Accurate reconstruction of insertion-deletion histories by statistical phylogenetics

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.

Machine Boss: rapid prototyping of bioinformatic automata

Motivation

Many software libraries for using Hidden Markov Models in bioinformatics focus on inference tasks, such as likelihood calculation, parameter-fitting and alignment. However, construction of the state machines can be a laborious task, automation of which would be time-saving and less error-prone.

Results

We present Machine Boss, a software tool implementing not just inference and parameter-fitting algorithms, but also a set of operations for manipulating and combining automata. The aim is to make prototyping of bioinformatics HMMs as quick and easy as the construction of regular expressions, with one-line ‘recipes’ for many common applications. We report data from several illustrative examples involving protein-to-DNA alignment, DNA data storage and nanopore sequence analysis.

Availability and implementation

Machine Boss is released under the BSD-3 open source license and is available from http://machineboss.org/.

Supplementary information

Supplementary data are available at Bioinformatics online.

A Model of Indel Evolution by Finite-State, Continuous-Time Machines

We introduce a systematic method of approximating finite-time transition probabilities for continuous-time insertion-deletion models on sequences. The method uses automata theory to describe the action of an infinitesimal evolutionary generator on a probability distribution over alignments, where both the generator and the alignment distribution can be represented by pair hidden Markov models (HMMs). In general, combining HMMs in this way induces a multiplication of their state spaces; to control this, we introduce a coarse-graining operation to keep the state space at a constant size. This leads naturally to ordinary differential equations for the evolution of the transition probabilities of the approximating pair HMM. The TKF91 model emerges as an exact solution to these equations for the special case of single-residue indels. For the more general case of multiple-residue indels, the equations can be solved by numerical integration. Using simulated data, we show that the resulting distribution over alignments, when compared to previous approximations, is a better fit over a broader range of parameters. We also propose a related approach to develop differential equations for sufficient statistics to estimate the underlying instantaneous indel rates by expectation maximization. Our code and data are available at https://github.com/ihh/trajectory-likelihood.

NGS-Indel Coder: A pipeline to code indel characters in phylogenomic data with an example of its application in milkweeds (Asclepias)

Targeted genome sequencing approaches allow characterization of evolutionary relationships using a considerable number of nuclear genes and informative characters. However, most phylogenomic analyses only utilize single nucleotide polymorphisms (SNPs). Studies at the species level, especially in groups that have recently radiated, often recover low amounts of phylogenetically informative variation in coding regions, and require non-coding sequences, which are richer in indels, to resolve gene trees. Here, NGS-Indel Coder, a pipeline to detect and omit false positive indels inferred from assemblies of short read sequence data, was developed to resolve the relationships among and within major clades of the American milkweeds (Asclepias), which are the result of a rapid and recent evolutionary radiation, and whose phylogeny has been difficult to resolve. This pipeline was applied to a Hyb-Seq data set of 768 loci including targeted exons and flanking intron regions from 33 milkweed species. Robust species tree inference was improved by excluding small alignment partitions (<100 bp) that increased gene tree ambiguity and incongruence. To further investigate the robustness of indel coding, data sets that included small and large indels were explored, and species trees derived from concatenated loci versus coalescent methods based on gene trees were compared. The phylogeny of Asclepias obtained using nuclear data was well resolved, and phylogenetic information from indels improved resolution of specific nodes. The Temperate North American, Mexican Highland, and Incarnatae clades were well supported as monophyletic. Asclepias coulteri, which has been considered part of the Sonoran Desert clade based on plastome analyses, was placed as sister to all the other milkweed species studied here, rather than as a member of that clade. Two groups within the Temperate North American and Mexican clades were not resolved, and the inferred relationships strongly conflicted when comparing results based on data sets that did or did not include indel characters. This new pipeline represents a step forward in making maximal use of the information content in phylogenomic data sets.

