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

Fast frequent pattern mining.

Yabo Xu.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 57-60).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Frequent Pattern Mining --- p.1Chapter 1.2 --- Biosequence Pattern Mining --- p.2Chapter 1.3 --- Organization of the Thesis --- p.4Chapter 2 --- PP-Mine: Fast Mining Frequent Patterns In-Memory --- p.5Chapter 2.1 --- Background --- p.5Chapter 2.2 --- The Overview --- p.6Chapter 2.3 --- PP-tree Representations and Its Construction --- p.7Chapter 2.4 --- PP-Mine --- p.8Chapter 2.5 --- Discussions --- p.14Chapter 2.6 --- Performance Study --- p.15Chapter 3 --- Fast Biosequence Patterns Mining --- p.20Chapter 3.1 --- Background --- p.21Chapter 3.1.1 --- Differences in Biosequences --- p.21Chapter 3.1.2 --- Mining Sequential Patterns --- p.22Chapter 3.1.3 --- Mining Long Patterns --- p.23Chapter 3.1.4 --- Related Works in Bioinformatics --- p.23Chapter 3.2 --- The Overview --- p.24Chapter 3.2.1 --- The Problem --- p.24Chapter 3.2.2 --- The Overview of Our Approach --- p.25Chapter 3.3 --- The Segment Phase --- p.26Chapter 3.3.1 --- Finding Frequent Segments --- p.26Chapter 3.3.2 --- The Index-based Querying --- p.27Chapter 3.3.3 --- The Compression-based Querying --- p.30Chapter 3.4 --- The Pattern Phase --- p.32Chapter 3.4.1 --- The Pruning Strategies --- p.34Chapter 3.4.2 --- The Querying Strategies --- p.37Chapter 3.5 --- Experiment --- p.40Chapter 3.5.1 --- Synthetic Data Sets --- p.40Chapter 3.5.2 --- Biological Data Sets --- p.46Chapter 4 --- Conclusion --- p.55Bibliography --- p.6

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

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

Informative Motif Detection Using Data Mining

Abstract: Motif finding in biological sequences is a fundamental problem in computational biology with important applications in understanding gene regulation, protein family identification and determination of functionally and structurally important identities. The large amounts of biological data let us solve the problem of discovering patterns in biological sequences computationally. In this research, we have developed an approach using a method of data mining to detect frequent residue informative motifs that are high in information content. The proposed approach modifies an existing method based on Apriori algorithm by using the Frequent Pattern tree (FP-tree) algorithm of data mining method. This method can efficiently detect novel motifs in biological sequences based on information content of the motifs and shows better performance than the existing method. Experiments on real biological sequence data sets demonstrate the effectiveness of the method

Discovering Protein Functional Regions and Protein-Protein Interaction using Co-occurring Aligned Pattern Clusters

Bioinformatics is a rapidly expanding field of research due to multiple recent advancements: 1) the advent of machine intelligence, 2) the increase of computing power, 3) our better understanding of the underlying biomolecular mechanisms, and 4) the drastic reduction of biosequencing cost and time. Since wet laboratory approaches to analysing the protein sequencing is still labour intensive and time consuming, more cost-effective computational approaches for analyzing protein sequences and their biochemical interactions are crucial. This is especially true when we encounter a large collection of protein sequences. Aligned Pattern CLustering (APCL), an algorithm which combines machine intelligence methodologies such as pattern recognition, pattern discovery, pattern clustering and alignment, formulated by my research group and myself, is one such technique. APCL discovers, prunes, and clusters aligned statistically significant patterns to assemble a related, or specifically, a homologous group of patterns in the form of an Aligned Pattern Cluster (APC). The APC obtained is found to correspond to statistically and functionally significant association patterns, which corresponds as conserved regions, such as binding segments within and between protein sequences as well as between Protein Transcription Factor (TF) and DNA Transcription Factor Binding Sites (TFBS) in many of our empirical experiments. While several known algorithms also exist to find functionally conserved segments in biosequences, they are less flexible and require more parameters than what APCL requires. Hence, APCL is a powerful tool to analyze biosequences. Because of its effectiveness, the usefulness of APCL is further expanded from the assist of discovering and analyzing functional regions of protein sequences to the exploration of co-occurrence of patterns on the same sequences or on interacting patterns between sequences from the discovered APCs. Two new algorithms are introduced and reported in this thesis in the exploration of 1) APCs containing patterns residing within the same biosequences and 2) APCs containing patterns residing between interacting biosequences. The first algorithm attempts to cluster APCs from APCs that share patterns on the same biosequences. It uses a co-occurrence score between APCs in a co-occurrence APC pair (two APCs containing co-occurrence patterns) to account for the proportion of biosequences of co-occurrence patterns they share against the total number of sequences containing them. Using this score as a similarity measure (or more precisely, as a co-occurring measure), we devise a Co-occurrence APC Clustering Algorithm to cluster APCs obtained from a collection of related biosequences into a Co-Occurrence Cluster of APCs abbreviated by cAPC. It is then analyzed and verified to see whether or not there are essential biological functions associating with the APCs within that cluster. Cytochrome c and ubiquitin families were analyzed in depth, and it was validated that members in the same cAPC do cover the functional regions that have essential cooperative biological functions. The second algorithm takes advantage of the effectiveness of APCL to create a protein-protein interaction (PPI) identification and prediction algorithm. PPI prediction is a hot research problem in bioinformatics and proteomic. A good number of algorithms exist. The state of the art algorithm is one which could achieve high success rate in prediction performance, but provides results that are difficult to interpret. The research in this thesis tries to overcome this hurdle. This second algorithm uses an APC-PPI score between two APCs to account for the proportion of patterns residing on two different protein sequences. This score measures how often patterns in both APCs co-occur in the sequence data of two known interacting proteins. The scores are then used to construct feature vectors to first train a learning model from the known PPI data and later used to predict the possible PPI between a protein pair. The algorithm performance was comparable to the state of the art algorithms, but provided results that are interpretable. The results from both algorithms built upon the extension of APCL in finding co-occurring patterns via co-occurrence of APCs are proved to be effective and useful since its performance in finding APCs is fast and effective. The first algorithm discovered biological insights, supported by biological literature, which are typically unable to be discovered solely through the analysis of biosequences. The second algorithm succeeded in providing accurate and descriptive PPI predictions. Hence, these two algorithms are useful in the analysis and prediction of proteins. In addition, through continued research and development to the second algorithm, it will be a powerful tool for the drug industry, as it can help find new PPI, an important step in developing new drugs for different drug targets

