Likun Wang

Abstract Summary: High-throughput RNA sequencing (RNA-seq) is rapidly emerging as a major quantitative transcriptome profiling platform. Here, we present DEGseq, an R package to identify differentially expressed genes or isoforms for RNA-seq data from different samples. In this package, we integrated three existing methods, and introduced two novel methods based on MA-plot to detect and visualize gene expression difference. Availability: The R package and a quick-start vignette is available at http://bioinfo.au.tsinghua.edu.cn/software/degseq Contact: xwwang@tsinghua.edu.cn; zhangxg@tsinghua.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Postmortem mRNA degradation is considered to be the major concern in gene expression research utilizing human postmortem tissues. A key factor in this process is the postmortem interval (PMI), which is defined as the interval between death and sample collection. However, global patterns of postmortem mRNA degradation at individual gene levels across diverse human tissues remain largely unknown. In this study, we performed a systematic analysis of alteration of gene expression associated with PMI in human tissues. From the Genotype-Tissue Expression (GTEx) database, we evaluated gene expression levels of 2,016 high-quality postmortem samples from 316 donors of European descent, with PMI ranging from 1 to 27 hours. We found that PMI-related mRNA degradation is tissue-specific, gene-specific, and even genotype-dependent, thus drawing a more comprehensive picture of PMI-associated gene expression across diverse human tissues. Additionally, we also identified 266 differentially variable (DV) genes, such as DEFB4B and IFNG, whose expression is significantly dispersed between short PMI (S-PMI) and long PMI (L-PMI) groups. In summary, our analyses provide a comprehensive profile of PMI-associated gene expression, which will help interpret gene expression patterns in the evaluation of postmortem tissues.

Significant effort has been made on designing user-acceptable driver assistance systems. To adapt to driver characteristics, this paper proposes a forward collision warning (FCW) algorithm that can adjust its warning thresholds in a real-time manner according to driver behavior changes, including both behavioral fluctuation and individual difference. This adaptive FCW algorithm overcomes the limit of traditional FCW with fixed risk evaluation models and fixed triggering thresholds by continuously monitoring driver braking behaviors in multiple lanes. A real-time identification algorithm for the warning thresholds is designed by using the recursive least squares method. Based on naturalistic experimental data, offline simulations show that this algorithm can match driver behavioral fluctuation and individual difference in long-time driving condition, and as time goes on, the adaptability to driver behavior is gradually improved, thus decreasing the false-alarm rate of FCW.

Protein lysine acetylation plays an important role in the normal functioning of cells, including gene expression regulation, protein stability and metabolism regulation. Although large amounts of lysine acetylation sites have been identified via large-scale mass spectrometry or traditional experimental methods, the lysine (K)-acetyl-transferase (KAT) responsible for the acetylation of a given protein or lysine site remains largely unknown due to the experimental limitations of KAT substrate identification. Hence, the in silico prediction of KAT-specific acetylation sites may provide direction for further experiments. In our previous study, we developed the acetylation set enrichment based (ASEB) computer program to predict which KAT-families are responsible for the acetylation of a given protein or lysine site. In this article, we provide KAT-specific acetylation site prediction as a web service. This web server not only provides the online tool and R package for the method in our previous study, but several useful services are also included, such as the integration of protein-protein interaction information to enhance prediction accuracy. This web server can be freely accessed at http://cmbi.bjmu.edu.cn/huac.