Briefings in Bioinformatics - current issue

Editorial

Dicks, J. — Fri, 20 Nov 2009 07:05:02 PST

Computational approaches and software tools for genetic linkage map estimation in plants

Cheema, J., Dicks, J. — Fri, 20 Nov 2009 07:05:02 PST

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.

De novo sequencing of plant genomes using second-generation technologies

Imelfort, M., Edwards, D. — Fri, 20 Nov 2009 07:05:02 PST

The ability to sequence the DNA of an organism has become one of the most important tools in modern biological research. Until recently, the sequencing of even small model genomes required substantial funds and international collaboration. The development of ‘second-generation’ sequencing technology has increased the throughput and reduced the cost of sequence generation by several orders of magnitude. These new methods produce vast numbers of relatively short reads, usually at the expense of read accuracy. Since the first commercial second-generation sequencing system was produced by 454 Technologies and commercialised by Roche, several other companies including Illumina, Applied Biosystems, Helicos Biosciences and Pacific Biosciences have joined the competition. Because of the relatively high error rate and lack of assembly tools, short-read sequence technology has mainly been applied to the re-sequencing of genomes. However, some recent applications have focused on the de novo assembly of these data. De novo assembly remains the greatest challenge for DNA sequencing and there are specific problems for second generation sequencing which produces short reads with a high error rate. However, a number of different approaches for short-read assembly have been proposed and some have been implemented in working software. In this review, we compare the current approaches for second-generation genome sequencing, explore the future direction of this technology and the implications for plant genome research.

Improved criteria and comparative genomics tool provide new insights into grass paleogenomics

Salse, J., Abrouk, M., Murat, F., Quraishi, U. M., Feuillet, C. — Fri, 20 Nov 2009 07:05:02 PST

In the past decade, a number of bioinformatics tools have been developed to perform comparative genomics studies in plants and animals. However, most of the publicly available and user friendly tools lack common standards for the identification of robust orthologous relationships between genomes leading non-specialists to often over interpret the results of large scale comparative sequence analyses. Recently, we have established a number of improved parameters and tools to define significant relationships between genomes as a basis to develop paleogenomics studies in grasses. Here, we describe our approaches and propose the development of community-based standards that can be used in comparative genomic studies to (i) identify robust sets of orthologous gene pairs, (ii) derive complete sets of chromosome to chromosome relationships within and between genomes and (iii) model common paleo-ancestor genome structures. The rice and sorghum genome sequences are used to exemplify step-by-step a methodology that should allow users to perform accurate comparative genome analyses in their favourite species. Finally, we describe two applications for accurate gene annotation and synteny-based cloning of agronomically important traits.

Common introns within orthologous genes: software and application to plants

Wilkerson, M. D., Ru, Y., Brendel, V. P. — Fri, 20 Nov 2009 07:05:02 PST

The residence of spliceosomal introns within protein-coding genes can fluctuate over time, with genes gaining, losing or conserving introns in a complex process that is not entirely understood. One approach for studying intron evolution is to compare introns with respect to position and type within closely related genes. Here, we describe new, freely available software called Common Introns Within Orthologous Genes (CIWOG), available at http://ciwog.gdcb.iastate.edu/, which detects common introns in protein-coding genes based on position and sequence conservation in the corresponding protein alignments. CIWOG provides dynamic web displays that facilitate detailed intron studies within orthologous genes. User-supplied options control how introns are clustered into sets of common introns. CIWOG also identifies special classes of introns, in particular those with GC- or U12-type donor sites, which enables analyses of these introns in relation to their counterparts in the other genes in orthologous groups. The software is demonstrated with application to a comprehensive study of eight plant transcriptomes. Three specific examples are discussed: intron class conversion from GT- to GC-donor-type introns in monocots, plant U12-type intron conservation and a global analysis of intron evolution across the eight plant species.

