Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

RANGER-DTL 2.0: rigorous reconstruction of gene-family evolution by duplication, transfer and loss.

ABSTRACT: Summary:RANGER-DTL 2.0 is a software program for inferring gene family evolution using Duplication-Transfer-Loss reconciliation. This new software is highly scalable and easy to use, and offers many new features not currently available in any other reconciliation program. RANGER-DTL 2.0 has a particular focus on reconciliation accuracy and can account for many sources of reconciliation uncertainty including uncertain gene tree rooting, gene tree topological uncertainty, multiple optimal reconciliations and alternative event cost assignments. RANGER-DTL 2.0 is open-source and written in C++?and Python. Availability and implementation:Pre-compiled executables, source code (open-source under GNU GPL) and a detailed manual are freely available from http://compbio.engr.uconn.edu/software/RANGER-DTL/. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Bansal MS

PROVIDER: S-EPMC6137995 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

RANGER-DTL 2.0: rigorous reconstruction of gene-family evolution by duplication, transfer and loss.

Bansal Mukul S MS Kellis Manolis M Kordi Misagh M Kundu Soumya S

Bioinformatics (Oxford, England) 20180901 18

<h4>Summary</h4>RANGER-DTL 2.0 is a software program for inferring gene family evolution using Duplication-Transfer-Loss reconciliation. This new software is highly scalable and easy to use, and offers many new features not currently available in any other reconciliation program. RANGER-DTL 2.0 has a particular focus on reconciliation accuracy and can account for many sources of reconciliation uncertainty including uncertain gene tree rooting, gene tree topological uncertainty, multiple optimal ...[more]

PMID: 29688310

Similar Datasets

GATC: a genetic algorithm for gene tree construction under the Duplication-Transfer-Loss model of evolution.

Project description:BACKGROUND:Several methods have been developed for the accurate reconstruction of gene trees. Some of them use reconciliation with a species tree to correct, a posteriori, errors in gene trees inferred from multiple sequence alignments. Unfortunately the best fit to sequence information can be lost during this process. RESULTS:We describe GATC, a new algorithm for reconstructing a binary gene tree with branch length. GATC returns optimal solutions according to a measure combining both tree likelihood (according to sequence evolution) and a reconciliation score under the Duplication-Transfer-Loss (DTL) model. It can either be used to construct a gene tree from scratch or to correct trees infered by existing reconstruction method, making it highly flexible to various input data types. The method is based on a genetic algorithm acting on a population of trees at each step. It substantially increases the efficiency of the phylogeny space exploration, reducing the risk of falling into local minima, at a reasonable computational time. We have applied GATC to a dataset of simulated cyanobacterial phylogenies, as well as to an empirical dataset of three reference gene families, and showed that it is able to improve gene tree reconstructions compared with current state-of-the-art algorithms. CONCLUSION:The proposed algorithm is able to accurately reconstruct gene trees and is highly suitable for the construction of reference trees. Our results also highlight the efficiency of multi-objective optimization algorithms for the gene tree reconstruction problem. GATC is available on Github at: https://github.com/UdeM-LBIT/GATC .

| S-EPMC5954287 | biostudies-literature

Origins and evolution of the recA/RAD51 gene family: evidence for ancient gene duplication and endosymbiotic gene transfer.

Project description:The bacterial recA gene and its eukaryotic homolog RAD51 are important for DNA repair, homologous recombination, and genome stability. Members of the recA/RAD51 family have functions that have differentiated during evolution. However, the evolutionary history and relationships of these members remains unclear. Homolog searches in prokaryotes and eukaryotes indicated that most eubacteria contain only one recA. However, many archaeal species have two recA/RAD51 homologs (RADA and RADB), and eukaryotes possess multiple members (RAD51, RAD51B, RAD51C, RAD51D, DMC1, XRCC2, XRCC3, and recA). Phylogenetic analyses indicated that the recA/RAD51 family can be divided into three subfamilies: (i) RADalpha, with highly conserved functions; (ii) RADbeta, with relatively divergent functions; and (iii) recA, functioning in eubacteria and eukaryotic organelles. The RADalpha and RADbeta subfamilies each contain archaeal and eukaryotic members, suggesting that a gene duplication occurred before the archaea/eukaryote split. In the RADalpha subfamily, eukaryotic RAD51 and DMC1 genes formed two separate monophyletic groups when archaeal RADA genes were used as an outgroup. This result suggests that another duplication event occurred in the early stage of eukaryotic evolution, producing the DMC1 clade with meiosis-specific genes. The RADbeta subfamily has a basal archaeal clade and five eukaryotic clades, suggesting that four eukaryotic duplication events occurred before animals and plants diverged. The eukaryotic recA genes were detected in plants and protists and showed strikingly high levels of sequence similarity to recA genes from proteobacteria or cyanobacteria. These results suggest that endosymbiotic transfer of recA genes occurred from mitochondria and chloroplasts to nuclear genomes of ancestral eukaryotes.

