Comparative RNA-seq analysis aids in diagnosis of a rare pediatric tumor.
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ABSTRACT:
SUBMITTER: Sanders LM
PROVIDER: S-EPMC6824255 | biostudies-literature | 2019 Oct
REPOSITORIES: biostudies-literature
Publications
Comparative RNA-seq analysis aids in diagnosis of a rare pediatric tumor.
Sanders Lauren M LM Rangaswami Arun A Bjork Isabel I Lam Du Linh DL Beale Holly C HC Kephart Ellen Towle ET Durbin Ann A Learned Katrina K Currie Rob R Lyle A Geoffrey AG Pfeil Jacob J Shah Avanthi Tayi AT Lee Alex G AG Leung Stanley G SG Behroozfard Inge H IH Breese Marcus R MR Peralez Jennifer J Hazard Florette K FK Lacayo Norman N Spunt Sheri L SL Haussler David D Salama Sofie R SR Sweet-Cordero E Alejandro EA Vaske Olena M OM
Cold Spring Harbor molecular case studies 20191023 5
Gliomatosis peritonei is a rare pathologic finding that is associated with ovarian teratomas and malignant mixed germ cell tumors. The occurrence of gliomatosis as a mature glial implant can impart an improved prognosis to patients with immature ovarian teratoma, making prompt and accurate diagnosis important. We describe a case of recurrent immature teratoma in a 10-yr-old female patient, in which comparative analysis of the RNA sequencing gene expression data from the patient's tumor was used ...[more]
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Project description:MotivationA critical task in high-throughput sequencing is aligning millions of short reads to a reference genome. Alignment is especially complicated for RNA sequencing (RNA-Seq) because of RNA splicing. A number of RNA-Seq algorithms are available, and claim to align reads with high accuracy and efficiency while detecting splice junctions. RNA-Seq data are discrete in nature; therefore, with reasonable gene models and comparative metrics RNA-Seq data can be simulated to sufficient accuracy to enable meaningful benchmarking of alignment algorithms. The exercise to rigorously compare all viable published RNA-Seq algorithms has not been performed previously.ResultsWe developed an RNA-Seq simulator that models the main impediments to RNA alignment, including alternative splicing, insertions, deletions, substitutions, sequencing errors and intron signal. We used this simulator to measure the accuracy and robustness of available algorithms at the base and junction levels. Additionally, we used reverse transcription-polymerase chain reaction (RT-PCR) and Sanger sequencing to validate the ability of the algorithms to detect novel transcript features such as novel exons and alternative splicing in RNA-Seq data from mouse retina. A pipeline based on BLAT was developed to explore the performance of established tools for this problem, and to compare it to the recently developed methods. This pipeline, the RNA-Seq Unified Mapper (RUM), performs comparably to the best current aligners and provides an advantageous combination of accuracy, speed and usability.AvailabilityThe RUM pipeline is distributed via the Amazon Cloud and for computing clusters using the Sun Grid Engine (http://cbil.upenn.edu/RUM).Contactggrant@pcbi.upenn.edu; epierce@mail.med.upenn.eduSupplementary informationThe RNA-Seq sequence reads described in the article are deposited at GEO, accession GSE26248.
| S-EPMC3167048 | biostudies-literature
Project description:Three principal approaches have been proposed for inferring the set of transcripts expressed in RNA samples using RNA-seq. The simplest approach uses curated annotations, which assumes the transcripts in a sample are a subset of the transcripts listed in a curated database. A more ambitious method involves aligning reads to a reference genome and using the alignments to infer the transcript structures, possibly with the aid of a curated transcript database. The most challenging approach is to assemble reads into putative transcripts de novo without the aid of reference data. We have systematically assessed the properties of these three approaches through a simulation study. We have found that the sensitivity of computational transcript set estimation is severely limited. Computational approaches (both genome-guided and de novo assembly) produce a large number of artefacts, which are assigned large expression estimates and absorb a substantial proportion of the signal when performing expression analysis. The approach using curated annotations shows good expression correlation even when the annotations are incomplete. Furthermore, any incorrect transcripts present in a curated set do not absorb much signal, so it is preferable to have a curation set with high sensitivity than high precision. Software to simulate transcript sets, expression values and sequence reads under a wider range of parameter values and to compare sensitivity, precision and signal-to-noise ratios of different methods is freely available online (https://github.com/boboppie/RSSS) and can be expanded by interested parties to include methods other than the exemplars presented in this article.
