Mining frequent biological sequences based on bitmap without candidate sequence generation

Biological sequences carry a lot of important genetic information of organisms. Furthermore, there is an inheritance law related to protein function and structure which is useful for applications such as disease prediction. Frequent sequence mining is a core technique for association rule discovery, but existing algorithms suffer from low efficiency or poor error rate because biological sequences differ from general sequences with more characteristics. In this paper, an algorithm for mining Frequent Biological Sequence based on Bitmap, FBSB, is proposed. FBSB uses bitmaps as the simple data structure and transforms each row into a quicksort list QS-list for sequence growth. For the continuity and accuracy requirement of biological sequence mining, tested sequences used during the mining process of FBSB are real ones instead of generated candidates, and all the frequent sequences can be mined without any errors. Comparing with other algorithms, the experimental results show that FBSB can achieve a better performance on both run time and scalability

10231 Abstracts Collection -- Structure Discovery in Biology: Motifs, Networks & Phylogenies

From 06.06. to 11.06.2010, the Dagstuhl Seminar 10231 ``Structure Discovery in Biology: Motifs, Networks & Phylogenies \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Interactive visualisation and exploration of biological data

International audienceno abstrac

Evaluating deterministic motif significance measures in protein databases

<p>Abstract</p> <p>Background</p> <p>Assessing the outcome of motif mining algorithms is an essential task, as the number of reported motifs can be very large. Significance measures play a central role in automatically ranking those motifs, and therefore alleviating the analysis work. Spotting the most interesting and relevant motifs is then dependent on the choice of the right measures. The combined use of several measures may provide more robust results. However caution has to be taken in order to avoid spurious evaluations.</p> <p>Results</p> <p>From the set of conducted experiments, it was verified that several of the selected significance measures show a very similar behavior in a wide range of situations therefore providing redundant information. Some measures have proved to be more appropriate to rank highly conserved motifs, while others are more appropriate for weakly conserved ones. Support appears as a very important feature to be considered for correct motif ranking. We observed that not all the measures are suitable for situations with poorly balanced class information, like for instance, when positive data is significantly less than negative data. Finally, a visualization scheme was proposed that, when several measures are applied, enables an easy identification of high scoring motifs.</p> <p>Conclusion</p> <p>In this work we have surveyed and categorized 14 significance measures for pattern evaluation. Their ability to rank three types of deterministic motifs was evaluated. Measures were applied in different testing conditions, where relations were identified. This study provides some pertinent insights on the choice of the right set of significance measures for the evaluation of deterministic motifs extracted from protein databases.</p

Analysis of the Relationships among Longest Common Subsequences, Shortest Common Supersequences and Patterns and its application on Pattern Discovery in Biological Sequences

For a set of mulitple sequences, their patterns,Longest Common Subsequences (LCS) and Shortest Common Supersequences (SCS) represent different aspects of these sequences profile, and they can all be used for biological sequence comparisons and analysis. Revealing the relationship between the patterns and LCS,SCS might provide us with a deeper view of the patterns of biological sequences, in turn leading to better understanding of them. However, There is no careful examinaton about the relationship between patterns, LCS and SCS. In this paper, we have analyzed their relation, and given some lemmas. Based on their relations, a set of algorithms called the PALS (PAtterns by Lcs and Scs) algorithms are propsoed to discover patterns in a set of biological sequences. These algorithms first generate the results for LCS and SCS of sequences by heuristic, and consequently derive patterns from these results. Experiments show that the PALS algorithms perform well (both in efficiency and in accuracy) on a variety of sequences. The PALS approach also provides us with a solution for transforming between the heuristic results of SCS and LCS.Comment: Extended version of paper presented in IEEE BIBE 2006 submitted to journal for revie

MAGIIC-PRO: detecting functional signatures by efficient discovery of long patterns in protein sequences

This paper presents a web service named MAGIIC-PRO, which aims to discover functional signatures of a query protein by sequential pattern mining. Automatic discovery of patterns from unaligned biological sequences is an important problem in molecular biology. MAGIIC-PRO is different from several previously established methods performing similar tasks in two major ways. The first remarkable feature of MAGIIC-PRO is its efficiency in delivering long patterns. With incorporating a new type of gap constraints and some of the state-of-the-art data mining techniques, MAGIIC-PRO usually identifies satisfied patterns within an acceptable response time. The efficiency of MAGIIC-PRO enables the users to quickly discover functional signatures of which the residues are not from only one region of the protein sequences or are only conserved in few members of a protein family. The second remarkable feature of MAGIIC-PRO is its effort in refining the mining results. Considering large flexible gaps improves the completeness of the derived functional signatures. The users can be directly guided to the patterns with as many blocks as that are conserved simultaneously. In this paper, we show by experiments that MAGIIC-PRO is efficient and effective in identifying ligand-binding sites and hot regions in protein–protein interactions directly from sequences. The web service is available at and a mirror site at

Query driven sequence pattern mining

The discovery of frequent patterns present in biological sequences has a large number of applications, ranging from classification, clustering and understanding sequence structure and function. This paper presents an algorithm that discovers frequent sequence patterns (motifs) present in a query sequence in respect to a database of sequences. The query is used to guide the mining process and thus only the patterns present in the query are reported. Two main types of patterns can be identified: flexible and rigid gap patterns. The user can choose to report all or only maximal patterns. Constraints and Substitution Sets are pushed directly into the mining process. Experimental evaluation shows the efficiency of the algorithm, the usefulness and the relevance of the extracted patterns.Fundação para a Ciência e a Tecnologia (FCT