Novel Algorithms for LDD Motif Search

Background: Motifs are crucial patterns that have numerous applications including the identification of transcription factors and their binding sites, composite regulatory patterns, similarity between families of proteins, etc. Several motif models have been proposed in the literature. The (l,d)-motif model is one of these that has been studied widely. However, this model will sometimes report too many spurious motifs than expected. We interpret a motif as a biologically significant entity that is evolutionarily preserved within some distance. It may be highly improbable that the motif undergoes the same number of changes in each of the species. To address this issue, in this paper, we introduce a new model which is more general than (l,d)-motif model. This model is called (l,d1,d2)-motif model (LDDMS) and is NP-hard as well. We present three elegant as well as efficient algorithms to solve the LDDMS problem, i.e., LDDMS1, LDDMS2 and LDDMS3. They are all exact algorithms. Results: We did both theoretical analyses and empirical tests on these algorithms. Theoretical analyses demonstrate that our algorithms have less computational cost than the pattern driven approach. Empirical results on both simulated datasets and real datasets show that each of the three algorithms has some advantages on some (l,d1,d2) instances. Conclusions: We proposed LDDMS model which is more practically relevant. We also proposed three exact efficient algorithms to solve the problem. Besides, our algorithms can be nicely parallelized. We believe that the idea in this new model can also be extended to other motif search problems such as Edit-distance-based Motif Search (EMS) and Simple Motif Search (SMS)

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

Finding DNA Motifs: A Probabilistic Suffix Tree Approach

We address the problem of de novo motif identification. That is, given a set of DNA sequences we try to identify motifs in the dataset without having any prior knowledge about existence of any motifs in the dataset. We propose a method based on Probabilistic Suffix Trees (PSTs) to identify fixed-length motifs from a given set of DNA sequences. Our experiments reveal that our approach successfully discovers true motifs. We compared our method with the popular MEME algorithm, and observed that it detects a larger number of correct and statistically significant motifs than MEME. Our method is highly efficient as compared to MEME in finding the motifs when processing datasets of 1000 or more sequences. We applied our method to sequences of mutant strains of Exophiala dermatitidis and successfully identified motifs which revealed several transcription factor binding sites. This information is important to biologists for performing experiments to understand their role in different regulatory pathways affected by cdc42. We also show that our PST approach to de novo motif discovery can be used successfully to identify motifs in ChIP-Seq datasets. These motifs in turn identify binding sites for proteins in the sequences

A Novel Tree Structure for Pattern Matching in Biological Sequences

This dissertation proposes a novel tree structure, Error Tree (ET), to more efficiently solve the Approximate Pattern Matching problem, a fundamental problem in bioinformatics and information retrieval. The problem involves different matching measures such as the Hamming distance, edit distance, and wildcard matching. The input is usually a text of length n over a fixed alphabet of size Σ, a pattern P of length m, and an integer k. The output is those subsequences in the text that are at a distance ≤ k from P by Hamming distance, edit distance, or wildcard matching. An immediate application of the approximate pattern matching is the Planted Motif Search, an important problem in many biological applications such as finding promoters, enhancers, locus control regions, transcription factors, etc. The (l, d)-Planted Motif Search is defined as the following: Given n sequences over an alphabet of size Σ, each of length m, and two integers l and d, find a motif M of length l, where in each sequence there is at least an l-mer (substring of length l) at a Hamming distance of ≤ d from M. Based on the ET structure, our algorithm ET-Motif solves this problem efficiently in time and space. The thesis also discusses how the ET structure may add efficiency when it comes to Genome Assembly and DNA Sequence Compression. Current high-throughput sequencing technologies generate millions or billions of short reads (100-1000 bases) that are sequenced from a genome of millions or billions bases long. The De novo Genome Assembly problem is to assemble the original genome as long and accurate as possible. Although high quality assemblies can be obtained by assembling multiple paired-end libraries with both short and long insert sizes, the latter is costly to generate. Moreover, the recent GAGE-B study showed that a remarkably good assembly quality can be obtained for bacterial genomes by state-of-the-art assemblers run on a single short-insert library with a very high coverage. This thesis introduces a novel Hierarchical Genome Assembly (HGA) method that takes further advantage of such high coverage by independently assembling disjoint subsets of reads, combining assemblies of the subsets, and finally re-assembling the combined contigs along with the original reads. We empirically evaluate this methodology for eight leading assemblers using seven GAGE-B bacterial datasets consisting of 100bp Illumina HiSeq and 250bp Illumina MiSeq reads with coverage ranging from 100x-∼200x. The results show that HGA leads to a significant improvement in the quality of the assembly for all evaluated assemblers and datasets. Still, the problem involves a major step which is overlapping the ends of the reads together and allowing few mismatches (i.e. the approximate matching problem). This requires computing the overlaps between the ends of all-against-all reads. The computation of such overlaps when allowing mismatches is intensive. The ET structure may further speed up this step. Lastly, due to the significant amount of DNA data generated by the Next- Generation-Sequencing machines, there is an increasing need to compress such data to reduce the storage space and transmission time. The Huffman encoding that incorporates DNA sequence characteristics proves to better compress DNA data. Different implementations of Huffman trees, centering on the selection of frequent repeats, are introduced in this thesis. Experimental results demonstrate improvement on the compression ratios for five genomes with lengths ranging from 5Mbp to 50Mbp, compared with the use of a standard Huffman tree algorithm. Hence, the thesis suggests an improvement on all DNA sequence compression algorithms that employ the conventional Huffman encoding. Moreover, approximate repeats can be compressed and further improve the results by encoding the Hamming or edit distance between these repeats. However, computing such distances requires additional costs in both time and space. These costs can be reduced by using the ET structure

