A probabilistic model for sequence alignment with context-sensitive indels

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.

EvoLSTM: context-dependent models of sequence evolution using a sequence-to-sequence LSTM

Motivation

Accurate probabilistic models of sequence evolution are essential for a wide variety of bioinformatics tasks, including sequence alignment and phylogenetic inference. The ability to realistically simulate sequence evolution is also at the core of many benchmarking strategies. Yet, mutational processes have complex context dependencies that remain poorly modeled and understood.

Results

We introduce EvoLSTM, a recurrent neural network-based evolution simulator that captures mutational context dependencies. EvoLSTM uses a sequence-to-sequence long short-term memory model trained to predict mutation probabilities at each position of a given sequence, taking into consideration the 14 flanking nucleotides. EvoLSTM can realistically simulate mammalian and plant DNA sequence evolution and reveals unexpectedly strong long-range context dependencies in mutation probabilities. EvoLSTM brings modern machine-learning approaches to bear on sequence evolution. It will serve as a useful tool to study and simulate complex mutational processes.

Availability and implementation

Code and dataset are available at https://github.com/DongjoonLim/EvoLSTM.

Supplementary information

Supplementary data are available at Bioinformatics online.

Solving the master equation for Indels

BACKGROUND

Despite the long-anticipated possibility of putting sequence alignment on the same footing as statistical phylogenetics, theorists have struggled to develop time-dependent evolutionary models for indels that are as tractable as the analogous models for substitution events.

MAIN TEXT

This paper discusses progress in the area of insertion-deletion models, in view of recent work by Ezawa (BMC Bioinformatics 17:304, 2016); (BMC Bioinformatics 17:397, 2016); (BMC Bioinformatics 17:457, 2016) on the calculation of time-dependent gap length distributions in pairwise alignments, and current approaches for extending these approaches from ancestor-descendant pairs to phylogenetic trees.

CONCLUSIONS

While approximations that use finite-state machines (Pair HMMs and transducers) currently represent the most practical approach to problems such as sequence alignment and phylogeny, more rigorous approaches that work directly with the matrix exponential of the underlying continuous-time Markov chain also show promise, especially in view of recent advances.

Probabilistic approaches to alignment with tandem repeats

Background

Short tandem repeats are ubiquitous in genomic sequences and due to their complex evolutionary history pose a challenge for sequence alignment tools.

Results

To better account for the presence of tandem repeats in pairwise sequence alignments, we propose a simple tractable pair hidden Markov model that explicitly models their presence. Using the framework of gain functions, we design several optimization criteria for decoding this model and describe resulting decoding algorithms, ranging from the traditional Viterbi and posterior decoding to block-based decoding algorithms tailored to our model. We compare the accuracy of individual decoding algorithms on simulated and real data and find that our approach is superior to the classical three-state pair HMM.

Conclusions

Our study illustrates versatility of pair hidden Markov models coupled with appropriate decoding criteria as a modeling tool for capturing complex sequence features.

Strong heterogeneity in mutation rate causes misleading hallmarks of natural selection on indel mutations in the human genome

Elucidating the mechanisms of mutation accumulation and fixation is critical to understand the nature of genetic variation and its contribution to genome evolution. Of particular interest is the effect of insertions and deletions (indels) on the evolution of genome landscapes. Recent population-scaled sequencing efforts provide unprecedented data for analyzing the relative impact of selection versus nonadaptive forces operating on indels. Here, we combined McDonald-Kreitman tests with the analysis of derived allele frequency spectra to investigate the dynamics of allele fixation of short (1-50 bp) indels in the human genome. Our analyses revealed apparently higher fixation probabilities for insertions than deletions. However, this fixation bias is not consistent with either selection or biased gene conversion and varies with local mutation rate, being particularly pronounced at indel hotspots. Furthermore, we identified an unprecedented number of loci with evidence for multiple indel events in the primate phylogeny. Even in nonrepetitive sequence contexts (a priori not prone to indel mutations), such loci are 60-fold more frequent than expected according to a model of uniform indel mutation rate. This provides evidence of as yet unidentified cryptic indel hotspots. We propose that indel homoplasy, at known and cryptic hotspots, produces systematic errors in determination of ancestral alleles via parsimony and advise caution interpreting classic selection tests given the strong heterogeneity in indel rates across the genome. These results will have great impact on studies seeking to infer evolutionary forces operating on indels observed in closely related species, because such mutations are traditionally presumed homoplasy-free.

A primer in macromolecular linguistics

Polymeric macromolecules, when viewed abstractly as strings of symbols, can be treated in terms of formal language theory, providing a mathematical foundation for characterizing such strings both as collections and in terms of their individual structures. In addition this approach offers a framework for analysis of macromolecules by tools and conventions widely used in computational linguistics. This article introduces the ways that linguistics can be and has been applied to molecular biology, covering the relevant formal language theory at a relatively nontechnical level. Analogies between macromolecules and human natural language are used to provide intuitive insights into the relevance of grammars, parsing, and analysis of language complexity to biology.