Patent Publication Number: US-2005136480-A1

Title: Computer based versatile method for identifying protein coding DNA sequences useful as drug targets

Description:
FIELD OF THE PRESENT INVENTION  
      This invention relates to a versatile method for identifying protein coding DNA sequences useful as drug targets. More particularly this invention relates to a method for identification of novel genes in genome sequence data of various organisms, useful as potential drug targets. This invention further provides a method for assignment of function to hypothetical Open Reading Frames (proteins) of unknown function through exact amino acid sequence identity signature.  
      Emergence of high throughput sequencing technologies has necessitated identification of novel protein coding DNA sequences (genes) in newly sequenced genomes. The invention provides a novel method of converting DNA sequence to alphanumeric sequence by the use of peptide library. The invention also provides a method for use of artificial neural network (feed forward back propagation topology) with one input layer, one hidden layer with 30 neurons and one output layer for identification protein coding DNA sequences. The invention further provides a method for training of neural networks using sigmoid as a learning function with five parameters namely total score, mean, fraction of zeroes, maximum continuous non-zero stretch and variance for identification of protein coding DNA sequence.  
     BACKGROUND AND PRIOR ART REFERENCES OF THE PRESENT INVENTION  
      The most reliable way to identify a protein coding DNA sequence (gene) in a newly sequenced genome is to find a close homolog from other organisms (BLAST (Altschul, S. F et al., 1990) and FASTA (Pearson, W. R., 1995)). Four nucleotides in a DNA sequence are not randomly distributed. The statistical distribution of nucleotides within a coding region is significantly different from the non-coding (Bird, A., 1987). Methods based on Hidden Markov Models (HMM) have used these statistical properties most efficiently (Salzberg, S. L et al., 1998; Delcher, A. L et al., 1999; Lukashin, A. V. and Borodovsky, M., 1998) and are able to predict ˜97-98% of all the genes in a genome when compared with published annotations (Delcher, A. L et al., 1999). Using HMM, various algorithms like GeneMark, Glimmer etc. have been developed to predict genes in prokaryotes. Glimmer 2.0 is the most successful method among all existing methods (Delcher, A. L et al., 1999). However, Glimmer also predicts 7-20% additional genes (false positives).  
      Each gene prediction method has its own strengths and weaknesses (Mathe, C. et al., 2002). Since the prediction is usually dependent on the training set, shortcomings arise because statistics for a coding region vary across various genomes. Also, these methods are unable to efficiently predict genes small in length (&lt;100 amino acids), because it&#39;s very difficult to detect these genes by similarity searches or by statistical analysis. The problem becomes more severe in case of horizontal gene transfer (Kehoe, M. A et al., 1996). In this case statistical distribution of the nucleotide sequence of these genes differs within a genome itself.  
      The said method of the invention is based upon the observation that the difference between total number of theoretically possible peptides of a given length and that which are actually observed in nature, increases drastically as this length of peptide increases. For example, only about 2% of the theoretically possible heptapeptides are observed in a pool of 56 completely sequenced prokaryotic genomes. At octapeptide level this number reduces to even less than 0.1%. Moreover, it is interesting to note that most of these peptides selected by nature are found only in the coding regions and very rarely in theoretically translated non-coding regions. This observation has prompted us to exploit this exclusivity of natural selection of peptides that are present in protein coding sequences to differentiate between coding and non-coding regions.  
      In principle, using longer peptides to score a query ORF is always preferable to using shorter ones (Salzberg, S. L. et al., 1998), but only if sufficient data is available to estimate statistical parameters required to train the prediction algorithm. In case we use peptides of length 8 or more amino acids, it is difficult to get sufficient data to estimate the training parameters. This is because likelihood of an octapeptide being shared between two polypeptides is less than that of a heptapeptide. So we consider the length of 7 amino acids as optimum for scoring of an ORF.  
      The novelty of the said method is that it works on the basis of protein coding sequences at amino acid, not at nucleotide sequence level. It is noteworthy that the method does not need an organism specific training set, which is an obvious advantage over other methods. Unlike other methods, GeneDecipher does not employ any landmarks like ribosome binding sites, promoter sequences, transcription start sites or codon usage biases to predict the coding genes and their start locations. In addition, this method overcomes the difficulties of gene prediction for smaller genomes (Chen, L et. al., 2003) like SARS-CoV. Other than gene prediction, this method can also be utilized for similarity searches for polypeptides, putative functional assignment to proteins (based on presence of the oligo-peptide motifs), and in phylogenetic domain analysis, indicating the generic-ness and versatility of the method.  
      Current computational methods like GeneMark.hmm (Lukashin and Borodovsky, 1998), Glimmer (Salzberg et al., 1998), etc. face difficulty in analyzing the small genomes such as of SARS. Methods based on Hidden Markov Models (HMM) require thousands of parameters for training. This makes these methods less suitable for analyzing smaller genomes. The problem compounds in the case of SARS-CoV genomes, which are about 30 kb length. Even the method most suitable for viral gene prediction till date ZCURVE_CoV (Chen et al., 2003) needs 33 parameters for training. GeneDecipher needs only 5 parameters and can analyze smaller genomes too. The applicants have trained the Artificial Neural Network on ecoli-k12 genome coding and non-coding regions (ORFs not reported as a gene). To predict protein coding genes using GeneDecipher on viral genomes no additional training is required. This is an obvious advantage of this method over other methods.  
     OBJECTS OF THE PRESENT INVENTION  
      The main object of the present invention is to provide a computer based method for predicting protein coding DNA sequences (genes) useful as drug targets.  
      Another main object of the present invention is to develop a versatile method of identifying genes using oligopeptides that are found to occur in the ORFs of other genomes using software GeneDecipher.  
      Still another object of the present invention is to develop a method applicable in the management of the diseases caused by the pathogenic organisms.  
      Still another object of the present invention is to develop a computer based system for performing the aforementioned methods.  
      Yet another object of the present invention is to develop a method useful for identification of novel protein coding DNA sequences useful as potential drug targets and can serve as drug screen for broad spectrum antibacterial as well as for specific diagnosis of infection. Still another object of the present invention is to identify strain specific or organism specific protein coding genes.  
      Yet another object of the method of invention is to identify protein coding DNA sequences (exons) in eukaryotic organisms.  
      Another object of the present invention is to assignment of function to hypothetical Open Reading Frames (proteins) of unknown function through exact amino acid sequence identity signature.  
     SUMMARY OF THE PRESENT INVENTION  
      The present invention relates to a versatile method of identifying genes using oligopeptides that are found to occur in the ORFs of other genomes and is also suitable for analyzing small genomes using software GeneDecipher, said method comprising steps of generating peptide libraries from the known genomes with peptide of length ‘N’ computationally arranged in an alphabetical order, artificially translating the test genome to obtain a polypeptide in each reading frame, converting each polypeptide sequence into an alphanumeric sequence with one corresponding to each reading frame on the basis of overlappings with the peptide libraries, training Artificial Neural Network (ANN) with sigmoidal learning function to the alphanumeric sequence, deciphering the protein coding regions in the test genome, thus, identifying longer streches of peptides mapping to large number of known genes and their corresponding proteins and lastly, a method of the management of the diseases caused by the pathogenic organisms comprising a step of evaluation of the proposed drug candidate by inhibiting the functioning of one or more proteins identified by the steps of the invention.  
     DETAILED DESCRIPTION OF THE PRESENT INVENTION  
      Accordingly, the present invention relates to a versatile method of identifying protein coding DNA sequences (genes) useful as drug targets in a genome using specially developed software GeneDecipher, said method comprising steps of generating peptide libraries from the known genomes with peptide of length ‘N’ computationally arranged in an alphabetical order, artificially translating the test genome to obtain a polypeptide corresponding to each reading frame, converting each polypeptide sequence into an alphanumeric sequence one corresponding to each reading frame on the basis of overlappings with the peptide libraries, training Artificial Neural Network (ANN) with sigmoidal learning function to the alphanumeric sequence, deciphering the protein coding regions in the test genome, thus, identifying longer streches of peptides mapping to large number of known genes and their corresponding proteins and lastly, a method of the management of the diseases caused by the pathogenic organisms comprising a step of evaluation of the proposed drug candidate by inhibiting the functioning of one or more proteins identified by the steps of the invention.  
      In an embodiment of the present invention, wherein a computer based versatile method for identifying protein coding DNA sequences useful as drug targets said method comprising steps of: 
          generating peptide libraries from the known genomes with oligopeptide of length ‘N’ computationally arranged in an alphabetical order,     artificially translating the test genome to obtain a polypeptide in each reading frame,     converting each polypeptide sequence into an alphanumeric sequence with one corresponding to each reading frame on the basis of occurrence of these oligopeptides in the peptide libraries,     training Artificial Neural Network (ANN) with sigmoidal learning function to the alphanumeric sequences corresponding to known protein coding DNA sequences and known non-coding regions,     deciphering the protein coding regions in the test genome, and     identifying longer stretches of peptides mapped to large number of known genes serving as functional signatures.        

      In another embodiment of the present invention, wherein the artificial neural network has one or more input layer, one or more hidden layer with varying number of neurons, and one or more output layer.  
      In yet another embodiment of the present invention, wherein the number of neurons in the hidden layer is preferably 30.  
      In still another embodiment of the present invention, wherein the value of the ‘N’ is 4 or more.  
      In still another embodiment of the present invention, wherein the sigmoidal learning function has five parameters comprising total score, mean, fraction of zeroes, maximum continuous non-zero stretch, and variance.  
      In still another embodiment of the present invention, wherein the method of identifying genes using oligopeptides that are found to occur in the ORFs of other genomes but not limited to genomes such as  H. influenzae, M. genitalium, E. coli, B. subtilis, A. fulgidis, M. tuberculosis, T. pallidum, T. maritima, Synecho cystis, H. pylori , and SARS-CoV.  
      In still another embodiment of the present invention, wherein a method claimed in claim  1 , wherein the peptide library data may be taken from any organism but not specifically limited to those used in the invention.  
      In still another embodiment of the present invention, wherein a set of genes of SEQ ID Nos. 1 to 44 of  H. influenzae , identified by using aforementioned method.  
      In still another embodiment of the present invention, wherein a set of proteins of SEQ ID Nos. 170 to 213 corresponding to genes of SEQ ID Nos 1 to 44 of  H. influenzae , identified by using aforementioned method.  
      In still another embodiment of the present invention, wherein a set of genes of SEQ ID Nos. 45 to 60 of  H. pylori , identified by using aforementioned method.  
      In still another embodiment of the present invention, wherein a set of proteins of SEQ ID Nos. 214 to 229 corresponding to genes of SEQ ID Nos 45 to 60 of  H. pylori  identified by using aforementioned method.  
      In still another embodiment of the present invention, wherein a set of genes of SEQ ID Nos. 61 to 165 of  M. tuberculosis , identified by using aforementioned method.  
      In still another embodiment of the present invention, wherein a set of proteins of SEQ ID Nos. 230 to 334 corresponding to genes of SEQ ID Nos 61 to 165 of  M. Tuberculosis , identified by using aforementioned method.  
      In still another embodiment of the present invention, wherein a set of genes of SEQ ID Nos. 166 to 169 of SARS-corona virus identified by using aforementioned method.  
      In still another embodiment of the present invention, wherein a set of proteins of SEQ ID Nos. 335 to 338 corresponding to genes of SEQ ID Nos 166 to 169 of SARS-corona virus, identified by using aforementioned method.  
      In still another embodiment of the present invention, wherein use of proteins of SEQ ID Nos. 170 to 338 corresponding to the genes of SEQ ID Nos. 1 to 169, as the drug target for the managing disease conditions caused by the pathogenic organisms in a subject in need thereof.  
      In still another embodiment of the present invention, wherein the pathogenic organisms are selected from a group comprising SARS-corona virus,  H. influenzae, M. tuberculosis , and  H. pylori.    
      In still another embodiment of the present invention, wherein the subject is an animal.  
      In still another embodiment of the present invention, wherein the subject is a human.  
      In still another embodiment of the present invention, wherein the use is extended to eukaryotes and multicellular organisms.  
      Emergence of high throughput sequencing technologies has necessitated identification of novel protein coding DNA sequences (genes) in newly sequenced genomes. The invention provides a novel method of converting DNA sequence to alphanumeric sequence by the use of peptide library. The invention also provides a method for use of artificial neural network (feed forward back propagation topology) with one input layer, one hidden layer with 30 neurons and one output layer for identification protein coding DNA sequences. The invention further provides a method for training of neural networks using sigmoid as a learning function with five parameters namely total score, mean, fraction of zeroes, maximum continuous non-zero stretch and variance for identification of protein coding DNA sequence.  
      The applicants have invented a novel computer based method to identify protein coding DNA sequences by comparing with peptide library containing millions of peptides obtained from protein sequences of many organisms that has withstood natural selection. The method describes a generic and versatile new approach for gene identification. The computational method determines gene candidates among all possible Open Reading Frames (ORF) of a given DNA sequence through the use of a peptide library and an artificial neural network. The peptide library consists of all possible overlapping heptapeptides derived from proteins of completely sequenced 56 or more prokaryotic genomes. A given query ORF qualifies as a gene based upon the abundance and distribution pattern of library heptapeptides (heptapeptides present in library) along the ORF. Performance of the method is characterized by simultaneous high values of sensitivity and specificity. An analysis of 10 completely sequenced prokaryotic genomes is provided to demonstrate the capabilities of the method of the invention.  
      The present method also allows prediction of alternate target against a specific peptide motif of a pathogenic organism or any host protein target responsible for a disease process. The method could be extended with different peptide lengths to obtain larger number of protein coding genes and also for eukaryotes and multicellular organisms.  
      The invention relates to a novel method of converting DNA sequence to alphanumeric sequence by the use of peptide library and the invention also provides a method for use of artificial neural network (feed forward back propagation topology) with one input layer, one hidden layer with 30 neurons and one output layer for identification protein coding DNA sequences. The invention further relates to a method for training of neural networks using sigmoid as a learning function with five parameters namely total score, mean, fraction of zeroes, maximum continuous non-zero stretch and variance for identification of protein coding DNA sequence and the present method is useful for identification of new protein coding regions which can serve as drug screen for broad-spectrum antibacterials as well as for specific diagnosis of infections, and in addition, for assignment of function to newly identified proteins of yet unknown functions. The method allows identification of species or strain specific protein coding genes. This method also can be extended to any protein coding sequence identification even in eukaryotic genomes.  
      Accordingly, present invention discloses a computer based versatile method for identifying protein coding DNA sequences useful as drug targets, said method comprising steps of: 
          a. generating peptide libraries from the known genomes with oligopeptide of length ‘N’ computationally arranged in an alphabetical order,     b. artificially translating the test genome to obtain a polypeptide in each reading frame,     c. converting each polypeptide sequence into an alphanumeric sequence with one corresponding to each reading frame on the basis of occurrence of these oligopeptides in the peptide libraries,     d. training Artificial Neural Network (ANN) with sigmoidal learning function to the alphanumeric sequences corresponding to known protein coding DNA sequences and known non-coding regions,     e. deciphering the protein coding regions in the test genome, and     f. identifying longer stretches of peptides (evolutionary conserved oligopeptides) mapped to large number of known genes serving as functional signatures.        

