The Complete Nucleotide Sequence of the Coffee (Coffea Arabica L.) Chloroplast Genome: Organization and Implications for Biotechnology and Phylogenetic Relationships Amongst Angiosperms

The chloroplast genome sequence of Coffea arabica L., the first sequenced member of the fourth largest family of angiosperms, Rubiaceae, is reported. The genome is 155 189 bp in length, including a pair of inverted repeats of 25 943 bp. Of the 130 genes present, 112 are distinct and 18 are duplicated in the inverted repeat. The coding region comprises 79 protein genes, 29 transfer RNA genes, four ribosomal RNA genes and 18 genes containing introns (three with three exons). Repeat analysis revealed five direct and three inverted repeats of 30 bp or longer with a sequence identity of 90% or more. Comparisons of the coffee chloroplast genome with sequenced genomes of the closely related family Solanaceae indicated that coffee has a portion of rps19 duplicated in the inverted repeat and an intact copy of infA. Furthermore, whole-genome comparisons identified large indels (\u3e 500 bp) in several intergenic spacer regions and introns in the Solanaceae, including trnE (UUC)–trnT (GGU) spacer, ycf4–cemA spacer, trnI (GAU) intron and rrn5–trnR (ACG) spacer. Phylogenetic analyses based on the DNA sequences of 61 protein-coding genes for 35 taxa, performed using both maximum parsimony and maximum likelihood methods, strongly supported the monophyly of several major clades of angiosperms, including monocots, eudicots, rosids, asterids, eurosids II, and euasterids I and II. Coffea (Rubiaceae, Gentianales) is only the second order sampled from the euasterid I clade. The availability of the complete chloroplast genome of coffee provides regulatory and intergenic spacer sequences for utilization in chloroplast genetic engineering to improve this important crop

Exact-IEBP: A New Technique For Estimating Evolutionary Distances Between Whole Genomes

Evolution operates on whole genomes by operations that change the order and strandedness of genes within the genomes. This type of data presents new opportunities for discoveries about deep evolutionary rearrangement events, provided that suciently accurate methods can be developed to reconstruct evolutionary trees in these models [3, 11, 13, 18]. A necessary component of any such method is the ability to accurately estimate the true evolutionary distance between two genomes, which is the number of rearrangement events that took place in the evolutionary history between them. We improve the technique (IEBP) in [21] with a new method, Exact-IEBP, for estimating the true evolutionary distance between two signed genomes. Our simulation study shows Exact-IEBP is a better estimation of true evolutionary distances. Furthermore, Exact-IEBP produces more accurate trees than IEBP when used with the popular distance-based method, neighbor joining [16]

A Linear-Time Algorithm for Computing Inversion Distance Between Signed Permutations with an Experimental Study

. Hannenhalli and Pevzner gave the first polynomial-time algorithm for computing the inversion distance between two signed permutations, as part of the larger task of determining the shortest sequence of inversions needed to transform one permutation into the other. Their algorithm (restricted to distance calculation) proceeds in two stages: in the first stage, the overlap graph induced by the permutation is decomposed into connected components, then in the second stage certain graph structures (hurdles and others) are identified. Berman and Hannenhalli avoided the explicit computation of the overlap graph and gave an O(na(n)) algorithm, based on a Union-Find structure, to find its connected components, where a is the inverse Ackerman function. Since for all practical purposes a(n) is a constant no larger than four, this algorithm has been the fastest practical algorithm to date. In this paper, we present a new linear-time algorithm for computing the connected components, which is more efficient than that of Berman and Hannenhalli in both theory and practice. Our algorithm uses only a stack and is very easy to implement. We give the results of computational experiments over a large range of permutation pairs produced through simulated evolution; our experiments show a speed-up by a factor of 2 to 5 in the computation of the connected components and by a factor of 1.3 to 2 in the overall distance computation.