Alignment Modulates Ancestral Sequence Reconstruction Accuracy

Accurate reconstruction of ancestral states is a critical evolutionary analysis when studying ancient proteins and comparing biochemical properties between parental or extinct species and their extant relatives. It relies on multiple sequence alignment (MSA) which may introduce biases, and it remains unknown how MSA methodological approaches impact ancestral sequence reconstruction (ASR). Here, we investigate how MSA methodology modulates ASR using a simulation study of various evolutionary scenarios. We evaluate the accuracy of ancestral protein sequence reconstruction for simulated data and compare reconstruction outcomes using different alignment methods. Our results reveal biases introduced not only by aligner algorithms and assumptions, but also tree topology and the rate of insertions and deletions. Under many conditions we find no substantial differences between the MSAs. However, increasing the difficulty for the aligners can significantly impact ASR. The MAFFT consistency aligners and PRANK variants exhibit the best performance, whereas FSA displays limited performance. We also discover a bias towards reconstructed sequences longer than the true ancestors, deriving from a preference for inferring insertions, in almost all MSA methodological approaches. In addition, we find measures of MSA quality generally correlate highly with reconstruction accuracy. Thus, we show MSA methodological differences can affect the quality of reconstructions and propose MSA methods should be selected with care to accurately determine ancestral states with confidence.

Enhancing Statistical Multiple Sequence Alignment and Tree Inference Using Structural Information

For highly divergent sequences, there is often insufficient information to reliably construct alignments and phylogenetic trees. Since protein structure may be strongly conserved despite large divergences in sequence, structural information can be used to help identify homology in such cases.While there exist well-studied models of sequence evolution, structurally informed alignment methods have typically made use of geometric measures of deviation that do not take into account the underlying mutational processes. In order to integrate structural information into sequence-based evolutionary models, we recently developed a stochastic model of structural evolution on a phylogenetic tree and implemented this as the StructAlign plugin for the StatAlign statistical alignment package.In this chapter, we will outline the types of analyses that can be carried out using StructAlign, illustrating how the inclusion of structural information can be used to inform joint estimation of alignments and trees. StructAlign can also be used to infer branch-specific rates of structural evolution, and analysis of an example globin dataset highlights strong variation in the inferred rate across the tree. While structure is more highly conserved within clades, the rate of structural divergence as a function of sequence variation is larger between functionally divergent proteins. Allowing for the rate of structural divergence to vary over the tree results in an improved fit to the empirically observed pairwise RMSD values.

A Poissonian Model of Indel Rate Variation for Phylogenetic Tree Inference

While indel rate variation has been observed and analyzed in detail, it is not taken into account by current indel-aware phylogenetic reconstruction methods. In this work, we introduce a continuous time stochastic process, the geometric Poisson indel process, that generalizes the Poisson indel process by allowing insertion and deletion rates to vary across sites. We design an efficient algorithm for computing the probability of a given multiple sequence alignment based on our new indel model. We describe a method to construct phylogeny estimates from a fixed alignment using neighbor joining. Using simulation studies, we show that ignoring indel rate variation may have a detrimental effect on the accuracy of the inferred phylogenies, and that our proposed method can sidestep this issue by inferring latent indel rate categories. We also show that our phylogenetic inference method may be more stable to taxa subsampling than methods that either ignore indels or indel rate variation. [evolutionary stochastic process; indel rate variation; Poisson indel process; TKF91.].

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.

Historian: accurate reconstruction of ancestral sequences and evolutionary rates

Motivation

Reconstruction of ancestral sequence histories, and estimation of parameters like indel rates, are improved by using explicit evolutionary models and summing over uncertain alignments. The previous best tool for this purpose (according to simulation benchmarks) was ProtPal, but this tool was too slow for practical use.

Results

Historian combines an efficient reimplementation of the ProtPal algorithm with performance-improving heuristics from other alignment tools. Simulation results on fidelity of rate estimation via ancestral reconstruction, along with evaluations on the structurally informed alignment dataset BAliBase 3.0, recommend Historian over other alignment tools for evolutionary applications.

