Benchmarking tools for the alignment of functional noncoding DNA
1 Biophysics Graduate Group, University of California, Berkeley, CA 94720, USA
2 Department of Genome Science, Life Science Division, Lawrence Orlando Berkeley National Laboratory, Berkeley, CA 94720, USA
3 Berkeley Drosophila Genome Project, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
4 Technische Fakultät, Universität Bielefeld, 33594 Bielefeld, Germany
5 Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA
6 Department of Genetics, University of Cambridge, Cambridge, UK CB2 3EH
BMC Bioinformatics 2004, 5:6 doi:10.1186/1471-2105-5-6Published: 21 January 2004
Numerous tools have been developed to align genomic sequences. However, their relative performance in specific applications remains poorly characterized. Alignments of protein-coding sequences typically have been benchmarked against "correct" alignments inferred from structural data. For noncoding sequences, where such independent validation is lacking, simulation provides an effective means to generate "correct" alignments with which to benchmark alignment tools.
Using rates of noncoding sequence evolution estimated from the genus Drosophila, we simulated alignments over a range of divergence times under varying models incorporating point substitution, insertion/deletion events, and short blocks of constrained sequences such as those found in cis-regulatory regions. We then compared "correct" alignments generated by a modified version of the ROSE simulation platform to alignments of the simulated derived sequences produced by eight pairwise alignment tools (Avid, BlastZ, Chaos, ClustalW, DiAlign, Lagan, Needle, and WABA) to determine the off-the-shelf performance of each tool. As expected, the ability to align noncoding sequences accurately decreases with increasing divergence for all tools, and declines faster in the presence of insertion/deletion evolution. Global alignment tools (Avid, ClustalW, Lagan, and Needle) typically have higher sensitivity over entire noncoding sequences as well as in constrained sequences. Local tools (BlastZ, Chaos, and WABA) have lower overall sensitivity as a consequence of incomplete coverage, but have high specificity to detect constrained sequences as well as high sensitivity within the subset of sequences they align. Tools such as DiAlign, which generate both local and global outputs, produce alignments of constrained sequences with both high sensitivity and specificity for divergence distances in the range of 1.25–3.0 substitutions per site.
For species with genomic properties similar to Drosophila, we conclude that a single pair of optimally diverged species analyzed with a high performance alignment tool can yield accurate and specific alignments of functionally constrained noncoding sequences. Further algorithm development, optimization of alignment parameters, and benchmarking studies will be necessary to extract the maximal biological information from alignments of functional noncoding DNA.