Paulson, J. | Search bioRxiv

bioRxiv2018

metagenomeFeatures: An R package for working with 16S rRNA reference databases and marker-gene survey feature data.

We developed the metagenomeFeatures R Bioconductor package along with annotation packages for the three primary 16S rRNA databases (Greengenes, RDP, and SILVA) to facilitate working with 16S rRNA sequence databases and marker-gene survey feature data. The metagenomeFeatures package defines two classes, MgDb for working with 16S rRNA sequence databases, and mgFeatures for working with marker-gene survey feature data. The associated annotation packages provide a consistent interface to the different 16S rRNA databases facilitating database comparison and exploration. The mgFeatures represents a crucial step in the development of a common data structure for working with 16S marker-gene survey data in R.\n\nAvailabilityhttps://bioconductor.org/packages/release/bioc/html/metagenomeFeatures.html\n\nContactnolson@nist.gov

bioinformatics↗

bioRxiv2016

Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data

Although ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data - critical first steps for any subsequent analysis. We find analysis of large RNA-Seq data sets requires both careful quality control and that one account for sparsity due to the heterogeneity intrinsic in multi-group studies. An R package instantiating our method for large-scale RNA-Seq normalization and preprocessing, YARN, is available at bioconductor.org/packages/yarn.\n\nHighlightsO_LIOverview of assumptions used in preprocessing and normalization\nC_LIO_LIPipeline for preprocessing, quality control, and normalization of large heterogeneous data\nC_LIO_LIA Bioconductor package for the YARN pipeline and easy manipulation of count data\nC_LIO_LIPreprocessed GTEx data set using the YARN pipeline available as a resource\nC_LI

bioinformatics↗