A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.

We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs, and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.

Densely interconnected transcriptional circuits control cell states in human hematopoiesis.

Though many individual transcription factors are known to regulate hematopoietic differentiation, major aspects of the global architecture of hematopoiesis remain unknown. Here, we profiled gene expression in 38 distinct purified populations of human hematopoietic cells and used probabilistic models of gene expression and analysis of cis-elements in gene promoters to decipher the general organization of their regulatory circuitry. We identified modules of highly coexpressed genes, some of which are restricted to a single lineage but most of which are expressed at variable levels across multiple lineages. We found densely interconnected cis-regulatory circuits and a large number of transcription factors that are differentially expressed across hematopoietic states. These findings suggest a more complex regulatory system for hematopoiesis than previously assumed.

Comparison of treatment effects during en-masse retraction of upper anterior teeth placed using mini-implants placed at infrazygomatic crest and interradicular sites: A randomized controlled trial.

INTRODUCTION: The objective of this prospective study was to examine the efficacy of posterior interradicular and infrazygomatic crest mini-implants for en-masse anterior retraction. METHODS: The 22 patients were divided into two groups. In group 1 (IZC n = 11), mini-implants were placed in the infrazygomatic crests and in group 2 (IR, n = 11), mini-implants were placed in the molar-premolar interradicular sites. Soft tissue, skeletal, and dental treatment effects between two groups were compared using lateral cephalometric measurements. RESULTS: The average angle between the cranial base and A point was 1.01 degrees (P = .004), and the linear distance between the upper incisor and A point was 2.67 to 5.2 millimetres (P = .00). In IZC group the maxillary incisor to the palatal plane moved upward by a mean of -5.20 mm (P = .059), whereas in IR group the incisor movement changed by -2.67 mm (P = .068). There was no significant difference between groups IZC and IR while comparing overall treatment changes on upper incisor position change, angle, and overjet. CONCLUSIONS: Mini-implants placed in between the molar and premolar as well as the infrazygomatic crest can withstand the deepening of the bite during retraction. Mini-implants in IZC are capable of causing intrusion of the anterior teeth and preventing intrusion of the molars, thereby providing absolute anchoring in all planes. Placement of the mini-implants in the infrazygomatic crest resulted in more linear retraction.

Assessment of Toxicity of Green Synthesized Silver Nanoparticle-coated Titanium Mini-implants with Uncoated Mini-implants: Comparison in an Animal Model Study.

AIM: To assess the potential for systemic toxicity when silver nanoparticle-coated mini-implants were implanted in Wistar albino rats conducted as a comparative study in the animal model by assessing the blood biochemistry, liver and kidney function, and histology of the implanted site. MATERIALS AND METHODS: The surface of the mini-implant was coated with a green-mediated silver nanoparticle. Uncoated mini-implants were placed in two groups of eight Wistar albino rats, and silver nanoparticle-coated mini-implants were placed in another eight rats. The bone's general conditions, blood biochemistry assessing for ALT, AST, GPT, GOT, and histological sections using H and E stain and Masson's Trichrome stain were examined at 7, 14, and 28-day intervals. RESULTS: The creatinine, urea, ALP, and ALT showed no signs of systemic toxicity during the 28-day follow-up period in the Wistar rats both in the test and control groups. The histological evaluation, which was conducted using HE and MTS stain, revealed osteogenesis and adequate healing of the insertion site in the group where coated mini-implant was placed. The bone sample revealed no abnormalities in the control group with uncoated mini-implants. CONCLUSION: Green synthesized silver nanoparticle-coated mini-implant does not cause systemic toxicity as indicated by no abnormalities in the levels of creatinine, urea, ALT, ALP, GPT, and GOT. The bone histology indicates that the coated mini-implants placed in animal bone healed with adequate osteogenesis. CLINICAL SIGNIFICANCE: Silver nanoparticles have potential for antimicrobial activity. Mini-implants placed as temporary anchorage devices in orthodontics often fail due to inflammation and plaque. Silver nanoparticle-coated mini-implants would reduce the risk of mini-implant failure as it would have antimicrobial potential and eliminate this cause for failure of mini-implants. How to cite this article: Sreenivasagan S, Subramanian AK, Mohanraj KG, et al. Assessment of Toxicity of Green Synthesized Silver Nanoparticle-coated Titanium Mini-implants with Uncoated Mini-implants: Comparison in an Animal Model Study. J Contemp Dent Pract 2023;24(12):944-950.

