Multiple RNA-seq Datasets Research Articles

Isoform function prediction based on bi-random walks on a heterogeneous network.

Alternative splicing contributes to the functional diversity of protein species and the proteoforms translated from alternatively spliced isoforms of a gene actually execute the biological functions. Computationally predicting the functions of genes has been studied for decades. However, how to distinguish the functional annotations of isoforms, whose annotations are essential for understanding developmental abnormalities and cancers, is rarely explored. The main bottleneck is that functional annotations of isoforms are generally unavailable and functional genomic databases universally store the functional annotations at the gene level. We propose IsoFun to accomplish Isoform Function prediction based on bi-random walks on a heterogeneous network. IsoFun firstly constructs an isoform functional association network based on the expression profiles of isoforms derived from multiple RNA-seq datasets. Next, IsoFun uses the available Gene Ontology annotations of genes, gene-gene interactions and the relations between genes and isoforms to construct a heterogeneous network. After this, IsoFun performs a tailored bi-random walk on the heterogeneous network to predict the association between GO terms and isoforms, thus accomplishing the prediction of GO annotations of isoforms. Experimental results show that IsoFun significantly outperforms the state-of-the-art algorithms and improves the area under the receiver-operating curve (AUROC) and the area under the precision-recall curve (AUPRC) by 17% and 44% at the gene-level, respectively. We further validated the performance of IsoFun on the genes ADAM15 and BCL2L1. IsoFun accurately differentiates the functions of respective isoforms of these two genes. The code of IsoFun is available at http://mlda.swu.edu.cn/codes.php? name=IsoFun. Supplementary data are available at Bioinformatics online.

ScMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets

Concerted examination of multiple collections of single-cell RNA sequencing (RNA-seq) data promises further biological insights that cannot be uncovered with individual datasets. Here we present scMerge, an algorithm that integrates multiple single-cell RNA-seq datasets using factor analysis of stably expressed genes and pseudoreplicates across datasets. Using a large collection of public datasets, we benchmark scMerge against published methods and demonstrate that it consistently provides improved cell type separation by removing unwanted factors; scMerge can also enhance biological discovery through robust data integration, which we show through the inference of development trajectory in a liver dataset collection.

Optimal Gene Filtering for Single-Cell data (OGFSC)-a gene filtering algorithm for single-cell RNA-seq data.

Single-cell transcriptomic data are commonly accompanied by extremely high technical noise due to the low RNA concentrations from individual cells. Precise identification of differentially expressed genes and cell populations are heavily dependent on the effective reduction of technical noise, e.g. by gene filtering. However, there is still no well-established standard in the current approaches of gene filtering. Investigators usually filter out genes based on single fixed threshold, which commonly leads to both over- and under-stringent errors. In this study, we propose a novel algorithm, termed as Optimal Gene Filtering for Single-Cell data, to construct a thresholding curve based on gene expression levels and the corresponding variances. We validated our method on multiple single-cell RNA-seq datasets, including simulated and published experimental datasets. The results show that the known signal and known noise are reliably discriminated in the simulated datasets. In addition, the results of seven experimental datasets demonstrate that these cells of the same annotated types are more sharply clustered using our method. Interestingly, when we re-analyze the dataset from an aging research recently published in Science, we find a list of regulated genes which is different from that reported in the original study, because of using different filtering methods. However, the knowledge based on our findings better matches the progression of immunosenescence. In summary, we here provide an alternative opportunity to probe into the true level of technical noise in single-cell transcriptomic data. https://github.com/XZouProjects/OGFSC.git. Supplementary data are available at Bioinformatics online.

