sequences,
int start, int end, boolean profile)
{
SequenceI[] seqs = new SequenceI[sequences.size()];
int width = 0;
synchronized (sequences)
{
for (int i = 0; i < sequences.size(); i++)
{
seqs[i] = sequences.get(i);
int length = seqs[i].getLength();
if (length > width)
{
width = length;
}
}
if (end >= width)
{
end = width;
}
ProfilesI reply = calculate(seqs, width, start, end, profile);
return reply;
}
}
/**
* Calculate the consensus symbol(s) for each column in the given range.
*
* @param sequences
* @param width
* the full width of the alignment
* @param start
* start column (inclusive, base zero)
* @param end
* end column (exclusive)
* @param saveFullProfile
* if true, store all symbol counts
*/
public static final ProfilesI calculate(final SequenceI[] sequences,
int width, int start, int end, boolean saveFullProfile)
{
// long now = System.currentTimeMillis();
int seqCount = sequences.length;
boolean nucleotide = false;
int nucleotideCount = 0;
int peptideCount = 0;
ProfileI[] result = new ProfileI[width];
for (int column = start; column < end; column++)
{
/*
* Apply a heuristic to detect nucleotide data (which can
* be counted in more compact arrays); here we test for
* more than 90% nucleotide; recheck every 10 columns in case
* of misleading data e.g. highly conserved Alanine in peptide!
* Mistakenly guessing nucleotide has a small performance cost,
* as it will result in counting in sparse arrays.
* Mistakenly guessing peptide has a small space cost,
* as it will use a larger than necessary array to hold counts.
*/
if (nucleotideCount > 100 && column % 10 == 0)
{
nucleotide = (9 * peptideCount < nucleotideCount);
}
ResidueCount residueCounts = new ResidueCount(nucleotide);
for (int row = 0; row < seqCount; row++)
{
if (sequences[row] == null)
{
System.err.println(
"WARNING: Consensus skipping null sequence - possible race condition.");
continue;
}
if (sequences[row].getLength() > column)
{
char c = sequences[row].getCharAt(column);
residueCounts.add(c);
if (Comparison.isNucleotide(c))
{
nucleotideCount++;
}
else if (!Comparison.isGap(c))
{
peptideCount++;
}
}
else
{
/*
* count a gap if the sequence doesn't reach this column
*/
residueCounts.addGap();
}
}
int maxCount = residueCounts.getModalCount();
String maxResidue = residueCounts.getResiduesForCount(maxCount);
int gapCount = residueCounts.getGapCount();
ProfileI profile = new Profile(seqCount, gapCount, maxCount,
maxResidue);
if (saveFullProfile)
{
profile.setCounts(residueCounts);
}
result[column] = profile;
}
return new Profiles(result);
// long elapsed = System.currentTimeMillis() - now;
// System.out.println(elapsed);
}
/**
* Make an estimate of the profile size we are going to compute i.e. how many
* different characters may be present in it. Overestimating has a cost of
* using more memory than necessary. Underestimating has a cost of needing to
* extend the SparseIntArray holding the profile counts.
*
* @param profileSizes
* counts of sizes of profiles so far encountered
* @return
*/
static int estimateProfileSize(SparseIntArray profileSizes)
{
if (profileSizes.size() == 0)
{
return 4;
}
/*
* could do a statistical heuristic here e.g. 75%ile
* for now just return the largest value
*/
return profileSizes.keyAt(profileSizes.size() - 1);
}
/**
* Derive the consensus annotations to be added to the alignment for display.
* This does not recompute the raw data, but may be called on a change in
* display options, such as 'ignore gaps', which may in turn result in a
* change in the derived values.
