Downloads & Free Reading Options - Results

Introduction [1] : Among different sheep breeds in the world, the Texel breed is known as a meaty and muscular breed . Skeletal muscle growth is a step-by-step and exponential process from differentiation, development and maturation, which is regulated by gene networks and cell signaling pathways, and several genes and factors are involved in the process of muscle fiber formation and their growth and hypertrophy ( Badday Betti et al . 2022 ). The study of gene expression is done with several methods, and this gene expression information is used in breeding programs as a tool to improve phenotypic choices. Databases are a large source of expression data that can be used by bioinformatics methods to integrate heterogeneous data from different studies and platforms. In this study, by integrating the microarray and RNA-Seq data available in the database belonging to the muscle tissue of Texel breed sheep, the transcriptomic profile of the muscle was compared at two ages of embryonic and adult. Materials and Methods: Microarray data related to longissimus dorsi muscle tissue with three replicates d-70 embryos from GEO database with accession number GSE23563 and RNA-Seq data related to muscle tissue from six samples with two replicates from adult individuals from ArrayExpress database were selected . Limma, Biobase and GEOquery software packages were used to calculate the expression values of the microarray data related to the embryonic age   in the R environment, and Tuxedo, HTSeq and DESeq2 packages were used in the Linux and R environment to calculate the expression values of the RNA-Seq data (Kamali et al . 2022 ; Sahraei et al . 2019 ). Then two types of expression values were integrated and to eliminate non-biological effects, the batch effects were also removed . Next, differential genes were identified with the limma software package. In order to identify the relationship between the identified differential genes, the gene network was drawn between them by software of Cytoscape version 3.7.1 and String 1.5.1 program. next, due to the vastness of the gene network, each network was clustered with MCODE 1.6.1 and CytoCluster 2.1.0 programs (ClusterOne algorithm) and significant clusters (P-value < 0.05) were identified (Saedi et al . 2022). In order to better understand the ontology and function of the identified differential genes, the Gene Ontology of the genes was investigate d using software of Cytoscape version 3.7.1 and ClueGO 2.5.9 and CluePedia 1.5.9 programs. After receiving the Gene Ontology results, significant Gene Ontology terms (P-Value < 0.05) related to functional groups were identified. Finally, the selected genes (Adj P-Value < 0.05) were identified and introduced in these two age groups. Results and Discussion: After quality control, correcting and normalizing the microarray data, the GPL10778 platform annotation file with 1042520 Probe ID was used to calculate their expression values. After relevant analyzes of 9289 Probe ID identified related to the data of this study, 7918 Gene Symbol was identified finally. After quality control, trimming and normalizing the RNA-Seq data in total, the number of Ensembl_Genes based on which the reading values were calculated by HTSeq was 27056. After removing IDs that had zero readings in all 6 samples, 10855 IDs remained. Then, these 10855 Ensembl ID were merged with the annotation file to obtain Gene Symbol, and finally 9417 common genes were identified between the six samples of adult age. The results of differential expression analysis showed that there were significant differences in the expression of 62 genes (37 increased and 25 decreased) in the muscle tissue between adult and embryonic age. By creating a gene network between differential genes, 15 selected genes were identified, including MYH1, ACTN3, CASQ1, TMOD4, FBP2, SLC2A4, MX1, COX4I1, SOD2, MFN2, UQCRB, UCP3, PRKAB2, PHKG2, PPP1R3C. The function of these genes has been proven in cell proliferation, protein synthesis, myofibril formation, and lipid metabolism. Differential gene enrichment analysis revealed some biological processes such as Vasculogenesis, positive regulation of ossification, positive regulation of muscle tissue development, regulation of muscle contraction, contractile fiber part, calcium signaling, calcineurin-NFAT signaling cascade and regulation of receptor signaling pathway via JAK-STAT , the molecular function of regulating cation channel activity and the cellular components of the contractile fiber. Conclusion: This study in addition to confirming the accuracy of the integration method of two types of heterogeneous data , provided a general view of the transcriptomic differences of Texel sheep muscle tissue at two important age points to be a useful source for biological investigations of genes related to muscle growth and development in sheep.

