International journal of plant genomics最新文献

Brassica rapa, which is closely related to Arabidopsis thaliana, is an important crop and a model plant for studying genome evolution via polyploidization. We report the current understanding of the genome structure of B. rapa and efforts for the whole-genome sequencing of the species. The tribe Brassicaceae, which comprises ca. 240 species, descended from a common hexaploid ancestor with a basic genome similar to that of Arabidopsis. Chromosome rearrangements, including fusions and/or fissions, resulted in the present-day "diploid" Brassica species with variation in chromosome number and phenotype. Triplicated genomic segments of B. rapa are collinear to those of A. thaliana with InDels. The genome triplication has led to an approximately 1.7-fold increase in the B. rapa gene number compared to that of A. thaliana. Repetitive DNA of B. rapa has also been extensively amplified and has diverged from that of A. thaliana. For its whole-genome sequencing, the Brassica rapa Genome Sequencing Project (BrGSP) consortium has developed suitable genomic resources and constructed genetic and physical maps. Ten chromosomes of B. rapa are being allocated to BrGSP consortium participants, and each chromosome will be sequenced by a BAC-by-BAC approach. Genome sequencing of B. rapa will offer a new perspective for plant biology and evolution in the context of polyploidization.

Detection of differentially expressed genes from expressed sequence tags (ESTs) data has received much attention. An empirical Bayesian method is introduced in which gene expression patterns are estimated and used to define detection statistics. Significantly differentially expressed genes can be declared given detection statistics. Simulation is done to evaluate the performance of proposed method. Two real applications are studied.

Functional annotation of novel sequence data is a primary requirement for the utilization of functional genomics approaches in plant research. In this paper, we describe the Blast2GO suite as a comprehensive bioinformatics tool for functional annotation of sequences and data mining on the resulting annotations, primarily based on the gene ontology (GO) vocabulary. Blast2GO optimizes function transfer from homologous sequences through an elaborate algorithm that considers similarity, the extension of the homology, the database of choice, the GO hierarchy, and the quality of the original annotations. The tool includes numerous functions for the visualization, management, and statistical analysis of annotation results, including gene set enrichment analysis. The application supports InterPro, enzyme codes, KEGG pathways, GO direct acyclic graphs (DAGs), and GOSlim. Blast2GO is a suitable tool for plant genomics research because of its versatility, easy installation, and friendly use.

Since Lander and Botstein proposed the interval mapping method for QTL mapping data analysis in 1989, tremendous progress has been made in the last many years to advance new and powerful statistical methods for QTL analysis. Recent research progress has been focused on statistical methods and issues for mapping multiple QTL together. In this article, we review this progress. We focus the discussion on the statistical methods for mapping multiple QTL by maximum likelihood and Bayesian methods and also on determining appropriate thresholds for the analysis.

The CCPMT is a free, web-based tool that allows plant investigators to rapidly determine if a given gene is present across various microarray platforms, which, of a list of genes, is present on array(s), and which gene a probe or probe set queries and vice versa, and to compare and contrast the gene contents of arrays. The CCPMT also maps a probe or probe sets to a gene or genes within and across species, and permits the mapping of the entire content from one array to another. By using the CCPMT, investigators will have a better understanding of the contents of arrays, a better ability to link data between experiments, ability to conduct meta-analysis and combine datasets, and an increased ability to conduct data mining projects.

Plant-parasitic nematodes are important pests of crop plants worldwide, and also among the most difficult animals to identify. Their identification based on nuclear ribosomal DNA (rDNA) cistron (18S, 28S, and 5.8S RNA genes, and internal transcribed spacers, ITS1 and ITS2) is becoming a popular tool. Sequences from nuclear ribosomal RNA repeats have been used to demonstrate the identity of isolates from various hosts and to unravel the relationships of cryptic and complex species. In addition, the availability of RNA sequences allows study of phylogenetic relationships between nematodes, also for more complete understanding of their biology as agricultural pests. PPNEMA is a plant-parasitic nematode bioinformatic resource. It consists of a database of ribosomal cistron sequences from various species grouped according to nematode genera, and a search system allowing data to be extracted according to both text and pattern searching. PPNEMA offers to the scientific community a preprocessed archive of plant parasitic nematode sequences useful for nematologists. It is a tool to retrieve plant nematode multialigned sequences for phylogenetic studies or to recognize a nematode by comparing its rDNA sequence with the PPNEMA available genus specific multialignments.

We propose a Bayesian procedure to cluster temporal gene expression microarray profiles, based on a mixed-effect smoothing-spline model, and design a Gibbs sampler to sample from the desired posterior distribution. Our method can determine the cluster number automatically based on the Bayesian information criterion, and handle missing data easily. When applied to a microarray dataset on the budding yeast, our clustering algorithm provides biologically meaningful gene clusters according to a functional enrichment analysis.

Citrus is one of the most widespread fruit crops globally, with great economic and health value. It is among the most difficult plants to improve through traditional breeding approaches. Currently, there is risk of devastation by diseases threatening to limit production and future availability to the human population. As technologies rapidly advance in genomic science, they are quickly adapted to address the biological challenges of the citrus plant system and the world's industries. The historical developments of linkage mapping, markers and breeding, EST projects, physical mapping, an international citrus genome sequencing project, and critical functional analysis are described. Despite the challenges of working with citrus, there has been substantial progress. Citrus researchers engaged in international collaborations provide optimism about future productivity and contributions to the benefit of citrus industries worldwide and to the human population who can rely on future widespread availability of this health-promoting and aesthetically pleasing fruit crop.

Compared to the conventional linkage mapping, linkage disequilibrium (LD)-mapping, using the nonrandom associations of loci in haplotypes, is a powerful high-resolution mapping tool for complex quantitative traits. The recent advances in the development of unbiased association mapping approaches for plant population with their successful applications in dissecting a number of simple to complex traits in many crop species demonstrate a flourish of the approach as a "powerful gene tagging" tool for crops in the plant genomics era of 21st century. The goal of this review is to provide nonexpert readers of crop breeding community with (1) the basic concept, merits, and simple description of existing methodologies for an association mapping with the recent improvements for plant populations, and (2) the details of some of pioneer and recent studies on association mapping in various crop species to demonstrate the feasibility, success, problems, and future perspectives of the efforts in plants. This should be helpful for interested readers of international plant research community as a guideline for the basic understanding, choosing the appropriate methods, and its application.