Academic literature on the topic 'Short-read sequencing'

La seqüenciació genòmica és un component clau en nous avenços en medicina, i la seva democratització és un pas important per millorar l’accessibilitat per al pacient. Els beneficis implícits en el descobriment de noves variants genètiques són molt amplis, incloent des de la detecció precoç de càncer com la medicina personalitzada, passant pel disseny de fàrmacs i l’edició genòmica. Tots aquests usos potencials han incrementat exponencialment l’interès de la comunitat científica en el camp de la bioinformàtica durant els últims anys. A més, el sorgiment dels mètodes de Seqüenciació de Nova Generació ha contribuït a la reducció ràpida dels costos de seqüenciació, permetent el desenvolupament de noves aplicacions genòmiques. El principal objectiu d’aquesta tesi és el de millorar el rendiment i precisió de l’estat de l’art de la seqüenciació genètica a través de l’ús de plataformes de còmput heterogeni i sistemes de computació híbrida. Més específicament, el treball s’ha centrat en l’acceleració de el problema de mapeig de reads curts, ja que es descriu com un dels estadis del pipeline amb un major cost computacional. De forma global, s’ aspirava a reduir el temps de processament i el cost de la seqüenciació genètica, incrementant la disponibilitat d’aquest tipus d’anàlisi. La principal contribució d’aquesta tesi és la integració GPU del mapper GEM3 (GEM3-GPU). Aquest mapper reporta les mateixes dades de sortida per CPU i GPU, i és un dels primers mappers GPU que permet l’alineament de reads llargs i variables. Les propostes han estat validades utilitzant dades reals, ja que el mapper ha estat corrent en producció en un centre de seqüenciació genòmica (Centre Nacional d’Anàlisi Genòmica (CNAG)). En conjunció amb el mapper GEM3-GPU, durant aquesta tesi s’ha creat una llibreria bioinformàtica en CUDA (GEM-cutter). La llibreria aporta blocs de primitives GPU bàsiques que han estat altament optimitzades. Gem-cutter ofereix una API basada en primitives send and receive (message passing), i incorpora un scheduler per balancejar el treball. A més, la llibreria suporta totes les arquitectures GPU i Multi-GPU.<br>La secuenciación genómica es un componente clave en nuevos avances en medicina, y su democratización es un paso importante hacia la accesibilidad para el paciente. Los beneficios implícitos en el descubrimiento de nuevas variantes genéticas son muy amplios, incluyendo desde la detección precoz de cáncer como la medicina personalizada, pasando por el diseño de fármaco y la edición genómica. Estos usos potenciales han incrementado exponencialmente el interés de la comunidad científica en el campo de la bioinformática durante los últimos años. Además, el surgimiento de los métodos de Secuenciación de Nueva Generación ha contribuido a la reducción rápida de los costes de secuenciación, permitiendo el desarrollo de nuevas aplicaciones genómicas. El principal objetivo de esta tesis es el de mejorar el rendimiento y precisión del estado del arte de la secuenciación genética a través del uso de plataformas de computo heterogéneo y sistemas de hardware híbridos. Más específicamente, el trabajo se ha centrado en la aceleración del problema del short-read mapping, dado que se describe como uno de los estadíos del pipeline con un mayor coste computacional. De forma global, se aspiraba a reducir el tiempo de procesado y el coste de la secuenciación genética, incrementando su disponibilidad. La principal contribución de esta tesis es la integración GPU del mapper GEM3 (GEM3-GPU). Este mapper reporta los mismos datos de salida para CPU y GPU, y es uno de los primeros mappers GPU que permite el alineamiento de reads largos y variables. Las propuestas han sido validadas utilizando datos reales, dado que el mapper ha estado corriendo en producción en un centro de secuenciación (Centro Nacional de Análisis Genómico (CNAG)). En conjunción con el mapper GEM3-GPU, durante esta tesis se ha creado una librería bioinformática en CUDA (GEM-cutter). La librería provee bloques de primitivas GPU básicas que han sido altamente optimizadas. Gem-cutter ofrece una API basada en primitivas de send and receive (message passing), e incorpora un scheduler para balancear el trabajo. Además, la librería soporta todas las arquitecturas GPU y Multi-GPU.