Citations

Abstract Motivation The significance of long non-coding RNAs (lncRNAs) in many biological processes and diseases has gained intense interests over the past several years. However, computational identification of lncRNAs in a wide range of species remains challenging; it requires prior knowledge of well-established sequences and annotations or species-specific training data, but the reality is that only a limited number of species have high-quality sequences and annotations. Results Here we first characterize lncRNAs in contrast to protein-coding RNAs based on feature relationship and find that the feature relationship between open reading frame length and guanine-cytosine (GC) content presents universally substantial divergence in lncRNAs and protein-coding RNAs, as observed in a broad variety of species. Based on the feature relationship, accordingly, we further present LGC, a novel algorithm for identifying lncRNAs that is able to accurately distinguish lncRNAs from protein-coding RNAs in a cross-species manner without any prior knowledge. As validated on large-scale empirical datasets, comparative results show that LGC outperforms existing algorithms by achieving higher accuracy, well-balanced sensitivity and specificity, and is robustly effective (>90% accuracy) in discriminating lncRNAs from protein-coding RNAs across diverse species that range from plants to mammals. To our knowledge, this study, for the first time, differentially characterizes lncRNAs and protein-coding RNAs based on feature relationship, which is further applied in computational identification of lncRNAs. Taken together, our study represents a significant advance in characterization and identification of lncRNAs and LGC thus bears broad potential utility for computational analysis of lncRNAs in a wide range of species. Availability and implementation LGC web server is publicly available at http://bigd.big.ac.cn/lgc/calculator. The scripts and data can be downloaded at http://bigd.big.ac.cn/biocode/tools/BT000004. Supplementary information Supplementary data are available at Bioinformatics online.

During his lifetime and a career spanning 42 years, James Watson Kernohan made numerous contributions to neuropathology, neurology, and neurosurgery. One of these, the phenomenon of ipsilateral, false localizing signs caused by compression of the contralateral cerebral peduncle against the tentorial edge, has widely become known as “Kernohan's notch” and continues to bear his name. The other is a grading system for gliomas from a neurosurgical viewpoint that continues to be relevant for grading of glial tumors 60 years after its introduction. In this paper, the authors analyze these two major contributions in detail within the context of Kernohan's career and explore how they contributed to the development of neurosurgical procedures.

Background— Long-QT syndrome (LQTS) is an inherited disorder associated with sudden cardiac death. The cytoskeletal protein syntrophin-α 1 (SNTA1) is known to interact with the cardiac sodium channel (hNa v 1.5), and we hypothesized that SNTA1 mutations might cause phenotypic LQTS in patients with genotypically normal hNa v 1.5 by secondarily disturbing sodium channel function. Methods and Results— Mutational analysis of SNTA1 was performed on 39 LQTS patients (QTc≥480 ms) with previously negative genetic screening for the known LQTS-causing genes. We identified a novel A257G- SNTA1 missense mutation, which affects a highly conserved residue, in 3 unrelated LQTS probands but not in 400 ethnic-matched control alleles. Only 1 of these probands had a preexisting family history of LQTS and sudden death with an additional intronic variant in KCNQ1. Electrophysiological analysis was performed using HEK-293 cells stably expressing hNa v 1.5 and transiently transfected with either wild-type or mutant SNTA1 and, in neonatal rat cardiomyocytes, transiently transfected with either wild-type or mutant SNTA1. In both HEK-293 cells and neonatal rat cardiomyocytes, increased peak sodium currents were noted along with a 10-mV negative shift of the onset and peak of currents of the current-voltage relationships. In addition, A257G-SNTA1 shifted the steady-state activation ( V h ) leftward by 9.4 mV, whereas the voltage-dependent inactivation kinetics and the late sodium currents were similar to wild-type SNTA1. Conclusion— SNTA1 is a new susceptibility gene for LQTS. A257G- SNTA1 can cause gain-of-function of Na v 1.5 similar to the LQT3.