Results of the application of network inference algorithms on the simulated dataset.

<p>PPV: Positive Predicted Value (or accuracy) defined as , where is true positive and is false positive; Se: Sensitivity defined as with false negative. : directed graph; : undirected graph. In bold are the results obtained by using our parallel implementation of the NIR algorithm which could not be obtained in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0010179#pone.0010179-Bansal2" target="_blank">[8]</a>. NIR performs significantly better than other software even for the 1000 gene networks.</p

Total execution times in seconds.

<p>Total execution times in seconds.</p

Spatial macrodomains.

<p>The contact maps for Chr19 obtained at the end of the steered-MD simulations and inferred from HiC data are shown on the left and right, respectively. The grey bands mark entries involving the centromere region. The boundaries of the principal spatial domains, identified with a clustering analysis of the contact maps, are overlaid on the matrices. The consistency of the two macrodomain subdivisions is visually conveyed in the chromosome sketch at the center. The overlapping portions of the domain subdivisions are colored (different colors are used for different domains). Non-overlapping regions are shown in white, while the centromere region is shown in grey. The overlapping regions accounts for of the chromosome (centromere excluded).</p

Increase of the percentage, , of Chr19 coregulated pairs which colocalize during the MD steering protocol, for the three variants of the native systems.

<p>The configurations reached at the end of the steering protocol are shown on the right. Chromosome regions that take part to the pairs of loci to be colocalized are highlighted in red.</p

Increase of the percentage, , of Chr19 coregulated pairs which colocalize during the MD steering protocol.

<p>The two curves reflect different initial conditions corresponding to the mitotic and the interphase conformations of panels (B) and (C) of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003019#pcbi-1003019-g001" target="_blank">Fig. 1</a>. The final configurations, corresponding to are shown on the right. Chromosome regions involved in the coregulatory network are highlighted in red. These and other graphical representations of model chromosomes were rendered with the VMD graphical package <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003019#pcbi.1003019-Humphrey1" target="_blank">[47]</a>.</p

Mitotic and interphase configurations of the model system chromosomes.

<p>(A) Initial mitotic-like arrangement, constituted by 6 copies of model human chromosome 19. Following ref. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003019#pcbi.1003019-Rosa1" target="_blank">[21]</a>, the chromatin fiber is helicoidally arranged into loops of each, and departing radially from a central axis. The six solenoidal arrangements were next placed in a random, but non-overlapping manner inside a cubic simulation box of side equal to and with periodic boundary conditions. (B) Chromosome spatial arrangement after short relaxation with a standard push-off protocol of MD time steps (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003019#s4" target="_blank">Materials and Methods</a>). (C) Interphase-like configuration obtained by evolving the initial mitotic configuration for MD time steps (approximately corresponding to hours in “real-time” <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003019#pcbi.1003019-Rosa1" target="_blank">[21]</a>). (<i>Inset</i>) The corresponding contact probabilities between <i>loci</i> of model interphase chromosomes decay as a power law of the genomic distance, , consistent with recent experimental observations <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003019#pcbi.1003019-LiebermanAiden1" target="_blank">[6]</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003019#pcbi.1003019-Rosa2" target="_blank">[29]</a>. In all panels, chromosome regions involved in the coregulatory network are highlighted in red.</p

Statistical analysis of mutual information.

<p>(A) Mutual information values for any pairs of probe sets on Chr19. The middle point of each probe set identifies its position along the chromosome. The gray stripes correspond to the centromere. (B) Histograms of values of mutual information for pairs of probe sets located at various intervals of their genomic separation. The black lines correspond to fitting the histograms with the theoretical (null case) MI distribution <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003019#pcbi.1003019-Goebel1" target="_blank">[36]</a>. The vertical black dashed lines correspond to the estimated threshold values (see next and main text). (C) Example of E-value (expected number of false positives) distribution for probe set pairs located at genomic separation in the range . The threshold is the value of mutual information at which the E-value is equal to . For different genomic separations, analogous curves were obtained. (D) Network of coregulated pairs of genes at separation. The analysis illustrated in (C) singles out significantly-high values of Mutual Information. These contributions corresponds to connections (<i>cyan links</i>) between coregulated gene pairs (<i>red dots</i>). The scale is in . (E) Networks of coregulated pairs of loci used to fix the spatial constraints between corresponding regions of the model chromosomes. For the sake of clarity, the whole network has been represented as three sub-networks for pairs of loci at genomic separations of 0–20 Mbp (<i>left</i>), 20–40 Mbp (<i>middle</i>) and 40–60 Mbp (<i>right</i>), respectively.</p

Variant systems subjected to the MD steering protocol.

<p>(A) Initial configuration of 6 random-walk like chains the linear size the model chromosome 19. (B) Model chromosomes were initially arranged as in the mitotic-like configuration of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003019#pcbi-1003019-g001" target="_blank">Fig. 1B</a>, but the pairings between genes were randomized. The randomization preserved the number of pairs that each probe set takes part to. (C) Model chromosomes were initially arranged as in the mitotic-like configuration of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003019#pcbi-1003019-g001" target="_blank">Fig. 1B</a>, but the gene positions along the chromosome were randomized. The randomization preserved the native pairings of the genes. In all panels chromosome regions involved in the native or randomized coregulatory network are highlighted in red. For all the three systems considered the same physical conditions of fiber density, stiffness and excluded volume interactions of the original system apply.</p

Crowd-Sourced Verification of Computational Methods and Data in Systems Toxicology: A Case Study with a Heat-Not-Burn Candidate Modified Risk Tobacco Product

Systems toxicology intends to quantify the effect of toxic molecules in biological systems and unravel their mechanisms of toxicity. The development of advanced computational methods is required for analyzing and integrating high throughput data generated for this purpose as well as for extrapolating predictive toxicological outcomes and risk estimates. To ensure the performance and reliability of the methods and verify conclusions from systems toxicology data analysis, it is important to conduct unbiased evaluations by independent third parties. As a case study, we report here the results of an independent verification of methods and data in systems toxicology by crowdsourcing. The sbv IMPROVER systems toxicology computational challenge aimed to evaluate computational methods for the development of blood-based gene expression signature classification models with the ability to predict smoking exposure status. Participants created/trained models on blood gene expression data sets including smokers/mice exposed to 3R4F (a reference cigarette) or noncurrent smokers/Sham (mice exposed to air). Participants applied their models on unseen data to predict whether subjects classify closer to smoke-exposed or nonsmoke exposed groups. The data sets also included data from subjects that had been exposed to potential modified risk tobacco products (MRTPs) or that had switched to a MRTP after exposure to conventional cigarette smoke. The scoring of anonymized participants’ predictions was done using predefined metrics. The top 3 performers’ methods predicted class labels with area under the precision recall scores above 0.9. Furthermore, although various computational approaches were used, the crowd’s results confirmed our own data analysis outcomes with regards to the classification of MRTP-related samples. Mice exposed directly to a MRTP were classified closer to the Sham group. After switching to a MRTP, the confidence that subjects belonged to the smoke-exposed group decreased significantly. Smoking exposure gene signatures that contributed to the group separation included a core set of genes highly consistent across teams such as AHRR, LRRN3, SASH1, and P2RY6. In conclusion, crowdsourcing constitutes a pertinent approach, in complement to the classical peer review process, to independently and unbiasedly verify computational methods and data for risk assessment using systems toxicology

Additional file 3 of Crowdsourced benchmarking of taxonomic metagenome profilers: lessons learned from the sbv IMPROVER Microbiomics challenge

Additional file 3