      In yet another embodiment of the present invention the ANN has one or more input layer, one or more hidden layer with varying number of neurons, and one or more output layer.  
      In still another embodiment of the present invention the number of neurons in the hidden layer is preferably 30.  
      In yet another embodiment of the present invention the value of the ‘N’ is 4 or more.  
      In yet another embodiment of the present invention the sigmoidal learning function has five parameters comprising total score, mean, fraction of zeroes, maximum continuous non-zero stretch, and variance.  
      One more embodiment of the present invention a method of identifying genes having evolutionary conserved peptide sequences which occur in ORFs of various genomes but not limited to genomes such as  H. influenzae, M. genitalium, E. coli, B. subtilis, A. fulgidis, M. tuberculosis, T. pallidum, T. maritima, Synecho cystis, H. pylori  and SARS-CoV.  
      In still another embodiment of the present invention the method identifies 169 novel genes identified in genomes of SARS-corona virus and  H. influenzae, M. tuberculosis, H. pylori  of SEQ IDs 1 to 169.  
      In further embodiment of the present invention, a method of the management of the diseases caused by the pathogenic organisms such as SARS-corona virus,  H. influenzae, M. tuberculosis  and  H. pylori , said method comprising step of evaluation of the proposed drug candidate for inhibition of the functioning of one or more evolutionary conserved peptide sequences identified by the instant method and selected from a group comprising proteins of SEQ IDs 170 to 338 corresponding to the novel genes of SEQ IDs 1 to 169.  
      In yet another embodiment of the present invention the peptide library data may be taken from any organism but not specifically limited to those used in the invention.  
      Detailed Methodology:  
      The method has been described in five major steps (as shown in  FIG. 1 ): 
          1. Generation of a peptide library     2. Artificial translation of a given genome into 6 reading frames     3. Conversion of each translated sequence into an alphanumeric sequence. (one corresponding to each reading frame)     4. Training of artificial neural network (ANN).        

      5. Deciphering genes using trained ANN.  
      1. Generation of Peptide Library  
      The method requires a reference peptide library to predict genes in a given genome. In the present invention, the applicants have used proteins from 56 completely sequenced prokaryotic genomes. The protein files for our database were obtained in FASTA format from ftp://ftp.ncbi.nlm.nih.gov/genomes. To prepare a peptide library for deciphering genes in a particular genome, the applicants exclude protein file(s) belonging to that particular species from our database in order to avoid any bias. For example, when analyzing  E. coli -k12 genome the protein files corresponding to all strains of  E. coli  were excluded from the database to create the peptide library. This has been done to eliminate the signal that is obtained from peptides of that organism, which would be the case while analyzing a newly sequenced genome. This strengthens the method in terms of gene prediction on a newly sequenced genome for which annotated protein file is not available. While creating peptide library all possible overlapping heptapeptides have been taken care of by shifting the window by one amino acid. Redundant peptides were eliminated from the peptide library and each peptide is given an occurrence value based on number of discrete organisms in which it is present.  
      This occurrence value is a measure of conservation of a heptapetide in coding regions. Presence of a heptapeptide with high occurrence value in an ORF increases the likelihood of that ORF being a protein coding gene. In our algorithm, occurrence value of 9 or more is treated as 9 based on the assumption that if a heptapeptide is present in 9 or more than 9 different organisms&#39; protein files, it can be considered as highly conserved heptapeptide. It is not worthwhile to use any higher value to further discriminate the amount of conservation.  
      The heptapeptide library database consists of two columns, first for heptapeptide sequence and second for score (occurrence value) of that heptapeptide. Heptapeptides are sorted in dictionary order. The peptide library database also retains other information about the heptapeptides, like the accession number and NCBI annotation of all proteins containing the particular heptapeptide. This can be utilized for putative function prediction of a given ORF. Same approach can be used for phylogenetic domain analysis also.  
      2. Artificial Translation of a Given Genome into 6 Reading Frames  
      Second step in the algorithm is artificial translation of the whole query genome in all six reading frames using a standard codon table. However user specified codon table may be used wherever necessary. Applicants used letter ‘z’ corresponding to the stop codons TTA, TAG and TGA, and letter ‘b’ for all triplets containing any non standard nucleotide(s) (K, N, W, R, and S etc.) while artificially translating the genome.  
      3. Conversion of Each Translated Sequence into an Alphanumeric Sequence (One Corresponding to Each Reading Frame)  
      The next step in our algorithm is to convert artificially translated amino acid sequence with stop codon (z) interruption, into an alphanumeric sequence. Applicants search each overlapping heptapeptide in the peptide library, assign a corresponding number (occurrence value), and append it to the alphanumeric sequence. If a heptapeptide is not present in the library applicants assign the number 0. If a heptapeptide begins with an amino acid corresponding to any of the start codon ATG, GTG and TTG applicants append character ‘s’ in the alphanumeric sequence. This will be helpful to detect the location of a probable start codon. In case a heptapeptide contains character ‘z’ applicants append a character ‘*’ corresponding to that heptapeptide. Thus consecutive seven ‘*’ (*******) in the alphanumeric sequence is a signal for stop codon. Applicants append ‘-’ character for any heptapeptide containing character ‘b’. This signals the presence of a non standard nucleotide character and conveys no information about sequence being a part of gene or non-gene. So, the alphanumeric sequence thus generated contain 13 characters viz. any integer (0-9), ‘s’, ‘*’, and ‘-’. In this way, applicants convert all six translated protein files into six alphanumeric sequences.  
      4. Training of Artificial Neural Network (ANN)  
      The neural network used here has a multi-layer feed-forward topology. It consists of one input layer, one hidden layer, and an output layer. This is a ‘fully-connected’ neural network where each neuron i is connected to each unit j of the next layer ( FIG. 2 ). The weight of each connection is denoted by w ij . The state I i  of each neuron in the input layer is assigned directly from the input data, whereas the states of hidden layer neurons are computed by using the sigmoid function, h j =1/(1+exp−λ(w j0 +Σw ij I i )), where, w j0  is the bias weight, and λ=1.  
      The back propagation algorithm is used to minimize the differences between the computed output and the desired output. One thousand cycles (epochs) of iterations are performed. Subsequently, the epoch with minimum error in validation set is identified and the corresponding weights (w ij ) are assigned as the final weights for the ANN. The network trains on the training set, checks error and optimizes using the validation set through back propagation.  
      The ‘training set’ consists of 1610  E. coli -k12 NCBI listed protein coding genes and 3000  E. coli -k12 ORFs (a stretch of sequence of length more than 20 amino acids and having start codon, stop codon in the same frame) which have not been reported as genes (non-genes). The ‘validation set’ has 1000 known genes and 1000 non-genes from  E. coli -k12, distinct from those used in the training set. The ‘test set’ contains another 1000 genes and 1000 non-genes from the same organism. For training of the ANN, genes and the non-genes are assigned a probability value of 1 and 0 respectively.  
      To train the neural network, first applicants convert all the  E. coli -k12 genes and non-genes into corresponding alphanumeric strings by the method described above (steps 2 and 3). Here it is important to note that the alphanumeric sequences corresponding to a gene is number rich compared to the alphanumeric sequences corresponding to non-genes. To quantify this number richness of an alphanumeric sequence, five parameters derived from the alphanumeric sequence have been selected. These five parameters are as follows:  
      (i). Total Score  
      This is an algebraic sum of all the integers of a given alphanumeric sequence. Here rule of thumb is higher the score, more are the chances to qualify as a gene.  
      (ii). Fraction of Zeroes  
      Fraction of zeroes equals to total no. of zero characters in the alphanumeric sequence divided by total no. of characters in the sequence. More the fraction of zeros, lesser is the chance to qualify as a gene.  
      (iii). Mean  
      Mean equals to total score divided by total length of the sequence. Higher the Mean, more is the chance to qualify as a gene. Virtually this parameter seems same as a total score but it is important because this incorporates the length of the sequence also (score per unit length)  
      (iv). Variance  
      It is the variance of occurrence values about the mean occurrence value for the whole ORF.  
      (v). Length of the Maximum Continuous Non Zero Stretch  
      Higher the value of this parameter more is the chance to qualify as a gene. Consider a sequence region like ‘45’. Here, ‘4’ denotes a heptapeptide conserved in 4 organisms, and the succeeding ‘5’ denotes an overlapping heptapeptide conserved in 5 organisms. So if there exists at least one organism which is common between these two sets, eventually applicants have an octapeptide common between that organism and the query ORF. This raises our confidence level in prediction of the coding region. For example, sequence ‘s45467000000*******’ is more likely to be a gene when compared to sequence ‘s40540607000*******’. This is because there are greater chances of presence of conserved longer peptide in the first sequence. Value of the parameter is 5 for first string and 2 for second one. However, other parameters used in the algorithm can not discriminate between these two sequences.  
      While calculating these parameters from the alphanumeric sequences, characters such as ‘s’, ‘*’ and ‘-’ have been excluded.  
      To find an optimum combination, the neural network is trained using all the five parameters together. Parameters corresponding to alphanumeric sequences of genes and non-genes are calculated. The training, validation and test sets contain 6 columns, first 5 columns contains values of the 5 parameters and the last column contains the number ‘1’ for genes and the number ‘0’ for non-genes.  
      The number of neurons in the input layer was equal to the number of input data points. The optimal number of neurons in the hidden layer was determined by hit and trial while minimizing the error at the best epoch for the network. Computer program to compute all 5 parameters and for the artificial neural network are written in C and executed on a PC under Red Hat Linux version 7.3 or 8.0.  
      Training of the ANN (step 4 of the algorithm) is generally executed only once, and the same trained neural network can be utilized to execute the method on any prokaryotic genome. Although if applicants use organism specific training set, results might improve in some cases, but it would be marginal. This is because our method predicts gene on the basis of the number distribution of the alphanumeric sequence of an ORF. So the gene prediction is more dependent on the peptide library used rather than training set.  
      5. Deciphering Genes Using Trained ANN  
      While creation of peptide library (step 1) and training of ANN (step 4) are considered as preparatory phases for executing the method of invention, step 2 and step 3 are mandatory for each genome sequence. After translating computationally a genome into all six reading frames and converting them into six alphanumeric sequences, deciphering genes using ANN is executed. This step can be further divided into following five sub-steps: 
          1. Breaking of all the six alphanumeric sequences into possible ORFs. (all possible fragments starting with ‘s’ and ending with ‘*’)     2. Calculate all the five parameters (total score, fraction of zeroes, mean, variance, and length of maximum continuous non zero stretch) for all possible ORFs (all the alphanumeric string sequences between ‘s’ and ‘*’)     3. Calculate the probability of the ORF corresponding to a given alphanumeric string as a protein coding gene, using the trained ANN.        

