Validation of predicted mRNA splicing mutations using high-throughput transcriptome data.
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ABSTRACT:
SUBMITTER: Viner C
PROVIDER: S-EPMC3983938 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We present a computational framework tailored for the modeling of the complex, dynamic relationships that are encountered in splicing regulation. The starting point is whole-genome transcriptomic data from high-throughput array or sequencing methods that are used to quantify gene expression and alternative splicing across multiple contexts. This information is used as input for state of the art methods for Graphical Model Selection in order to recover the structure of a composite network that simultaneously models exon co-regulation and their cognate regulators. Community structure detection and social network analysis methods are used to identify distinct modules and key actors within the network. As a proof of concept for our framework we studied the splicing regulatory network for Drosophila development using the publicly available modENCODE data. The final model offers a comprehensive view of the splicing circuitry that underlies fly development. Identified modules are associated with major developmental hallmarks including maternally loaded RNAs, onset of zygotic gene expression, transitions between life stages and sex differentiation. Within-module key actors include well-known developmental-specific splicing regulators from the literature while additional factors previously unassociated with developmental-specific splicing are also highlighted. Finally we analyze an extensive battery of Splicing Factor knock-down transcriptome data and demonstrate that our approach captures true regulatory relationships.
| S-EPMC4603746 | biostudies-literature
Project description:Genomic alignments of sequenced cellular messenger RNA contain gapped alignments which are interpreted as consequence of intron removal. The resulting gap-sites, genomic locations of alignment gaps, are landmarks representing potential splice-sites. As alignment algorithms report gap-sites with a considerable false discovery rate, validations are required. We describe two quality scores, gap quality score (gqs) and weighted gap information score (wgis), developed for validation of putative splicing events: While gqs solely relies on alignment data wgis additionally considers information from the genomic sequence. FASTQ files obtained from 54 human dermal fibroblast samples were aligned against the human genome (GRCh38) using TopHat and STAR aligner. Statistical properties of gap-sites validated by gqs and wgis were evaluated by their sequence similarity to known exon-intron borders. Within the 54 samples, TopHat identifies 1,000,380 and STAR reports 6,487,577 gap-sites. Due to the lack of strand information, however, the percentage of identified GT-AG gap-sites is rather low. While gap-sites from TopHat contain ?89% GT-AG, gap-sites from STAR only contain ?42% GT-AG dinucleotide pairs in merged data from 54 fibroblast samples. Validation with gqs yields 156,251 gap-sites from TopHat alignments and 166,294 from STAR alignments. Validation with wgis yields 770,327 gap-sites from TopHat alignments and 1,065,596 from STAR alignments. Both alignment algorithms, TopHat and STAR, report gap-sites with considerable false discovery rate, which can drastically be reduced by validation with gqs and wgis.
| S-EPMC5485934 | biostudies-literature
Project description:MotivationInference of gene regulatory networks from high throughput measurement of gene and protein expression is particularly attractive because it allows the simultaneous discovery of interactive molecular signals for numerous genes and proteins at a relatively low cost.ResultsWe developed two score-based local causal learning algorithms that utilized the Markov blanket search to identify direct regulators of target mRNAs and proteins. These two algorithms were specifically designed for integrated high throughput RNA and protein data. Simulation study showed that these algorithms outperformed other state-of-the-art gene regulatory network learning algorithms. We also generated integrated miRNA, mRNA, and protein expression data based on high throughput analysis of primary trophoblasts, derived from term human placenta and cultured under standard or hypoxic conditions. We applied the new algorithms to these data and identified gene regulatory networks for a set of trophoblastic proteins found to be differentially expressed under the specified culture conditions.
| S-EPMC4443676 | biostudies-literature
Project description:The widespread and rapid adoption of high-throughput sequencing technologies has afforded researchers the opportunity to gain a deep understanding of genome level processes that underlie evolutionary change, and perhaps more importantly, the links between genotype and phenotype. In particular, researchers interested in functional biology and adaptation have used these technologies to sequence mRNA transcriptomes of specific tissues, which in turn are often compared to other tissues, or other individuals with different phenotypes. While these techniques are extremely powerful, careful attention to data quality is required. In particular, because high-throughput sequencing is more error-prone than traditional Sanger sequencing, quality trimming of sequence reads should be an important step in all data processing pipelines. While several software packages for quality trimming exist, no general guidelines for the specifics of trimming have been developed. Here, using empirically derived sequence data, I provide general recommendations regarding the optimal strength of trimming, specifically in mRNA-Seq studies. Although very aggressive quality trimming is common, this study suggests that a more gentle trimming, specifically of those nucleotides whose Phred score <2 or <5, is optimal for most studies across a wide variety of metrics.
| S-EPMC3908319 | biostudies-literature
Project description:Alternative splicing (AS) is an efficient mechanism that involves the generation of transcriptome and protein diversity from a single gene. Defects in pre-messenger RNA (mRNA) splicing are an important cause of numerous diseases, including cancer. AS of pre-mRNA as a target for cancer therapy has not been well studied. We have reported previously that a splicing factor, polypyrimidine tract-binding protein (PTB), is overexpressed in ovarian tumors compared with matched normal controls, and knockdown of PTB expression by short-hairpin RNA impairs ovarian tumor cell growth, colony formation, and invasiveness. Given the complexity of PTB's molecular functions, a chemical method for controlling PTB activity might provide a therapeutic and experimental tool. However, no commercially available PTB inhibitors have yet been described. To expand our ability to find novel inhibitors, we developed a robust, fluorometric, cell-based high-throughput screening assay in 96-well plates that reports on the splicing activity of PTB. In an attempt to use the cells for large-scale chemical screens to identify PTB modulators, we established cell lines stably expressing the reporter gene. Our results suggest that this high-throughput assay could be used to identify small-molecule modulators of PTB activity. Based on these findings and the role that upregulated PTB has on cell proliferation and malignant properties of tumors, targeting PTB for inhibition with small molecules offers a promising strategy for cancer therapy.
