High-throughput sequencing of polyA+ RNA (RNA-Seq) in human cancer shows remarkable potential to identify both novel markers of disease and uncharacterized aspects of tumor biology, particularly non-coding RNA (ncRNA) species. We employed RNA-Seq on a cohort of 102 prostate tissues and cells lines and performed ab initio transcriptome assembly to discover unannotated ncRNAs. We nominated 121 such Prostate Cancer Associated Transcripts (PCATs) with cancer-specific expression patterns. Among these, we characterized PCAT-1 as a novel prostate-specific regulator of cell proliferation and target of the Polycomb Repressive Complex 2 (PRC2). We further found that high PCAT-1 and PRC2 expression stratified patient tissues into molecular subtypes distinguished by expression signatures of PCAT-1-repressed target genes. Taken together, the findings presented herein identify PCAT-1 as a novel transcriptional repressor implicated in subset of prostate cancer patients. These findings establish the utility of RNA-Seq to identify disease-associated ncRNAs that may improve the stratification of cancer subtypes.

Recall the data generating equation ( 1), and assume that U ∈ R n is an absolutely continuous random vector with a joint density f U (u), defined with respect to the Lebesgue measure on R n , continuous on its support U. We need the following assumptions.Assumption A.1. The function G has continuous partial derivatives with respect to all variables θ j , j = 1, . . . , p and u i , i = 1, . . . n.Assumption A.2. For each y and θ there is at most one u ∈ U so that y = G(u, θ). For the observed data y there is a θ and u ∈ U so that y = G(u, θ). Additionally, the determinant of the n × n Jacobian matrix det d du G(u, θ) = 0 for all θ ∈ Θ and u ∈ U. Assumption A.3. The n × p Jacobian matrix d dθ G(U , θ) is of rank p.For Part (iii) of Theorem 1 we will also need the following assumption.

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A likelihood ratio (LR) system is defined as the entire pipeline of the measurement and interpretation processes where probabilistic genotyping software (PGS) is a piece of the whole LR system. To gain understanding on how two LR systems perform, a total of 154 two-person, 147 three-person, and 127 four-person mixture profiles of varying DNA quality, DNA quantity, and mixture ratios were obtained from the filtered (.CSV) files of the GlobalFiler 29 cycles 15s PROVEDIt dataset and deconvolved in two independently developed fully continuous programs, STRmix v2.6 and EuroForMix v2.1.0. Various parameters were set in each software and LR computations obtained from the two software were based on same/fixed EPG features, same pair of propositions, number of contributors, theta, and population allele frequencies. The ability of each LR system to discriminate between contributor (H1-true) and non-contributor (H2-true) scenarios was evaluated qualitatively and quantitatively. Differences in the numeric LR values and their corresponding verbal classifications between the two LR systems were compared. The magnitude of the differences in the assigned LRs and the potential explanations for the observed differences greater than or equal to 3 on the log10 scale were described. Cases of LR < 1 for H1-true tests and LR > 1 for H2-true tests were also discussed. Our intent is to demonstrate the value of using a publicly available ground truth known mixture dataset to assess discrimination performance of any LR system and show the steps used to understand similarities and differences between different LR systems. We share our observations with the forensic community and describe how examining more than one PGS with similar discrimination power can be beneficial, help analysts compare interpretation especially with low-template profiles or minor contributor cases, and be a potential additional diagnostic check even if software in use does contain certain diagnostic statistics as part of the output.

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Background: Currently, most tests of differential gene expression using Affymetrix expression array data are performed using expression summary values representing each probe set on a microarray. Recently testing methods have been proposed which incorporate probe level information. We propose a new approach that uses Fisher's method of combining evidence from multiple sources of information. Specifically, we combine p-values from probe level tests of significance.