Availability and Implementation

Historian is available at https://github.com/evoldoers/historian under the Creative Commons Attribution 3.0 US license.

Contact

ihholmes+historian@gmail.com.

General continuous-time Markov model of sequence evolution via insertions/deletions: local alignment probability computation

Background

Insertions and deletions (indels) account for more nucleotide differences between two related DNA sequences than substitutions do, and thus it is imperative to develop a method to reliably calculate the occurrence probabilities of sequence alignments via evolutionary processes on an entire sequence. Previously, we presented a perturbative formulation that facilitates the ab initio calculation of alignment probabilities under a continuous-time Markov model, which describes the stochastic evolution of an entire sequence via indels with quite general rate parameters. And we demonstrated that, under some conditions, the ab initio probability of an alignment can be factorized into the product of an overall factor and contributions from regions (or local alignments) delimited by gapless columns.

Results

Here, using our formulation, we attempt to approximately calculate the probabilities of local alignments under space-homogeneous cases. First, for each of all types of local pairwise alignments (PWAs) and some typical types of local multiple sequence alignments (MSAs), we numerically computed the total contribution from all parsimonious indel histories and that from all next-parsimonious histories, and compared them. Second, for some common types of local PWAs, we derived two integral equation systems that can be numerically solved to give practically exact solutions. We compared the total parsimonious contribution with the practically exact solution for each such local PWA. Third, we developed an algorithm that calculates the first-approximate MSA probability by multiplying total parsimonious contributions from all local MSAs. Then we compared the first-approximate probability of each local MSA with its absolute frequency in the MSAs created via a genuine sequence evolution simulator, Dawg. In all these analyses, the total parsimonious contributions approximated the multiplication factors fairly well, as long as gap sizes and branch lengths are at most moderate. Examination of the accuracy of another indel probabilistic model in the light of our formulation indicated some modifications necessary for the model’s accuracy improvement.

Conclusions

At least under moderate conditions, the approximate methods can quite accurately calculate ab initio alignment probabilities under biologically more realistic models than before. Thus, our formulation will provide other indel probabilistic models with a sound reference point.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1167-6) contains supplementary material, which is available to authorized users.

General continuous-time Markov model of sequence evolution via insertions/deletions: are alignment probabilities factorable?

Background

Insertions and deletions (indels) account for more nucleotide differences between two related DNA sequences than substitutions do, and thus it is imperative to develop a stochastic evolutionary model that enables us to reliably calculate the probability of the sequence evolution through indel processes. Recently, indel probabilistic models are mostly based on either hidden Markov models (HMMs) or transducer theories, both of which give the indel component of the probability of a given sequence alignment as a product of either probabilities of column-to-column transitions or block-wise contributions along the alignment. However, it is not a priori clear how these models are related with any genuine stochastic evolutionary model, which describes the stochastic evolution of an entire sequence along the time-axis. Moreover, currently none of these models can fully accommodate biologically realistic features, such as overlapping indels, power-law indel-length distributions, and indel rate variation across regions.

Results

Here, we theoretically dissect the ab initio calculation of the probability of a given sequence alignment under a genuine stochastic evolutionary model, more specifically, a general continuous-time Markov model of the evolution of an entire sequence via insertions and deletions. Our model is a simple extension of the general “substitution/insertion/deletion (SID) model”. Using the operator representation of indels and the technique of time-dependent perturbation theory, we express the ab initio probability as a summation over all alignment-consistent indel histories. Exploiting the equivalence relations between different indel histories, we find a “sufficient and nearly necessary” set of conditions under which the probability can be factorized into the product of an overall factor and the contributions from regions separated by gapless columns of the alignment, thus providing a sort of generalized HMM. The conditions distinguish evolutionary models with factorable alignment probabilities from those without ones. The former category includes the “long indel” model (a space-homogeneous SID model) and the model used by Dawg, a genuine sequence evolution simulator.

Conclusions

With intuitive clarity and mathematical preciseness, our theoretical formulation will help further advance the ab initio calculation of alignment probabilities under biologically realistic models of sequence evolution via indels.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1105-7) contains supplementary material, which is available to authorized users.