| S-EPMC4652615 | biostudies-literature
Project description:ImportancePediatric cancers are epigenetic diseases; therefore, considering tumor gene expression information is necessary for a complete understanding of the tumorigenic processes.ObjectiveTo evaluate the feasibility and utility of incorporating comparative gene expression information into the precision medicine framework for difficult-to-treat pediatric and young adult patients with cancer.Design, setting, and participantsThis cohort study was conducted as a consortium between the University of California, Santa Cruz (UCSC) Treehouse Childhood Cancer Initiative and clinical genomic trials. RNA sequencing (RNA-Seq) data were obtained from the following 4 clinical sites and analyzed at UCSC: British Columbia Children's Hospital (n = 31), Lucile Packard Children's Hospital at Stanford University (n = 80), CHOC Children's Hospital and Hyundai Cancer Institute (n = 46), and the Pacific Pediatric Neuro-Oncology Consortium (n = 24). The study dates were January 1, 2016, to March 22, 2017.ExposuresParticipants underwent tumor RNA-Seq profiling as part of 4 separate clinical trials at partner hospitals. The UCSC either downloaded RNA-Seq data from a partner institution for analysis in the cloud or provided a Docker pipeline that performed the same analysis at a partner institution. The UCSC then compared each participant's tumor RNA-Seq profile with more than 11 000 uniformly analyzed tumor profiles from pediatric and young adult patients with cancer, downloaded from public data repositories. These comparisons were used to identify genes and pathways that are significantly overexpressed in each patient's tumor. Results of the UCSC analysis were presented to clinical partners.Main outcomes and measuresFeasibility of a third-party institution (UCSC Treehouse Childhood Cancer Initiative) to obtain tumor RNA-Seq data from patients, conduct comparative analysis, and present analysis results to clinicians; and proportion of patients for whom comparative tumor gene expression analysis provided useful clinical and biological information.ResultsAmong 144 samples from children and young adults (median age at diagnosis, 9 years; range, 0-26 years; 72 of 118 [61.0%] male [26 patients sex unknown]) with a relapsed, refractory, or rare cancer treated on precision medicine protocols, RNA-Seq-derived gene expression was potentially useful for 99 of 144 samples (68.8%) compared with DNA mutation information that was potentially useful for only 34 of 74 samples (45.9%).Conclusions and relevanceThis study's findings suggest that tumor RNA-Seq comparisons may be feasible and highlight the potential clinical utility of incorporating such comparisons into the clinical genomic interpretation framework for difficult-to-treat pediatric and young adult patients with cancer. The study also highlights for the first time to date the potential clinical utility of harmonized publicly available genomic data sets.
| S-EPMC6822083 | biostudies-literature
Project description:Clinical detection of sequence and structural variants in known cancer genes points to viable treatment options for a minority of children with cancer.1 To increase the number of children who benefit from genomic profiling, gene expression information must be considered alongside mutations.2,3 Although high expression has been used to nominate drug targets for pediatric cancers,4,5 its utility has not been evaluated in a systematic way.6 We describe a child with a rare sarcoma that was profiled with whole-genome and RNA sequencing (RNA-Seq) techniques. Although the tumor did not harbor DNA mutations targetable by available therapies, incorporation of gene expression information derived from RNA-Seq analysis led to a therapy that produced a significant clinical response. We use this case to describe a framework for inclusion of gene expression into the clinical genomic evaluation of pediatric tumors.
| S-EPMC6675034 | biostudies-other
Project description:RNA-Seq analysis carried out in three different backcrosses based on Iberian breed with Landrace, Pietrain and Duroc breeds
2017-10-31 | PRJEB23068 | EVA
Project description:BackgroundOver the last few years transcriptome sequencing (RNA-Seq) has almost completely taken over microarrays for high-throughput studies of gene expression. Currently, the most popular use of RNA-Seq is to identify genes which are differentially expressed between two or more conditions. Despite the importance of Gene Set Analysis (GSA) in the interpretation of the results from RNA-Seq experiments, the limitations of GSA methods developed for microarrays in the context of RNA-Seq data are not well understood.ResultsWe provide a thorough evaluation of popular multivariate and gene-level self-contained GSA approaches on simulated and real RNA-Seq data. The multivariate approach employs multivariate non-parametric tests combined with popular normalizations for RNA-Seq data. The gene-level approach utilizes univariate tests designed for the analysis of RNA-Seq data to find gene-specific P-values and combines them into a pathway P-value using classical statistical techniques. Our results demonstrate that the Type I error rate and the power of multivariate tests depend only on the test statistics and are insensitive to the different normalizations. In general standard multivariate GSA tests detect pathways that do not have any bias in terms of pathways size, percentage of differentially expressed genes, or average gene length in a pathway. In contrast the Type I error rate and the power of gene-level GSA tests are heavily affected by the methods for combining P-values, and all aforementioned biases are present in detected pathways.ConclusionsOur result emphasizes the importance of using self-contained non-parametric multivariate tests for detecting differentially expressed pathways for RNA-Seq data and warns against applying gene-level GSA tests, especially because of their high level of Type I error rates for both, simulated and real data.
