Granatum a graphical single-cell RNA-Seq analysis pipeline for genomics scientists, bioRxiv, 2017-02-23

AbstractBackgroundSingle-cell RNA sequencing (scRNA-Seq) is an increasingly popular platform to study heterogeneity at the single-cell level.Computational methods to process scRNA-Seq have limited accessibility to bench scientists as they require significant amounts of bioinformatics skills.ResultsWe have developed Granatum, a web-based scRNA-Seq analysis pipeline to make analysis more broadly accessible to researchers. Without a single line of programming code, users can click through the pipeline, setting parameters and visualizing results via the interactive graphical interface Granatum conveniently walks users through various steps of scRNA-Seq analysis. It has a comprehensive list of modules, including plate merging and batch-effect removal, outlier-sample removal, gene filtering, geneexpression normalization, cell clustering, differential gene expression analysis, pathwayontology enrichment analysis, protein-networ interaction visualization, and pseudo-time cell series construction.ConclusionsGranatum enables broad adoption of scRNA-Seq technology by empowering the bench scientists with an easy-to-use graphical interface for scRNA-Seq data analysis. The package is freely available for research use at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpgarmiregroup.orggranatumapp>httpgarmiregroup.orggranatumapp<jatsext-link>

biorxiv bioinformatics 0-100-users 2017

W2RAP a pipeline for high quality, robust assemblies of large complex genomes from short read data, bioRxiv, 2017-02-23

AbstractProducing high-quality whole-genome shotgun de novo assemblies from plant and animal species with large and complex genomes using low-cost short read sequencing technologies remains a challenge. But when the right sequencing data, with appropriate quality control, is assembled using approaches focused on robustness of the process rather than maximization of a single metric such as the usual contiguity estimators, good quality assemblies with informative value for comparative analyses can be produced. Here we present a complete method described from data generation and qc all the way up to scaffold of complex genomes using Illumina short reads and its application to data from plants and human datasets. We show how to use the w2rap pipeline following a metric-guided approach to produce cost-effective assemblies. The assemblies are highly accurate, provide good coverage of the genome and show good short range contiguity. Our pipeline has already enabled the rapid, cost-effective generation of de novo genome assemblies from large, polyploid crop species with a focus on comparative genomics.Availabilityw2rap is available under MIT license, with some subcomponents under GPL-licenses. A ready-to-run docker with all software pre-requisites and example data is also available.<jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpgithub.combioinfologicsw2rap>httpgithub.combioinfologicsw2rap<jatsext-link><jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpgithub.combioinfologicsw2rap-contigger>httpgithub.combioinfologicsw2rap-contigger<jatsext-link>

biorxiv bioinformatics 0-100-users 2017

mixOmics an R package for ‘omics feature selection and multiple data integration, bioRxiv, 2017-02-15

AbstractThe advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently.We introducemixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a system biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latestmixOmicsintegrative frameworks for the multivariate analyses of ‘omics data available from the package.

biorxiv bioinformatics 100-200-users 2017

 

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