Bio::EnsEMBL::Analysis::Runnable BlastMiniBuilder
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build_runnablescode    nextTop
  Args [1]   : @features - list of FeaturePair objects 
constructed from blast align features. Within
each FeaturePair, feature1 and feature2 should
correspond to the genomic sequence and the
protein/est/cdna sequence.
Example : $self->build_runnables(@features);
Description: Builds the runnable objects for conducting
BlastMini(Genewise/Est2Genome) runs. In
the simplest case this
method creates a single runnable object. The
method itself was written to cope with
situations where the presence of repeated genes
means that more than one whole gene of high
homology is present in a minigenomic sequence.
This module separates possible repeated genes
into clusters of exons such that multiple
minigenomic sequence fragments can be
generated for passing to genewise/est2genome.
When this happens this method returns
multiple runnables that examine smaller
portions of genomic DNA.
Returntype : A list of Bio::EnsEMBL::Analysis::Runnable
- the exact identity of the runnable sub-class
is determined by the calling class (as the
actual final runnable construction is done in
the inheriting class.)
Exceptions : None.
Caller : $self->run
  Args [1]   : protein id - id used by seqfetcher to retrieve
protein object.
Args [2] : feature listref - reference to list of align features
that align to parts of the protein specified in Arg [1]
Example : $self->check_coverage($seqname, $features);
Description: Using a protein/est and a list of corresponding align
features, calculates the percentage of the protein/est
that the features cover.
Returntype : A real (e.g. a percentage like 0.80)
Exceptions : none
Caller : $self->make_runnables
  Args [1]   : query_feature - any kind of align feature.
Args [2] : other feature - another align feature that is
to be checked for substantial overlap with the
query feature.
Example : $self->check_overlap($query_feature, $other_feature);
Description: Checks two features for greater than 90% hit overlap.
Features must be blast-generated align features
resultant from a blast run against a common hit
Returntype : 0 or 1
Exceptions : none
Caller : $self->cluster_features
    Title   :   check_repeated
Usage : $self->check_repeated(1)
Function: Get/Set method for check_repeated
Returns : 0 (Off) or 1 (On)
Args : 0 (Off) or 1 (On)
  Args [1]   : $features - reference to a list of 
FeaturePairs derived from the align feature
objects resulting from a blast run.
Ideally, these features should have been
filtered to remove garbage - say, filter by
percent_id > 80.
Example : $self->cluster_features($features);
Description: Takes a list of features from a blast run
and attempts to cluster these into groups
of high homology. If the blast features
result from matches to a region with several
highly similar copies of the same gene this
clustering should produce a series of exon
clusters. If this is not the case only a
couple of grubby clusters will be produced.
Returntype : A reference to an array of clusters. Each
cluster in the array is an array of similar
Exceptions : none
Caller : self->build_runnables
  Args [1]   : Array of exon clusters generated by
$self->cluster_features (an array of arrays,
each sub-array containing similar blast
Example : $self->for_gene_clusters($exon_clusters);
Description: Using an array of exons clusters (similar
blast features) to build genes with a feature
from each cluster.
Returntype : A reference to an array of arrays, each sub-
array containing features that should
correspond to exons.
Exceptions : none
Caller : $self->build_runnables
  Args [1]   : minigenomic sequence object
Args [2] : reference to a list of FeaturePairs
Example : $self->make_object($miniseq, $features);
Description: This is an interface that must be implemented
in inheriting classes. This method handles
the ultimate creation of the runnable object.
Returntype : 0
Exceptions : Throws an error if the class has not been
implemented by the inheriting class.
Caller : self->build_runnables
Methods code
sub build_runnables {
  my ($self, @features) = @_;

  my @runnables;