*
* @param consensus
* the annotation row to add annotations to
* @param profiles
* the source consensus data
* @param startCol
* start column (inclusive)
* @param endCol
* end column (exclusive)
* @param ignoreGaps
* if true, normalise residue percentages ignoring gaps
* @param showSequenceLogo
* if true include all consensus symbols, else just show modal
* residue
* @param nseq
* number of sequences
*/
public static void completeConsensus(AlignmentAnnotation consensus,
ProfilesI profiles, int startCol, int endCol, boolean ignoreGaps,
boolean showSequenceLogo, long nseq)
{
// long now = System.currentTimeMillis();
if (consensus == null || consensus.annotations == null
|| consensus.annotations.length < endCol)
{
/*
* called with a bad alignment annotation row
* wait for it to be initialised properly
*/
return;
}
for (int i = startCol; i < endCol; i++)
{
ProfileI profile = profiles.get(i);
if (profile == null)
{
/*
* happens if sequences calculated over were
* shorter than alignment width
*/
consensus.annotations[i] = null;
return;
}
final int dp = getPercentageDp(nseq);
float value = profile.getPercentageIdentity(ignoreGaps);
String description = getTooltip(profile, value, showSequenceLogo,
ignoreGaps, dp);
String modalResidue = profile.getModalResidue();
if ("".equals(modalResidue))
{
modalResidue = "-";
}
else if (modalResidue.length() > 1)
{
modalResidue = "+";
}
consensus.annotations[i] = new Annotation(modalResidue, description,
' ', value);
}
// long elapsed = System.currentTimeMillis() - now;
// System.out.println(-elapsed);
}
/**
* Derive the gap count annotation row.
*
* @param gaprow
* the annotation row to add annotations to
* @param profiles
* the source consensus data
* @param startCol
* start column (inclusive)
* @param endCol
* end column (exclusive)
*/
public static void completeGapAnnot(AlignmentAnnotation gaprow,
ProfilesI profiles, int startCol, int endCol, long nseq)
{
if (gaprow == null || gaprow.annotations == null
|| gaprow.annotations.length < endCol)
{
/*
* called with a bad alignment annotation row
* wait for it to be initialised properly
*/
return;
}
// always set ranges again
gaprow.graphMax = nseq;
gaprow.graphMin = 0;
double scale = 0.8 / nseq;
for (int i = startCol; i < endCol; i++)
{
ProfileI profile = profiles.get(i);
if (profile == null)
{
/*
* happens if sequences calculated over were
* shorter than alignment width
*/
gaprow.annotations[i] = null;
return;
}
final int gapped = profile.getNonGapped();
String description = "" + gapped;
gaprow.annotations[i] = new Annotation("", description, '\0', gapped,
jalview.util.ColorUtils.bleachColour(Color.DARK_GRAY,
(float) scale * gapped));
}
}
/**
* Returns a tooltip showing either
*
* - the full profile (percentages of all residues present), if
* showSequenceLogo is true, or
* - just the modal (most common) residue(s), if showSequenceLogo is
* false
*
* Percentages are as a fraction of all sequence, or only ungapped sequences
* if ignoreGaps is true.
*
* @param profile
* @param pid
* @param showSequenceLogo
* @param ignoreGaps
* @param dp
* the number of decimal places to format percentages to
* @return
*/
static String getTooltip(ProfileI profile, float pid,
boolean showSequenceLogo, boolean ignoreGaps, int dp)
{
ResidueCount counts = profile.getCounts();
String description = null;
if (counts != null && showSequenceLogo)
{
int normaliseBy = ignoreGaps ? profile.getNonGapped()
: profile.getHeight();
description = counts.getTooltip(normaliseBy, dp);
}
else
{
StringBuilder sb = new StringBuilder(64);
String maxRes = profile.getModalResidue();
if (maxRes.length() > 1)
{
sb.append("[").append(maxRes).append("]");
}
else
{
sb.append(maxRes);
}
if (maxRes.length() > 0)
{
sb.append(" ");
Format.appendPercentage(sb, pid, dp);
sb.append("%");
}
description = sb.toString();
}
return description;
}
/**
* Returns the sorted profile for the given consensus data. The returned array
* contains
*
*
* [profileType, numberOfValues, nonGapCount, charValue1, percentage1, charValue2, percentage2, ...]
