Sensitive, reliable and robust circRNA detection from RNA-seq with CirComPara2.
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ABSTRACT:
SUBMITTER: Gaffo E
PROVIDER: S-EPMC8769706 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MotivationPaired-end whole transcriptome sequencing provides evidence for fusion transcripts. However, due to the repetitiveness of the transcriptome, many reads have multiple high-quality mappings. Previous methods to find gene fusions either ignored these reads or required additional longer single reads. This can obscure up to 30% of fusions and unnecessarily discards much of the data.ResultsWe present a method for using paired-end reads to find fusion transcripts without requiring unique mappings or additional single read sequencing. Using simulated data and data from tumors and cell lines, we show that our method can find fusions with ambiguously mapping read pairs without generating numerous spurious fusions from the many mapping locations.AvailabilityA C++ and Python implementation of the method demonstrated in this article is available at http://exon.ucsd.edu/ShortFuse.Contactmckinsel@ucsd.eduSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC3072550 | biostudies-literature
Project description:We describe a cost-effective, highly sensitive, and quantitative method for in situ detection of RNA molecules in tissue sections. This method, dubbed Yn-situ, standing for Y-branched probe in situ hybridization, uses a single-strand DNA preamplifier with multiple initiation sites that trigger a hybridization chain reaction (HCR) to detect polynucleotides. By characterizing the performance of this method, we show that the Yn-situ method, in conjunction with an improved fixation step, is sensitive enough to allow detection of RNA molecules using fewer probes targeting short nucleotide sequences than existing methods. A set of five probes can produce quantitative results with smaller puncta and higher signal-to-noise ratio than the 20-probe sets commonly required for HCR and RNAscope. We show that the high sensitivity and wide dynamic range allow quantification of genes expressed at different levels in the olfactory sensory neurons. We describe key steps of this method to enable broad utility by individual laboratories.
| S-EPMC9046451 | biostudies-literature
Project description:BackgroundHigh throughput RNA sequencing is a powerful approach to study gene expression. Due to the complex multiple-steps protocols in data acquisition, extreme deviation of a sample from samples of the same treatment group may occur due to technical variation or true biological differences. The high-dimensionality of the data with few biological replicates make it challenging to accurately detect those samples, and this issue is not well studied in the literature currently. Robust statistics is a family of theories and techniques aim to detect the outliers by first fitting the majority of the data and then flagging data points that deviate from it. Robust statistics have been widely used in multivariate data analysis for outlier detection in chemometrics and engineering. Here we apply robust statistics on RNA-seq data analysis.ResultsWe report the use of two robust principal component analysis (rPCA) methods, PcaHubert and PcaGrid, to detect outlier samples in multiple simulated and real biological RNA-seq data sets with positive control outlier samples. PcaGrid achieved 100% sensitivity and 100% specificity in all the tests using positive control outliers with varying degrees of divergence. We applied rPCA methods and classical principal component analysis (cPCA) on an RNA-Seq data set profiling gene expression of the external granule layer in the cerebellum of control and conditional SnoN knockout mice. Both rPCA methods detected the same two outlier samples but cPCA failed to detect any. We performed differentially expressed gene detection before and after outlier removal as well as with and without batch effect modeling. We validated gene expression changes using quantitative reverse transcription PCR and used the result as reference to compare the performance of eight different data analysis strategies. Removing outliers without batch effect modeling performed the best in term of detecting biologically relevant differentially expressed genes.ConclusionsrPCA implemented in the PcaGrid function is an accurate and objective method to detect outlier samples. It is well suited for high-dimensional data with small sample sizes like RNA-seq data. Outlier removal can significantly improve the performance of differential gene detection and downstream functional analysis.