Alignment, Clustering and Extraction of Structured Motifs in DNA Promoter Sequences

A simple motif is a short DNA sequence found in the promoter region and believed to act as a binding site for a transcription factor protein. A structured motif is a sequence of simple motifs (boxes) separated by short sequences (gaps). Biologists theorize that the presence of these motifs play a key role in gene expression regulation. Discovering these patterns is an important step towards understanding protein-gene and gene-gene interaction thus facilitates the building of accurate gene regulatory network models. DNA sequence motif extraction is an important problem in bioinformatics. Many studies have proposed algorithms to solve the problem instance of simple motif extraction. Only in the past decade has the more complex structured motif extraction problem been examined by researchers. The problem is inherently challenging as structured motif patterns are segmented into several boxes separated by variable size gaps for each instance. These boxes may not be exact copies, but may have multiple mismatched positions. The challenge is extenuated by the lack of resources for real datasets covering a wide range of possible cases. Also, incomplete annotation of real data leads to the discovery of unknown motifs that may be regarded as false positives. Furthermore, current algorithms demand unreasonable amount of prior knowledge to successfully extract the target pattern. The contributions of this research are four new algorithms. First, SMGenerate generates simulated datasets of implanted motifs that covers a wide range of biologically possible cases. Second, SMAlign aligns a pair of structured motifs optimally and efficiently given their gap constraints. Third, SMCluster produces multiple alignment of structured motifs through hierarchical clustering using SMAlign\u27s affinity score. Finally, SMExtract extracts structured motifs from a set of sequences by using SMCluster to construct the target pattern from the top reported two-box patterns (fragments), extracted using an existing algorithm (Exmotif) and a two-box template. The main advantage of SMExtract is its efficiency to extract longer degenerate patterns while requiring less prior knowledge, about the pattern to be extracted, than current algorithms

Adaptation and Stochasticity of Natural Complex Systems

The methods that fueled the microscale revolution (top-down design/fabrication, combined with application of forces large enough to overpower stochasticity) constitute an approach that will not scale down to nanoscale systems. In contrast, in nanotechnology, we strive to embrace nature’s quite different paradigms to create functional systems, such as self-assembly to create structures, exploiting stochasticity, rather than overwhelming it, in order to create deterministic, yet highly adaptable, behavior. Nature’s approach, through billions of years of evolutionary development, has achieved self-assembling, self-duplicating, self-healing, adaptive systems. Compared to microprocessors, nature’s approach has achieved eight orders of magnitude higher memory density and three orders of magnitude higher computing capacity while utilizing eight orders of magnitude less power. Perhaps the most complex of functions, homeostatis by a biological cell – i.e., the regulation of its internal environment to maintain stability and function – in a fluctuating and unpredictable environment, emerges from the interactions between perhaps 50M molecules of a few thousand different types. Many of these molecules (e.g. proteins, RNA) are produced in the stochastic processes of gene expression, and the resulting populations of these molecules are distributed across a range of values. So although homeostasis is maintained at the system (i.e. cell) level, there are considerable and unavoidable fluctuations at the component (protein, RNA) level. While on at least some level, we understand the variability in individual components, we have no understanding of how to integrate these fluctuating components together to achieve complex function at the system level. This thesis will explore the regulation and control of stochasticity in cells. In particular, the focus will be on (1) how genetic circuits use noise to generate more function in less space; (2) how stochastic and deterministic responses are co-regulated to enhance function at a system level; and (3) the development of high-throughput analytical techniques that enable a comprehensive view of the structure and distribution of noise on a whole organism level

92nd Annual Meeting of the Virginia Academy of Science: Proceedings

Full proceedings of the 92nd Annual Meeting of the Virginia Academy of Science, May 13-15, 2014, Virginia Commonwealth University, Richmond, Virgini