SPACE: an algorithm to predict and quantify alternatively spliced isoforms using microarrays

Antón, Miguel; Gorostiaga, Dorleta; Guruceaga, Elizabeth; Segura, Víctor; Carmona-Sáez, Pedro; Pascual-Montano, Alberto; Pı́o, Rubén; Montuenga, Luis M.; Rubio, Ángel

doi:10.1186/gb-2008-9-2-r46

Cited by 28 publications

(28 citation statements)

References 29 publications

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“…Consequently, the current trend is to work with arrays that include probes in annotated junctions. Unfortunately, there are very few algorithms that take advantage of the redundancy provided by junction probes (17)(18)(19)(20)(21)(22)(23)(24)(25).…”

Section: Introductionmentioning

confidence: 99%

Identification of Alternative Splicing Events Regulated by the Oncogenic Factor SRSF1 in Lung Cancer

et al. 2014

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Abnormal alternative splicing has been associated with cancer. Genome-wide microarrays can be used to detect differential splicing events. In this study, we have developed ExonPointer, an algorithm that uses data from exon and junction probes to identify annotated cassette exons. We used the algorithm to profile differential splicing events in lung adenocarcinoma A549 cells after downregulation of the oncogenic serine/arginine-rich splicing factor 1 (SRSF1). Data were generated using two different microarray platforms. The PCR-based validation rate of the top 20 ranked genes was 60% and 100%. Functional enrichment analyses found a substantial number of splicing events in genes related to RNA metabolism. These analyses also identified genes associated with cancer and developmental and hereditary disorders, as well as biologic processes such as cell division, apoptosis, and proliferation. Most of the top 20 ranked genes were validated in other adenocarcinoma and squamous cell lung cancer cells, with validation rates of 80% to 95% and 70% to 75%, respectively. Moreover, the analysis allowed us to identify four genes, ATP11C, IQCB1, TUBD1, and proline-rich coiled-coil 2C (PRRC2C), with a significantly different pattern of alternative splicing in primary non-small cell lung tumors compared with normal lung tissue. In the case of PRRC2C, SRSF1 downregulation led to the skipping of an exon overexpressed in primary lung tumors. Specific siRNA downregulation of the exon-containing variant significantly reduced cell growth. In conclusion, using a novel analytical tool, we have identified new splicing events regulated by the oncogenic splicing factor SRSF1 in lung cancer. Cancer Res; 74(4); 1105-15. Ó2013 AACR.

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Section: Introductionmentioning

confidence: 99%

Identification of Alternative Splicing Events Regulated by the Oncogenic Factor SRSF1 in Lung Cancer

et al. 2014

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“…example, spliced aligned reads (exon-exon junction reads) are beneficial in identifying the spliced mRNA structures [17,18]. As another example, non-negative matrix factorization (NMF) has been used to decompose data into essential patterns and predict AS patterns from microarray data [19] and RNA-seq data [20].…”

Section: Introductionmentioning

confidence: 99%

A NMF-based approach to discover overlooked differentially expressed gene regions from single-cell RNA-seq data

Matsumoto

Hayashi

Ozaki

et al. 2019

Preprint

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Single-cell RNA sequencing has enabled researchers to quantify the transcriptomes of individual cells, infer cell types, and investigate differential expression among cell types, which will lead to a better understanding of the regulatory mechanisms of cell states. Transcript diversity caused by phenomena such as aberrant splicing events have been revealed, and differential expression of previously unannotated transcripts might be overlooked by annotation-based analyses. Accordingly, we have developed an approach to discover overlooked differentially expressed (DE) gene regions that complements annotation-based methods. We applied our algorithm to two datasets and discovered several intriguing DE transcripts, including a transcript related to the modulation of neural stem/progenitor cell differentiation.

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“…This approach, however, only measures annotated transcripts and is hence unable to detect and quantify novel isoforms. Despite this obstacle, methods to estimate isoform concentrations using microarrays have been proposed [20][21][22][23]. A recent comparison of the isoform deconvolution using both RNA-seq and microarrays has been published using non-junction Affymetrix HuEx arrays [24].…”

Section: Introductionmentioning

confidence: 99%

Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm

Romero

Ortiz-Estévez

Muniategui

et al. 2017

Preprint

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Abstract: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.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. 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.

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SPACE: an algorithm to predict and quantify alternatively spliced isoforms using microarrays

Cited by 28 publications

References 29 publications

Identification of Alternative Splicing Events Regulated by the Oncogenic Factor SRSF1 in Lung Cancer

Identification of Alternative Splicing Events Regulated by the Oncogenic Factor SRSF1 in Lung Cancer

A NMF-based approach to discover overlooked differentially expressed gene regions from single-cell RNA-seq data

Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm

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