      4. Filter out the protein coding ORFs from the non coding ones by using a cutoff probability value.  
      5. Remove all the encapsulated protein coding regions (Shibuya, T. and Rigoutsos, I., 2002). 
          If two ORFs are predicted in distinct translation frames, such that one&#39;s span completely encapsulates other, it is a commonly believed that only one of them can be an actual gene. In this case the applicants report the ORF with a higher probability value as a gene. In case of same probability value applicants take longer ORF as a gene.        

      The method of the invention predicts a probability value corresponding to a query ORF being a protein coding region. The training of ANN is done using a sigmoid learning function with =1 (probability ‘1’for genes and ‘0’ for non-genes); therefore most of the time this probability value lies either below 0:1 or above 0.9. Due to this any cutoff value lying between 0.1 and 0.9 generate very similar results. In our analysis applicants use a default cutoff value of 0.5. It&#39;s important to note that the method does not require a trade-off between sensitivity and specificity because the choice of cut-off probability has no major consequences on the results.  
      Other and further aspects, features and advantages of the present invention will be apparent from the following description of the presently preferred embodiments of the invention given for the purpose of disclosures.  
      Brief Description of the Computer Programs:  
      1. File Name: genedcodchr.cxx  
      Application: Translation of nucleotide sequence (FASTA file format) into 6 hypothetical polypeptides in 6 respective frames.  
                                                  Input format : &lt;Program_name&gt; &lt;Nucleotide_file&gt; &lt;Output1&gt; &lt;Output2&gt;                     &lt;frame&gt; e.g.,   ./genedcodchr ecoli.fna pf1 pr1 0                             Output   format:                 AGTFYRYmGHVNMKIYTASLPTYRYGYFSHRED.....HGOIEKSDWEzDFGTRE                  
 
 2. File Name: searchchr.cxx 
 
      Application: Converts the polypeptide file into an alphanumeric sequence through a heptapetide library (given as an input) search.  
                                   Input format :&lt; Program_name&gt; 7 &lt;peptide library file name&gt;        out Y &lt;Input1&gt;                     &lt;Input2&gt;   &lt;Output1&gt; &lt;Output 2&gt;                      e.g.,   ./searchchr 7 ecoli.peplib out Y pf1 pr1 bf1 br1                      Output   format:                  s1124500001090003000020000023000000000*******0001000..........                  
 
 3. File Name: cutf.c 
          Application: Cuts all possible ORFs (i.e., all ‘s’ to ‘*’ regions) from the alphanumeric sequence of forward strand and generates a file containing locations of all the ‘s’ in alphanumeric sequence.     Input format:&lt;Program_name&gt;&lt;Input file name&gt;&lt;Output1&gt;&lt;Output2&gt;e.g./cutf bf1 unknown_bf1 bf1_location     Output format: output1—s1111000s00000000563*, output2—starting locations of ‘s’ in a column. 
 
 4. File Name: cutr.c 
       

      Application: Cuts the all possible ORFs (all ‘s’ to ‘* regions) from the reverse strand&#39;s alphanumeric sequences and produces a file which contains the starting locations in alphanumeric sequence file for all 3 forward frames corresponding to all ORFs.  
                                                  Input format :&lt; Program_name&gt; &lt;Input file name&gt; &lt;Output1&gt;           &lt;Output2&gt;                             e.g.   ./cutr br1 unknown_br1 br1_location                         Outputformat:           output1-*010340000222200067900000s000001000200s00230000s,                      
          output2—starting location of ‘s’
 
 5. File Name: stat.c 
       

      Application: Calculates the five parameters: fraction of zeros, mean, total score, length of maximum continuous stretch, and variance for a given alphanumeric sequence.  
                                                  Input format :&lt; Program_name&gt; &lt;Input file name&gt;&lt;Output&gt; 1                             e.g.   ./stat unknown_bf1 bf1.data 1                                         Output format: 0.334   3.2   48   15   0.452 1                      
 
 6. File Name: train .c 
 
      Application: Training of Artificial Neural Network (single hidden layer, 1 input and 1 output layer) with feed forward back propagation algorithm and using sigmoid (=1) as a learning function.  
                                                  Input format :&lt; Program_name&gt; &lt;Input specification file           name&gt; &lt;Input1&gt;                         &lt;Input2&gt; &lt;Input3&gt; &gt; output                             e.g.   ./train train.spec.fast trainset.data                         validateset.data testset.data &gt;                         train.net                      
          Output format: output containing the final neural network wieghts in a single column. 
 
 7. File Name: recognize.c 
       

      Application: Recognizes a given pattern on the basis of trained weights and generates a probability value as output.  
                                                  Input tormat :&lt; Program_name&gt; &lt;Input specification file           name&gt; &lt;Input1&gt;                         &lt;Input2&gt;                         &lt;Output&gt;                             e.g.   ./recognize recognize.spec bf1.data train.net           f1.out                         Output format: pat1 probability &lt;value&gt;                      
 
 8. File Name: Filter_prediction.c 
 
      Application: Filters out the completely overlapping ORFs in same frame based on probability and length parameter.  
                                                  Input format :&lt; Program_name&gt; &lt;Input1&gt; &lt;Input2&gt; &lt;Output&gt;                             e.g.   ./Filter_prediction f1.out unknown_bf1 bf1.out.res                                 Output format: pat1 probability   &lt;value&gt;   &lt;integer string&gt;                      
 
 9. File Name: locationf.c 
 
      Application: Filters out the genes of length&lt;20 amino acids, and reports starting location of the remaining ones with the alphanumeric sequence for all 3 forward frames.  
                                                  Input format :&lt; Program_name&gt; &lt;Input1&gt; &lt;Output&gt; &lt;Input2&gt;                             e.g.   ./locationf bf1.out.res bf1.out.res1 bf1_location                         Output format:&lt;Pattern No&gt; &lt;Probability value&gt; &lt;integer string&gt;           &lt;Start&gt; &lt;End&gt;                      
 
 10. File Name: locationr.c 
 
      Application: Filters out the genes of length&lt;20 amino acids, and reports starting location of the remaining ones with the alphanumeric sequence for all 3 reverse frames.  
                                                  Input format :&lt; Program_name&gt; &lt;Input1&gt; &lt;Output&gt; &lt;Input2&gt;                             e.g.   ./locationr br1.out.res br1.out.res1 br1_location                         Output format:&lt;Pattern No&gt; &lt;Probability value&gt; &lt;integer string&gt;           &lt;Start&gt; &lt;End&gt;                      
 
 11. File Name: finalf.c 
 
      Application: Converts the start and end locations of the alphanumeric sequence into the corresponding genome locations for 3 forward frames.  
                                                  Input format :&lt; Program_name&gt; &lt;Input1&gt; &lt;Input2&gt; &lt;Input3&gt;           &lt;Output&gt;                             e.g.   ./finalf bf1.out.res1 bf2.out.res1 bf3.out.res1                         Final_outputf           Output format:&lt;Start&gt; &lt;End&gt; &lt;frame&gt; &lt;length&gt; &lt;Probability           value&gt; &lt;integer                 string&gt;                  
 
 12. File Name: finalr.c 
 
      Application: Converts the start and end locations of the alphanumeric sequence into the corresponding genome locations for 3 reverse frames.  
                                                  Input format :&lt; Program_name&gt; &lt;Input1&gt; &lt;Input2&gt; &lt;Input3&gt;           &lt;Output&gt;                             e.g.   ./finalf br1.out.res1 br2.out.res1 br3.out.res1                         Final_outputr           Output format:&lt;Start&gt; &lt;End&gt; &lt;frame&gt; &lt;length&gt; &lt;Probability value&gt;           &lt;integer                 string&gt;                  
 
 13. File Name: sort.c 
          File Name: sort.c        

      Applications: Prints the finally predicted genes into descending order along the genome start location.  
                                  Input format :&lt; Program_name&gt; &lt;Input1&gt; &lt;Input2&gt; &lt;Input3&gt; &lt;Output&gt;                             e.g.   ./sort   Final_outputf   Final_outputr                 OUTPUTF_with_encap       OUTPUTR_with_encap OUTPUT       Output format:&lt;Start&gt; &lt;End&gt; &lt;Probability value&gt;                  
 
 14. File Name: removeencap.c 
 
      Application: Removes encapsulated genes found in other five frames.  
                                                  Input format :&lt; Program_name&gt; &lt;Input1&gt; &lt;Input2&gt; &lt;Input3&gt;           &lt;Output&gt;                                 e.g.   ./removeencap   OUTPUTF_with_encap                 OUTPUTR_with_encap                         OUTPUT OUTPUTF OUTPUTR           Output format:&lt;Start&gt; &lt;End&gt; &lt;frame&gt; &lt;length&gt; &lt;Probability           value&gt; &lt;integer                 string&gt;                  
 
      The present invention relates to a novel computer based method for predicting protein coding DNA sequences useful as drug targets. In this method occurrence of oligopeptide signatures have been used as probes. The method is versatile and does not necessarily require organism specific training set for the Artificial Neural Network. The method is not only dependent on statistical analysis but also integrates with the biological information that is retained in the conserved peptides, which withstood evolutionary pressure. Logical extension of the method will be to predict protein coding DNA sequences (exons) in eukaryotic genomes. 
    
    
     BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS  
       FIG. 1  shows a logic circuit of GeneDecipher.  
       FIG. 2  shows a architecture of neural network.  
       FIG. 3  shows analysis of results of GeneDecipher on 10 organisms. 
    
    
      The particulars of the organisms used for the invention comprising name, strain, accession number and other details are given below.  
                                                                       Date of       S. No.   Genome   Strain   Accession Number   Total Base Sequences   Completion                                                        1     H. Influenzae     Rd   NC_000907   1830138   Sep. 30, 1996                 Fleischmann, R. D. et. al Science 269 (5223), 496-512 (1995)                                     2     M. Genitalium     —   NC_000908   580074   Jan. 8, 2001                 Fraser, C. M., et. al Science 270 (5235), 397-403 (1995                                     3     E. coli     K-12   NC_000913   4639221   Oct. 15, 2001.                 Blattner, F. R. et. al Science 277 (5331), 1453-1474 (1997)                                     4     B. Subtilis      168   NC_000964   4214814   Nov. 20, 1997                 Kunst, F. et. al Nature 390 (6657), 249-256 (1997)                                     5     A. Fulgidis     DSM 4304   NC_000917   2178400   Dec. 17, 1997                 Klenk, H. P. et. al Nature 390 (6658), 364-370 (1997)                                     6     M. Tuberculosis     H37RV   NC_000962   4411529   Sep. 7, 2001                 Cole, S. T. et. al Nature 393 (6685), 537-544 (1998)                                     7     T. Pallidum     —   NC_000919   1138011   Sep. 7, 2001                 Fraser, C. M., et. al Science 281 (5375), 375-388 (1998)                                     8     T. Maritima     —   NC_000853   1860725   Sep. 10, 2001.                 Nelson, K. E. et. al Nature 399 (6734), 323-329 (1999)                                     9     Synecho cystis     PCC6803   NC_000911   3573470   Oct. 30, 1996                 Kaneko, T. et. al DNA Res. 3(3), 109-136 (1996)                                     10     H. Pylori     26695   NC_000915   1667867   Sep. 7, 2001                 Tomb, J. -F. et. al Nature 388 (6642), 539-547 (1997)                  
 