Bioinformatics in the orphan crops

Armstead, I., Huang, L., Ravagnani, A., Robson, P., Ougham, H. — Fri, 20 Nov 2009 07:05:02 PST

Orphan crops are those which are grown as food, animal feed or other crops of some importance in agriculture, but which have not yet received the investment of research effort or funding required to develop significant public bioinformatics resources. Where an orphan crop is related to a well-characterised model plant species, comparative genomics and bioinformatics can often, though not always, be exploited to assist research and crop improvement. This review addresses some challenges and opportunities presented by bioinformatics in the orphan crops, using three examples: forage grasses from the genera Lolium and Festuca, forage legumes and the second generation energy crop Miscanthus.

Computational techniques for elucidating plant-pathogen interactions from large-scale experiments on fungi and oomycetes

Soderlund, C. — Fri, 20 Nov 2009 07:05:03 PST

Eukaryotic plant pathogens are responsible for the destruction of billions of dollars worth of crops each year. With large-scale genomics of both pathogens and hosts and the corresponding computational analysis, biologists are now able to gain knowledge about many pathogenic and defense genes concurrently. To study the interactions between these two organism groups, it is necessary to design experiments to elucidate the genes being expressed during the invasion of the pathogen into the host. For the most part, this does not require new software development, though it does require the use of existing software in novel ways. This article provides a broad overview of several key and illustrative experiments and the corresponding computational analyses, outlining the knowledge gained in each. It goes on to describe databases for plant–pathogen data and important initiatives such as Plant-Associated Microbe Gene Ontology. It discusses how various emerging approaches will increase the power of computers in host-pathogen interaction studies.

Software engineering the mixed model for genome-wide association studies on large samples

Zhang, Z., Buckler, E. S., Casstevens, T. M., Bradbury, P. J. — Fri, 20 Nov 2009 07:05:03 PST

Mixed models improve the ability to detect phenotype-genotype associations in the presence of population stratification and multiple levels of relatedness in genome-wide association studies (GWAS), but for large data sets the resource consumption becomes impractical. At the same time, the sample size and number of markers used for GWAS is increasing dramatically, resulting in greater statistical power to detect those associations. The use of mixed models with increasingly large data sets depends on the availability of software for analyzing those models. While multiple software packages implement the mixed model method, no single package provides the best combination of fast computation, ability to handle large samples, flexible modeling and ease of use. Key elements of association analysis with mixed models are reviewed, including modeling phenotype-genotype associations using mixed models, population stratification, kinship and its estimation, variance component estimation, use of best linear unbiased predictors or residuals in place of raw phenotype, improving efficiency and software–user interaction. The available software packages are evaluated, and suggestions made for future software development.

Data integration for plant genomics--exemplars from the integration of Arabidopsis thaliana databases

Lysenko, A., Hindle, M. M., Taubert, J., Saqi, M., Rawlings, C. J. — Fri, 20 Nov 2009 07:05:03 PST

The development of a systems based approach to problems in plant sciences requires integration of existing information resources. However, the available information is currently often incomplete and dispersed across many sources and the syntactic and semantic heterogeneity of the data is a challenge for integration. In this article, we discuss strategies for data integration and we use a graph based integration method (Ondex) to illustrate some of these challenges with reference to two example problems concerning integration of (i) metabolic pathway and (ii) protein interaction data for Arabidopsis thaliana. We quantify the degree of overlap for three commonly used pathway and protein interaction information sources. For pathways, we find that the AraCyc database contains the widest coverage of enzyme reactions and for protein interactions we find that the IntAct database provides the largest unique contribution to the integrated dataset. For both examples, however, we observe a relatively small amount of data common to all three sources. Analysis and visual exploration of the integrated networks was used to identify a number of practical issues relating to the interpretation of these datasets. We demonstrate the utility of these approaches to the analysis of groups of coexpressed genes from an individual microarray experiment, in the context of pathway information and for the combination of coexpression data with an integrated protein interaction network.