No evidence for phylostratigraphic bias impacting inferences on patterns of gene emergence and evolution

Abstract

Phylostratigraphy is a computational framework for dating the emergence of DNA and protein sequences in a phylogeny. It has been extensively applied to make inferences on patterns of genome evolution, including patterns of disease gene evolution, ontogeny and de novo gene origination. Phylostratigraphy typically relies on BLAST searches along a species tree, but new simulation studies have raised concerns about the ability of BLAST to detect remote homologues and its impact on phylostratigraphic inferences. Here, we re-assessed these simulations. We found that, even with a possible overall BLAST false negative rate between 11–15%, the large majority of sequences assigned to a recent evolutionary origin by phylostratigraphy is unaffected by technical concerns about BLAST. Where the results of the simulations did cast doubt on previously reported findings, we repeated the original analyses but now excluded all questionable sequences. The originally described patterns remained essentially unchanged. These new analyses strongly support phylostratigraphic inferences, including: genes that emerged after the origin of eukaryotes are more likely to be expressed in the ectoderm than in the endoderm or mesoderm in Drosophila, and the de novo emergence of protein-coding genes from non-genic sequences occurs through proto-gene intermediates in yeast. We conclude that BLAST is an appropriate and sufficiently sensitive tool in phylostratigraphic analysis that does not appear to introduce significant biases into evolutionary pattern inferences.

Further Simulations and Analyses Demonstrate Open Problems of Phylostratigraphy

“…In their recent critique, Domazet-Lošo et al (2017) claimed that the trend they discovered still holds when genes found to be subject to BLAST error in simulation are excluded. This control of error, however, is incomplete for the following reason.…”

“…When we used conservative methods by including the overlapped regions in phylostratigraphy and excluding them in purifying selection tests, no gene was found to be S. cerevisiae -specific and selected (Moyers and Zhang 2016). This is not simply a discrepancy in results between two equally valid approaches, as implied by Domazet-Lošo et al (2017). The convention has always been to use the conservative method, or at least both methods rather than just the liberal method.…”

Origins and structural properties of novel andde novoprotein domains during insect evolution

“…The results presented here contribute to two fields, the one of modular protein evolution and the one of emergence of novel protein-coding genes. For the latter debate there are two major issues outstanding: first, the question if phylostratigraphy in combination with simple sequence search tools, such as BLAST, is sufficient to reliably tell fast evolving from true de novo genes [44][45][46]82]. Since we usenext to Pfam -SEG-HCA, which does not require prior homology detection for domain identification, highly sensitive and selective HMMs and convey of synteny information to narrowing down the genomic origins of novel domains, this 'street light effect' is negligible in our study.…”

Evolutionary and genetic features of drug targets

“…[45][46][47] On the other hand, the developers of phylostratigraphy showed that the age of some genes may be underestimated; however, irrespective of whether these genes were excluded, the patterns of the emergence and evolution of genes remain the same. 48 The above-mentioned arguments reflect the major difficulty in gene-age estimation: it is extremely difficult to detect all the correct homologs for genes in multiple species. 48 The inherent bias of current homology detection algorithms generates inconsistent results, and may impair downstream analyses.…”

“…48 The above-mentioned arguments reflect the major difficulty in gene-age estimation: it is extremely difficult to detect all the correct homologs for genes in multiple species. 48 The inherent bias of current homology detection algorithms generates inconsistent results, and may impair downstream analyses. Thus, it is noticeable that a relatively consensus gene-age data set was provided by Liebeskind and colleagues.…”