| S-EPMC4265362 | biostudies-literature
Project description:BackgroundBud dormancy is an important biological phenomenon of perennial plants that enables them to survive under harsh environmental circumstances. Grape (Vitis vinifera) is one of the most grown fruit crop worldwide; however, underlying mechanisms involved in grape bud dormancy are not yet clear. This work was aimed to explore the underlying molecular mechanism regulating bud dormancy in grape.ResultsWe have performed transcriptome and differential transcript expression analyses of "Shine Muscat" grape buds using the Illumina RNA-seq system. Comparisons of transcript expression levels among three stages of dormancy, paradormancy (PD) vs endodormancy (ED), summer buds (SB) vs ED and SB vs PD, resulted in the detection of 8949, 9780 and 3938 differentially expressed transcripts, respectively. Out of approximately 78 million high-quality generated reads, 6096 transcripts were differentially expressed (log2 ratio ≥ 1, FDR ≤ 0.001). Grape reference genome was used for alignment of sequence reads and to measure the expression level of transcripts. Furthermore, findings obtained were then compared using two different databases; Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), to annotate the transcript descriptions and to assign a pathway to each transcript. KEGG analysis revealed that secondary metabolites biosynthesis and plant hormone signaling was found most enriched out of the 127 total pathways. In the comparisons of the PD vs ED and SB vs ED stages of grape buds, the gibberellin (GA) and abscisic acid (ABA) pathways were found to be the most enriched. The ABA and GA pathways were further analyzed to observe the expression pattern of differentially expressed transcripts. Transcripts related to the PP2C family (ABA pathway) were found to be up-regulated in the PD vs ED comparison and down-regulated in the SB vs ED and SB vs PD comparisons. GID1 family transcripts (GA pathway) were up-regulated while DELLA family transcripts were down-regulated during the three dormancy stages. Differentially expressed transcripts (DEGs) related to redox activity were abundant in the GO biological process category. RT-qPCR assay results for 12 selected transcripts validated the data obtained by RNA-seq.ConclusionAt this stage, taking into account the results obtained so far, it is possible to put forward a hypothesis for the molecular mechanism underlying grape bud dormancy, which may pave the way for ultimate improvements in the grape industry.
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Project description:Odontogenic keratocysts (OKCs) are a common presentation in almost all patients with nevoid basal cell carcinoma syndrome (NBCCS) or Gorlin syndrome, irrespective of race. In most patients with NBCCS, OKC presents as multiple lesions affecting the jaws which makes it a signpost for the investigation of patients with the syndrome. In approximately 40% of pediatric patients, the initial presentation is that of a single OKC, which may often result in missing the diagnosis of NBCCS. This is particularly common in patients without clinically apparent NBCCS-related manifestations. This review examines the clinicopathological features that clinicians and oral pathologists may look for in pediatric patient with OKC and OKC surgical specimens that may serve as indicators for the diagnosis of NBCCS. Although these features do not diagnose NBCCS by themselves, they may significantly help in initiating the diagnostic process at an early stage with an obvious benefit to the child and relatives.
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Project description:In recent years, high-throughput next-generation sequencing technology has allowed a rapid increase in diagnostic capacity and precision through different bioinformatics processing algorithms, tools, and pipelines. The identification, annotation, and classification of sequence variants within different target regions are now considered a gold standard in clinical genetic diagnosis. However, this procedure lacks the ability to link regulatory events such as differential splicing to diseases. RNA-seq is necessary in clinical routine in order to interpret and detect among others splicing events and splicing variants, as it would increase the diagnostic rate by up to 10-35%. The transcriptome has a very dynamic nature, varying according to tissue type, cellular conditions, and environmental factors that may affect regulatory events such as splicing and the expression of genes or their isoforms. RNA-seq offers a robust technical analysis of this complexity, but it requires a profound knowledge of computational/statistical tools that may need to be adjusted depending on the disease under study. In this article we will cover RNA-seq analyses best practices applied to clinical routine, bioinformatics procedures, and present challenges of this approach.
| S-EPMC6861419 | biostudies-literature
Project description:Single cell RNA-Seq (scRNA-seq) studies profile thousands of cells in heterogeneous environments. Current methods for characterizing cells perform unsupervised analysis followed by assignment using a small set of known marker genes. Such approaches are limited to a few, well characterized cell types. We developed an automated pipeline to download, process, and annotate publicly available scRNA-seq datasets to enable large scale supervised characterization. We extend supervised neural networks to obtain efficient and accurate representations for scRNA-seq data. We apply our pipeline to analyze data from over 500 different studies with over 300 unique cell types and show that supervised methods outperform unsupervised methods for cell type identification. A case study highlights the usefulness of these methods for comparing cell type distributions in healthy and diseased mice. Finally, we present scQuery, a web server which uses our neural networks and fast matching methods to determine cell types, key genes, and more.
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