The wealth of molecular information provided by high-throughput technologies has enhanced the efforts dedicated to the reconstruction of regulatory networks in diverse biological systems. This information, however, ha...
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The wealth of molecular information provided by high-throughput technologies has enhanced the efforts dedicated to the reconstruction of regulatory networks in diverse biological systems. This information, however, has proven to be insufficient for the construction of quantitative models due to the absence of sufficiently accurate measurements of kinetic constants. As a result, there have been efforts to develop methodologies that permit the use of qualitative information about patterns of expression to infer the regulatory networks that generate such patterns. One of these approaches is the SQUAD method, which approximates a Boolean network with the use of a set of ordinary differential equations. The main benefit of the SQUAD method over purely Boolean approaches is the possibility of evaluating the effect of continuous external signals, which are pervasive in biological phenomena. A brief description and code on how to implement this method can be found at the following link: https://***/caramirezal/SQUADBookChapter . less
Chemogenomic profiling is a powerful and unbiased approach to elucidate pharmacological targets and the mechanism of bioactive compounds. It is based on identifying cellular hypersensitivity and resistance caused by i...
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Chemogenomic profiling is a powerful and unbiased approach to elucidate pharmacological targets and the mechanism of bioactive compounds. It is based on identifying cellular hypersensitivity and resistance caused by individual gene modulations with genome-wide coverage. Due to the requirement of bar-coded, genome-wide deletion collections, high-resolution experiments of this nature have historically been limited to fungal systems. Pooled RNAi reagents have enabled similar attempts in mammalian cells but efforts have been hampered by significant off-target effects and experimental noise. The CRISPR/Cas9 system for the first time enables precise DNA editing at defined loci in a genome-wide fashion. Here we present the detailed protocol that leverages the CRISPR/Cas9 system for chemogenomic profiling and target identification of diverse chemical probes. less
The recent transition in gene expression analysis technology to ultra high-throughput cDNA sequencing provides a means for higher quantitation sensitivity across a wider dynamic range than previously possible. Sensiti...
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The recent transition in gene expression analysis technology to ultra high-throughput cDNA sequencing provides a means for higher quantitation sensitivity across a wider dynamic range than previously possible. Sensitivity of detection is mostly a function of the sheer number of sequence reads generated. Typically, RNA is converted to cDNA using random hexamers and the cDNA is subsequently sequenced (RNA-Seq). With this approach, higher read numbers are generated for long transcripts as compared to short ones. This length bias necessitates the generation of very high read numbers to achieve sensitive quantitation of short, low-expressed genes. To eliminate this length bias, we have developed an ultra high-throughput sequencing approach where only a single read is generated for each transcript molecule (single-molecule sequencing Digital Gene Expression (smsDGE)). So, for example, equivalent quantitation accuracy of the yeast transcriptome can be achieved by smsDGE using only 25% of the reads that would be required using RNA-Seq. For sample preparation, RNA is first reverse-transcribed into single-stranded cDNA using oligo-dT as a primer. A poly-A tail is then added to the 3′ ends of cDNA to facilitate the hybridization of the sample to the Helicos® single-molecule sequencing Flow-Cell to which a poly dT oligo serves as the substrate for subsequent sequencing by synthesis. No PCR, sample-size selection, or ligation steps are required, thus avoiding possible biases that may be introduced by such manipulations. Each tailed cDNA sample is injected into one of 50 flow-cell channels and sequenced on the Helicos® Genetic Analysis System. Thus, 50 samples are sequenced simultaneously generating 10–20 million sequence reads on average for each sample channel. The sequence reads can then be aligned to the reference of choice such as the transcriptome, for quantitation of known transcripts, or the genome for novel transcript discovery. This chapter provides a summary of the met
Many bioinformatics tasks involve creating a computational pipeline from existing software components and algorithms. The traditional approach is to glue components together using scripts written in a programming lang...
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Many bioinformatics tasks involve creating a computational pipeline from existing software components and algorithms. The traditional approach is to glue components together using scripts written in a programming language such as Perl. However, a new, more powerful approach is emerging that promises to revolutionise the way bioinformaticians create applications from existing components, an approach based on the concept of the scientific workflow. Scientific workflows are created in graphical environments known as workflow management systems. They have many benefits over traditional program scripts, including speed of development, portability, and their suitability for developing complex, distributed applications. This chapter explains how to design and implement bioinformatics workflows using free, Open Source software tools, such as the Tav-erna workflow management system. We also demonstrate how new and existing tools can be deployed as Web services so that they can be easily integrated into novel computational pipelines using the scientific workflow paradigm. less
Deep sequencing has many possible applications; one of them is the identification and quantification of RNA editing sites. The most common type of RNA editing is adenosine to inosine (A-to-I) editing. A prerequisite f...
