Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map

The application of RNA interference (RNAi) to mammalian cells has provided the means to perform phenotypic screens to determine the functions of genes. Although RNAi has revolutionized loss-of-function genetic experiments, it has been difficult to systematically assess the prevalence and consequences of off-target effects. The Connectivity Map (CMAP) represents an unprecedented resource to study the gene expression consequences of expressing short hairpin RNAs (shRNAs). Analysis of signatures for over 13,000 shRNAs applied in 9 cell lines revealed that microRNA (miRNA)-like off-target effects of RNAi are far stronger and more pervasive than generally appreciated. We show that mitigating off-target effects is feasible in these datasets via computational methodologies to produce a consensus gene signature (CGS). In addition, we compared RNAi technology to clustered regularly interspaced short palindromic repeat (CRISPR)-based knockout by analysis of 373 single guide RNAs (sgRNAs) in 6 cells lines and show that the on-target efficacies are comparable, but CRISPR technology is far less susceptible to systematic off-target effects. These results will help guide the proper use and analysis of loss-of-function reagents for the determination of gene function

Assessment of network module identification across complex diseases

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology

Exploring Cosmic Origins with CORE: Cosmological Parameters

We forecast the main cosmological parameter constraints achievable with theCORE space mission which is dedicated to mapping the polarisation of the CosmicMicrowave Background (CMB). CORE was recently submitted in response to ESA'sfifth call for medium-sized mission proposals (M5). Here we report the resultsfrom our pre-submission study of the impact of various instrumental options, inparticular the telescope size and sensitivity level, and review the great,transformative potential of the mission as proposed. Specifically, we assessthe impact on a broad range of fundamental parameters of our Universe as afunction of the expected CMB characteristics, with other papers in the seriesfocusing on controlling astrophysical and instrumental residual systematics. Inthis paper, we assume that only a few central CORE frequency channels areusable for our purpose, all others being devoted to the cleaning ofastrophysical contaminants. On the theoretical side, we assume LCDM as ourgeneral framework and quantify the improvement provided by CORE over thecurrent constraints from the Planck 2015 release. We also study the jointsensitivity of CORE and of future Baryon Acoustic Oscillation and Large ScaleStructure experiments like DESI and Euclid. Specific constraints on the physicsof inflation are presented in another paper of the series. In addition to thesix parameters of the base LCDM, which describe the matter content of aspatially flat universe with adiabatic and scalar primordial fluctuations frominflation, we derive the precision achievable on parameters like thosedescribing curvature, neutrino physics, extra light relics, primordial heliumabundance, dark matter annihilation, recombination physics, variation offundamental constants, dark energy, modified gravity, reionization and cosmicbirefringence. (ABRIDGED

Integral-geometry characterization of photobiomodulation effects on retinal vessel morphology

The morphological characterization of quasi-planar structures represented by gray-scale images is challenging when object identification is sub-optimal due to registration artifacts. We propose two alternative procedures that enhances object identification in the integral-geometry morphological image analysis (MIA) framework. The first variant streamlines the framework by introducing an active contours segmentation process whose time step is recycled as a multi-scale parameter. In the second variant, we used the refined object identification produced in the first variant to perform the standard MIA with exact dilation radius as multi-scale parameter. Using this enhanced MIA we quantify the extent of vaso-obliteration in oxygen-induced retinopathic vascular growth, the preventative effect (by photobiomodulation) of exposure during tissue development to near-infrared light (NIR, 670 nm), and the lack of adverse effects due to exposure to NIR light.This work was supported by Grant CE0561903 from the Australian Research Council through the ARC Centre of Excellence in Vision Science

Advancing computational biology and bioinformatics research through open innovation competitions.

Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research in which the use of competitions has yielded significant performance gains over established algorithms. These include algorithms for antibody clustering, imputing gene expression data, and querying the Connectivity Map (CMap). Performance gains are evaluated quantitatively using realistic, albeit sanitized, data sets. The solutions produced through these competitions are then examined with respect to their utility and the prospects for implementation in the field. We present the decision process and competition design considerations that lead to these successful outcomes as a model for researchers who want to use competitions and non-domain crowds as collaborators to further their research

CGSs and investigation of PC1.

