/**
* Documentation for this function.
*/
function makeSummaryStatsTable($resolution, $clash, $rama, $rota, $cbdev, $pperp, $suites, $bbonds, $bangles, $cablam, $omega)
{
$entry = "";
$bgPoor = '#ff9999';
$bgFair = '#ffff99';
$bgGood = '#99ff99';
$entry .= "<p><table border='1' width='100%'>\n";
if (is_array($clash)) {
$clashPct = runClashStats($resolution, $clash['scoreAll'], $clash['scoreBlt40']);
if ($clashPct['pct_rank'] < 33) {
$bg = $bgPoor;
} elseif ($clashPct['pct_rank'] < 66) {
$bg = $bgFair;
} else {
$bg = $bgGood;
}
if ($clash['scoreAll'] < 0) {
$bg = $bgFair;
}
// for catching a bug with probe giving clashscore = -1
$entry .= "<tr><td rowspan='2' align='center'>All-Atom<br>Contacts</td>\n";
$entry .= "<td>Clashscore, all atoms:</td><td colspan='2' bgcolor='{$bg}'>{$clash['scoreAll']}</td>\n";
if ($clash['scoreAll'] < 0) {
$entry .= "<td>unknown percentile<sup>*</sup> (N={$clashPct['n_samples']}, {$clashPct['minresol']}Å - {$clashPct['maxresol']}Å)</td></tr>\n";
$entry .= "<tr><td colspan='3'>An error has occurred; clashscore should not be negative! Please report this bug.</td></tr>\n";
} else {
//$entry .= "<td>$clashPct[pct_rank]<sup>".ordinalSuffix($clashPct['pct_rank'])."</sup> percentile<sup>*</sup> (N=$clashPct[n_samples], $clashPct[minresol]Å - $clashPct[maxresol]Å)</td></tr>\n";
if ($clashPct[minresol] == 0 && $clashPct[maxresol] == 9999) {
$percentileOut = "all resolutions";
} else {
$diff = $resolution - $clashPct[minresol];
//echo $diff." ".gettype($diff)." ";
$diff2 = -($resolution - $clashPct[maxresol]);
//echo $diff2." ".gettype($diff2)." ";
//$test = ($diff2 > 0.245 && $diff2 < 0.255);
//echo "diff2 = 0.25:".$test;
if ($diff > 0.245 && $diff2 > 0.245 && $diff < 0.255 && $diff2 < 0.255) {
$percentileOut = "{$resolution}Å ± {$diff}Å";
} else {
$percentileOut = "{$clashPct['minresol']}Å - {$clashPct['maxresol']}Å";
}
}
$entry .= "<td>{$clashPct['pct_rank']}<sup>" . ordinalSuffix($clashPct['pct_rank']) . "</sup> percentile<sup>*</sup> (N={$clashPct['n_samples']}, {$percentileOut})</td></tr>\n";
$entry .= "<tr><td colspan='4'>Clashscore is the number of serious steric overlaps (> 0.4 Å) per 1000 atoms.</td></tr>\n";
}
//if($clashPct['pct_rank40'] <= 33) $bg = $bgPoor;
//elseif($clashPct['pct_rank40'] <= 66) $bg = $bgFair;
//else $bg = $bgGood;
//$entry .= "<tr><td>Clashscore, B<40:</td><td bgcolor='$bg'>$clash[scoreBlt40]</td>\n";
//$entry .= "<td>$clashPct[pct_rank40]<sup>".ordinalSuffix($clashPct['pct_rank40'])."</sup> percentile<sup>*</sup> (N=$clashPct[n_samples], $clashPct[minresol]Å - $clashPct[maxresol]Å)</td></tr>\n";
}
$proteinRows = 0;
if (is_array($rama)) {
$proteinRows += 2;
}
if (is_array($rota)) {
$proteinRows += 2;
}
if (is_array($cbdev)) {
$proteinRows += 1;
}
if (is_array($clash) && is_array($rota) && is_array($rama)) {
$proteinRows += 1;
}
if (hasMoltype($bbonds, "protein")) {
$proteinRows += 1;
}
if (hasMoltype($bangles, "protein")) {
$proteinRows += 1;
