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      Comparison of RNA-seq and microarray platforms for splice event detection using a cross-platform algorithm

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          Abstract

          Background

          RNA-seq is a reference technology for determining alternative splicing at genome-wide level. Exon arrays remain widely used for the analysis of gene expression, but show poor validation rate with regard to splicing events. Commercial arrays that include probes within exon junctions have been developed in order to overcome this problem.

          We compare the performance of RNA-seq (Illumina HiSeq) and junction arrays (Affymetrix Human Transcriptome array) for the analysis of transcript splicing events. Three different breast cancer cell lines were treated with CX-4945, a drug that severely affects splicing. To enable a direct comparison of the two platforms, we adapted EventPointer, an algorithm that detects and labels alternative splicing events using junction arrays, to work also on RNA-seq data. Common results and discrepancies between the technologies were validated and/or resolved by over 200 PCR experiments.

          Results

          As might be expected, RNA-seq appears superior in cases where the technologies disagree and is able to discover novel splicing events beyond the limitations of physical probe-sets. We observe a high degree of coherence between the two technologies, however, with correlation of EventPointer results over 0.90. Through decimation, the detection power of the junction arrays is equivalent to RNA-seq with up to 60 million reads.

          Conclusions

          Our results suggest, therefore, that exon-junction arrays are a viable alternative to RNA-seq for detection of alternative splicing events when focusing on well-described transcriptional regions.

          Electronic supplementary material

          The online version of this article (10.1186/s12864-018-5082-2) contains supplementary material, which is available to authorized users.

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          Most cited references 23

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          Computational methods for transcriptome annotation and quantification using RNA-seq.

          High-throughput RNA sequencing (RNA-seq) promises a comprehensive picture of the transcriptome, allowing for the complete annotation and quantification of all genes and their isoforms across samples. Realizing this promise requires increasingly complex computational methods. These computational challenges fall into three main categories: (i) read mapping, (ii) transcriptome reconstruction and (iii) expression quantification. Here we explain the major conceptual and practical challenges, and the general classes of solutions for each category. Finally, we highlight the interdependence between these categories and discuss the benefits for different biological applications.
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            Assessment of transcript reconstruction methods for RNA-seq

            RNA sequencing (RNA-seq) is transforming genome biology, enabling comprehensive transcriptome profiling with unprecendented accuracy and detail. Due to technical limitations of current high-throughput sequencing platforms, transcript identity, structure and expression level must be inferred programmatically from partial sequence reads of fragmented gene products. We evaluated 24 protocol variants of 14 independent computational methods for exon identification, transcript reconstruction and expression level quantification from RNA-seq data. Our results show that most algorithms are able to identify discrete transcript components with high success rates, but that assembly of complete isoform structures poses a major challenge even when all constituent elements are identified. Expression level estimates also varied widely across methods, even when based on similar transcript models. Consequently, the complexity of higher eukaryotic genomes imposes severe limitations in transcript recall and splice product discrimination that are likely to remain limiting factors for the analysis of current-generation RNA-seq data.
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              Alternative splicing and differential gene expression in colon cancer detected by a whole genome exon array

