Jia Meng

lncRScan-SVM: A Tool for Predicting Long Non-Coding RNAs Using Support Vector Machine

Functional long non-coding RNAs (lncRNAs) have been bringing novel insight into biological study, however it is still not trivial to accurately distinguish the lncRNA transcripts (LNCTs) from the protein coding ones (PCTs). As various information and data about lncRNAs are preserved by previous studies, it is appealing to develop novel methods to identify the lncRNAs more accurately. Our method lncRScan-SVM aims at classifying PCTs and LNCTs using support vector machine (SVM). The gold-standard datasets for lncRScan-SVM model training, lncRNA prediction and method comparison were constructed according to the GENCODE gene annotations of human and mouse respectively. By integrating features derived from gene structure, transcript sequence, potential codon sequence and conservation, lncRScan-SVM outperforms other approaches, which is evaluated by several criteria such as sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC) and area under curve (AUC). In addition, several known human lncRNA datasets were assessed using lncRScan-SVM. LncRScan-SVM is an efficient tool for predicting the lncRNAs, and it is quite useful for current lncRNA study.

MeT-DB: a database of transcriptome methylation in mammalian cells

Methyltranscriptome is an exciting new area that studies the mechanisms and functions of methylation in transcripts. The MethylTranscriptome DataBase (MeT-DB, http://compgenomics.utsa.edu/methylation/) is the first comprehensive resource for N6-methyladenosine (m6A) in mammalian transcriptome. It includes a database that records publicaly available data sets from methylated RNA immunoprecipitation sequencing (MeRIP-Seq), a recently developed technology for interrogating m6A methyltranscriptome. MeT-DB includes ∼300k m6A methylation sites in 74 MeRIP-Seq samples from 22 different experimental conditions predicted by exomePeak and MACS2 algorithms. To explore this rich information, MeT-DB also provides a genome browser to query and visualize context-specific m6A methylation under different conditions. MeT-DB also includes the binding site data of microRNA, splicing factor and RNA binding proteins in the browser window for comparison with m6A sites and for exploring the potential functions of m6A. Analysis of differential m6A methylation and the related differential gene expression under two conditions is also available in the browser. A global perspective of the genome-wide distribution of m6A methylation in all the data is provided in circular ideograms, which also act as a navigation portal. The query results and the entire data set can be exported to assist publication and additional analysis.

A novel algorithm for calling mRNA m6A peaks by modeling biological variances in MeRIP-seq data

Motivation: N6-methyl-adenosine (m6A) is the most prevalent mRNA methylation but precise prediction of its mRNA location is important for understanding its function. A recent sequencing technology, known as Methylated RNA Immunoprecipitation Sequencing technology (MeRIP-seq), has been developed for transcriptome-wide profiling of m6A. We previously developed a peak calling algorithm called exomePeak. However, exomePeak over-simplifies data characteristics and ignores the reads’ variances among replicates or reads dependency across a site region. To further improve the performance, new model is needed to address these important issues of MeRIP-seq data. Results: We propose a novel, graphical model-based peak calling method, MeTPeak, for transcriptome-wide detection of m6A sites from MeRIP-seq data. MeTPeak explicitly models read count of an m6A site and introduces a hierarchical layer of Beta variables to capture the variances and a Hidden Markov model to characterize the reads dependency across a site. In addition, we developed a constrained Newton’s method and designed a log-barrier function to compute analytically intractable, positively constrained Beta parameters. We applied our algorithm to simulated and real biological datasets and demonstrated significant improvement in detection performance and robustness over exomePeak. Prediction results on publicly available MeRIP-seq datasets are also validated and shown to be able to recapitulate the known patterns of m6A, further validating the improved performance of MeTPeak. Availability and implementation: The package ‘MeTPeak’ is implemented in R and C ++, and additional details are available at https://github.com/compgenomics/MeTPeak Contact: yufei.huang@utsa.edu or xdchoi@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

HEPeak: an HMM-based exome peak-finding package for RNA epigenome sequencing data

