTSB & t ^ ^ /x—x C ii> *#5iSi9;iii!f:K-F‘ Rehovot T^gf, ISREC (A 4 A) T&2:o?)7 7/f ^ (303kB, Release 48, 1996¥ 10 H) t LXWM$ fiX v>-2> (ftp://expasy.hcuge . ch/databases/epd) 0 EPD t± EMBL T— 9 • 7 i 7° 7 0 database "C, EPD CM#*# &#RNA#9;(7-'tniCj:9mm$fi& nx^&o $frT 0 , EMBL^titthML &--S:L&v>0 @3-5 CXn^-XSSl?)—£X FP Hs c-myc P2+:+S PRI:HSMYCC 1+ 2490; 11148.053 010*2 XX DO Experimental evidence: 4,4#,<2> DO Expression/Regulation: +mitogen RF Cell34:779 EMBOJ2:2375 MCB7:1393 MCB7:2988 n ______) @3-5 EPDfiWM l), #7A3-5M8T, T^XHl*7A72emx.4rWo m{&mc@i-&##e^tfFP^fT^$D, (//) T#t»&oFP^ii, yo HO# (*7^6-30), 6 EMBLE^J#m^^&@K (ijy A31-55), (* 7^68 -72) yn —105 — ■ 7')V — 7\ @d£!j<7)M (circular/linear), strand(+/-) #<7)# WLiiFPmz^tft&o EI3-5<7)FPtf(±x iiiiiHs (U), c-myc, fflteV T h t± P2x subset status /5 sindependent, H£q-+hT h <7)M/5ssingle, EMBL division code /$' PRI, Entry name ^HSMYCC, @d£!l <7)1!! ^'linear, strand/5s+, 2490, entry code /5s 11148, homology group number /5s 053, alternative promoter identification code ^'010*2 ’Qfa'b Z. t £:Si~o 7°n^e— DOlf "C (4* (D 0 —o <7) DO If Ir c S 1 & (7), Jk.U length measurement of an RNA product t- X & i> 0 , %%i± mitogen tl&o Transcription Factor Database (TFD) 2) ti, # < (7)(: Mi" 6 Bd^lj'l## ^##L, j: 0#^$ft"CV''6o David Ghosh (ghoshd@rockvax. rockefeller.edu)C<7) i ## i i ##m&#<7)m5-ciw CLONES (2106 records) DOMAINS (1016 records) FACTORS (523 records) POLYPEPTIDES (1626 records) SITES (2155 records) METHODS (38 records) N_POINTERS (2876 records) REFERENCES (8509 records) X_POINTERS (5757 records) -106- Cl C X\S3fllf*3t± Release 7.5(1996 ¥ 3 J), ftp://ncbi.nlm.nih.gov/repository/TFD ) CLONESliK^BfcDNAcoK^J DOMAINS / FACTORS Izmir & -®-ltIE£ x POLYPEPTIDES 5 / ESd^iJ £ x SITESl±K#Bf C1: METHODS(i^#^&e, REFERENCES ii 31 ffl il© 7 7" X o N_POINTERS t X_POINTERS - ^^-7 (Genbank, EMBL, SwissProt, PIR, Genpept, NCBI-Backbone) k TFD t % ') > tirZtzibiztiiFfl SttSo Kk±. TFD r— ? ^ - x cb SITESt - 7Gv f & £ 0 CtU±, CZ m-7ACf ^<7)fm± 14i@<7)7 i ~)V Kiz-lflE^SBESnTv^o ::tf, SITE_ID (± Sites entry identifier, FAC_NAME SEQ_NAME liSi^J £ /;SxW> hco^BUx NA_SEQ immm. SEQ_TYPE mm ^&m*x LOCAT-REFt±, LOCATIONSS<7)#bs ti;, LOCATION limRNA start U METHOD N_PROB (2^<7) SITES@W^-yA@^J±[:#^I:m6fi6 # $, REF_N