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Test your microbiome analysis pipeline with PLuMA

Study of the microbiome and metagenomics are actively developing areas of research. In the meantime, analyzing microbiome data has become increasingly complex, with multiple tools required to be run sequentially. Running these so-called “pipelines” of tools can be challenging because of the diversity of coding languages and compatibility issues.   To overcome this problem, Trevor Cickovski and Giri Narasimhan from the Bioinformatics Research Group of the Florida International University have developed PLuMA, a Plugin-Based Microbiome Analysis lightweight back end pipeline that supports multiple dynamically loaded plugin extensions. Here, they describe their tool and its main features.   Plugin-Based Microbiome Analysis   If you are an algorithm developer who wants to prototype, test and debug a new pipeline stage in your …

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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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Explore molecular interactions across cancer types with CancerNet

Cancer is a complex disease characterized by a large number of molecular interaction alterations. Knowledge of molecular interactions is quite useful for discovering the functions of molecules and the processes they are involved in. However, there is little systematic insight into the nature and scale of the potential interactions in human cancers.     To solve this gap, Dr. Ming Chen and his colleagues from the Institute of Bioinformatics in Zhejiang University constructed CancerNet, a database for decoding multilevel molecular interactions across diverse cancer types. Here, they describe the features of CancerNet. The CancerNet database   CancerNet aims to provide cancer-specific molecular interaction networks across multiple cancer types. It includes 33 human cancer types. The interactions contain protein-protein interactions (PPIs), miRNA–target …

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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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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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Your top 3 MS-based proteomics analysis tools

Proteomics are the next step after genomics and transcriptomics to study biological systems. However, analyzing the proteome is much more difficult than the genome or transcriptome, because each cell expresses its own set of proteins. Mass spectrometry (MS) has emerged as the most important and popular tool to identify, characterize, and quantify proteins and their post-translational modifications with high throughput and on a large scale (Zhang et al.). To help you perform your experiments in the best conditions, we asked OMICtools members to choose their favorite MS-based untargeted proteomics analysis tools. LC-MS based untargeted proteomics Two-dimension liquid-chromatography (LC) coupled with mass-spectrometry (LC-MS) is the leading technology for high-throughput proteomics. LC is used to separate proteins from different samples in parallel, and is …

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Finding scientific resources with semantic search engines

  The exponential growth of the number of biological studies over the past decade has created an enormous challenge to make effective use of the accumulated information. Owing to this size, simple web-style text search engines are often not yielding the best results and a lot of important information remains buried in the masses of text (Doms, 2005). Semantic technologies have been introduced for better question answering and faster literature exploration (Doms, 2008). They use ontologies to give an overview over large query results and guide scientists for discoveries. Starting your research in biology but don’t know which platform to use to find impactful studies? Here is a list of useful resources you will love using to retrieve articles and …

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