Characterization of multiple sequence alignment errors using complete-likelihood score and position-shift map

BACKGROUND

Reconstruction of multiple sequence alignments (MSAs) is a crucial step in most homology-based sequence analyses, which constitute an integral part of computational biology. To improve the accuracy of this crucial step, it is essential to better characterize errors that state-of-the-art aligners typically make. For this purpose, we here introduce two tools: the complete-likelihood score and the position-shift map.

RESULTS

The logarithm of the total probability of a MSA under a stochastic model of sequence evolution along a time axis via substitutions, insertions and deletions (called the "complete-likelihood score" here) can serve as an ideal score of the MSA. A position-shift map, which maps the difference in each residue's position between two MSAs onto one of them, can clearly visualize where and how MSA errors occurred and help disentangle composite errors. To characterize MSA errors using these tools, we constructed three sets of simulated MSAs of selectively neutral mammalian DNA sequences, with small, moderate and large divergences, under a stochastic evolutionary model with an empirically common power-law insertion/deletion length distribution. Then, we reconstructed MSAs using MAFFT and Prank as representative state-of-the-art single-optimum-search aligners. About 40-99% of the hundreds of thousands of gapped segments were involved in alignment errors. In a substantial fraction, from about 1/4 to over 3/4, of erroneously reconstructed segments, reconstructed MSAs by each aligner showed complete-likelihood scores not lower than those of the true MSAs. Out of the remaining errors, a majority by an iterative option of MAFFT showed discrepancies between the aligner-specific score and the complete-likelihood score, and a majority by Prank seemed due to inadequate exploration of the MSA space. Analyses by position-shift maps indicated that true MSAs are in considerable neighborhoods of reconstructed MSAs in about 80-99% of the erroneous segments for small and moderate divergences, but in only a minority for large divergences.

CONCLUSIONS

The results of this study suggest that measures to further improve the accuracy of reconstructed MSAs would substantially differ depending on the types of aligners. They also re-emphasize the importance of obtaining a probability distribution of fairly likely MSAs, instead of just searching for a single optimum MSA.

Simultaneous Bayesian estimation of alignment and phylogeny under a joint model of protein sequence and structure

For sequences that are highly divergent, there is often insufficient information to infer accurate alignments, and phylogenetic uncertainty may be high. One way to address this issue is to make use of protein structural information, since structures generally diverge more slowly than sequences. In this work, we extend a recently developed stochastic model of pairwise structural evolution to multiple structures on a tree, analytically integrating over ancestral structures to permit efficient likelihood computations under the resulting joint sequence-structure model. We observe that the inclusion of structural information significantly reduces alignment and topology uncertainty, and reduces the number of topology and alignment errors in cases where the true trees and alignments are known. In some cases, the inclusion of structure results in changes to the consensus topology, indicating that structure may contain additional information beyond that which can be obtained from sequences. We use the model to investigate the order of divergence of cytoglobins, myoglobins, and hemoglobins and observe a stabilization of phylogenetic inference: although a sequence-based inference assigns significant posterior probability to several different topologies, the structural model strongly favors one of these over the others and is more robust to the choice of data set.

Phylogenetic quantification of intra-tumour heterogeneity

Intra-tumour genetic heterogeneity is the result of ongoing evolutionary change within each cancer. The expansion of genetically distinct sub-clonal populations may explain the emergence of drug resistance, and if so, would have prognostic and predictive utility. However, methods for objectively quantifying tumour heterogeneity have been missing and are particularly difficult to establish in cancers where predominant copy number variation prevents accurate phylogenetic reconstruction owing to horizontal dependencies caused by long and cascading genomic rearrangements. To address these challenges, we present MEDICC, a method for phylogenetic reconstruction and heterogeneity quantification based on a Minimum Event Distance for Intra-tumour Copy-number Comparisons. Using a transducer-based pairwise comparison function, we determine optimal phasing of major and minor alleles, as well as evolutionary distances between samples, and are able to reconstruct ancestral genomes. Rigorous simulations and an extensive clinical study show the power of our method, which outperforms state-of-the-art competitors in reconstruction accuracy, and additionally allows unbiased numerical quantification of tumour heterogeneity. Accurate quantification and evolutionary inference are essential to understand the functional consequences of tumour heterogeneity. The MEDICC algorithms are independent of the experimental techniques used and are applicable to both next-generation sequencing and array CGH data.

