Bioinformatic Methods for the Analysis of High-Throughput RNA Sequencing in Arbuscular Mycorrhizal Fungi

RNA-seq is a powerful method for transcriptome profiling that allows the detection of total RNA present in a single cell, tissues, or organs. mRNA-seq is focused on protein-coding RNAs, and results in large datasets of reads, or portion of sequenced mRNA that can be assembled back to the original transcripts to reconstruct a virtual gene catalog. Studies on the biology of arbuscular mycorrhizal fungi (AMF) often took great advantage of mRNA-seq, and several attempts to decipher their coding potential relied on de novo transcriptome assembly. As the transcriptional profile of an organism is modulated depending on cell types, and in response to specific biological conditions, mRNA-seq is an attractive approach to study the physiology of AMF, which are axenically unculturable and genetically intractable. mRNA-seq analyses require bioinformatic workflows to manipulate the huge amount of raw data generated by the sequencing run, with several crucial steps (e.g., library trimming, reads mapping, normalization, and differential expression calculation) which can strongly affect the final results. Here, we propose a standard workflow for de novo transcriptome assembly and differential expression calculation for AMF, which considers the most common technical issues of working in the absence of reference sequences and with mixed biological samples.