GUItars: a GUI tool for analysis of high-throughput RNA interference screening data.

High-throughput RNA interference (RNAi) screening has become a widely used approach to elucidating gene functions. However, analysis and annotation of large data sets generated from these screens has been a challenge for researchers without a programming background. Over the years, numerous data analysis methods were produced for plate quality control and hit selection and implemented by a few open-access software packages. Recently, strictly standardized mean difference (SSMD) has become a widely used method for RNAi screening analysis mainly due to its better control of false negative and false positive rates and its ability to quantify RNAi effects with a statistical basis. We have developed GUItars to enable researchers without a programming background to use SSMD as both a plate quality and a hit selection metric to analyze large data sets.The software is accompanied by an intuitive graphical user interface for easy and rapid analysis workflow. SSMD analysis methods have been provided to the users along with traditionally-used z-score, normalized percent activity, and t-test methods for hit selection. GUItars is capable of analyzing large-scale data sets from screens with or without replicates. The software 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. Graphical outputs are also written in HTML format for easy access, and a comprehensive summary of screening results is written into tab-delimited output files.With GUItars, we demonstrated robust SSMD-based analysis workflow on a 3840-gene small interfering RNA (siRNA) library and identified 200 siRNAs that increased and 150 siRNAs that decreased the assay activities with moderate to stronger effects. GUItars enables rapid analysis and illustration of data from large- or small-scale RNAi screens using SSMD and other traditional analysis methods. The software is freely available at http://sourceforge.net/projects/guitars/

siRNA counts classified by effect sizes.

<p>GUItars output with gene counts ranked based upon the criteria presented by Zhang <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049386#pone.0049386-Zhang4" target="_blank">[12]</a>. Data is generated from a 12-plate luminescence-based assay with 3840 total genes.</p

General workflow of high-throughput data analysis with GUItars.

<p>General workflow of high-throughput data analysis with GUItars.</p

User input files required by GUItars.

<p>Three separate input files are required by GUItars: A “data file directory” containing individual files for each plate, an “annotation file” with first two columns containing RNAi source plate ID and assay plate well ID with a single header line, and a “plate ID file” with a single header line. An “annotation file” and a “plate ID” file are mandatory only if the “hit mapping to the RNAi annotation file” option is checked.</p

Excel readable output file.

<p>Individual tab-delimited output files as well as a comprehensive Excel file are generated with the following information: Plate QC calculations before and after control outlier knockout, scores for all wells classified by well type, scores for hit wells classified by hit type (i.e., signal-increasing or signal-decreasing), and annotated hit list (optional) with corresponding scores.</p

Graphical outputs demonstrated on a 12-plate siRNA screen analyzed with the robust SSMD method with GUItars.

<p>(A) Raw data (left) and log₂-transformed data (right) histograms of each plate showing the original data distribution and effect of data transformation (one representative plate is shown). (B) Original scale (left) and rescaled (right) heat maps of each plate helping to capture systematic errors (one representative plate is shown). (C) Column-wise plate-series plot. (D) Screen-wise line plot for average control readings showing a clear separation between negative control and positive controls that is consistent throughout the screen. (E) Screen-wise SSMD score scatter plots with cutoff lines at 1.28 and −1.28 for signal-increasing and signal-decreasing hits, respectively. (F) Hit distribution heat maps for signal-increasing (top) and signal-decreasing (bottom) hits. (G) Screen-wise hit counts for signal-increasing (top) and signal-decreasing (bottom) hits.</p

High-throughput screening reveals alsterpaullone, 2-cyanoethyl as a potent p27Kip1 transcriptional inhibitor.

p27Kip1 is a cell cycle inhibitor that prevents cyclin dependent kinase (CDK)/cyclin complexes from phosphorylating their targets. p27Kip1 is a known tumor suppressor, as the germline loss of p27Kip1 results in sporadic pituitary formation in aged rodents, and its presence in human cancers is indicative of a poor prognosis. In addition to its role in cancer, loss of p27Kip1 results in regenerative phenotypes in some tissues and maintenance of stem cell pluripotency, suggesting that p27Kip1 inhibitors could be beneficial for tissue regeneration. Because p27Kip1 is an intrinsically disordered protein, identifying direct inhibitors of the p27Kip1 protein is difficult. Therefore, we pursued a high-throughput screening strategy to identify novel p27Kip1 transcriptional inhibitors. We utilized a luciferase reporter plasmid driven by the p27Kip1 promoter to transiently transfect HeLa cells and used cyclohexamide as a positive control for non-specific inhibition. We screened a "bioactive" library consisting of 8,904 (4,359 unique) compounds, of which 830 are Food and Drug Administration (FDA) approved. From this screen, we successfully identified 111 primary hits with inhibitory effect against the promoter of p27Kip1. These hits were further refined using a battery of secondary screens. Here we report four novel p27Kip1 transcriptional inhibitors, and further demonstrate that our most potent hit compound (IC50 = 200 nM) Alsterpaullone 2-cyanoethyl, inhibits p27Kip1 transcription by preventing FoxO3a from binding to the p27Kip1 promoter. This screen represents one of the first attempts to identify inhibitors of p27Kip1 and may prove useful for future tissue regeneration studies