Prioritizing MCDC test cases by spectral analysis of Boolean functions

Test case prioritization aims at scheduling test cases in an order that improves some performance goal. One performance goal is a measure of how quickly faults are detected. Such prioritization can be performed by exploiting the Fault Exposing Potential (FEP) parameters associated to the test cases. FEP is usually approximated by mutation analysis under certain fault assumptions. Although this technique is effective, it could be relatively expensive compared to the other prioritization techniques. This study proposes a cost-effective FEP approximation for prioritizing Modified Condition Decision Coverage (MCDC) test cases. A strict negative correlation between the FEP of a MCDC test case and the influence value of the associated input condition allows to order the test cases easily without the need of an extensive mutation analysis. The method is entirely based on mathematics and it provides useful insight into how spectral analysis of Boolean functions can benefit software testing

Highly enriched repeat types among DUX4 binding sites.

1<p>We show only repeat types with ≥2-fold enrichment among either the ChIP-seq peaks (showing only those with ≥100 peaks) or among the ChIP-seq reads (showing only those with ≥1000 reads). Repeat types are shown sorted by read-based enrichment estimate. Full results for all repeat types are shown in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003947#pgen.1003947.s015" target="_blank">Table S2</a>, and are given aggregated by repeat family in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003947#pgen.1003947.s016" target="_blank">Table S3</a>.</p

Many repeat types are enriched among DUX4 binding sites.

<p>(A) ∼2/3 of DUX4 binding-sites are in repetitive elements, compared to ∼45% of the human genome. (B) Comparing repeat family proportions among DUX4 binding-sites with genome-wide fractions shows ∼10-fold MaLR enrichment. (C) A peak-based method of estimating repeat enrichment uses uniquely-mapped reads, so is blind to recently active repeats; however, it ignores background reads so provides a more sensitive enrichment measure than the read-based estimate (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003947#pgen-1003947-g001" target="_blank">Figures 1D, 1E</a>). 32 repeat types (red) are enriched ≥2-fold with ≥100 peaks (arbitrary thresholds) (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003947#pgen-1003947-t001" target="_blank">Tables 1</a>, <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003947#pgen.1003947.s015" target="_blank">S2</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003947#pgen.1003947.s016" target="_blank">S3</a>); 21 (orange) are rarer in the genome (10–99 peaks) but enriched ≥4-fold. The log10-scaled x-axis shows the proportion of peaks expected to overlap each repeat type if DUX4 binding sites had uniform genomic distribution; the log10-scaled y-axis shows observed proportions. The dashed line represents no enrichment. In all panels, “+” symbols represent MaLR elements and “x” datapoints represent repeat types for which no peaks/reads were observed – these are given an arbitrary low (non-zero) value to ensure visibility on log-scaled plots. (D) The read-based enrichment estimation method examines highly similar repeats as well as uniquely-mappable sequences, but gives a “dampened” enrichment measure due to background reads in ChIP-seq samples (see Methods). 25 repeat types (dark blue) are enriched ≥2-fold, with ≥1000 reads (arbitrary thresholds); 3 (light blue) are rarer among ChIP-seq reads (100–999 reads) but enriched ≥4-fold. (E) The peak-based (x-axis) and read-based (y-axis) methods yield similar results. 19 repeat types (green datapoints, thresholds as in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003947#pgen-1003947-g001" target="_blank">Figures 1A and B</a> for red and dark blue points) were enriched in both analyses; 13 enriched only by the peak-based method (red), and 6 enriched only by the read-based method (blue). Additional repeats (gray datapoints, upper-right quadrant) appear enriched by both methods, but are rare in the genome so do not exceed our arbitrary peak/read thresholds.</p

DUX4-bound regions are similarly activated in FSHD patient myotubes and in <i>DUX4</i>-transduced myoblasts.

<p>(A) DUX4-bound regions show correlated activation levels in FSHD patient myotubes and in our <i>DUX4</i>-transduced myoblast experimental system. We show log2-activation levels in each system, counting RNA-seq reads within an arbitrary 1 kb of DUX4-bound regions as for <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003947#pgen-1003947-g003" target="_blank">Figure 3A</a>. “+” symbols show bound repetitive elements and dot symbols are unique regions. Green symbols show regions that reach statistical significance in only the <i>DUX4</i>-transduced myoblasts, blue symbols are significant in only the FSHD myotubes, and red symbols are significant in both comparisons. The dotted line is a regression line (slope 0.265). (B) Internal regions of ERV and MaLR repeats show correlated activation levels in FSHD patient myotubes and in our <i>DUX4</i>-transduced myoblast experimental system. Colors as in panel A. The dotted line is a regression line (slope 0.270).</p

Examples of DUX4-bound repeats that function as alternative promoters for annotated genes.

<p>In each panel, thin red boxes depict DUX4-bound repetitive elements. Exons of previously annotated transcripts are depicted as green boxes, with alternative DUX4-induced exons as empty boxes (not to scale). Arrows show the direction of transcription. Diagonal lines show splicing, with alternative splice forms shown above and below the exons. (A) <i>HEY1</i>. (B) <i>PPCS</i>. An upstream, divergently transcribed gene, <i>ZMYDN12</i>, is shown in gray. (C) <i>NT5C1B</i>.</p

Examples of DUX4-bound repeats that function as alternative promoters for lncRNAs or antisense transcripts.

