Transcriptomics

Best bioinformatics software for single-cell RNA sequencing

RNA-sequencing is often performed on well-identified groups of cells thought to be homogeneous. However, quantification of molecular changes is made by estimating the mean value from millions of cells and averaging the signal of individual cells, thus ignoring cell-to-cell heterogeneity. Single-cell RNA-sequencing (scRNAseq) enables to unravel the heterogeneity of cell genotype, phenotype, and function within a given subpopulation.   ScRNA-seq now has a wide variety of applications, and numerous tools were developed to analyze this new kind of sequencing data. To help you perform your experiments in the best conditions, we asked OMICtools members to choose their favorite scRNA-seq analysis tools.   Main applications for scRNA-sequencing   Single-cell RNA sequencing finds its main applications in immunology, cancerology, and the study …

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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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qPCR techniques and analysis software tools

Quantitative real-time polymerase chain reaction (qPCR) is widely used for the detection of specific nucleic acids, measurement of RNA transcript abundance and validation of high-throughput experimental results. While high-throughput sequencing (HTS) has revolutionized the fields of genomics and transcriptomics, qPCR techniques have evolved and can now be performed in a high-throughput fashion. What use for qPCR? qPCR is an easy-to-perform technique to evaluate the relative (or absolute) expression of genes as compared to each other and/or a reference. Traditional qPCR is often used to validate results from gene arrays or HTS, as it is more precise, rapid to perform, and cheap.   Recently, new technologies have been developed to increase the number of genes that can be tested simultaneously. Next …

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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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Best bioinformatics software for RNA-seq quantification and differential expression

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 the OMICtools community to choose the best quantification and differential expression tools.   1. DESeq 2. Limma 3. EdgeR 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 …

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Best bioinformatics software for RNA-seq read alignment

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 the OMICtools community to choose the best analysis tools step by step.   Mapping reads to reference genome  After a first step of quality control, 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 genome …

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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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