Motif Discovery in Protein Sequences

Biology has become a data‐intensive research field. Coping with the flood of data from the new genome sequencing technologies is a major area of research. The exponential increase in the size of the datasets produced by “next‐generation sequencing” (NGS) poses unique computational challenges. In this context, motif discovery tools are widely used to identify important patterns in the sequences produced. Biological sequence motifs are defined as short, usually fixed length, sequence patterns that may represent important structural or functional features in nucleic acid and protein sequences such as transcription binding sites, splice junctions, active sites, or interaction interfaces. They can occur in an exact or approximate form within a family or a subfamily of sequences. Motif discovery is therefore an important field in bioinformatics, and numerous methods have been developed for the identification of motifs shared by a set of functionally related sequences. This chapter will review the existing motif discovery methods for protein sequences and their ability to discover biologically important features as well as their limitations for the discovery of new motifs. Finally, we will propose new horizons for motif discovery in order to address the short comings of the existent methods

Efficient algorithms for mining clickstream patterns using pseudo-IDLists

Sequential pattern mining is an important task in data mining. Its subproblem, clickstream pattern mining, is starting to attract more research due to the growth of the Internet and the need to analyze online customer behaviors. To date, only few works are dedicately proposed for the problem of mining clickstream patterns. Although one approach is to use the general algorithms for sequential pattern mining, those algorithms’ performance may suffer and the resources needed are more than would be necessary with a dedicated method for mining clickstreams. In this paper, we present pseudo-IDList, a novel data structure that is more suitable for clickstream pattern mining. Based on this structure, a vertical format algorithm named CUP (Clickstream pattern mining Using Pseudo-IDList) is proposed. Furthermore, we propose a pruning heuristic named DUB (Dynamic intersection Upper Bound) to improve our proposed algorithm. Four real-life clickstream databases are used for the experiments and the results show that our proposed methods are effective and efficient regarding runtimes and memory consumption. © 2020 Elsevier B.V.Vietnam National Foundation for Science and Technology Development (NAFOSTED)National Foundation for Science & Technology Development (NAFOSTED) [02/2019/TN

EXMOTIF: efficient structured motif extraction

BACKGROUND: Extracting motifs from sequences is a mainstay of bioinformatics. We look at the problem of mining structured motifs, which allow variable length gaps between simple motif components. We propose an efficient algorithm, called EXMOTIF, that given some sequence(s), and a structured motif template, extracts all frequent structured motifs that have quorum q. Potential applications of our method include the extraction of single/composite regulatory binding sites in DNA sequences. RESULTS: EXMOTIF is efficient in terms of both time and space and is shown empirically to outperform RISO, a state-of-the-art algorithm. It is also successful in finding potential single/composite transcription factor binding sites. CONCLUSION: EXMOTIF is a useful and efficient tool in discovering structured motifs, especially in DNA sequences. The algorithm is available as open-source at: