Dipeptide repeat peptides on the attack: Certain neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), are associated with expanded dipeptides translated from RNA transcripts of disease-associated genes (see the Perspective by West and Gitler). Kwon et al. show that the peptides encoded by the expanded repeats in the C9orf72 gene interfere with the way cells make RNA and kill cells. These effects may account for how this genetic form of ALS causes disease. Working in Drosophila , Mizielinska et al. aimed to distinguish between the effects of repeat-containing RNAs and the dipeptide repeat peptides that they encode. The findings provide evidence that dipeptide repeat proteins can cause toxicity directly. Science , this issue p. 1139 and p. 1192 ; see also p. 1118

CLIP-seq is widely used to study genome-wide interactions between RNA-binding proteins and RNAs. However, there are few tools available to analyze CLIP-seq data, thus creating a bottleneck to the implementation of this methodology. Here, we present PIPE-CLIP, a Galaxy framework-based comprehensive online pipeline for reliable analysis of data generated by three types of CLIP-seq protocol: HITS-CLIP, PAR-CLIP and iCLIP. PIPE-CLIP provides both data processing and statistical analysis to determine candidate cross-linking regions, which are comparable to those regions identified from the original studies or using existing computational tools. PIPE-CLIP is available at http://pipeclip.qbrc.org/.

Co-expression analysis is widely used to predict gene function and to identify functionally related gene sets. However, co-expression analysis using human cancer transcriptomic data is confounded by somatic copy number alterations (SCNA), which produce co-expression signatures based on physical proximity rather than biological function. To better understand gene–gene co-expression based on biological regulation but not SCNA, we describe a method termed “Genomic Regression Analysis of Coordinated Expression” (GRACE) to adjust for the effect of SCNA in co-expression analysis. The results from analyses of TCGA, CCLE, and NCI60 data sets show that GRACE can improve our understanding of how a transcriptional network is re-wired in cancer. A user-friendly web database populated with data sets from The Cancer Genome Atlas (TCGA) is provided to allow customized query.

Particle filters have been widely used for online filtering problems in state–space models (SSMs). The current available proposal distributions depend either only on the state dynamics, or only on the observation, or on both sources of information but are not available for general SSMs. In this article, we develop a new particle filtering algorithm, called the augmented particle filter (APF), for online filtering problems in SSMs. The APF combines two sets of particles from the observation equation and the state equation, and the state space is augmented to facilitate the weight computation. Theoretical justification of the APF is provided, and the connection between the APF and the optimal particle filter (OPF) in some special SSMs is investigated. The APF shares similar properties as the OPF, but the APF can be applied to a much wider range of models than the OPF. Simulation studies show that the APF performs similarly to or better than the OPF when the OPF is available, and the APF can perform better than other filtering algorithms in the literature when the OPF is not available.

This paper formally addresses the asymptotic convergence of the ensemble Kalman filter (EnKF), a state estimation procedure that, when combined with a technique called localization, provides computationally tractable solutions to large-dimensional state estimation problems. The proof presented in this paper shows that the estimates given by the EnKF converge to the optimal estimates given by the Kalman filter (KF) and provides a formal justification for the use of the EnKF in dynamic remote sensing image formation. The implications of the proof are twofold: it shows that the EnKF converges to a well-defined limit and provides a formal argument that the EnKF is in fact a Monte Carlo algorithm that converges to the KF.