Fast and accurate read mapping with approximate seeds and multiple backtracking

We present Masai, a read mapper representing the state-of-the-art in terms of speed and accuracy. Our tool is an order of magnitude faster than RazerS 3 and mrFAST, 2-4 times faster and more accurate than Bowtie 2 and BWA. The novelties of our read mapper are filtration with approximate seeds and a method for multiple backtracking. Approximate seeds, compared with exact seeds, increase filtration specificity while preserving sensitivity. Multiple backtracking amortizes the cost of searching a large set of seeds by taking advantage of the repetitiveness of next-generation sequencing data. Combined together, these two methods significantly speed up approximate search on genomic data sets. Masai is implemented in C++ using the SeqAn library. The source code is distributed under the BSD license and binaries for Linux, Mac OS X and Windows can be freely downloaded from http://www.seqan.de/projects/masai

Novel computational techniques for mapping and classifying Next-Generation Sequencing data

Since their emergence around 2006, Next-Generation Sequencing technologies have been revolutionizing biological and medical research. Quickly obtaining an extensive amount of short or long reads of DNA sequence from almost any biological sample enables detecting genomic variants, revealing the composition of species in a metagenome, deciphering cancer biology, decoding the evolution of living or extinct species, or understanding human migration patterns and human history in general. The pace at which the throughput of sequencing technologies is increasing surpasses the growth of storage and computer capacities, which creates new computational challenges in NGS data processing. In this thesis, we present novel computational techniques for read mapping and taxonomic classification. With more than a hundred of published mappers, read mapping might be considered fully solved. However, the vast majority of mappers follow the same paradigm and only little attention has been paid to non-standard mapping approaches. Here, we propound the so-called dynamic mapping that we show to significantly improve the resulting alignments compared to traditional mapping approaches. Dynamic mapping is based on exploiting the information from previously computed alignments, helping to improve the mapping of subsequent reads. We provide the first comprehensive overview of this method and demonstrate its qualities using Dynamic Mapping Simulator, a pipeline that compares various dynamic mapping scenarios to static mapping and iterative referencing. An important component of a dynamic mapper is an online consensus caller, i.e., a program collecting alignment statistics and guiding updates of the reference in the online fashion. We provide Ococo, the first online consensus caller that implements a smart statistics for individual genomic positions using compact bit counters. Beyond its application to dynamic mapping, Ococo can be employed as an online SNP caller in various analysis pipelines, enabling SNP calling from a stream without saving the alignments on disk. Metagenomic classification of NGS reads is another major topic studied in the thesis. Having a database with thousands of reference genomes placed on a taxonomic tree, the task is to rapidly assign a huge amount of NGS reads to tree nodes, and possibly estimate the relative abundance of involved species. In this thesis, we propose improved computational techniques for this task. In a series of experiments, we show that spaced seeds consistently improve the classification accuracy. We provide Seed-Kraken, a spaced seed extension of Kraken, the most popular classifier at present. Furthermore, we suggest ProPhyle, a new indexing strategy based on a BWT-index, obtaining a much smaller and more informative index compared to Kraken. We provide a modified version of BWA that improves the BWT-index for a quick k-mer look-up

