RNAi is a process in which gene expression is inhibited by small RNA molecules such as small interfering RNAs (siRNAs) and short hairpin RNAs. High-throughput RNAi screening is a breakthrough technology for functional genomics and for drug target discovery. A frequently used screening platform uses 96-well or 384-well microtiter plates, on which each well contains siRNA oligos designed to target a specific gene.
Assists users in gene dependency assessing. DEMETER is an application that aims to deduce cancer cell line genetic dependencies from RNAi screens. This model integrates the evaluation of several screen normalization parameters from the data. It intends to simplify comparisons between cell lines by targeting and eliminating several sources of cell line- and screen-related systematic bias.
Assists users with the analysis of high-throughput screens. web cellHTS2 is a web application useful for large cell-based screening experiments. This software includes features for a variety of standardization and standardization methods, annotation of data sets and a complete HTML report of the screening data analysis, including a results ranking-list.
A software package implemented in Bioconductor/R to analyze cell-based high-throughput RNAi screens. The cellHTS2 package is the new version of the cellHTS package, offering improved functionality for the analysis and integration of multi-channel screens and multiple screens. We advise users starting new projects to use cellHTS2. For your existing projects, you may keep using cellHTS, which is still supported.
Integrates analysis and visualization of RNAi screen data. CARD allows the user to seamlessly carry out sequential steps in a rigorous data analysis workflow, including normalization, off-target analysis, integration of gene expression data, optimal thresholds for hit selection and network/pathway analysis. CARD also uses cutting-edge data visualization techniques that allow users to interact dynamically with the figures and tables displayed on the web-browser to facilitate the hit selection process.
Takes advantage of patterns in RNAi data across multiple samples to enrich for RNAi reagents. For each gene in a screen, ATARiS identifies sets of reagents with similar behaviour. It produces quantitative, gene-level phenotype values, which provide an intuitive measure of the effect of gene suppression in each sample. Our method uses only data from reagents determined to have primarily on-target effects, discarding data from reagents with off-target effects. One key advance of ATARiS lies in the ability to distinguish reagents with on-target effects and reject reagents with significant off-target effects by mining patterns across multisample screens.
Estimates hidden gene-specific phenotypes (GSPs) from observed reagent-specific phenotypes (RSPs). gespeR is a statistical model that can be applied to any RNA interference (RNAi) screening data set confounded by off-target effects. This tool assists users in study of gene-specific phenotypes for all pathogens from different sets. It allows correction of confounding off-target effects and inferring GSPs.
A one-stop solution for chemical compound screens, siRNA knock-down and CRISPR/Cas9 knock-out screens, as well as microRNA inhibitor and -mimics screens. HiTSeekR exploits HTS screening data in quite heterogeneous contexts to generate novel hypotheses for follow-up experiments: (i) a genome-wide RNAi screen to uncover modulators of TNF, (ii) a combined siRNA and miRNA mimics screen on vorinostat resistance and (iii) a small compound screen on KRAS synthetic lethality. HiTSeekR is the first approach to close the gap between raw data processing, network enrichment and wet lab target generation for various HTS screen types.
A computational approach to prioritize candidate drug targets for NSCLC by subdividing cell lines into different groups and identifying genetic vulnerabilities targeted to each group. NSCLC uses k-means clustering to calculate degree of bimodal response of cell lines to loss of biological systems. This tool provides an opportunity for drug repurposing, which could lead to reduced time in the drug development pipeline.
A flexible software to build integrated analysis pipelines for high-throughput screen (HTS) data that contains over-representation analysis, gene set enrichment analysis, comparative gene set analysis and rich sub-network identification. HTSanalyzeR interfaces with commonly used pre-processing packages for HTS data and presents its results as HTML pages and network plots.
A package for the free statistical environment R which performs an analysis of high-throughput RNA interference (RNAi) knock-down experiments, generating lists of relevant genes and pathways out of raw experimental data. The library provides a quality assessment of the signal intensities, as well as a broad range of options for data normalization, different statistical tests for the identification of significant siRNAs, and a significance analysis of the biological processes involving corresponding genes. The results of the analysis are presented as a set of HTML pages. Additionally, all values and plots are available as either text files or pdf and png files.