* in descending order of percentage value
*
*
* @param profile
* the data object from which to extract and sort values
* @param ignoreGaps
* if true, only non-gapped values are included in percentage
* calculations
* @return
*/
public static int[] extractProfile(ProfileI profile, boolean ignoreGaps)
{
int[] rtnval = new int[64];
ResidueCount counts = profile.getCounts();
if (counts == null)
{
return null;
}
SymbolCounts symbolCounts = counts.getSymbolCounts();
char[] symbols = symbolCounts.symbols;
int[] values = symbolCounts.values;
QuickSort.sort(values, symbols);
int nextArrayPos = 2;
int totalPercentage = 0;
final int divisor = ignoreGaps ? profile.getNonGapped()
: profile.getHeight();
/*
* traverse the arrays in reverse order (highest counts first)
*/
for (int i = symbols.length - 1; i >= 0; i--)
{
int theChar = symbols[i];
int charCount = values[i];
rtnval[nextArrayPos++] = theChar;
final int percentage = (charCount * 100) / divisor;
rtnval[nextArrayPos++] = percentage;
totalPercentage += percentage;
}
rtnval[0] = symbols.length;
rtnval[1] = totalPercentage;
int[] result = new int[rtnval.length + 1];
result[0] = AlignmentAnnotation.SEQUENCE_PROFILE;
System.arraycopy(rtnval, 0, result, 1, rtnval.length);
return result;
}
/**
* Extract a sorted extract of cDNA codon profile data. The returned array
* contains
*
*
* [profileType, numberOfValues, totalCount, charValue1, percentage1, charValue2, percentage2, ...]
* in descending order of percentage value, where the character values encode codon triplets
*
*
* @param hashtable
* @return
*/
public static int[] extractCdnaProfile(Hashtable hashtable,
boolean ignoreGaps)
{
// this holds #seqs, #ungapped, and then codon count, indexed by encoded
// codon triplet
int[] codonCounts = (int[]) hashtable.get(PROFILE);
int[] sortedCounts = new int[codonCounts.length - 2];
System.arraycopy(codonCounts, 2, sortedCounts, 0,
codonCounts.length - 2);
int[] result = new int[3 + 2 * sortedCounts.length];
// first value is just the type of profile data
result[0] = AlignmentAnnotation.CDNA_PROFILE;
char[] codons = new char[sortedCounts.length];
for (int i = 0; i < codons.length; i++)
{
codons[i] = (char) i;
}
QuickSort.sort(sortedCounts, codons);
int totalPercentage = 0;
int distinctValuesCount = 0;
int j = 3;
int divisor = ignoreGaps ? codonCounts[1] : codonCounts[0];
for (int i = codons.length - 1; i >= 0; i--)
{
final int codonCount = sortedCounts[i];
if (codonCount == 0)
{
break; // nothing else of interest here
}
distinctValuesCount++;
result[j++] = codons[i];
final int percentage = codonCount * 100 / divisor;
result[j++] = percentage;
totalPercentage += percentage;
}
result[2] = totalPercentage;
/*
* Just return the non-zero values
*/
// todo next value is redundant if we limit the array to non-zero counts
result[1] = distinctValuesCount;
return Arrays.copyOfRange(result, 0, j);
}
/**
* Compute a consensus for the cDNA coding for a protein alignment.
*
* @param alignment
* the protein alignment (which should hold mappings to cDNA
* sequences)
* @param hconsensus
* the consensus data stores to be populated (one per column)
*/
public static void calculateCdna(AlignmentI alignment,
Hashtable[] hconsensus)
{
final char gapCharacter = alignment.getGapCharacter();
List mappings = alignment.getCodonFrames();
if (mappings == null || mappings.isEmpty())
{
return;
}
int cols = alignment.getWidth();
for (int col = 0; col < cols; col++)
{
// todo would prefer a Java bean for consensus data
Hashtable columnHash = new Hashtable();
// #seqs, #ungapped seqs, counts indexed by (codon encoded + 1)
int[] codonCounts = new int[66];
codonCounts[0] = alignment.getSequences().size();
int ungappedCount = 0;
for (SequenceI seq : alignment.getSequences())
{
if (seq.getCharAt(col) == gapCharacter)
{
continue;
}
List codons = MappingUtils.findCodonsFor(seq, col,
mappings);
for (char[] codon : codons)
{
int codonEncoded = CodingUtils.encodeCodon(codon);
if (codonEncoded >= 0)
{
codonCounts[codonEncoded + 2]++;
ungappedCount++;
}
}
}
codonCounts[1] = ungappedCount;
// todo: sort values here, save counts and codons?
columnHash.put(PROFILE, codonCounts);
hconsensus[col] = columnHash;
}
}
/**
* Derive displayable cDNA consensus annotation from computed consensus data.