Unique Molecular Identifiers reveal a novel sequencing artefact with implications for RNA-Seq based gene expression analysis

Attaching Unique Molecular Identifiers (UMI) to RNA molecules in the first step of sequencing library preparation establishes a distinct identity for each input molecule. This makes it possible to eliminate the effects of PCR amplification bias, which is particularly important where many PCR cycles are required, for example, in single cell studies. After PCR, molecules sharing a UMI are assumed to be derived from the same input molecule. In our single cell RNA-Seq studies of Physcomitrellapatens, we discovered that reads sharing a UMI, and therefore presumed to be derived from the same mRNA molecule, frequently map to different, but closely spaced locations. This behaviour occurs in all such libraries that we have produced, and in multiple other UMI-containing RNA-Seq data sets in the public domain. This apparent paradox, that reads of identical origin map to distinct genomic coordinates may be partially explained by PCR stutter, which is often seen in low-entropy templates and those containing simple tandem repeats. In the absence of UMI this artefact is undetectable. We show that the common assumption that sequence reads having different mapping coordinates are derived from different starting molecules does not hold. Unless taken into account, this artefact is likely to result in over-estimation of certain transcript abundances, depending on the counting method employed.

MSIQ: JOINT MODELING OF MULTIPLE RNA-SEQ SAMPLES FOR ACCURATE ISOFORM QUANTIFICATION.

Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq data offer insight into gene expression levels and transcriptome structures, enabling us to better understand the regulation of gene expression and fundamental biological processes. Accurate isoform quantification from RNA-seq data is challenging due to the information loss in sequencing experiments. A recent accumulation of multiple RNA-seq data sets from the same tissue or cell type provides new opportunities to improve the accuracy of isoform quantification. However, existing statistical or computational methods for multiple RNA-seq samples either pool the samples into one sample or assign equal weights to the samples when estimating isoform abundance. These methods ignore the possible heterogeneity in the quality of different samples and could result in biased and unrobust estimates. In this article, we develop a method, which we call "joint modeling of multiple RNA-seq samples for accurate isoform quantification" (MSIQ), for more accurate and robust isoform quantification by integrating multiple RNA-seq samples under a Bayesian framework. Our method aims to (1) identify a consistent group of samples with homogeneous quality and (2) improve isoform quantification accuracy by jointly modeling multiple RNA-seq samples by allowing for higher weights on the consistent group. We show that MSIQ provides a consistent estimator of isoform abundance, and we demonstrate the accuracy and effectiveness of MSIQ compared with alternative methods through simulation studies on D. melanogaster genes. We justify MSIQ's advantages over existing approaches via application studies on real RNA-seq data from human embryonic stem cells, brain tissues, and the HepG2 immortalized cell line. We also perform a comprehensive analysis of how the isoform quantification accuracy would be affected by RNA-seq sample heterogeneity and different experimental protocols.

Removing bias against short sequences enables northern blotting to better complement RNA-seq for the study of small RNAs

Changes in small non-coding RNAs such as micro RNAs (miRNAs) can serve as indicators of disease and can be measured using next-generation sequencing of RNA (RNA-seq). Here, we highlight the need for approaches that complement RNA-seq, discover that northern blotting of small RNAs is biased against short sequences and develop a protocol that removes this bias. We found that multiple small RNA-seq datasets from the worm Caenorhabditis elegans had shorter forms of miRNAs that appear to be degradation products that arose during the preparatory steps required for RNA-seq. When using northern blotting during these studies, we discovered that miRNA-length probes can have ∼1000-fold bias against detecting even synthetic sequences that are 8 nt shorter. By using shorter probes and by performing hybridization and washes at low temperatures, we greatly reduced this bias to enable nearly equivalent detection of 24 to 14 nt RNAs. Our protocol can discriminate RNAs that differ by a single nucleotide and can detect specific miRNAs present in total RNA from C. elegans and pRNAs in total RNA from bacteria. This improved northern blotting is particularly useful to analyze products of RNA processing or turnover, and functional RNAs that are shorter than typical miRNAs.

