Single-cell RNA sequencing (scRNA-seq) has been used extensively to study cell-specific gene expression in animals, but it has not been widely applied to plants. Here, we describe the use of a commercially available droplet-based microfluidics platform for high-throughput scRNA-seq to obtain single-cell transcriptomes from protoplasts of more than 10,000 Arabidopsis (Arabidopsis thaliana) root cells. We find that all major tissues and developmental stages are represented in this single-cell transcriptome population. Further, distinct subpopulations and rare cell types, including putative quiescent center cells, were identified. A focused analysis of root epidermal cell transcriptomes defined developmental trajectories for individual cells progressing from meristematic through mature stages of root-hair and nonhair cell differentiation. In addition, single-cell transcriptomes were obtained from root epidermis mutants, enabling a comparative analysis of gene expression at single-cell resolution and providing an unprecedented view of the impact of the mutated genes. Overall, this study demonstrates the feasibility and utility of scRNA-seq in plants and provides a first-generation gene expression map of the Arabidopsis root at single-cell resolution.

The analysis of next-generation sequencing data is computationally and statistically challenging because of the massive volume of data and imperfect data quality. We present GotCloud, a pipeline for efficiently detecting and genotyping high-quality variants from large-scale sequencing data. GotCloud automates sequence alignment, sample-level quality control, variant calling, filtering of likely artifacts using machine-learning techniques, and genotype refinement using haplotype information. The pipeline can process thousands of samples in parallel and requires less computational resources than current alternatives. Experiments with whole-genome and exome-targeted sequence data generated by the 1000 Genomes Project show that the pipeline provides effective filtering against false positive variants and high power to detect true variants. Our pipeline has already contributed to variant detection and genotyping in several large-scale sequencing projects, including the 1000 Genomes Project and the NHLBI Exome Sequencing Project. We hope it will now prove useful to many medical sequencing studies.

Summary: A genetic variant can be represented in the Variant Call Format (VCF) in multiple different ways. Inconsistent representation of variants between variant callers and analyses will magnify discrepancies between them and complicate variant filtering and duplicate removal. We present a software tool vt normalize that normalizes representation of genetic variants in the VCF. We formally define variant normalization as the consistent representation of genetic variants in an unambiguous and concise way and derive a simple general algorithm to enforce it. We demonstrate the inconsistent representation of variants across existing sequence analysis tools and show that our tool facilitates integration of diverse variant types and call sets. Availability and implementation: The source code is available for download at http://github.com/atks/vt. More detailed documentation is available at http://genome.sph.umich.edu/wiki/Variant_Normalization. Contact:hmkang@umich.edu Supplementary information: Supplementary data are available at Bioinformatics online.

How genes affect human obesity: Obesity is linked to many human diseases, including diabetes, cancer, and heart disease. There is thus great interest in understanding how genes predispose individuals to, or protect individuals from, obesity. Akbari et al. sequenced more than 600,000 exomes from the United Kingdom, the United States, and Mexico and identified 16 rare coding variants (see the Perspective by Yeo and O'Rahilly). Some of the alleles associated with body mass index (BMI) were brain-expressed G protein–coupled receptors. One variant allele was found in Mexican populations at low frequency and was associated with lower BMI. Deletion of this gene in mice resulted in a resistance to weight gain, suggesting that this gene provides an avenue of study for the prevention or treatment of obesity. Science , abf8683, this issue p. eabf8683 ; see also abh3556, p. 30

Background: Although analysis pipelines have been developed to use RNA-seq to identify long non-coding RNAs (lncRNAs), inference of their biological and pathological relevance remains a challenge. As a result, most transcriptome studies of autoimmune disease have only assessed protein-coding transcripts. Results: We used RNA-seq data from 99 lesional psoriatic, 27 uninvolved psoriatic, and 90 normal skin biopsies, and applied computational approaches to identify and characterize expressed lncRNAs. We detect 2,942 previously annotated and 1,080 novel lncRNAs which are expected to be skin specific. Notably, over 40% of the novel lncRNAs are differentially expressed and the proportions of differentially expressed transcripts among protein-coding mRNAs and previously-annotated lncRNAs are lower in psoriasis lesions versus uninvolved or normal skin. We find that many lncRNAs, in particular those that are differentially expressed, are co-expressed with genes involved in immune related functions, and that novel lncRNAs are enriched for localization in the epidermal differentiation complex. We also identify distinct tissue-specific expression patterns and epigenetic profiles for novel lncRNAs, some of which are shown to be regulated by cytokine treatment in cultured human keratinocytes. Conclusions: Together, our results implicate many lncRNAs in the immunopathogenesis of psoriasis, and our results provide a resource for lncRNA studies in other autoimmune diseases.