High-throughput mRNA sequencing (RNA-Seq) is widely used for transcript quantification of gene isoforms. Since RNA-Seq data alone is often not sufficient to accurately identify the read origins from the isoforms for quantification, we propose to explore protein domain-domain interactions as prior knowledge for integrative analysis with RNA-seq data. We introduce a Network-based method for RNA-Seq-based Transcript Quantification (Net-RSTQ) to integrate protein domain-domain interaction network with short read alignments for transcript abundance estimation. Based on our observation that the abundances of the neighboring isoforms by domain-domain interactions in the network are positively correlated, Net-RSTQ models the expression of the neighboring transcripts as Dirichlet priors on the likelihood of the observed read alignments against the transcripts in one gene. The transcript abundances of all the genes are then jointly estimated with alternating optimization of multiple EM problems. In simulation Net-RSTQ effectively improved isoform transcript quantifications when isoform co-expressions correlate with their interactions. qRT-PCR results on 25 multi-isoform genes in a stem cell line, an ovarian cancer cell line, and a breast cancer cell line also showed that Net-RSTQ estimated more consistent isoform proportions with RNA-Seq data. In the experiments on the RNA-Seq data in The Cancer Genome Atlas (TCGA), the transcript abundances estimated by Net-RSTQ are more informative for patient sample classification of ovarian cancer, breast cancer and lung cancer. All experimental results collectively support that Net-RSTQ is a promising approach for isoform quantification.

This article is from Genome Biology , volume 14 . Abstract A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.

This article is from Bioinformatics , volume 30 . Abstract Motivation: Gene expression profiling using RNA-seq is a powerful technique for screening RNA species’ landscapes and their dynamics in an unbiased way. While several advanced methods exist for differential expression analysis of RNA-seq data, proper tools to anal.yze RNA-seq time-course have not been proposed.Results: In this study, we use RNA-seq to measure gene expression during the early human T helper 17 (Th17) cell differentiation and T-cell activation (Th0). To quantify Th17-specific gene expression dynamics, we present a novel statistical methodology, DyNB, for analyzing time-course RNA-seq data. We use non-parametric Gaussian processes to model temporal correlation in gene expression and combine that with negative binomial likelihood for the count data. To account for experiment-specific biases in gene expression dynamics, such as differences in cell differentiation efficiencies, we propose a method to rescale the dynamics between replicated measurements. We develop an MCMC sampling method to make inference of differential expression dynamics between conditions. DyNB identifies several known and novel genes involved in Th17 differentiation. Analysis of differentiation efficiencies revealed consistent patterns in gene expression dynamics between different cultures. We use qRT-PCR to validate differential expression and differentiation efficiencies for selected genes. Comparison of the results with those obtained via traditional timepoint-wise analysis shows that time-course analysis together with time rescaling between cultures identifies differentially expressed genes which would not otherwise be detected.Availability: An implementation of the proposed computational methods will be available at http://research.ics.aalto.fi/csb/software/Contact:[email protected] or [email protected] information:Supplementary data are available at Bioinformatics online.

This article is from BMC Genomics , volume 14 . Abstract Background: Data normalization is a key step in gene expression analysis by qPCR. Endogenous control genes are used to estimate variations and experimental errors occurring during sample preparation and expression measurements. However, the transcription level of the most commonly used reference genes can vary considerably in samples obtained from different individuals, tissues, developmental stages and under variable physiological conditions, resulting in a misinterpretation of the performance of the target gene(s). This issue has been scarcely approached in woody species such as grapevine. Results: A statistical criterion was applied to select a sub-set of 19 candidate reference genes from a total of 242 non-differentially expressed (NDE) genes derived from a RNA-Seq experiment comprising ca. 500 million reads obtained from 14 table-grape genotypes sampled at four phenological stages. From the 19 candidate reference genes, VvAIG1 (AvrRpt2-induced gene) and VvTCPB (T-complex 1 beta-like protein) were found to be the most stable ones after comparing the complete set of genotypes and phenological stages studied. This result was further validated by qPCR and geNorm analyses. Conclusions: Based on the evidence presented in this work, we propose to use the grapevine genes VvAIG1 or VvTCPB or both as a reference tool to normalize RNA expression in qPCR assays or other quantitative method intended to measure gene expression in berries and other tissues of this fruit crop, sampled at different developmental stages and physiological conditions.

As gene expression measurement technology is shifting from microarrays to sequencing, the statistical tools available for their analysis must be adapted since RNA-seq data are measured as counts. Recently, it has been proposed to tackle the count nature of these data by modeling log-count reads per million as continuous variables, using nonparametric regression to account for their inherent heteroscedasticity. Adopting such a framework, we propose tcgsaseq, a principled, model-free and efficient top-down method for detecting longitudinal changes in RNA-seq gene sets. Considering gene sets defined a priori, tcgsaseq identifies those whose expression vary over time, based on an original variance component score test accounting for both covariates and heteroscedasticity without assuming any specific parametric distribution for the transformed counts. We demonstrate that despite the presence of a nonparametric component, our test statistic has a simple form and limiting distribution, and both may be computed quickly. A permutation version of the test is additionally proposed for very small sample sizes. Applied to both simulated data and two real datasets, the proposed method is shown to exhibit very good statistical properties, with an increase in stability and power when compared to state of the art methods ROAST, edgeR and DESeq2, which can fail to control the type I error under certain realistic settings. We have made the method available for the community in the R package tcgsaseq.

