Algorithm for Physiological Interpretation of Transcriptome Profiling Data for Non-Model Organisms

Abstract

© 2018, Pleiades Publishing, Inc. Modern techniques of next-generation sequencing (NGS) allow obtaining expression profile of all genes and provide an essential basis for characterizing metabolism in the organism of interest on a broad scale. An important condition for obtaining a demonstrative physiological picture using high throughput sequencing data is the availability of the genome sequence and its sufficient annotation for the target organism. However, a list of species with properly annotated genomes is limited. Transcriptome profiling is often performed in the so-called non-model organisms, which are those with unknown or poorly assembled and/or annotated genome sequences. The transcriptomes of non-model organisms are possible to investigate using algorithms of de novo assembly of the transcripts from sequences obtained as the result of RNA sequencing. A physiological interpretation of the data is difficult in this case because of the absence of annotation of the assembled transcripts and their classification by metabolic pathway and functional category. An algorithm for transcriptome profiling in non-model organisms was developed, and a transcriptome analysis was performed for the basidiomycete Lentinus edodes. The algorithm includes open access software and custom scripts and encompasses a complete analysis pipeline from the selection of cDNA reads to the functional classification of differentially expressed genes and the visualization of the results. Based on this algorithm, a comparative transcriptome analysis of the nonpigmented mycelium and brown mycelial mat was performed in L. edodes. The comparison revealed physiological differences between the two morphogenetic stages, including an induction of cell wall biogenesis, intercellular communication, ion transport, and melanization in the brown mycelial mat.