1. Volume 4, Issue 2, Pages 182-193.e4 (February 2017)
Explicit Modeling of siRNA-Dependent On- and Off-Target Repression Improves the Interpretation of Screening Results 
Andrea Riba, Mario Emmenlauer, Amy Chen, Frederic Sigoillot, Feng Cong, Christoph Dehio, Jeremy Jenkins, Mihaela Zavolan 
Cell Systems 
Volume 4, Issue 2, Pages e4 (February 2017)
DOI: /j.cels
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2. Cell Systems 2017 4, 182-193.e4DOI: (10.1016/j.cels.2017.01.011)
Copyright © 2017 The Author(s) Terms and Conditions

3. Figure 1 Design of PheLiM
The input consists of transcripts, siRNA sequences, and associated phenotype measurements. siRNA sequences are used to predict off-target downregulation with a miRNA target prediction tool and on-target repression with a linear model that takes into account the position-dependent nucleotide composition, overall A/U content and self-folding energy of the siRNA, the accessibility of the target site, and the stability of the siRNA-target duplex. Gene contributions to phenotype are inferred by partial least-squares regression on a training subset of siRNAs, and principal components are selected such as to maximize the correlation of predicted and measured phenotypes obtained with a seed-disjoint, validation set of siRNAs. The final predictions of gene contributions to the phenotype are obtained by averaging models obtained with different partitions of the siRNAs into training and validation sets.
Cell Systems 2017 4, e4DOI: ( /j.cels )
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4. Figure 2
Evaluation of the Accuracy with which Computational Methods Identify the Targeted Pathway
Predictions of the indicated computational methods—PheLiM (this study) gespeR (Schmich et al., 2015), mean (see text), HayStack (Buehler et al., 2012), and RSA (König et al., 2007)—were obtained for the four siRNA screening experiments.
(A and B) The concordance between a method’s predictions and the expected KEGG pathway was evaluated by the number (A) and the significance of the enrichment (−log10(p value) of the hypergeometric test) (B) of genes from the expected KEGG pathway.
(C and D) The total protein-protein interaction score (C) and mean length (number of links) (D) of all the shortest protein-protein interaction paths that linked a predicted gene to genes in the expected pathway in the STRING database (Szklarczyk et al., 2015).
(E) The significance of the enrichment (−log10(p value) of the hypergeometric test) of genes found by CRISPR screening to contribute to the phenotype (Table S1) among the top predictions of individual methods. The pathway genes in the top 300 predictions for each method are listed in Table S6.
Cell Systems 2017 4, e4DOI: ( /j.cels )
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5. Figure 3
Features Influencing the Relative Performance of Computational Methods
(A) Scatterplots of gene contributions to the phenotype estimated by PheLiM and gespeR from BMP and NF-κB screens carried out with low and high concentrations of stimulant. Pearson correlations are consistently high for PheLiM’s estimates but vary over a wide range for gespeR’s estimates. The correlations for the genes from the expected pathway (in red) are significantly higher than for other genes (in black) (p values from the Fisher z transformation: 2.65e-12 for BMP and 3.43e-9 for NF-κB).
(B) Comparison between PheLiM and gespeR using off-target predictions by TargetScan (Agarwal et al., 2015), seed-MIRZA-G (Gumienny and Zavolan, 2015), and PITA (Kertesz et al., 2007).
(C) Reproducibility of estimated gene contributions to the phenotype generated by gespeR and PheLiM. 30 partitions of the siRNA set into seed-disjoint subsets were generated, predictions were obtained separately with each method for the two subsets, and the distribution of correlation coefficients of gene contributions to phenotype inferred from the two subsets is shown.
Cell Systems 2017 4, e4DOI: ( /j.cels )
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6. Figure 4 Comparison of PheLiM and CRISPR/Cas9 Targets
Scatterplots of PheLiM and CRISPR scores inferred for individual genes in the four experimental conditions. Red dots identify genes within the targeted pathways and blue dots identify the genes with the highest 100 PPI scores with all genes in the targeted pathway. Black lines separate top 1% targets inferred with the two methods and the black arc shows the 1% contour line of the two-dimensional histogram of scores.
Cell Systems 2017 4, e4DOI: ( /j.cels )
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7. Figure 5
Identification of Pathways Involved in a Wide Range of Phenotypes
Plots show the normalized ratio of the contribution to (A) SMAD2 translocation, (B) survival of myeloma cells, and (C) the number of cells of genes within a specific pathway compared to all other genes on the x axis and the p value calculated through the Browne-Forsythe (BF) test on the y axis (Brown and Forsythe, 1974; Levene, 1961) (pathways with p values in the variance test < are marked in red). In (C), because the correlations between the gene-specific contributions to cell numbers inferred from the three infections were high, the average gene contributions were used in the BF test.
(D) Pairwise Pearson correlations between gene contributions to the number of cells (left) and frequency of infection (right) in the three infection systems. Venn diagrams show the overlap between the sets of 100 genes with highest absolute contributions to the respective phenotype in individual siRNA screens.
Cell Systems 2017 4, e4DOI: ( /j.cels )
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8. Figure 6
Pathways and Genes Contributing Most to the Infection with Brucella abortus, Bartonella henselae, and Salmonella typhimurium
(A, C, and E) Pathways inferred by the variance test on PheLiM results to contribute most to infection (marked in red are pathways with p values < in the variance test).
(B, D, and F) STRING graph of protein-protein interactions for top 100 genes inferred by PheLiM to be associated with the infection process. Only proteins with at least one connection are shown.
Cell Systems 2017 4, e4DOI: ( /j.cels )
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