Reconstruction Of Ancestral Gene Orders Research Articles

AGO, a Framework for the Reconstruction of Ancestral Syntenies and Gene Orders.

Reconstructing ancestral gene orders from the genome data of extant species is an important problem in comparative and evolutionary genomics. In a phylogenomics setting that accounts for gene family evolution through gene duplication and gene loss, the reconstruction of ancestral gene orders involves several steps, including multiple sequence alignment, the inference of reconciled gene trees, and the inference of ancestral syntenies and gene adjacencies. For each of the steps of such a process, several methods can be used and implemented using a growing corpus of, often parameterized, tools; in practice, interfacing such tools into an ancestral gene order reconstruction pipeline is far from trivial. This chapter introduces AGO, a Python-based framework aimed at creating ancestral gene order reconstruction pipelines allowing to interface and parameterize different bioinformatics tools. The authors illustrate the features of AGO by reconstructing ancestral gene orders for the X chromosome of three ancestral Anopheles species using three different pipelines. AGO is freely available at https://github.com/cchauve/AGO-pipeline .

Phylogenetic signal from rearrangements in 18 Anopheles species by joint scaffolding extant and ancestral genomes

BackgroundGenomes rearrangements carry valuable information for phylogenetic inference or the elucidation of molecular mechanisms of adaptation. However, the detection of genome rearrangements is often hampered by current deficiencies in data and methods: Genomes obtained from short sequence reads have generally very fragmented assemblies, and comparing multiple gene orders generally leads to computationally intractable algorithmic questions.ResultsWe present a computational method, ADseq, which, by combining ancestral gene order reconstruction, comparative scaffolding and de novo scaffolding methods, overcomes these two caveats. ADseq provides simultaneously improved assemblies and ancestral genomes, with statistical supports on all local features. Compared to previous comparative methods, it runs in polynomial time, it samples solutions in a probabilistic space, and it can handle a significantly larger gene complement from the considered extant genomes, with complex histories including gene duplications and losses. We use ADseq to provide improved assemblies and a genome history made of duplications, losses, gene translocations, rearrangements, of 18 complete Anopheles genomes, including several important malaria vectors. We also provide additional support for a differentiated mode of evolution of the sex chromosome and of the autosomes in these mosquito genomes.ConclusionsWe demonstrate the method’s ability to improve extant assemblies accurately through a procedure simulating realistic assembly fragmentation. We study a debated issue regarding the phylogeny of the Gambiae complex group of Anopheles genomes in the light of the evolution of chromosomal rearrangements, suggesting that the phylogenetic signal they carry can differ from the phylogenetic signal carried by gene sequences, more prone to introgression.

The complete mitogenome of Orcula dolium (Draparnaud, 1801); ultra-deep sequencing from a single long-range PCR using the Ion-Torrent PGM

BackgroundWith the increasing capacity of present-day next-generation sequencers the field of mitogenomics is rapidly changing. Enrichment of the mitochondrial fraction, is no longer necessary for obtaining mitogenomic data. Despite the benefits, shotgun sequencing approaches also have disadvantages. They do not guarantee obtaining the complete mitogenome, generally require larger amounts of input DNA and coverage is low compared to sequencing with enrichment strategies. If the mitogenome could be amplified in a single amplification, additional time and costs for sample preparation might outweigh these disadvantages.ResultsA sequence of the complete mitochondrial genome of the pupilloid landsnail Orcula dolium is presented. The mitogenome was amplified in a single long-range (LR) PCR and sequenced on an Ion Torrent PGM (Life Technologies). The length is 14,063 nt and the average depth of coverage is 1112 X. This is the first published mitogenome for a member of the family Orculidae. It has the typical metazoan makeup of 13 protein coding genes (PCGs), 2 ribosomal RNAs (12S and 16S) and 22 transfer RNAs (tRNAs). Orcula is positioned between Pupilla and the Vertiginidae as the sister-group of Gastrocopta and Vertigo, together. An ancestral gene order reconstruction shows that Orthurethra in contrast to other Stylommatophora, have tRNA-H before tRNA-G and that the gene order in the ‘non-achatinoid’ clade is identical to that of closely related non-stylommatophoran taxa.ConclusionsWe show it is feasible to ultra-deep sequence a mitogenome from a single LR-PCR. This approach is particularly relevant to studies that have low concentrations of input DNA. It results in a more efficient use of NGS capacity (only the targeted fraction is sequenced) and is an effective selection against nuclear mitochondrial inserts (NUMTS). In contrast to previous studies based in particular on 28S, our results indicate that phylogeny reconstructions based on complete mitogenomes might be more suitable to resolve deep relationships within Stylommatophora. Ancestral gene order reconstructions reveal rearrangements that characterize systematic groups.