      The following examples are given by way of illustration of the present invention and should not be construed to limit the scope of the present invention.  
     EXAMPLE 1  
      Conversion of DNA Sequence into Alphanumeric Sequence  
      The purpose of this module in our software is to translate computationally the whole query genome (DNA sequence) in all six reading frames using a specified codon table. Applicants used letter ‘z’ corresponding to the stop codons TTA, TAG and TGA, and letter ‘b’ for all triplets containing any non standard nucleotide(s) (K, N, W, R, and S etc.) while artificially translating the genome. Subsequently the translated genome sequence is converted computationally into an alphanumeric sequence ([0-9], ‘s’, ‘*’, and ‘-’). Applicants search each overlapping heptapeptide in the peptide library, assign a corresponding number (occurrence value), and append it to the alphanumeric sequence. If a heptapeptide is not present in the library applicants assign the number 0. If a heptapeptide begins with an amino acid corresponding to any of the start codon ATG, GTG and TTG Applicants append character ‘s’ in the alphanumeric sequence. This will be helpful to detect the location of a probable start codon. In case a heptapeptide contains character ‘z’ applicants append a character ‘*’ corresponding to that heptapeptide. Thus consecutive seven ‘*’ (*******) in the alphanumeric sequence is a signal for stop codon. Applicants append a ‘-’ character for any heptapeptide containing character ‘b’. This signals the presence of a non-standard nucleotide character.  
      The aforementioned conversion is further elaborated with the help of following six sequences.  
                          SEQ ID No. 12                                             GDC_HINF_243018   243018   243215   65   +   Cell wall-associated                               hydrolase                             &gt;gi_GDC_HINF_243018           GTGATGAGCCGACATCGAGGTGCCAAACACCGCCGTCGATATGAACTCTTGGG               CGGTATCAGCCTGTTATCCCCGGAGTACCTTTTATCCGTTGAGCGATGGCCCTT               CCATTCAGAACCACCGGATCACTATGACCTACTTTCGTACCTGCTCGACTTGTC               TGTCTCGCAGTTAAGCTTGCTTATACCATTGCACTAA          
 
      Computationally Translated Protein Sequence  
                          &gt;gi_GDC_HINF_243018           VMSRHRGAKHRRRYELLGGISLLSPEYLLSVERWPFHSEPPDHYDLLSYLLDLSVSQLSLLIPLH          
 
 Computationally Generated Alphanumeric Sequence 
 
      ss10000000000001s03111431000000000000000000110000100s001030* 
                          SEQ ID No. 4                                             GDC_HINF_170553   170553   170732   59   −   dicarboxylate transport protein                               homolog HI0153                             &gt;gi_GDC_HINF_170553           GTGTTTATGCTTTATTTAGAATTTTTATTTTTACTATTAATGCTCTATATCGGTA               GCCGTTACGGCGGTATCGGATTAGGTGTTGTTTCTGGTATCGGTCTTGCTATCG               AGGTTTTCGTATTTCGTATGCCAGTGGGGAAGCACCGATTGATGTTATGCTTAT               CATTCTTGCAGTGGTGA          
 
      Computationally Translated Protein Sequence  
                          &gt;gi_GDC_HINF_170553           VFMLYLEFLFLLLMLYIGSRYGGIGLGVVSGIGLAIEVFVFRMPVGKHRLMLCLSFLQW          
 
 Computationally Generated Alphanumeric Sequence 
 
      s0s1131231142s1111445232254238000000000000s0s0000ss00* 
                          SEQ ID No. 73                                             GDC_MTUB_688806   688806   689060   84   +   MCE-FAMILY PROTEIN                               MCE2B                             &gt;gi_GDC_MTUB_688806           TTGCTGCACAGCAGCTTCGGGCACCTCGAGGGCATCCAGCAGCCGCTCATAGA               CGAGCTGGCAGAACTCGACCACGTGTTGGGCAAGCTGCCGGACGCCTACCGGA               TCATCGGCCGCGCCGGCGGCATATACGGTGACTTCTTCAACTTCTATCTGTGTG               ACATCTCACTGAAAGTCAACGGATTACAGCCTGGAGGTCCGGTACGCACCGTC               AAGTTGTTCGGCCAGCCGACCGGCAGGTGCACACCGCAATGA          
 
      Computationally Translated Protein Sequence  
                          &gt;gi_GDC_MTUB_688806           LLHSSFGHLEGIQQPLIDELAELDHVLGKLPDAYRIIGRAGGIYGDFFNFYLCDISLK               VNGLQPGGPVRTVKLFGQPTGRCTPQ          
 
 Computationally Generated Alphanumeric Sequence 
 
      s000000000110110530100000ss000000000000100000000000000000001111210000000s00100* 
                          SEQ ID No. 92                                             GDC_MTUB_1286282   1286282   1286587   101   −   pterin-4-alpha-                               carbinolamine                           dehydratase                             &gt;gi_GDC_MTUB_1286282           GTGACGGTATACCGTCGAGGTATGGCTGTGTTAACGGATGAGCAGGTCGACGC               CGCACTGCACGACCTCAACGGCTGGCAGCGCGCCGGTGGTGTCCTGCGTAGGT               CAATCAAGTTTCCGACGTTTATGGCCGGTATCGACGCCGTACGCCGGGTGGCC               GAGCGAGCCGAGGAGGTAAATCATCATCCGGACATCGATATCCGTTGGCGAAC               AGTAACTTTCGCGCTGGTTACGCATGCGGTAGGTGGTATCACGGAAAACGACA               TTGCGATGGCGCACGATATCGACGCAATGTTTGGGGCCTAA          
 
      Computationally Translated Protein Sequence  
                          &gt;gi_GDC_MTUB_1286282           VTVYRRGMAVLTDEQVDAALHDLNGWQRAGGVLRRSIKFPTFMAGIDAVRRVA               ERAEEVNHHPDIDIRWRTVTFALVTHAVGGITENDIAMAHDIDAMFGA          
 
 Computationally Generated Alphanumeric Sequence 
 
      s000000s0s21110001000000300000000011000000s01031100s00020000110000000030000000013310000000s0001* 
                          SEQ ID No. 49                                             GDC_HPYL_583607   583607   583876   89   +   probable DNA                               helicase                             &gt;gi_GDC_HPYL_583607           TTGATGGAATTTGATGTTACCATCATAGATGAGACAGGCAGGGCCACAGCACC               AGAAATCTTGATTCCTGCACTTCGCACTAAAAAACTGATCTTAATAGGCGATC               ACAACCAGCTCCCACCTAGCATTGATAGGTACCTCCTAGAACAATTAGAGAGC               GATGATATTCAAAACTTGGATGCCATTGATCGCCAATTATTGGAAGAGAGTTT               TTTTGAAAATCTCTATAAGTATATTCCAGAGAGTAATAAGGCCATGCTTAATG               AGTAA          
 
      Computationally Translated Protein Sequence  
                          &gt;gi_GDC_HPYL_583607           LMEFDVTIIDETGRATAPEILIPALRTKKLILIGDHNQLPPSIDRYLLEQLESDDIQNL               DAIDRQLLEESFFENLYKYIPESNKAMLNE          
 
 Computationally Generated Alphanumeric Sequence 
 
      ss001000000001000000s0000011000020000000000030310000000002s0003020s0000000000000000* 
                          SEQ ID No. 54                                             GDC_HPYL_954846   954846   955217   123   −   PHOSPHOTRANSACETY                               LASE                             &gt;gi_GDC_HPYL_954846           GTGAGCCTGGTTTCAAGCGTGTTTTTAATGTGTTTAGACACTCAAGTGCTAGTC               TTTGGGGATTGCGCGATTATCCCTAACCCTAGCCCTAAAGAATTAGCCGAGAT               CGCTACCACTTCCGCACAAACCGCCAAGCAATTCAATATTGCGCCTAAAGTGG               CCTTGCTTTCTTATGCGACAGGCGATTCCGCTCAAGGCGAAATGATAGACAAA               ATCAACGAAGCTTTAACAATCGCTCAAAAGTTGGATCCCCAATTAGAAATTGA               TGGCCCCTTACAATTTGACGCTTCCATTGATAAAAGCGTAGCCAAGAAAAAAT               GCCTAACAGCCAAGTGGCTGGGCAAGCTAGCGTTTTTATTTTCCCGGATTTAA          
 
      Computationally Translated Protein Sequence  
                          &gt;gi_GDC_HPYL_954846           VSLVSSVFLMCLDTQVLVFGDCAIIPNPSPKELAEIATTSAQTAKQFNIAPKVALLS               YATGDSAQGEMIDKINEALTIAQKLDPQLEIDGPLQFDASIDKSVAKKKCLTAKWL               GKLAFLFSRI          
 
 Computationally Generated Alphanumeric Sequence 
      s80000s00s00002s200222000000003100000000000000000010s0s100000000000s0000000100000s00000000000000000000000000030000010*    

     EXAMPLE 2  
      Training of Artificial Neural Network (ANN)  
      The purpose of this module in the software is to train the designed neural network ( FIG. 2 ) with a specified no. of genes and non-genes. In this example the training set consists of 1610  E. coli -k12 NCBI listed protein coding genes and 3000  E. coli -k12 ORFs which have not been reported as genes (non-genes). The validation set has 1000 known genes and 1000 non-genes from  E. coli -k12, distinct from those used in the training set. The test set contains another 1000 genes and 1000 non-genes from the same organism. For training of the ANN, genes and the non-genes are assigned a probability value of 1 and 0 respectively. To train the neural network, first applicants convert all the  E. coli -k12 genes and non-genes into corresponding alphanumeric strings by the method described above (steps 2 and 3). Samples of two  E. coli -k12 genes and two non-genes in alphanumeric sequence format are shown in  FIG. 3 . Here it is important to note that the alphanumeric sequences corresponding to a gene is number rich compared to the alphanumeric sequences corresponding to non-genes. This supports our hypothesis. To quantify this number richness of an alphanumeric sequence, five parameters derived from the alphanumeric sequence have been selected. These five parameters are as follows:  
      Total Score (algebraic sum of all the integers of a given alphanumeric sequence), Fraction of zeroes (total no. of zero characters in the alphanumeric sequence divided by total no. of characters in the sequence), Mean (total score divided by total length of the sequence), Variance (variance of occurrence values about the mean occurrence value for the whole ORF), Length of the maximum continuous non zero stretch (represents the occupancy of uninterrupted non-zero numbers in a sequence) as explained in table 1(a) and 1(b).  
               TABLE 1(a)                          Training of ANN (genes)                                                         Biggest               S.   Fraction   Total       Continuous       No   of Zeros   Score   Average   stretch   Variance   Probability                                                 1   0.663116   587   0.7816   19   2.10146   1       2   0.693950   214   0.7616   18   2.43068   1       3   0.597436   412   1.0590   13   3.16832   1       4   0.898876   12   0.1348   4   0.20654   1                  
 
     
       
         
           
               
             
               
                 TABLE 1(b) 
               
             
            
               
                   
               
               
                   
               
               
                 Training of ANN (Non-genes) 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                 Biggest 
                   
                   
               
               
                 S. 
                 Fraction 
                 Total 
                   
                 Continuous 
               
               
                 No 
                 of Zeros 
                 Score 
                 Average 
                 stretch 
                 Variance 
                 Probability 
               
               
                   
               
               
                 1 
                 0.946429 
                 3 
                 0.0536 
                 2 
                 0.05070 
                 0 
               
               
                 2 
                 1.000000 
                 0 
                 0.0000 
                 0 
                 0.00000 
                 0 
               
               
                 3 
                 0.955556 
                 2 
                 0.0444 
                 1 
                 0.04247 
                 0 
               
               
                 4 
                 0.956522 
                 2 
                 0.0435 
                 1 
                 0.04159 
                 0 
               
               
                   
               
            
           
         
       
     