}
if ($proteinRows > 0) {
$entry .= "<tr><td rowspan='{$proteinRows}' align='center'>Protein<br>Geometry</td>\n";
$firstRow = true;
if (is_array($rota)) {
$rotaOut = count(findRotaOutliers($rota));
$rotaTot = count($rota);
$rotaOutPct = sprintf("%.2f", 100.0 * $rotaOut / $rotaTot);
if ($firstRow) {
$firstRow = false;
} else {
$entry .= "<tr>";
}
//Rotamer outliers:
//if($rotaOutPct+0 <= 1) $bg = $bgGood; //old top500 cutoff, matched outlier cutoff pct
if ($rotaOutPct + 0 <= 0.3) {
$bg = $bgGood;
} elseif ($rotaOutPct + 0 <= 1.5) {
$bg = $bgFair;
} else {
$bg = $bgPoor;
}
$entry .= "<td>Poor rotamers</td><td bgcolor='{$bg}'>{$rotaOut}</td><td bgcolor='{$bg}'>{$rotaOutPct}%</td>\n";
$entry .= "<td>Goal: <0.3%</td></tr>\n";
//Rotamer Favored:
foreach ($rota as $r) {
if ($r['eval'] == "Favored") {
$rotaFav++;
}
}
$rotaFavPct = sprintf("%.2f", 100.0 * $rotaFav / $rotaTot);
if ($rotaFavPct + 0 >= 98) {
$bg = $bgGood;
} elseif ($rotaFavPct + 0 >= 95) {
$bg = $bgFair;
} else {
$bg = $bgPoor;
}
$entry .= "<tr><td>Favored rotamers</td><td bgcolor='{$bg}'>{$rotaFav}</td><td bgcolor='{$bg}'>{$rotaFavPct}%</td>\n";
$entry .= "<td>Goal: >98%</td></tr>\n";
}
if (is_array($rama)) {
$ramaOut = count(findRamaOutliers($rama));
foreach ($rama as $r) {
if ($r['eval'] == "Favored") {
$ramaFav++;
}
}
$ramaTot = count($rama);
$ramaOutPct = sprintf("%.2f", 100.0 * $ramaOut / $ramaTot);
$ramaFavPct = sprintf("%.2f", 100.0 * $ramaFav / $ramaTot);
if ($firstRow) {
$firstRow = false;
} else {
$entry .= "<tr>";
}
if ($ramaOutPct + 0 <= 0.05) {
$bg = $bgGood;
} elseif ($ramaOut == 1 || $ramaOutPct + 0 <= 0.5) {
$bg = $bgFair;
} else {
$bg = $bgPoor;
}
$entry .= "<td>Ramachandran outliers</td><td bgcolor='{$bg}'>{$ramaOut}</td><td bgcolor='{$bg}'>{$ramaOutPct}%</td>\n";
$entry .= "<td>Goal: <0.05%</td></tr>\n";
if ($ramaFavPct + 0 >= 98) {
$bg = $bgGood;
} elseif ($ramaFavPct + 0 >= 95) {
$bg = $bgFair;
} else {
$bg = $bgPoor;
}
$entry .= "<tr><td>Ramachandran favored</td><td bgcolor='{$bg}'>{$ramaFav}</td><td bgcolor='{$bg}'>{$ramaFavPct}%</td>\n";
$entry .= "<td>Goal: >98%</td></tr>\n";
}
if (is_array($clash) && is_array($rota) && is_array($rama)) {
if ($firstRow) {
$firstRow = false;
} else {
$entry .= "<tr>";
}
$axr = $resolution;
// Actual Xtalographic Resolution
$mer = getEffectiveResolution($clash, $rota, $rama);
// MolProbity Effective Resolution
$mer_pct = getEffectiveResolutionPercentile($mer, $axr);
/*if(!$axr)*/
$bg = $bgFair;
// unknown AXR
//elseif($mer < $axr) $bg = $bgGood; // below
//elseif(abs(($mer-$axr)/$axr) <= 0.20) $bg = $bgFair; // within 20% of actual
//else $bg = $bgPoor; // more than 20% above
if ($mer_pct['pct_rank'] < 33) {
$bg = $bgPoor;
}
// switch to using percentiles for bg colors vbc 120629
if ($mer_pct['pct_rank'] >= 66) {
$bg = $bgGood;
}
// to try to compensate for high-res structures looking worse than they are
if (is_infinite($mer)) {
$mer = -1;
$bg = $bgFair;
}