Fast ancestral gene order reconstruction of genomes with unequal gene content.

BackgroundDuring evolution, genomes are modified by large scale structural events, such as rearrangements, deletions or insertions of large blocks of DNA. Of particular interest, in order to better understand how this type of genomic evolution happens, is the reconstruction of ancestral genomes, given a phylogenetic tree with extant genomes at its leaves. One way of solving this problem is to assume a rearrangement model, such as Double Cut and Join (DCJ), and find a set of ancestral genomes that minimizes the number of events on the input tree. Since this problem is NP-hard for most rearrangement models, exact solutions are practical only for small instances, and heuristics have to be used for larger datasets. This type of approach can be called event-based. Another common approach is based on finding conserved structures between the input genomes, such as adjacencies between genes, possibly also assigning weights that indicate a measure of confidence or probability that this particular structure is present on each ancestral genome, and then finding a set of non conflicting adjacencies that optimize some given function, usually trying to maximize total weight and minimizing character changes in the tree. We call this type of methods homology-based.ResultsIn previous work, we proposed an ancestral reconstruction method that combines homology- and event-based ideas, using the concept of intermediate genomes, that arise in DCJ rearrangement scenarios. This method showed better rate of correctly reconstructed adjacencies than other methods, while also being faster, since the use of intermediate genomes greatly reduces the search space. Here, we generalize the intermediate genome concept to genomes with unequal gene content, extending our method to account for gene insertions and deletions of any length. In many of the simulated datasets, our proposed method had better results than MLGO and MGRA, two state-of-the-art algorithms for ancestral reconstruction with unequal gene content, while running much faster, making it more scalable to larger datasets.ConclusionStuding ancestral reconstruction problems under a new light, using the concept of intermediate genomes, allows the design of very fast algorithms by greatly reducing the solution search space, while also giving very good results. The algorithms introduced in this paper were implemented in an open-source software called RINGO (ancestral Reconstruction with INtermediate GenOmes), available at https://github.com/pedrofeijao/RINGO.

Reconstruction of ancestral gene orders using intermediate genomes.

BackgroundThe problem of reconstructing ancestral genomes in a given phylogenetic tree arises in many different comparative genomics fields. Here, we focus on reconstructing the gene order of ancestral genomes, a problem that has been largely studied in the past 20 years, especially with the increasing availability of whole genome DNA sequences. There are two main approaches to this problem: event-based methods, that try to find the ancestral genomes that minimize the number of rearrangement events in the tree; and homology-based, that look for conserved structures, such as adjacent genes in the extant genomes, to build the ancestral genomes.ResultsWe propose algorithms that use the concept of intermediate genomes, arising in optimal pairwise rearrangement scenarios. We show that intermediate genomes have combinatorial properties that make them easy to reconstruct, and develop fast algorithms with better reconstructed ancestral genomes than current event-based methods. The proposed framework is also designed to accept extra information, such as results from homology-based approaches, giving rise to combined algorithms with better results than the original methods.

ProCARs: Progressive Reconstruction of Ancestral Gene Orders.

BackgroundIn the context of ancestral gene order reconstruction from extant genomes, there exist two main computational approaches: rearrangement-based, and homology-based methods. The rearrangement-based methods consist in minimizing a total rearrangement distance on the branches of a species tree. The homology-based methods consist in the detection of a set of potential ancestral contiguity features, followed by the assembling of these features into Contiguous Ancestral Regions (CARs).ResultsIn this paper, we present a new homology-based method that uses a progressive approach for both the detection and the assembling of ancestral contiguity features into CARs. The method is based on detecting a set of potential ancestral adjacencies iteratively using the current set of CARs at each step, and constructing CARs progressively using a 2-phase assembling method.ConclusionWe show the usefulness of the method through a reconstruction of the boreoeutherian ancestral gene order, and a comparison with three other homology-based methods: AnGeS, InferCARs and GapAdj. The program, written in Python, and the dataset used in this paper are available at http://bioinfo.lifl.fr/procars/.

