Metabolic pathway activity estimation from RNA-Seq data

An interesting approach to study the metabolic differences between species is metabolic pathway. In this study, we characterize pathways activity levels of two samples. We applied our proposed methods on RNA-SeqBugula neritina metage- nomics data. We successfully identified several differential pathway activity and we selected 3 of them for qPCR validation. For the past several years, transcriptome sequencing through deep sequencing tech- nologies or RNA-Seq, has revolutionized sequencing technologies with the many ad- vantages it provides. Because of RNA-Seq, it is easier to characterize transcripts and their isoforms, to detect genes without need of prior information in the form of probe, also RNA-Seq can estimate expression level of transcript over a wide range with good precision. The problem of transcriptome quantification has been recently shown extremely im- portant since an estimated rate of 84% of protein level variation can be explained by transcription alone without taking in account variation in translation and degradation (1, 2) while the rate drops to only 73% for microarray data. This paper primary goal is to develop highly accurate algorithms for metabolic pathway activity level estimation and testing differential pathway activity. Activity levels will be inferred using expectation maximization algorithms applied to novel uniform binary and maximum likelihood models while robust testing of pathway significance will be achieved by employing a novel graph-based approach. In contrast to array-based methods, pathway analysis based on RNA-seq data does not measure gene expression directly but allows inference based on total RNA con- tent. When applied to metatranscriptome data, the first challenge of pathway analysis is to decide which metabolic pathways are active in the sampled community (i.e., path- way activity detection). Recent software tools (MEGAN4 (3) and MetaPathways (4) using SEED and KEGG (5) annotations) enable the organization of transcripts into