The RNA-sequencing (RNA-seq) is becoming increasingly popular for quantifying gene expression levels. Since the RNA-seq measurements are relative in nature, between-sample normalization of counts is an essential step in differential expression (DE) analysis. The normalization of existing DE detection algorithms is ad hoc and performed once for all prior to DE detection, which may be suboptimal since ideally normalization should be based on non-DE genes only and thus coupled with DE detection. We propose a unified statistical model for joint normalization and DE detection of log-transformed RNA-seq data. Sample-specific normalization factors are modeled as unknown parameters in the gene-wise linear models and jointly estimated with the regression coefficients. By imposing sparsity-inducing L1 penalty (or mixed L1/L2-norm for multiple treatment conditions) on the regression coefficients, we formulate the problem as a penalized least-squares regression problem and apply the augmented lagrangian method to solve it. Simulation studies show that the proposed model and algorithms outperform existing methods in terms of detection power and false-positive rate when more than half of the genes are differentially expressed and/or when the up- and down-regulated genes among DE genes are unbalanced in amount.

RNA-Seq technology allows for studying the transcriptional state of the cell at an unprecedented level of detail. Beyond quantification of whole-gene expression, it is now possible to disentangle the abundance of individual alternatively spliced transcript isoforms of a gene. A central question is to understand the regulatory processes that lead to differences in relative abundance variation due to external and genetic factors. Here, we present a mixed model approach that allows for (i) joint analysis and genetic mapping of multiple transcript isoforms and (ii) mapping of isoform-specific effects. Central to our approach is to comprehensively model the causes of variation and correlation between transcript isoforms, including the genomic background and technical quantification uncertainty. As a result, our method allows to accurately test for shared as well as transcript-specific genetic regulation of transcript isoforms and achieves substantially improved calibration of these statistical tests. Experiments on genotype and RNA-Seq data from 126 human HapMap individuals demonstrate that our model can help to obtain a more fine-grained picture of the genetic basis of gene expression variation.

RNA-Seq technology allows for studying the transcriptional state of the cell at an unprecedented level of detail. Beyond quantification of whole-gene expression, it is now possible to disentangle the abundance of individual alternatively spliced transcript isoforms of a gene. A central question is to understand the regulatory processes that lead to differences in relative abundance variation due to external and genetic factors. Here, we present a mixed model approach that allows for (i) joint analysis and genetic mapping of multiple transcript isoforms and (ii) mapping of isoform-specific effects. Central to our approach is to comprehensively model the causes of variation and correlation between transcript isoforms, including the genomic background and technical quantification uncertainty. As a result, our method allows to accurately test for shared as well as transcript-specific genetic regulation of transcript isoforms and achieves substantially improved calibration of these statistical tests. Experiments on genotype and RNA-Seq data from 126 human HapMap individuals demonstrate that our model can help to obtain a more fine-grained picture of the genetic basis of gene expression variation.

RNA-Seq technology allows for studying the transcriptional state of the cell at an unprecedented level of detail. Beyond quantification of whole-gene expression, it is now possible to disentangle the abundance of individual alternatively spliced transcript isoforms of a gene. A central question is to understand the regulatory processes that lead to differences in relative abundance variation due to external and genetic factors. Here, we present a mixed model approach that allows for (i) joint analysis and genetic mapping of multiple transcript isoforms and (ii) mapping of isoform-specific effects. Central to our approach is to comprehensively model the causes of variation and correlation between transcript isoforms, including the genomic background and technical quantification uncertainty. As a result, our method allows to accurately test for shared as well as transcript-specific genetic regulation of transcript isoforms and achieves substantially improved calibration of these statistical tests. Experiments on genotype and RNA-Seq data from 126 human HapMap individuals demonstrate that our model can help to obtain a more fine-grained picture of the genetic basis of gene expression variation.

RNA-Seq technology allows for studying the transcriptional state of the cell at an unprecedented level of detail. Beyond quantification of whole-gene expression, it is now possible to disentangle the abundance of individual alternatively spliced transcript isoforms of a gene. A central question is to understand the regulatory processes that lead to differences in relative abundance variation due to external and genetic factors. Here, we present a mixed model approach that allows for (i) joint analysis and genetic mapping of multiple transcript isoforms and (ii) mapping of isoform-specific effects. Central to our approach is to comprehensively model the causes of variation and correlation between transcript isoforms, including the genomic background and technical quantification uncertainty. As a result, our method allows to accurately test for shared as well as transcript-specific genetic regulation of transcript isoforms and achieves substantially improved calibration of these statistical tests. Experiments on genotype and RNA-Seq data from 126 human HapMap individuals demonstrate that our model can help to obtain a more fine-grained picture of the genetic basis of gene expression variation.