Reconstruction of ancestral gene orders using probabilistic and gene encoding approaches.

Current tools used in the reconstruction of ancestral gene orders often fall into event-based and adjacency-based methods according to the principles they follow. Event-based methods such as GRAPPA are very accurate but with extremely high complexity, while more recent methods based on gene adjacencies such as InferCARsPro is relatively faster, but often produces an excessive number of chromosomes. This issue is mitigated by newer methods such as GapAdj, however it sacrifices a considerable portion of accuracy. We recently developed an adjacency-based method in the probabilistic framework called PMAG to infer ancestral gene orders. PMAG relies on calculating the conditional probabilities of gene adjacencies that are found in the leaf genomes using the Bayes' theorem. It uses a novel transition model which accounts for adjacency changes along the tree branches as well as a re-rooting procedure to prevent any information loss. In this paper, we improved PMAG with a new method to assemble gene adjacencies into valid gene orders, using an exact solver for traveling salesman problem (TSP) to maximize the overall conditional probabilities. We conducted a series of simulation experiments using a wide range of configurations. The first set of experiments was to verify the effectiveness of our strategy of using the better transition model and re-rooting the tree under the targeted ancestral genome. PMAG was then thoroughly compared in terms of three measurements with its four major competitors including InferCARsPro, GapAdj, GASTS and SCJ in order to assess their performances. According to the results, PMAG demonstrates superior performance in terms of adjacency, distance and assembly accuracies, and yet achieves comparable running time, even all TSP instances were solved exactly. PMAG is available for free at http://phylo.cse.sc.edu.

Probabilistic Reconstruction of Ancestral Gene Orders with Insertions and Deletions.

Changes of gene orderings have been extensively used as a signal to reconstruct phylogenies and ancestral genomes. Inferring the gene order of an extinct species has a wide range of applications, including the potential to reveal more detailed evolutionary histories, to determine gene content and ordering, and to understand the consequences of structural changes for organismal function and species divergence. In this study, we propose a new adjacency-based method, PMAG(+) , to infer ancestral genomes under a more general model of gene evolution involving gene insertions and deletions (indels), in addition to gene rearrangements. PMAG(+) improves on our previous method PMAG by developing a new approach to infer ancestral gene contents and reducing the adjacency assembly problem to an instance of TSP. We designed a series of experiments to extensively validate PMAG(+) and compared the results with the most recent and comparable method GapAdj. According to the results, ancestral gene contents predicted by PMAG(+) coincides highly with the actual contents with error rates less than 1 percent. Under various degrees of indels, PMAG(+) consistently achieves more accurate prediction of ancestral gene orders and at the same time, produces contigs very close to the actual chromosomes.

On the Identification of Conflicting Contiguities in Ancestral Genome Reconstruction

In computional biology, up-to-date homology-based methods for the reconstruction of ancestral gene orders usually rely on two phases. First, potential ancestral co-localizations of some genomic markers are detected from homologies between extant species. Next, the assembling phase mainly consists in resolving the conflicts between the potential ancestral features. This can be done using many methods, but one of the most advanced solutions is to identify and discard from the set of potential features those that belong to inclusivewise minimal conflicting sets of features. It relies on the consecutive ones property (C1P), and the notion of minimal conflicting set (MCS), widely used in physical mapping problems. Let C be a finite set of n elements and R= {r(1)‚ r(2)‚ . . . ‚ r(m)} a family of m subsets of C. A subset X of R satisfies the C1P if there exists a permutation P of C such that each r(i) in X is an interval of P. An MCS S ⊆ R is a subset of R that does not satisfy the C1P, but such that any of its proper subsets does. In this article, we present a new simpler and faster algorithm to decide if a given element r ∈ R belongs to at least one MCS. Our algorithm runs in O(n(2)m(2) + nm(7)), largely improving upon the current O(m(6)n(5)(m + n)(2) log(m + n)) fastest algorithm. The new algorithm is based on an alternative approach considering minimal forbidden induced subgraphs of interval graphs instead of Tucker matrices.