      While calculating these parameters from the alphanumeric sequences characters ‘s’, ‘*’ and ‘-’ have been excluded. To determine the contribution of each parameter towards discriminating genes from non-genes, the neural network is trained using all the five parameters together. Parameters corresponding to alphanumeric sequences of genes and non-genes are calculated. The training, validation and test sets contain 6 columns, first 5 columns contains values of the 5 parameters and the last column contains the number ‘I’ for genes and the number ‘0’ for non-genes.  
     EXAMPLE 3  
      The applicants have analyzed 10 prokaryotic genomes using the method of invention. Efficiency of the method has been defined as percentage of the NCBI listed protein coding regions predicted by said method. All the encapsulated protein coding regions have been eliminated automatically by a specifically developed program. The method is able to predict on an average 92.7% of the NCBI listed genes with a standard deviation of 2.8%. Both sensitivity and specificity values of the method are high except in  M. tuberculosis  H37RV genome (as shown in FIG. No. 3).  
     EXAMPLE 4  
      Prediction of Start Site of Protein Coding DNA Sequences  
      Correct start site prediction rate of the method of invention varies from 49.5% in  M. tuberculosis  H37Rv (where specificity is also least) to 81.1% in  H. pylori  26695. The applicants method decides start location based on the presence of start codon plus conservation of the surrounding heptapeptides. This method can also be utilized to predict the start site of a query protein coding DNA sequences predicted by some other method. This can be done by simply converting the protein sequence into corresponding integer sequence and then deciding the valid start site ‘s’ on the basis of surrounding heptapeptides. The applicants report three such cases from  E. coli  K-12 genome (two from the forward strand and one from the reverse strand), to exemplify the start site prediction (as shown below).  
      In prediction of start site there is a trade-off between number richness and length of the ORF. In Case 1 (PID 16132273), the start location of the gene has been shifted from location 85540 to 85630 by NCBI. By visual inspection of the integer sequences corresponding to this gene it is evident that earlier there was a region after ‘s’ which was full of zeroes; or in other terms not a number rich region (bold region in Case 1 of figure shown below). The start site has now been shifted so that it now lies before a number rich region as predicted by the said method of invention. Case 2 is an example of 5′ upstream shifting of the start codon because there is a number rich region (‘2011111’ and one ‘3’ and one ‘2’) upstream of this start codon. So this has been shifted to location 4611050 from 4611194. Case 3 is another example of shifting of start site in the reverse strand where there is a number rich region (‘16531311’ and many other numbers in the string) upstream of the earlier NCBI start location.  
                                                                                        s0s0000000000000s000000000s000s 2ss4222s111000000000999922224210000s00s40004       466442223s0s0120000000177s9999855553239888440s001111000113002s1116311112ss       22222s430100000000100s0100000639977100011100100000001000000000s2000010030       000011110111100000161171000000000s201s12s0000002ss10000000001099s76s621110       0s0s0000s00014444441111100000000000234331211000s033221s000000014s000s00000       002000000000001110000000000000000000s000001s000000s48976531s11111100012234       59999999s92554010010s0s0002s2236667778s75221001s000s000ss00000066ss11111s32       11100000s000002204332110000000000210010010000s00000s11000000354211s000000s       00s22*******                  
 
     
       
         
           
               
             
               
                   
               
               
                   
               
             
            
               
                   
               
               
                 
                   
                     
                     
                         
                         
                     
                   
                 
               
               
                   
               
            
           
           
               
            
               
                 
                   s00020111110000000000000300000000020000010000030ss000000001110s0s000ss0000 
                 
               
               
                 0s102110000000100ss3s2000000000000000000000100021100011s110000000000s00000 
               
               
                 000001s10100000010100002222222000000000000000010321002s3321111s1101111001 
               
               
                 0000000s00s000s00101010100s00000******* 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                   
               
               
                   
               
             
            
               
                   
               
               
                 
                   
                     
                     
                         
                         
                     
                   
                 
               
               
                   
               
            
           
         
       
     
     EXAMPLE 5  
      Prediction of Protein Coding DNA Sequences  
      The method is utilized for prediction of protein coding DNA sequences for various genomes in a publicly available database (NCBI) by employing the following steps: 
      i) generating computationally overlapping peptide libraries from all the protein sequences of the selected organisms available at http://www.ncbi.nlm.nih.gov,     ii) sorting computationally the peptides of length ‘N’ obtained as above, alphabetically, according to single letter amino acid code,     iii) cataloging every peptide and their unique occurrence different organisms,     iv) converting DNA sequence to alphanumeric sequence using peptide library obtained from steps 1 and 2,     v) retrieving all possible open reading frames (ORFs) from the alphanumeric sequence,     vi) training of the modified neural network for discriminating protein coding and non-coding DNA sequences,     vii) predicting DNA coding sequences in the open reading frames (obtained in step 4) using trained neural network,     viii) removing the encapsulated protein coding DNA sequences (genes within genes)    