This article is from BMC Bioinformatics , volume 14 . Abstract Background: Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results: To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions: GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

High-throughput sequencing of RNA transcripts (RNA-seq) has become the method of choice for detection of differential expression (DE). Concurrent with the growing popularity of this technology there has been a significant research effort devoted towards understanding the statistical properties of this data and the development of analysis methods. We report on a comprehensive evaluation of the commonly used DE methods using the SEQC benchmark data set. We evaluate a number of key features including: assessment of normalization, accuracy of DE detection, modeling of genes expressed in only one condition, and the impact of sequencing depth and number of replications on identifying DE genes. We find significant differences among the methods with no single method consistently outperforming the others. Furthermore, the performance of array-based approach is comparable to methods customized for RNA-seq data. Perhaps most importantly, our results demonstrate that increasing the number of replicate samples provides significantly more detection power than increased sequencing depth.

This article is from Genes , volume 1 . Abstract The recent arrival of ultra-high throughput, next generation sequencing (NGS) technologies has revolutionized the genetics and genomics fields by allowing rapid and inexpensive sequencing of billions of bases. The rapid deployment of NGS in a variety of sequencing-based experiments has resulted in fast accumulation of massive amounts of sequencing data. To process this new type of data, a torrent of increasingly sophisticated algorithms and software tools are emerging to help the analysis stage of the NGS applications. In this article, we strive to comprehensively identify the critical challenges that arise from all stages of NGS data analysis and provide an objective overview of what has been achieved in existing works. At the same time, we highlight selected areas that need much further research to improve our current capabilities to delineate the most information possible from NGS data. The article focuses on applications dealing with ChIP-Seq and RNA-Seq.

This article is from Scientific Reports , volume 4 . Abstract RNA-Seq is quickly becoming the preferred method for comprehensively characterizing whole transcriptome activity, and the analysis of count data from RNA-Seq requires new computational tools. We developed GSAASeqSP, a novel toolset for genome-wide gene set association analysis of sequence count data. This toolset offers a variety of statistical procedures via combinations of multiple gene-level and gene set-level statistics, each having their own strengths under different sample and experimental conditions. These methods can be employed independently, or results generated from multiple or all methods can be integrated to determine more robust profiles of significantly altered biological pathways. Using simulations, we demonstrate the ability of these methods to identify association signals and to measure the strength of the association. We show that GSAASeqSP analyses of RNA-Seq data from diverse tissue samples provide meaningful insights into the biological mechanisms that differentiate these samples. GSAASeqSP is a powerful platform for investigating molecular underpinnings of complex traits and diseases arising from differential activity within the biological pathways. GSAASeqSP is available at http://gsaa.unc.edu.

This article is from Scientific Reports , volume 4 . Abstract RNA-Seq is quickly becoming the preferred method for comprehensively characterizing whole transcriptome activity, and the analysis of count data from RNA-Seq requires new computational tools. We developed GSAASeqSP, a novel toolset for genome-wide gene set association analysis of sequence count data. This toolset offers a variety of statistical procedures via combinations of multiple gene-level and gene set-level statistics, each having their own strengths under different sample and experimental conditions. These methods can be employed independently, or results generated from multiple or all methods can be integrated to determine more robust profiles of significantly altered biological pathways. Using simulations, we demonstrate the ability of these methods to identify association signals and to measure the strength of the association. We show that GSAASeqSP analyses of RNA-Seq data from diverse tissue samples provide meaningful insights into the biological mechanisms that differentiate these samples. GSAASeqSP is a powerful platform for investigating molecular underpinnings of complex traits and diseases arising from differential activity within the biological pathways. GSAASeqSP is available at http://gsaa.unc.edu.