i± reference number ~Qhho TFD Offi'C, ^CHCE& t — 7"(±, RT-co Sit k ## e Ei£ L fc DOMAINS *C* & £» DOMAINS MW £ 0 3-7B "C, 5«3*me TTAA7CCCTT eeen-ski ppei Science 25* UNA »t»rt »• LOCATION -1S62/-15S3 9 DOMAIN ID D00293 tAC _HAME C-Myc STPVC_CLAS heli<-lo»p-helix AA START 1*6.00 AA^SEQ VKRRTmtVLtRORRNELKRSrrAt.RDQIPELENNEKAPIcVVTLXKATAYILSVQAEEOXL rUVC_CLAS dimeritition CCWENTS Hyc ainvi let ity region MAIM_REf Genes Dev « 167-79 <19901 R£r_N 902*972* a. sttes-t-r^ns.m 0 3-6 TFD -107- DOMAIN_ID t± Domains entry identifier, strnc_clas $ A: lit-f — 7^#, DOMAIN_NUM t±amino terminus IZft-fZ K* T AA_START l± 9 >/ & starting position, AA_END t± stop position, AA_SEQ (± 7 < / gtgd^J, FUNC_CLAS &« 1993 Mizushima ^ i±TFD £ tC t:!£¥0¥ 7'- 7 ^ - X (TFDB) TRANSFAC t±, d cis-acting SW DNA> h t trans-acting H ifH-IM] f TRANSFAC (± 1988¥^^ep@iJt/ 5) a L"CXf- b L, 1990¥¥m¥lb5n^ (http://transfac.gbf- braunschweig.de) o TRANSFAC7* — 7(i± Release 3.0 1. SITE (4299 entries) 2. FACTOR (1763 entries) 3. CELL (816 entries) 4. CLASS (27 entries) Identifier : IIS$PK_4 Key: TRANSFAC flag : R Sequence Binding Factor: AIM References: 111 Ilia, 121 I40aa l-MBI. II): ItSKNKVIimS:) :ry Dale: 20.06.1990 : R. Wlngendcr Updated by: T. Helm HTOB ENTRY) Factors F.nlry No. : 0034 Synonyms: AP4; (JT-lIBalpha/bela ? References Factor : AP-4 Cell specificity: 4ft kl)a rSDS) Class: bill,If-ZIP iral features: 2 let zippers: 1.4. 1.2E1.2; Q. Pi Interactions: AP-4; (I.cu ZIP truncated variants:) EI2 Functional properties: functional interaction with AP I It la. I40aa. xfi2, Matrix F.MBI./SWISSPROIVIMR: Added by : E. Wingvnder Undated by: USE 03-8 TRANSFAC r-^-X -108- 5. MATRIX (246 entries) B3-8C. SITES &FACTORS####M&#f*'o SITES###;:#. h I'). ^%^c#A##. -g-#^#. gtSi. Rtf^H C#T-7;i/#(I h Af#^/f h#. MS i"£ EMBUGenBanktmC-Sl $ tvC v S (Comment 11)» FACTORS#. Ztib SITES i> FACTORS &mHV X b^#^^ CELLS# #"4-4 CLASS#^Bf^7A;:o^-c#^###$r Gtto MATRIX# (^i:%ftm») $5^BT-<7)*§^-tM h izmth nuleotide distribution matrix £ 'j- A 6 c TRANSFAC #GBF#7n y^ 9 b#-g|VZihhiiK Z tit Mil# fc* European Comission# TRAD AT ~fxiVs.it H±, TRANSFAC 6 r"- ? ^ - X ###1: M##%DNAK^I#^@^mr7-;i/#m%$'BmL"Cv^o #####$#hL-c. ho (2) mfKfW#y<^l/-ya>#^aLT#. l77-y#M%mm%. ya9y37/<^# tryptophan operon # $'] # # ri^lfhhh 9' 0 17 7 - y #7:#mm%f & virus ? 