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Genomics

Explore gene-expression datasets with the Omics Dashboard

The advent of omics technologies has fostered the generation of a flood of complex, high-resolution datasets, the analysis of which remains a major hurdle and requires conversion into actionable biological knowledge. To address this challenge, Peter Karp and his colleagues from the SRI Bioinformatics Research Group have developed the Omics Dashboard within the BioCyc.org website. Here, they present their tool and its main features. The Omics Dashboard The Dashboard provides a multi-level visual read out of an expression dataset, from the cellular level to the gene level.  The user can probe their data in a fast and intuitive manner to gain a deep understanding of the data at multiple biological levels.   At the highest echelon (Figure 1) the Dashboard provides …

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Virtualize your computing with IaaS

Since the advent of high-throughput sequencing, the number and complexity of biological data has exploded. Though information technology and computers have paralleled this development, researchers rarely meet the computational power and storage capability required to perform the analysis of their data (Ben Langmead and Abhinav Nellore). Infrastructure as a service to the rescue For this, infrastructures as a service (IaaS) have been created to let users run large-scale computing workloads on the cloud – that is, on virtual machines hosted on dedicated infrastructures – and to stock up to exabits of data.   According to the National Institute of Standards and Technology, IaaS can be described as:   The capability provided to the consumer is to provision processing, storage, networks, …

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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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Promoting standards in biological research

Most research advances are built upon pre-existing data and findings. This paradigm implies that researchers must be able to reproduce data on which they base their own research. Yet a number of promising pre-clinical researches do not go into clinics because of the inability to reproduce the work (Freedman and Inglese).   For this, it has become essential to set standards and good practices in life sciences research. Initiatives promoting standards in biology A number of initiatives have been created to set and promote standards in biology research. Some society are proposing standards to cover all areas of basic research and clinics, while others are focusing on specific research fields. Here are some of the most advanced organizations which develop …

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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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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 …

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Predict transcription factor binding from DNase footprints with Sasquatch

Predicting the impact of regulatory sequence variation on transcription factor (TF) binding is an important challenge as the vast majority of disease associated SNPs are found in the non-coding genome (Vaquerizas, Kummerfeld, Teichmann, & Luscombe, 2009)⁠. Most existing approaches rely on large catalogs of cell type and TF specific functional annotations. As only a minority of TFs is well characterized (Maurano et al., 2015; Rockman & Kruglyak, 2006)⁠, identifying the relevant factors and probing them in the appropriate cell types represents a major limitation of TF centric approaches. With this in mind, Ron Schwessinger and colleagues from University of Oxford have developed the Sasquatch tool to use DNase footprinting data to estimate and visualize the effects of non-coding variants on …

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