Recent advances in next-generation sequencing technology allow high-throughput cDNA sequencing (RNA-Seq) to be widely applied in transcriptomic studies, in particular for detecting differentially expressed genes between groups. Many software packages have been developed for the identification of differentially expressed genes (DEGs) between treatment groups based on RNA-Seq data. However, there is a lack of consensus on how to approach an optimal study design and choice of suitable software for the analysis. In this comparative study we evaluate the performance of three of the most frequently used software tools: Cufflinks-Cuffdiff2, DESeq and edgeR. A number of important parameters of RNA-Seq technology were taken into consideration, including the number of replicates, sequencing depth, and balanced vs. unbalanced sequencing depth within and between groups. We benchmarked results relative to sets of DEGs identified through either quantitative RT-PCR or microarray. We observed that edgeR performs slightly better than DESeq and Cuffdiff2 in terms of the ability to uncover true positives. Overall, DESeq or taking the intersection of DEGs from two or more tools is recommended if the number of false positives is a major concern in the study. In other circumstances, edgeR is slightly preferable for differential expression analysis at the expense of potentially introducing more false positives.

This article is from BMC Bioinformatics , volume 14 . Abstract Background: Finding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, DNA microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of cDNA (RNA-seq) has emerged as a powerful competitor. As the cost of sequencing decreases, it is conceivable that the use of RNA-seq for differential expression analysis will increase rapidly. To exploit the possibilities and address the challenges posed by this relatively new type of data, a number of software packages have been developed especially for differential expression analysis of RNA-seq data. Results: We conducted an extensive comparison of eleven methods for differential expression analysis of RNA-seq data. All methods are freely available within the R framework and take as input a matrix of counts, i.e. the number of reads mapping to each genomic feature of interest in each of a number of samples. We evaluate the methods based on both simulated data and real RNA-seq data. Conclusions: Very small sample sizes, which are still common in RNA-seq experiments, impose problems for all evaluated methods and any results obtained under such conditions should be interpreted with caution. For larger sample sizes, the methods combining a variance-stabilizing transformation with the ‘limma’ method for differential expression analysis perform well under many different conditions, as does the nonparametric SAMseq method.

This article is from BMC Bioinformatics , volume 14 . Abstract 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.

This article is from BMC Bioinformatics , volume 15 . Abstract None

This article is from BMC Bioinformatics , volume 14 . Abstract Background: Qualitative and quantitative analysis of small non-coding RNAs by next generation sequencing (smallRNA-Seq) represents a novel technology increasingly used to investigate with high sensitivity and specificity RNA population comprising microRNAs and other regulatory small transcripts. Analysis of smallRNA-Seq data to gather biologically relevant information, i.e. detection and differential expression analysis of known and novel non-coding RNAs, target prediction, etc., requires implementation of multiple statistical and bioinformatics tools from different sources, each focusing on a specific step of the analysis pipeline. As a consequence, the analytical workflow is slowed down by the need for continuous interventions by the operator, a critical factor when large numbers of datasets need to be analyzed at once. Results: We designed a novel modular pipeline (iMir) for comprehensive analysis of smallRNA-Seq data, comprising specific tools for adapter trimming, quality filtering, differential expression analysis, biological target prediction and other useful options by integrating multiple open source modules and resources in an automated workflow. As statistics is crucial in deep-sequencing data analysis, we devised and integrated in iMir tools based on different statistical approaches to allow the operator to analyze data rigorously. The pipeline created here proved to be efficient and time-saving than currently available methods and, in addition, flexible enough to allow the user to select the preferred combination of analytical steps. We present here the results obtained by applying this pipeline to analyze simultaneously 6 smallRNA-Seq datasets from either exponentially growing or growth-arrested human breast cancer MCF-7 cells, that led to the rapid and accurate identification, quantitation and differential expression analysis of ~450 miRNAs, including several novel miRNAs and isomiRs, as well as identification of the putative mRNA targets of differentially expressed miRNAs. In addition, iMir allowed also the identification of ~70 piRNAs (piwi-interacting RNAs), some of which differentially expressed in proliferating vs growth arrested cells. Conclusion: The integrated data analysis pipeline described here is based on a reliable, flexible and fully automated workflow, useful to rapidly and efficiently analyze high-throughput smallRNA-Seq data, such as those produced by the most recent high-performance next generation sequencers. iMir is available at http://www.labmedmolge.unisa.it/inglese/research/imir.

