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A number of algorithms exist for analysing RNA-sequencing data to infer profiles of differential gene expression. Problems inherent in building algorithms around statistical models of over dispersed count data are formidable and frequently lead to non-uniform p-value distributions for null-hypothesis data and to inaccurate estimates of false discovery rates (FDRs). This can lead to an inaccurate measure of significance and loss of power to detect differential expression. We use synthetic and real biological data to assess the ability of several available R packages to accurately estimate FDRs. The packages surveyed are based on statistical models of overdispersed Poisson data and include edgeR, DESeq, DESeq2, PoissonSeq and QuasiSeq. Also tested is an add-on package to edgeR and DESeq which we introduce called Polyfit. Polyfit aims to address the problem of a non-uniform null p-value distribution for two-class datasets by adapting the Storey-Tibshirani procedure. We find the best performing package in the sense that it achieves a low FDR which is accurately estimated over the full range of p-values, albeit with a very slow run time, is the QLSpline implementation of QuasiSeq. This finding holds provided the number of biological replicates in each condition is at least 4. The next best performing packages are edgeR and DESeq2. When the number of biological replicates is sufficiently high, and within a range accessible to multiplexed experimental designs, the Polyfit extension improves the performance DESeq (for approximately 6 or more replicates per condition), making its performance comparable with that of edgeR and DESeq2 in our tests with synthetic data.

The D(2) statistic, defined as the number of matches of words of some pre-specified length k, is a computationally fast alignment-free measure of biological sequence similarity. However there is some debate about its suitability for this purpose as the variability in D(2) may be dominated by the terms that reflect the noise in each of the single sequences only. We examine the extent of the problem and the effectiveness of overcoming it by using two mean-centred variants of this statistic, D(2)* and D(2c). We conclude that all three statistics are potentially useful measures of sequence similarity, for which reasonably accurate p-values can be estimated under a null hypothesis of sequences composed of identically and independently distributed letters. We show that D(2) and D(2)c, and to a somewhat lesser extent D(2)*, perform well in tests to classify moderate length query sequences as putative cis-regulatory modules.

The cause of common polygenic autoimmune diseases is not understood because of genetic and cellular complexity. Here, we pinpoint the action of a subset of autoimmune susceptibility loci in the NOD mouse strain linked to D1mit181, D2mit490, D7mit101, and D15mit229, which cause a generalized resistance to thymic deletion in vivo that applies equally to Aire-induced organ-specific gene products in the thymic medulla and to systemic antigens expressed at high levels throughout the thymus and affects CD4(+), CD4(+)8(+), and CD4(+)25(+) thymocytes. Resistance to thymic deletion does not reflect a general deficit in TCR signaling to calcineurin- or ERK-induced genes, imbalance in constitutive regulators of apoptosis, nor excessive signaling to prosurvival genes but is distinguished by failure to induce the proapoptotic gene and protein, Bim, during in vivo encounter with high-avidity autoantigen. These findings establish defects in thymic deletion and Bim induction as a key mechanism in the pathogenesis of autoimmunity.

BACKGROUND The matrix metalloproteinases (MMP) are a family of proteolytic enzymes involved in facilitating cancer metastasis. Protease-activated receptors (PARs) have previously been shown to be involved in pathways of MMP upregulation by tumor cells. METHODS Two androgen independent prostate cancer cell lines, PC3 and DU-145, and one androgen dependent prostate cancer line LNCaP, were investigated. PAR expression was detected using RT-PCR and immunofluorochemistry (IFC) techniques. MMP activity assays were used to quantify the levels of MMP-2 and -9 on all three prostate cell lines after PAR activation. RESULTS RT-PCR and IFC showed the presence of PAR-1 and PAR-2 in all cell lines investigated, only LNCaP showed PAR-3 and PAR-4 expression. Increased levels of MMP-2 and MMP-9 activity, up to sevenfold depending on prostate cancer cell line, following PAR activation by specific PAR peptides was shown. CONCLUSION Preliminary studies show the activation of PAR-1 or PAR-2 produced increased levels of MMP-2 and MMP-9 activity in prostate cancer cell lines, indicating their potential role in the metastasis of prostate cancer cells. © 2004 Wiley-Liss, Inc.

This article discusses certain aspects of determining heritability estimates from the genetic analyses of data. First, a brief review is given of the major statistical techniques for fitting models to quantitative family data. Then, by reference to a general schematic framework, the basic models that have been proposed for such data will be described. Some new environmental models are also developed. Finally, by reference to some data sets, the interactive approach to data analysis is proposed, advocating the determination of adequacy of fit of all reasonable models to available data.