6 & d*. f ###E »#%## ^ #;&&## ^ac(ri#m#. #m dysis) &##%. ±mmc«B%Ld:i t7^ac#i§m (lysogeny) ±#m(:#%L^17 7-y#%A##. 77-yd^#±mm;:#%L"c#@i-&,:a^m#fi77-y#migm@#y < ^ F-y a 7CUBLT#MeyersCi^^tirW^vE, 10)= C###T#. 7°v?? y a —109 — > • 3 McAdams t Shapiro (±> itfe 5 JL !/-'> 3 > 2: LT##L, -ZrcoZamZ i> izx&fflftt LX, Shimada b Hagiya bcojLW- h-£z>iMz, h &o -%(=, M &if-then-else ;v-;v*et± , roa_h«;^;vi5i^l-cUi\ ES ^y ; al/-y 3 > j^{ii3£& V'ijl'a ii%%r t'0 17r-’/®'/5 J- V — i/ 3 > {= £> V-1 "C & > f C"C, if-then-else^~* X$li&1rS:i©(D*4^8&&(=#L XV^;v 2: LTV^O y 3 7i/3 >//< f #y;i/-7T6&o =1 ay 9 y/*9Kl£embryo @2£* LT& D. c<7)^Ki##e^A/'c^(ofTyy' - ? 3 C(Ot^y 3 7$"6h(: LT, C(7)7^:<7)% embryo l±14#(7)t^ y 3 71=^^^ft, 14#<0# Meinhardt(i^T;i/-^ - ?7/ -c c ^ u ^ ^. Reimtz t Sharp(i> y^^Mt^T )V-)V • ? & £ d J — 9^ simulated annealing (= £ o T^*Tv^ 0 -€"<7) i|g#aL"C. «*-CiSV'£ t ^»i:/TLf;14)o Arita t Hagiya $> t±. Simfly (Simfly-l 15> b Simfly-2^') -110- ^h#x.x ^ LO#5#m&? >^?Xffico±£frfj: FL#t'#J ^r^^ik^LtZo Simfly i±, embryo%###CBc^^ < a. L- if amount (bed) > 3 & amount (nos) < 1 then create (hb) ±/ X - 9 iLMLfco L<7)L§ W6 ^VXAemv^T##f&o Simny-g-Cd, f^-vX- ^^/^gX^-X&^X;!/- Karp(±x tryptophan operonLXx £nISi^--X ^fflvXcTEtttoX s a- y~y a y*'il^tzll)o tm> C(7)y < jLP-^eGENSIMh^#^:o Cftli. 7 L- tryptophan aLTm%L^Bm^-x (PKB) &6&&0 PKB(ilb#KB(D-'jR^7xaa6(:# (BI3-9)c GENSIMlix JELt'y Mavrovouniotis (DT ivziv X-AbfiZ> 0 #l±x BSlxS(zfrfrfo&WX ^6GibbsLx Gibbs J:^Kfo -111- Diescalation.Processes Mutually.Exclusive.Binding Naked.Binding.Pres R. Ef f icient .MutEx^Bifsciinp R. Hut Ex. Binding^.*'" Fill.BindinsKtfiVes Rel eg.»€T&i-rfding .Sites ... Antbfani late-Synthetase .Assembly lemblye^^ f Binding.Processes Trp-Synthetase.Assembly } Repressor.Binds.Operator V Ribosome. x X Binds. RBS ein. in MukableSite.Binds.MutableObj Asite. Binds . 8 Mut^fcleSite.Binds.Obj Muta&leSite.Binds.MutableSite MutabfpObj.Binds.MutableSite MutableObjy^inds.Obji ''V' Polymerase.Binds.Promoter / \ Trp-Repressor.Binds.Operator Mutable^Cj.Binds.Muta^leObj Generic.Protease.Catalysis Processes PA-Isomerase.Catalysis ’ RNAse - Catalysis Mutation.Checkl *"""**• Trp-Synthetase. Alpha .Catalysis Trp-Synthetase.Beta.Catalysis iTrp-Synthetase.Catalysis ^nthranilate-Synthetase . 