Lateral gene transfer, rearrangement, reconciliation

BackgroundModels of ancestral gene order reconstruction have progressively integrated different evolutionary patterns and processes such as unequal gene content, gene duplications, and implicitly sequence evolution via reconciled gene trees. These models have so far ignored lateral gene transfer, even though in unicellular organisms it can have an important confounding effect, and can be a rich source of information on the function of genes through the detection of transfers of clusters of genes.ResultWe report an algorithm together with its implementation, DeCoLT, that reconstructs ancestral genome organization based on reconciled gene trees which summarize information on sequence evolution, gene origination, duplication, loss, and lateral transfer. DeCoLT optimizes in polynomial time on the number of rearrangements, computed as the number of gains and breakages of adjacencies between pairs of genes. We apply DeCoLT to 1099 gene families from 36 cyanobacteria genomes.ConclusionDeCoLT is able to reconstruct adjacencies in 35 ancestral bacterial genomes with a thousand gene families in a few hours, and detects clusters of co-transferred genes. DeCoLT may also be used with any relationship between genes instead of adjacencies, to reconstruct ancestral interactions, functions or complexes.Availabilityhttp://pbil.univ-lyon1.fr/software/DeCoLT/

Gene order in rosid phylogeny, inferred from pairwise syntenies among extant genomes

BackgroundAncestral gene order reconstruction for flowering plants has lagged behind developments in yeasts, insects and higher animals, because of the recency of widespread plant genome sequencing, sequencers' embargoes on public data use, paralogies due to whole genome duplication (WGD) and fractionation of undeleted duplicates, extensive paralogy from other sources, and the computational cost of existing methods.ResultsWe address these problems, using the gene order of four core eudicot genomes (cacao, castor bean, papaya and grapevine) that have escaped any recent WGD events, and two others (poplar and cucumber) that descend from independent WGDs, in inferring the ancestral gene order of the rosid clade and those of its main subgroups, the fabids and malvids. We improve and adapt techniques including the OMG method for extracting large, paralogy-free, multiple orthologies from conflated pairwise synteny data among the six genomes and the PATHGROUPS approach for ancestral gene order reconstruction in a given phylogeny, where some genomes may be descendants of WGD events. We use the gene order evidence to evaluate the hypothesis that the order Malpighiales belongs to the malvids rather than as traditionally assigned to the fabids.ConclusionsGene orders of ancestral eudicot species, involving 10,000 or more genes can be reconstructed in an efficient, parsimonious and consistent way, despite paralogies due to WGD and other processes. Pairwise genomic syntenies provide appropriate input to a parameter-free procedure of multiple ortholog identification followed by gene-order reconstruction in solving instances of the "small phylogeny" problem.

Pathgroups, a dynamic data structure for genome reconstruction problems

Ancestral gene order reconstruction problems, including the median problem, quartet construction, small phylogeny, guided genome halving and genome aliquoting, are NP hard. Available heuristics dedicated to each of these problems are computationally costly for even small instances. We present a data structure enabling rapid heuristic solution to all these ancestral genome reconstruction problems. A generic greedy algorithm with look-ahead based on an automatically generated priority system suffices for all the problems using this data structure. The efficiency of the algorithm is due to fast updating of the structure during run time and to the simplicity of the priority scheme. We illustrate with the first rapid algorithm for quartet construction and apply this to a set of yeast genomes to corroborate a recent gene sequence-based phylogeny. http://albuquerque.bioinformatics.uottawa.ca/pathgroup/Quartet.html chunfang313@gmail.com Supplementary data are available at Bioinformatics online.

Towards Improved Reconstruction of Ancestral Gene Order in Angiosperm Phylogeny

Whole genome doubling (WGD), a frequent occurrence during the evolution of the angiosperms, complicates ancestral gene order reconstruction due to the multiplicity of solutions to the genome halving process. Using the genome of a related species (the outgroup) to guide the halving of a WGD descendant attenuates this problem. We investigate a battery of techniques for further improvement, including an unbiased version of the guided genome halving algorithm, reference to two related genomes instead of only one to guide the reconstruction, use of draft genome sequences in contig form only, incorporation of incomplete sets of homology correspondences among the genomes, and addition of large numbers of "singleton" correspondences. We make use of genomic distance, breakpoint reuse rate, dispersion of sets of alternate solutions, and other means to evaluate these techniques, and employ the papaya (Carica papaya) and grapevine (Vitis vinifera) genomes to reconstruct the pre-WGD ancestor of poplar (Populus trichocarpa), as well as an early rosid ancestor. A significant result is that the papaya genome has rearranged at a greater rate from the rosid ancestor than phylogenetic relationships would predict.