Phylogenetic Tree Estimation With and Without Alignment: New Distance Methods and Benchmarking

Abstract

Abstract.-Phylogenetic tree inference is a critical component of many systematic and evolutionary studies. The majority of these studies are based on the two-step process of multiple sequence alignment followed by tree inference, despite persistent evidence that the alignment step can lead to biased results. Here we present a two-part study that first presents PaHMMTree, a novel neighbor joining-based method that estimates pairwise distances without assuming a single alignment. We then use simulations to benchmark its performance against a wide-range of other phylogenetic tree inference methods, including the first comparison of alignment-free distance-based methods against more conventional tree estimation methods. Our new method for calculating pairwise distances based on statistical alignment provides distance estimates that are as accurate as those obtained using standard methods based on the true alignment. Pairwise distance estimates based on the two-step process tend to be substantially less accurate. This improved performance carries through to tree inference, where PaHMM-Tree provides more accurate tree estimates than all of the pairwise distance methods assessed. For close to moderately divergent sequence data we find that the two-step methods using statistical inference, where information from all sequences is included in the estimation procedure, tend to perform better than PaHMM-Tree, particularly full statistical alignment, which simultaneously estimates both the tree and the alignment. For deep divergences we find the alignment step becomes so prone to error that our distance-based PaHMM-Tree outperforms all other methods of tree inference. Finally, we find that the accuracy of alignment-free methods tends to decline faster than standard two-step methods in the presence of alignment uncertainty, and identify no conditions where alignment-free methods are equal to or more accurate than standard phylogenetic methods even in the presence of substantial alignment error. [Alignment-free; distance-based phylogenetics; pair Hidden Markov Models; phylogenetic inference; statistical alignment.]

Identifying Clusters of High Confidence Homologies in Multiple Sequence Alignments

“…In order to compute PPs, our Divvier software integrates a modified version of Zorro, which in turn uses the model initially proposed by MAVID (Bray and Pachter 2004). This model is trained on data that is much more divergent than that usually used in phylogenomic analyses and the fit of that model, along with the resultant PPs, might be improved by using data dependent models such as those implemented in (e.g.,) PaHMM-Tree (Bogusz and Whelan 2017). An alternative to Zorro might also enable divvier to work on nucleotide sequences, although the inherent difficulty of aligning four character sequences means that clusters of high confidence characters might be harder to identify (Yang 2014).…”

Comparative Genomics Reveals Thousands of Novel Chemosensory Genes and Massive Changes in Chemoreceptor Repertories across Chelicerates

“…Besides, for very large families, such as the Gr , the bootstrap analysis was unfeasible in the practice due to excessive computation times. Given that PaHMM-Tree is an alignment-free approach, which allow us to utilize all the amino acid positions to reconstruct the trees, and that the results obtained by Bogusz and Whelan (2017) point to a better performance of this approach for highly divergent sequences without the need for a previous filtering step, here, we decided to report the results based on this method. However, a more exhaustive study comparing these and other tree reconstruction methods, using both real and simulated data and under different degrees of divergence, would be necessary to know whether this method actually improves the phylogenetic analysis.…”

“…2006 ), building a reliable MSA to estimate the phylogenetic relationships is not straightforward. To address this long-standing problem, we applied the MSA-free HMM distance-based method ( Bogusz and Whelan 2017 ) implemented in the PaHMM-Tree software, which outperforms MSA-based methods when dealing with the high alignment uncertainty that is usually associated with large divergences. All the phylogenies except those of the IR family (see Results for more details about this family) were based on complete sequences.…”

Druggability for COVID19 – in Silico Discovery of Potential Drug Compounds Against Nucleocapsid (N) Protein of SARS-CoV-2

“…The phylogenetic tree was constructed using Neighbour Joining (NJ) method 14 with tree evaluation step for 1000 bootstrap replicates. The resulted rooted tree ( sequence within clade-I with branch frequency of 100% which pointed out regarding their significant evolutionary closeness.…”