advertisement
Print

Relational Modeling of Biological Data: Trees and Graphs
Pages: 1, 2

Generating left_id and right_id

So where do these magic left_id and right_id values come from? To generate these values, we must resort to a procedural language (in this case Perl), in which we "walk" down the tree, visiting all of a taxon's children before moving on to a taxon's sibling. (For example, we perform a depth-first traversal of the tree). Whenever we first visit a taxon, we set its left_id value before moving on to its children. When we're finished with all of its children, we then set its right_id. We keep a continuously increasing counter to use for the values. This process is diagrammed in Figure 1, and shown in the Perl code in Example 1 below: 


#!/usr/bin/perl -w

# depth-first tree traversal to generate left_id, right_id

use strict;
use DBI;

my $dbh = DBI->connect("dbi:mysql:seqdb_demo",
                       "seqdb_user",
                       "seqdb_pass")
  or die $DBI::errstr;

my $children = $dbh->prepare("SELECT taxon_id
                              FROM taxon
                              WHERE parent_id = ?");
my $setleft  = $dbh->prepare("UPDATE taxon
                              SET left_id = ?
                              WHERE taxon_id = ?");
my $setright = $dbh->prepare("UPDATE taxon
                              SET right_id = ?
                              WHERE taxon_id = ?");

my $ctr = 1;
my $rootid = 1;

walktree($rootid);
$dbh->disconnect;

sub walktree {
  my $id = shift;
  $setleft->execute($ctr++, $id);
  $children->execute($id);
  while(my ($id) = $children->fetchrow_array) {
    walktree($id);
  }
  $setright->execute($ctr++, $id);
}
Figure 1
Figure 1

On a modest database server running MySQL, this process takes about 15 minutes to execute with the NCBI Taxonomy data. Since we only update our local database weekly (by reloading the entire NCBI Taxonomy datasets), we rebuild the entire nested-set representation after each database load. Methods to maintain the values in an incremental update/delete scenario are well known but will not be discussed here. For further information and an excellent treatment of adjacency list, and nested-set representations for tree-like data, Joe Celko's book, SQL For Smarties is highly recommended.

O'Reilly Bioinformatics Conference.

Representing Graphs

To build upon our simple biosequence database, we'll bring in additional information from the Gene Ontology project. Just as we previously asked for all sequences from any Primate, we now want to ask for all sequences that are either enzymes, or are found in mitochondria, or are enzymes found in mitochondria.

The difference between the Taxonomy data and the GO data is that specific GO terms may have multiple general parent terms. For instance, the focal adhesion kinase term has parental kinase, transferase, and signal tranducer terms. Fortunately, no term's parents can also be a child of the term. The hierarchy is one way, or directed. Generally, the GO term ontology is implemented as a specific graph structure called a directed acyclic graph or DAG.

Storing Nodes and Edges in a Graph

Unlike the adjacency-list representation for trees, graphs cannot generally be stored in a single table. The GO terms (or other graph nodes) are stored in one table, and a second table is used to store a list of edges between nodes, with pairs of foreign keys referencing the node table:

Definition: go_term
  field datatype
PK go_acc CHAR(10)
  name TEXT
  class CHAR(1)
Definition: go_edge
  field datatype
PK go_edge_id INT
FK child CHAR(10) REFERENCES (go_term.go_acc)
FK parent CHAR(10) REFERENCES (go_term.go_acc)

The go_edge table is another form of the adjacency list. To move up and down the hierarchy requires a procedural language. Unfortunately, there is no equivalent to the nested-set representation for these more general graph structures. Instead, we'll build an entirely separate accessory table that contains one row for every unique pair of nodes that can be connected transitively; that is, if A is connected to B, and B is connected to C, then A is transitively connected to C. Our transitive closure tables will contain three rows corresponding to this relationship, one each for A & B, B & C, and A & C; see the example below and Figure 2.

