Said Sadique Adi

Novel insights into the genomic basis of citrus canker based on the genome sequences of two strains of Xanthomonas fuscans subsp. aurantifolii

BackgroundCitrus canker is a disease that has severe economic impact on the citrus industry worldwide. There are three types of canker, called A, B, and C. The three types have different phenotypes and affect different citrus species. The causative agent for type A is Xanthomonas citri subsp. citri, whose genome sequence was made available in 2002. Xanthomonas fuscans subsp. aurantifolii strain B causes canker B and Xanthomonas fuscans subsp. aurantifolii strain C causes canker C.ResultsWe have sequenced the genomes of strains B and C to draft status. We have compared their genomic content to X. citri subsp. citri and to other Xanthomonas genomes, with special emphasis on type III secreted effector repertoires. In addition to pthA, already known to be present in all three citrus canker strains, two additional effector genes, xopE3 and xopAI, are also present in all three strains and are both located on the same putative genomic island. These two effector genes, along with one other effector-like gene in the same region, are thus good candidates for being pathogenicity factors on citrus. Numerous gene content differences also exist between the three cankers strains, which can be correlated with their different virulence and host range. Particular attention was placed on the analysis of genes involved in biofilm formation and quorum sensing, type IV secretion, flagellum synthesis and motility, lipopolysacharide synthesis, and on the gene xacPNP, which codes for a natriuretic protein.ConclusionWe have uncovered numerous commonalities and differences in gene content between the genomes of the pathogenic agents causing citrus canker A, B, and C and other Xanthomonas genomes. Molecular genetics can now be employed to determine the role of these genes in plant-microbe interactions. The gained knowledge will be instrumental for improving citrus canker control.

Repetition-free longest common subsequence

We study the following problem. Given two sequences x and y over a finite alphabet, find a repetition-free longest common subsequence of x and y. We show several algorithmic results, a computational complexity result, and we describe a preliminary experimental study based on the proposed algorithms. We also show that this problem is APX-hard.

Analysis of membrane protein genes in a Brazilian isolate of Anaplasma marginale.

The sequencing of the complete genome of Anaplasma marginale has enabled the identification of several genes that encode membrane proteins, thereby increasing the chances of identifying candidate immunogens. Little is known regarding the genetic variability of genes that encode membrane proteins in A. marginale isolates. The aim of the present study was to determine the degree of conservation of the predicted amino acid sequences of OMP1, OMP4, OMP5, OMP7, OMP8, OMP10, OMP14, OMP15, SODb, OPAG1, OPAG3, VirB3, VirB9-1, PepA, EF-Tu and AM854 proteins in a Brazilian isolate of A. marginale compared to other isolates. Hence, primers were used to amplify these genes: omp1, omp4, omp5, omp7, omp8, omp10, omp14, omp15, sodb, opag1, opag3, virb3, VirB9-1, pepA, ef-tu and am854. After polimerase chain reaction amplification, the products were cloned and sequenced using the Sanger method and the predicted amino acid sequence were multi-aligned using the CLUSTALW and MEGA 4 programs, comparing the predicted sequences between the Brazilian, Saint Maries, Florida and A. marginale centrale isolates. With the exception of outer membrane protein (OMP) 7, all proteins exhibited 92-100% homology to the other A. marginale isolates. However, only OMP1, OMP5, EF-Tu, VirB3, SODb and VirB9-1 were selected as potential immunogens capable of promoting cross-protection between isolates due to the high degree of homology (over 72%) also found with A. (centrale) marginale.

Gene prediction by multiple spliced alignment

With recent advances in sequencing technologies, a huge amount of DNA sequences become available year after year. In order to obtain useful information on these sequences, we need to process them in search of biologically meaningful regions. The genes are amongst the most important regions of a genome and the task of locating them in a DNA of interest is called the gene prediction problem. This problem can be addressed in several ways, and one of the most promising methods relies on homology information between the genomic DNA and previous annotated sequences (proteins, cDNAs and ESTs). In this paper we generalize a traditional formulation of the gene prediction problem and use this new formulation in the development of three gene identification tools. All these programs were tested on a benchmark of 240 human genomic sequences and the results obtained compare favorably with those achieved by other gene prediction tools available in the literature.