Linear programming for phylogenetic reconstruction based on gene rearrangements

Phylogenetic reconstruction from gene rearrangements has attracted increasing attention from biologists and computer scientists over the last few years. Methods used in reconstruction include distance-based methods, parsimony methods using sequence-based encodings, and direct optimization. The latter, pioneered by Sankoff and extended by us with the software suite GRAPPA, is the most accurate approach, but has been limited to small genomes because the running time of its scoring algorithm grows exponentially with the number of genes in the genome. We report here on a new method to compute a tight lower bound on the score of a given tree, using a set of linear constraints generated through selective applications of the triangle inequality. Our method generates an integer linear program with a carefully limited number of constraints, rapidly solves its relaxed version, and uses the result to provide a tight lower bound. Since this bound is very close to the optimal tree score, it can be used directly as a selection criterion, thereby enabling us to bypass entirely the expensive scoring procedure. We have implemented this method within our GRAPPA software and run several series of experiments on both biological and simulated datasets to assess its accuracy. Our results show that using the bound as a selection criterion yields excellent trees, with error rates below 5 % up to very large evolutionary distances, consistently beating the baseline Neighbor-Joining. Our new method enables us to extend the range of applicability of the direct optimization method to chromosomes of size comparable to those of bacteria, as well as to datasets with complex combinations of evolutionary events.

Phylogenetic reconstruction from gene rearrangement data with unequal gene contents

Abstract. Phylogenetic reconstruction from gene-rearrangement data has seen increased attention over the last five years. Existing methods are limited computationally and by the assumption (highly unrealistic in practice) that all genomes have the same gene content. We have recently shown that we can scale our reconstruction tool, GRAPPA, to instances with up to a thousand genomes with no loss of accuracy and at minimal computational cost. Computing genomic distances between two genomes with unequal gene contents has seen much progress recently, but that progress has not yet been reflected in phylogenetic reconstruction methods. In this paper, we present extensions to our GRAPPA approach that can handle limited numbers of duplications (one of the main requirements for analyzing genomic data from organelles) and a few deletions. Although GRAPPA is based on exhaustive search, we show that, in practice, our bounding functions suffice to prune away almost all of the search space (our pruning rates never fall below 99.995%), resulting in high accuracy and fast running times. The range of values within which we have tested our approach encompasses mitochondria and chloroplast organellar genomes, whose phylogenetic analysis is providing new insights on evolution. Keywords computational biology, phylogenetic reconstruction, gene-order data, whole-genome data, signe

Quartet-Based Phylogeny Reconstruction from Gene Orders

Phylogenetic reconstruction from gene-rearrangement data is attracting increasing attention from biologists and computer scientists. Methods used in reconstruction include distance-based methods, parsimony methods using sequence encodings, and direct optimization. The latter, pioneered by Sankoff and extended by us with the software suiteGRAPPA, is the most accurate approach; however, its exhaustive approach means that it can be applied only to small datasets of fewer than 15 taxa. While we have successfully scaled it up to 1,000 genomes by integrating it with a disk-covering method (DCM-GRAPPA), the recursive decomposition may need many levels of recursion to handle datasets with 1,000 or more genomes. We thus investigated quartet-based approaches, which directly decompose the datasets into subsets of four taxa each; such approaches have been well studied for sequence data, but not for gene-rearrangement data. We give an optimization algorithm for the NP-hard problem of computing optimal trees for each quartet, present a variation of the dyadic method (using heuristics to choose suitable short quartets), and use both in simulation studies. We find that our quartet-based method can handle more genomes than the base version of GRAPPA, thus enabling us to reduce the number of levels of recursion in DCM-GRAPPA, but is more sensitive to the rate of evolution, with error rates rapidly increasing when saturation is approached

Quartet methods for phylogeny reconstruction from gene orders

Abstract. Phylogenetic reconstruction from gene-rearrangement data has attracted increasing attention from biologists and computer scientists. Methods used in reconstruction include distance-based methods, parsimony methods using sequence-based encodings, and direct optimization. The latter, pioneered by Sankoff and extended by us with the software suite GRAPPA, is the most accurate approach; however, its exhaustive approach means that it can be applied only to small datasets of fewer than 15 taxa. While we have successfully scaled it up to 1,000 genomes by integrating it with a diskcovering method (DCM-GRAPPA), the recursive decomposition may need many levels of recursion to handle datasets with 1,000 or more genomes. We thus investigated quartet-based approaches, which directly decompose the datasets into subsets of four taxa each; such approaches have been well studied for sequence data, but not for gene-rearrangement data. We give an optimization algorithm for the NP-hard problem of computing optimal trees for each quartet, present a variation of the dyadic method (using heuristics to choose suitable short quartets), and use both in simulation studies. We find that our quartet-based method can handle more genomes than the base version of GRAPPA, thus enabling us to reduce the number of levels of recursion in DCM-GRAPPA, but is more sensitive to the rate of evolution, with error rates rapidly increasing when saturation is approached.