<p>In each panel, thin red boxes depict DUX4-bound repetitive elements. As in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003947#pgen-1003947-g004" target="_blank">Figure 4</a>, exons of previously annotated transcripts are depicted as green boxes, with alternative DUX4-induced exons as empty boxes (not to scale). Arrows show the direction of transcription. Diagonal lines show splicing, with alternative splice forms shown above and below the exons. (A) A lncRNA initiated at an MLT1C element. Two exons of the DUX4-activated lncRNA overlap with exons of previously described lncRNAs TCONS_00003193, TCONS_00002742, TCONS_00002660 and TCONS_00003194 <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003947#pgen.1003947-Cabili1" target="_blank">[42]</a>. (B) A lncRNA initiated at an THE1C element that shares the second exon of lncRNA TCONS_00022347 <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003947#pgen.1003947-Cabili1" target="_blank">[42]</a>. Additional lncRNAs on the opposite strand also initiate in this region - for clarity, only one is depicted here. (C) An antisense RNA initiated at a DUX4-bound MLT1D element that overlaps the first exon of <i>DDX10</i>.</p

DUX4-bound motifs are evolutionarily conserved.

<p>(A) DUX4 motifs are conserved among placental mammals. We obtained phylogenetic conservation scores for placental mammals (phyloP scores) via the UCSC genome browser for the top-scoring DUX4 motif in each of the 63,795 DUX4 ChIP-seq peaks, along with scores for 20 bp flanking regions on each side. For each position in the motif, we show the mean phyloP score across all 63,795 motifs (black datapoints) or across just motifs overlapping repetitive elements (gray datapoints). The dashed lines show overall mean scores for the entire motif plus their flanking regions. We use blue and red background shading to indicate the 17 nucleotide positions that comprise the DUX4 motif, with light red shading showing the least variable nucleotide positions of the motif. A sequence logo of the motif is shown above each graph. It is clear that most of the nucleotide positions that are least variable in the DUX4 motif have higher conservation scores than the surrounding positions. (B) DUX4 motifs are conserved among primates. Primate phyloP scores, plotted as in panel A. (C) Common SNPs are under-represented in DUX4-bound motifs. We used the UCSC genome browser to determine the locations of common SNPs (≥1% minor allele frequency) within any of the 63,795 DUX4-bound motifs and their flanking regions. We plot the number of SNPs found at each position in any of the 63,795 DUX4-bound motifs (black datapoints), or in just motifs overlapping repetitive elements (gray datapoints). It is clear that there are fewer SNPs in most of the nucleotide positions that are least variable in the DUX4 motif than in the surrounding positions.</p

Some DUX4-bound repetitive elements are transcriptionally activated by DUX4.

<p>(A) ∼1% of DUX4-bound regions show statistically-significant activation in response to DUX4 in our conservative analysis. We show normalized RNA-seq read counts within an arbitrary distance of 1 kb from DUX4-bound regions (peaks), comparing counts averaged over two myoblast lines that do not express <i>DUX4</i> (x-axis) to counts averaged over two myoblast lines transduced with lentiviral <i>DUX4</i> (y-axis). Only regions with at least 10 reads summed over all four samples are shown, and counts are plotted on a log scale. The 738 regions shown as green points show statistically significant activation in response to DUX4 (≥2-fold activation, FDR-adjusted p-value≤0.1) and the 6 orange points show significant repression (same thresholds). Regions surrounding DUX4-bound repeats are shown as “+” symbols, with dots representing DUX4-bound unique regions. Some regions (“x” symbols) had normalized counts of 0 in one condition and are plotted at an arbitrary low value so they appear on a log-scale. (B) DUX4-bound regions with more predicted DUX4 binding motifs are more likely to be transcriptionally activated. The y-axis value gives the percentage of regions that are transcriptionally activated by DUX4 (≥2-fold, FDR-adjusted p-value≤0.1). (C) DUX4-bound regions with more predicted DUX4 binding motifs have greater DUX4 occupancy, using ChIP-seq peak height as a proxy for DUX4 occupancy (shown on a log-scale).</p

RT-PCR confirms novel transcripts and shows their presence in testis and FHSD patient cells.

1<p>“+” symbols indicate that an RT-PCR product was obtained and verified by Sanger sequencing.</p><p>“−” symbols indicate samples that were either negative, or gave only bands derived from unrelated loci (our primers recognize repetitive elements, so clean amplification is sometimes difficult).</p><p>“(+)” symbols indicate that an RT-PCR product of the expected size was obtained, but could not be cloned for sequencing.</p><p>No products were obtained from corresponding negative control samples prepared without reverse transcriptase.</p

Additional file 3: Table S2. of BET bromodomain inhibitors and agonists of the beta-2 adrenergic receptor identified in screens for compounds that inhibit DUX4 expression in FSHD muscle cells

Collection of compounds that target epigenetic modifier proteins. (XLSX 33 kb