$entry .= "<td>MolProbity score<sup><small>^</small></sup></td><td colspan='2' bgcolor='{$bg}'>";
$entry .= sprintf('%.2f', $mer);
//$entry .= "</td><td>Goal: <$axr</td></tr>\n";
if ($mer == -1) {
$entry .= "</td><td>unknown percentile<sup>*</sup> (N={$mer_pct['n_samples']}, {$mer_pct['minresol']}Å - {$mer_pct['maxresol']}Å)</td></tr>\n";
} else {
if ($mer_pct[minresol] == 0 && $mer_pct[maxresol] == 9999) {
$percentileOut = "all resolutions";
} else {
$diff = $resolution - $mer_pct[minresol];
$diff2 = -($resolution - $mer_pct[maxresol]);
if ($diff > 0.245 && $diff2 > 0.245 && $diff < 0.255 && $diff2 < 0.255) {
$percentileOut = "{$resolution}Å ± {$diff}Å";
} else {
$percentileOut = "{$mer_pct['minresol']}Å - {$mer_pct['maxresol']}Å";
}
}
$entry .= "</td><td>{$mer_pct['pct_rank']}<sup>" . ordinalSuffix($mer_pct['pct_rank']) . "</sup> percentile<sup>*</sup> (N={$mer_pct['n_samples']}, {$percentileOut})</td></tr>\n";
//$entry .= "</td><td>$mer_pct[pct_rank]<sup>".ordinalSuffix($mer_pct['pct_rank'])."</sup> percentile<sup>*</sup> (N=$mer_pct[n_samples], $mer_pct[minresol]Å - $mer_pct[maxresol]Å)</td></tr>\n";
}
}
if (is_array($cbdev)) {
$cbOut = count(findCbetaOutliers($cbdev));
$cbTot = count($cbdev);
$cbOutPct = sprintf("%.2f", 100.0 * $cbOut / $cbTot);
if ($firstRow) {
$firstRow = false;
} else {
$entry .= "<tr>";
}
if ($cbOut == 0) {
$bg = $bgGood;
} else {
$bg = $bgFair;
}
if ($cbOut / $cbTot > 0.05) {
$bg = $bgPoor;
}
$entry .= "<td>Cβ deviations >0.25Å</td><td bgcolor='{$bg}'>{$cbOut}</td><td bgcolor='{$bg}'>{$cbOutPct}%</td>\n";
$entry .= "<td>Goal: 0</td></tr>\n";
}
if (hasMoltype($bbonds, "protein")) {
if ($firstRow) {
$firstRow = false;
} else {
$entry .= "<tr>";
}
$total = 0;
$outCount = 0;
foreach ($bbonds as $cnit => $item) {
if ($item['type'] == 'protein') {
if ($item['isOutlier']) {
$outCount += $item['outCount'];
}
$total += $item['bondCount'];
}
}
$geomOutPct = sprintf("%.2f", 100.0 * $outCount / $total);
if ($outCount / $total < 0.002) {
$bg = $bgFair;
}
if ($outCount / $total < 0.0001) {
$bg = $bgGood;
} else {
$bg = $bgPoor;
}
$entry .= "<td>Bad bonds:</td><td bgcolor='{$bg}'>{$outCount} / {$total}</td><td bgcolor='{$bg}'>{$geomOutPct}%</td>\n<td>Goal: 0%</td></tr>\n";
}
if (hasMoltype($bangles, "protein")) {
if ($firstRow) {
$firstRow = false;
} else {
$entry .= "<tr>";
}
$total = 0;
$outCount = 0;
foreach ($bangles as $cnit => $item) {
if ($item['type'] == 'protein') {
if ($item['isOutlier']) {
$outCount += $item['outCount'];
}
$total += $item['angCount'];
}
}
$geomOutPct = sprintf("%.2f", 100.0 * $outCount / $total);
if ($outCount / $total < 0.005) {
$bg = $bgFair;
}
if ($outCount / $total < 0.001) {
$bg = $bgGood;
} else {
$bg = $bgPoor;
}
$entry .= "<td>Bad angles:</td><td bgcolor='{$bg}'>{$outCount} / {$total}</td><td bgcolor='{$bg}'>{$geomOutPct}%</td>\n<td>Goal: <0.1%</td></tr>\n";
}
}
// end of protein-specific stats
if (is_array($omega)) {
$totalres = 0;
$prototal = 0;
$nonprototal = 0;
