TY - GEN
T1 - SPARSE
T2 - 17th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2013
AU - Will, Sebastian
AU - Schmiedl, Christina
AU - Miladi, Milad
AU - Möhl, Mathias
AU - Backofen, Rolf
PY - 2013/4/3
Y1 - 2013/4/3
N2 - Motivation: There is increasing evidence of pervasive transcription, resulting in hundreds of thousands of ncRNAs of unknown function. Standard computational analysis tasks for inferring functional annotations like clustering require fast and accurate RNA comparisons based on sequence and structure similarity. The gold standard for the latter is Sankoff's algorithm [3], which simultaneously aligns and folds RNAs. Because of its extreme time complexity of O(n6), numerous faster "Sankoff-style" approaches have been suggested. Several such approaches introduce heuristics based on sequence alignment, which compromises the alignment quality for RNAs with sequence identities below 60% [1]. Avoiding such heuristics, as e.g. in LocARNA [4], has been assumed to prohibit time complexities better than O(n 4), which strongly limits large-scale applications.
AB - Motivation: There is increasing evidence of pervasive transcription, resulting in hundreds of thousands of ncRNAs of unknown function. Standard computational analysis tasks for inferring functional annotations like clustering require fast and accurate RNA comparisons based on sequence and structure similarity. The gold standard for the latter is Sankoff's algorithm [3], which simultaneously aligns and folds RNAs. Because of its extreme time complexity of O(n6), numerous faster "Sankoff-style" approaches have been suggested. Several such approaches introduce heuristics based on sequence alignment, which compromises the alignment quality for RNAs with sequence identities below 60% [1]. Avoiding such heuristics, as e.g. in LocARNA [4], has been assumed to prohibit time complexities better than O(n 4), which strongly limits large-scale applications.
UR - https://www.scopus.com/pages/publications/84875529774
U2 - 10.1007/978-3-642-37195-0_28
DO - 10.1007/978-3-642-37195-0_28
M3 - Conference contribution
AN - SCOPUS:84875529774
SN - 9783642371943
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 289
EP - 290
BT - Research in Computational Molecular Biology - 17th Annual International Conference, RECOMB 2013, Proceedings
Y2 - 7 April 2013 through 10 April 2013
ER -