1Institute of Human Genetics, CNRS UPR1142, Machine learning and gene regulation, University of Montpellier, Montpellier, France.
2AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France.
Commun Biol. 2019 Jun 20;2:222. doi: 10.1038/s42003-019-0456-9. eCollection 2019.
Comparative analysis of high throughput sequencing data between multiple conditions often involves mapping of sequencing reads to a reference and downstream bioinformatics analyses. Both of these steps may introduce heavy bias and potential data loss. This is especially true in studies where patient transcriptomes or genomes may vary from their references, such as in cancer. Here we describe a novel approach and associated software that makes use of advances in genetic algorithms and feature selection to comprehensively explore massive volumes of sequencing data to classify and discover new sequences of interest without a mapping step and without intensive use of specialized bioinformatics pipelines. We demonstrate that our approach called GECKO for GEnetic Classification using Optimization is effective at classifying and extracting meaningful sequences from multiple types of sequencing approaches including mRNA, microRNA, and DNA methylome data.
对多种条件下的高通量测序数据进行比较分析通常涉及将测序reads 映射到参考基因组上，以及下游的生物信息学分析。这两个步骤都可能引入严重的偏差和潜在的数据丢失。在研究中，当患者的转录组或基因组与参考基因组不同时，这种情况尤其如此，例如在癌症中。在这里，我们描述了一种新的方法和相关软件，该方法利用遗传算法和特征选择的进展，全面探索大量测序数据，无需映射步骤和密集使用专门的生物信息学管道，即可对感兴趣的新序列进行分类和发现。我们证明，我们的方法称为 GECKO（使用优化进行基因分类），可以有效地对包括 mRNA、microRNA 和 DNA 甲基化组数据在内的多种测序方法进行分类和提取有意义的序列。