Assessment of Soft Tissue, Airway Dimension and Hyoid Bone Position in Class II Patients Treated by PowerScope Class 2 Corrector.

AIM: This study aims to assess the changes in the soft tissue, pharyngeal airway dimensions, and hyoid bone position in patients treated with PowerScope Class 2 corrector to correct the skeletal Class II pattern. MATERIALS AND METHODS: This study was conducted on a sample of 20 cases diagnosed with Class II malocclusion. The lateral cephalograms were taken before (T1) and after functional appliance therapy (T2) and were traced. The outcomes were compared for the mean changes in soft tissue, airway way dimension, and hyoid bone position. The paired t-test was used for the data comparisons wherein p < 0.05 was kept for statistical significance. RESULTS: The mean values before and after treatment for H angle, mentolabial angle, lower lip E-line, upper lip S-line, lower lip S-line, and lip strain were 19.88 ± 2.77 vs 17.13 ± 1.659, 94.09 ± 12.164 vs 101.75 ± 11.28, -2.47 ± 1.213 vs -1.38 ± 0.976, 3.99 ± 0.19 vs 2.64 ± 0.32, 9.01 ± 0.247 vs 9.43 ± 0.238, 10.24 ± 0.510 vs 10.64 ± 0.52, respectively, which were statistically significant (p < 0.05). All airway spaces (except for lower pharyngeal space) and hyoid bone parameters were significantly improved posttreatment. CONCLUSION: The facial convexity, upper E-line, Z-angle, nasolabial angle, and lower pharyngeal space did not show statistically significant changes. The rest of the soft tissue parameters, oropharyngeal air spaces, and hyoid positioning measured in the study showed significant improvement after treatment with the PowerScope appliance in Class II patients. CLINICAL SIGNIFICANCE: Class II malocclusion is the most common dental anomaly with a high degree of prevalence in the population. This study will help the clinician in understanding the improvement of soft tissue, airway dimension, and hyoid bone position changes on treatment with a fixed functional appliance for the correction of Class II cases, thereby ensuring the greater success of orthodontic therapy in the future.

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.

Improving deconvolution methods in biology through open innovation competitions: an application to the connectivity map.

MOTIVATION: Do machine learning methods improve standard deconvolution techniques for gene expression data? This article uses a unique new dataset combined with an open innovation competition to evaluate a wide range of approaches developed by 294 competitors from 20 countries. The competition's objective was to address a deconvolution problem critical to analyzing genetic perturbations from the Connectivity Map. The issue consists of separating gene expression of individual genes from raw measurements obtained from gene pairs. We evaluated the outcomes using ground-truth data (direct measurements for single genes) obtained from the same samples. RESULTS: We find that the top-ranked algorithm, based on random forest regression, beat the other methods in accuracy and reproducibility; more traditional gaussian-mixture methods performed well and tended to be faster, and the best deep learning approach yielded outcomes slightly inferior to the above methods. We anticipate researchers in the field will find the dataset and algorithms developed in this study to be a powerful research tool for benchmarking their deconvolution methods and a resource useful for multiple applications. AVAILABILITY AND IMPLEMENTATION: The data is freely available at clue.io/data (section Contests) and the software is on GitHub at https://github.com/cmap/gene_deconvolution_challenge. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comparative Assessment of pH and Degree of Surface Roughness of Enamel When Etched with Five Commercially Available Etchants: An In Vitro Study.

AIM: To evaluate the pH and degree of surface roughness caused by five commercially and readily available etchants on tooth enamel. MATERIALS AND METHODS: Five different etchants were chosen. An electric pH meter was utilized to test the pH of the etchants employed. Fifteen maxillary bicuspids that had been extracted were cleansed and stored in thymol solution. The samples were sorted into five groups of three each. A noncontact profilometer was employed to assess the microsurface changes of the pre-etched enamel. The teeth were then etched for 30 seconds with respect to the group to which they belonged before being cleaned and dried. The surface roughness after etching was analyzed, measured and values were tabulated. Descriptive statistics and paired t-test were done. RESULTS: The pH of the etchants and surface roughness of the enamel are varied across the five groups, though they have the same composition of 37% orthophosphoric acid. Etchant from Group C was found to be most acidic while the one manufactured by Group E was least acidic. Ivoclar, DPI, and DTECH showed a statistically significant value in surface roughness parameter post-etching (p <0.05). A statistical difference that was significant was observed with the Kruskal-Wallis test for surface roughness parameter (p <0.05). CONCLUSION: All five etchants had varied pH and the amount of surface roughness was also varied though the composition was the same. Further elemental analysis of these etchants has to be done to validate the results obtained. CLINICAL SIGNIFICANCE: Etchants of the same composition should ideally produce the same effect on the tooth enamel surface, but etchants from different manufacturers produce different levels of surface roughness which could be due to differences in the composition of the prepared etchant. The study was conducted to assist in making an educated selection about the most cost-effective but efficient etchant for clinical application.