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Deep sequencing has many possible applications; one of them is the identification and quantification of RNA editing sites. The most common type of RNA editing is adenosine to inosine (A-to-I) editing. A prerequisite for this editing process is a double-stranded RNA (dsRNA) structure. Such dsRNAs are formed as part of the microRNA (miRNA) maturation process, and it is therefore expected that miRNAs are affected by A-to-I editing. Indeed, tens of editing sites were found in miRNAs, some of which change the miRNA binding specificity. Here, we describe a protocol for the identification of RNA editing sites in mature miRNAs using deep sequencing data. less
The detection of antibody responses using serological tests provides means to diagnose infections, follow disease transmission, and monitor vaccination responses. The coronavirus disease 2019 (COVID-19) pandemic, caus...
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The detection of antibody responses using serological tests provides means to diagnose infections, follow disease transmission, and monitor vaccination responses. The coronavirus disease 2019 (COVID-19) pandemic, caused by the SARS-CoV-2 virus, highlighted the need for rapid development of robust and reliable serological tests to follow disease spreading. Moreover, the rise of SARS-CoV-2 variants emphasized the need to monitor their transmission and prevalence in the population. For this reason, multiplex and flexible serological assays are needed to allow for rapid inclusion of antigens representing new variants as soon as they appear. In this chapter, we describe the generation and application of a multiplex serological test, based on bead array technology, to detect anti-SARS-CoV-2 antibodies in a high-throughput manner, using only a few microliters of sample. This method is currently expanding to include a multi-disease antigen panel that will allow parallel detection of antibodies towards several infectious agents. less
Single cell experimental techniques now allow us to quantify gene expression in up to thousands of individual cells. These data reveal the changes in transcriptional state that occur as cells progress through developm...
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Single cell experimental techniques now allow us to quantify gene expression in up to thousands of individual cells. These data reveal the changes in transcriptional state that occur as cells progress through development and adopt specialized cell fates. In this chapter we describe in detail how to use our network inference algorithm (PIDC)—and the associated software package ***—to infer functional interactions between genes from the observed gene expression patterns. We exploit the large sample sizes and inherent variability of single cell data to detect statistical dependencies between genes that indicate putative (co-)regulatory relationships, using multivariate information measures that can capture complex statistical relationships. We provide guidelines on how best to combine this analysis with other complementary methods designed to explore single cell data, and how to interpret the resulting gene regulatory network models to gain insight into the processes regulating cell differentiation. less
Transcriptomics has played an essential role as proof of concept in the development of experimental and bioinformatics approaches for the generation and analysis of Omics data. We are giving an introduction on how lar...
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Transcriptomics has played an essential role as proof of concept in the development of experimental and bioinformatics approaches for the generation and analysis of Omics data. We are giving an introduction on how large-scale technologies for gene expression profiling, especially microarrays, have changed the view from studying single molecular events to a systems level view of global mechanisms in a cell, the biological processes, and their pathological mutations. The main platforms available for gene expression profiling (from microarrays to RNA-seq) are presented and the general concepts that need to be taken into account for proper data analysis in order to extract objective and general conclusions from transcriptomics experiments are introduced. We also describe the available main bioinformatics resources used for this purpose. less
We provide a detailed protocol for robot-assisted, genome-wide measurement of fitness in the model yeast Saccharomyces cerevisiae using Quantitative Fitness Analysis (QFA). We first describe how we construct thousands...
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We provide a detailed protocol for robot-assisted, genome-wide measurement of fitness in the model yeast Saccharomyces cerevisiae using Quantitative Fitness Analysis (QFA). We first describe how we construct thousands of double or triple mutant yeast strains in parallel using Synthetic Genetic Array (SGA) procedures. Strains are inoculated onto solid agar surfaces by liquid spotting followed by repeated photography of agar plates. Growth curves are constructed and the fitness of each strain is estimated. Robot-assisted QFA, can be used to identify genetic interactions and chemical sensitivity/resistance in genome-wide experiments, but QFA can also be used in smaller scale, manual workflows. less
Gene regulatory network (GRN) models have been shown to predict and represent interactions among sets of genes. Here, we first show the basic steps to implement a simple but computationally efficient algorithm to infe...
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Gene regulatory network (GRN) models have been shown to predict and represent interactions among sets of genes. Here, we first show the basic steps to implement a simple but computationally efficient algorithm to infer GRNs based on dynamic Bayesian networks (DBNs), and we then explain how to approximate DBN-based GRN models with continuous models. In addition, we show a MATLAB implementation of the key steps of this method, which we use to infer an Arabidopsis root GRN. less
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