| S-EPMC7324992 | biostudies-literature
Project description:BACKGROUND: In eukaryotes, alternative splicing often generates multiple splice variants from a single gene. Here we explore the use of RNA sequencing (RNA-Seq) datasets to address the isoform quantification problem. Given a set of known splice variants, the goal is to estimate the relative abundance of the individual variants. METHODS: Our method employs a linear models framework to estimate the ratios of known isoforms in a sample. A key feature of our method is that it takes into account the non-uniformity of RNA-Seq read positions along the targeted transcripts. RESULTS: Preliminary tests indicate that the model performs well on both simulated and real data. In two publicly available RNA-Seq datasets, we identified several alternatively-spliced genes with switch-like, on/off expression properties, as well as a number of other genes that varied more subtly in isoform expression. In many cases, genes exhibiting differential expression of alternatively spliced transcripts were not differentially expressed at the gene level. CONCLUSIONS: Given that changes in isoform expression level frequently involve a continuum of isoform ratios, rather than all-or-nothing expression, and that they are often independent of general gene expression changes, we anticipate that our research will contribute to revealing a so far uninvestigated layer of the transcriptome. We believe that, in the future, researchers will prioritize genes for functional analysis based not only on observed changes in gene expression levels, but also on changes in alternative splicing.
| S-EPMC2863065 | biostudies-literature
Project description:Circular RNAs (circRNAs) are a group of single-stranded RNAs in closed circular form. They are splicing-generated, widely expressed in various tissues and have functional implications in development and diseases. To facilitate genome-wide characterization of circRNAs using RNA-Seq data, we present a freely available software package named acfs. Acfs allows de novo, accurate and fast identification and abundance quantification of circRNAs from single- and paired-ended RNA-Seq data. On simulated datasets, acfs achieved the highest F1 accuracy and lowest false discovery rate among current state-of-the-art tools. On real-world datasets, acfs efficiently identified more bona fide circRNAs. Furthermore, we demonstrated the power of circRNA analysis on two leukemia datasets. We identified a set of circRNAs that are differentially expressed between AML and APL samples, which might shed light on the potential molecular classification of complex diseases using circRNA profiles. Moreover, chromosomal translocation, as manifested in numerous diseases, could produce not only fusion transcripts but also fusion circRNAs of clinical relevance. Featured with high accuracy, low FDR and the ability to identify fusion circRNAs, we believe that acfs is well suited for a wide spectrum of applications in characterizing the landscape of circRNAs from non-model organisms to cancer biology.
| S-EPMC5144000 | biostudies-literature
Project description:Ultra-deep RNA sequencing (RNA-Seq) has become a powerful approach for genome-wide analysis of pre-mRNA alternative splicing. We previously developed multivariate analysis of transcript splicing (MATS), a statistical method for detecting differential alternative splicing between two RNA-Seq samples. Here we describe a new statistical model and computer program, replicate MATS (rMATS), designed for detection of differential alternative splicing from replicate RNA-Seq data. rMATS uses a hierarchical model to simultaneously account for sampling uncertainty in individual replicates and variability among replicates. In addition to the analysis of unpaired replicates, rMATS also includes a model specifically designed for paired replicates between sample groups. The hypothesis-testing framework of rMATS is flexible and can assess the statistical significance over any user-defined magnitude of splicing change. The performance of rMATS is evaluated by the analysis of simulated and real RNA-Seq data. rMATS outperformed two existing methods for replicate RNA-Seq data in all simulation settings, and RT-PCR yielded a high validation rate (94%) in an RNA-Seq dataset of prostate cancer cell lines. Our data also provide guiding principles for designing RNA-Seq studies of alternative splicing. We demonstrate that it is essential to incorporate biological replicates in the study design. Of note, pooling RNAs or merging RNA-Seq data from multiple replicates is not an effective approach to account for variability, and the result is particularly sensitive to outliers. The rMATS source code is freely available at rnaseq-mats.sourceforge.net/. As the popularity of RNA-Seq continues to grow, we expect rMATS will be useful for studies of alternative splicing in diverse RNA-Seq projects.
