Algorithms for Molecular Biology最新文献

We present a novel problem, called MetaEC, which aims to infer gene-species assignments in a collection of partially leaf-labeled gene trees labels by minimizing the size of duplication episode clustering (EC). This problem is particularly relevant in metagenomics, where incomplete data often poses a challenge in the accurate reconstruction of gene histories. To solve MetaEC, we propose a polynomial time dynamic programming (DP) formulation that verifies the existence of a set of duplication episodes from a predefined set of episode candidates. In addition, we design a method to infer distributions of gene-species mappings. We then demonstrate how to use DP to design an algorithm that solves MetaEC. Although the algorithm is exponential in the worst case, we introduce a heuristic modification of the algorithm that provides a solution with the knowledge that it is exact. To evaluate our method, we perform two computational experiments on simulated and empirical data containing whole genome duplication events, showing that our algorithm is able to accurately infer the corresponding events.

Background: Horizontal gene transfer inference approaches are usually based on gene sequences: parametric methods search for patterns that deviate from a particular genomic signature, while phylogenetic methods use sequences to reconstruct the gene and species trees. However, it is well-known that sequences have difficulty identifying ancient transfers since mutations have enough time to erase all evidence of such events. In this work, we ask whether character-based methods can predict gene transfers. Their advantage over sequences is that homologous genes can have low DNA similarity, but still have retained enough important common motifs that allow them to have common character traits, for instance the same functional or expression profile. A phylogeny that has two separate clades that acquired the same character independently might indicate the presence of a transfer even in the absence of sequence similarity.

Our contributions: We introduce perfect transfer networks, which are phylogenetic networks that can explain the character diversity of a set of taxa under the assumption that characters have unique births, and that once a character is gained it is rarely lost. Examples of such traits include transposable elements, biochemical markers and emergence of organelles, just to name a few. We study the differences between our model and two similar models: perfect phylogenetic networks and ancestral recombination networks. Our goals are to initiate a study on the structural and algorithmic properties of perfect transfer networks. We then show that in polynomial time, one can decide whether a given network is a valid explanation for a set of taxa, and show how, for a given tree, one can add transfer edges to it so that it explains a set of taxa. We finally provide lower and upper bounds on the number of transfers required to explain a set of taxa, in the worst case.

Background: Scaffolding is an intermediate stage of fragment assembly. It consists in orienting and ordering the contigs obtained by the assembly of the sequencing reads. In the general case, the problem has been largely studied with the use of distances data between the contigs. Here we focus on a dedicated scaffolding for the chloroplast genomes. As these genomes are small, circular and with few specific repeats, numerous approaches have been proposed to assemble them. However, their specificities have not been sufficiently exploited.

Results: We give a new formulation for the scaffolding in the case of chloroplast genomes as a discrete optimisation problem, that we prove the decision version to be [Formula: see text]-Complete. We take advantage of the knowledge of chloroplast genomes and succeed in expressing the relationships between a few specific genomic repeats in mathematical constraints. Our approach is independent of the distances and adopts a genomic regions view, with the priority on scaffolding the repeats first. In this way, we encode the structural haplotype issue in order to retrieve several genome forms that coexist in the same chloroplast cell. To solve exactly the optimisation problem, we develop an integer linear program that we implement in Python3 package khloraascaf. We test it on synthetic data to investigate its performance behaviour and its robustness against several chosen difficulties.

Conclusions: We succeed to model biological knowledge on genomic structures to scaffold chloroplast genomes. Our results suggest that modelling genomic regions is sufficient for scaffolding repeats and is suitable for finding several solutions corresponding to several genome forms.

Pangenome reference graphs are useful in genomics because they compactly represent the genetic diversity within a species, a capability that linear references lack. However, efficiently aligning sequences to these graphs with complex topology and cycles can be challenging. The seed-chain-extend based alignment algorithms use co-linear chaining as a standard technique to identify a good cluster of exact seed matches that can be combined to form an alignment. Recent works show how the co-linear chaining problem can be efficiently solved for acyclic pangenome graphs by exploiting their small width and how incorporating gap cost in the scoring function improves alignment accuracy. However, it remains open on how to effectively generalize these techniques for general pangenome graphs which contain cycles. Here we present the first practical formulation and an exact algorithm for co-linear chaining on cyclic pangenome graphs. We rigorously prove the correctness and computational complexity of the proposed algorithm. We evaluate the empirical performance of our algorithm by aligning simulated long reads from the human genome to a cyclic pangenome graph constructed from 95 publicly available haplotype-resolved human genome assemblies. While the existing heuristic-based algorithms are faster, the proposed algorithm provides a significant advantage in terms of accuracy. Implementation ( https://github.com/at-cg/PanAligner ).