GeNets: a unified web platform for network-based genomic analyses.

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to compare the signal-to-noise ratios of different networks and to identify the optimal network with which to interpret a particular genetic dataset. We present GeNets, a platform in which users can train a machine-learning model (Quack) to carry out these comparisons and execute, store, and share analyses of genetic and RNA-sequencing datasets.

Assessment of Insertion Torque of Mini-implant and Its Correlation with Primary Stability and Pain Levels in Orthodontic Patients.

AIM: The aim of this study was to assess the insertion torque of the mini-implant used in orthodontic patients and to assess the correlation between the insertion torque, primary stability, and perception of pain in patients undergoing orthodontic therapy with mini-implant-augmented anchorage. MATERIAL AND METHODS: Among the patients undergoing orthodontic therapy, 31 samples who required mini-implant for anchorage purpose were selected. A total of 59 mini-implants were placed in these patients. This included interradicular mini-implants and extra-alveolar mini-screws. Immediately after placement, the insertion torque in all these was measured using a digital torque meter and primary stability was assessed by identifying any mobility of the implant placed. Primary stability was noted at two time intervals immediate post-placement and 1 week after that. All the mini-implants that were considered in this study were immediately loaded. Patients were asked to record any pain experienced on the visual analog scale (VAS) score sheet at 24 hours and 1 week post-placement. RESULTS: Among the mini-implants placed, those with 2 mm diameter needed higher placement torque, i.e., infrazygomatic crest mini-implants and buccal shelf mini-implants were placed with average placement torque of 10.08 and 10.25 N cm, respectively. Extra-alveolar screws caused more pain, especially higher in the mandible than the maxilla. Decrease in pain scores was noted from T0 to T1 in almost all the cases. CONCLUSION: Thicker mini-implant needed more insertion torque and highest insertion torque was recorded with extra-alveolar screws. No direct correlation could be obtained with the pain levels experienced by the patients and with the primary stability of the mini-implants. CLINICAL SIGNIFICANCE: Mini-implants placed with an insertion torque above the recommended range tend to fail and break more often. Patients placed with extra-alveolar bone screws reported more pain than that of the smaller-dimension mini-implant.

The GCTx format and cmap{Py, R, M, J} packages: resources for optimized storage and integrated traversal of annotated dense matrices.

MOTIVATION: Facilitated by technological improvements, pharmacologic and genetic perturbational datasets have grown in recent years to include millions of experiments. Sharing and publicly distributing these diverse data creates many opportunities for discovery, but in recent years the unprecedented size of data generated and its complex associated metadata have also created data storage and integration challenges. RESULTS: We present the GCTx file format and a suite of open-source packages for the efficient storage, serialization and analysis of dense two-dimensional matrices. We have extensively used the format in the Connectivity Map to assemble and share massive datasets currently comprising 1.3 million experiments, and we anticipate that the format's generalizability, paired with code libraries that we provide, will lower barriers for integrated cross-assay analysis and algorithm development. AVAILABILITY AND IMPLEMENTATION: Software packages (available in Python, R, Matlab and Java) are freely available at https://github.com/cmap. Additional instructions, tutorials and datasets are available at clue.io/code. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Activation of WNT and CREB signaling pathways in human neuronal cells in response to the Omega-3 fatty acid docosahexaenoic acid (DHA).

A subset of individuals with major depressive disorder (MDD) elects treatment with complementary and alternative medicines (CAMs), including the omega-3 fatty acids docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA). Previous studies in rodents suggest that DHA modulates neurodevelopmental processes, including adult neurogenesis and neuroplasticity, but the molecular and cellular mechanisms of DHA's potential therapeutic effect in the context of human neurobiology have not been well established. Here we sought to address this knowledge gap by investigating the effects of DHA using human iPSC-derived neural progenitor cells (NPCs) and post-mitotic neurons using pathway-selective reporter genes, multiplexed mRNA expression profiling, and a panel of metabolism-based viability assays. Finally, real-time, live-cell imaging was employed to monitor neurite outgrowth upon DHA treatment. Overall, these studies showed that DHA treatment (0-50 µM) significantly upregulated both WNT and CREB signaling pathways in human neuronal cells in a dose-dependent manner with 2- to 3-fold increases in pathway activation. Additionally, we observed that DHA treatment enhanced survival of iPSC-derived NPCs and differentiation of post-mitotic neurons with live-cell imaging, revealing increased neurite outgrowth with DHA treatment within 24 h. Taken together, this study provides evidence that DHA treatment activates critical pathways regulating neuroplasticity, which may contribute to enhanced neuronal cell viability and neuronal connectivity. The extent to which these pathways represent molecular mechanisms underlying the potential beneficial effects of omega-3 fatty acids in MDD and other brain disorders merits further investigation.