Tissue resolved, gene structure refined equine transcriptome

BackgroundTranscriptome interpretation relies on a good-quality reference transcriptome for accurate quantification of gene expression as well as functional analysis of genetic variants. The current annotation of the horse genome lacks the specificity and sensitivity necessary to assess gene expression especially at the isoform level, and suffers from insufficient annotation of untranslated regions (UTR) usage. We built an annotation pipeline for horse and used it to integrate 1.9 billion reads from multiple RNA-seq data sets into a new refined transcriptome.ResultsThis equine transcriptome integrates eight different tissues from 59 individuals and improves gene structure and isoform resolution, while providing considerable tissue-specific information. We utilized four levels of transcript filtration in our pipeline, aimed at producing several transcriptome versions that are suitable for different downstream analyses. Our most refined transcriptome includes 36,876 genes and 76,125 isoforms, with 6474 candidate transcriptional loci novel to the equine transcriptome.ConclusionsWe have employed a variety of descriptive statistics and figures that demonstrate the quality and content of the transcriptome. The equine transcriptomes that are provided by this pipeline show the best tissue-specific resolution of any equine transcriptome to date and are flexible for several downstream analyses. We encourage the integration of further equine transcriptomes with our annotation pipeline to continue and improve the equine transcriptome.

Identifying stably expressed genes from multiple RNA-Seq data sets.

We examined RNA-Seq data on 211 biological samples from 24 different Arabidopsis experiments carried out by different labs. We grouped the samples according to tissue types, and in each of the groups, we identified genes that are stably expressed across biological samples, treatment conditions, and experiments. We fit a Poisson log-linear mixed-effect model to the read counts for each gene and decomposed the total variance into between-sample, between-treatment and between-experiment variance components. Identifying stably expressed genes is useful for count normalization and differential expression analysis. The variance component analysis that we explore here is a first step towards understanding the sources and nature of the RNA-Seq count variation. When using a numerical measure to identify stably expressed genes, the outcome depends on multiple factors: the background sample set and the reference gene set used for count normalization, the technology used for measuring gene expression, and the specific numerical stability measure used. Since differential expression (DE) is measured by relative frequencies, we argue that DE is a relative concept. We advocate using an explicit reference gene set for count normalization to improve interpretability of DE results, and recommend using a common reference gene set when analyzing multiple RNA-Seq experiments to avoid potential inconsistent conclusions.

TACO produces robust multisample transcriptome assemblies from RNA-seq.

Accurate transcript structure and abundance inference from RNA-Seq data is foundational for molecular discovery. Here we present TACO, a computational method to reconstruct a consensus transcriptome from multiple RNA-Seq datasets. TACO employs novel change-point detection to demarcate transcript start and end sites, leading to dramatically improved reconstruction accuracy compared to other tools in its class. The tool is available at http://tacorna.github.io and can be readily incorporated into RNA-Seq analysis workflows.

Systematic Discovery of Chromatin-Bound Protein Complexes from ChIP-seq Datasets.

Chromatin immunoprecipitation followed by sequencing is an invaluable assay for identifying the genomic binding sites of transcription factors. However, transcription factors rarely bind chromatin alone but often bind together with other cofactors, forming protein complexes. Here, we describe a computational method that integrates multiple ChIP-seq and RNA-seq datasets to discover protein complexes and determine their role as activators or repressors. This chapter outlines a detailed computational pipeline for discovering and predicting binding partners from ChIP-seq data and inferring their role in regulating gene expression. This work aims at developing hypotheses about gene regulation via binding partners and deciphering the combinatorial nature of DNA-binding proteins.

OEFinder: a user interface to identify and visualize ordering effects in single-cell RNA-seq data.

Summary: A recent article identified an artifact in multiple single-cell RNA-seq (scRNA-seq) datasets generated by the Fluidigm C1 platform. Specifically, Leng et al. showed significantly increased gene expression in cells captured from sites with small or large plate output IDs. We refer to this artifact as an ordering effect (OE). Including OE genes in downstream analyses could lead to biased results. To address this problem, we developed a statistical method and software called OEFinder to identify a sorted list of OE genes. OEFinder is available as an R package along with user-friendly graphical interface implementations which allows users to check for potential artifacts in scRNA-seq data generated by the Fluidigm C1 platform.Availability and implementation: OEFinder is freely available at https://github.com/lengning/OEFinderContact: rstewart@morgridge.org or lengning1@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online.

