SUFFIX TREE, MINWISE HASHING AND STREAMING ALGORITHMS FOR BIG DATA ANALYSIS IN BIOINFORMATICS

In this dissertation, we worked on several algorithmic problems in bioinformatics using mainly three approaches: (a) a streaming model, (b) sux-tree based indexing, and (c) minwise-hashing (minhash) and locality-sensitive hashing (LSH). The streaming models are useful for large data problems where a good approximation needs to be achieved with limited space usage. We developed an approximation algorithm (Kmer-Estimate) using the streaming approach to obtain a better estimation of the frequency of k-mer counts. A k-mer, a subsequence of length k, plays an important role in many bioinformatics analyses such as genome distance estimation. We also developed new methods that use sux tree, a trie data structure, for alignment-free, non-pairwise algorithms for a conserved non-coding sequence (CNS) identification problem. We provided two different algorithms: STAG-CNS to identify exact-matched CNSs and DiCE to identify CNSs with mismatches. Using our algorithms, CNSs among various grass species were identified. A different approach was employed for identification of longer CNSs ( 100 bp, mostly found in animals). In our new method (MinCNE), the minhash approach was used to estimate the Jaccard similarity. Using also LSH, k-mers extracted from genomic sequences were clustered and CNSs were identified. Another new algorithm (MinIsoClust) that also uses minhash and LSH techniques was developed for an isoform clustering problem. Isoforms are generated from the same gene but by alternative splicing. As the isoform sequences share some exons but in different combinations, regular sequencing clustering methods do not work well. Our algorithm generates clusters for isoform sequences based on their shared minhash signatures. Finally, we discuss de novo transcriptome assembly algorithms and how to improve the assembly accuracy using ensemble approaches. First, we did a comprehensive performance analysis on different transcriptome assemblers using simulated benchmark datasets. Then, we developed a new ensemble approach (Minsemble) for the de novo transcriptome assembly problem that integrates isoform-clustering using minhash technique to identify potentially correct transcripts from various de novo transcriptome assemblers. Minsemble identified more correctly assembled transcripts as well as genes compared to other de novo and ensemble methods. Adviser: Jitender S. Deogu

The Role of Distributed Computing in Big Data Science: Case Studies in Forensics and Bioinformatics

2014 - 2015The era of Big Data is leading the generation of large amounts of data, which require storage and analysis capabilities that can be only ad- dressed by distributed computing systems. To facilitate large-scale distributed computing, many programming paradigms and frame- works have been proposed, such as MapReduce and Apache Hadoop, which transparently address some issues of distributed systems and hide most of their technical details. Hadoop is currently the most popular and mature framework sup- porting the MapReduce paradigm, and it is widely used to store and process Big Data using a cluster of computers. The solutions such as Hadoop are attractive, since they simplify the transformation of an application from non-parallel to the distributed one by means of general utilities and without many skills. However, without any algorithm engineering activity, some target applications are not alto- gether fast and e cient, and they can su er from several problems and drawbacks when are executed on a distributed system. In fact, a distributed implementation is a necessary but not su cient condition to obtain remarkable performance with respect to a non-parallel coun- terpart. Therefore, it is required to assess how distributed solutions are run on a Hadoop cluster, and/or how their performance can be improved to reduce resources consumption and completion times. In this dissertation, we will show how Hadoop-based implementations can be enhanced by using carefully algorithm engineering activity, tuning, pro ling and code improvements. It is also analyzed how to achieve these goals by working on some critical points, such as: data local computation, input split size, number and granularity of tasks, cluster con guration, input/output representation, etc. i In particular, to address these issues, we choose some case studies coming from two research areas where the amount of data is rapidly increasing, namely, Digital Image Forensics and Bioinformatics. We mainly describe full- edged implementations to show how to design, engineer, improve and evaluate Hadoop-based solutions for Source Camera Identi cation problem, i.e., recognizing the camera used for taking a given digital image, adopting the algorithm by Fridrich et al., and for two of the main problems in Bioinformatics, i.e., alignment- free sequence comparison and extraction of k-mer cumulative or local statistics. The results achieved by our improved implementations show that they are substantially faster than the non-parallel counterparts, and re- markably faster than the corresponding Hadoop-based naive imple- mentations. In some cases, for example, our solution for k-mer statis- tics is approximately 30× faster than our Hadoop-based naive im- plementation, and about 40× faster than an analogous tool build on Hadoop. In addition, our applications are also scalable, i.e., execution times are (approximately) halved by doubling the computing units. Indeed, algorithm engineering activities based on the implementation of smart improvements and supported by careful pro ling and tun- ing may lead to a much better experimental performance avoiding potential problems. We also highlight how the proposed solutions, tips, tricks and insights can be used in other research areas and problems. Although Hadoop simpli es some tasks of the distributed environ- ments, we must thoroughly know it to achieve remarkable perfor- mance. It is not enough to be an expert of the application domain to build Hadop-based implementations, indeed, in order to achieve good performance, an expert of distributed systems, algorithm engi- neering, tuning, pro ling, etc. is also required. Therefore, the best performance depend heavily on the cooperation degree between the domain expert and the distributed algorithm engineer. [edited by Author]XIV n.s