Reduced-representation Phosphosignatures Measured by Quantitative Targeted MS Capture Cellular States and Enable Large-scale Comparison of Drug-induced Phenotypes.

Profiling post-translational modifications represents an alternative dimension to gene expression data in characterizing cellular processes. Many cellular responses to drugs are mediated by changes in cellular phosphosignaling. We sought to develop a common platform on which phosphosignaling responses could be profiled across thousands of samples, and created a targeted MS assay that profiles a reduced-representation set of phosphopeptides that we show to be strong indicators of responses to chemical perturbagens.To develop the assay, we investigated the coordinate regulation of phosphosites in samples derived from three cell lines treated with 26 different bioactive small molecules. Phosphopeptide analytes were selected from these discovery studies by clustering and picking 1 to 2 proxy members from each cluster. A quantitative, targeted parallel reaction monitoring assay was developed to directly measure 96 reduced-representation probes. Sample processing for proteolytic digestion, protein quantification, peptide desalting, and phosphopeptide enrichment have been fully automated, making possible the simultaneous processing of 96 samples in only 3 days, with a plate phosphopeptide enrichment variance of 12%. This highly reproducible process allowed ∼95% of the reduced-representation phosphopeptide probes to be detected in ∼200 samples.The performance of the assay was evaluated by measuring the probes in new samples generated under treatment conditions from discovery experiments, recapitulating the observations of deeper experiments using a fraction of the analytical effort. We measured these probes in new experiments varying the treatments, cell types, and timepoints to demonstrate generalizability. We demonstrated that the assay is sensitive to disruptions in common signaling pathways (e.g. MAPK, PI3K/mTOR, and CDK). The high-throughput, reduced-representation phosphoproteomics assay provides a platform for the comparison of perturbations across a range of biological conditions, suitable for profiling thousands of samples. We believe the assay will prove highly useful for classification of known and novel drug and genetic mechanisms through comparison of phosphoproteomic signatures.

Chronic cisplatin treatment promotes enhanced damage repair and tumor progression in a mouse model of lung cancer.

Chemotherapy resistance is a major obstacle in cancer treatment, yet the mechanisms of response to specific therapies have been largely unexplored in vivo. Employing genetic, genomic, and imaging approaches, we examined the dynamics of response to a mainstay chemotherapeutic, cisplatin, in multiple mouse models of human non-small-cell lung cancer (NSCLC). We show that lung tumors initially respond to cisplatin by sensing DNA damage, undergoing cell cycle arrest, and inducing apoptosis-leading to a significant reduction in tumor burden. Importantly, we demonstrate that this response does not depend on the tumor suppressor p53 or its transcriptional target, p21. Prolonged cisplatin treatment promotes the emergence of resistant tumors with enhanced repair capacity that are cross-resistant to platinum analogs, exhibit advanced histopathology, and possess an increased frequency of genomic alterations. Cisplatin-resistant tumors express elevated levels of multiple DNA damage repair and cell cycle arrest-related genes, including p53-inducible protein with a death domain (Pidd). We demonstrate a novel role for PIDD as a regulator of chemotherapy response in human lung tumor cells.

High-throughput validation of ceRNA regulatory networks.