A FASTQ compressor based on integer-mapped k-mer indexing for biologist

Next generation sequencing (NGS) technologies have gained considerable popularity among biologists. For example, RNA-seq, which provides both genomic and functional information, has been widely used by recent functional and evolutionary studies, especially in non-model organisms. However, storing and transmitting these large data sets (primarily in FASTQ format) have become genuine challenges, especially for biologists with little informatics experience. Data compression is thus a necessity. KIC, a FASTQ compressor based on a new integer-mapped k-mer indexing method, was developed (available at http://www.ysunlab.org/kic.jsp). It offers high compression ratio on sequence data, outstanding user-friendliness with graphic user interfaces, and proven reliability. Evaluated on multiple large RNA-seq data sets from both human and plants, it was found that the compression ratio of KIC had exceeded all major generic compressors, and was comparable to those of the latest dedicated compressors. KIC enables researchers with minimal informatics training to take advantage of the latest sequence compression technologies, easily manage large FASTQ data sets, and reduce storage and transmission cost.

Identification of important long non-coding RNAs and highly recurrent aberrant alternative splicing events in hepatocellular carcinoma through integrative analysis of multiple RNA-Seq datasets.

Hepatocellular carcinoma (HCC) is an aggressive and deadly cancer. The molecular pathogenesis of the disease remains poorly understood. To better understand HCC biology and explore potential biomarkers and therapeutic targets, we investigated the whole transcriptome of HCC. Considering the genetic heterogeneity of HCC, four datasets from four studies consisting of 15 pairs of HCC and adjacent normal samples were analyzed. We observed that the number of lncRNAs expressed in each HCC sample was consistently greater than the adjacent normal sample. Moreover, 15 lncRNAs were identified expressed in five to seven HCC tissues but were not detected in any adjacent normal tissue. Differential expression analysis detected 35 up- and 80 down-regulated lncRNAs in HCC samples compared with adjacent normal samples. In addition, five differentially expressed lncRNAs were predicted to play a role in oxidation and reduction process. With regard to splicing alterations, we identified nine highly recurrent differential splicing events belonging to eight genes USO1, RPS24, CCDC50, THNSL2, NUMB, FN1 (two events), SLC39A14 and NR1I3. Of them, splicing alterations of SLC39A14 and NR1I3 were reported for the association with HCC for the first time. The splicing dysregulation in HCC may be influenced by three splicing factors ESRP2, CELF2 and SRSF5 which were significantly down-regulated in HCC samples. This study revealed uncharacterized aspects of HCC transcriptome and identified important lncRNAs and splicing isoforms with the potential to serve as biomarkers and therapeutic targets for the disease.

Accurate inference of isoforms from multiple sample RNA-Seq data.

BackgroundRNA-Seq based transcriptome assembly has become a fundamental technique for studying expressed mRNAs (i.e., transcripts or isoforms) in a cell using high-throughput sequencing technologies, and is serving as a basis to analyze the structural and quantitative differences of expressed isoforms between samples. However, the current transcriptome assembly algorithms are not specifically designed to handle large amounts of errors that are inherent in real RNA-Seq datasets, especially those involving multiple samples, making downstream differential analysis applications difficult. On the other hand, multiple sample RNA-Seq datasets may provide more information than single sample datasets that can be utilized to improve the performance of transcriptome assembly and abundance estimation, but such information remains overlooked by the existing assembly tools.ResultsWe formulate a computational framework of transcriptome assembly that is capable of handling noisy RNA-Seq reads and multiple sample RNA-Seq datasets efficiently. We show that finding an optimal solution under this framework is an NP-hard problem. Instead, we develop an efficient heuristic algorithm, called Iterative Shortest Path (ISP), based on linear programming (LP) and integer linear programming (ILP). Our preliminary experimental results on both simulated and real datasets and comparison with the existing assembly tools demonstrate that (i) the ISP algorithm is able to assemble transcriptomes with a greatly increased precision while keeping the same level of sensitivity, especially when many samples are involved, and (ii) its assembly results help improve downstream differential analysis. The source code of ISP is freely available at http://alumni.cs.ucr.edu/~liw/isp.html.