$cisprocount = 0;
$cisnonprocount = 0;
$twistcount = 0;
foreach ($omega as $cnit => $item) {
//$totalres += 1;
if ($item['type'] == 'Pro') {
$prototal += 1;
if ($item['conf'] == 'Cis') {
$cisprocount += 1;
} elseif ($item['conf'] == 'Twisted') {
$twistcount += 1;
}
} elseif ($item['type'] == 'General') {
$nonprototal += 1;
if ($item['conf'] == 'Cis') {
$cisnonprocount += 1;
} elseif ($item['conf'] == 'Twisted') {
$twistcount += 1;
}
}
}
$totalres = $prototal + $nonprototal;
$cispropct = sprintf("%.2f", 100.0 * $cisprocount / $prototal);
$cisnonpropct = sprintf("%.2f", 100.0 * $cisnonprocount / $nonprototal);
$twistpct = sprintf("%.2f", 100.0 * $twistcount / $totalres);
//stoplights:
//cis pro yellow is 5 to 10%
//cis nonpro is 0.05 to 0.1%
//twisted is 0 to 0.1%
//This is gonna get ugly. The trick is: always print the cisPRO line
//Print other lines only if relevant
$cispepentry = "";
$cispepRows = 1;
//if($cispropct <= 5.0) $bg = $bgGood; //5% is statistical expectation for cis-Pro
//elseif($cispropct <= 10.0) $bg = $bgFair; //set at 2x statistical expectations
//else $bg = $bgPoor;
$cisentry .= "<td>Cis Prolines:</td><td> {$cisprocount} / {$prototal}</td><td> {$cispropct}% </td>\n<td>Expected: ≤1 per chain, or ≤5%</td></tr>\n";
if ($cisnonprocount > 0) {
$cispepRows += 1;
if ($cisnonpropct <= 0.05) {
$bg = $bgGood;
} elseif ($cisnonpropct <= 0.1) {
$bg = $bgFair;
} else {
$bg = $bgPoor;
}
$cisentry .= "<td>Cis nonProlines:</td><td bgcolor='{$bg}'> {$cisnonprocount} / {$nonprototal}</td><td bgcolor='{$bg}'> {$cisnonpropct}% </td>\n<td>Goal: <0.05%</td></tr>\n";
}
if ($twistcount > 0) {
$cispepRows += 1;
if ($twistpct == 0) {
$bg = $bgGood;
} elseif ($twistpct <= 0.1) {
$bg = $bgFair;
} else {
$bg = $bgPoor;
}
$cisentry .= "<td>Twisted Peptides:</td><td bgcolor='{$bg}'> {$twistcount} / {$totalres}</td><td bgcolor='{$bg}'> {$twistpct}% </td>\n<td>Goal: 0</td></tr>\n";
}
$entry .= "<tr><td rowspan='{$cispepRows}' align='center'>Peptide Omegas</td>\n";
$firstRow = true;
$entry .= $cisentry;
}
$nucleicRows = 0;
if (is_array($pperp)) {
$nucleicRows += 1;
}
if (is_array($suites)) {
$nucleicRows += 1;
}
if (hasMoltype($bbonds, "rna")) {
$nucleicRows += 1;
}
if (hasMoltype($bangles, "rna")) {
$nucleicRows += 1;
}
if ($nucleicRows > 0) {
$entry .= "<tr><td rowspan='{$nucleicRows}' align='center'>Nucleic Acid<br>Geometry</td>\n";
$firstRow = true;
if (is_array($pperp)) {
if ($firstRow) {
$firstRow = false;
} else {
$entry .= "<tr>";
}
$pperpOut = count(findBasePhosPerpOutliers($pperp));
$pperpTot = count($pperp);
$pperpOutPct = sprintf("%.2f", 100.0 * $pperpOut / $pperpTot);
$bg = $bgFair;
if ($pperpOut == 0) {
$bg = $bgGood;
}
if ($pperpOut / $pperpTot > 0.05) {
$bg = $bgPoor;
}
$entry .= "<td>Probably wrong sugar puckers:</td><td bgcolor='{$bg}'>{$pperpOut}</td><td bgcolor='{$bg}'>{$pperpOutPct}%</td>\n";
$entry .= "<td>Goal: 0</td></tr>\n";
}
if (is_array($suites)) {
if ($firstRow) {
$firstRow = false;
