Statistical analysis and significance testing of serial analysis of gene expression data using a Poisson mixture model

A model-based clustering via mixture of hierarchical models with covariate adjustment for detecting differentially expressed genes from paired design

The causes of many complex human diseases are still largely unknown. Genetics plays an important role in uncovering the molecular mechanisms of complex human diseases. A key step to characterize the genetics of a complex human disease is to unbiasedly identify disease-associated gene transcripts on a whole-genome scale. Confounding factors could cause false positives. Paired design, such as measuring gene expression before and after treatment for the same subject, can reduce the effect of known confounding factors. However, not all known confounding factors can be controlled in a paired/match design. Model-based clustering, such as mixtures of hierarchical models, has been proposed to detect gene transcripts differentially expressed between paired samples. To the best of our knowledge, no model-based gene clustering methods have the capacity to adjust for the effects of covariates yet. In this article, we proposed a novel mixture of hierarchical models with covariate adjustment in identifying differentially expressed transcripts using high-throughput whole-genome data from paired design. Both simulation study and real data analysis show the good performance of the proposed method.

A Bayesian Semi-parametric Approach for the Differential Analysis of Sequence Counts Data

Data obtained using modern sequencing technologies are often summarized by recording the frequencies of observed sequences. Examples include the analysis of T cell counts in immunological research and studies of gene expression based on counts of RNA fragments. In both cases the items being counted are sequences, of proteins and base pairs, respectively. The resulting sequence-abundance distribution is usually characterized by overdispersion. We propose a Bayesian semi-parametric approach to implement inference for such data. Besides modeling the overdispersion, the approach takes also into account two related sources of bias that are usually associated with sequence counts data: some sequence types may not be recorded during the experiment and the total count may differ from one experiment to another. We illustrate our methodology with two data sets, one regarding the analysis of CD4+ T cell counts in healthy and diabetic mice and another data set concerning the comparison of mRNA fragments recorded in a Serial Analysis of Gene Expression (SAGE) experiment with gastrointestinal tissue of healthy and cancer patients.

Differential expression analysis for paired RNA-Seq data

Background

RNA-Seq technology measures the transcript abundance by generating sequence reads and counting their frequencies across different biological conditions. To identify differentially expressed genes between two conditions, it is important to consider the experimental design as well as the distributional property of the data. In many RNA-Seq studies, the expression data are obtained as multiple pairs, e.g., pre- vs. post-treatment samples from the same individual. We seek to incorporate paired structure into analysis.

Results

We present a Bayesian hierarchical mixture model for RNA-Seq data to separately account for the variability within and between individuals from a paired data structure. The method assumes a Poisson distribution for the data mixed with a gamma distribution to account variability between pairs. The effect of differential expression is modeled by two-component mixture model. The performance of this approach is examined by simulated and real data.

Conclusions

In this setting, our proposed model provides higher sensitivity than existing methods to detect differential expression. Application to real RNA-Seq data demonstrates the usefulness of this method for detecting expression alteration for genes with low average expression levels or shorter transcript length.

miREvo: an integrative microRNA evolutionary analysis platform for next-generation sequencing experiments

Background

MicroRNAs (miRNAs) are small (~19-24nt) non-coding RNAs that play important roles in various biological processes. To date, the next-generation sequencing (NGS) technology has been widely used to discover miRNAs in plants and animals. Although evolutionary analysis is important to reveal the functional dynamics of miRNAs, few computational tools have been developed to analyze the evolution of miRNA sequence and expression across species, especially the newly emerged ones,

Results

We developed miREvo, an integrated software platform with a graphical user interface (GUI), to process deep-sequencing data of small RNAs and to analyze miRNA sequence and expression evolution based on the multiple-species whole genome alignments (WGAs). Three major features are provided by miREvo: (i) to identify novel miRNAs in both plants and animals, based on a modified miRDeep algorithm, (ii) to detect miRNA homologs and measure their pairwise evolutionary distances among multiple species based on a WGA, and (iii) to profile miRNA expression abundances and analyze expression divergence across multiple species (small RNA libraries). Moreover, we demonstrated the utility of miREvo with Illumina data sets from Drosophila melanogaster and Arabidopsis, respectively.

Conclusion

This work presents an integrated pipline, miREvo, for exploring the expressional and evolutionary dynamics of miRNAs across multiple species. MiREvo is standalone, modular, and freely available at http://evolution.sysu.edu.cn/software/mirevo.htm under the GNU/GPL license.

Accelerating pairwise statistical significance estimation for local alignment by harvesting GPU's power

Background

Pairwise statistical significance has been recognized to be able to accurately identify related sequences, which is a very important cornerstone procedure in numerous bioinformatics applications. However, it is both computationally and data intensive, which poses a big challenge in terms of performance and scalability.

Results

We present a GPU implementation to accelerate pairwise statistical significance estimation of local sequence alignment using standard substitution matrices. By carefully studying the algorithm's data access characteristics, we developed a tile-based scheme that can produce a contiguous data access in the GPU global memory and sustain a large number of threads to achieve a high GPU occupancy. We further extend the parallelization technique to estimate pairwise statistical significance using position-specific substitution matrices, which has earlier demonstrated significantly better sequence comparison accuracy than using standard substitution matrices. The implementation is also extended to take advantage of dual-GPUs. We observe end-to-end speedups of nearly 250 (370) × using single-GPU Tesla C2050 GPU (dual-Tesla C2050) over the CPU implementation using Intel^© Core™i7 CPU 920 processor.

Conclusions

Harvesting the high performance of modern GPUs is a promising approach to accelerate pairwise statistical significance estimation for local sequence alignment.

Case studies in reproducibility

Reproducible research is a concept of providing access to data and software along with published scientific findings. By means of some case studies from different disciplines, we will illustrate reasons why readers should be given the possibility to look at the data and software independently from the authors of the original publication. We report results of a survey comprising 100 papers recently published in Bioinformatics. The main finding is that authors of this journal share a culture of making data available. However, the number of papers where source code for simulation studies or analyzes is available is still rather limited.

A novel approach to detect differentially expressed genes from count-based digital databases by normalizing with housekeeping genes

Sequence tag count-based gene expression analysis is potent for the identification of candidate genes relevant to the cancerous phenotype. With the public availability of count-based data, the computational approaches for differentially expressed genes, which are mainly based on Binomial or beta-Binomial distribution, become practical and important in cancer biology. It remains a permanent need to select a proper statistical model for these methods. In this study, we developed a novel Bayesian algorithm-based method, Electronic Differential Gene Expression Screener (EDGES), in which a statistical model was determined by geometric averaging of 12 common housekeeping genes. EDGES identified a set of differentially expressed genes in lung, breast and colorectal cancers by using publically available Serial Analysis of Gene Expression (SAGE) and Expressed Sequence Tag (EST data). Gene expression microarray analysis and quantitative reverse transcription real-time PCR demonstrated the effectiveness of this procedure. We conclude that current normalization of calibrators provides a new insight into count-based digital subtraction in cancer research.

Clustering-based approaches to SAGE data mining

Serial analysis of gene expression (SAGE) is one of the most powerful tools for global gene expression profiling. It has led to several biological discoveries and biomedical applications, such as the prediction of new gene functions and the identification of biomarkers in human cancer research. Clustering techniques have become fundamental approaches in these applications. This paper reviews relevant clustering techniques specifically designed for this type of data. It places an emphasis on current limitations and opportunities in this area for supporting biologically-meaningful data mining and visualisation.