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Polyester: simulating RNA-seq datasets with differential transcript expression.

ABSTRACT: Statistical methods development for differential expression analysis of RNA sequencing (RNA-seq) requires software tools to assess accuracy and error rate control. Since true differential expression status is often unknown in experimental datasets, artificially constructed datasets must be utilized, either by generating costly spike-in experiments or by simulating RNA-seq data.Polyester is an R package designed to simulate RNA-seq data, beginning with an experimental design and ending with collections of RNA-seq reads. Its main advantage is the ability to simulate reads indicating isoform-level differential expression across biological replicates for a variety of experimental designs. Data generated by Polyester is a reasonable approximation to real RNA-seq data and standard differential expression workflows can recover differential expression set in the simulation by the user.Polyester is freely available from Bioconductor (http://bioconductor.org/).jtleek@gmail.comSupplementary data are available at Bioinformatics online.

SUBMITTER: Frazee AC

PROVIDER: S-EPMC4635655 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Polyester: simulating RNA-seq datasets with differential transcript expression.

Frazee Alyssa C AC Jaffe Andrew E AE Langmead Ben B Leek Jeffrey T JT

Bioinformatics (Oxford, England) 20150428 17

<h4>Motivation</h4>Statistical methods development for differential expression analysis of RNA sequencing (RNA-seq) requires software tools to assess accuracy and error rate control. Since true differential expression status is often unknown in experimental datasets, artificially constructed datasets must be utilized, either by generating costly spike-in experiments or by simulating RNA-seq data.<h4>Results</h4>Polyester is an R package designed to simulate RNA-seq data, beginning with an experi ...[more]

PMID: 25926345

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Project description:BackgroundHepatocellular carcinoma (HCC) is one of the leading causes of cancer death in the world owing to limitations in its prognosis. The current prognosis approaches include radiological examination and detection of serum biomarkers, however, both have limited efficiency and are ineffective in early prognosis. Due to such limitations, we propose to use RNA-Seq data for evaluating putative higher accuracy biomarkers at the transcript level that could help in early prognosis.MethodsTo identify such potential transcript biomarkers, RNA-Seq data for healthy liver and various HCC cell models were subjected to five different machine learning algorithms: random forest, K-nearest neighbor, Naïve Bayes, support vector machine, and neural networks. Various metrics, namely sensitivity, specificity, MCC, informedness, and AUC-ROC (except for support vector machine) were evaluated. The algorithms that produced the highest values for all metrics were chosen to extract the top features that were subjected to recursive feature elimination. Through recursive feature elimination, the least number of features were obtained to differentiate between the healthy and HCC cell models.ResultsFrom the metrics used, it is demonstrated that the efficiency of the known protein biomarkers for HCC is comparatively lower than complete transcriptomics data. Among the different machine learning algorithms, random forest and support vector machine demonstrated the best performance. Using recursive feature elimination on top features of random forest and support vector machine three transcripts were selected that had an accuracy of 0.97 and kappa of 0.93. Of the three transcripts, two were protein coding (PARP2-202 and SPON2-203) and one was a non-coding transcript (CYREN-211). Lastly, we demonstrated that these three selected transcripts outperformed randomly taken three transcripts (15,000 combinations), hence were not chance findings, and could then be an interesting candidate for new HCC biomarker development.ConclusionUsing RNA-Seq data combined with machine learning approaches can aid in finding novel transcript biomarkers. The three biomarkers identified: PARP2-202, SPON2-203, and CYREN-211, presented the highest accuracy among all other transcripts in differentiating the healthy and HCC cell models. The machine learning pipeline developed in this study can be used for any RNA-Seq dataset to find novel transcript biomarkers. Code: www.github.com/rajinder4489/ML_biomarkers.

Project description:Polymorphisms in the interferon regulatory factor 5 (IRF5) gene have been consistently replicated and shown to confer risk for or protection from the development of systemic lupus erythematosus (SLE). IRF5 expression is significantly upregulated in SLE patients and upregulation associates with IRF5-SLE risk haplotypes. IRF5 alternative splicing has also been shown to be elevated in SLE patients. Given that human IRF5 exists as multiple alternatively spliced transcripts with distinct function(s), it is important to determine whether the IRF5 transcript profile expressed in healthy donor immune cells is different from that expressed in SLE patients. Moreover, it is not currently known whether an IRF5-SLE risk haplotype defines the profile of IRF5 transcripts expressed. Using standard molecular cloning techniques, we identified and isolated 14 new differentially spliced IRF5 transcript variants from purified monocytes of healthy donors and SLE patients to generate an IRF5 variant transcriptome. Next-generation sequencing was then used to perform in-depth and quantitative analysis of full-length IRF5 transcript expression in primary immune cells of SLE patients and healthy donors by next-generation sequencing. Evidence for additional alternatively spliced transcripts was obtained from de novo junction discovery. Data from these studies support the overall complexity of IRF5 alternative splicing in SLE. Results from next-generation sequencing correlated with cloning and gave similar abundance rankings in SLE patients thus supporting the use of this new technology for in-depth single gene transcript profiling. Results from this study provide the first proof that 1) SLE patients express an IRF5 transcript signature that is distinct from healthy donors, 2) an IRF5-SLE risk haplotype defines the top four most abundant IRF5 transcripts expressed in SLE patients, and 3) an IRF5 transcript signature enables clustering of SLE patients with the H2 risk haplotype.

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Polyester: simulating RNA-seq datasets with differential transcript expression.

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Polyester: simulating RNA-seq datasets with differential transcript expression.

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