} else {
$entry .= "<tr>";
}
$suitesOut = count(findSuitenameOutliers($suites));
$suitesTot = count($suites);
$suitesOutPct = sprintf("%.2f", 100.0 * $suitesOut / $suitesTot);
$bg = $bgFair;
if ($suitesOut / $suitesTot <= 0.05) {
$bg = $bgGood;
}
if ($suitesOut / $suitesTot > 0.15) {
$bg = $bgPoor;
}
$entry .= "<td>Bad backbone conformations<sup><small>#</small></sup>:</td><td bgcolor='{$bg}'>{$suitesOut}</td><td bgcolor='{$bg}'>{$suitesOutPct}%</td>\n";
$entry .= "<td>Goal: <= 5%</td></tr>\n";
}
if (hasMoltype($bbonds, "rna")) {
if ($firstRow) {
$firstRow = false;
} else {
$entry .= "<tr>";
}
$total = 0;
$outCount = 0;
foreach ($bbonds as $cnit => $item) {
if ($item['type'] == 'rna') {
if ($item['isOutlier']) {
$outCount += $item['outCount'];
}
$total += $item['bondCount'];
}
}
$geomOutPct = sprintf("%.2f", 100.0 * $outCount / $total);
if ($outCount / $total < 0.002) {
$bg = $bgFair;
}
if ($outCount / $total < 0.0001) {
$bg = $bgGood;
} else {
$bg = $bgPoor;
}
$entry .= "<td>Bad bonds:</td><td bgcolor='{$bg}'>{$outCount} / {$total}</td><td bgcolor='{$bg}'>{$geomOutPct}%</td>\n<td>Goal: 0%</td></tr>\n";
}
if (hasMoltype($bangles, "rna")) {
if ($firstRow) {
$firstRow = false;
} else {
$entry .= "<tr>";
}
$total = 0;
$outCount = 0;
foreach ($bangles as $cnit => $item) {
if ($item['type'] == 'rna') {
if ($item['isOutlier']) {
$outCount += $item['outCount'];
}
$total += $item['angCount'];
}
}
$geomOutPct = sprintf("%.2f", 100.0 * $outCount / $total);
if ($outCount / $total < 0.005) {
$bg = $bgFair;
}
if ($outCount / $total < 0.001) {
$bg = $bgGood;
} else {
$bg = $bgPoor;
}
$entry .= "<td>Bad angles:</td><td bgcolor='{$bg}'>{$outCount} / {$total}</td><td bgcolor='{$bg}'>{$geomOutPct}%</td>\n<td>Goal: <0.1%</td></tr>\n";
}
}
//Lowres
$lowresRows = 0;
if (is_array($cablam)) {
$lowresRows += 2;
}
if ($lowresRows > 0) {
//Following text should be added to help instead of entry:
//$entry .= "<td>Because the submitted structure has a resolution lower than 2.5Å or because low-resolution measures were selected, the following criteria are provided that may be helpful at low resolution.</td></tr>\n";
$entry .= "<tr><td rowspan='{$lowresRows}' align='center'>Low-resolution Criteria</td>\n";
$firstRow = true;
if (is_array($cablam)) {
$cablamOut = 0;
$caGeomOut = 0;
//$cablamFav = 0;
foreach ($cablam as $c) {
if ($c['outlierType'] == " CaBLAM Outlier ") {
$cablamOut++;
} elseif ($c['outlierType'] == " CA Geom Outlier ") {
$caGeomOut++;
}
//elseif($c['outlierType'] != " CaBLAM Disfavored ") $cablamFav++;
//The disfavored category was deemed more info than necessary for the moment in this chart
}
$cablamTot = count($cablam);
$cablamOutPct = sprintf("%.2f", 100.0 * $cablamOut / $cablamTot);
//$cablamFavPct = sprintf("%.2f", 100.0 * $cablamFav/$cablamTot);
$caGeomPct = sprintf("%.2f", 100.0 * $caGeomOut / $cablamTot);
if ($firstRow) {
$firstRow = false;
} else {
$entry .= "<tr>";
}
//CaBLAMout 1% and 5%
