Unlock your biological data

?

Try: RNA sequencing CRISPR Genomic databases DESeq

Transcriptomics

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 …

Read this post
comment
0

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 …

Read this post
comment
0

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. Introduction 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 generation qPCR platforms …

Read this post
comment
0

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 …

Read this post
comment
0

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. …

Read this post
comment
0

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 …

Read this post
comment
0

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 …

Read this post
comment
0

Map functional networks of ncRNAs with circlncRNAnet

Long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) lack protein-coding potential but have nonetheless emerged as key determinants in gene regulation, acting to fine-tune transcriptional and signaling output. These noncoding RNA transcripts are known to affect expression of messenger RNAs (mRNAs) via epigenetic and post-transcriptional regulation. To fully capture, from a network perspective, the functional implications of lncRNAs or circRNAs of interest, Dr. Bertrand Chin-MingTan and his team have implemented an integrative bioinformatics approach to examine in silico the functional networks of non-coding RNAs. Here, they present their web server tool “circlncRNAnet” and discuss its main features. In-depth analyses of non-coding RNA biology The main purpose for implementing this web server is to provide biologists with a user-friendly, “one-stop” web tool …

Read this post
comment
0
< Previous 1 2 Next >

By using OMICtools you acknowledge that you have read and accepted the terms of the end user license agreement.