By Yelena Frid, Dan Gusfield (auth.), Vincent Moulton, Mona Singh (eds.)
This e-book constitutes the refereed court cases of the tenth foreign Workshop on Algorithms in Bioinformatics, WABI 2010, held in Liverpool, united kingdom, in September 2010. The 30 revised complete papers offered have been rigorously reviewed and chosen from eighty three submissions. The papers are geared up in topical sections on biomolecular constitution: RNA, protein and molecular comparability; comparative genomics; haplotype and genotype research; high-throughput facts research: subsequent iteration sequencing and stream cytometry; networks; phylogenetics; and sequences, strings and motifs.
Read Online or Download Algorithms in Bioinformatics: 10th International Workshop, WABI 2010, Liverpool, UK, September 6-8, 2010. Proceedings PDF
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Additional resources for Algorithms in Bioinformatics: 10th International Workshop, WABI 2010, Liverpool, UK, September 6-8, 2010. Proceedings
Intensity measurements). Rfens ree measures the collective ability of the ensemble to represent the data. Small RMSD ranges observed for the ensemble highlight the challenges of identifying true variability in real data. 8 No. 1 0 1 2 3 4 5 6 7 Lasso Iteration 8 9 10 (c) Fig. 5. Interpretation of ensembles on real data. A) Lasso tests on 9ilb:124-132 classiﬁes 2 structures as non-variable (light gray). B) For the same loop, structures classiﬁed as truly variable (dark gray) deviate more from the PDB structure (black).
Additionally, it is known that the N-terminus is important for Ubc9s speciﬁcity for SUMO rather than ubiquitin . However, the molecular mechanisms responsible for substrate identiﬁcation and interaction are not well understood . Tatham et al. (2003) conducted site-directed mutagenesis experiments on Ubc9 to discover that mutations R13A/K14A and R17A/K18A disrupted Ubc9’s interaction with SUMO-1. More recently, through a crystal structure of Ubc9SUMO-1 complex, R13 and R17 have been observed to be involved in key noncovalent interactions with SUMO-1 .
Among all DP based pseudoknot prediction algorithms, we focus on the fastest algorithm (R&G) and the most general one (R&E) and develop sparse variants of these dynamic programming algorithms. Due to sparsiﬁcation, the resulting algorithms need to consider only a limited number of candidates substructures compared to the original algorithms. As a result, we analyze the theoretical worst case complexities in terms of the number of candidate substructures. We also present experimental results, comparing our implementations of the original and sparsiﬁed R&G algorithm.