1 Mutation.Check2^ Catalysis , Enzyme.Binds.Inhibitor .Binds.SmallMoieiuleC^ Repressor.Binds.Cofactor Naked.Sma11Molecule.Check RNA.Binds.SmalIMolecule5 tRNA.Charging trp.tRNA.Charging / Leaky.Transcription.Termination Transcription.Elongation Transcription.Processes , Transcription.inrti.tion • Transcription.Termination , Trans1 at ion.Elongation Translation.Processes *-• Trans 1 at ion.Initiation -Translation.Termination ytj-tz ■ ?7xii, 7xciygr*eiftLrv'5 c %4T0T#^7'O tT. • 4 >7. ? >7.l±, -etl-bmtz ?7*IZ @3-9 Karp (Oynta&i SS/utx^olfflCU) , Z Z Karp (oevcm 6Z 9 (: j: & y < ih-va me? £ ffi 1) Buchner, P. and Trifonov, E. 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L. “Artificial Intelligence and Molecular Biology ”, pp.325 - 364, The MIT Press (1993) 4. 4-1 * t L-CV'So f 5 2 a i: j: N J Aft & & a t' 9 & o t'-c V1 -?>o -113- 4-1 -1 NMR 1C «£ 5 DNA, RNA, ##^^(7)&##M<7)NMR(Nuclear Magnetic Resonance)# C <£ 1980 ¥ ft U Wiirich b «-T< ^^ y 1990^ftCA&t, Mf, "C, =H) (3^7C#ia) ^>XX@(7)AL#: $^NMR#-e(i, #2Mf '=N, "C/H) #G#&<7)-e, <%#%## (mmm#) Kf&&k'?)f##;M#&fi&c k 4)±# O-C6&0 >/ ?>/b9;kK/ Wc ltv ^c: 5 • ®^VA:;i/-eoS)aX)BfftillE ’e i-&Z kli'&m-C'&Zc MC, >'S?Wk DNAW^#(complex)<7) 3'A &, #% -114- ccrti, %M ^#(7)NMR&(: NMR&-ea'(7)j:9 frlzirho Wurich3~6) 6>(±> '> a ^ y a ^ Antenapedia <7) * * jr V J 4 >5r&H<7) 60m #^(7)2^, S^TcNMR^m&L. C(7)#emti, 4# NOE(7)X^-> ti&Vo fc>~f /Hr^ V 7 ^ X II ^BL-Cv^ck^m*6fi^o ##faDNAm (14#*) 6BmDNA<7)#m^hc Tj3 0, 6^:# NMR 'i£Rl/ distance geometry StSi£ £ ffl v> X #$f Lfco H-NS 9 's R<7) C )K^<7) 47?$S<7) ^ t:(ix 11@<7) a-helixS.?/1 1@ #H-NS ^ DNAh^-a-L-cv^c: Schumachu £>8) ti> Lac V 7°V -y- 1)"—<7)^ot:'dfe-£79 V > • })XU ■y-f—9 >/S?K (PuR)<7) DNA In a" IwOV't #% =tr fr v \ C(7)^ >/<^%<7)N t± helix-turn-helix <7) rigid&#m&2:cTj3lX C^m^DNAh^LtTV'&Ck^^^^CL^o DNA<7)yat-^-#^(:Bg^|##A(M:i|g4'f6i6^afe&6c- Myb trout: NMR & t?ffl& L tz „ c-Myb ti 521@(7)T 5 3o<7)|| I) SLE ^|J(R1, R2, R3)e2:^t:^l). DNA@8&#AACNGm=]>t>'9-x@d#l&mmi-a. # 6RR1R2R3 5:R2R3ti^^'-E-^DNA complex ;$$M S fi> ^<7)2^, 3^tcNMR nmmfrtifzo m&nmrx ^? y )v Rl<7)%mu\ DNA^4-#t:±#»^#i:2iU3:$^^o A*Z:Lt:, ri, R2, e3«3>7m-'>3 >iii —115 — mutant<£> NMR& ¥ <7)#%##, D. R. 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