Efficient approximate string matching techniques for sequence alignment

One of the outstanding milestones achieved in recent years in the field of biotechnology research has been the development of high-throughput sequencing (HTS). Due to the fact that at the moment it is technically impossible to decode the genome as a whole, HTS technologies read billions of relatively short chunks of a genome at random locations. Such reads then need to be located within a reference for the species being studied (that is aligned or mapped to the genome): for each read one identifies in the reference regions that share a large sequence similarity with it, therefore indicating what the read¿s point or points of origin may be. HTS technologies are able to re-sequence a human individual (i.e. to establish the differences between his/her individual genome and the reference genome for the human species) in a very short period of time. They have also paved the way for the development of a number of new protocols and methods, leading to novel insights in genomics and biology in general. However, HTS technologies also pose a challenge to traditional data analysis methods; this is due to the sheer amount of data to be processed and the need for improved alignment algorithms that can generate accurate results quickly. This thesis tackles the problem of sequence alignment as a step within the analysis of HTS data. Its contributions focus on both the methodological aspects and the algorithmic challenges towards efficient, scalable, and accurate HTS mapping. From a methodological standpoint, this thesis strives to establish a comprehensive framework able to assess the quality of HTS mapping results. In order to be able to do so one has to understand the source and nature of mapping conflicts, and explore the accuracy limits inherent in how sequence alignment is performed for current HTS technologies. From an algorithmic standpoint, this work introduces state-of-the-art index structures and approximate string matching algorithms. They contribute novel insights that can be used in practical applications towards efficient and accurate read mapping. More in detail, first we present methods able to reduce the storage space taken by indexes for genome-scale references, while still providing fast query access in order to support effective search algorithms. Second, we describe novel filtering techniques that vastly reduce the computational requirements of sequence mapping, but are nonetheless capable of giving strict algorithmic guarantees on the completeness of the results. Finally, this thesis presents new incremental algorithmic techniques able to combine several approximate string matching algorithms; this leads to efficient and flexible search algorithms allowing the user to reach arbitrary search depths. All algorithms and methodological contributions of this thesis have been implemented as components of a production aligner, the GEM-mapper, which is publicly available, widely used worldwide and cited by a sizeable body of literature. It offers flexible and accurate sequence mapping while outperforming other HTS mappers both as to running time and to the quality of the results it produces.Uno de los avances más importantes de los últimos años en el campo de la biotecnología ha sido el desarrollo de las llamadas técnicas de secuenciación de alto rendimiento (high-throughput sequencing, HTS). Debido a las limitaciones técnicas para secuenciar un genoma, las técnicas de alto rendimiento secuencian individualmente billones de pequeñas partes del genoma provenientes de regiones aleatorias. Posteriormente, estas pequeñas secuencias han de ser localizadas en el genoma de referencia del organismo en cuestión. Este proceso se denomina alineamiento - o mapeado - y consiste en identificar aquellas regiones del genoma de referencia que comparten una alta similaridad con las lecturas producidas por el secuenciador. De esta manera, en cuestión de horas, la secuenciación de alto rendimiento puede secuenciar un individuo y establecer las diferencias de este con el resto de la especie. En última instancia, estas tecnologías han potenciado nuevos protocolos y metodologías de investigación con un profundo impacto en el campo de la genómica, la medicina y la biología en general. La secuenciación alto rendimiento, sin embargo, supone un reto para los procesos tradicionales de análisis de datos. Debido a la elevada cantidad de datos a analizar, se necesitan nuevas y mejoradas técnicas algorítmicas que puedan escalar con el volumen de datos y producir resultados precisos. Esta tesis aborda dicho problema. Las contribuciones que en ella se realizan se enfocan desde una perspectiva metodológica y otra algorítmica que propone el desarrollo de nuevos algoritmos y técnicas que permitan alinear secuencias de manera eficiente, precisa y escalable. Desde el punto de vista metodológico, esta tesis analiza y propone un marco de referencia para evaluar la calidad de los resultados del alineamiento de secuencias. Para ello, se analiza el origen de los conflictos durante la alineación de secuencias y se exploran los límites alcanzables en calidad con las tecnologías de secuenciación de alto rendimiento. Desde el punto de vista algorítmico, en el contexto de la búsqueda aproximada de patrones, esta tesis propone nuevas técnicas algorítmicas y de diseño de índices con el objetivo de mejorar la calidad y el desempeño de las herramientas dedicadas a alinear secuencias. En concreto, esta tesis presenta técnicas de diseño de índices genómicos enfocados a obtener un acceso más eficiente y escalable. También se presentan nuevas técnicas algorítmicas de filtrado con el fin de reducir el tiempo de ejecución necesario para alinear secuencias. Y, por último, se proponen algoritmos incrementales y técnicas híbridas para combinar métodos de alineamiento y mejorar el rendimiento en búsquedas donde el error esperado es alto. Todo ello sin degradar la calidad de los resultados y con garantías formales de precisión. Para concluir, es preciso apuntar que todos los algoritmos y metodologías propuestos en esta tesis están implementados y forman parte del alineador GEM. Este versátil alineador ofrece resultados de alta calidad en entornos de producción siendo varias veces más rápido que otros alineadores. En la actualidad este software se ofrece gratuitamente, tiene una amplia comunidad de usuarios y ha sido citado en numerosas publicaciones científicas