GO transitive closure table go_tc
Definition: go_tc
  field datatype
FK child CHAR(10) REFERENCES (go_term.go_acc)
FK parent CHAR(10) REFERENCES (go_term.go_acc)
  length INT
PRIMARY KEY (child, parent)
Example data:
child parent length
A B 1
B C 1
C D 1
C E 1
A C 2
A D 3
A E 3
B D 2
B E 2
Figure 2
Figure 2

To fill the go_tc table, we will again require a procedural, iterative approach. We begin by first copying all the rows from go_edge into go_tc, setting length to one (for completeness, we also add all the terms in go, setting length to zero). We then iteratively add each new pair of terms that are two edges away from each other, three edges away, four edges away, and so on until no new rows have been inserted: 


#!/usr/bin/perl -w

use strict;
use DBI;

my $dbh = DBI->connect("dbi:mysql:seqdb_demo",
                       "seqdb_user",
                       "seqdb_pass")
  or die $DBI::errstr;

$dbh->do("INSERT INTO go_tc
          SELECT go_acc AS child,
                 go_acc AS parent,
                 0 AS length
          FROM   go");

$dbh->do("INSERT INTO go_tc
          SELECT child, parent, 1 AS length
          FROM   go_edge");

my $select = $dbh->prepare(q{
  SELECT DISTINCT tc1.child,
                  tc2.parent,
                  tc1.length + 1
  FROM   go_tc AS tc1
         INNER JOIN go_tc AS tc2
           ON (tc1.parent = tc2.child)
  WHERE  tc2.length = 1
    AND  tc1.length = ?
});

my $insert = $dbh->prepare(q{
  REPLACE INTO go_tc (child, parent, length)
              VALUES (    ?,      ?,      ?)
});

my ($oldsize) =
  $dbh->selectrow_array("SELECT COUNT(*) FROM go_tc");
my $newsize;

my $len = 1;
while (!$newsize || $oldsize < $newsize) {
  $oldsize = $newsize || $oldsize;
  $newsize = $oldsize;
  $select->execute($len++);
  while(my @data = $select->fetchrow_array) {
    $insert->execute(@data);
    $newsize++;
  }
}

Building the go_tc table on our system takes just a few minutes. As with the nested-set tree data, we won't bother to figure out how to make incremental updates to go_tc when data changes in go_edge. We'll simply run the transitive closure script immediately following any changes to go or go_edge.

The last part of the GO data is the associations between some protein sequences and GO terms; unlike an organism in the Taxonomy tree, a protein may have multiple GO terms with which it's associated. Furthermore, the associations are often made to SwissProt or TREMBL accessions, not NCBI GI numbers, so instead of keying off of gi, we'll reference the acc field of the descr table: 


SELECT descr.gi, descr.descr, seq.seq
FROM   seq
       INNER JOIN descr USING (seq_id)
       INNER JOIN go_assoc ON (descr.acc = go_assoc.seq_acc)
       INNER JOIN go_tc ON (go_assoc.go_acc = go_tc.child)
       INNER JOIN go_term ON (go_tc.parent = go_term.go_acc)
WHERE  go_term.name = "enzyme"
  AND  descr.gi = ( SELECT MAX(gi)
                    FROM descr AS d
                    WHERE d.seq_id = descr.seq_id )

Unfortunately, because of MySQL's continued lack of support for SQL statements with sub-selects, we cannot use the previous solution directly. Instead, we break the previous statement into two parts: first we create a temporary table full of GI numbers from the sequence we want, and second, we use the temporary table to pull out the rest of the sequence-specific information: 


CREATE TEMPORARY TABLE go_gi
SELECT MAX(descr.gi) AS gi
FROM   descr
       INNER JOIN go_assoc ON (descr.acc = go_assoc.seq_acc)
       INNER JOIN go_tc ON (go_assoc.go_acc = go_tc.child)
       INNER JOIN go_term ON (go_tc.parent = go_term.go_acc)
WHERE  go_term.name = 'enzyme'
GROUP BY descr.seq_id

Then, to get at the corresponding sequence data: 


SELECT descr.gi, descr.descr, seq.seq
FROM   seq
       INNER JOIN descr USING (seq_id)
       INNER JOIN go_gi USING (gi)

The approach we've shown here can be extended to include any number of boolean conditions, such as "enzyme AND mitochondrion;" the trick will be to find sequences that have an accession (or accessions) associated with both terms. 


CREATE TEMPORARY TABLE go_gi
SELECT MAX(descr.gi) AS gi,
       COUNT(DISTINCT go_term.name) AS count
FROM   descr
       INNER JOIN go_assoc ON (descr.acc = go_assoc.seq_acc)
       INNER JOIN go_tc ON (go_assoc.go_acc = go_tc.child)
       INNER JOIN go_term ON (go_tc.parent = go_term.go_acc)
WHERE  ( go_term.name = "enzyme"
         OR  go_term.name = "mitochondrion"
       )
GROUP BY descr.seq_id
HAVING count = 2

We've done a few things differently here. For each unique sequence, we're collecting the count of how many different requested GO terms it satisfies. To enforce the boolean logic for "enzyme AND mitochondrion," we use an accessory value, count, and require (via the HAVING count = 2 clause) that the rows returned have the correct number of GO terms associated with them. To get "enzyme OR mitochondrion," simply remove the HAVING clause, and the associated count field.

To implement exclusions, such as "enzyme AND mitochondrion NOT mitochondrial matrix," the usual mechanism is to extend the WHERE clause with a NOT EXISTS sub-select: 


WHERE ( go_term.name = "enzyme"
        OR go_term.name = "mitochondrion"
      ) AND NOT EXISTS (

SELECT seq_id
FROM   descr AS d
       INNER JOIN go_assoc ON (d.acc = go_assoc.seq_acc)
       INNER JOIN go_tc ON (go_assoc.go_acc = go_tc.child)
       INNER JOIN go_term ON (go_tc.parent = go_term.go_acc)
WHERE  go_term.name = "mitochondrial matrix"
  AND  descr.seq_id = d.seq_id

      )

But again, since MySQL doesn't support subselect statements, we have to resort to placing the results of the subselect statement into a temporary table, go_gi_exclude, and altering our normal SELECT statement to implement a LEFT JOIN:


CREATE TEMPORARY TABLE go_gi
SELECT MAX(descr.gi) AS gi,
       COUNT(DISTINCT go_term.name) AS count
FROM   descr
       INNER JOIN go_assoc ON (descr.acc = go_assoc.seq_acc)
       INNER JOIN go_tc ON (go_assoc.go_acc = go_tc.child)
       INNER JOIN go_term ON (go_tc.parent = go_term.go_acc)
       LEFT JOIN go_gi_exclude
         ON (descr.seq_id = go_gi_exclude.seq_id)
WHERE  ( go_term.name = "enzyme"
         OR  go_term.name = "mitochondrion"
       ) AND go_gi_exclude.seq_id IS NULL
GROUP BY descr.seq_id
HAVING count = 2

Between the inclusionary HAVING count = n mechanism and the exclusionary NOT EXISTS (or LEFT JOIN alternative) mechanism, all manners of boolean queries can be constructed.

It's All in the Way You Look at Things

The lesson here is that often the obvious way to model data (such as adjacency lists of tree or graph edges) is not the most efficient data model with which to query or compute. Alternatives such as the nested-set implicit representation of a tree-like hierarchy, or the transitive closure of a graph structure costs very little to create, and provides one-step mechanisms to extract informative subsets of the data. Commercial relational database products that advertise native support for hierarchical data are often simply transparently building and managing these same representations. Now you know how to it yourself.

In the next installment, we'll look at some alternatives to modeling temporal or historical biological data.

Aaron Mackey is involved with the BioPerl and GMOD software projects, and distributes his own simple biosequence relational database tool package, seqdb, for educational purposes.