BackgroundMethylated RNA Immunoprecipatation combined with RNA sequencing (MeRIP-seq) is revolutionizing the de novo study of RNA epigenomics at a higher resolution. However, this new technology poses unique bioinformatics problems that call for novel and sophisticated statistical computational solutions, aiming at identifying and characterizing transcriptome-wide methyltranscriptome.ResultsWe developed HEP, a Hidden Markov Model (HMM)-based Exome Peak-finding algorithm for predicting transcriptome methylation sites using MeRIP-seq data. In contrast to exomePeak, our previously developed MeRIP-seq peak calling algorithm, HEPeak models the correlation between continuous bins in an m6A peak region and it is a model-based approach, which admits rigorous statistical inference. HEPeak was evaluated on a simulated MeRIP-seq dataset and achieved higher sensitivity and specificity than exomePeak. HEPeak was also applied to real MeRIP-seq datasets from human HEK293T cell line and mouse midbrain cells and was shown to be able to recapitulate known m6A distribution in transcripts and identify novel m6A sites in long non-coding RNAs.ConclusionsIn this paper, a novel HMM-based peak calling algorithm, HEPeak, was developed for peak calling for MeRIP-seq data. HEPeak is written in R and is publicly available.

Enrichment constrained time-dependent clustering analysis for finding meaningful temporal transcription modules

MOTIVATION Clustering is a popular data exploration technique widely used in microarray data analysis. When dealing with time-series data, most conventional clustering algorithms, however, either use one-way clustering methods, which fail to consider the heterogeneity of temporary domain, or use two-way clustering methods that do not take into account the time dependency between samples, thus producing less informative results. Furthermore, enrichment analysis is often performed independent of and after clustering and such practice, though capable of revealing biological significant clusters, cannot guide the clustering to produce biologically significant result. RESULT We present a new enrichment constrained framework (ECF) coupled with a time-dependent iterative signature algorithm (TDISA), which, by applying a sliding time window to incorporate the time dependency of samples and imposing an enrichment constraint to parameters of clustering, allows supervised identification of temporal transcription modules (TTMs) that are biologically meaningful. Rigorous mathematical definitions of TTM as well as the enrichment constraint framework are also provided that serve as objective functions for retrieving biologically significant modules. We applied the enrichment constrained time-dependent iterative signature algorithm (ECTDISA) to human gene expression time-series data of Kaposis sarcoma-associated herpesvirus (KSHV) infection of human primary endothelial cells; the result not only confirms known biological facts, but also reveals new insight into the molecular mechanism of KSHV infection. AVAILABILITY Data and Matlab code are available at http://engineering.utsa.edu/ approximately yfhuang/ECTDISA.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Guitar: An R/Bioconductor Package for Gene Annotation Guided Transcriptomic Analysis of RNA-Related Genomic Features.

Biological features, such as genes and transcription factor binding sites, are often denoted with genome-based coordinates as the genomic features. While genome-based representation is usually very effective in correlating various biological features, it can be tedious to examine the relationship between RNA-related genomic features and the landmarks of RNA transcripts with existing tools due to the difficulty in the conversion between genome-based coordinates and RNA-based coordinates. We developed here an open source Guitar R/Bioconductor package for sketching the transcriptomic view of RNA-related biological features represented by genome based coordinates. Internally, Guitar package extracts the standardized RNA coordinates with respect to the landmarks of RNA transcripts, with which hundreds of millions of RNA-related genomic features can then be efficiently analyzed within minutes. We demonstrated the usage of Guitar package in analyzing posttranscriptional RNA modifications (5-methylcytosine and N6-methyladenosine) derived from high-throughput sequencing approaches (MeRIP-Seq and RNA BS-Seq) and show that RNA 5-methylcytosine (m5C) is enriched in 5′UTR. The newly developed Guitar R/Bioconductor package achieves stable performance on the data tested and revealed novel biological insights. It will effectively facilitate the analysis of RNA methylation data and other RNA-related biological features in the future.

