BAliBASE 3.0: Latest developments of the multiple sequence alignment benchmark

Abstract

Multiple sequence alignment is one of the cornerstones of modern molecular biology. It is used to identify conserved motifs, to determine protein domains, in 2D/3D structure prediction by homology and in evolutionary studies. Recently, high-throughput technologies such as genome sequencing and structural proteomics have lead to an explosion in the amount of sequence and structure information available. In response, several new multiple alignment methods have been developed that improve both the efficiency and the quality of protein alignments. Consequently, the benchmarks used to evaluate and compare these methods must also evolve. We present here the latest release of the most widely used multiple alignment benchmark, BAliBASE, which provides high quality, manually refined, reference alignments based on 3D structural superpositions. Version 3.0 of BAliBASE includes new, more challenging test cases, representing the real problems encountered when aligning large sets of complex sequences. Using a novel, semiautomatic update protocol, the number of protein families in the benchmark has been increased and representative test cases are now available that cover most of the protein fold space. The total number of proteins in BAliBASE has also been significantly increased from 1444 to 6255 sequences. In addition, full-length sequences are now provided for all test cases, which represent difficult cases for both global and local alignment programs. Finally, the BAliBASE Web site (http://www-bio3d-igbmc.u-strasbg.fr/balibase) has been completely redesigned to provide a more user-friendly, interactive interface for the visualization of the BAliBASE reference alignments and the associated annotations.

Partitioning into Colorful Components by Minimum Edge Deletions

“…We take advantage of this in data reduction rules, a simplified branching, and a new heuristic. With the branching algorithms, we can solve to optimality more than half of the instances generated from the BAliBASE 3.0 benchmark [15] each time within five minutes on a standard PC, with up to 5 000 vertices and 13 000 edges. Our heuristic has an average error of 0.6 %, a large improvement over the 29.2 % of the previously suggested min-cut heuristic [6].…”

Protein Progressive MSA Using 2-Opt Method

“…These methods have the advantage of being fast, simple as well as reasonably sensitive. Their main drawback is that they can be trapped in local minima that stems from the greedy nature of the algorithm (Thompson et al 2005). The other major drawback is that any progressive MSA solution cannot be globally optimal, since it is heavily influenced by the initial choice.…”

Analysis of a Scientific Protocol: Selecting Suitable Resources

“…They include semantic benchmarks such as BaliBASE 3.0 [17], OxBench [18], and ZDOCK [19] that compare the quality of the results obtained by the tools, and efficiency benchmarks Bioperf [20] and Biobench and Bioparallel [21,22] that focus on space and time.…”

“…BaliBASE 3.0 [17] and OxBench [18] address similarity issues for protein sequences as well as nucleotide sequences, whereas ZDOCK [19] only focuses on protein sequences. They respectively use NCBI Nucleotide and PDB or Swiss-Prot as instances.…”