      Using the steps of the invention the inventors have arrived at disclosure of novel 169 genes from the genomes of organisms selected from SARS-corona virus,  H. influenzae, M. tuberculosis , and  H. pylori  as detailed in the table 2. The Table No. 2 provides the said novel genes in the sequence of SEQ ID No. 1 to SEQ ID No. 169.  
                                       TABLE 2                          1   GDC_HINF —     5641   6273   210   +   Formate dehydrogenase major           5641                   subunit       2   GDC_HINF —     6322   8748   808   +   Formate dehydrogenase major           6322                   subunit       3   GDC_HINF —     124181   124378   65   +   Cell wall-associated hydrolase           124181       4   GDC_HINF —     170553   170732   59   −   dicarboxylate transport protein           170553                   homolog HI0153       5   GDC_HINF —     231874   232173   99   +   type I restriction system           231874                   adenine methylase       6   GDC_HINF —     232170   232991   273   +   type I restriction system           232170                   adenine methylase       7   GDC_HINF —     232813   233139   108   +   type I restriction system           232813                   adenine methylase       8   GDC_HINF —     233190   233393   67   +   Type I restriction enzyme           233190                   EcoprrI M protein       9   GDC_HINF —     235441   235932   163   +   prrD protein homolog           235441       10   GDC_HINF —     235913   238519   868   +   Type I restriction enzyme           235913                   EcoR124II R protein       11   GDC_HINF —     240336   241379   347   −   Aerobic respiration control           240336                   sensor protein       12   GDC_HINF —     243018   243215   65   +   Cell wall-associated hydrolase           243018       13   GDC_HINF —     274892   276853   653   −   Adhesion and penetration           274892                   protein precursor       14   GDC_HINF —     276992   279121   709   −   Adhesion and penetration           276992                   protein precursor       15   GDC_HINF —     370413   370808   131   +   NapA           370413       16   GDC_HINF —     370747   372912   721   +   NapA           370747       17   GDC_HINF —     628407   628604   65   −   Cell wall-associated hydrolase           628407       18   GDC_HINF —     654365   655015   216   −   Probable D-methionine           654365                   transport system permease       19   GDC_HINF —     661444   661641   65   −   Cell wall-associated hydrolase           661444       20   GDC_HINF —     737160   737297   45   +   glycerophosphodiester           737160                   phosphodiesterase       21   GDC_HINF —     775792   775989   65   −   Cell wall-associated hydrolase           775792       22   GDC_HINF —     848166   848678   170   −   ribosomal protein           848166       23   GDC_HINF —     928073   929080   335   +   Peptidase B (Aminopeptidase           928073                   B)       24   GDC_HINF —     929037   929402   121   +   Peptidase B (Aminopeptidase           929037                   B)       25   GDC_HINF —     1018846   1021371   841   −   Isoleucyl-tRNA synthetase           1018846       26   GDC_HINF —     1021582   1021683   33   −   Isoleucyl-tRNA synthetase           1021582       27   GDC_HINF —     1082407   1082514   35   −   protein V6, truncated -           1082407                     Haemophilus influenzae         28   GDC_HINF —     1144501   1145004   167   −   PnuC transporter           1144501       29   GDC_HINF —     1279189   1279935   248   −   Peptide chain release factor 2           1279189                   (RF-2)       30   GDC_HINF —     1347200   1347445   81   +   putative ABC transport protein           1347200       31   GDC_HINF —     1347942   1348478   178   +   putative iron compound ABC           1347942                   transporter       32   GDC_HINF —     1476415   1476615   66   −   PstB           1476415       33   GDC_HINF —     1476557   1477183   208   −   PstB           1476557       34   GDC_HINF —     1505851   1506048   65   −   terminase large subunit           1505851       35   GDC_HINF —     1524561   1525421   286   −   ThiI           1524561       36   GDC_HINF —     1568974   1569300   108   +   DNA-binding protein rdgB           1568974                   homolog       37   GDC_HINF —     1586944   1587765   273   +   putative tail protein           1586944       38   GDC_HINF —     1594339   1594854   171   −   NifC           1594339       39   GDC_HINF —     1634710   1636722   670   +   Probable hemoglobin and           1634710                   hemoglobin-haptoglobin       40   GDC_HINF —     1638626   1639372   248   −   Putative integrase/recombinase           1638626                   HI1572       41   GDC_HINF —     1639409   1639726   105   −   Putative integrase/recombinase           1639409                   HI1572       42   GDC_HINF —     1660491   1662080   529   −   Cell division protein ftsK           1660491                   homolog       43   GDC_HINF —     1807963   1808859   298   −   adhesin homolog HI1732           1807963       44   GDC_HINF —     1817220   1817417   65   +   Cell wall-associated hydrolase           1817220       45   GDC_HPYL —     51094   51432   112   −   putative HP0052-like protein           51094       46   GDC_HPYL —     155367   156164   265   −   2-oxoglutarate/malate           155367                   translocator       47   GDC_HPYL —     447632   447850   72   −   Cell wall-associated hydrolase           447632       48   GDC_HPYL —     506250   507134   294   +   site-specific DNA-           506250                   methyltransferase       49   GDC_HPYL —     583607   583876   89   +   probable DNA helicase           583607       50   GDC_HPYL —     583883   584437   184   +   probable DNA helicase           583883       51   GDC_HPYL —     665045   665695   216   +   putative lipopolysaccharide           665045                   biosynthesis protein       52   GDC_HPYL —     953783   954664   293   −   acetate kinase           953783       53   GDC_HPYL —     954679   954900   73   −   phosphate acetyltransferase           954679       54   GDC_HPYL —     954846   955217   123   −   PHOSPHOTRANSACETYLASE           954846       55   GDC_HPYL —     955261   955557   98   −   phosphate acetyltransferase           955261       56   GDC_HPYL —     1068602   1069459   285   −   IS606 TRANSPOSASE           1068602       57   GDC_HPYL —     1069456   1069929   157   −   transposase-like protein,           1069456                   PS3IS       58   GDC_HPYL —     1376803   1377126   107   +   ribosomal protein           1376803       59   GDC_HPYL —     1474291   1474509   72   +   Cell wall-associated hydrolase           1474291       60   GDC_HPYL —     1600102   1600689   195   −   TYPE III DNA           1600102                   MODIFICATION ENZYME       61   GDC_MTUB —     26830   27534   234   −   putative protoporphyrinogen           26830                   oxidase       62   GDC_MTUB —     36276   36785   169   −   fibronectin-attachment protein           36276                   FAP-P       63   GDC_MTUB —     76032   76595   187   +   retinoblastoma inhibiting gene           76032                   1       64   GDC_MTUB —     80423   81214   263   −   mucin 5           80423       65   GDC_MTUB —     167239   168084   281   +   putative secreted peptidase           167239       66   GDC_MTUB —     214625   215116   163   −   glycoprotein gp2           214625       67   GDC_MTUB —     424142   424657   171   −   PPE FAMILY PROTEIN           424142       68   GDC_MTUB —     459316   461076   586   +   63 kDa protein           459316       69   GDC_MTUB —     549643   550758   371   −   carR           549643       70   GDC_MTUB —     566823   567284   153   +   MAPK-interacting and           566823                   spindle-stabilizing protein       71   GDC_MTUB —     591109   591345   78   +   excisionase, putative           591109       72   GDC_MTUB —     663028   663426   132   +   PROBABLE           663028                   RIBONUCLEOSIDE-                               DIPHOSPHATE                               REDUCTASE       73   GDC_MTUB —     688806   689060   84   +   MCE-FAMILY PROTEIN           688806                   MCE2B       74   GDC_MTUB —     701762   702643   293   −   u1764ad           701762       75   GDC_MTUB —     731710   731877   55   +   ribosomal protein L33           731710       76   GDC_MTUB —     772761   773402   213   −   ENSANGP00000004917           772761       77   GDC_MTUB —     868821   869216   131   −   cold-shock induced protein of           868821                   the Srp1p/Tip1p       78   GDC_MTUB —     890358   891254   298   −   orf2           890358       79   GDC_MTUB —     904043   904840   265   +   aminoimidazole ribotide           904043                   synthetase       80   GDC_MTUB —     1045383   1046129   248   +   u650i           1045383       81   GDC_MTUB —     1068100   1068726   208   −   anchorage subunit of a-           1068100                   agglutinin; Aga1p       82   GDC_MTUB —     1115707   1116369   220   −   mucin 7 precursor, salivary           1115707       83   GDC_MTUB —     1124996   1125712   238   −   putative oxidoreductase           1124996       84   GDC_MTUB —     1138949   1139665   238   −   platelet binding protein GspB           1138949       85   GDC_MTUB —     1170285   1170749   154   −   MC8           1170285       86   GDC_MTUB —     1176592   1176858   88   +   gp85           1176592       87   GDC_MTUB —     1202653   1203198   181   −   s19 chorion protein           1202653       88   GDC_MTUB —     1231843   1232460   205   +   carboxylesterase           1231843       89   GDC_MTUB —     1241031   1241468   145   −   PE           1241031       90   GDC_MTUB —     1252888   1253748   286   −   ppg3           1252888       91   GDC_MTUB —     1264312   1264554   80   +   ketoacyl-CoA thiolase-related           1264312                   protein       92   GDC_MTUB —     1286282   1286587   101   −   pterin-4-alpha-carbinolamine           1286282                   dehydratase       93   GDC_MTUB —     1301742   1302053   103   −   similar to ORF starts at 87,           1301742                   first start codon       94   GDC_MTUB —     1351907   1352614   235   −   ppg3           1351907       95   GDC_MTUB —     1476279   1476647   122   −   Cell wall-associated hydrolase           1476279       96   GDC_MTUB —     1485311   1486399   362   −   4-hydroxyphenylpyruvate           1485311                   dioxygenase C terminal       97   GDC_MTUB —     1486309   1487727   472   −   cell wall surface anchor family           1486309                   protein       98   GDC_MTUB —     1515112   1515846   244   −   putative ABC transporter ATP           1515112                   binding protein       99   GDC_MTUB —     1515464   1516198   244   −   extracellular protein, gamma-           1515464                   D-glutamate-meso-d . . .       100   GDC_MTUB —     1596569   1596892   107   −   putative translation initiation           1596569                   factor IF-2       101   GDC_MTUB —     1600905   1601861   318   −   carboxylesterase family           1600905                   protein       102   GDC_MTUB —     1616064   1616951   295   −   PUTATIVE           1616064                   TRANSCRIPTION                               REGULATOR PROTEIN       103   GDC_MTUB —     1672449   1673216   255   +   MAV278           1672449       104   GDC_MTUB —     1673708   1675000   430   −   MAV301           1673708       105   GDC_MTUB —     1699549   1700226   225   +   gmdA           1699549       106   GDC_MTUB —     1742061   1742858   265   −   ENSANGP00000020758           1742061       107   GDC_MTUB —     1782153   1782932   259   +   GLP_26_54603_52153           1782153       108   GDC_MTUB —     2060659   2061114   151   +   nuclear factor of kappa light           2060659                   polypeptide gene       109   GDC_MTUB —     2093062   2093994   310   −   PROBABLE 6-           2093062                   PHOSPHOGLUCONATE                               DEHYDROGENASE GND1       110   GDC_MTUB —     2105797   2106912   371   +   ATP-binding subunit of ABC-           2105797                   transport system       111   GDC_MTUB —     2133554   2134069   171   −   KIAA0324 protein           2133554       112   GDC_MTUB —     2183418   2184026   202   −   putative transport protein           2183418       113   GDC_MTUB —     2192571   2193488   305   −   putative oxidoreductase           2192571       114   GDC_MTUB —     2234641   2234889   82   −   DNA-binding protein, CopG           2234641                   family       115   GDC_MTUB —     2320829   2321062   77   +   DNA-binding protein, CopG           2320829                   family       116   GDC_MTUB —     2321250   2322509   419   −   cell wall surface anchor family           2321250                   protein       117   GDC_MTUB —     2487508   2488524   338   −   ORF1           2487508       118   GDC_MTUB —     2567990   2568457   155   +   B1158F07.3           2567990       119   GDC_MTUB —     2577106   2577699   197   +   POSSIBLE CONSERVED           2577106                   MEMBRANE PROTEIN       120   GDC_MTUB —     2577486   2577920   144   +   POSSIBLE CONSERVED           2577486                   MEMBRANE PROTEIN       121   GDC_MTUB —     2690012   2690509   165   +   PROBABLE CONSERVED           2690012                   INTEGRAL MEMBRANE                               PROTEIN       122   GDC_MTUB —     2698040   2698243   67   −   POSSIBLE CONSERVED           2698040                   MEMBRANE PROTEIN       123   GDC_MTUB —     2712275   2714008   577   +   MLCL536.10 protein           2712275       124   GDC_MTUB —     2725593   2725859   88   −   PROBABLE HYDROGEN           2725593                   PEROXIDE-INDUCIBLE                               GENES       125   GDC_MTUB —     2733212   2734420   402   −   lycoprotein gp2           2733212       126   GDC_MTUB —     2828257   2828937   226   +   MC8           2828257       127   GDC_MTUB —     2895354   2897222   622   +   antigen T5           2895354       128   GDC_MTUB —     2983047   2984033   328   −   MC8           2983047       129   GDC_MTUB —     3005316   3005696   126   −   ABC transporter, ATP-binding           3005316                   protein       130   GDC_MTUB —     3048559   3049095   178   −   recX protein           3048559       131   GDC_MTUB —     3065095   3066549   484   +   ppg3           3065095       132   GDC_MTUB —     3100192   3100452   86   −   IS1537, transposase           3100192       133   GDC_MTUB —     3129118   3129594   158   −   KIAA1139 protein           3129118       134   GDC_MTUB —     3237815   3238096   93   −   acylphosphatase           3237815       135   GDC_MTUB —     3283182   3283718   178   −   Putative mycocerosyl           3283182                   transferase in MAS 5′r . . .       136   GDC_MTUB —     3289702   3290232   176   +   POSSIBLE TRANSPOSASE           3289702       137   GDC_MTUB —     3319076   3319546   156   −   u0002d           3319076       138   GDC_MTUB —     3339006   3339851   281   −   membrane glycoprotein           3339006       139   GDC_MTUB —     3356995   3357831   278   −   sensor histidine kinase           3356995       140   GDC_MTUB —     3381198   3381755   185   +   MC8           3381198       141   GDC_MTUB —     3388071   3389003   310   +   cellulosomal scaffoldin           3388071                   anchoring protein C       142   GDC_MTUB —     3482312   3482770   152   −   MC8           3482312       143   GDC_MTUB —     3581973   3582620   215   +   similar to mucin, submaxillary -           3581973                   pig       144   GDC_MTUB —     3711717   3712613   298   −   orf2           3711717       145   GDC_MTUB —     3716987   3718534   515   −   similar to profilaggrin - human           3716987                   (fragments)       146   GDC_MTUB —     3754581   3755711   376   −   putative transposase           3754581       147   GDC_MTUB —     3794808   3795026   72   −   deoxyxylulose-5-phosphate           3794808                   synthase       148   GDC_MTUB —     3796793   3797512   239   +   membrane glycoprotein           3796793                   [imported] - equine                               herpesvirus       149   GDC_MTUB —     3879013   3879534   173   −   ribosomal protein S11           3879013       150   GDC_MTUB —     3921024   3921665   213   −   3-oxoacyl-(acyl-carrier-           3921024                   protein) reductase       151   GDC_MTUB —     3974481   3975056   191   +   mucin 10           3974481       152   GDC_MTUB —     3994808   3995446   212   +   MAV278           3994808       153   GDC_MTUB —     3998938   3999642   234   −   protease inhibitor/seed           3998938                   storage/lipid transfer       154   GDC_MTUB —     4021183   4021425   80   −   PUTATIVE TRNA/RRNA           4021183                   METHYLTRANSFERASE       155   GDC_MTUB —     4045946   4046290   114   −   chalcone/stilbene synthase           4045946                   family protein       156   GDC_MTUB —     4053033   4053635   200   +   putative protein (2G313)           4053033       157   GDC_MTUB —     4140236   4140460   74   −   DNA-binding protein, CopG           4140236                   family       158   GDC_MTUB —     4169350   4169706   118   +   PROBABLE CUTINASE           4169350                   PRECURSOR CUT5       159   GDC_MTUB —     4170798   4171211   137   +   PUTATIVE           4170798                   OXIDOREDUCTASE       160   GDC_MTUB —     4252190   4252921   243   +   Salivary gland secretion 1           4252190                   CG3047-PA       161   GDC_MTUB —     4260620   4261213   197   +   SPAPB15E9.01c           4260620       162   GDC_MTUB —     4302166   4302858   230   +   u1764ad           4302166       163   GDC_MTUB —     4317863   4318309   148   +   POSSIBLE TRANSPOSASE           4317863                   [SECOND PART]       164   GDC_MTUB —     4341852   4342388   178   −   GLP_49_64409_65443           4341852       165   GDC_MTUB —     4391527   4391988   153   −   AT9S           4391527       166   gi!Sars174_ref   701   1225   174   +   ABC transporter ATP binding           seq_OUTPUT                   protein/Cytochrome c oxidase           F_GDC_701 —                     folding protein           1225       167   gi!Sars68_refs   1397   1603   68   +   Major facilitator for           eq_OUTPUTF —                     superfamily protein or           GDC_1397 —                     serine/threonine kinase 2           1603       168   gi!Sars61_refs   8828   9013   61   +   Putative protein           eq_OUTPUTF —             GDC_8828 —             9013       169   gi!Sars78_refs   24492   24764   90   +   NADH dehydrogenase I chain           eq_OUTPUTF —             GDC_28559 —             28795                  
 