| S-EPMC1502457 | biostudies-literature

Gene duplication, gene loss and evolution of expression domains in the vertebrate nuclear receptor NR5A (Ftz-F1) family.

Project description:Fushi tarazu factor 1 (Ftz-F1, NR5A) is a zinc-finger transcription factor that belongs to the nuclear receptor superfamily and regulates genes that are involved in sterol and steroid metabolism in gonads, adrenals, liver and other tissues. To understand the evolutionary origins and developmental genetic relationships of the Ftz-F1 genes, we have cloned four homologous Ftz-f1 genes in zebrafish, called ff1a, ff1b, ff1c and ff1d. These four genes have different temporal and spatial expression patterns during development, indicating that they have distinct mechanisms of genetic regulation. Among them, the ff1a expression pattern is similar to mammalian Nr5a2, while the ff1b pattern is similar to that of mammalian Nr5a1. Genetic mapping experiments show that these four ff1 genes are located on chromosome segments conserved between the zebrafish and human genomes, indicating a common ancestral origin. Phylogenetic and conserved synteny analysis show that ff1a is the orthologue of NR5A2, and that ff1b and ff1d genes are co-orthologues of NR5A1 that arose by a gene-duplication event, probably a whole-genome duplication, in the ray-fin lineage, and each gene is located next to an NR6A1 co-orthologue as in humans, showing that the tandem duplication occurred before the divergence of human and zebrafish lineages. ff1c does not have a mammalian counterpart. Thus we have characterized the phylogenetic relationships, expression patterns and chromosomal locations of these Ftz-F1 genes, and have demonstrated their identities as NR5A genes in relation to the orthologous genes in other species.

| S-EPMC1184535 | biostudies-literature

GeneRax: A Tool for Species-Tree-Aware Maximum Likelihood-Based Gene Family Tree Inference under Gene Duplication, Transfer, and Loss.

Project description:Inferring phylogenetic trees for individual homologous gene families is difficult because alignments are often too short, and thus contain insufficient signal, while substitution models inevitably fail to capture the complexity of the evolutionary processes. To overcome these challenges, species-tree-aware methods also leverage information from a putative species tree. However, only few methods are available that implement a full likelihood framework or account for horizontal gene transfers. Furthermore, these methods often require expensive data preprocessing (e.g., computing bootstrap trees) and rely on approximations and heuristics that limit the degree of tree space exploration. Here, we present GeneRax, the first maximum likelihood species-tree-aware phylogenetic inference software. It simultaneously accounts for substitutions at the sequence level as well as gene level events, such as duplication, transfer, and loss relying on established maximum likelihood optimization algorithms. GeneRax can infer rooted phylogenetic trees for multiple gene families, directly from the per-gene sequence alignments and a rooted, yet undated, species tree. We show that compared with competing tools, on simulated data GeneRax infers trees that are the closest to the true tree in 90% of the simulations in terms of relative Robinson-Foulds distance. On empirical data sets, GeneRax is the fastest among all tested methods when starting from aligned sequences, and it infers trees with the highest likelihood score, based on our model. GeneRax completed tree inferences and reconciliations for 1,099 Cyanobacteria families in 8 min on 512 CPU cores. Thus, its parallelization scheme enables large-scale analyses. GeneRax is available under GNU GPL at https://github.com/BenoitMorel/GeneRax (last accessed June 17, 2020).

| S-EPMC8312565 | biostudies-literature

DTL-DephosSite: Deep Transfer Learning Based Approach to Predict Dephosphorylation Sites.

