UNLABELLED: TURNIP comprises a suite of Perl scripts and modules that facilitates the resolution of microheterogeneity within hard-to-assemble repetitive DNA sequences. TURNIP was originally developed for the Saccharomyces Genome Resequencing Project (SGRP) within which the ribosomal DNA (rDNA) of 36 strains of S.cerevisiae were analysed to investigate the occurrence of potential polymorphisms. Here, 'partially resolved SNPs', or pSNPs, as well as indels, were found to be far more prevalent than previously suspected. More generally, the TURNIP software ascertains degrees of variation between large tandem repeats within a single locus, offering insights into mechanisms of genome stability and gene conversion in any organism for which genome sequence data are available. AVAILABILITY: The TURNIP source code, results files and online help are available at http://www.ncyc.co.uk/software/turnip.html.

Genetic maps are an important component within the plant biologist's toolkit, underpinning crop plant improvement programs. The estimation of plant genetic maps is a conceptually simple yet computationally complex problem, growing ever more so with the development of inexpensive, high-throughput DNA markers. The challenge for bioinformaticians is to develop analytical methods and accompanying software tools that can cope with datasets of differing sizes, from tens to thousands of markers, that can incorporate the expert knowledge that plant biologists typically use when developing their maps, and that facilitate user-friendly approaches to achieving these goals. Here, we aim to give a flavour of computational approaches for genetic map estimation, discussing briefly many of the key concepts involved, and describing a selection of software tools that employ them. This review is intended both for plant geneticists as an introduction to software tools with which to estimate genetic maps, and for bioinformaticians as an introduction to the underlying computational approaches.