This article is from Microbiome , volume 2 . Abstract Background: Experimental designs that take advantage of high-throughput sequencing to generate datasets include RNA sequencing (RNA-seq), chromatin immunoprecipitation sequencing (ChIP-seq), sequencing of 16S rRNA gene fragments, metagenomic analysis and selective growth experiments. In each case the underlying data are similar and are composed of counts of sequencing reads mapped to a large number of features in each sample. Despite this underlying similarity, the data analysis methods used for these experimental designs are all different, and do not translate across experiments. Alternative methods have been developed in the physical and geological sciences that treat similar data as compositions. Compositional data analysis methods transform the data to relative abundances with the result that the analyses are more robust and reproducible. Results: Data from an in vitro selective growth experiment, an RNA-seq experiment and the Human Microbiome Project 16S rRNA gene abundance dataset were examined by ALDEx2, a compositional data analysis tool that uses Bayesian methods to infer technical and statistical error. The ALDEx2 approach is shown to be suitable for all three types of data: it correctly identifies both the direction and differential abundance of features in the differential growth experiment, it identifies a substantially similar set of differentially expressed genes in the RNA-seq dataset as the leading tools and it identifies as differential the taxa that distinguish the tongue dorsum and buccal mucosa in the Human Microbiome Project dataset. The design of ALDEx2 reduces the number of false positive identifications that result from datasets composed of many features in few samples. Conclusion: Statistical analysis of high-throughput sequencing datasets composed of per feature counts showed that the ALDEx2 R package is a simple and robust tool, which can be applied to RNA-seq, 16S rRNA gene sequencing and differential growth datasets, and by extension to other techniques that use a similar approach.

This article is from Microbiome , volume 2 . Abstract Background: Experimental designs that take advantage of high-throughput sequencing to generate datasets include RNA sequencing (RNA-seq), chromatin immunoprecipitation sequencing (ChIP-seq), sequencing of 16S rRNA gene fragments, metagenomic analysis and selective growth experiments. In each case the underlying data are similar and are composed of counts of sequencing reads mapped to a large number of features in each sample. Despite this underlying similarity, the data analysis methods used for these experimental designs are all different, and do not translate across experiments. Alternative methods have been developed in the physical and geological sciences that treat similar data as compositions. Compositional data analysis methods transform the data to relative abundances with the result that the analyses are more robust and reproducible. Results: Data from an in vitro selective growth experiment, an RNA-seq experiment and the Human Microbiome Project 16S rRNA gene abundance dataset were examined by ALDEx2, a compositional data analysis tool that uses Bayesian methods to infer technical and statistical error. The ALDEx2 approach is shown to be suitable for all three types of data: it correctly identifies both the direction and differential abundance of features in the differential growth experiment, it identifies a substantially similar set of differentially expressed genes in the RNA-seq dataset as the leading tools and it identifies as differential the taxa that distinguish the tongue dorsum and buccal mucosa in the Human Microbiome Project dataset. The design of ALDEx2 reduces the number of false positive identifications that result from datasets composed of many features in few samples. Conclusion: Statistical analysis of high-throughput sequencing datasets composed of per feature counts showed that the ALDEx2 R package is a simple and robust tool, which can be applied to RNA-seq, 16S rRNA gene sequencing and differential growth datasets, and by extension to other techniques that use a similar approach.

This article is from BMC Research Notes , volume 5 . Abstract Background: RNA sequencing (RNA-Seq) is emerging as a highly accurate method to quantify transcript abundance. However, analyses of the large data sets obtained by sequencing the entire transcriptome of organisms have generally been performed by bioinformatics specialists. Here we provide a step-by-step guide and outline a strategy using currently available statistical tools that results in a conservative list of differentially expressed genes. We also discuss potential sources of error in RNA-Seq analysis that could alter interpretation of global changes in gene expression. Findings: When comparing statistical tools, the negative binomial distribution-based methods, edgeR and DESeq, respectively identified 11,995 and 11,317 differentially expressed genes from an RNA-seq dataset generated from soybean leaf tissue grown in elevated O3. However, the number of genes in common between these two methods was only 10,535, resulting in 2,242 genes determined to be differentially expressed by only one method. Upon analysis of the non-significant genes, several limitations of these analytic tools were revealed, including evidence for overly stringent parameters for determining statistical significance of differentially expressed genes as well as increased type II error for high abundance transcripts. Conclusions: Because of the high variability between methods for determining differential expression of RNA-Seq data, we suggest using several bioinformatics tools, as outlined here, to ensure that a conservative list of differentially expressed genes is obtained. We also conclude that despite these analytical limitations, RNA-Seq provides highly accurate transcript abundance quantification that is comparable to qRT-PCR.