Project description:Phosphorylation, which is mediated by protein kinases and opposed by protein phosphatases, is an important post-translational modification that regulates many cellular processes, including cellular metabolism, cell migration, and cell division. Due to its essential role in cellular physiology, a great deal of attention has been devoted to identifying sites of phosphorylation on cellular proteins and understanding how modification of these sites affects their cellular functions. This has led to the development of several computational methods designed to predict sites of phosphorylation based on a protein's primary amino acid sequence. In contrast, much less attention has been paid to dephosphorylation and its role in regulating the phosphorylation status of proteins inside cells. Indeed, to date, dephosphorylation site prediction tools have been restricted to a few tyrosine phosphatases. To fill this knowledge gap, we have employed a transfer learning strategy to develop a deep learning-based model to predict sites that are likely to be dephosphorylated. Based on independent test results, our model, which we termed DTL-DephosSite, achieved efficiency scores for phosphoserine/phosphothreonine residues of 84%, 84% and 0.68 with respect to sensitivity (SN), specificity (SP) and Matthew's correlation coefficient (MCC). Similarly, DTL-DephosSite exhibited efficiency scores of 75%, 88% and 0.64 for phosphotyrosine residues with respect to SN, SP, and MCC.

| S-EPMC8264445 | biostudies-literature

Lineage-specific gene duplication and loss in human and great ape evolution

Project description:Given that gene duplication is a major driving force of evolutionary change and the key mechanism underlying the emergence of new genes and biological processes, this study sought to use a novel genome-wide approach to identify genes that have undergone lineage-specific duplications or contractions among several hominoid lineages. Interspecies cDNA array-based comparative genomic hybridization was used to individually compare copy number variation for 39,711 cDNAs, representing 29,619 human genes, across five hominoid species, including human. We identified 1,005 genes, either as isolated genes or in clusters positionally biased toward rearrangement-prone genomic regions, that produced relative hybridization signals unique to one or more of the hominoid lineages. Measured as a function of the evolutionary age of each lineage, genes showing copy number expansions were most pronounced in human (134) and include a number of genes thought to be involved in the structure and function of the brain. This work represents, to our knowledge, the first genome-wide gene-based survey of gene duplication across hominoid species. The genes identified here likely represent a significant majority of the major gene copy number changes that have occurred over the past 15 million years of human and great ape evolution and are likely to underlie some of the key phenotypic characteristics that distinguish these species. A species experiment design type assays differences between distinct species. Using regression correlation, arrayCGH, acCGH

2006-01-05 | E-GEOD-3980 | biostudies-arrayexpress

Ranger Michael Whelan

Project description: Not available

| S-EPMC1125440 | biostudies-literature

Efficient algorithms for the reconciliation problem with gene duplication, horizontal transfer and loss.

Project description:MotivationGene family evolution is driven by evolutionary events such as speciation, gene duplication, horizontal gene transfer and gene loss, and inferring these events in the evolutionary history of a given gene family is a fundamental problem in comparative and evolutionary genomics with numerous important applications. Solving this problem requires the use of a reconciliation framework, where the input consists of a gene family phylogeny and the corresponding species phylogeny, and the goal is to reconcile the two by postulating speciation, gene duplication, horizontal gene transfer and gene loss events. This reconciliation problem is referred to as duplication-transfer-loss (DTL) reconciliation and has been extensively studied in the literature. Yet, even the fastest existing algorithms for DTL reconciliation are too slow for reconciling large gene families and for use in more sophisticated applications such as gene tree or species tree reconstruction.ResultsWe present two new algorithms for the DTL reconciliation problem that are dramatically faster than existing algorithms, both asymptotically and in practice. We also extend the standard DTL reconciliation model by considering distance-dependent transfer costs, which allow for more accurate reconciliation and give an efficient algorithm for DTL reconciliation under this extended model. We implemented our new algorithms and demonstrated up to 100 000-fold speed-up over existing methods, using both simulated and biological datasets. This dramatic improvement makes it possible to use DTL reconciliation for performing rigorous evolutionary analyses of large gene families and enables its use in advanced reconciliation-based gene and species tree reconstruction methods.AvailabilityOur programs can be freely downloaded from http://compbio.mit.edu/ranger-dtl/.

| S-EPMC3371857 | biostudies-literature