Accelerating edit-distance sequence alignment on GPU using the wavefront algorithm

Sequence alignment remains a fundamental problem with practical applications ranging from pattern recognition to computational biology. Traditional algorithms based on dynamic programming are hard to parallelize, require significant amounts of memory, and fail to scale for large inputs. This work presents eWFA-GPU, a GPU (graphics processing unit)-accelerated tool to compute the exact edit-distance sequence alignment based on the wavefront alignment algorithm (WFA). This approach exploits the similarities between the input sequences to accelerate the alignment process while requiring less memory than other algorithms. Our implementation takes full advantage of the massive parallel capabilities of modern GPUs to accelerate the alignment process. In addition, we propose a succinct representation of the alignment data that successfully reduces the overall amount of memory required, allowing the exploitation of the fast shared memory of a GPU. Our results show that our GPU implementation outperforms by 3- 9× the baseline edit-distance WFA implementation running on a 20 core machine. As a result, eWFA-GPU is up to 265 times faster than state-of-the-art CPU implementation, and up to 56 times faster than state-of-the-art GPU implementations.This work was supported in part by the European Unions’s Horizon 2020 Framework Program through the DeepHealth Project under Grant 825111; in part by the European Union Regional Development Fund within the Framework of the European Regional Development Fund (ERDF) Operational Program of Catalonia 2014–2020 with a Grant of 50% of Total Cost Eligible through the Designing RISC-V-based Accelerators for next-generation Computers Project under Grant 001-P-001723; in part by the Ministerio de Ciencia e Innovacion (MCIN) Agencia Estatal de Investigación (AEI)/10.13039/501100011033 under Contract PID2020-113614RB-C21 and Contract TIN2015-65316-P; and in part by the Generalitat de Catalunya (GenCat)-Departament de Recerca i Universitats (DIUiE) (GRR) under Contract 2017-SGR-313, Contract 2017-SGR-1328, and Contract 2017-SGR-1414. The work of Miquel Moreto was supported in part by the Spanish Ministry of Economy, Industry and Competitiveness under Ramon y Cajal Fellowship under Grant RYC-2016-21104.Peer ReviewedPostprint (published version

Algorithms for Peptide Identification from Mixture Tandem Mass Spectra

The large amount of data collected in an mass spectrometry experiment requires effective computational approaches for the automated analysis of those data. Though extensive research has been conducted for such purpose by the proteomics community, there are still remaining challenges, among which, one particular challenge is that the identification rate of the MS/MS spectra collected is rather low. One significant reason that contributes to this situation is the frequently observed mixture spectra, which result from the concurrent fragmentation of multiple precursors in a single MS/MS spectrum. However, nearly all the mainstream computational methods still take the assumption that the acquired spectra come from a single precursor, thus they are not suitable for the identification of mixture spectra. In this research, we focused on developing effective algorithms for the purpose of interpreting mixture tandem mass spectra, and our research work is mainly comprised of two components: de novo sequencing of mixture spectra and mixture spectra identification by database search. For the de novo sequencing approach, firstly we formulated the mixture spectra de novo sequencing problem mathematically, and proposed a dynamic programming algorithm for the problem. Additionally, we use both simulated and real mixture spectra datasets to verify the efficiency of the algorithm described in the research. For the database search identification, we proposed an approach for matching mixture tandem mass spectra with a pair of peptide sequences acquired from the protein sequence database by incorporating a special de novo assisted filtration strategy. Besides the filtration strategy, we also introduced in the research a method to give an reasonable estimation of the mixture coefficient which represents the relative abundance level of the co-sequenced precursors. The preliminary experimental results demonstrated the efficiency of the integrated filtration strategy and mixture coefficient estimating method in reducing examination space and also verified the effectiveness of the proposed matching scheme

