Differential expression analysis of RNA sequencing data by incorporating non-exonic mapped reads

Combining p-values from various statistical methods for microbiome data

MOTIVATION

In the field of microbiome analysis, there exist various statistical methods that have been developed for identifying differentially expressed features, that account for the overdispersion and the high sparsity of microbiome data. However, due to the differences in statistical models or test formulations, it is quite often to have inconsistent significance results across statistical methods, that makes it difficult to determine the importance of microbiome taxa. Thus, it is practically important to have the integration of the result from all statistical methods to determine the importance of microbiome taxa. A standard meta-analysis is a powerful tool for integrative analysis and it provides a summary measure by combining p-values from various statistical methods. While there are many meta-analyses available, it is not easy to choose the best meta-analysis that is the most suitable for microbiome data.

RESULTS

In this study, we investigated which meta-analysis method most adequately represents the importance of microbiome taxa. We considered Fisher's method, minimum value of p method, Simes method, Stouffer's method, Kost method, and Cauchy combination test. Through simulation studies, we showed that Cauchy combination test provides the best combined value of p in the sense that it performed the best among the examined methods while controlling the type 1 error rates. Furthermore, it produced high rank similarity with the true ranks. Through the real data application of colorectal cancer microbiome data, we demonstrated that the most highly ranked microbiome taxa by Cauchy combination test have been reported to be associated with colorectal cancer.

Identifying genes with conserved splicing structure and orthologous isoforms in human, mouse and dog

BACKGROUND

In eukaryote transcriptomes, a significant amount of transcript diversity comes from genes' capacity to generate different transcripts through alternative splicing. Identifying orthologous alternative transcripts across multiple species is of particular interest for genome annotators. However, there is no formal definition of transcript orthology based on the splicing structure conservation. Likewise there is no public dataset benchmark providing groups of orthologous transcripts sharing a conserved splicing structure.

RESULTS

We introduced a formal definition of splicing structure orthology and we predicted transcript orthologs in human, mouse and dog. Applying a selective strategy, we analyzed 2,167 genes and their 18,109 known transcripts and identified a set of 253 gene orthologs that shared a conserved splicing structure in all three species. We predicted 6,861 transcript CDSs (coding sequence), mainly for dog, an emergent model species. Each predicted transcript was an ortholog of a known transcript: both share the same CDS splicing structure. Evidence for the existence of the predicted CDSs was found in external data.

CONCLUSIONS

We generated a dataset of 253 gene triplets, structurally conserved and sharing all their CDSs in human, mouse and dog, which correspond to 879 triplets of spliced CDS orthologs. We have released the dataset both as an SQL database and as tabulated files. The data consists of the 879 CDS orthology groups with their detailed splicing structures, and the predicted CDSs, associated with their experimental evidence. The 6,861 predicted CDSs are provided in GTF files. Our data may contribute to compare highly conserved genes across three species, for comparative transcriptomics at the isoform level, or for benchmarking splice aligners and methods focusing on the identification of splicing orthologs. The data is available at https://data-access.cesgo.org/index.php/s/V97GXxOS66NqTkZ .

Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease

Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.

Gene expression differences between matched pairs of ovarian cancer patient tumors and patient-derived xenografts

As patient derived xenograft (PDX) models are increasingly used for preclinical drug development, strategies to account for the nonhuman component of PDX RNA expression data are critical to its interpretation. A bioinformatics pipeline to separate donor tumor and mouse stroma transcriptome profiles was devised and tested. To examine the molecular fidelity of PDX versus donor tumors, we compared mRNA differences between paired PDX-donor tumors from nine ovarian cancer patients. 1,935 differentially expressed genes were identified between PDX and donor tumors. Over 90% (n = 1767) of these genes were down-regulated in PDX models and enriched in stroma-specific functions. Several protein kinases were also differentially expressed in PDX tumors, e.g. PDGFRA, PDGFRB and CSF1R. Upon in silico removal of these PDX-donor tumor differentially expressed genes, a stronger transcriptional resemblance between PDX-donor tumor pairs was seen (average correlation coefficient increases from 0.91 to 0.95). We devised and validated an effective bioinformatics strategy to separate mouse stroma expression from human tumor expression for PDX RNAseq. In addition, we showed most of the PDX-donor differentially expressed genes were implicated in stromal components. The molecular similarities and differences between PDX and donor tumors have implications in future therapeutic trial designs and treatment response evaluations using PDX models.

Ex Vivo Hepatocyte Reprograming Promotes Homology-Directed DNA Repair to Correct Metabolic Disease in Mice After Transplantation

Ex vivo CRISPR/Cas9-mediated gene editing in hepatocytes using homology-directed repair (HDR) is a potential alternative curative therapy to organ transplantation for metabolic liver disease. However, a major limitation of this approach in quiescent adult primary hepatocytes is that nonhomologous end-joining is the predominant DNA repair pathway for double-strand breaks (DSBs). This study explored the hypothesis that ex vivo hepatocyte culture could reprogram hepatocytes to favor HDR after CRISPR/Cas9-mediated DNA DSBs. Quantitative PCR (qPCR), RNA sequencing, and flow cytometry demonstrated that within 24 hours, primary mouse hepatocytes in ex vivo monolayer culture decreased metabolic functions and increased expression of genes related to mitosis progression and HDR. Despite the down-regulation of hepatocyte function genes, hepatocytes cultured for up to 72 hours could robustly engraft in vivo. To assess functionality long-term, primary hepatocytes from a mouse model of hereditary tyrosinemia type 1 bearing a single-point mutation were transduced ex vivo with two adeno-associated viral vectors to deliver the Cas9 nuclease, target guide RNAs, and a 1.2-kb homology template. Adeno-associated viral Cas9 induced robust cutting at the target locus, and, after delivery of the repair template, precise correction of the point mutation occurred by HDR. Edited hepatocytes were transplanted into recipient fumarylacetoacetate hydrolase knockout mice, resulting in engraftment, robust proliferation, and prevention of liver failure. Weight gain and biochemical assessment revealed normalization of metabolic function. Conclusion: The results of this study demonstrate the potential therapeutic effect of ex vivo hepatocyte-directed gene editing after reprogramming to cure metabolic disease in a preclinical model of hereditary tyrosinemia type 1.

