Supersymmetric dS/CFT

We put forward new explicit realisations of dS/CFT that relate ${\cal N}=2$ supersymmetric Euclidean vector models with reversed spin-statistics in three dimensions to specific supersymmetric Vasiliev theories in four-dimensional de Sitter space. The partition function of the free supersymmetric vector model deformed by a range of low spin deformations that preserve supersymmetry appears to specify a well-defined wave function with asymptotic de Sitter boundary conditions in the bulk. In particular we find the wave function is globally peaked at undeformed de Sitter space, with a low amplitude for strong deformations. This suggests that supersymmetric de Sitter space is stable in higher-spin gravity and in particular free from ghosts. We speculate this is a limiting case of the de Sitter realizations in exotic string theories.Comment: V2: references and comments added, typos corrected, version published in JHEP; 27 pages, 3 figures, 1 tabl

Venn diagram showing the overlap in the SNP calls made using data from the three sequencing technologies.

<p>We display the sizes of each of the seven categories of overlaps among the variant calls in the three technologies. (a) depicts the overlaps when all substitution calls are used, (b) depicts the overlaps when all calls from Illumina and SOLiD are used but only the high-confidence subset of the 454 dataset is used, and (c) depicts the overlaps when only the variants in the uniquely alignable regions of the reference sequence are used.</p

Variation of coverage with GC content in the three sequencing technologies.

<p>The red line shows the mean coverage across the whole genome. Each point on the plot reflects the mean coverage and fraction of GC content in 50 kbp non-overlapping window. The y-axis shows the coverage whereas the x-axis shows the fraction of C, G nucleotides in the window. This does not include secondary alignments and potential PCR duplicates.</p

Discrepant SNP calls from each platform.

<p>The categories on the x-axis are (1) no coverage at location (2) not enough coverage at location (3) more than expected coverage (4) alternate allele not seen (5) alternate allele seen just once (6) too many SNPs around location (7) close to a high-quality indel (8) low RMS mapping quality (9) low SNP quality. The y-axis depicts the number of locations (frequency) in each category. a) Comparison of SOLiD generated sequences with other sequences based on SNP calls and alignments. (i) SNPs called using 454 and Illumina sequences but not called using SOLiD reads. (ii) SNPs called only by SOLiD sequences. We investigate why they were not called using Illumina alignments. (iii) SNPs called only by SOLiD sequences. We investigate why they were not called using 454 alignments. b) Comparison of Illumina generated sequences with other sequences based on SNP calls and alignments. (i) SNPs called using 454 and SOLiD reads but not called using Illumina reads. (ii) SNPs called only by Illumina sequences. We investigate why they were not called using SOLiD alignments. (iii) SNPs called only by SOLiD sequences. We investigate why they were not called using 454 alignments. c) Comparison of 454 generated sequences with other sequences based on SNP calls and alignments. (i) SNPs called using SOLiD and Illumina reads but not called using 454 reads. (ii) SNPs called only by 454 sequences. We investigate why they were not called using SOLiD alignments. (iii) SNPs called only by 454 sequences. We investigate why they were not called using Illumina alignments.</p

Depth of coverage distribution for the three platforms.

<p>The y-axis indicates the fraction of the bases in the reference sequence that has a particular coverage. This does not include secondary alignments and potential PCR duplicates. The dashed lighter curves depict the coverage distribution as calculated using a Poisson model for each sequencing technology.</p

Sequencing and alignment statistics. Coverage is calculated with and without the putative PCR duplicates.

<p>The number of aligned reads includes the duplicate reads.</p

SNP Validation using Mass spectroscopy.

<p>Validation of 300 putative SNP locations from each of the six sets of SNP calls in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055089#pone-0055089-g003" target="_blank">Figure 3a</a>, where not all three technologies agree on the computed genotype. The categories on x-axis are “454” (SNPs called by 454 only), “Illumina” (SNPs called by Illumina only), “SOLiD” (SNPs called by SOLiD only), “454 & Illumina” (SNPs called by 454 and Illumina), “454 & SOLiD” (SNPs called by 454 and SOLiD), “Illumina & SOLiD” (SNPs called by Illumina and SOLiD). The color categories include “Primer Failure” (Primer extension failure), “Assay Failure” (Assay Failure), “Validated” and “Not Validated”.</p

Prediction and Quantification of Splice Events from RNA-Seq Data

<div><p>Analysis of splice variants from short read RNA-seq data remains a challenging problem. Here we present a novel method for the genome-guided prediction and quantification of splice events from RNA-seq data, which enables the analysis of unannotated and complex splice events. Splice junctions and exons are predicted from reads mapped to a reference genome and are assembled into a genome-wide splice graph. Splice events are identified recursively from the graph and are quantified locally based on reads extending across the start or end of each splice variant. We assess prediction accuracy based on simulated and real RNA-seq data, and illustrate how different read aligners (GSNAP, HISAT2, STAR, TopHat2) affect prediction results. We validate our approach for quantification based on simulated data, and compare local estimates of relative splice variant usage with those from other methods (MISO, Cufflinks) based on simulated and real RNA-seq data. In a proof-of-concept study of splice variants in 16 normal human tissues (Illumina Body Map 2.0) we identify 249 internal exons that belong to known genes but are not related to annotated exons. Using independent RNA samples from 14 matched normal human tissues, we validate 9/9 of these exons by RT-PCR and 216/249 by paired-end RNA-seq (2 x 250 bp). These results indicate that <i>de novo</i> prediction of splice variants remains beneficial even in well-studied systems. An implementation of our method is freely available as an R/Bioconductor package .</p></div

Splice graph and analysis workflow.

<p>A) Splice graph derived from four annotated transcript isoforms for gene <i>SLC39A14</i>. B) Schematic of analysis workflow. Discrete transcript features (splice junctions and exons) are predicted from RNA-seq reads mapped to a reference genome and are assembled into a splice graph. Splice events, characterized by two or more splice variants, are identified from the graph and estimates for relative variant usage Ψ are obtained based on reads spanning event boundaries.</p

Estimates of relative usage Ψ for candidate novel exons across normal human tissues and validation by RT-PCR.

<p>A-I) Splice graph of predicted events. Grey and red indicate annotated and unannotated transcript features, respectively. Introns are not drawn to scale. PhastCons scores indicating evolutionary conservation are shown in green below each splice graph. Heatmaps illustrate estimates for variant frequency Ψ. In each panel, heatmaps for 14 tissues are based on RNA-seq data from the Illumina Body Map (bottom) and validation samples (top). RT-PCR results were obtained with primers targeting flanking exons.</p