Efficient approximate string matching techniques for sequence alignment

One of the outstanding milestones achieved in recent years in the field of biotechnology research has been the development of high-throughput sequencing (HTS). Due to the fact that at the moment it is technically impossible to decode the genome as a whole, HTS technologies read billions of relatively short chunks of a genome at random locations. Such reads then need to be located within a reference for the species being studied (that is aligned or mapped to the genome): for each read one identifies in the reference regions that share a large sequence similarity with it, therefore indicating what the read¿s point or points of origin may be. HTS technologies are able to re-sequence a human individual (i.e. to establish the differences between his/her individual genome and the reference genome for the human species) in a very short period of time. They have also paved the way for the development of a number of new protocols and methods, leading to novel insights in genomics and biology in general. However, HTS technologies also pose a challenge to traditional data analysis methods; this is due to the sheer amount of data to be processed and the need for improved alignment algorithms that can generate accurate results quickly. This thesis tackles the problem of sequence alignment as a step within the analysis of HTS data. Its contributions focus on both the methodological aspects and the algorithmic challenges towards efficient, scalable, and accurate HTS mapping. From a methodological standpoint, this thesis strives to establish a comprehensive framework able to assess the quality of HTS mapping results. In order to be able to do so one has to understand the source and nature of mapping conflicts, and explore the accuracy limits inherent in how sequence alignment is performed for current HTS technologies. From an algorithmic standpoint, this work introduces state-of-the-art index structures and approximate string matching algorithms. They contribute novel insights that can be used in practical applications towards efficient and accurate read mapping. More in detail, first we present methods able to reduce the storage space taken by indexes for genome-scale references, while still providing fast query access in order to support effective search algorithms. Second, we describe novel filtering techniques that vastly reduce the computational requirements of sequence mapping, but are nonetheless capable of giving strict algorithmic guarantees on the completeness of the results. Finally, this thesis presents new incremental algorithmic techniques able to combine several approximate string matching algorithms; this leads to efficient and flexible search algorithms allowing the user to reach arbitrary search depths. All algorithms and methodological contributions of this thesis have been implemented as components of a production aligner, the GEM-mapper, which is publicly available, widely used worldwide and cited by a sizeable body of literature. It offers flexible and accurate sequence mapping while outperforming other HTS mappers both as to running time and to the quality of the results it produces.Uno de los avances más importantes de los últimos años en el campo de la biotecnología ha sido el desarrollo de las llamadas técnicas de secuenciación de alto rendimiento (high-throughput sequencing, HTS). Debido a las limitaciones técnicas para secuenciar un genoma, las técnicas de alto rendimiento secuencian individualmente billones de pequeñas partes del genoma provenientes de regiones aleatorias. Posteriormente, estas pequeñas secuencias han de ser localizadas en el genoma de referencia del organismo en cuestión. Este proceso se denomina alineamiento - o mapeado - y consiste en identificar aquellas regiones del genoma de referencia que comparten una alta similaridad con las lecturas producidas por el secuenciador. De esta manera, en cuestión de horas, la secuenciación de alto rendimiento puede secuenciar un individuo y establecer las diferencias de este con el resto de la especie. En última instancia, estas tecnologías han potenciado nuevos protocolos y metodologías de investigación con un profundo impacto en el campo de la genómica, la medicina y la biología en general. La secuenciación alto rendimiento, sin embargo, supone un reto para los procesos tradicionales de análisis de datos. Debido a la elevada cantidad de datos a analizar, se necesitan nuevas y mejoradas técnicas algorítmicas que puedan escalar con el volumen de datos y producir resultados precisos. Esta tesis aborda dicho problema. Las contribuciones que en ella se realizan se enfocan desde una perspectiva metodológica y otra algorítmica que propone el desarrollo de nuevos algoritmos y técnicas que permitan alinear secuencias de manera eficiente, precisa y escalable. Desde el punto de vista metodológico, esta tesis analiza y propone un marco de referencia para evaluar la calidad de los resultados del alineamiento de secuencias. Para ello, se analiza el origen de los conflictos durante la alineación de secuencias y se exploran los límites alcanzables en calidad con las tecnologías de secuenciación de alto rendimiento. Desde el punto de vista algorítmico, en el contexto de la búsqueda aproximada de patrones, esta tesis propone nuevas técnicas algorítmicas y de diseño de índices con el objetivo de mejorar la calidad y el desempeño de las herramientas dedicadas a alinear secuencias. En concreto, esta tesis presenta técnicas de diseño de índices genómicos enfocados a obtener un acceso más eficiente y escalable. También se presentan nuevas técnicas algorítmicas de filtrado con el fin de reducir el tiempo de ejecución necesario para alinear secuencias. Y, por último, se proponen algoritmos incrementales y técnicas híbridas para combinar métodos de alineamiento y mejorar el rendimiento en búsquedas donde el error esperado es alto. Todo ello sin degradar la calidad de los resultados y con garantías formales de precisión. Para concluir, es preciso apuntar que todos los algoritmos y metodologías propuestos en esta tesis están implementados y forman parte del alineador GEM. Este versátil alineador ofrece resultados de alta calidad en entornos de producción siendo varias veces más rápido que otros alineadores. En la actualidad este software se ofrece gratuitamente, tiene una amplia comunidad de usuarios y ha sido citado en numerosas publicaciones científicas.Postprint (published version