A few years ago, when asked what I worked on by new acquaintances at conferences, I occasionally found myself looked at rather quizzically upon answering ‘Plant Bioinformatics’. True, there were yet no plant genome sequences, at a time when conferences were buzzing with excitement at newly sequenced mammals or microbes. And also true, plant research did not at first glance have the relevance to human medical advancement that motivates many in the bioinformatics field. Indeed, the former fact was a frustration that has only been remedied relatively recently. However, in my responses to my new-found friends, I tried to get across just how fascinating, relevant and rewarding plant bioinformatics can be. Plant genomes have an awful lot in common with other genomes. Indeed that is what brought me into this field after my PhD, discovering that the evolutionary processes underlying plant genomic evolution appeared to have many similarities with those in the mammalian species that I had been analysing in my previous studies. But plants also have their own special characteristics and idiosyncrasies, such as the ubiquitous nature of polyploidy and the extraordinary expansion of repeat sequences seen in many crops, which add to the analytical challenge but always keep you interested. Perhaps today the relevance of plant research is no longer in doubt. An abundance of food can no longer be taken for granted in even the wealthiest of nations. It is also common knowledge that food production must be further stepped up while improving methods of sustainable agriculture. Plants are being scrutinized for their potential as biofuels and for their role in areas such as healthy ageing. These are all scientific challenges that plant bioinformatics can help to advance and underpin, and as a plant bioinformatician, understanding your role in the plant improvement process can be deeply rewarding. This special issue contains eight reviews that showcase bioinformatics themes related to plant genomics. However, these papers should not be seen as solely relevant to researchers working on plants. Rather, researchers working on other organism groups should find much of relevance to their own research, both in understanding the broad mechanisms and techniques of a theme, and then in recognizing the differences between the analysis of their own favourite genomes and of plants. We begin this special issue with a paper from my own research group, because the area it discusses represents the starting point of many genomic analyses. Cheema and Dicks review analytical approaches and software for the construction of genetic linkage maps in plants. This problem is almost 100 years old but the authors focus on the history of solutions starting from the proliferation of computers in the 1980s. The generation of dense marker data sets from high-throughput technologies is reinvigorating this field and presents new challenges both to plant geneticists with data sets to analyse and to plant bioinformaticians wishing to make advances in this area. Next, we move on to the process of sequencing and assembling a plant genome. Imelfort and Edwards cover second (or next) generation sequencing technologies, describing the different sequencing systems and assembly algorithms available and their current and future application to de novo sequencing and assembly of plant genomes. The authors discuss the challenges presented by de novo assembly of short reads in plants, for example due to the large number of intergenic repeats in the genomes of many key crops, and explore the future of this area both in terms of new technologies and algorithms. With several cereal genomes now sequenced, and with more underway, it becomes both possible and desirable to compare them in a variety of ways. Salse et al. focus on paleogenomics, describing how their recent research comparing the coding regions of grass genomes has given new insights on the chromosomal structure of their ancestor. The authors discuss the bioinformatics methods and the Narcisse-Cereals, a web-based tool they developed in their research and how various parameters, and the values to which they are set, can be influential in making evolutionary inferences. They call on the plant bioinformatics community to investigate and establish standards for these and other parameters for future studies. Also drawing upon the theme of plant comparative genomics is a paper by Wilkerson et al. Here the authors discuss the rather different problem of identifying and classifying common introns between orthologous gene sequences. They introduce their new CIWOG software, within which the user-customizable CIDA algorithm may be used to generate MySQL databases of common introns, such as the ciwogPlants database that the authors generate from eight plant species. In a series of case studies, Wilkerson et al. describe how CIWOG can be used to investigate different aspects of intron variation and evolution, such as inference of intron gain and loss over an evolutionary tree. Before we become complacent about the burgeoning plant genomic resources and those bioinformatics resources that derive from them, Armstead et al. offer an insightful ‘reality check’ on plant bioinformatics for the orphan crops. Using illustrative grass and legume case studies, the authors describe ways in which information valuable to research on those crops can be leveraged through, for example, comparative genomics with better resourced relatives. Although Armstead et al. note that second generation sequencing technologies such as those described here by Imelfort and Edwards will undoubtedly help to reduce the number of orphan crops, significant challenges will remain and should not be underestimated. One of the great challenges of plant research is protecting crops from pathogenic attack. Soderlund describes a range of large-scale experimental studies for the analysis of plant–pathogen interactions, describing the bioinformatics approaches and tools that have been influential in gaining new knowledge from these data sets. She distinguishes between those problems that may be solved using generic bioinformatics methodologies and those for which bespoke solutions are required. Soderlund discusses key resources including fungal and oomycete databases and initiatives such as the PAMGO ontology, emphasizing the role of improved web-based resources in the knowledge discovery process. Next, we shift our focus towards the important theme of understanding the genetic basis of plant traits. Zhang et al. describe methodologies for inferring genotype–phenotype associations through whole genome studies, focusing on the use of mixed model. The authors show that although many parts of the analytical process are rooted in animal studies and while some transfer directly to plant studies, others do not and need to be adapted. They discuss challenges to software engineering of the various methodologies involved, particularly in light of new high-throughput marker data sets, and discuss a number of software tools with regard to many key user requirements. Finally, Lysenko et al. describe a problem that has arisen due to our capability to develop the data sets such as those described throughout this issue, that of developing software systems to integrate across information resources. The authors describe some of the problems inherent to data integration and discuss and contrast methods which are being used to solve them, while also noting the benefits of such approaches in generating new knowledge. They discuss two distinct, plant-oriented data integration case studies in the model plant Arabidopsis thaliana with reference to their own Ondex system, and look to new challenges in the form of high-throughput data sets, standards and ontologies. Plant research is undergoing enormous change with the generation of plant genome sequences and high-throughput marker data sets. This change brings with it new analytical challenges and difficult decisions about how best to exploit these new data in the crop improvement process. Above all though, it brings excitement. Plant research is under the spotlight and some of us bioinformaticians, perhaps contrary to our stereotypes, do like a little light sometimes.

