Karol Kozak

The evolution of RNA interference (RNAi) and the development of technologies exploiting its biology have enabled scientists to rapidly examine the consequences of depleting a particular gene product in cells. Design tools have been developed based on experimental data to increase the knockdown efficiency of siRNAs. Not all siRNAs that are developed to a given target mRNA are equally effective. Currently available design algorithms take an accession, identify conserved regions among their transcript space, find accessible regions within the mRNA, design all possible siRNAs for these regions, filter them based on multi-scores thresholds, and then perform off-target filtration. These different criteria are used by commercial suppliers to produce siRNA genome-wide libraries for different organisms. In this article, we analyze existing siRNA design algorithms and evaluate weight of design parameters for libraries produced in the last decade. We proved that not all essential parameters are currently applied by siRNA vendors. Based on our evaluation results, we were able to suggest an siRNA sequence pattern. The findings in our study can be useful for commercial vendors improving the design of RNAi constructs, by addressing both the issue of potency and the issue of specificity.

Detection of potential cross-reaction between a short oligonucleotide sequence and a longer (unintended) sequence is crucial for many biological applications, such as high content screening (HCS), microarray nucleotide probes, or short interfering RNAs (siRNAs). However, owing to a tolerance for mismatches and gaps in base-pairing with target transcripts, siRNAs could have up to hundreds of potential target sequences in a genome, and some small RNAs in mammalian systems have been shown to affect the levels of many messenger RNAs (off-targets) besides their intended target transcripts (on-targets). The reference sequence (RefSeq) collection aims to provide a comprehensive, integrated, nonredundant, well-annotated set of sequences, including mRNA transcripts. We performed a detailed off-target analysis of three most commonly used kinome siRNA libraries based on the latest RefSeq version. To simplify the access to off-target transcripts, we created a SeedSeq database, a new unique format to store off-target information.

Biological systems present multiple scales of complexity, ranging from molecules to entire populations. Light microscopy is one of the least invasive techniques used to access information from various biological scales in living cells. The combination of molecular biology and imaging provides a bottom-up tool for direct insight into how molecular processes work on a cellular scale. However, imaging can also be used as a top-down approach to study the behavior of a system without detailed prior knowledge about its underlying molecular mechanisms. In this review, we highlight the recent developments on microscopy-based systems analyses and discuss the complementary opportunities and different challenges with high-content screening and high-throughput imaging. Furthermore, we provide a comprehensive overview of the available platforms that can be used for image analysis, which enable community-driven efforts in the development of image-based systems biology.

Large-scale RNA interference experiments, especially the ones based on short-interfering RNA (siRNA) technology have become increasingly popular over the past years. Obviously, the sequence (and structure) of the corresponding siRNA is a key factor in obtaining reliable results in these large-scale studies, and the companies use a variety of algorithms to design them. Design tools have been developed based on experimental data to increase the knockdown efficiency of siRNAs. Nevertheless, as the genome annotations are still continuously changing, siRNAs may become obsolete, so siRNA reagents should be periodically re-annotated according the latest version of sequence database. In this article, existing siRNA design algorithms, design parameters, and siRNAs are evaluated. A new approach for systematic analysis and re-annotation of siRNAs libraries produced in the last decade is introduced here.

Technological advancements are constantly increasing the size and complexity of data resulting from large-scale RNA interference screens. This fact has led biologists to ask complex questions, which the existing, fully automated analyses are often not adequate to answer. We present a concept of 1Click1View (1C1V) as a methodology for interactive analytic software tools. 1C1V can be applied for two-dimensional visualization of image-based screening data sets from High Content Screening (HCS). Through an easy-to-use interface, one-click, one-view concept, and workflow based architecture, visualization method facilitates the linking of image data with numeric data. Such method utilizes state-of-the-art interactive visualization tools optimized for fast visualization of large scale image data sets. We demonstrate our method on an HCS dataset consisting of multiple cell features from two screening assays.

RNA interference (RNAi) high-content screening (HCS) enables massive parallel gene silencing and is increasingly being used to reveal novel connections between genes and disease-relevant phenotypes. The application of genome-scale RNAi relies on the development of high quality HCS assays. The Z' factor statistic provides a way to evaluate whether or not screening run conditions (reagents, protocols, instrumentation, kinetics, and other conditions not directly related to the test compounds) are optimized. Z' factor, introduced by Zhang et al., ( 1) is a dimensionless value that represents both the variability and the dynamic range between two sets of sample control data. This paper describe a new extension of the Z' factor, which integrates bioinformatics RNAi non-target compounds for screening quality assessment. Currently presented Z' factor is based on positive and negative control, which may not be sufficient for RNAi experiments including oligonucleotides (oligo) with lack of knock-down. This paper proposes an algorithm which extends existing algorithm by using additional controls generetaed from on-target analysis.

Large-scale RNA interference (RNAi) experiments, especially the ones based on short-interfering RNA (siRNA) technology became increasingly popular over the past years. For such knock-down/screening purposes, different companies offer sets of oligos/reagents targeting the whole genome or a subset of it for various organisms. Obviously, the sequence (and structure) of the corresponding oligos is a key factor in obtaining reliable results in these large-scale studies and the companies use a variety of (often not fully public) algorithms to design them. Nevertheless, as the genome annotations are still continuously changing, oligos may become obsolete, so siRNA reagents should be periodically re-annotated according to the latest version of the sequence database (which of course has serious consequences also on the interpretation of the screening results). In our article, we would like to introduce a new software/database tool, the RNAiAtlas. It has been created for exploration, analysis and distribution of large scale RNAi libraries (currently limited to the human genome) with their latest annotation (including former history) but in addition it contains also specific on-target analysis results (design quality, side effects, off-targets). Database URL: http://www.rnaiatlas.ethz.ch.

RNA interference (RNAi) high-content screening (HCS) enables massive parallel gene silencing and is increasingly being used to reveal novel connections between genes and disease-relevant phenotypes. The application of genome-scale RNAi relies on the development of high quality HCS assays. The Z’ factor statistic provides a way to evaluate whether or not screening run conditions (reagents, protocols, instrumentation, kinetics, and other conditions not directly related to the test compounds) are optimized. Z’ factor, introduced by Zhang et al.¹, is a dimensionless value that represents both the variability and the dynamic range between two sets of sample control data. This paper describes a new extension of the Z' factor, which integrates multiple readouts for screening quality assessment. Currently presented multivariate Z’ factor is based on linear projection, which may not be suitable for data with nonlinear structure. This paper proposes an algorithm which extends existing algorithm to deal with nonlinear data by using the kernel function. Using kernel methods for projections, multiple readouts are condensed to a single parameter, based on which the screening run quality is monitored.

Light microscopy is of central importance in cell biology. The recent introduction of automated high content screening has expanded this technology towards automation of experiments and performing large scale perturbation assays. Nevertheless, evaluation of microscopy data continues to be a bottleneck in many projects. Currently, among open source software, CellProfiler and its extension Analyst are widely used in automated image processing. Even though revolutionizing image analysis in current biology, some routine and many advanced tasks are either not supported or require programming skills of the researcher. This represents a significant obstacle in many biology laboratories.