NBPMF: Novel Network-Based Inference Methods for Peptide Mass Fingerprinting

Proteins are large, complex molecules that perform a vast array of functions in every living cell. A proteome is a set of proteins produced in an organism, and proteomics is the large-scale study of proteomes. Several high-throughput technologies have been developed in proteomics, where the most commonly applied are mass spectrometry (MS) based approaches. MS is an analytical technique for determining the composition of a sample. Recently it has become a primary tool for protein identification, quantification, and post translational modification (PTM) characterization in proteomics research. There are usually two different ways to identify proteins: top-down and bottom-up. Top-down approaches are based on subjecting intact protein ions and large fragment ions to tandem MS directly, while bottom-up methods are based on mass spectrometric analysis of peptides derived from proteolytic digestion, usually with trypsin. In bottom-up techniques, peptide mass fingerprinting (PMF) is widely used to identify proteins from MS dataset. Conventional PMF representatives such as probabilistic MOWSE algorithm, is based on mass distribution of tryptic peptides. In this thesis, we developed a novel network-based inference software termed NBPMF. By analyzing peptide-protein bipartite network, we designed new peptide protein matching score functions. We present two methods: the static one, ProbS, is based on an independent probability framework; and the dynamic one, HeatS, depicts input dataset as dependent peptides. Moreover, we use linear regression to adjust the matching score according to the masses of proteins. In addition, we consider the order of retention time to further correct the score function. In the post processing, we design two algorithms: assignment of peaks, and protein filtration. The former restricts that a peak can only be assigned to one peptide in order to reduce random matches; and the latter assumes each peak can only be assigned to one protein. In the result validation, we propose two new target-decoy search strategies to estimate the false discovery rate (FDR). The experiments on simulated, authentic, and simulated authentic dataset demonstrate that our NBPMF approaches lead to significantly improved performance compared to several state-of-the-art methods

A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges

Measuring and evaluating source code similarity is a fundamental software engineering activity that embraces a broad range of applications, including but not limited to code recommendation, duplicate code, plagiarism, malware, and smell detection. This paper proposes a systematic literature review and meta-analysis on code similarity measurement and evaluation techniques to shed light on the existing approaches and their characteristics in different applications. We initially found over 10000 articles by querying four digital libraries and ended up with 136 primary studies in the field. The studies were classified according to their methodology, programming languages, datasets, tools, and applications. A deep investigation reveals 80 software tools, working with eight different techniques on five application domains. Nearly 49% of the tools work on Java programs and 37% support C and C++, while there is no support for many programming languages. A noteworthy point was the existence of 12 datasets related to source code similarity measurement and duplicate codes, of which only eight datasets were publicly accessible. The lack of reliable datasets, empirical evaluations, hybrid methods, and focuses on multi-paradigm languages are the main challenges in the field. Emerging applications of code similarity measurement concentrate on the development phase in addition to the maintenance.Comment: 49 pages, 10 figures, 6 table