Gene prediction by multiple syntenic alignment

Summary Given the increasing number of available genomic sequences, one now faces the task of identifying their functional parts, like the protein coding regions. The gene prediction problem can be addressed in several ways. One of the most promising methods makes use of similarity information between the genomic DNA and previously annotated sequences (proteins, cDNAs and ESTs). Recently, given the huge amount of newly sequenced genomes, new similarity-based methods are being successfully applied in the task of gene prediction. The so-called comparative-based methods lie in the similarities shared by regions of two evolutionary related genomic sequences. Despite the number of different gene prediction approaches in the literature, this problem remains challenging. In this paper we present a new comparative-based approach to the gene prediction problem. It is based on a syntenic alignment of three or more genomic sequences. With syntenic alignment we mean an alignment that is constructed taking into account the fact that the involved sequences include conserved regions intervened by unconserved ones. We have implemented the proposed algorithm in a computer program and confirm the validity of the approach on a benchmark including triples of human, mouse and rat genomic sequences.

The maximum similarity partitioning problem and its application in the transcriptome reconstruction and quantification problem

Reconstruct and quantify the RNA molecules in a cell at a given moment is an important problem in molecular biology that allows one to know which genes are being expressed and at which intensity level. Such problem is known as transcriptome reconstruction and quantification problem TRQP. Although several approaches were already designed for the TRQP, none of them model it as a combinatorial optimisation problem over strings. In order to narrow this gap, we present here a new combinatorial optimisation problem called maximum similarity partitioning problem MSPP that models the TRQP. In addition, we prove that the MSPP is NP-complete in the strong sense and present a greedy heuristic for it and some experimental results.

The Maximum Similarity Partitioning Problem and its Application in the Transcriptome Reconstruction and Quantification Problem

Reconstruct and quantify the RNA molecules in a cell at a given moment is an important problem in molecular biology that allows one to know which genes are being expressed and at which intensity level. Such problem is known as Transcriptome Reconstruction and Quantification Problem (TRQP). Although several approaches were already designed that solve the TRQP, none of them model it as a combinatorial optimization problem. In order to narrow this gap, we present here a new combinatorial optimization problem called Maximum Similarity Partitioning Problem (MSPP) that models the TRQP. In addition, we prove that the MSPP is NP-complete in the strong sense and present a greedy heuristic for it.

A 3-Approximation Algorithm for the Multiple Spliced Alignment Problem and Its Application to the Gene Prediction Task

The Spliced Alignment Problem is a well-known problem in Bioinformatics with application to the gene prediction task. This problem consists in finding an ordered subset of non-overlapping substrings of a subject sequence g that best fits a target sequence t. In this work we present an approximation algorithm for a variant of the Spliced Alignment Problem, called Multiple Spliced Alignment Problem, that involves more than one target sequence. Under a metric, this algorithm is proved to be a 3-approximation for the problem and its good practical results compare to those obtained by four heuristics already developed for the Multiple Spliced Alignment Problem.

Syntenic global alignment and its application to the gene prediction problem

Given the increasing number of available genomic sequences, one now faces the task of identifying their protein coding regions. The gene prediction problem can be addressed in several ways, and one of the most promising methods makes use of information derived from the comparison of homologous sequences. In this work, we develop a new comparative-based gene prediction program, called Exon_Finder2. This tool is based on a new type of alignment we propose, called syntenic global alignment, that can deal satisfactorily with sequences that share regions with different rates of conservation. In addition to this new type of alignment itself, we also describe a dynamic programming algorithm that computes a best syntenic global alignment of two sequences, as well as its related score. The applicability of our approach was validated by the promising initial results achieved by Exon_Finder2. On a benchmark including 120 pairs of human and mouse genomic sequences, most of their encoded genes were successfully identified by our program.

Mapping contigs onto reference genomes

This work presents a preliminary comparative study of some tools for mapping annotated contigs onto a close-related complete annotated genome. This kind of mapping could help scientists in generating additional sequences to fill in gaps in finishing genome projects, or even in getting relevant functional information, specially when annotations of the complete genome and contigs are available.