*
* @param consensusAnnotation
* the annotation row to be populated for display
* @param consensusData
* the computed consensus data
* @param showProfileLogo
* if true show all symbols present at each position, else only the
* modal value
* @param nseqs
* the number of sequences in the alignment
*/
public static void completeCdnaConsensus(
AlignmentAnnotation consensusAnnotation,
Hashtable[] consensusData, boolean showProfileLogo, int nseqs)
{
if (consensusAnnotation == null
|| consensusAnnotation.annotations == null
|| consensusAnnotation.annotations.length < consensusData.length)
{
// called with a bad alignment annotation row - wait for it to be
// initialised properly
return;
}
// ensure codon triplet scales with font size
consensusAnnotation.scaleColLabel = true;
for (int col = 0; col < consensusData.length; col++)
{
Hashtable hci = consensusData[col];
if (hci == null)
{
// gapped protein column?
continue;
}
// array holds #seqs, #ungapped, then codon counts indexed by codon
final int[] codonCounts = (int[]) hci.get(PROFILE);
int totalCount = 0;
/*
* First pass - get total count and find the highest
*/
final char[] codons = new char[codonCounts.length - 2];
for (int j = 2; j < codonCounts.length; j++)
{
final int codonCount = codonCounts[j];
codons[j - 2] = (char) (j - 2);
totalCount += codonCount;
}
/*
* Sort array of encoded codons by count ascending - so the modal value
* goes to the end; start by copying the count (dropping the first value)
*/
int[] sortedCodonCounts = new int[codonCounts.length - 2];
System.arraycopy(codonCounts, 2, sortedCodonCounts, 0,
codonCounts.length - 2);
QuickSort.sort(sortedCodonCounts, codons);
int modalCodonEncoded = codons[codons.length - 1];
int modalCodonCount = sortedCodonCounts[codons.length - 1];
String modalCodon = String
.valueOf(CodingUtils.decodeCodon(modalCodonEncoded));
if (sortedCodonCounts.length > 1 && sortedCodonCounts[codons.length
- 2] == sortedCodonCounts[codons.length - 1])
{
/*
* two or more codons share the modal count
*/
modalCodon = "+";
}
float pid = sortedCodonCounts[sortedCodonCounts.length - 1] * 100
/ (float) totalCount;
/*
* todo ? Replace consensus hashtable with sorted arrays of codons and
* counts (non-zero only). Include total count in count array [0].
*/
/*
* Scan sorted array backwards for most frequent values first. Show
* repeated values compactly.
*/
StringBuilder mouseOver = new StringBuilder(32);
StringBuilder samePercent = new StringBuilder();
String percent = null;
String lastPercent = null;
int percentDecPl = getPercentageDp(nseqs);
for (int j = codons.length - 1; j >= 0; j--)
{
int codonCount = sortedCodonCounts[j];
if (codonCount == 0)
{
/*
* remaining codons are 0% - ignore, but finish off the last one if
* necessary
*/
if (samePercent.length() > 0)
{
mouseOver.append(samePercent).append(": ").append(percent)
.append("% ");
}
break;
}
int codonEncoded = codons[j];
final int pct = codonCount * 100 / totalCount;
String codon = String
.valueOf(CodingUtils.decodeCodon(codonEncoded));
StringBuilder sb = new StringBuilder();
Format.appendPercentage(sb, pct, percentDecPl);
percent = sb.toString();
if (showProfileLogo || codonCount == modalCodonCount)
{
if (percent.equals(lastPercent) && j > 0)
{
samePercent.append(samePercent.length() == 0 ? "" : ", ");
samePercent.append(codon);
}
else
{
if (samePercent.length() > 0)
{
mouseOver.append(samePercent).append(": ").append(lastPercent)
.append("% ");
}
samePercent.setLength(0);
samePercent.append(codon);
}
lastPercent = percent;
}
}
consensusAnnotation.annotations[col] = new Annotation(modalCodon,
mouseOver.toString(), ' ', pid);
}
}
/**
* Returns the number of decimal places to show for profile percentages. For
* less than 100 sequences, returns zero (the integer percentage value will be
* displayed). For 100-999 sequences, returns 1, for 1000-9999 returns 2, etc.
*
* @param nseq
* @return
*/
protected static int getPercentageDp(long nseq)
{
int scale = 0;
while (nseq >= 100)
{
scale++;
nseq /= 10;
}
return scale;
}
}