Peroxisome proliferator-activated receptor ␥ (PPAR␥) is a nuclear receptor that mediates the antidiabetic effects of thiazolidinediones. PPAR␥ is present in adipose tissue and becomes elevated in fatty livers, but the roles of specific tissues in thiazolidinedione actions are unclear. We studied the function of liver PPAR␥ in both lipoatrophic A-ZIP/F-1 (AZIP) and wild type mice. In AZIP mice, ablation of liver PPAR␥ reduced the hepatic steatosis but worsened the hyperlipidemia, triglyceride clearance, and muscle insulin resistance. Inactivation of AZIP liver PPAR␥ also abolished the hypoglycemic and hypolipidemic effects of rosiglitazone, demonstrating that, in the absence of adipose tissue, the liver is a primary and major site of thiazolidinedione action. In contrast, rosiglitazone remained effective in non-lipoatrophic mice lacking liver PPAR␥, suggesting that adipose tissue is the major site of thiazolidinedione action in typical mice with adipose tissue. Interestingly, mice without liver PPAR␥, but with adipose tissue, developed relative fat intolerance, increased adiposity, hyperlipidemia, and insulin resistance. Thus, liver PPAR␥ regulates triglyceride homeostasis, contributing to hepatic steatosis, but protecting other tissues from triglyceride accumulation and insulin resistance.

Correct annotation metadata is critical for reproducible and accurate RNA-seq analysis. When files are shared publicly or among collaborators with incorrect or missing annotation metadata, it becomes difficult or impossible to reproduce bioinformatic analyses from raw data. It also makes it more difficult to locate the transcriptomic features, such as transcripts or genes, in their proper genomic context, which is necessary for overlapping expression data with other datasets. We provide a solution in the form of an R/Bioconductor package tximeta that performs numerous annotation and metadata gathering tasks automatically on behalf of users during the import of transcript quantification files. The correct reference transcriptome is identified via a hashed checksum stored in the quantification output, and key transcript databases are downloaded and cached locally. The computational paradigm of automatically adding annotation metadata based on reference sequence checksums can greatly facilitate genomic workflows, by helping to reduce overhead during bioinformatic analyses, preventing costly bioinformatic mistakes, and promoting computational reproducibility. The tximeta package is available at https://bioconductor.org/packages/tximeta.

Background De novo transcriptome assemblies are required prior to analyzing RNA sequencing data from a species without an existing reference genome or transcriptome. Despite the prevalence of transcriptomic studies, the effects of using different workflows, or “pipelines," on the resulting assemblies are poorly understood. Here, a pipeline was programmatically automated and used to assemble and annotate raw transcriptomic short-read data collected as part of the Marine Microbial Eukaryotic Transcriptome Sequencing Project. The resulting transcriptome assemblies were evaluated and compared against assemblies that were previously generated with a different pipeline developed by the National Center for Genome Research. Results New transcriptome assemblies contained the majority of previous contigs as well as new content. On average, 7.8% of the annotated contigs in the new assemblies were novel gene names not found in the previous assemblies. Taxonomic trends were observed in the assembly metrics. Assemblies from the Dinoflagellata showed a higher number of contigs and unique k -mers than transcriptomes from other phyla, while assemblies from Ciliophora had a lower percentage of open reading frames compared to other phyla. Conclusions Given current bioinformatics approaches, there is no single “best” reference transcriptome for a particular set of raw data. As the optimum transcriptome is a moving target, improving (or not) with new tools and approaches, automated and programmable pipelines are invaluable for managing the computationally intensive tasks required for re-processing large sets of samples with revised pipelines and ensuring a common evaluation workflow is applied to all samples. Thus, re-assembling existing data with new tools using automated and programmable pipelines may yield more accurate identification of taxon-specific trends across samples in addition to novel and useful products for the community.

Background De novo transcriptome assemblies are required prior to analyzing RNAseq data from a species without an existing reference genome or transcriptome. Despite the prevalence of transcriptomic studies, the e ects of using di erent work ows, or "pipelines", on the resulting assemblies are poorly understood. Here, a pipeline was programmatically automated and used to assemble and annotate raw transcriptomic short read data collected by the Marine Microbial Eukaryotic Transcriptome Sequencing Project (MMETSP). The resulting transcriptome assemblies were evaluated and compared against assemblies that were previously generated with a di erent pipeline developed by the National Center for Genome Research (NCGR). Results New transcriptome assemblies contained the majority of previous contigs as well as new content. On average, 7.8% of the annotated contigs in the new assemblies were novel gene names not found in the previous assemblies. Taxonomic trends were observed in the assembly metrics, with assemblies from the Dino agellata and Ciliophora phyla showing a higher percentage of open reading frames and number of contigs than transcriptomes from other phyla. Conclusions Given current bioinformatics approaches, there is no single 'best' reference transcriptome for a particular set of raw data. As the optimum transcriptome is a moving target, improving (or not) with new tools and approaches, automated and programmable pipelines are invaluable for managing the computationally-intensive tasks required for re-processing large sets of samples with revised pipelines and ensuring a common evaluation work ow is applied to all samples. Thus, re-assembling existing data with new tools using automated and programmable pipelines may yield more accurate identi cation of taxon-speci c trends across samples in addition to novel and useful products for the community.• Re-assembly with new tools can yield new results • Automated and programmable pipelines can be used to process arbitrarily many samples. • Analyzing many samples using a common pipeline identi es taxon-speci c trends.