Analysis of 52 Rab GTPases from channel catfish and their involvement in immune responses after bacterial infections

Rab genes, encoding a large family of monomeric G-proteins, contain over 60 members in the human genome. They have been recognized as crucial regulators for membrane trafficking including cargo sorting, vesicle formation, budding, motility, docking, fusion, secretory and endocytic pathway of host immune responses. However, little is known of the Rab gene family in teleost fish species. The development of full-length transcripts and whole genome sequences allow the identification and annotation of Rab GTPase gene family in catfish. In this study, a total of 52 Rab genes were identified from catfish cDNA and genome databases. Phylogenetic analysis assigned them into eleven subfamilies. Most Rab GTPases are conserved among vertebrates, though some of which are absent in fish genomes. Analysis of multiple RNA-seq datasets, along with real time PCR analysis revealed up-regulation of 10 Rab genes after bacterial infection. These included Rab3a, Rab4a, Rab4b, Rab5a, Rab5c, Rab7a, Rab9a, Rab11a, Rab11b, and Rab33a. Their up-regulation are temporally and spatially regulated in various tissues, but mostly induced at early stages after infection and in the gill and liver tissues, with the exception of Rab5c that is mostly up-regulated in the head kidney and trunk kidney. The complex pattern of their induced expression suggested both specific and cooperative actions of a these Rab genes in the acute immune responses to bacterial infection.

High-resolution functional annotation of human transcriptome: predicting isoform functions by a novel multiple instance-based label propagation method

Alternative transcript processing is an important mechanism for generating functional diversity in genes. However, little is known about the precise functions of individual isoforms. In fact, proteins (translated from transcript isoforms), not genes, are the function carriers. By integrating multiple human RNA-seq data sets, we carried out the first systematic prediction of isoform functions, enabling high-resolution functional annotation of human transcriptome. Unlike gene function prediction, isoform function prediction faces a unique challenge: the lack of the training data—all known functional annotations are at the gene level. To address this challenge, we modelled the gene–isoform relationships as multiple instance data and developed a novel label propagation method to predict functions. Our method achieved an average area under the receiver operating characteristic curve of 0.67 and assigned functions to 15 572 isoforms. Interestingly, we observed that different functions have different sensitivities to alternative isoform processing, and that the function diversity of isoforms from the same gene is positively correlated with their tissue expression diversity. Finally, we surveyed the literature to validate our predictions for a number of apoptotic genes. Strikingly, for the famous ‘TP53’ gene, we not only accurately identified the apoptosis regulation function of its five isoforms, but also correctly predicted the precise direction of the regulation.

Inferring Polymorphism-Induced Regulatory Gene Networks Active in Human Lymphocyte Cell Lines by Weighted Linear Mixed Model Analysis of Multiple RNA-Seq Datasets

Single-nucleotide polymorphisms (SNPs) contribute to the between-individual expression variation of many genes. A regulatory (trait-associated) SNP is usually located near or within a (host) gene, possibly influencing the gene’s transcription or/and post-transcriptional modification. But its targets may also include genes that are physically farther away from it. A heuristic explanation of such multiple-target interferences is that the host gene transfers the SNP genotypic effects to the distant gene(s) by a transcriptional or signaling cascade. These connections between the host genes (regulators) and the distant genes (targets) make the genetic analysis of gene expression traits a promising approach for identifying unknown regulatory relationships. In this study, through a mixed model analysis of multi-source digital expression profiling for 140 human lymphocyte cell lines (LCLs) and the genotypes distributed by the international HapMap project, we identified 45 thousands of potential SNP-induced regulatory relationships among genes (the significance level for the underlying associations between expression traits and SNP genotypes was set at FDR < 0.01). We grouped the identified relationships into four classes (paradigms) according to the two different mechanisms by which the regulatory SNPs affect their cis- and trans- regulated genes, modifying mRNA level or altering transcript splicing patterns. We further organized the relationships in each class into a set of network modules with the cis- regulated genes as hubs. We found that the target genes in a network module were often characterized by significant functional similarity, and the distributions of the target genes in three out of the four networks roughly resemble a power-law, a typical pattern of gene networks obtained from mutation experiments. By two case studies, we also demonstrated that significant biological insights can be inferred from the identified network modules.