      A systematic sensitivity and specificity analysis of GeneDecipher has been done on 10 microbial genomes ( FIG. 3 ). Further analysis of GeneDecipher on viral genomes is presented here.  
      SARS-CoV genome sequence:Sequences of the 18 SARS-CoV strains available in the GenBank database (http://www.ncbi.nlm.nih.gov/Entrez/genomes/viruses) were downloaded and analyzed. These include SARS-CoV Refseq (NC — 004718.3), SARS-CoV TWC(AY32118), SIN2774(AY283798), SIN2748(AY283797) SIN267{circumflex over ( )}(AY283796), SIN2677(AY283794), SIN25ti6(AY283794), Frankfurt 1 (AY291315), BJ04(AY279354) BJ03(AY278490), BJ02(AY278487), GZ01(AY278848), CUHKW1(AY278554), TOR2(AY274119), TW1(AY291451), BJ01(AY278488), Urban(AY278741), HKU-39849(AY278491). Other information related to protein coding genes was retrieved from http://www.ncbi.nlm.nih.gov/genomes/SARS/SAks.html.  
      Testing of GeneDecipher on Viral Genomes:  
      To test our method on viral genomes the applicants first analyzed Human Respiratory Syncytial Virus (HRSV), complete genome using GeneDecipher. Comparison of GeneDecipher results with state of the art method ZCURVE_CoV has been done (Table 3). ZCURVE_CoV is able to predict 8 annotated proteins out of 11 reported at NCBI without any false positives. ZCURVE_CoV was unable to predict the following three genes: PID 9629200 (location 626 . . . 1000, non-structural protein2 (NS2)); PID 9629205 (location 4690 . . . 5589, attachment glycoprotein (G)); and PID 9629208 (location 8171 . . . 8443, matrix protein 2(M2)). GeneDecipher predicted 10 out of total 11 annotated proteins of HRSV without any false positives. The gene missed by GeneDecipher was PID 9629208 (location 8171 . . . 8443, matrix protein 2) which was notably missed by ZCURVE_CoV too.  
      This successful prediction of protein coding regions in HRSV genome increases our confidence to predict protein coding regions on newly sequenced SARS-CoV genomes.  
      Analysis of SARS-CoV Using GeneDecipher:  
      The applicants analyzed all 18 strains of SARS-CoV using GeneDecipher. (Detailed results are available on the website given above). GeneDecipher predicts a total of 15 protein coding regions in SARS-CoV genomes including both the polyproteins 1a, 1ab (Sars2628 C-terminal end of Polyprotein 1ab), and all four known structural proteins (M, N, S, and E) for each of the 18 strains. GeneDecipher also predicts 6 to 8 additional coding regions depending on the genome sequence of the strain used. The length of these additional coding regions varied between 61 and 274 amino acids.  
      GeneDecipher predicts 12 coding regions which are common to all 18 strains (Table 4), and one coding region (Sars63, sars6 at NCBI refseq genome) present in 5 strains. GeneDecipher predicts gene Sars90 in GZ01 strain, and Sars154 (Sars 3b at NCBI refseq genome) in BJ02 strain specifically.  
      These 12 common protein coding regions consist of the 6 basic proteins of SARS-CoV (2 polyproteins and the 4 structural proteins); Sars274 (Sars3a at NCBI refseq database), Sars122 (Sars7a at NCBI refseq database), Sars78 (already reported with start shifted as ORF14/Sars9c in TOR2 strain); and three newly predicted (false positives with respect to current annotation at NCBI) protein coding regions Sars 174, Sars68, and Sars61. The three newly predicted genes lie completely within polyprotein 1a genomic region. Although our method discards such genes in bacterial genomes, possibility of finding such genes in viral genomes has not been ruled out. As these genes are present in all 18 strains it is likely that they are protein coding genes.  
      The applicants predict three more coding regions Sars63, Sars154, and Sars90 apart from the 12 discussed above. Sars63 is identified in 5 strains and not identified in remaining 13 strains. This coding region is already reported in NCBI refseq (Sars6). Here the applicants can not comment much about the existence of Sars63 (Sars6 at NCBI refseq) because it is identified in 5 strains and not identified in rest 13. This is due to high density of non-synonymous mutations across strains in this region. Two coding regions Sars154 (sars3b at NCBI), and Sars90 (newly predicted in GZ01 starin) are identified in only one strain. Since these two coding regions are identified in only one strain, they are less likely to be protein coding regions, as also suggested by ZCURVE_CoV (Chen et al., 2003) analysis. The locations of these three genes in different strains are provided in Table 5.  
      Since the peptide libraries are made from the genome sequences of various organisms, the evolutionary origin of a given protein can be traced. If the protein is rich in heptapeptides found occurring in viral genomes then that protein is considered to be of viral origin. The applicants found that 5 core proteins (two polyproteins and three structural proteins M, N, and S) are of viral origin. The remaining, including 3 new predictions, are of prokaryotic origin. It is interesting to that from the same DNA region the applicants are getting proteins in different frames which contain peptides from different origin. Here, how same DNA sequence can code for both bacterial and viral origin is intriguing. This might explain why these new protein coding genes were not detected in primary attempts based on homology to other known viral genome sequences.  
      Comparison with the Existing System—ZCURVE_CoV.  
      Comparison of GeneDecipher, ZCURVE_CoV results with the known annotations for Urbani and TOR2 strains of SARS-CoV are presented in Tables 6a and 6b.  
      In general, GeneDecipher results are in good agreement with the known annotations. In case of Urbani strain GeneDecipher predicts all the known genes except Sars84(X5), Sars63(X3) and Sars154(X2). Sars84(X5) and Sars63(X3) are supported by ZCURVE_CoV whereas Sars154(X2) is missed by both the methods. GeneDecipher predicts four new genes in this strain which incidentally are not supported by ZCURVE_CoV. It is noticeable that out of these four genes Sars78 is already known for strain TOR2 as ORF14/Sars9c. This supports the likelihood of the gene being present in Urbani strain. However, ZCURVE_CoV predicts 2 new genes which are not supported by GeneDecipher either.  
      GeneDecipher predictions for TOR2 strain are identical with those for Urbani strain. In this strain GeneDecipher predicts 9 known genes but fails to predict 6 genes with known annotations. These 6 genes are: Sars154 (ORF4), Sars98 (ORF13), Sars63 (ORF7), Sars44 (ORF9), Sars39 (ORF10), and Sars84 (ORF11). Of these, Sars154 (ORF4) and Sars98 (ORF13) are also missed by ZCURVE_CoV. It is to be noted that both Sars44 (ORF9) and Sars39 (ORF10) are ORFs very small in length (44 and 39 amino acids respectively), and their presence too is not consistent across various SARS strains. Sars63 (ORF7) has been predicted by GeneDecipher in 5 other strains but not in the two strains considered here.  
      Mutation Analysis:  
      Analysis using multiple sequence alignment (ClustalW) for 3 newly predicted protein coding genes Sars174, Sars68 and Sars61 across all 18 strains shows: 
          1. Sars68 has one point mutation at location 80 GAT-&gt;GGT (D-&gt;G) SIN2677 strain.     2. Sars174 has two synonymous point mutations at location 204 CGA-&gt;CGC in GZ01 strain and at location 447 CTG-&gt;CTT in BJ04 strain.     3. Sars61 has one point mutation at location 119 CTG-&gt;CAG (L-&gt;Q) in GZ01 strain.        

      These three newly predicted genes are present in all 18 strains without significant mutations and has no significant hits with BLASTP in non-redundant database. This indicates that these three proteins might have crucial biological functions specific to SARS-CoV. Therefore these coding sequences might serve as candidate drug targets against SARS.  
      Function Assignment:  
      In total the applicants predict 15 coding regions in SARS-CoV out of which functions of the four structural proteins (M, N, S and E) have already been assigned. Although the polyprotein 1ab has been assigned only replicase activity, our analysis implies that the replicase activity is associated with Sars2628 (C terminal of ORF 1ab) fragment. The complete 1ab polyprotein contains 6 functional signatures of which polyprotein 1a contains signatures associated with metabolic enzymes (Table 7a). Functions were assigned to the polyproteins on the basis of peptides (length 7 or more amino acids) occurring in proteins having similar functions in at least 5 different organisms. Other predicted genes/protein coding regions contain peptides which occur in fewer genomes. Based on these peptides the applicants suggest functions, albeit with lesser confidence (Table 7b). The biological relevance of these finding remains to be explored.  
               TABLE 3                          Comparison of GeneDecipher results with ZCURVE_CoV results       on HRSV genome, with respect to annotated genes                         Annotated genes   ZCURVE_CoV   GeneDecipher                                                 Start   End   Length   Start   End   Length   Start   End   Length                                                         99   518   139   99   518   139   99   518   139       626   1000   124   —   —   —   626   1000   124       1140   2315   391   1140   2315   391   1140   2315   391       2348   3073   241   2348   3073   241   2348   3073   241       3263   4033   256   3158   4033   291   3158   4033   291       4303   4500   65   4303   4500   65   4303   4500   65       4690   5589   299   —   —   —   4690   5589   299       5666   7390   574   5666   7390   574   5621   7390   589       7618   8205   195   7618   8205   195   7618   8205   195       8171   8443   90   —   —   —   —   —   —       8509   15009   2166   8443   15009   2188   8443   15009   2188                  
 
     
       
         
           
               
             
               
                 TABLE 4 
               
             
            
               
                   
               
               
                   
               
               
                 Protein coding genes predicted by GeneDecipher 
               
               
                 in SARS-CoV Refseq common to all 18 strains. 
               
            
           
           
               
               
               
               
            
               
                 S. 
                   
                 Length 
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 No. 
                 Start 
                 Stop 
                 Frame 
                 bp 
                 aa 
                 Feature 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 1 
                 265 
                 13413 
                 1+ 
                 13149 
                 4382 
                 Sars1a polyprotein 
               
               
                 2 
                 701 
                 1225 
                 2+ 
                 525 
                 174 
                 Sars174(new predic- 
               
               
                   
                   
                   
                   
                   
                   
                 tion) 
               
               
                 3 
                 1397 
                 1603 
                 2+ 
                 207 
                 68 
                 Sars68(new predic- 
               
               
                   
                   
                   
                   
                   
                   
                 tion) 
               
               
                 4 
                 8828 
                 9013 
                 2+ 
                 186 
                 61 
                 Sars61(new predic- 
               
               
                   
                   
                   
                   
                   
                   
                 tion) 
               
               
                 5 
                 13599 
                 21485 
                 3+ 
                 7887 
                 2628 
                 Sars2628(C-terminal 
               
               
                   
                   
                   
                   
                   
                   
                 end of polyprotein 
               
               
                   
                   
                   
                   
                   
                   
                 lab) 
               
               
                 6 
                 21492 
                 25259 
                 3+ 
                 3768 
                 1255 
                 Spike (S) protein 
               
               
                 7 
                 25268 
                 26092 
                 2+ 
                 825 
                 274 
                 Sars274(Sars 3a) 
               
               
                 8 
                 26117 
                 26347 
                 2+ 
                 231 
                 76 
                 Sars76(Sars4) 
               
               
                 9 
                 26398 
                 27063 
                 1+ 
                 666 
                 221 
                 Sars221(Sars5) 
               
               
                 10 
                 27273 
                 27641 
                 3+ 
                 369 
                 122 
                 Sars122(Sars7a) 
               
               
                 11 
                 28120 
                 29388 
                 1+ 
                 1269 
                 422 
                 Sars422(Sars9a) 
               
               
                 12 
                 28559 
                 28795 
                 2+ 
                 237 
                 78 
                 Sars78 (Identical 
               
               
                   
                   
                   
                   
                   
                   
                 to ORF 14/Sars9c 
               
               
                   
                   
                   
                   
                   
                   
                 in TOR2 with 
               
               
                   
                   
                   
                   
                   
                   
                 shifted start) 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 5 
               
             
            
               
                   
               
               
                   
               
               
                 Identification of Sars90, Sars63, Sars154 as protein coding 
               
               
                 genes by GeneDecipher in various strains of SARS-CoV 
               
            
           
           
               
               
               
               
               
            
               
                 S. 
                 Strain 
                 Sars90 (New 
                 Sars63(Sars6 
                 Sars154(Sars 
               
               
                 No. 
                 name 
                 prediction) 
                 at NCBI) 
                 3b at NCBI) 
               
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 1 
                 SIN2748 
                 — 
                 — 
                 — 
               
               
                 2 
                 BJ01 
                 — 
                 27055 . . . 27246 
                 — 
               
               
                 3 
                 BJ02 
                 — 
                 27074 . . . 27265 
                 25689 . . . 
               
               
                   
                   
                   
                   
                 26153 
               
               
                 4 
                 BJ03 
                 — 
                 27070 . . . 27261 
                 — 
               
               
                 5 
                 BJ04 
                 — 
                 27058 . . . 27249 
                 — 
               
               
                 6 
                 Frank- 
                 — 
                 — 
                 — 
               
               
                   
                 furtt1 
               
               
                 7 
                 Urbani 
                 — 
                 — 
                 — 
               
               
                 8 
                 GZ01 
                 24492 . . . 24764 
                 27058 . . . 27249 
                 — 
               
               
                 9 
                 SIN2500 
                 — 
                 — 
                 — 
               
               
                 10 
                 SIN2677 
                 — 
                 — 
                 — 
               
               
                 11 
                 SIN2679 
                 — 
                 — 
                 — 
               
               
                 12 
                 SIN2774 
                 — 
                 — 
                 — 
               
               
                 13 
                 CHUKW1 
                 — 
                 — 
                 — 
               
               
                 14 
                 TW1 
                 — 
                 — 
                 — 
               
               
                 15 
                 TWC 
                 — 
                 — 
                 — 
               
               
                 16 
                 HKU- 
                 — 
                 — 
                 — 
               
               
                   
                 39849 
               
               
                 17 
                 Refseq 
                 — 
                 — 
                 — 
               
               
                 18 
                 TOR2 
                 — 
                 — 
                 — 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 6(a) 
               
             
            
               
                   
               
               
                   
               
               
                 Comparison of GeneDecipher results with ZCURVE_CoV results on 
               
               
                 SARS-CoV genome Urbani strain, with respect to annotated genes 
               
            
           
           
               
               
               
               
            
               
                 Annotated genes 
                 ZCURVE_CoV 
                 GeneDecipher 
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
               
            
               
                 Start 
                 End 
                 Length 
                 Start 
                 End 
                 Length 
                 Start 
                 End 
                 Length 
                 Features 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
               
            
               
                 265 
                 13398 
                 4377 
                 265 
                 13398 
                 4377 
                 265 
                 13413 
                 4382 
                 ORF 1a 
               
               
                 — 
                 — 
                 — 
                 — 
                 — 
                 — 
                 701 
                 1225 
                 174 
                 Sars174(New 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 prediction by 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 GeneDecipher) 
               
               
                 — 
                 — 
                 — 
                 — 
                 — 
                 — 
                 1397 
                 1603 
                 68 
                 Sars68(New 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 prediction by 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 GeneDecipher) 
               
               
                 — 
                 — 
                 — 
                 — 
                 — 
                 — 
                 8828 
                 9013 
                 61 
                 Sars61(New 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 prediction by 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 GeneDecipher) 
               
               
                 13398 
                 21485 
                 2695 
                 13398 
                 21485 
                 2695 
                 13599 
                 21485 
                 2628 
                 ORF 1b 
               
               
                 21492 
                 25259 
                 1255 
                 21492 
                 25259 
                 1255 
                 21492 
                 25259 
                 1255 
                 S protein 
               
               
                 25268 
                 26092 
                 274 
                 25268 
                 26092 
                 274 
                 25268 
                 26092 
                 274 
                 Sars274(X1) 
               