Viral and cellular N6-methyladenosine and N6,2′-O-dimethyladenosine epitranscriptomes in the KSHV life cycle

N6-methyladenosine (m6A) and N6,2′-O-dimethyladenosine (m6Am) modifications (m6A/m) of messenger RNA mediate diverse cellular functions. Oncogenic Kaposi’s sarcoma-associated herpesvirus (KSHV) has latent and lytic replication phases that are essential for the development of KSHV-associated cancers. To date, the role of m6A/m in KSHV replication and tumorigenesis is unclear. Here, we provide mechanistic insights by examining the viral and cellular m6A/m epitranscriptomes during KSHV latent and lytic infection. KSHV transcripts contain abundant m6A/m modifications during latent and lytic replication, and these modifications are highly conserved among different cell types and infection systems. Knockdown of YTHDF2 enhanced lytic replication by impeding KSHV RNA degradation. YTHDF2 binds to viral transcripts and differentially mediates their stability. KSHV latent infection induces 5′ untranslated region (UTR) hypomethylation and 3′UTR hypermethylation of the cellular epitranscriptome, regulating oncogenic and epithelial-mesenchymal transition pathways. KSHV lytic replication induces dynamic reprogramming of epitranscriptome, regulating pathways that control lytic replication. These results reveal a critical role of m6A/m modifications in KSHV lifecycle and provide rich resources for future investigations.This study reports the viral and cellular N6-methyladenosine (m6A) and N6,2′-O-dimethyladenosine (m6Am) epitranscriptomes during KSHV latent and lytic infection, and shows that lytic replication induces dynamic epitranscriptome reprogramming of host pathways that control this process.

MeTDiff: A Novel Differential RNA Methylation Analysis for MeRIP-Seq Data

N6-Methyladenosine (m6A) transcriptome methylation is an exciting new research area that just captures the attention of research community. We present in this paper, MeTDiff, a novel computational tool for predicting differential m6A methylation sites from Methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data. Compared with the existing algorithm exomePeak, the advantages of MeTDiff are that it explicitly models the reads variation in data and also devices a more power likelihood ratio test for differential methylation site prediction. Comprehensive evaluation of MeTDiffs performance using both simulated and real datasets showed that MeTDiff is much more robust and achieved much higher sensitivity and specificity over exomePeak. The R package “MeTDiff” and additional details are available at: https://github.com/compgenomics/MeTDiff.

RNA methylation and diseases: experimental results, databases, Web servers and computational models

Ribonucleic acid (RNA) methylation is a type of posttranscriptional modifications occurring in all kingdoms of life. It is strongly related to important biological process, thus making it linked to a number of human diseases. Owing to the development of high-throughput sequencing technology, plenty of achievement had been obtained in RNA methylation research recently. Meanwhile, various computational models have been developed to analyze and mining increasing RNA methylation data. In this review, we first made a brief introduction about eight types of most popular RNA methylation, the biological functions of RNA methylation, the relationship between RNA methylation and disease and five important RNA methylation-related diseases. The research of RNA methylation is based on sequencing data processing, and effective bioinformatics techniques can benefit better understanding of RNA methylation. We further introduced seven publicly available RNA methylation-related databases, and some important publicly available RNA-methylation-related Web servers and software for RNA methylation site identification, differential analysis and so on. Furthermore, we provided detailed analysis of the state-of-the-art computational models used in these Web servers and software. We also analyzed the limitations of these models and discussed the future directions of developing computational models for RNA methylation research.

QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model

BackgroundAs a newly emerged research area, RNA epigenetics has drawn increasing attention recently for the participation of RNA methylation and other modifications in a number of crucial biological processes. Thanks to high throughput sequencing techniques, such as, MeRIP-Seq, transcriptome-wide RNA methylation profile is now available in the form of count-based data, with which it is often of interests to study the dynamics at epitranscriptomic layer. However, the sample size of RNA methylation experiment is usually very small due to its costs; and additionally, there usually exist a large number of genes whose methylation level cannot be accurately estimated due to their low expression level, making differential RNA methylation analysis a difficult task.ResultsWe present QNB, a statistical approach for differential RNA methylation analysis with count-based small-sample sequencing data. Compared with previous approaches such as DRME model based on a statistical test covering the IP samples only with 2 negative binomial distributions, QNB is based on 4 independent negative binomial distributions with their variances and means linked by local regressions, and in the way, the input control samples are also properly taken care of. In addition, different from DRME approach, which relies only the input control sample only for estimating the background, QNB uses a more robust estimator for gene expression by combining information from both input and IP samples, which could largely improve the testing performance for very lowly expressed genes.ConclusionQNB showed improved performance on both simulated and real MeRIP-Seq datasets when compared with competing algorithms. And the QNB model is also applicable to other datasets related RNA modifications, including but not limited to RNA bisulfite sequencing, m1A-Seq, Par-CLIP, RIP-Seq, etc.