<p>(A) Schematic of the weighted average procedure for combining individual shRNA signatures targeting the same gene into a CGS. The shRNAs are weighted by the sum of their correlations to other same-gene shRNAs and then averaged. (B) CGSs made from random groups of shRNAs show increasing variance of Spearman correlation with larger numbers of component shRNAs. Because these are random groups, there should not be a consistent signal; the increasing probability of very large correlations reveals a spurious signal that we attribute to the PC1 of the data. (C) Comparison of the fraction of variance explained by PC1 for either CMAP build 02, which used Affymetrix arrays to profile small molecules [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003213#pbio.2003213.ref001" target="_blank">1</a>], or the expansion of CMAP, which uses L1000 technology [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003213#pbio.2003213.ref005" target="_blank">5</a>] with different types of perturbation. Level 5 data were used. The shRNA CGS has a notably larger PC1. See <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003213#pbio.2003213.s007" target="_blank">S3 Data</a>. (D) Pearson correlation of PC1 across RNA measurement platforms and perturbation types in level 5 data. (E) For genes with 6 or more shRNAs, a fraction of statistically significant holdout results at different <i>q</i>-value-corrected false discovery rate thresholds, comparing PC1 retained or PC1 removed. Analysis was performed separately for each cell line and data for all cell lines are shown as a single distribution. Because holdout analysis combines multiple shRNA signatures, removal of PC1 decreases the background caused by the general increase in correlations shown in panel (B) and thus improves the performance of this particular analysis. (F) Removal of PC1 does not diminish the magnitude of the seed effect. After removal of PC1, distribution of pairwise Spearman correlations in HT29 (as a representative cell line) for pairs of shRNAs with the same gene target, the same 6- and 7-mer seed sequence, and all pairs of shRNAs. Compare to <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003213#pbio.2003213.g001" target="_blank">Fig 1C</a>. (G) Effect of PC1 of CMAP queries. For small molecules previously profiled in CMAP build 02 by Affymetrix technology, the rank of the matched compound when queried against small molecule L1000 data, with either PC1 retained or removed. CGS, consensus gene signature; CMAP, Connectivity Map; PC1, first principal component; shRNA, short hairpin RNA.</p

Decomposition by projection.

<p>(A) For shRNAs, the magnitude of the off-target effect comparing the leave-one-out CSS to projection. Pearson correlation coefficient = 0.43. (B) For individual shRNAs (top) and sgRNAs (bottom), in which the on-target magnitude passes FDR < 25%, distribution of on- and off-target magnitudes, as assessed by projection decomposition. (C) Scatter plots of on-target and off-target projection magnitudes for RNAi (top) and CRISPR (bottom) for all of the signatures of reagents in the dataset. While the 2 technologies show similar on-target activities, RNAi shows large off-target effects. CRISPR, clustered regularly interspaced short palindromic repeat; CSS, consensus seed signature; FDR, false discovery rate; RNAi, RNA interference; sgRNA, single guide RNA; shRNA, short hairpin RNA.</p

RNAi reagents have widespread off-target effects.

<p>(A) Heat map of Spearman correlations among pairs of shRNAs targeting control genes. Correlation on the diagonal reveals a gene expression signal that is reproducible and specific to each shRNA, despite the absence of a target. Control genes are labeled as follows: GFP, LUC, RFP, and LAC. Additional control treatments are grouped under Ctrl; 1: pgw, a lentivirus with no U6 promoter and no shRNA; 2: empty_vector, a lentivirus with a run of 5 thymidines immediately after the U6 promoter, to terminate transcription; 3: UnTrt, wells that did not receive any lentivirus. (B) Distribution of pairwise correlations of shRNA signatures with the same gene target, the same 6- and 7-mer seed sequence, and all pairs of shRNAs. Data shown are from HT29 cells. Pairs of shRNAs with the same seed correlate much better than those with the same gene, which correlate only marginally better than random pairs. (C) The fraction of pairs of shRNA signatures with the same target gene (red) or the same 6-mer seed (blue) that are statistically significant (<i>q</i> < 0.25) in each cell line. In all cell lines, correlation due to seed is more often significant than correlation due to gene. See <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003213#pbio.2003213.s006" target="_blank">S2 Data</a>. Ctrl, control; GFP, green fluorescent protein; LAC, beta-galactosidase; LUC, firefly luciferase; pgw, puromycin-GFP-WPRE; RFP, red fluorescence protein; RNAi, RNA interference; shRNA, short hairpin RNA; U6, human U6 polymerase III promoter; UnTrt, untreated.</p