MOTIVATION: The rise in the number of genotyped ancient individuals provides an opportunity to estimate population admixture models for many populations. However, in models describing modern populations as mixtures of ancient ones, it is typically difficult to estimate the model mixing coefficients and to evaluate its fit to the data. RESULTS: We present LINADMIX, designed to tackle this problem by solving a constrained linear model when both the ancient and the modern genotypes are represented in a low-dimensional space. LINADMIX estimates the mixing coefficients and their standard errors, and computes a P-value for testing the model fit to the data. We quantified the performance of LINADMIX using an extensive set of simulated studies. We show that LINADMIX can accurately estimate admixture coefficients, and is robust to factors such as population size, genetic drift, proportion of missing data and various types of model misspecification. AVAILABILITY AND IMPLEMENTATION: LINADMIX is available as a python code at https://github.com/swidler/linadmix. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: It is not fully understood how a termination codon is recognized as premature (PTC) by the nonsense-mediated decay (NMD) machinery. This is particularly true for transcripts lacking an exon junction complex (EJC) along their 3' untranslated region (3'UTR), and thus degrade through the EJC-independent NMD pathway. RESULTS: Here, we analyzed data of transcript stability change following NMD repression and identified over 200 EJC-independent NMD-targets. We examined many features characterizing these transcripts, and compared them to NMD-insensitive transcripts, as well as to a group of transcripts that are destabilized following NMD repression (destabilized transcripts). CONCLUSIONS: We found that none of the known NMD-triggering features, such as the presence of upstream open reading frames, significantly characterizes EJC-independent NMD-targets. Instead, we saw that NMD-targets are strongly enriched with G nucleotides upstream of the termination codon, and even more so along their 3'UTR. We suggest that high G content around the termination codon impedes translation termination as a result of mRNA folding, thus triggering NMD. We also suggest that high G content in the 3'UTR helps to activate NMD by allowing for the accumulation of UPF1, or other NMD-promoting proteins, along the 3'UTR.

The serotonin 2C receptor (5-HT(2C)R)-a key regulator of diverse neurological processes-exhibits functional variability derived from editing of its pre-mRNA by site-specific adenosine deamination (A-to-I pre-mRNA editing) in five distinct sites. Here we describe a statistical technique that was developed for analysis of the dependencies among the editing states of the five sites. The statistical significance of the observed correlations was estimated by comparing editing patterns in multiple individuals. For both human and rat 5-HT(2C)R, the editing states of the physically proximal sites A and B were found to be strongly dependent. In contrast, the editing states of sites C and D, which are also physically close, seem not to be directly dependent but instead are linked through the dependencies on sites A and B, respectively. We observed pronounced differences between the editing patterns in humans and rats: in humans site A is the key determinant of the editing state of the other sites, whereas in rats this role belongs to site B. The structure of the dependencies among the editing sites is notably simpler in rats than it is in humans implying more complex regulation of 5-HT(2C)R editing and, by inference, function in the human brain. Thus, exhaustive statistical analysis of the 5-HT(2C)R editing patterns indicates that the editing state of sites A and B is the primary determinant of the editing states of the other three sites, and hence the overall editing pattern. Taken together, these findings allow us to propose a mechanistic model of concerted action of ADAR1 and ADAR2 in 5-HT(2C)R editing. Statistical approach developed here can be applied to other cases of interdependencies among modification sites in RNA and proteins.

Although the family of genes encoding for olfactory receptors was identified more than 15 years ago, the difficulty of functionally expressing these receptors in an heterologous system has, with only some exceptions, rendered the receptive range of given olfactory receptors largely unknown. Furthermore, even when successfully expressed, the task of probing such a receptor with thousands of odors/ligands remains daunting. Here we provide proof of concept for a solution to this problem. Using computational methods, we tune an electronic nose to the receptive range of an olfactory receptor. We then use this electronic nose to predict the receptors' response to other odorants. Our method can be used to identify the receptive range of olfactory receptors, and can also be applied to other questions involving receptor-ligand interactions in non-olfactory settings.

We present an algorithm for drawing directed graphs which is based on rapidly solving a unique one-dimensional optimization problem for each of the axes. The algorithm results in a clear description of the hierarchy structure of the graph. Nodes are not restricted to lie on fixed horizontal layers, resulting in layouts that convey the symmetries of the graph very naturally. The algorithm can be applied without change to cyclic or acyclic digraphs and even to graphs containing both directed and undirected edges. We also derive a hierarchy index from the input digraph, which quantitatively measures its amount of hierarchy.

We present a novel family of data-driven linear transformations, aimed at finding low-dimensional embeddings of multivariate data, in a way that optimally preserves the structure of the data. The well-studied PCA and Fisher's LDA are shown to be special members in this family of transformations, and we demonstrate how to generalize these two methods such as to enhance their performance. Furthermore, our technique is the only one, to the best of our knowledge, that reflects in the resulting embedding both the data coordinates and pairwise relationships between the data elements. Even more so, when information on the clustering (labeling) decomposition of the data is known, this information can also be integrated in the linear transformation, resulting in embeddings that clearly show the separation between the clusters, as well as their internal structure. All of this makes our technique very flexible and powerful, and lets us cope with kinds of data that other techniques fail to describe properly.