Evolutionary origins of a bioactive peptide buried within Preproalbumin

The de novo evolution of proteins is now considered a frequented route for biological innovation, but the genetic and biochemical processes that lead to each newly created protein are often poorly documented. The common sunflower (Helianthus annuus) contains the unusual gene PawS1 (Preproalbumin with SFTI-1) that encodes a precursor for seed storage albumin; however, in a region usually discarded during albumin maturation, its sequence is matured into SFTI-1, a protease-inhibiting cyclic peptide with a motif homologous to unrelated inhibitors from legumes, cereals, and frogs. To understand how PawS1 acquired this additional peptide with novel biochemical functionality, we cloned PawS1 genes and showed that this dual destiny is over 18 million years old. This new family of mostly backbone-cyclic peptides is structurally diverse, but the protease-inhibitory motif was restricted to peptides from sunflower and close relatives from its subtribe. We describe a widely distributed, potential evolutionary intermediate PawS-Like1 (PawL1), which is matured into storage albumin, but makes no stable peptide despite possessing residues essential for processing and cyclization from within PawS1. Using sequences we cloned, we retrodict the likely stepwise creation of PawS1's additional destiny within a simple albumin precursor. We propose that relaxed selection enabled SFTI-1 to evolve its inhibitor function by converging upon a successful sequence and structure.

A note on probabilistic models over strings: the linear algebra approach

Probabilistic models over strings have played a key role in developing methods that take into consideration indels as phylogenetically informative events. There is an extensive literature on using automata and transducers on phylogenies to do inference on these probabilistic models, in which an important theoretical question is the complexity of computing the normalization of a class of string-valued graphical models. This question has been investigated using tools from combinatorics, dynamic programming, and graph theory, and has practical applications in Bayesian phylogenetics. In this work, we revisit this theoretical question from a different point of view, based on linear algebra. The main contribution is a set of results based on this linear algebra view that facilitate the analysis and design of inference algorithms on string-valued graphical models. As an illustration, we use this method to give a new elementary proof of a known result on the complexity of inference on the "TKF91" model, a well-known probabilistic model over strings. Compared to previous work, our proving method is easier to extend to other models, since it relies on a novel weak condition, triangular transducers, which is easy to establish in practice. The linear algebra view provides a concise way of describing transducer algorithms and their compositions, opens the possibility of transferring fast linear algebra libraries (for example, based on GPUs), as well as low rank matrix approximation methods, to string-valued inference problems.

Sequence context of indel mutations and their effect on protein evolution in a bacterial endosymbiont

Indel mutations play key roles in genome and protein evolution, yet we lack a comprehensive understanding of how indels impact evolutionary processes. Genome-wide analyses enabled by next-generation sequencing can clarify the context and effect of indels, thereby integrating a more detailed consideration of indels with our knowledge of nucleotide substitutions. To this end, we sequenced Blochmannia chromaiodes, an obligate bacterial endosymbiont of carpenter ants, and compared it with the close relative, B. pennsylvanicus. The genetic distance between these species is small enough for accurate whole genome alignment but large enough to provide a meaningful spectrum of indel mutations. We found that indels are subjected to purifying selection in coding regions and even intergenic regions, which show a reduced rate of indel base pairs per kilobase compared with nonfunctional pseudogenes. Indels occur almost exclusively in repeat regions composed of homopolymers and multimeric simple sequence repeats, demonstrating the importance of sequence context for indel mutations. Despite purifying selection, some indels occur in protein-coding genes. Most are multiples of three, indicating selective pressure to maintain the reading frame. The deleterious effect of frameshift-inducing indels is minimized by either compensation from a nearby indel to restore reading frame or the indel's location near the 3'-end of the gene. We observed amino acid divergence exceeding nucleotide divergence in regions affected by frameshift-inducing indels, suggesting that these indels may either drive adaptive protein evolution or initiate gene degradation. Our results shed light on how indel mutations impact processes of molecular evolution underlying endosymbiont genome evolution.

