Photos of CIBCB 2016 are now available! Thank you to the organizers for a great event.

Congratulations to Manuel Belmadani for his accepted paper at CIBCB 2016 : Manuel Belmadani and Marcel Turcotte. MotifGP: Using multi-objective evolutionary computing for mining network expressions in DNA sequences. In IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2016), Chiang Mai, Thailand, October, 5-7, 2016 2016 (Accepted).

»» The annual IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2016)

Congratulations to Manuel Belmadani on obtaining the first prize in the Computer Science section of the 8th Edition of the Engineering and Computer Science Graduate Poster Competition.

Mining DNA Network Expression with Evolutionary Multi-Objective Programming

Where a wall poster can be your ticket to a great career via Ottawa Business Journal

»» 8th Edition of the Engineering and Computer Science Graduate Poster Competition

The discovery of common RNA secondary structure motifs is an important problem in bioinformatics. The presence of such motifs is usually associated with key biological functions. However, the identification of structural motifs is far from easy. Unlike motifs in sequences, which have conserved bases, structural motifs have common structure arrangements even if the underlying sequences are different. Over the past few years, hundreds of algorithms have been published for the discovery of sequential motifs, while less work has been done for the structural motifs case. Current structural motif discovery algorithms are limited in terms of accuracy and scalability. In this paper, we present an incremental and scalable algorithm for discovering RNA secondary structure motifs, namely IncMD. We consider the structural motif discovery as a frequent pattern mining problem and tackle it using a modified a priori algorithm. IncMD uses data structures, trie-based linked lists of prefixes (LLP), to accelerate the search and retrieval of patterns, support counting, and candidate generation. We modify the candidate generation step in order to adapt it to the RNA secondary structure representation. IncMD constructs the frequent patterns incrementally from RNA secondary structure basic elements, using nesting and joining operations. The notion of a motif group is introduced in order to simulate an alignment of motifs that only differ in the number of unpaired bases. In addition, we use a cluster beam approach to select motifs that will survive to the next iterations of the search. Results indicate that IncMD can perform better than some of the available structural motif discovery algorithms in terms of sensitivity (Sn), positive predictive value (PPV), and specificity (Sp). The empirical results also show that the algorithm is scalable and runs faster than all of the compared algorithms.

»» http://dx.doi.org/10.1142/S0219720014500279 (Open Access)

We welcome a new group member, Wejdan Al-Kaldi!

Isra Al-Turaiki, Ghada Badr, Marcel Turcotte, and Hassan Mathkour. Incremental trie-based structural motif discovery algorithm. Accepted for publication in Journal of Bioinformatics and Computational Biology; Manuscript number JBCB-553R1; 2014-09-05, September 2014.

"RiboFSM: Frequent Subgraph Mining for the Discovery of RNA Structures and Interactions" by Alexander Gawronski and Marcel Turcotte was accepted for publication in BMC Bioinformatics.

Presented Misha and Oksana's work at the first Bioinformatics Scientific Meeting at King Saud University (BioSM-KSU).

»» The slides are available at induce.eecs.uottawa.ca/BioSM-KSU2014.pdf.

Frequent subgraph mining is a useful method for extracting meaningful patterns from a set of graphs or a single large graph. Here, the graph represents all possible RNA structures and interactions. Patterns that are significantly more frequent in this graph over a random graph are extracted. We hypothesize that these patterns are most likely to represent a biological mechanisms. The graph representation used is a directed dual graph, extended to handle intermolecular interactions. The graph is sampled for subgraphs, which are labeled using a canonical labeling method and counted. The resulting patterns are compared to those created from a randomized dataset and scored. The algorithm was applied to the mitochondrial genome of the kinetoplastid species Trypanosoma brucei. This species has a unique RNA editing mechanism that has been well studied, making it a good model organism to test RiboFSM. The most significant patterns contain two stem-loops, indicative of gRNA, and represent interactions of these structures with target mRNA.

"Novel Framework for the Discovery of RNA Elements and its Application to Euglenozoa" by Alexander Gawronski and Marcel Turcotte was accepted for both short abstract electronic distribution and oral presentation at 9th International Symposium on Bioinformatics Research and Applications.

»» 9th International Symposium on Bioinformatics Research and Applications

"Incremental Structural Motif Discovery" by Isra Al-Turaiki, Ghada Badr, Marcel Turcotte, and Hassan Mathkour was accepted for a poster presentation at 9th International Symposium on Bioinformatics Research and Applications.

»» 9th International Symposium on Bioinformatics Research and Applications

Isra Al-Turaiki is a Ph.D. student working under the supervision of Ghada Badr. She is doing an internship with us from October 1 2012 until February 12 2013.

"Learning relationships between over-represented motifs in a set of DNA sequences" by Oksana Korol and Marcel Turcotte was accepted at CIBCB 2012 : IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

»» IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

Oskana Korol successfully defended her thesis today!

»» ModuleInducer: Automating the Extraction of Knowledge from Biological Sequences

Misha Jiline successfully defended his thesis today!

»» Annotation Concept Synthesis and Enrichment Analysis: a Logic-Based Approach to the Interpretation of High-Throughput Biological Experiments