M. tuberculosis genes

Data Type

Abstract

Data giving characteristics of each ORF (potential gene) in the M. tuberculosis bacterium. Sequence, homology (similarity to other genes) and structural information, and function (if known) are provided.

Sources

Original Owner and Donor

  Ross D. King
  Department of Computer Science, 
  University of Wales Aberystwyth, 
  SY23 3DB, Wales
  rdk@aber.ac.uk

Data Characteristics

The data was collected from several sources, including the Sanger Centre and SWISSPROT. Structure prediction was made by PROF. Homology search was made by PSI-BLAST.

The data is in Datalog format. Missing values are not explicit, but some genes have more relationships than others.

Dependencies

M. tuberculosis genes (ORFs) are related to each other by the predicate tb_to_tb_evalue(TBNumber,E-value). They are related to other (SWISSPROT) proteins by the predicate e_val(AccNo,E-value). All the data for a single gene (ORF) is enclosed between delimiters of the form:

   begin(model(TBNumber)).
   end(model(TBNumber)).

Other Relevant Information

The gene functional classes are in a hierarchy. See http://www.sanger.ac.uk/Projects/M_tuberculosis/Gene_list/.

Data Format

tb_functions.pl

   class([1,0,0,0],"Small-molecule metabolism ").
   class([1,1,0,0],"Degradation ").
   class([1,1,1,0],"Carbon compounds ").  

Arguments are a list of 4 numbers (describing class at the 4 different levels), followed by a string class description. For example,

   function(tb186,[1,1,1,0],'bglS',"beta-glucosidase"). 

Arguments are ORF number, list of 4 class numbers, gene name (or null if no gene name) in single quotes, ORF description in double quotes.

tb_data.pl

   begin(model(X)).
   end(model(X)).

   tb_protein(X).    % X is gene number
   function(2,1,5,0,'gyrA','DNA gyrase subunit A').  % 4 levels of functional hierarchy, gene name, description
   coding_region(7302,9815). % start,end. integers
   tb_mol_wt(19934).  % integer
   access(1,e,20). % int (position), {e,i,b}, int (length) 
   access_exposed(1,20). % int (position), int (length) 
   access_intermediate(26,1). % int (position), int (length) 
   access_burried(1,2). % int (position), int (length) 
   access_dist(b,42.8). % {e,i,b}, float (percentage)
   sec_struc(1,c,23). % int (position), {a,b,c}, int (length)
   sec_struc_coil(1,23). % int (position), int (length)
   sec_struc_alpha(1,15). % int (position), int (length)
   sec_struc_beta(1,6). % int (position), int (length)
   struc_dist(a,32.1). % {a,b,c}, float (percentage)
   sec_struc_conf(78.8). % float (confidence)
   sec_struc_conf_alpha(88.9). % float (confidence)
   sec_struc_conf_beta(58.0). % float (confidence)
   sec_struc_conf_coil(77.7). % float (confidence)
   psi_sequences_found(1,7). % how many found, which iteration
   psi_sequences_found_again(2,7).  % how many found, which iteration
   psi_sequences_found_new(2,0). % how many found, which iteration
   amino_acid_ratio(a,11.2). % amino acid letter, float
   amino_acid_pair_ratio(a,c,0.0). % amino acid letter, amino acid letter, float (out of 1000, ie 2.8 = 0.28%)
   sequence_length(187).  % integer
   tb_to_tb_evalue(tb3671,1.100000e-01). % ORF number, e-value (double)  
   e_val(p35925,7.0e-59). % SWISSPROT accession no, e-value (double)
   species(p35925,'streptomyces_coelicolor'). % SWISSPROT acc no, string
   classification(p35925,bacteria). % SWISSPROT acc no, name
   mol_wt(p35925,19772). % SWISSPROT acc no, integer
   keyword(p35925,'hypothetical_protein'). % SWISSPROT acc no, string
   db_ref(p35925,embl,l27063,g436026,null). % SWISSPROT acc no, db id, primary id, secondary id, status id
   signalip(c,35,no). % {c,y,s}, int (signal peptide c/y/s score), yes/no
   signalip(ss,1,34,no). % ss, int, int, yes/no
   signalip(cleavage,59,60). % cleavage, int/null, int/null
   hydro_cons(-0.498,-0.474,0.624,3.248,0.278). % double, double, double, double, double
   gene_name(p41514,'gyrb'). % SWISSPROT acc no, string

Past Usage

King, R. and Karwath, A. and Clare, A. and Dehaspe, L. (2000). Genome Scale Prediction of Protein Functional Class from Sequence Using Data Mining, In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

Acknowledgements, Copyright Information, and Availability

Usage Restrictions

There are no restrictions. This data is provided "as is" and without any express or implied warranties, including, without limitation, the implied warranties of merchantibility and fitness for a particular purpose.

Citation Requests

Please cite King~et. al (2000).

Acknowledgements

References and Further Information

King, R. and Karwath, A. and Clare, A. and Dehaspe, L. (2000). Accurate prediction of protein functional class in the M. tuberculosis and E. coli genomes using data mining, Comparative and Functional Genomics, 17, pp 283--293.