lncDIFF: a novel distribution-free method for differential expression analysis of long non-coding RNA

ABSTRACTMotivationLong non-coding RNA expression data has been increasingly used in finding diagnostic and prognostic biomarkers in cancer studies. Existing differential analysis tools for RNA sequencing does not effectively accommodate low abundant genes, as commonly observed in lncRNA. We propose a novel and robust statistical method lncDIFF to detect differential expressed (DE) genes without assuming the true density on normalized counts.ResultslncDIFF adopts the generalized linear model with zero-inflated exponential quasi likelihood to estimate group effect on normalized counts, and employs the likelihood ratio test to detect differential expressed genes. The proposed method and tool is suitable for data processed with standard RNA-Seq preprocessing and normalization pipelines. Simulation results illustrate that lncDIFF detects DE genes with more power and lower false discovery rate regardless of the data pattern. The analysis on a head and neck squamous cell carcinomas study also confirms that lncDIFF has better sensitivity in identifying novel lncRNA genes with relatively large fold change and prognostic value.Availability and ImplementationlncDIFF is an R package available athttps://github.com/qianli10000/lncDIFF.Supplementary InformationSupplementary Data are available at Bioinformatics online.

Detecting Large Indels Using Optical Map Data

Optical Maps (OM) provide reads that are very long, and thus can be used to detect large indels not detectable by the shorter reads provided by sequence-based technologies such as Illumina and PacBio. Two existing tools for detecting large indels from OM data are BioNano Solve and OMSV. However, these two tools may miss indels with weak signals. We propose a local-assembly based approach, OMIndel, to detect large indels with OM data. The results of applying OMIndel to empirical data demonstrate that it is able to detect indels with weak signal. Furthermore, compared with the other two OM-based methods, OMIndel has a lower false discovery rate. We also investigated the indels that can only be detected by OM but not Illumina, PacBio or 10X, and we found that they mostly fall into two categories: complex events or indels on repetitive regions. This implies that adding the OM data to sequence-based technologies can provide significant progress towards a more complete characterization of structural variants (SVs). The algorithm has been implemented in Perl and is publicly available onhttps://bitbucket.org/xianfan/optmethod.

PEAK DETECTION IN MASS SPECTROMETRY BY GABOR FILTERS AND ENVELOPE ANALYSIS

Mass Spectrometry (MS) is increasingly being used to discover diseases-related proteomic patterns. The peak detection step is one of the most important steps in the typical analysis of MS data. Recently, many new algorithms have been proposed to increase true position rate with low false discovery rate in peak detection. Most of them follow two approaches: one is the denoising approach and the other is the decomposing approach. In the previous studies, the decomposition of MS data method shows more potential than the first one. In this paper, we propose two novel methods, named GaborLocal and GaborEnvelop, both of which can detect more true peaks with a lower false discovery rate than previous methods. We employ the method of Gaussian local maxima to detect peaks, because it is robust to noise in signals. A new approach, peak rank, is defined for the first time to identify peaks instead of using the signal-to-noise ratio. Meanwhile, the Gabor filter is used to amplify important information and compress noise in the raw MS signal. Moreover, we also propose the envelope analysis to improve the quantification of peaks and remove more false peaks. The proposed methods have been performed on the real SELDI-TOF spectrum with known polypeptide positions. The experimental results demonstrate that our methods outperform other commonly used methods in the Receiver Operating Characteristic (ROC) curve.