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

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

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

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

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

Additional file 4: Figure S4. of Massively parallel nanowell-based single-cell gene expression profiling

Heatmaps illustrating the total number of detected transcripts for each well selected for downstream processing. Data are for three microchips, each with 5184 wells arranged in a 72 × 72 square layout. Microchips 72,618 and 72,598 were used for profiling human and mouse cell lines (names of cell lines indicated in the plot). Microchip 72,625 was used for profiling pancreatic islets. For microchips with multiple dispensed samples, the dispense area for each sample is indicated. (PDF 93 kb

Additional file 5: Figure S5. of Massively parallel nanowell-based single-cell gene expression profiling

Percentage of mitochondrial transcripts plotted against total number of detected transcripts for mouse Ba/F3 cells (a), human cell lines (b), mouse cell lines (c), and pancreatic islets (d). Dashed lines indicate the minimum number of detected transcripts required as a cell QC filter for each data set. (PDF 409 kb

Additional file 2: Figure S2. of Massively parallel nanowell-based single-cell gene expression profiling

Checkerboard assay. (a) Image of a microchip where the right half contains negative control master mix (NTC wells, n = 2520) and the left half contains lambda DNA master mix master (Positive wells, n = 1024) and negative control master mix (Test wells, n = 1496) in a checkerboard pattern. (b) Number of Test wells with signal, number of NTC wells with signal, and calculated misalignment rate for 11 MSNDs and 19 microchips. (PDF 1288 kb