              Background Alternative splicing is a mechanism for increasing protein diversity by excluding or including exons during post-transcriptional processing. Alternatively spliced proteins are particularly relevant in oncology since they may contribute to the etiology of cancer, provide selective drug targets, or serve as a marker set for cancer diagnosis. While conventional identification of splice variants generally targets individual genes, we present here a new exon-centric array (GeneChip Human Exon 1.0 ST) that allows genome-wide identification of differential splice variation, and concurrently provides a flexible and inclusive analysis of gene expression. Results We analyzed 20 paired tumor-normal colon cancer samples using a microarray designed to detect over one million putative exons that can be virtually assembled into potential gene-level transcripts according to various levels of prior supporting evidence. Analysis of high confidence (empirically supported) transcripts identified 160 differentially expressed genes, with 42 genes occupying a network impacting cell proliferation and another twenty nine genes with unknown functions. A more speculative analysis, including transcripts based solely on computational prediction, produced another 160 differentially expressed genes, three-fourths of which have no previous annotation. We also present a comparison of gene signal estimations from the Exon 1.0 ST and the U133 Plus 2.0 arrays. Novel splicing events were predicted by experimental algorithms that compare the relative contribution of each exon to the cognate transcript intensity in each tissue. The resulting candidate splice variants were validated with RT-PCR. We found nine genes that were differentially spliced between colon tumors and normal colon tissues, several of which have not been previously implicated in cancer. Top scoring candidates from our analysis were also found to substantially overlap with EST-based bioinformatic predictions of alternative splicing in cancer. Conclusion Differential expression of high confidence transcripts correlated extremely well with known cancer genes and pathways, suggesting that the more speculative transcripts, largely based solely on computational prediction and mostly with no previous annotation, might be novel targets in colon cancer. Five of the identified splicing events affect mediators of cytoskeletal organization (ACTN1, VCL, CALD1, CTTN, TPM1), two affect extracellular matrix proteins (FN1, COL6A3) and another participates in integrin signaling (SLC3A2). Altogether they form a pattern of colon-cancer specific alterations that may particularly impact cell motility.
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                Author and article information

                Contributors
                jpromero@tecnun.es
                mortiz@celgene.com
                amuniategui@tecnun.es
                scarrancio@celgene.com
                fsanchezde@lumni.unav.es
                fcarazo@tecnun.es
                lmontuenga@unav.es
                rloos@celgene.com
                rpio@unav.es
                mtrotter@celgene.com
                arubio@tecnun.es
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                25 September 2018
                25 September 2018
                2018
                : 19
                Affiliations
                [1 ]ISNI 0000000419370271, GRID grid.5924.a, CEIT and Tecnun, , University of Navarra, ; Parque Tecnológico de San Sebastián, Paseo Mikeletegi 48, 20009 San Sebastián, Gipuzkoa Spain
                [2 ]Celgene Institute for Translational Research Europe, Celgene Corporation, Parque Científico y Tecnológico Cartuja 93, Centro de Empresas Pabellón de Italia, Isaac Newton, 4, E-41092 Seville, Spain
                [3 ]ISNI 0000000419370271, GRID grid.5924.a, Program in Solid Tumors and Biomarkers, CIMA, , University of Navarra, ; Avda. Pío XII, 55, E-31008 Pamplona, Navarra Spain
                [4 ]ISNI 0000000419370271, GRID grid.5924.a, Department of Histology and Pathology, , University of Navarra, ; Campus Universitario, 31009 Pamplona, Navarra Spain
                [5 ]GRID grid.497559.3, IdiSNA, Navarra Institute for Health Research, Recinto de Complejo Hospitalario de Navarra, ; Irunlarrea 3, 31008 Pamplona, Navarra Spain
                [6 ]ISNI 0000000419370271, GRID grid.5924.a, Department of Biochemistry and Genetics, , University of Navarra, ; Campus Universitario, 31009 Pamplona, Navarra Spain
                [7 ]ISNI 0000 0000 9314 1427, GRID grid.413448.e, CIBERONC, Centro de Investigación Biomédica en Red, , Instituto de Salud Carlos III, ; Calle Monforte de Lemos 3-5, Pabellón 11. Planta 0, 28029 Madrid, Spain
                Article
                5082
                10.1186/s12864-018-5082-2
                6156849
                30253752
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003086, Eusko Jaurlaritza;
                Award ID: PRE_2016_1_0194
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100007136, Secretaría de Estado de Investigación, Desarrollo e Innovación;
                Award ID: PI14/00806
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003330, Secretaría de Estado de Investigacion, Desarrollo e Innovacion;
                Award ID: PI14/00806
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002704, Fundación Científica Asociación Española Contra el Cáncer;
                Award ID: GCB14-2170
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2018

                Genetics

                microarrays, rna-seq, alternative splicing

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