if ($cablamOutPct + 0 <= 1.0) {
$bg = $bgGood;
} elseif ($cablamOutPct + 0 <= 5.0) {
$bg = $bgFair;
} else {
$bg = $bgPoor;
}
$entry .= "<td>CaBLAM outliers</td><td bgcolor='{$bg}'>{$cablamOut}</td><td bgcolor='{$bg}'>{$cablamOutPct}%</td>\n";
$entry .= "<td>Goal: <1.0%</td></tr>\n";
//if($cablamFavPct+0 >= 95.0) $bg = $bgGood;
//elseif($cablamFavPct+0 >=90.0) $bg = $bgFair;
//else $bg = $bgPoor;
//$entry .= "<tr><td>CaBLAM favored</td><td bgcolor='$bg'>$cablamFav</td><td bgcolor='$bg'>$cablamFavPct%</td>\n";
//$entry .= "<td>Goal: >95.0%</td></tr>\n";
if ($caGeomPct + 0 <= 0.5) {
$bg = $bgGood;
} elseif ($caGeomPct + 0 <= 1.0) {
$bg = $bgFair;
} else {
$bg = $bgPoor;
}
$entry .= "<tr><td>CA Geometry outliers</td><td bgcolor='{$bg}'>{$caGeomOut}</td><td bgcolor='{$bg}'>{$caGeomPct}%</td>\n";
$entry .= "<td>Goal: <0.5%</td></tr>\n";
}
}
$entry .= "</table>\n";
//Table ended
$firstRow = true;
if (is_array($rota) || is_array($rama) || is_array($cbdev) || is_array($bbonds) || is_array($bangles) || is_array($pperp) || is_array($suites)) {
if ($firstRow) {
$firstRow = false;
} else {
$entry .= "<br>";
}
$entry .= "<small>In the two column results, the left column gives the raw count, right column gives the percentage.</small>\n";
}
if (is_array($clash)) {
if ($firstRow) {
$firstRow = false;
} else {
$entry .= "<br>";
}
$entry .= "<small>* 100<sup>th</sup> percentile is the best among structures of comparable resolution; 0<sup>th</sup> percentile is the worst. For clashscore the comparative set of structures was selected in 2004, for MolProbity score in 2006.</small>\n";
}
if (is_array($suites)) {
if ($firstRow) {
$firstRow = false;
} else {
$entry .= "<br>";
}
$entry .= "<small><sup>#</sup> RNA backbone was recently shown to be rotameric. Outliers are RNA suites that don't fall into recognized rotamers.</small>\n";
}
if (is_array($clash) && is_array($rota) && is_array($rama)) {
if ($firstRow) {
$firstRow = false;
} else {
$entry .= "<br>";
}
//$entry .= "<small><sup>^</sup> MolProbity score is defined as the following: 0.42574*log(1+clashscore) + 0.32996*log(1+max(0,pctRotOut-1)) + 0.24979*log(1+max(0,100-pctRamaFavored-2)) + 0.5</small>\n";
$entry .= "<small><sup>^</sup> MolProbity score combines the clashscore, rotamer, and Ramachandran evaluations into a single score, normalized to be on the same scale as X-ray resolution.</small>\n";
}
$entry .= "</p>\n";
// end of summary stats table
return $entry;
}
function short_date($date)
{
$minute = $date['minutes'];
$minute = $minute < 10 ? '0' . $minute : (string) $minute;
$ordinal = ordinalSuffix($date['mday']);
if ($date['hours'] == '12') {
$hour = '12';
$ampm = 'pm';
} else {
if ($date['hours'] == '24') {
$hour = '12';
$ampm = 'am';
} else {
if ($date['hours'] > 12) {
$hour = (int) $date['hours'] - 12;
$ampm = 'pm';
} else {
$hour = $date['hours'];
$ampm = 'am';
}
}
}
return "{$date['month']} {$date['mday']}{$ordinal} @ {$hour}:{$minute}{$ampm}";
}