This article is from BMC Genomics , volume 14 . Abstract Background: The species Brassica rapa (2n=20, AA) is an important vegetable and oilseed crop, and serves as an excellent model for genomic and evolutionary research in Brassica species. With the availability of whole genome sequence of B. rapa, it is essential to further determine the activity of all functional elements of the B. rapa genome and explore the transcriptome on a genome-wide scale. Here, RNA-seq data was employed to provide a genome-wide transcriptional landscape and characterization of the annotated and novel transcripts and alternative splicing events across tissues. Results: RNA-seq reads were generated using the Illumina platform from six different tissues (root, stem, leaf, flower, silique and callus) of the B. rapa accession Chiifu-401-42, the same line used for whole genome sequencing. First, these data detected the widespread transcription of the B. rapa genome, leading to the identification of numerous novel transcripts and definition of 5'/3' UTRs of known genes. Second, 78.8% of the total annotated genes were detected as expressed and 45.8% were constitutively expressed across all tissues. We further defined several groups of genes: housekeeping genes, tissue-specific expressed genes and co-expressed genes across tissues, which will serve as a valuable repository for future crop functional genomics research. Third, alternative splicing (AS) is estimated to occur in more than 29.4% of intron-containing B. rapa genes, and 65% of them were commonly detected in more than two tissues. Interestingly, genes with high rate of AS were over-represented in GO categories relating to transcriptional regulation and signal transduction, suggesting potential importance of AS for playing regulatory role in these genes. Further, we observed that intron retention (IR) is predominant in the AS events and seems to preferentially occurred in genes with short introns. Conclusions: The high-resolution RNA-seq analysis provides a global transcriptional landscape as a complement to the B. rapa genome sequence, which will advance our understanding of the dynamics and complexity of the B. rapa transcriptome. The atlas of gene expression in different tissues will be useful for accelerating research on functional genomics and genome evolution in Brassica species.

This article is from BMC Bioinformatics , volume 15 . Abstract Background: High-throughput sequencing is now regularly used for studies of the transcriptome (RNA-seq), particularly for comparisons among experimental conditions. For the time being, a limited number of biological replicates are typically considered in such experiments, leading to low detection power for differential expression. As their cost continues to decrease, it is likely that additional follow-up studies will be conducted to re-address the same biological question. Results: We demonstrate how p-value combination techniques previously used for microarray meta-analyses can be used for the differential analysis of RNA-seq data from multiple related studies. These techniques are compared to a negative binomial generalized linear model (GLM) including a fixed study effect on simulated data and real data on human melanoma cell lines. The GLM with fixed study effect performed well for low inter-study variation and small numbers of studies, but was outperformed by the meta-analysis methods for moderate to large inter-study variability and larger numbers of studies. Conclusions: The p-value combination techniques illustrated here are a valuable tool to perform differential meta-analyses of RNA-seq data by appropriately accounting for biological and technical variability within studies as well as additional study-specific effects. An R package metaRNASeq is available on the CRAN (http://cran.r-project.org/web/packages/metaRNASeq).

This article is from BMC Systems Biology , volume 5 . Abstract Background: Many groups, including our own, have proposed the use of DNA methylation profiles as biomarkers for various disease states. While much research has been done identifying DNA methylation signatures in cancer vs. normal etc., we still lack sufficient knowledge of the role that differential methylation plays during normal cellular differentiation and tissue specification. We also need thorough, genome level studies to determine the meaning of methylation of individual CpG dinucleotides in terms of gene expression. Results: In this study, we have used (insert statistical method here) to compile unique DNA methylation signatures from normal human heart, lung, and kidney using the Illumina Infinium 27 K methylation arraysand compared those to gene expression by RNA sequencing. We have identified unique signatures of global DNA methylation for human heart, kidney and liver, and showed that DNA methylation data can be used to correctly classify various tissues. It indicates that DNA methylation reflects tissue specificity and may play an important role in tissue differentiation. The integrative analysis of methylation and RNA-Seq data showed that gene methylation and its transcriptional levels were comprehensively correlated. The location of methylation markers in terms of distance to transcription start site and CpG island showed no effects on the regulation of gene expression by DNA methylation in normal tissues. Conclusions: This study showed that an integrative analysis of methylation array and RNA-Seq data can be utilized to discover the global regulation of gene expression by DNA methylation and suggests that DNA methylation plays an important role in normal tissue differentiation via modulation of gene expression.

RNA-sequencing (RNA-Seq) has become a powerful technology to characterize gene expression profiles because it is more accurate and comprehensive than microarrays. Although statistical methods that have been developed for microarray data can be applied to RNA-Seq data, they are not ideal due to the discrete nature of RNA-Seq data. The Poisson distribution and negative binomial distribution are commonly used to model count data. Recently, Witten (2011) proposed a Poisson linear discriminant analysis for RNA-Seq data. The Poisson assumption may not be as appropriate as negative binomial distribution when biological replicates are available and in the presence of overdispersion (i.e., when the variance is larger than the mean). However, it is more complicated to model negative binomial variables because they involve a dispersion parameter that needs to be estimated. In this paper, we propose a negative binomial linear discriminant analysis for RNA-Seq data. By Bayes' rule, we construct the classifier by fitting a negative binomial model, and propose some plug-in rules to estimate the unknown parameters in the classifier. The relationship between the negative binomial classifier and the Poisson classifier is explored, with a numerical investigation of the impact of dispersion on the discriminant score. Simulation results show the superiority of our proposed method. We also analyze four real RNA-Seq data sets to demonstrate the advantage of our method in real-world applications.