Systematically profiling and annotating long intergenic non-coding RNAs in human embryonic stem cell

BackgroundWhile more and more long intergenic non-coding RNAs (lincRNAs) were identified to take important roles in both maintaining pluripotency and regulating differentiation, how these lincRNAs may define and drive cell fate decisions on a global scale are still mostly elusive. Systematical profiling and comprehensive annotation of embryonic stem cells lincRNAs may not only bring a clearer big picture of these novel regulators but also shed light on their functionalities.ResultsBased on multiple RNA-Seq datasets, we systematically identified 300 human embryonic stem cell lincRNAs (hES lincRNAs). Of which, one forth (78 out of 300) hES lincRNAs were further identified to be biasedly expressed in human ES cells. Functional analysis showed that they were preferentially involved in several early-development related biological processes. Comparative genomics analysis further suggested that around half of the identified hES lincRNAs were conserved in mouse. To facilitate further investigation of these hES lincRNAs, we constructed an online portal for biologists to access all their sequences and annotations interactively. In addition to navigation through a genome browse interface, users can also locate lincRNAs through an advanced query interface based on both keywords and expression profiles, and analyze results through multiple tools.ConclusionsBy integrating multiple RNA-Seq datasets, we systematically characterized and annotated 300 hES lincRNAs. A full functional web portal is available freely at http://scbrowse.cbi.pku.edu.cn. As the first global profiling and annotating of human embryonic stem cell lincRNAs, this work aims to provide a valuable resource for both experimental biologists and bioinformaticians.

Gene expression rate comparison for multiple high‐throughput datasets

Microarray provides genome-wide transcript profiles, whereas RNA-seq is an alternative approach applied for transcript discovery and genome annotation. Both high-throughput techniques show quantitative measurement of gene expression. To explore differential gene expression rates and understand biological functions, the authors designed a system which utilises annotations from Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathways and Gene Ontology (GO) associations for integrating multiple RNA-seq or microarray datasets. The developed system is initiated by either estimating gene expression levels from mapping next generation sequencing short reads onto reference genomes or performing intensity analysis from microarray raw images. Normalisation procedures on expression levels are evaluated and compared through different approaches including Reads Per Kilobase per Million mapped reads (RPKM) and housekeeping gene selection. Such gene expression levels are shown in different colour shades and graphically displayed in designed temporal pathways. To enhance importance of functional relationships of clustered genes, representative GO terms associated with differentially expressed gene cluster are visually illustrated in a tag cloud representation.

A two-parameter generalized Poisson model to improve the analysis of RNA-seq data

Deep sequencing of RNAs (RNA-seq) has been a useful tool to characterize and quantify transcriptomes. However, there are significant challenges in the analysis of RNA-seq data, such as how to separate signals from sequencing bias and how to perform reasonable normalization. Here, we focus on a fundamental question in RNA-seq analysis: the distribution of the position-level read counts. Specifically, we propose a two-parameter generalized Poisson (GP) model to the position-level read counts. We show that the GP model fits the data much better than the traditional Poisson model. Based on the GP model, we can better estimate gene or exon expression, perform a more reasonable normalization across different samples, and improve the identification of differentially expressed genes and the identification of differentially spliced exons. The usefulness of the GP model is demonstrated by applications to multiple RNA-seq data sets.