  my %unfiltered_partitioned_features;
  my $hseqname;
  foreach my $raw_feat (@features){
    push (@{$unfiltered_partitioned_features{$raw_feat->hseqname}}, $raw_feat);
    $hseqname = $raw_feat->hseqname;
  # We partition the features by sequence id such that we can
# iterate for each id. Most ids will fall through
# to the simplest case where we make a single runnable with one
# unique gene per minigenomic sequence fragment. In cases
# where multiple similar genes are likely to be present in
# the minigenomic sequence we switch to the
# special case algorithm. When this happens we cluster the
# likely exons into groups of similar exons, which we then
# use to make a set of distinct genes. For each likely gene
# a minigenomic sequence is made that tightly spans the
# likely location of the gene - and most probably excludes
# any similar genes nearby. With these smaller minigenomic
# sequence fragments a runnable is made using ALL of the
# original blast-derived feature pairs.
my %partitioned_features; foreach my $raw_feat (@features){ # Imporantly, the features are filtered for low identity matches.
# This is just for the clustering process. The full set of features
# are still passed to the runnable at the end.
if($raw_feat->percent_id >= 80){ push (@{$partitioned_features{$raw_feat->hseqname}}, $raw_feat); } } my %runnable_featids; foreach my $seqname (keys %partitioned_features){ # Preliminary check to see whether our filtered features
# give enough coverage to make it worth continuing.
my $coverage = $self->check_coverage($seqname, $partitioned_features{$seqname}); # If only sparse coverage, go home early.
unless ($coverage > 0.5) { my $runnable = $self->make_object($self->genomic_sequence, $unfiltered_partitioned_features{$seqname}); push (@runnables, $runnable); $runnable_featids{$seqname}++; next } # Now, we attempt to cluster our features into groups of
# similar exons.
my $clustered_features = $self->cluster_features(\@{$partitioned_features{$seqname}}); # We have to do a little test to see if the clusters
# represent the bulk of the features present in the
# genomic region. This test helps avoid the situation
# where a non-repeated gene has exons that are very
# similar to one another. If the flag $clusters_seem_real
# is not set to 1, the the analysis will default to the
# simplest, non-repeated gene way of doing things.
my $clusters_seem_real = 0; if (@$clustered_features) { my $number_of_clustered_features = 0; foreach my $cluster (@$clustered_features) { foreach my $clust_feat (@$cluster){ $number_of_clustered_features++; } } if ($number_of_clustered_features >= (0.7 * (scalar @{$partitioned_features{$seqname}}))) { $clusters_seem_real = 1; } } # If we have clusters in such numbers that it looks like
# there might be repeated genes, we break the mini genomic
# sequence into fragments and construct multiple runnables.
# Otherwise, we default to the normal way of building our
# runnable.
if ((@$clustered_features)&&($clusters_seem_real > 0)) { # Write to logfile
print "Multi-gene code has turned itself on.\nProtein sequence is $seqname\nGenomic region is " . $self->genomic_sequence->id . "\tLength " . $self->genomic_sequence->length . "\n"; # print STDERR "Minigenomic sequence could contain a number "
# . "of highly similar genes. Fragmenting the minigenomic "
# . "sequence to try to resolve these genes individually.\n";
my $gene_clusters = $self->form_gene_clusters($clustered_features); # Determine the extreme start and ends of each gene
# cluster. Decide on positions to split the genomic
# sequence for genomewise - mid-way between
# ends and starts of neighboring clusters. The
# fragments from clusters that don't have the maximum
# number of members are a problem - these presumably
# will fall at the ends of the genomic fragment and
# should just be discarded.
GENE: foreach my $gene_cluster (@$gene_clusters){ my @sorted_gene_cluster = sort {$a->start <=> $b->start;} @$gene_cluster; my $rough_gene_length = $sorted_gene_cluster[-1]->end - $sorted_gene_cluster[0]->start; my $padding_length; if ($rough_gene_length > 10000) { $padding_length = 10000; } else { $padding_length = 1000; } my $cluster_start = $sorted_gene_cluster[0]->start - $padding_length; my $cluster_end = $sorted_gene_cluster[-1]->end + $padding_length; if ($cluster_start < 1) { if ($sorted_gene_cluster[0]->end > 0) { $cluster_start = 1; } } if ($cluster_end > $self->genomic_sequence->length) { if ($sorted_gene_cluster[0]->start <= $self->genomic_sequence->length) { $cluster_end = $self->genomic_sequence->length; } } # Here we create our genomic fragment for passing to
# our runnable. This is the fragment of genomic sequence
# that we have calculated should only contain the
# exons of one single gene. It is padded with Ns to
# maintain the coordinate system of the features that
# may be returned. Another way of looking at this is
# that we make a genomic sequence where the regions
# flanking our gene are masked.
# my $string_seq = ('N' x ($cluster_start - 1)) .
# $self->genomic_sequence->subseq($cluster_start, $cluster_end)
# . ('N' x ($self->genomic_sequence->length - ($cluster_end + 1)));
#print " Fragmented mini-genomic sequence - Start $cluster_start\tEnd $cluster_end\n";
#print "Length of string_seq = " . length($string_seq) . "\n";
# my $genomic_subseq = Bio::EnsEMBL::Slice->new
# (
# -seq => $string_seq,
# -seq_region_name => $self->genomic_sequence->seq_region_name,
# -start => 1,
# -end => length($string_seq),
# -coord_system => $self->genomic_sequence->coord_system,
# );
#print STDERR "Have genomic subseq ".$genomic_subseq->name."\n";
# my $runnable = $self->make_object($genomic_subseq, $unfiltered_partitioned_features{$seqname});
my $runnable = $self->make_object($self->genomic_sequence, $unfiltered_partitioned_features{$seqname}, $cluster_start, $cluster_end); push (@runnables, $runnable); $runnable_featids{$seqname}++; } }else{ # This is what we do when we dont have multiple genes
# in our genomic fragment. This is "Normal mode".
my $runnable = $self->make_object($self->genomic_sequence, $unfiltered_partitioned_features{$seqname}); push (@runnables, $runnable); $runnable_featids{$seqname}++; } } # Gah, what if we havent constructed a runnable for each
# feature id by this late stage?
# It means that the homology of the blast features for
# an id was so low that we threw all the features
# away. Had better just make a default runnable...
my %feature_ids; foreach my $feature (@features){ $feature_ids{$feature->hseqname}++; } foreach my $feature_id (keys %feature_ids){ unless ($runnable_featids{$feature_id}){ my $runnable = $self->make_object($self->genomic_sequence, $unfiltered_partitioned_features{$feature_id}); push (@runnables, $runnable); } } return\@ runnables;
sub check_coverage {
  my ($self, $seqname, $features) = @_;