XBSeq2: a fast and accurate quantification of differential expression and differential polyadenylation

Background

RNA sequencing (RNA-seq) is a high throughput technology that profiles gene expression in a genome-wide manner. RNA-seq has been mainly used for testing differential expression (DE) of transcripts between two conditions and has recently been used for testing differential alternative polyadenylation (APA). In the past, many algorithms have been developed for detecting differentially expressed genes (DEGs) from RNA-seq experiments, including the one we developed, XBSeq, which paid special attention to the context-specific background noise that is ignored in conventional gene expression quantification and DE analysis of RNA-seq data.

Results

We present several major updates in XBSeq2, including alternative statistical testing and parameter estimation method for detecting DEGs, capacity to directly process alignment files and methods for testing differential APA usage. We evaluated the performance of XBSeq2 against several other methods by using simulated datasets in terms of area under the receiver operating characteristic (ROC) curve (AUC), number of false discoveries and statistical power. We also benchmarked different methods concerning execution time and computational memory consumed. Finally, we demonstrated the functionality of XBSeq2 by using a set of in-house generated clear cell renal carcinoma (ccRCC) samples.

Conclusions

We present several major updates to XBSeq. By using simulated datasets, we demonstrated that, overall, XBSeq2 performs equally well as XBSeq in terms of several statistical metrics and both perform better than DESeq2 and edgeR. In addition, XBSeq2 is faster in speed and consumes much less computational memory compared to XBSeq, allowing users to evaluate differential expression and APA events in parallel. XBSeq2 is available from Bioconductor: http://bioconductor.org/packages/XBSeq/

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1803-9) contains supplementary material, which is available to authorized users.

MBDDiff: an R package designed specifically for processing MBDcap-seq datasets

Background

Since its initial discovery in 1975, DNA methylation has been intensively studied and shown to be involved in various biological processes, such as development, aging and tumor progression. Many experimental techniques have been developed to measure the level of DNA methylation. Methyl-CpG binding domain-based capture followed by high-throughput sequencing (MBDCap-seq) is a widely used method for characterizing DNA methylation patterns in a genome-wide manner. However, current methods for processing MBDCap-seq datasets does not take into account of the region-specific genomic characteristics that might have an impact on the measurements of the amount of methylated DNA (signal) and background fluctuation (noise). Thus, specific software needs to be developed for MBDCap-seq experiments.

Results

A new differential methylation quantification algorithm for MBDCap-seq, MBDDiff, was implemented. To evaluate the performance of the MBDDiff algorithm, a set of simulated signal based on negative binomial and Poisson distribution with parameters estimated from real MBDCap-seq datasets accompanied with different background noises were generated, and then performed against a set of commonly used algorithms for MBDCap-seq data analysis in terms of area under the ROC curve (AUC), number of false discoveries and statistical power. In addition, we also demonstrated the effective of MBDDiff algorithm to a set of in-house prostate cancer samples, endometrial cancer samples published earlier, and a set of public-domain triple negative breast cancer samples to identify potential factors that contribute to cancer development and recurrence.

Conclusions

In this paper we developed an algorithm, MBDDiff, designed specifically for datasets derived from MBDCap-seq. MBDDiff contains three modules: quality assessment of datasets and quantification of DNA methylation; determination of differential methylation of promoter regions; and visualization functionalities. Simulation results suggest that MBDDiff performs better compared to MEDIPS and DESeq in terms of AUC and the number of false discoveries at different levels of background noise. MBDDiff outperforms MEDIPS with increased backgrounds noise, but comparable performance when noise level is lower. By applying MBDDiff to several MBDCap-seq datasets, we were able to identify potential targets that contribute to the corresponding biological processes. Taken together, MBDDiff provides user an accurate differential methylation analysis for data generated by MBDCap-seq, especially under noisy conditions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2794-z) contains supplementary material, which is available to authorized users.

Long noncoding RNAs in T lymphocytes

Long noncoding RNAs are recently discovered regulatory RNA molecules that do not code for proteins but influence a vast array of biologic processes. In vertebrates, the number of long noncoding RNA genes is thought to greatly exceed the number of protein-coding genes. It is also thought that long noncoding RNAs drive the biologic complexity observed in vertebrates compared with that in invertebrates. Evidence of this complexity has been found in the T-lymphocyte compartment of the adaptive immune system. In the present review, we describe our current level of understanding of the expression of specific long or large intergenic or intervening long noncoding RNAs during T-lymphocyte development in the thymus and differentiation in the periphery and highlight the mechanisms of action that specific long noncoding RNAs employ to regulate T-lymphocyte function, both in vitro and in vivo.

Education, collaboration, and innovation: intelligent biology and medicine in the era of big data

Here we present a summary of the 2014 International Conference on Intelligent Biology and Medicine (ICIBM 2014) and the editorial report of the supplement to BMC Genomics and BMC Systems Biology that includes 20 research articles selected from ICIBM 2014. The conference was held on December 4-6, 2014 at San Antonio, Texas, USA, and included six scientific sessions, four tutorials, four keynote presentations, nine highlight talks, and a poster session that covered cutting-edge research in bioinformatics, systems biology, and computational medicine.