SUFFIX TREE, MINWISE HASHING AND STREAMING ALGORITHMS FOR BIG DATA ANALYSIS IN BIOINFORMATICS

In this dissertation, we worked on several algorithmic problems in bioinformatics using mainly three approaches: (a) a streaming model, (b) sux-tree based indexing, and (c) minwise-hashing (minhash) and locality-sensitive hashing (LSH). The streaming models are useful for large data problems where a good approximation needs to be achieved with limited space usage. We developed an approximation algorithm (Kmer-Estimate) using the streaming approach to obtain a better estimation of the frequency of k-mer counts. A k-mer, a subsequence of length k, plays an important role in many bioinformatics analyses such as genome distance estimation. We also developed new methods that use sux tree, a trie data structure, for alignment-free, non-pairwise algorithms for a conserved non-coding sequence (CNS) identification problem. We provided two different algorithms: STAG-CNS to identify exact-matched CNSs and DiCE to identify CNSs with mismatches. Using our algorithms, CNSs among various grass species were identified. A different approach was employed for identification of longer CNSs ( 100 bp, mostly found in animals). In our new method (MinCNE), the minhash approach was used to estimate the Jaccard similarity. Using also LSH, k-mers extracted from genomic sequences were clustered and CNSs were identified. Another new algorithm (MinIsoClust) that also uses minhash and LSH techniques was developed for an isoform clustering problem. Isoforms are generated from the same gene but by alternative splicing. As the isoform sequences share some exons but in different combinations, regular sequencing clustering methods do not work well. Our algorithm generates clusters for isoform sequences based on their shared minhash signatures. Finally, we discuss de novo transcriptome assembly algorithms and how to improve the assembly accuracy using ensemble approaches. First, we did a comprehensive performance analysis on different transcriptome assemblers using simulated benchmark datasets. Then, we developed a new ensemble approach (Minsemble) for the de novo transcriptome assembly problem that integrates isoform-clustering using minhash technique to identify potentially correct transcripts from various de novo transcriptome assemblers. Minsemble identified more correctly assembled transcripts as well as genes compared to other de novo and ensemble methods. Adviser: Jitender S. Deogu

Suffix Tree, Minwise Hashing and Streaming Algorithms for Big Data Analysis in Bioinformatics