Graph-based modeling of tandem repeats improves global multiple sequence alignment

Tandem repeats (TRs) are often present in proteins with crucial functions, responsible for resistance, pathogenicity and associated with infectious or neurodegenerative diseases. This motivates numerous studies of TRs and their evolution, requiring accurate multiple sequence alignment. TRs may be lost or inserted at any position of a TR region by replication slippage or recombination, but current methods assume fixed unit boundaries, and yet are of high complexity. We present a new global graph-based alignment method that does not restrict TR unit indels by unit boundaries. TR indels are modeled separately and penalized using the phylogeny-aware alignment algorithm. This ensures enhanced accuracy of reconstructed alignments, disentangling TRs and measuring indel events and rates in a biologically meaningful way. Our method detects not only duplication events but also all changes in TR regions owing to recombination, strand slippage and other events inserting or deleting TR units. We evaluate our method by simulation incorporating TR evolution, by either sampling TRs from a profile hidden Markov model or by mimicking strand slippage with duplications. The new method is illustrated on a family of type III effectors, a pathogenicity determinant in agriculturally important bacteria Ralstonia solanacearum. We show that TR indel rate variation contributes to the diversification of this protein family.

Evolutionary inference via the Poisson Indel Process

We address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formulated in terms of string-valued evolutionary processes along the branches of a phylogenetic tree. The classic evolutionary process, the TKF91 model [Thorne JL, Kishino H, Felsenstein J (1991) J Mol Evol 33(2):114-124] is a continuous-time Markov chain model composed of insertion, deletion, and substitution events. Unfortunately, this model gives rise to an intractable computational problem: The computation of the marginal likelihood under the TKF91 model is exponential in the number of taxa. In this work, we present a stochastic process, the Poisson Indel Process (PIP), in which the complexity of this computation is reduced to linear. The Poisson Indel Process is closely related to the TKF91 model, differing only in its treatment of insertions, but it has a global characterization as a Poisson process on the phylogeny. Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model. We present illustrative experiments in which Bayesian inference under the PIP model is compared with separate inference of phylogenies and alignments.

Exploiting ancestral mammalian genomes for the prediction of human transcription factor binding sites

Background

The computational prediction of Transcription Factor Binding Sites (TFBS) remains a challenge due to their short length and low information content. Comparative genomics approaches that simultaneously consider several related species and favor sites that have been conserved throughout evolution improve the accuracy (specificity) of the predictions but are limited due to a phenomenon called binding site turnover, where sequence evolution causes one TFBS to replace another in the same region. In parallel to this development, an increasing number of mammalian genomes are now sequenced and it is becoming possible to infer, to a surprisingly high degree of accuracy, ancestral mammalian sequences.

Results

We propose a TFBS prediction approach that makes use of the availability of inferred ancestral mammalian genomes to improve its accuracy. This method aims to identify binding loci, which are regions of a few hundred base pairs that have preserved their potential to bind a given transcription factor over evolutionary time. After proposing a neutral evolutionary model of predicted TFBS counts in a DNA region of a given length, we use it to identify regions that have preserved the number of predicted TFBS they contain to an unexpected degree given their divergence. The approach is applied to human chromosome 1 and shows significant gains in accuracy as compared to both existing single-species and multi-species TFBS prediction approaches, in particular for transcription factors that are subject to high turnover rates.

Availability

The source code and predictions made by the program are available at http://www.cs.mcgill.ca/~blanchem/bindingLoci.