| S-EPMC4280593 | biostudies-literature
Project description:Identifying genomic variation is a crucial step for unraveling the relationship between genotype and phenotype and can yield important insights into human diseases. Prevailing methods rely on cost-intensive whole-genome sequencing (WGS) or whole-exome sequencing (WES) approaches while the identification of genomic variants from often existing RNA sequencing (RNA-seq) data remains a challenge because of the intrinsic complexity in the transcriptome. Here, we present a highly accurate approach termed SNPiR to identify SNPs in RNA-seq data. We applied SNPiR to RNA-seq data of samples for which WGS and WES data are also available and achieved high specificity and sensitivity. Of the SNPs called from the RNA-seq data, >98% were also identified by WGS or WES. Over 70% of all expressed coding variants were identified from RNA-seq, and comparable numbers of exonic variants were identified in RNA-seq and WES. Despite our method's limitation in detecting variants in expressed regions only, our results demonstrate that SNPiR outperforms current state-of-the-art approaches for variant detection from RNA-seq data and offers a cost-effective and reliable alternative for SNP discovery.
| S-EPMC3791257 | biostudies-literature
Project description:Identifying variants from RNA-seq (transcriptome sequencing) data is a cost-effective and versatile alternative to whole-genome sequencing. However, current variant callers do not generally behave well with RNA-seq data due to reads encompassing intronic regions. We have developed a software programme called Opossum to address this problem. Opossum pre-processes RNA-seq reads prior to variant calling, and although it has been designed to work specifically with Platypus, it can be used equally well with other variant callers such as GATK HaplotypeCaller. In this work, we show that using Opossum in conjunction with either Platypus or GATK HaplotypeCaller maintains precision and improves the sensitivity for SNP detection compared to the GATK Best Practices pipeline. In addition, using it in combination with Platypus offers a substantial reduction in run times compared to the GATK pipeline so it is ideal when there are only limited time or computational resources available.
| S-EPMC5322827 | biostudies-literature
Project description:CircRNAs are a group of endogenous noncoding RNAs. The quickly developing high throughput RNA sequencing technologies along with novel bioinformatics approaches have enabled researchers to systematically identify circRNAs and their biological functions in cells. Deep sequencing of rRNA-depleted RNAs treated with RNase R, which digests linear RNAs and leaves circRNAs enriched, is an efficient way to identify circRNAs. However, very few of RNase R treated data are at hand but a large amount of total RNA-Seq data with no sequencing costs is available, for circRNA predictions. In this study, we systematically investigated the prediction bias from total RNA-Seq data as well as the influence of sequencing depth, sequencing quality and single-end or paired-end sequencing strategy on the predictions. We also identified circRNA properties that may contribute to the improved prediction performance. Our analysis shows that circRNA predictions from total RNA-Seq data gain ∼50% true positive. Sequencing error dramatically worsens the predictions, rather than single-end sequencing strategy or low sequencing depth. However, false positive can be carefully controlled by using data with good quality and narrowing down circRNAs guided by their properties.
| S-EPMC5762294 | biostudies-literature
Project description:Complex glycans play vital roles in many biological processes, ranging from intracellular signaling and organ development to tumor growth. Glycan expression is routinely assessed by the application of glycan-specific antibodies to cells and tissues. However, glycan-specific antibodies quite often show a large number of bands on immunoblots and it is hard to interpret the data when reliable controls are lacking. This limits the scope of glycobiology studies and poses challenges for replication. We sought to resolve this issue by developing a novel strategy that utilizes an immunoreaction enhancing technology to vastly improve the speed and quality of glycan-based immunoblots. As a representative case study, we used chondroitin sulfate glycosaminoglycan (CS-GAG) chains as the carbohydrate target and a monoclonal antibody, CS-56, as the probe. We discovered that preincubation of the antibody with its antigenic CS-GAG chain distinguishes true-positive signals from false-positive ones. We successfully applied this strategy to 10E4, a monoclonal anti heparan sulfate GAGs (HS-GAGs) antibody, where true-positive signals were confirmed by chemical HS-GAG depolymerization on the membrane. This evidence that glycan-specific antibodies can generate clear and convincing data on immunoblot with highly replicable results opens new opportunities for many facets of life science research in glycobiology.
| S-EPMC7874388 | biostudies-literature