In this dissertation, we worked on several algorithmic problems in bioinformatics using mainly three approaches: (a) a streaming model, (b) suffix-tree based indexing, and (c) minwise-hashing (minhash) and locality-sensitive hashing (LSH). The streaming models are useful for large data problems where a good approximation needs to be achieved with limited space usage. We developed an approximation algorithm (KmerEstimate) using the streaming approach to obtain a better estimation of the frequency of k-mer counts. A k-mer, a subsequence of length k, plays an important role in many bioinformatics analyses such as genome distance estimation. We also developed new methods that use suffix tree, a trie data structure, for alignment-free, non-pairwise algorithms for a conserved non-coding sequence (CNS) identification problem. We provided two different algorithms: STAG-CNS to identify exact-matched CNSs and DiCE to identify CNSs with mismatches. Using our algorithms, CNSs among various grass species were identified. A different approach was employed for identification of longer CNSs (≥ 100 bp, mostly found in animals). In our new method (MinCNE), the minhash approach was used to estimate the Jaccard similarity. Using also LSH, k-mers extracted from genomic sequences were clustered and CNSs were identified. Another new algorithm (MinIsoClust) that also uses minhash and LSH techniques was developed for an isoform clustering problem. Isoforms are generated from the same gene but by alternative splicing. As the isoform sequences share some exons but in different combinations, regular sequencing clustering methods do not work well. Our algorithm generates clusters for isoform sequences based on their shared minhash signatures. Finally, we discuss de novo transcriptome assembly algorithms and how to improve the assembly accuracy using ensemble approaches. First, we did a comprehensive performance analysis on different transcriptome assemblers using simulated benchmark datasets. Then, we developed a new ensemble approach (Minsemble) for the de novo transcriptome assembly problem that integrates isoform-clustering using minhash technique to identify potentially correct transcripts from various de novo transcriptome assemblers. Minsemble identified more correctly assembled transcripts as well as genes compared to other de novo and ensemble methods

Suffix Tree, Minwise Hashing and Streaming Algorithms for Big Data Analysis in Bioinformatics

In this dissertation, we worked on several algorithmic problems in bioinformatics using mainly three approaches: (a) a streaming model, (b) suffix-tree based indexing, and (c) minwise-hashing (minhash) and locality-sensitive hashing (LSH). The streaming models are useful for large data problems where a good approximation needs to be achieved with limited space usage. We developed an approximation algorithm (KmerEstimate) using the streaming approach to obtain a better estimation of the frequency of k-mer counts. A k-mer, a subsequence of length k, plays an important role in many bioinformatics analyses such as genome distance estimation. We also developed new methods that use suffix tree, a trie data structure, for alignment-free, non-pairwise algorithms for a conserved non-coding sequence (CNS) identification problem. We provided two different algorithms: STAG-CNS to identify exact-matched CNSs and DiCE to identify CNSs with mismatches. Using our algorithms, CNSs among various grass species were identified. A different approach was employed for identification of longer CNSs (≥ 100 bp, mostly found in animals). In our new method (MinCNE), the minhash approach was used to estimate the Jaccard similarity. Using also LSH, k-mers extracted from genomic sequences were clustered and CNSs were identified. Another new algorithm (MinIsoClust) that also uses minhash and LSH techniques was developed for an isoform clustering problem. Isoforms are generated from the same gene but by alternative splicing. As the isoform sequences share some exons but in different combinations, regular sequencing clustering methods do not work well. Our algorithm generates clusters for isoform sequences based on their shared minhash signatures. Finally, we discuss de novo transcriptome assembly algorithms and how to improve the assembly accuracy using ensemble approaches. First, we did a comprehensive performance analysis on different transcriptome assemblers using simulated benchmark datasets. Then, we developed a new ensemble approach (Minsemble) for the de novo transcriptome assembly problem that integrates isoform-clustering using minhash technique to identify potentially correct transcripts from various de novo transcriptome assemblers. Minsemble identified more correctly assembled transcripts as well as genes compared to other de novo and ensemble methods

How Can Bring the Big Data Analysis of High-Throughput Sequencing Technologies into the Routine Clinical Diagnostic Assays?

Whilst the widespread use of whole genome sequencing and metagenomics sequencing techniques is becoming applicable as a routine tool for diagnostic and public health microbiology, the computational analysis is likely to be a few years away. Due to the massive data volumes and complexity of the analysis, computational resources are expensive and time-consuming. Moreover, the complexity of the applications requires adequate knowledge of computer science. Hence, these problems are the barriers for the applicability of such analysis as a routine technique for the entire research community. Sequencebased identication methods can revolutionize the ability of medicine laboratories for early and precise detection of infectious pathogens in order to have a more accurate diagnosis and treatment decision. The overriding purpose of this research is to overcome the limitations in the computational analysis of rapid diagnostic identication and characterization of species and infectious pathogens from raw reads sequencing data, in particular, complex metagenomics data. This research has focused on reducing the cost and time-consumption of the computational resources by the use of parallel and distributed computing and related optimization techniques, in order to utilize ordinary desktop computers for Big Data analysis in bioinformatics