Building Phylogenies from Transcriptomic Data

Transcriptomic data (obtained from RNA sequencing) has become a very powerful source of information to reconstruct the evolutionary relationships among organisms. Although phylogenetic inference using transcriptomes retains the same core steps as when working with few molecular markers (viz., nucleic acid extraction and sequencing, sequence treatment, and tree inference), all of them show significant differences. First, the needed quantity and quality of the extracted RNA has to be very high. Although this may not represent a challenge when working with certain organisms, it may well be a headache with others, especially for those with small body sizes. Second, the tremendous increase in the quantity of sequences obtained requires a high computational power for both treating the sequences and inferring the subsequent phylogenies. This means that transcriptomic data can no longer be analyzed using personal computers nor local programs with a graphical interface. This, in turn, implies the requirement of an increased set of bioinformatic skills from the researchers. Finally, the genomic peculiarities of each group of organisms, such as the level of heterozygosity or the percentage of base composition, also need to be considered when inferring phylogenies using transcriptomic data.