An applications-focused review of comparative genomics tools : Capabilities , limitations and future challenges

A team at the Lawrence Livermore National Laboratory (LLNL) was given the task of using computational tools to speed up the development of DNA diagnostics for pathogen detection. This work will be described in another paper in this issue (see pages 133–149). To achieve this goal it was necessary to understand the merits and limitations of the various available comparative genomics tools. A review of some recent tools for multisequence/genome alignment and substring comparison is presented, within the general framework of applicability to a large-scale application. We note that genome alignments are important for many things, only one of which is pathogen detection. Understanding gene function, gene regulation, gene networks, phylogenetic studies and other aspects of evolution all depend on accurate nucleic acid and protein sequence alignment. Selecting appropriate tools can make a large difference in the quality of results obtained and the effort required. A SURVEY OF GENOMIC ALIGNMENT AND COMPARISON TOOLS FOR WHOLE-GENOME PATHOGEN DNA SIGNATURE DEVELOPMENT The output of sequence data from worldwide sequencing centres with constantly increasing sequencing capacities has been rising at an exponential rate for the past decade or two. The first two publications of microbial whole genome sequencing projects appeared in 1995. Only eight years later, there are almost 100 completed bacterial genomes available. Most of these are eubacteria and archaebacteria but there is also a completed yeast, along with draft analyses of several multicellular eukaryotes including nematode, fly, human, weed, mouse, mosquito, rat, rice, fungi and fish. More still are currently underway; the TIGR Microbial Database In-Progress currently has over 180 entries and over 1,200 viral genomes have been submitted to NCBI. The increase in sequencing efficiency suggests that the bottleneck is not the accumulation of raw data but the annotation and analysis of sequences and genomes. There have been waits of longer than a year after the end of sequencing for some teams to analyse and publish genomes before the entire sequences were made publicly available. At least some of this delay can be attributed to the tools being used to perform the analyses. While the desire to postpone data release until publication is understandable, in some cases lengthy delays have prevented us from being able to create effective pathogen diagnostics in a timely fashion. Given the continuing improvements in high-throughput genomic sequencing and & HENRY STEWART PUBLICATIONS 1467-5463. B R I E F I N G S I N B I O I N F O R M A T I CS . VOL 4. NO 2. 105–123. JUNE 2003 1 0 5 Accurate wholegenome comparisons are needed to solve many problems related to modern genomics Every alignment is based on a collinear arrangement of sequence similarities Alignments have been used to compare coding and non-coding regions from different species A genome comparison does not assume colinearity of sequence similarities the ever-expanding sequence databases, new advances in software programs for post-sequencing functional analysis are being demanded by the general scientific community. Whole genome comparisons have been heralded as the next logical step toward solving genomic puzzles, such as determining coding regions, discovering regulatory signals, and deducing the mechanisms and history of genome evolution. As noted above, these tools are also required for the specific problem of determining unique DNA and protein sequence for diagnostic assays. However, before any such detailed analyses can be addressed, methods are required for comparing and visualising such large sequences. These two topics are reviewed below. COMPARATIVE GENOMICS A brief comment on terminology is in order. In our opinion, every alignment is based on a colinear arrangement of sequence similarities (Figure 1). All current alignment tools assume this colinearity. We will use a more general notion of genome comparison when we are discussing situations where an assumption of colinear similarities is not required (Figure 2). Searching for apparently unique regions of pathogen nucleic acid or protein sequence to create diagnostics is one such situation. To date the authors are aware of no general, versatile genome comparison tools; ad hoc techniques are used to identify syntenic regions, which then can be individually aligned. It is an overloading of terminology that ‘comparative genomics’ is a commonly used term that encompasses the capabilities of both alignments and genome comparison tools. It is generally believed that homologous genes are relatively well preserved, while non-coding regions tend to show varying degrees of conservation. Non-coding regions that do show conservation are thought important for regulating gene expression, maintaining the structural organisation of the genome and possibly have other, as-yet unknown, functions. Several comparative sequence analysis approaches using alignments have recently been used to analyse corresponding coding and non-coding regions from different species, although mainly between human and mouse. Of course, the utility of cross-species comparative genomics in the identification of such regions is greatly influenced by the evolutionary distance of the species in question. The use of alignment-based comparisons to uncover conserved functional elements has been termed ‘phylogenetic footprinting’. Of importance to annotation, this approach obviates the need for a priori knowledge of a protein sequence motif and provides a complement for algorithmic analyses. Comparative analysis of a number of phylogenetically diverse genomes may provide clues about the selective pressures governing gene/operon clustering and may offer insights into mechanisms of evolution or show patterns in acquisition of foreign material via horizontal gene transfer. This may prove especially important for exploiting common mechanisms of virulence, antibiotic Figure 1: A cartoon representation of a set of colinear segments between three genomes Figure 2: A representation of non-colinear relationships between homologous segments of three genomes 106 & HENRY STEWART PUBLICATIONS 1467-5463. BRIEF INGS IN BIOINFORMATICS . VOL 4. NO 2. 105–123. JUNE 2003 Chain et al.