UNLABELLED: MPP is a Java application, encompassing both new and established algorithms, for the analysis of gene and marker content datasets arising from high-throughput microarray techniques. MPP analyses flat file output from microarray experiments to determine the probability of the presence or absence of genes or markers within a genome. MPP can construct gene or marker content datasets for a number of genomes and can use the data to estimate an evolutionary tree or network. Results from gene content analyses may be validated by comparing them to known gene contents. MPP was initially developed to analyse data derived from comparative genome hybridization (CGH) microarray experiments in fungi and bacteria. It has recently been adapted to analyse retrotransposon-based insertion polymorphism (RBIP) marker scores derived from tagged microarray marker (TAM) experiments in pea. New analytical procedures may be added easily to MPP as plugins in order to increase the scope of the software. AVAILABILITY: MPP source code, executables and online help are available at http://cbr.jic.ac.uk/dicks/software/

MOTIVATION: Recombination can be a prevailing drive in shaping genome evolution. RAT (Recombination Analysis Tool) is a Java-based tool for investigating recombination events in any number of aligned sequences (protein or DNA) of any length (short viral sequences to full genomes). It is an uncomplicated and intuitive application and allows the user to view only the regions of sequence alignments they are interested in. RESULTS: RAT was applied to viral sequences. Its utility was demonstrated through the detection of a known recombinant of HIV and a detailed analysis of Noroviruses, the most common cause of viral gastroenteritis in humans. AVAILABILITY: RAT, along with a user's guide, is freely available from http://jic-bioinfo.bbsrc.ac.uk/bioinformatics-research/staff/graham_etherington/RAT.htm.

UNLABELLED: newicktree is a PSTricks-based LATEX package which enables phylogenetic trees described in the Newick format to be drawn directly into LATEX documents. mswordtree is a macro for producing phylogenetic trees using the drawing elements available in Microsoft Word. AVAILABILITY: Both programs are available free from the John Innes Centre's Bioinformatics Research Group website at http://jic-bioinfo.bbsrc.ac.uk/bioinformatics-research/software/index.html. SUPPLEMENTARY INFORMATION: A full user-guide for newicktree and installation and usage instructions for mswordtree and available at http://jic-bioinfo.bbsrc.ac.uk/bioinformatics-research/software/index.html

It has long been known that evolutionary trees (phylogenies) can be estimated by comparing the DNA or protein sequences of homologous genes across different organisms. More recently, attempts have been made to estimate phylogenies by comparing entire genomes. These attempts have focused largely on comparisons of gene content and gene order. Many different methods have been proposed for making these comparisons. These include primarily maximum parsimony and distance methods, although more recently maximum likelihood and Bayesian methods are being developed. This paper discusses each of these approaches in turn, including their merits and limitations, and any software which is available to make use of them.

MOTIVATION: Yeasts are often still identified with physiological growth tests, which are both time consuming and unsuitable for detection of a mixture of organisms. Hence, there is a need for molecular methods to identify yeast species. RESULTS: A hashing technique has been developed to search for unique DNA sequences in 702 26S rRNA genes. A unique DNA sequence has been found for almost every yeast species described to date. The locations of the unique defining sequences are in accordance with the variability map of large subunit ribosomal RNA and provide detail of the evolution of the D1/D2 region. This approach will be applicable to the rapid identification of unique sequences in other DNA sequence sets. AVAILABILITY: Freely available upon request from the authors. SUPPLEMENTARY INFORMATION: Results are available at http://www.sys.uea.ac.uk/~jjw/project/paper

Plant genome databases play an important role in the archiving and dissemination of data arising from the international genome projects. Recent developments in bioinformatics, such as new software tools, programming languages and standards, have produced better access across the Internet to the data held within them. An increasing emphasis is placed on data analysis and indeed many resources now provide tools allied to the databases, to aid in the analysis and interpretation of the data. However, a considerable wealth of information lies untapped by considering the databases as single entities and will only be exploited by linking them with a wide range of data sources. Data from research programs such as comparative mapping and germplasm studies may be used as tools, to gain additional knowledge but without additional experimentation. To date, the current plant genome databases are not yet linked comprehensively with each other or with these additional resources, although they are clearly moving toward this. Here, the current wealth of public plant genome databases is reviewed, together with an overview of initiatives underway to bind them to form a single plant genome infrastructure.