| S-EPMC3541474 | biostudies-literature
Project description:MotivationRNA sequencing is now widely performed to study differential expression among experimental conditions. As tests are performed on a large number of genes, stringent false-discovery rate control is required at the expense of detection power. Ad hoc filtering techniques are regularly used to moderate this correction by removing genes with low signal, with little attention paid to their impact on downstream analyses.ResultsWe propose a data-driven method based on the Jaccard similarity index to calculate a filtering threshold for replicated RNA sequencing data. In comparisons with alternative data filters regularly used in practice, we demonstrate the effectiveness of our proposed method to correctly filter lowly expressed genes, leading to increased detection power for moderately to highly expressed genes. Interestingly, this data-driven threshold varies among experiments, highlighting the interest of the method proposed here.AvailabilityThe proposed filtering method is implemented in the R package HTSFilter available on Bioconductor.
| S-EPMC3740625 | biostudies-literature
Project description:Analyze gene expression levels in primary trophoblasts, derived from term human placenta and cultured under standard or hypoxic conditions
2015-03-01 | GSE60432 | GEO
Project description:Splicing-affecting mutations can disrupt gene function by altering the transcript assembly. To ascertain splicing dysregulation principles, we modified a minigene assay for the parallel high-throughput evaluation of different mutations by next-generation sequencing. In our model system, all exonic and six intronic positions of the SMN1 gene's exon 7 were mutated to all possible nucleotide variants, which amounted to 180 unique single-nucleotide mutants and 470 double mutants. The mutations resulted in a wide range of splicing aberrations. Exonic splicing-affecting mutations resulted either in substantial exon skipping, supposedly driven by predicted exonic splicing silencer or cryptic donor splice site (5'ss) and de novo 5'ss strengthening and use. On the other hand, a single disruption of exonic splicing enhancer was not sufficient to cause major exon skipping, suggesting these elements can be substituted during exon recognition. While disrupting the acceptor splice site led only to exon skipping, some 5'ss mutations potentiated the use of three different cryptic 5'ss. Generally, single mutations supporting cryptic 5'ss use displayed better pre-mRNA/U1 snRNA duplex stability and increased splicing regulatory element strength across the original 5'ss. Analyzing double mutants supported the predominating splicing regulatory elements' effect, but U1 snRNA binding could contribute to the global balance of splicing isoforms. Based on these findings, we suggest that creating a new splicing enhancer across the mutated 5'ss can be one of the main factors driving cryptic 5'ss use.
| S-EPMC6779402 | biostudies-literature
Project description:BackgroundThe advances in high-throughput sequencing technologies and growth in data sizes has highlighted the need for scalable tools to perform quality assurance testing. These tests are necessary to ensure that data is of a minimum necessary standard for use in downstream analysis. In this paper we present the SAMQA tool to rapidly and robustly identify errors in population-scale sequence data.ResultsSAMQA has been used on samples from three separate sets of cancer genome data from The Cancer Genome Atlas (TCGA) project. Using technical standards provided by the SAM specification and biological standards defined by researchers, we have classified errors in these sequence data sets relative to individual reads within a sample. Due to an observed linearithmic speedup through the use of a high-performance computing (HPC) framework for the majority of tasks, poor quality data was identified prior to secondary analysis in significantly less time on the HPC framework than the same data run using alternative parallelization strategies on a single server.ConclusionsThe SAMQA toolset validates a minimum set of data quality standards across whole-genome and exome sequences. It is tuned to run on a high-performance computational framework, enabling QA across hundreds gigabytes of samples regardless of coverage or sample type.
| S-EPMC3170309 | biostudies-literature
Project description:Efficient high-throughput transcriptomics (HTT) tools promise inexpensive, rapid assessment of possible biological consequences of human and environmental exposures to tens of thousands of chemicals in commerce. HTT systems have used relatively small sets of gene expression measurements coupled with mathematical prediction methods to estimate genome-wide gene expression and are often trained and validated using pharmaceutical compounds. It is unclear whether these training sets are suitable for general toxicity testing applications and the more diverse chemical space represented by commercial chemicals and environmental contaminants. In this work, we built predictive computational models that inferred whole genome transcriptional profiles from a smaller sample of surrogate genes. The model was trained and validated using a large scale toxicogenomics database with gene expression data from exposure to heterogeneous chemicals from a wide range of classes (the Open TG-GATEs data base). The method of predictor selection was designed to allow high fidelity gene prediction from any pre-existing gene expression data set, regardless of animal species or data measurement platform. Predictive qualitative models were developed with this TG-GATES data that contained gene expression data of human primary hepatocytes with over 941 samples covering 158 compounds. A sequential forward search-based greedy algorithm, combining different fitting approaches and machine learning techniques, was used to find an optimal set of surrogate genes that predicted differential expression changes of the remaining genome. We then used pathway enrichment of up-regulated and down-regulated genes to assess the ability of a limited gene set to determine relevant patterns of tissue response. In addition, we compared prediction performance using the surrogate genes found from our greedy algorithm (referred to as the SV2000) with the landmark genes provided by existing technologies such as L1000 (Genometry) and S1500 (Tox21), finding better predictive performance for the SV2000. The ability of these predictive algorithms to predict pathway level responses is a positive step toward incorporating mode of action (MOA) analysis into the high throughput prioritization and testing of the large number of chemicals in need of safety evaluation.
| S-EPMC6176017 | biostudies-literature