BACKGROUND: MicroRNAs (miRNAs) play multiple roles in tumor biology. Interestingly, reports from multiple groups suggest that miRNA targets may be coupled through competitive stoichiometric sequestration. Specifically, computational models predicted and experimental assays confirmed that miRNA activity is dependent on miRNA target abundance, and consequently, changes in the abundance of some miRNA targets lead to changes to the regulation and abundance of their other targets. The resulting indirect regulatory influence between miRNA targets resembles competition and has been dubbed competitive endogenous RNA (ceRNA). Recent studies have questioned the physiological relevance of ceRNA interactions, our ability to accurately predict these interactions, and the number of genes that are impacted by ceRNA interactions in specific cellular contexts. RESULTS: To address these concerns, we reverse engineered ceRNA networks (ceRNETs) in breast and prostate adenocarcinomas using context-specific TCGA profiles, and tested whether ceRNA interactions can predict the effects of RNAi-mediated gene silencing perturbations in PC3 and MCF7 cells._ENREF_22 Our results, based on tests of thousands of inferred ceRNA interactions that are predicted to alter hundreds of cancer genes in each of the two tumor contexts, confirmed statistically significant effects for half of the predicted targets. CONCLUSIONS: Our results suggest that the expression of a significant fraction of cancer genes may be regulated by ceRNA interactions in each of the two tumor contexts.

Gene expression inference with deep learning.

MOTIVATION: Large-scale gene expression profiling has been widely used to characterize cellular states in response to various disease conditions, genetic perturbations, etc. Although the cost of whole-genome expression profiles has been dropping steadily, generating a compendium of expression profiling over thousands of samples is still very expensive. Recognizing that gene expressions are often highly correlated, researchers from the NIH LINCS program have developed a cost-effective strategy of profiling only ∼1000 carefully selected landmark genes and relying on computational methods to infer the expression of remaining target genes. However, the computational approach adopted by the LINCS program is currently based on linear regression (LR), limiting its accuracy since it does not capture complex nonlinear relationship between expressions of genes. RESULTS: We present a deep learning method (abbreviated as D-GEX) to infer the expression of target genes from the expression of landmark genes. We used the microarray-based Gene Expression Omnibus dataset, consisting of 111K expression profiles, to train our model and compare its performance to those from other methods. In terms of mean absolute error averaged across all genes, deep learning significantly outperforms LR with 15.33% relative improvement. A gene-wise comparative analysis shows that deep learning achieves lower error than LR in 99.97% of the target genes. We also tested the performance of our learned model on an independent RNA-Seq-based GTEx dataset, which consists of 2921 expression profiles. Deep learning still outperforms LR with 6.57% relative improvement, and achieves lower error in 81.31% of the target genes. AVAILABILITY AND IMPLEMENTATION: D-GEX is available at https://github.com/uci-cbcl/D-GEX CONTACT: xhx@ics.uci.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genetic modifiers of EGFR dependence in non-small cell lung cancer.

Lung adenocarcinomas harboring activating mutations in the epidermal growth factor receptor (EGFR) represent a common molecular subset of non-small cell lung cancer (NSCLC) cases. EGFR mutations predict sensitivity to EGFR tyrosine kinase inhibitors (TKIs) and thus represent a dependency in NSCLCs harboring these alterations, but the genetic basis of EGFR dependence is not fully understood. Here, we applied an unbiased, ORF-based screen to identify genetic modifiers of EGFR dependence in EGFR-mutant NSCLC cells. This approach identified 18 kinase and kinase-related genes whose overexpression can substitute for EGFR in EGFR-dependent PC9 cells, and these genes include seven of nine Src family kinase genes, FGFR1, FGFR2, ITK, NTRK1, NTRK2, MOS, MST1R, and RAF1. A subset of these genes can complement loss of EGFR activity across multiple EGFR-dependent models. Unbiased gene-expression profiling of cells overexpressing EGFR bypass genes, together with targeted validation studies, reveals EGFR-independent activation of the MEK-ERK and phosphoinositide 3-kinase (PI3K)-AKT pathways. Combined inhibition of PI3K-mTOR and MEK restores EGFR dependence in cells expressing each of the 18 EGFR bypass genes. Together, these data uncover a broad spectrum of kinases capable of overcoming dependence on EGFR and underscore their convergence on the PI3K-AKT and MEK-ERK signaling axes in sustaining EGFR-independent survival.

Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling.

High-throughput screening has become a mainstay of small-molecule probe and early drug discovery. The question of how to build and evolve efficient screening collections systematically for cell-based and biochemical screening is still unresolved. It is often assumed that chemical structure diversity leads to diverse biological performance of a library. Here, we confirm earlier results showing that this inference is not always valid and suggest instead using biological measurement diversity derived from multiplexed profiling in the construction of libraries with diverse assay performance patterns for cell-based screens. Rather than using results from tens or hundreds of completed assays, which is resource intensive and not easily extensible, we use high-dimensional image-based cell morphology and gene expression profiles. We piloted this approach using over 30,000 compounds. We show that small-molecule profiling can be used to select compound sets with high rates of activity and diverse biological performance.

LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures.

For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites.