Algorithms for differential splicing detection using exon arrays: a comparative assessment

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• 作者：Karin Zimmermann (1)
  Marcel Jentsch (2)
  Axel Rasche (3)
  Michael Hummel (4)
  Ulf Leser (1)
  
  1. Department of Computer Science ; Knowledge Management in Bioinformatics ; Humboldt Universitaet zu Berlin ; Rudower Chaussee 25 ; Berlin ; 12489 ; Germany
  2. Department of Mathematics and Computer Science ; Freie Universitaet Berlin ; Berlin ; Germany
  3. Department of Vertebrate Genomics ; Max Planck Institute for Molecular Genetics ; Ihnestr. 63-73 ; Berlin ; 14195 ; Germany
  4. Institut fuer Pathologie CBF ; Charite - Universitaetsmedizin Berlin ; Hindenburgdamm 30 ; Berlin ; 12200 ; Germany
  
• 关键词：Alternative splicing ; Differential splicing ; Exon arrays ; Method comparison ; Parameter influence
• 刊名：BMC Genomics
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：16
• 期：1
• 全文大小：702 KB
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• 刊物主题：Life Sciences, general; Microarrays; Proteomics; Animal Genetics and Genomics; Microbial Genetics and Genomics; Plant Genetics & Genomics;
• 出版者：BioMed Central
• ISSN：1471-2164

文摘

Background The analysis of differential splicing (DS) is crucial for understanding physiological processes in cells and organs. In particular, aberrant transcripts are known to be involved in various diseases including cancer. A widely used technique for studying DS are exon arrays. Over the last decade a variety of algorithms for the detection of DS events from exon arrays has been developed. However, no comprehensive, comparative evaluation including sensitivity to the most important data features has been conducted so far. To this end, we created multiple data sets based on simulated data to assess strengths and weaknesses of seven published methods as well as a newly developed method, KLAS. Additionally, we evaluated all methods on two cancer data sets that comprised RT-PCR validated results. Results Our studies indicated ARH as the most robust methods when integrating the results over all scenarios and data sets. Nevertheless, special cases or requirements favor other methods. While FIRMA was highly sensitive according to experimental data, SplicingCompass, MIDAS and ANOSVA showed high specificity throughout the scenarios. On experimental data ARH, FIRMA, MIDAS, and KLAS performed best. Conclusions Each method shows different characteristics regarding sensitivity, specificity, interference to certain data settings and robustness over multiple data sets. While some methods can be considered as generally good choices over all data sets and scenarios, other methods show heterogeneous prediction quality on the different data sets. The adequate method has to be chosen carefully and with a defined study aim in mind.