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Improve your differential gene expression analysis with EPEE

Differential gene expression is highly common. The standard paradigm is to quantify the significance of differences in the individual gene expression values, commonly known as differential expression (DE) analysis. One opportunity to improve the widely used DE analysis is to incorporate known gene regulation relationships. Without regulatory knowledge, DE methods cannot discover any perturbation/regulation events due to post-transcriptional and/or translational mechanisms. To address this challenge, Murat Cobanoglu and his colleagues from Lyda Hill Department of Bioinformatics have developed EPEE. Here, they present their tool and its main features. EPEE features To more accurately analyze differential gene expression data, we need algorithms that account for differential regulation (DR). However, most currently existing DR methods do not strictly integrate existing knowledge of transcriptional regulation …

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Evaluate the quality of your read alignment with GeneQC

RNA-sequencing has replaced gene array and is now the leading technology in gene expression analysis. After a sequencing step, reads need to be mapped to a reference genome. However, this step is not perfect and errors can impact all downstream analyses. To address this issue, Adam McDermaid and colleagues have developed GeneQC, a tool to evaluate the quality of read alignment. Here, he describes GeneQC and its features. Quality control of read alignment One of the main benefits of using modern RNA-Sequencing (RNA-Seq) technology is the more accurate gene expression estimations compared with previous generations of expression data, such as the microarray. However, numerous issues can result in the possibility that an RNA-Seq read can be mapped to multiple locations …

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Visualize differential gene expression with ViDGER

Differential gene expression (DGE) analysis is one of the most common applications of RNA-seq data. This process allows for the elucidation of differentially expressed genes (DEGs) across two or more conditions. Interpretation of the DGE results can be non-intuitive and time-consuming due to the variety of formats based on the tool of choice and the numerous pieces of information provided in these results files. To address this challenge, Adam McDermaid and his colleagues from South Dakota State University have developed ViDGER. Here, they present their tool and its main features.   Interpretation and Visualization of Differential Gene Expression through ViDGER   One of the most straightforward ways to gain a broader understanding of the tens-of-thousands of pieces of information generated …

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Your top 3 RNA-seq quantification and differential expression tools

RNA-sequencing is progressively replacing microarrays for the study of transcriptomes, and comparison of gene expression. One advantage of this technique is the ability to identify and quantify the expression of isoforms and unknown transcripts.   To help you perform your experiments in the best conditions, we are closing our series of surveys on RNA-sequencing by asking OMICtools members to choose their favorite quantification and differential expression tools. RNA quantification and differential expression While microarrays produce a numerical estimate of the relative expression of genes across the genome, RNA-sequencing experiments rely on read-count distributions. After mapping reads to a reference genome, the expression level for each gene or isoform are estimated and normalized, and finally differentially-expressed genes are identified using statistical methods. …

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Your top 3 RNA-seq read alignment tools

RNA-sequencing (RNA-seq) is currently the leading technology for transcriptome analysis. RNA-seq has a wide range of applications, from the study of alternative gene splicing, post-transcriptional modifications, to comparison of relative gene expression between different biological samples.   To help you perform your RNA-seq experiments in the best conditions, we are continuing our series of surveys by asking you to choose your favorite analysis tools step by step. Mapping reads to reference genome  After a first step of quality control (previous blog post here), the next step in the analysis of your RNA-seq experiment is alignment of reads to a reference genome or a transcriptome database.   There are two types of aligners: Splice-unaware and splice-aware. Splice-unaware aligners are able to align continuous reads to a …

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Link splice-isoform expression to cancer metabolism with GEMsplice

Metabolic models rely on genes and proteins expression to estimate or predict a metabolic cell phenotype. In the case of cancer, it is now admitted that metabolism dysregulations play a crucial role in cancer onset and proliferation. However, most metabolic models only rely on gene expression, and do not account for splice-isoform expression and/or alteration.   To solve this gap, Claudio Angione developed GEMsplice, a desktop application that allows to link splice-isoform gene expression data to cancer metabolism. Here, he describes the features and benefits of GEMsplice. Solving the gap in cancer metabolism models Despite being often perceived as the main contributors to cell fate and physiology, genes alone cannot predict the cellular phenotype. A genome-scale analysis of cancer metabolism captures …

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Towards standardized protocols for microbiome studies

The study of the microbiome – that is, the ensemble of microbial communities living inside us – has become a major application for high-throughput DNA sequencing. Functional changes in the composition of the gut microbiome have been implicated in multiple human diseases. Due to its complexity, the analysis of sequencing data from microbiome study typically involves a lot of different protocols and bioinformatics tools. From sample collection and DNA extraction to sequencing and computational analysis, technical errors and bias can occur at each step, rendering the uniformization of protocols a complex task. To this end, two consortia recently proposed to examine the sources of inter-laboratory variability in various aspects of microbiome data generation. This work was published in the last …

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Your top 3 RNA-seq quality control tools

RNA-sequencing (RNA-seq) is currently the leading technology for transcriptome analysis. RNA-seq has a wide range of applications, from the study of alternative gene splicing, post-transcriptional modifications, to comparison of relative gene expression between different biological samples. To help you prepare and analyse your RNA-seq experiments under the best conditions, we have launched a new series of surveys focused on the best tools for each fundamental step of an RNA-seq experiment. Starting your analysis with quality control The first step in the analysis of an RNA-seq experiment is quality control. This crucial step will ensure that your data are of the best quality to perform the subsequent steps of your analysis. Quality control usually includes sequence quality, sequencing depth, reads duplication rates (clonal reads), …

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