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.

MOTIVATION: Extensions have been made to the RHMAPPER-1.1 package. One set of extensions computes the totally linked markers and uses the results as input to the salient RHMAPPER functions. The second set of extensions uses TKperl to provide an interactive interface for ease of querying the database and displaying maps. AVAILABILITY: The extensions can be obtained via ftp.sanger.ac.uk/pub/zmapper. SUPPLEMENTARY INFORMATION: The User's Manual can be viewed from http:/www.sanger.ac.uk/Users/cari/Z.shtml. CONTACT: [email protected]

MOTIVATION: To meet the demands of large-scale sequencing, thousands of clones must be fingerprinted and assembled into contigs. To determine the order of clones, a typical experiment is to digest the clones with one or more restriction enzymes and measure the resulting fragments. The probability of two clones overlapping is based on the similarity of their fragments. A contig contains two or more overlapping clones and a minimal tiling path of clones is selected to be sequenced. Interactive software with algorithmic support is necessary to assemble the clones into contigs quickly. RESULTS: FPC (fingerprinted contigs) is an interactive program for building contigs from restriction fingerprinted clones. FPC uses an algorithm to cluster clones into contigs based on their probability of coincidence score. For each contig, it builds a consensus band (CB) map which is similar to a restriction map; but it does not try to resolve all the errors. The CB map is used to assign coordinates to the clones based on their alignment to the map and to provide a detailed visualization of the clone overlap. FPC has editing facilities for the user to refine the coordinates and to remove poorly fingerprinted clones. Functions are available for updating an FPC database with new clones. Contigs can easily be merged, split or deleted. Markers can be added to clones and are displayed with the appropriate contig. Sequence-ready clones can be selected and their sequencing status displayed. As such, FPC is an integrated program for the assembly of sequence-ready clones for large-scale sequencing projects.

SAM (system for assembling markers) is a system which supports man-machine problem solving for iteratively ordering a set of markers. SAM aids the user in partially ordering a set of markers based on incomplete and uncertain data. As data is added and modified, SAM aids the user in updating the previously assembled maps. The input is a file of clones and for each clone, a list of the markers contained within it. The objective is to order the set of markers such that the markers contained in each clone are consecutive. The user directs the map building by selecting functions to assemble a region of markers, order the clones to fit the order of the markers and position new markers within an ordered set of markers. The user can edit the input data, edit the assembled map and add clones to the map based on their marker content. The results are displayed graphically and can be saved in a solution file. Based on the partial map, the user designs new experiments or edits the existing data to fill gaps and resolve ambiguities. When a previously assembled map is loaded into SAM, it is automatically updated with the new or altered data. SAM treats all markers as points, but has special features for multiple copy and long markers so that they can be used in the map building process. This system has supported the building of a YAC map of human chromosome 22 at the Sanger Centre, where use of Alu-PCR product markers is a major component in determining clone overlap and where we have an on-going effort to accumulate data from various sources. SAM is also being used at various other laboratories.

GRAM (Genomic Restriction map AsseMbly) takes as input single-digest restriction fragments for a set of overlapping clones and outputs one or more plausible partially ordered restriction maps. For each restriction map, GRAM shows the corresponding alignment of the input clone fragments. Due to the error and uncertainty in experimental data, this problem is computationally difficult to solve; therefore, the principle objective in the design of GRAM is to facilitate man-machine collaborative problem solving. GRAM quickly approximates a solution, as follows. (i) A clustering algorithm determines a probable set of restriction fragments. (ii) An assembly algorithm permutes the set of restriction fragments such that the maximal number of clone fragments are contiguous. The output of the GRAM algorithm is displayed for the user to query and edit. This paper describes the stochastic assembly algorithm and shows how it works with the interactive graphics to support man-machine problem solving. In order to test and verify the performance of GRAM, we have developed a program called genfragII to simulate the digestion of clones and fragments; this program is described and results are presented. GRAM is also being used for a number of genome mapping projects.