               
                 25689 
                 26153 
                 154 
                 — 
                 — 
                 — 
                 — 
                 — 
                 — 
                 Sars154(X2) 
               
               
                 26117 
                 26347 
                 76 
                 26117 
                 26347 
                 76 
                 26117 
                 26347 
                 76 
                 E protein 
               
               
                 26398 
                 27063 
                 221 
                 26398 
                 27063 
                 221 
                 26389 
                 27063 
                 224 
                 M protein 
               
               
                 27074 
                 27265 
                 63 
                 27074 
                 27265 
                 63 
                 — 
                 — 
                 — 
                 Sars63(X3) 
               
               
                 27273 
                 27641 
                 122 
                 27273 
                 27641 
                 122 
                 27273 
                 27641 
                 122 
                 Sars122(X4) 
               
               
                 — 
                 — 
                 — 
                 27638 
                 27772 
                 44 
                 — 
                 — 
                 — 
                 Sars44 
               
               
                 — 
                 — 
                 — 
                 27779 
                 27898 
                 39 
                 — 
                 — 
                 — 
                 Sars39 
               
               
                 27864 
                 28118 
                 84 
                 27864 
                 28118 
                 84 
                 — 
                 — 
                 — 
                 Sars84(X5) 
               
               
                 28120 
                 29388 
                 422 
                 28120 
                 29388 
                 422 
                 28120 
                 29388 
                 422 
                 N protein 
               
               
                 — 
                 — 
                 — 
                 — 
                 — 
                 — 
                 28559 
                 28795 
                 78 
                 Sars78(Identical 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 to ORF 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 14/Sars9c in 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 TOR2 with 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 shifted start) 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 6(b) 
               
             
            
               
                   
               
               
                   
               
               
                 Comparison of GeneDecipher results with ZCURVE_CoV results on 
               
               
                 SARS-CoV genome TOR2 strain, with respect to annotated genes 
               
            
           
           
               
               
               
               
            
               
                   
                 ZCURVE_CoV 
                 GeneDecipher 
                   
               
               
                 Annotated genes 
                 predicted genes 
                 predicted genes 
               
            
           
           
               
               
               
               
               
               
               
               
               
               
            
               
                 Start 
                 End 
                 Length 
                 Start 
                 End 
                 Length 
                 Start 
                 End 
                 Length 
                 Features 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
               
            
               
                 265 
                 13398 
                 4377 
                 265 
                 13398 
                 4377 
                 265 
                 13413 
                 4382 
                 ORF 1a 
               
               
                 — 
                 — 
                 — 
                 — 
                 — 
                 — 
                 701 
                 1225 
                 174 
                 Sars174(New 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 prediction by 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 GeneDecipher) 
               
               
                 — 
                 — 
                 — 
                 — 
                 — 
                 — 
                 1397 
                 1603 
                 68 
                 Sars68(New 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 prediction by 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 GeneDecipher) 
               
               
                 — 
                 — 
                 — 
                 — 
                 — 
                 — 
                 8828 
                 9013 
                 61 
                 Sars61(New 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 prediction by 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 GeneDecipher) 
               
               
                 13398 
                 21485 
                 2695 
                 13398 
                 21485 
                 2695 
                 13599 
                 21485 
                 2628 
                 ORF 1b 
               
               
                 21492 
                 25259 
                 1255 
                 21492 
                 25259 
                 1255 
                 21492 
                 25259 
                 1255 
                 S protein 
               
               
                 25268 
                 26092 
                 274 
                 25268 
                 26092 
                 274 
                 25268 
                 26092 
                 274 
                 ORF3(Sars274) 
               
               
                 25689 
                 26153 
                 154 
                 — 
                 — 
                 — 
                 — 
                 — 
                 — 
                 ORF4(Sars154) 
               
               
                 26117 
                 26347 
                 76 
                 26117 
                 26347 
                 76 
                 26117 
                 26347 
                 76 
                 E protein 
               
               
                 26398 
                 27063 
                 221 
                 26398 
                 27063 
                 221 
                 26389 
                 27063 
                 224 
                 M protein 
               
               
                 27074 
                 27265 
                 63 
                 27074 
                 27265 
                 63 
                 — 
                 — 
                 — 
                 Sars63(ORF7) 
               
               
                 27273 
                 27641 
                 122 
                 27273 
                 27641 
                 122 
                 27273 
                 27641 
                 122 
                 Sars122(ORF8) 
               
               
                 27638 
                 27772 
                 44 
                 27638 
                 27772 
                 44 
                 — 
                 — 
                 — 
                 Sars44(ORF9) 
               
               
                 27779 
                 27898 
                 39 
                 27779 
                 27898 
                 39 
                 — 
                 — 
                 — 
                 Sars39(ORF10) 
               
               
                 27864 
                 28118 
                 84 
                 27864 
                 28118 
                 84 
                 — 
                 — 
                 — 
                 Sars84(ORF11) 
               
               
                 28120 
                 29388 
                 422 
                 28120 
                 29388 
                 422 
                 28120 
                 29388 
                 422 
                 N protein 
               
               
                 28130 
                 28426 
                 98 
                 — 
                 — 
                 — 
                 — 
                 — 
                 — 
                 ORF13 
               
               
                 28583 
                 28795 
                 70 
                 — 
                 — 
                 — 
                 28559 
                 28795 
                 78 
                 Sars78(Identical 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 to ORF 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 14/Sars9c in 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 TOR2 with 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 shifted start) 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 7(a) 
               
             
            
               
                   
               
               
                   
               
               
                 Functional assignment of polyproteins 
               
               
                 in SARS (Urbani) Genome using PLHOST 
               
            
           
           
               
               
               
               
            
               
                 S. 
                 NCBI 
                 Conserved peptide 
                   
               
               
                 No. 
                 annotation 
                 signature 
                 Function assigned 
               
               
                   
               
               
                 1 
                 Sars1ab 
                 RIRASLPT 
                 Phosphoglycerate kinase 
               
               
                   
                 (Poly 
               
               
                   
                 protein1ab) 
               
               
                   
                   
                 RSETLLPL 
                 Sulfite reductase (NADPH), 
               
               
                   
                   
                   
                 Flavoprotein 
               
               
                   
                   
                   
                 beta subunit 
               
               
                   
                   
                 LDKLKSLL 
                 Probable acyl-CoA thiolase 
               
               
                   
                   
                 ATVVIGTS 
                 cell division protein ftsZ 
               
               
                   
                   
                 NVAITRAK 
                 DNA-binding protein, 
               
               
                   
                   
                   
                 probably DNA 
               
               
                   
                   
                   
                 helicase 
               
               
                   
                   
                 LQGPPGTGK 
                 DNA helicase related 
               
               
                   
                   
                   
                 protein 
               
               
                 2 
                 Sars1a poly 
                 RIRASLPT 
                 Phosphoglycerate kinase 
               
               
                   
                 protein1a 
               
               
                   
                   
                 RSETLLPL 
                 Sulfite reductase (NADPH), 
               
               
                   
                   
                   
                 Flavoprotein 
               
               
                   
                   
                   
                 beta subunit 
               
               
                   
                   
                 LDKLKSLL 
                 Probable acyl-CoA thiolase 
               
               
                 3 
                 Sars 2628 
                 ATVVIGTS 
                 cell division protein ftsZ 
               
               
                   
                 (C terminal 
               
               
                   
                 of Sars1ab) 
               
               
                   
                   
                 NVAITRAK 
                 DNA-binding protein, 
               
               
                   
                   
                   
                 probably DNA 
               
               
                   
                   
                   
                 helicase 
               
               
                   
                   
                 LQGPPGTGK 
                 DNA helicase related 
               
               
                   
                   
                   
                 protein 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 7(b) 
               
             
            
               
                   
               
               
                   
               
               
                 Suggested functions for some of the non-structural 
               
               
                 genes in SARS-CoV using PLHOST 
               
            
           
           
               
               
               
               
            
               
                 S. 
                   
                 Peptide 
                   
               
               
                 No. 
                 Gene 
                 Signature 
                 Suggested function 
               
               
                   
               
            
           
           
               
               
               
               
            
               
                 1 
                 Sars174(new 
                 TLSKGNAQ 
                 ABC transporter ATP 
               
               
                   
                 prediction) 
                   
                 binding protein 
               
               
                   
                   
                   
                 [ Lactococcus lactis  subsp. 
               
               
                   
                   
                   
                   lactis ] 
               
               
                   
                   
                 VAQMGTLL 
                 Cytochrome c oxidase 
               
               
                   
                   
                   
                 folding protein 
               
               
                   
                   
                   
                 [ Synechocystis  sp. 
               
               
                   
                   
                   
                 PCC 6803] 
               
               
                 2 
                 Sars68(new 
                 LVLVLILA 
                 putative major facilitator 
               
               
                   
                 prediction) 
                   
                 superfamily protein 
               
               
                   
                   
                   
                 [ Schizosaccharomyces   
               
               
                   
                   
                   
                   pombe ] 
               
               
                   
                   
                 TQTLKLDS 
                 serine/threonine kinase 2; 
               
               
                   
                   
                   
                 Serine/threonine 
               
               
                   
                   
                   
                 protein kinase-2 
               
               
                   
                   
                   
                 [ Homo sapiens ] 
               
               
                 3* 
                 Sars90(new 
                 GLLHRGT 
                 NADH Dehydrogenase I 
               
               
                   
                 prediction 
                   
                 Chain 
               
               
                   
                 only in 
               
               
                   
                 GZ01 strain) 
               
               
                 4 
                 Sars61(new 
                 LLPLLAFL 
                 Putative protein 
               
               
                   
                 prediction) 
                   
                 (Conserved across 2 
               
               
                   
                   
                   
                 organisms) 
               
               
                 5 
                 Sars274(Sars3a) 
                 LLLFVTIY 
                 Polyamine transport 
               
               
                   
                   
                   
                 protein; Tpo1p 
               
               
                   
                   
                   
                 [ Saccharomyces   
               
               
                   
                   
                   
                   cerevisiae ] 
               
               
                 6 
                 Sars154(Sars3b) 
                 QTLVLKML 
                 K550.3.p [ Caenorhabditis   
               
               
                   
                   
                   
                   elegans ] 
               
               
                 7 
                 Sars63(Sars6) 
                 DDEELMEL 
                 Elongation factor Tu 
               
               
                   
                   
                   
                 [ Lactococcus lactis   
               
               
                   
                   
                   
                 subsp.  lactis ] 
               
               
                 8 
                 Sars122(Sars7a) 
                 LIVAALVF 
                 Putative transport 
               
               
                   
                   
                   
                 transmembrane protein 
               
               
                   
                   
                   
                 [ Sinorhizobium meliloti ] 
               
               
                   
                   
                 RARSVSPK 
                 Src homology domain 3 
               
               
                   
                   
                   
                 [ Caenorhabditis   
               
               
                   
                   
                   
                   elegans ] 
               
               
                 9* 
                 Sars78(Sars9c) 
                 QLLAAVG 
                 Gamma-glutamate kinase 
               
               
                   
                   
                   
                 (Conserved across 
               
               
                   
                   
                   
                 8 organisms) 
               
               
                   
               
               
                   *No conserved octapeptide was found. However, function has been assigned on the basis of the highly conserved heptapeptide.    
               
            
           
         
       
     
      From the aforementioned The applicants have disclosed 4 new genes including Sars78 in SARS-CoV. The analysis further corroborates the finding of ZCURVE_CoV (Chen et al., 2003) that ORF Sars154 (listed in Refseq as Sars3b) is unlikely to be a coding region. The applicants have also assigned functions to the two polyproteins 1ab and 1a. In addition to replication associated function of C-terminal of 1ab polyprotein, the applicants&#39; analysis implies that the polyprotein 1a may be associated with metabolic enzyme like functions. In all, six peptide signatures are present in polyprotein 1ab. The applicants have suggested putative function for other 9 proteins including ones newly predicted Ly GeneDecipher.  
      Advantages:  
     
         
         
           
              1. Main advantage of the present invention is to provide a new method for prediction of protein coding DNA sequences without using any external evidences like ribosome binding sites, promoter sequences, transcription start sites or codon usage biases.  
              2. It provides a method for statistical analysis of protein coding DNA sequences that utilizes the biological information retained in the conserved peptides which withstood evolutionary pressure.  
              3. It provides a simple method for start site prediction of a protein coding gene.  
              4. It provides a method to detect organism specific, strain specific protein coding DNA sequences.  
              5. It provides novel protein coding DNA sequences, which could be used as potential drug targets.  
           
         
       
    
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