Insertions and deletions as phylogenetic signal in an alignment-free context

Insertions and Deletions: Computational Methods, Evolutionary Dynamics, and Biological Applications

Insertions and deletions constitute the second most important source of natural genomic variation. Insertions and deletions make up to 25% of genomic variants in humans and are involved in complex evolutionary processes including genomic rearrangements, adaptation, and speciation. Recent advances in long-read sequencing technologies allow detailed inference of insertions and deletion variation in species and populations. Yet, despite their importance, evolutionary studies have traditionally ignored or mishandled insertions and deletions due to a lack of comprehensive methodologies and statistical models of insertions and deletion dynamics. Here, we discuss methods for describing insertions and deletion variation and modeling insertions and deletions over evolutionary time. We provide practical advice for tackling insertions and deletions in genomic sequences and illustrate our discussion with examples of insertions and deletion-induced effects in human and other natural populations and their contribution to evolutionary processes. We outline promising directions for future developments in statistical methodologies that would allow researchers to analyze insertions and deletion variation and their effects in large genomic data sets and to incorporate insertions and deletions in evolutionary inference.

Please Mind the Gap: Indel-Aware Parsimony for Fast and Accurate Ancestral Sequence Reconstruction and Multiple Sequence Alignment Including Long Indels

Despite having important biological implications, insertion, and deletion (indel) events are often disregarded or mishandled during phylogenetic inference. In multiple sequence alignment, indels are represented as gaps and are estimated without considering the distinct evolutionary history of insertions and deletions. Consequently, indels are usually excluded from subsequent inference steps, such as ancestral sequence reconstruction and phylogenetic tree search. Here, we introduce indel-aware parsimony (indelMaP), a novel way to treat gaps under the parsimony criterion by considering insertions and deletions as separate evolutionary events and accounting for long indels. By identifying the precise location of an evolutionary event on the tree, we can separate overlapping indel events and use affine gap penalties for long indel modeling. Our indel-aware approach harnesses the phylogenetic signal from indels, including them into all inference stages. Validation and comparison to state-of-the-art inference tools on simulated data show that indelMaP is most suitable for densely sampled datasets with closely to moderately related sequences, where it can reach alignment quality comparable to probabilistic methods and accurately infer ancestral sequences, including indel patterns. Due to its remarkable speed, our method is well suited for epidemiological datasets, eliminating the need for downsampling and enabling the exploitation of the additional information provided by dense taxonomic sampling. Moreover, indelMaP offers new insights into the indel patterns of biologically significant sequences and advances our understanding of genetic variability by considering gaps as crucial evolutionary signals rather than mere artefacts.

Deep learning for predicting 16S rRNA gene copy number

Culture-independent 16S rRNA gene metabarcoding is a commonly used method for microbiome profiling. To achieve more quantitative cell fraction estimates, it is important to account for the 16S rRNA gene copy number (hereafter 16S GCN) of different community members. Currently, there are several bioinformatic tools available to estimate the 16S GCN values, either based on taxonomy assignment or phylogeny. Here we present a novel approach ANNA16, Artificial Neural Network Approximator for 16S rRNA gene copy number, a deep learning-based method that estimates the 16S GCN values directly from the 16S gene sequence strings. Based on 27,579 16S rRNA gene sequences and gene copy number data from the rrnDB database, we show that ANNA16 outperforms the commonly used 16S GCN prediction algorithms. Interestingly, Shapley Additive exPlanations (SHAP) shows that ANNA16 can identify unexpected informative positions in 16S rRNA gene sequences without any prior phylogenetic knowledge, which suggests potential applications beyond 16S GCN prediction.

KINN: An alignment-free accurate phylogeny reconstruction method based on inner distance distributions of k-mer pairs in biological sequences

Alignment-based methods have faced disadvantages in sequence comparison and phylogeny reconstruction due to their high computational complexity. Alignment-free methods for sequence comparison and phylogeny inference have attracted a great deal of attention in recent years. Here, we explore an alignment-free approach that uses inner distance distributions of k-mer pairs in biological sequences for phylogeny inference. For every sequence in a dataset, our method transforms the sequence into a numeric feature vector consisting of features each representing a specific k-mer pair's contribution to the characterization of the sequentiality uniqueness of the sequence. This newly defined k-mer pair's contribution is an integration of the reverse Kullback-Leibler divergence, pseudo mode and the classic entropy of an inner distance distribution of the k-mer pair in the sequence. Our method has been tested on datasets of complete genome sequences, complete protein sequences, and gene sequences of rRNA of various lengths. Our method achieves the best performance in comparison with state-of-the-art alignment-free methods as measured by the Robinson-Foulds distance between the reference and the constructed phylogeny trees.