This article is from PLoS ONE , volume 9 . Abstract Recent advances in next-generation sequencing technology allow high-throughput cDNA sequencing (RNA-Seq) to be widely applied in transcriptomic studies, in particular for detecting differentially expressed genes between groups. Many software packages have been developed for the identification of differentially expressed genes (DEGs) between treatment groups based on RNA-Seq data. However, there is a lack of consensus on how to approach an optimal study design and choice of suitable software for the analysis. In this comparative study we evaluate the performance of three of the most frequently used software tools: Cufflinks-Cuffdiff2, DESeq and edgeR. A number of important parameters of RNA-Seq technology were taken into consideration, including the number of replicates, sequencing depth, and balanced vs. unbalanced sequencing depth within and between groups. We benchmarked results relative to sets of DEGs identified through either quantitative RT-PCR or microarray. We observed that edgeR performs slightly better than DESeq and Cuffdiff2 in terms of the ability to uncover true positives. Overall, DESeq or taking the intersection of DEGs from two or more tools is recommended if the number of false positives is a major concern in the study. In other circumstances, edgeR is slightly preferable for differential expression analysis at the expense of potentially introducing more false positives.

This article is from BMC Bioinformatics , volume 14 . Abstract Background: RNA-seq, a massive parallel-sequencing-based transcriptome profiling method, provides digital data in the form of aligned sequence read counts. The comparative analyses of the data require appropriate statistical methods to estimate the differential expression of transcript variants across different cell/tissue types and disease conditions. Results: We developed a novel nonparametric empirical Bayesian-based approach (NPEBseq) to model the RNA-seq data. The prior distribution of the Bayesian model is empirically estimated from the data without any parametric assumption, and hence the method is “nonparametric” in nature. Based on this model, we proposed a method for detecting differentially expressed genes across different conditions. We also extended this method to detect differential usage of exons from RNA-seq data. The evaluation of NPEBseq on both simulated and publicly available RNA-seq datasets and comparison with three popular methods showed improved results for experiments with or without biological replicates. Conclusions: NPEBseq can successfully detect differential expression between different conditions not only at gene level but also at exon level from RNA-seq datasets. In addition, NPEBSeq performs significantly better than current methods and can be applied to genome-wide RNA-seq datasets. Sample datasets and R package are available at http://bioinformatics.wistar.upenn.edu/NPEBseq.

This article is from BMC Bioinformatics , volume 14 . Abstract Background: Sample size calculation is an important issue in the experimental design of biomedical research. For RNA-seq experiments, the sample size calculation method based on the Poisson model has been proposed; however, when there are biological replicates, RNA-seq data could exhibit variation significantly greater than the mean (i.e. over-dispersion). The Poisson model cannot appropriately model the over-dispersion, and in such cases, the negative binomial model has been used as a natural extension of the Poisson model. Because the field currently lacks a sample size calculation method based on the negative binomial model for assessing differential expression analysis of RNA-seq data, we propose a method to calculate the sample size. Results: We propose a sample size calculation method based on the exact test for assessing differential expression analysis of RNA-seq data. Conclusions: The proposed sample size calculation method is straightforward and not computationally intensive. Simulation studies to evaluate the performance of the proposed sample size method are presented; the results indicate our method works well, with achievement of desired power.

This article is from Biostatistics (Oxford, England) , volume 15 . Abstract RNA-sequencing (RNA-seq) is a flexible technology for measuring genome-wide expression that is rapidly replacing microarrays as costs become comparable. Current differential expression analysis methods for RNA-seq data fall into two broad classes: (1) methods that quantify expression within the boundaries of genes previously published in databases and (2) methods that attempt to reconstruct full length RNA transcripts. The first class cannot discover differential expression outside of previously known genes. While the second approach does possess discovery capabilities, statistical analysis of differential expression is complicated by the ambiguity and variability incurred while assembling transcripts and estimating their abundances. Here, we propose a novel method that first identifies differentially expressed regions (DERs) of interest by assessing differential expression at each base of the genome. The method then segments the genome into regions comprised of bases showing similar differential expression signal, and then assigns a measure of statistical significance to each region. Optionally, DERs can be annotated using a reference database of genomic features. We compare our approach with leading competitors from both current classes of differential expression methods and highlight the strengths and weaknesses of each. A software implementation of our method is available on github (https://github.com/alyssafrazee/derfinder).