BAliBASE 3.0: Latest developments of the multiple sequence alignment benchmark

By Julie Dawn Thompson, Patrice Koehl, Raymond Ripp, Olivier Poch. Proteins: Structure, Function, and Bioinformatics, 2005

445 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/73bd4ed4fd01dcb985033dbff670cbd286074542

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Gerard Talavera
2. J. Castresana
3. Tal Pupko
4. Haim Ashkenazy
5. Manuel Gil
6. Sing-Hoi Sze
7. Simon Whelan
8. Benjamin P. Blackburne

In Molecular Biology

1. Kazutaka Katoh
2. Tal Pupko
3. Haim Ashkenazy
4. Christophe Dessimoz
5. Gerard Talavera
6. Dan-Ovidiu Graur
7. Osnat Penn
8. J. Castresana

Citations in the corpus

• The accuracy of several multiple sequence alignment programs for proteins (2006, methodology): “The accuracy assessment of MSA programs is often done by employing manually (or semi automatically) curated sequence databases such as BAliBASE [1], PREFAB [2] and SABmark [3].”
• Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. (2007, background): “There are some alignment sets curated from structural information that can be used to test alignment accuracy (Thompson et al., 2005), but the phylogenetic tree is unknown in these sets, thus making problematic their use for proving phylogenetic accuracy.”
• Class of multiple sequence alignment algorithm affects genomic analysis. (2013, methodology): “For example, in Blackburne and Whelan (2012), we show that the evolution-MSAM Prank performs poorly on BaliBase but does well on simulated data where the generative model resembles that used in Prank.”
• Phylogenetic assessment of alignments reveals neglected tree signal in gaps (2010, background): “Trusted benchmark alignments such as Balibase, Prefab, Homstrad, or Sabmark [5-8] are all derived from protein structure information, exploiting the tendency of structure to evolve more slowly than sequence [9].”
• DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment (2015, methodology): “This method of comparison requires that the secondary structure of reference sequences be available, which excludes the popular BAliBASE benchmarks [52] because the corresponding secondary structure of most BAliBASE sequences is unknown [53].”
• Kalign2: high-performance multiple alignment of protein and nucleotide sequences allowing external features (2008, methodology): “Since the reference alignments in Balibase3 are derived from the structural annotation themselves, this test is circular and the reported accuracy cannot be used for comparison to other methods.”
• An alignment confidence score capturing robustness to guide tree uncertainty. (2010, methodology): “We applied the GUIDANCEmeasure, using theMAFFT alignment algorithm, to the BAliBASE benchmark (Thompson et al. 2005), which is based on structural homology of protein families.”
• Multiple Sequence Alignment Based on Profile Alignment of Intermediate Sequences (2007, methodology): “We compare the performance of our algorithm to MAFFT (Katoh et al. 2005) and ProbCons (Do et al. 2005), which are among the best multiple alignment algorithms that do not utilize additional information, and SPEM (Zhou and Zhou 2005), which is among the best multiple alignment algorithms that utilize”
• GUIDANCE: a web server for assessing alignment confidence scores (2010, methodology): “These studies included both simulated protein sequence benchmarks, using the simulators ROSE (7) and INDELible (8), and the widely-accepted real biological benchmark BAliBASE (9).”