The problem of sequence identification or matching-determining the subset of reference sequences from a given collection that are likely to contain a short, queried nucleotide sequence-is relevant for many important tasks in Computational Biology, such as metagenomics and pangenome analysis. Due to the complex nature of such analyses and the large scale of the reference collections a resource-efficient solution to this problem is of utmost importance. This poses the threefold challenge of representing the reference collection with a data structure that is efficient to query, has light memory usage, and scales well to large collections. To solve this problem, we describe an efficient colored de Bruijn graph index, arising as the combination of a k-mer dictionary with a compressed inverted index. The proposed index takes full advantage of the fact that unitigs in the colored compacted de Bruijn graph are monochromatic (i.e., all k-mers in a unitig have the same set of references of origin, or color). Specifically, the unitigs are kept in the dictionary in color order, thereby allowing for the encoding of the map from k-mers to their colors in as little as 1 + o(1) bits per unitig. Hence, one color per unitig is stored in the index with almost no space/time overhead. By combining this property with simple but effective compression methods for integer lists, the index achieves very small space. We implement these methods in a tool called Fulgor, and conduct an extensive experimental analysis to demonstrate the improvement of our tool over previous solutions. For example, compared to Themisto-the strongest competitor in terms of index space vs. query time trade-off-Fulgor requires significantly less space (up to 43% less space for a collection of 150,000 Salmonella enterica genomes), is at least twice as fast for color queries, and is 2-6[Formula: see text] faster to construct.

The last decade of phylogenetics has seen the development of many methods that leverage constraints plus dynamic programming. The goal of this algorithmic technique is to produce a phylogeny that is optimal with respect to some objective function and that lies within a constrained version of tree space. The popular species tree estimation method ASTRAL, for example, returns a tree that (1) maximizes the quartet score computed with respect to the input gene trees and that (2) draws its branches (bipartitions) from the input constraint set. This technique has yet to be used for parsimony problems where the input are binary characters, sometimes with missing values. Here, we introduce the clade-constrained character parsimony problem and present an algorithm that solves this problem for the Dollo criterion score in [Formula: see text] time, where n is the number of leaves, k is the number of characters, and [Formula: see text] is the set of clades used as constraints. Dollo parsimony, which requires traits/mutations to be gained at most once but allows them to be lost any number of times, is widely used for tumor phylogenetics as well as species phylogenetics, for example analyses of low-homoplasy retroelement insertions across the vertebrate tree of life. This motivated us to implement our algorithm in a software package, called Dollo-CDP, and evaluate its utility for analyzing retroelement insertion presence / absence patterns for bats, birds, toothed whales as well as simulated data. Our results show that Dollo-CDP can improve upon heuristic search from a single starting tree, often recovering a better scoring tree. Moreover, Dollo-CDP scales to data sets with much larger numbers of taxa than branch-and-bound while still having an optimality guarantee, albeit a more restricted one. Lastly, we show that our algorithm for Dollo parsimony can easily be adapted to Camin-Sokal parsimony but not Fitch parsimony.

Cancer progression and treatment can be informed by reconstructing its evolutionary history from tumor cells. Although many methods exist to estimate evolutionary trees (called phylogenies) from molecular sequences, traditional approaches assume the input data are error-free and the output tree is fully resolved. These assumptions are challenged in tumor phylogenetics because single-cell sequencing produces sparse, error-ridden data and because tumors evolve clonally. Here, we study the theoretical utility of methods based on quartets (four-leaf, unrooted phylogenetic trees) in light of these barriers. We consider a popular tumor phylogenetics model, in which mutations arise on a (highly unresolved) tree and then (unbiased) errors and missing values are introduced. Quartets are then implied by mutations present in two cells and absent from two cells. Our main result is that the most probable quartet identifies the unrooted model tree on four cells. This motivates seeking a tree such that the number of quartets shared between it and the input mutations is maximized. We prove an optimal solution to this problem is a consistent estimator of the unrooted cell lineage tree; this guarantee includes the case where the model tree is highly unresolved, with error defined as the number of false negative branches. Lastly, we outline how quartet-based methods might be employed when there are copy number aberrations and other challenges specific to tumor phylogenetics.