A handy freeware utility that can be used to process high-throughput screening data. The HTS Helper utility has been created to facilitate the systematic error correction of experimental HTS data. It implements several error correction and normalization methods: (i) matrix error amendment (MEA); (ii) partial mean polish (PMP); (iii) well correction; (iv) Z-score normalization and (v) B-score normalization. By design, HTS Helper completes its work in three steps. First, it reads an HTS dataset from the input data file. Second, it applies the selected data processing method, if any. And finally, it saves the modified dataset into the output data file.
An open source application dedicated to high content screening (HCS) multivariate data processing and analysis. HCS-Analyzer includes: (i) a user-friendly interface specifically dedicated to HCS readouts, (ii) an automated approach to identify systematic errors potentially occurring during screening and (iii) a set of tools to classify, cluster and identify phenotypes of interest among large and multivariate data.
A statistical modeling framework that is based on experimental designs with at least two controls profiled throughout the experiment, and a normalization and variance estimation procedure with linear mixed-effects models. HTSmix (i) can be used in conjunction with practical experimental designs; (ii) allows extensions to alternative experimental workflows; (iii) enables a sensitive discovery of biologically meaningful changes; and (iv) strongly outperforms the existing noise reduction procedures.
Provides an analytic tool that can generate figures for displaying data and hit selection results from HTS experiments. displayHTS can be used to generate not only useful distinctive graphics including the plate-well series plot, plate image and dual-flashlight plot but also other commonly used figures such as volcano plot and plate correlation plot. The visualization of data and hit selection results enabled by displayHTS is critical to reveal various patterns of spatial effects and assay quality issues in HTS experiments for both small molecules and siRNAs.
Provides an R/Shiny open-source web application for interactive visualization and exploratory analysis of arrayed high-throughput data. Using a light-weight infrastructure suitable for desktop computers, HTSvis can be used to visualize raw data, perform quality control and interactively visualize screening results from single- to multi-channel measurements, such as image-based, screens. Input data can either be a result file obtained upon analysis with cellHTS or a generic table with raw or analyzed data from, e.g. a high-content microscopy screen.
Represents an improvement over existing methods of image non-uniformity (NU) correction used in high-content screening (HCS), which are based on varying degrees of simplification to a linear model approximation of NU bias. By estimating the full non-linear form of the NU bias, the IQEM method essentially applies a correction factor that is appropriate to each intensity quantile in the measured image. The method is particularly pertinent for the quantification of extremely low-intensity cell phenotypes, where multiplicative correction provides an inaccurate fit to the low-intensity image NU, and where background subtraction does not adequately model the range of dim intensity levels. An additional positive feature of the IQEM algorithm is that it can be applied on a batch-specific basis such that a unique image NU correction is estimated for each batch.
Finds hits in cell-based high-throughput screening (HTS) / high-content screening (HCS). Φ-score aims to overcome the pitfalls and drawbacks of the Z-score. It employs rank-based statistics and considers the number of cells per perturbation to correct for variability. This approach was applied to real data from a screen designed to identify new factors participating in oxidized DNA repair.
Enables researchers without a programming background to use strictly standardized mean difference (SSMD) as both a plate quality and a hit selection metric to analyze large data sets. GUItars is capable of analyzing large-scale data sets from screens with or without replicates. It is designed to automatically generate and save numerous graphical outputs known to be among the most informative high-throughput data visualization tools capturing plate-wise and screen-wise performances. The tool enables rapid analysis and illustration of data from large- or small-scale RNAi screens using SSMD and other traditional analysis methods.
An R package for the statistical modeling and visualization of image-based high-content RNAi screening. The iScreen package provides visualization tools to examine raw data, display analysis results, and conduct quality control. Two case studies were used to demonstrate the capability and efficiency of the iScreen package.
The RVM t-test, available in the SIGHTS software, is a good alternative that provides all of the advantages of formal statistical testing with as few as two replicates, although a minimum of three is generally recommended. The p-values generated from the statistical tests can then be used to balance false positives and false negatives based on objective statistical benchmarks.