  my $protein = $self->get_Sequence($seqname);  
  my $protein_length = $protein->length;

  my @voider;
  my @score_keeper;

  foreach my $feature (@$features){

    my $start = $feature->hstart;
    my $end = $feature->hend;
    #print STDERR "Have ".$seqname." start ".$start." end ".$end."\n";
($start, $end) = sort {$a <=> $b} ($start, $end); for (my $j = $start; $j <= $end; $j++){ $score_keeper[$j] = 1; } } my $tally = 0; #print "Have " . scalar(@score_keeper) . " 1s and 0s!\n";
foreach my $score (@score_keeper){ $tally++ if $score; } return ($tally/$protein_length);
sub check_overlap {
  my ($self, $query_feature, $other_feature) = @_;

  my $query_start = $query_feature->hstart;
  my $query_end   = $query_feature->hend;

  if ($query_start > $query_end){($query_start, $query_end) = ($query_end, $query_start)}
  my $other_start = $other_feature->hstart;
  my $other_end   = $other_feature->hend;
  if ($other_start > $other_end) {($other_start, $other_end) = ($other_end, $other_start)}
  unless ($query_end < $other_start || $query_start > $other_end){
    # We have overlapping features
my $query_length = $query_end - $query_start; my $other_length = $other_end - $other_start; my ($start1, $start2, $end1, $end2) = sort {$a <=> $b} ($query_start, $other_start, $query_end, $other_end); my $overlap = $end1 - $start2; if (($overlap >= (0.9 * $query_length))&&($overlap >= (0.9 * $other_length))){ return 1 } } return 0;
sub check_repeated {
  my( $self, $value ) = @_;    

  if ($value) {
    $self->{'_check_repeated'} = $value;

  return $self->{'_check_repeated'};

return 1;
sub cluster_features {
  my ($self, $features) = @_;

  # Sort the list of features according to their percent id.
my @sorted_features = sort { $b->percent_id <=> $a->percent_id;} @$features; # With the sorted features, we iteratively check each
# highest scoring hit against each lesser scoring
# sequence. Features that correspond to the same part of
# the hit sequence (ie, are overlapping) are clustered
# in an array of 'exons' (and removed from the
# sorted_features array).
my @all_feature_clusters; while (@sorted_features) { my $top_feature = shift @sorted_features; my @similar_features; push (@similar_features, $top_feature); my @subtracted_sorted_features; foreach my $lesser_feature (@sorted_features) { if ($self->check_overlap($top_feature,$lesser_feature)){ push (@similar_features, $lesser_feature); } else { push (@subtracted_sorted_features, $lesser_feature); } } @sorted_features = @subtracted_sorted_features; unless ((scalar @similar_features) == 1){ push (@all_feature_clusters,\@ similar_features); } } return\@ all_feature_clusters;
sub form_gene_clusters {
  my ($self, $exon_clusters) = @_;

  # Sort clusters according to their location in our
# hit sequence.
my @sorted_by_start = sort {$a->[0]->hstart <=> $b->[0]->hstart} @$exon_clusters; my @final_gene_clusters; my $total_clustered_exons = 0; foreach my $cluster (@sorted_by_start){ foreach my $exon (@$cluster){ $total_clustered_exons++; } } my $remaining_unclustered_exons = $total_clustered_exons; my @other_clusters = @sorted_by_start; while ($remaining_unclustered_exons > (0.2*$total_clustered_exons)) { my $this_cluster = shift @other_clusters; while (@$this_cluster){ my $seed_exon = shift @$this_cluster; $remaining_unclustered_exons--; my @gene_cluster; push (@gene_cluster, $seed_exon); foreach my $other_cluster (@other_clusters) { if (@$other_cluster){ my @subtracted_other_cluster; my $closest = 1000000; my $closest_feature; foreach my $candidate_exon (@$other_cluster){ my $distance = abs($candidate_exon->start - $seed_exon->start); # The criteria below allows matches so long as the candidate exon
# is close enough and that the start of the next exon is greater
# than the start of the previous exon (it is desirable to allow
# overlapping exons because this clusters features that are fragments
# of one another).
if(($distance < $closest)&& ($candidate_exon->hstart > $seed_exon->hstart)&& ($distance < 20000)){ if (defined $closest_feature){ push (@subtracted_other_cluster, $closest_feature); } $closest = $distance; $closest_feature = $candidate_exon; } else { push (@subtracted_other_cluster, $candidate_exon); } } @$other_cluster = @subtracted_other_cluster; if (defined $closest_feature) { push (@gene_cluster, $closest_feature); $remaining_unclustered_exons--; } undef $closest_feature; } } push (@final_gene_clusters,\@ gene_cluster); } } return\@ final_gene_clusters;
sub genomic_sequence {
  my $self = shift;
  my $slice = shift;
    throw("Must pass Runnable::query a Bio::PrimarySeqI not a ".
          $slice) unless($slice->isa('Bio::PrimarySeqI'));
    $self->{'query'} = $slice;
  return $self->{'query'};
sub make_object {
  my ($self) = @_;

  $self->throw("make_object method must be implemented in the inheriting class.");

  # Sample implementation for Runnable::MultiMiniGenewise:
# my ($self, $miniseq, $features) = @_;
# my $mg = new Bio::EnsEMBL::Analysis::Runnable::MultiMiniGenewise(
# '-genomic' => $miniseq,
# '-features' => \@newf,
# '-seqfetcher' => $self->seqfetcher,
# '-endbias' => $self->endbias
# );
# return $mg;
General documentation
Dan Andrews <>
The rest of the documentation details each of the object methods.
Internal methods are usually preceded with a _