<br>Genomic sequencing is the key component of new advances in medicine, and its democratization is an important step in improving accessibility for the patient. The benefits involved in discovering new genomic variations are vast and include everything from early cancer detection to personalized medicine, drug design and genome editing. All of these potential uses have greatly increased the interest of the scientific community in the field of bioinformatics in recent years. Moreover, the emergence of next-generation sequencing methods has contributed to the rapid reduction of sequencing costs, enabling new applications of genomics in precision medicine. The main goal of this thesis is to improve the state of the art in performance and accuracy for genome sequencing through the use of heterogeneous computing platforms and hybrid hardware systems. More specifically, the work is focused on accelerating the problem of short-read mapping, as it is described as one of the most computationally expensive parts of the pipeline process. Overall, we aim to reduce the processing time and cost of genome sequencing, and then increasing the availability of this type analysis. The main contribution of this thesis is the full GPU integration of the GEM3 mapper (GEM3-GPU), reporting significant improvements in performance and competitive accuracy results. The mapper reports the same output files for CPU and GPU and is one of the first GPU mappers to allow very long and variable read alignment. The proposals have been validated using real data, since the mapper has been running in production at a genomic sequencing center (Centro Nacional de Análisis Genómico (CNAG)). Together with the GEM3-GPU mapper, a complete bioinformatics CUDA library (GEM-cutter) has been created. The library provides the basic building blocks for genomic applications, which are highly optimised to run on GPUs. Gem-cutter offers an API based on send and receive primitives (message passing) and incorporates a scheduler to balance the work. Furthermore, the library supports all GPU architectures and Multi-GPU execution.<br>Universitat Autònoma de Barcelona. Programa de Doctorat en Informàtica

L’adaptation des organismes aux changements environnementaux est devenue une question fondamentale de la recherche, en particulier face aux impacts du réchauffement climatique. Un axe clé de recherche consiste à comprendre comment les éléments génétiques sous jacent, tels que les éléments transposables (ET). Les ET sont des séquences d'ADN répétés présentes chez tous les Eucaryotes, possédant la capacité unique de se déplacer au sein du génome, un phénomène appelé transposition active. Ainsi, ils peuvent provoquer des mutations en générant des insertions polymorphiques d'ET (TIPs) entre individus, voire des insertions somatiques. En général, les ET restent inactifs grâce à des mécanismes épigénétiques qui limitent leur prolifération incontrôlée. Cependant, ils peuvent être réactivés par divers stimuli environnementaux, rendant la transposition active relativement rare. Cette mobilité des ET peut être révélée en utilisant l'ADN circulaire extrachromosomique (ADNecc) comme marqueur de transposition. Le paysage transpostionnel des TEs et leur activité récente ont été décrits chez des organismes modèles, mais restent inexploités chez les espèces pérennes comme les arbres. Cette étude vise à explorer l’activité transpositionelle récente et la mobilité en cours des ET chez des espèces pérennes non modèles en utilisant le hêtre européen (Fagus sylvatica) comme notre modèle d’étude. Nous avons cherché à étudier l'activité récente des ET et leur mobilité continue en identifiant les variants causés par les ET au sein d'une population et chez un individu (à l'échelle somatique) en utilisant le séquençage du génome complet (WGS) et le séquençage du mobilome (ou ADNecc). Nous avons réalisé le séquençage WGS et du mobilome d'arbres de la forêt de Verzy, connue pour abriter des hêtres nains et tortillards, également appelés « mutants ». Ces arbres présentent des traits morphologiques instables, avec chez certains arbres de nouvelles branches qui se développent avec une forme normale. Nous avons identifié deux ET appartenant au type des Miniature Inverted Repeats Transposable Elements (MITEs), nommés SQUIRREL1 et SQUIRREL2, qui se mobilisent activement dans ces arbres, produisant une grande quantité dADNecc et causant même des variations somatiques. SQUIRREL1 et SQUIRREL2 sont également actifs dans les hêtres de la forêt de la Massane. De plus, dans tous ces arbres, plusieurs d’autres ET, principalement des MITEs, produisent une grande quantité dADNecc, bien que leur niveau d’activité semble varier en fonction des tissus, suggérant que l'activité des ET varie selon le stade de développement et indiquant une transposition dominée par les MITEs chez le hêtre. Parallèlement, nous avons étudié les TIPs dans une population de hêtres de la forêt de la Massane, une forêt ancienne classée au patrimoine mondial de l'UNESCO. En séquençant 150 arbres, nous avons cherché à comprendre comment les ET contribuent à la diversité génétique de l'ensemble de la population en détectant les TIPs générés par les Long Terminal Repeats rétrotransposons (LTR RT) et les MITEs en utilisant le séquençage WGS. Nous avons détecté environ 30 000 TIPs de LTR-RT chez chaque individu, contre 70 000 TIPs de MITEs. La plupart de ces TIPs restent à faible fréquence mais de nombreux MITE-TIPs restent localisés près de gènes fonctionnels et conservés au sein de la population. À partir des TIPs, nous avons identifié plusieurs points chauds de variation et des régions conservées le long du génome du hêtre permettant d’abordant la structuration du génome chez cette espèce. Pour conclure, notre étude met en lumière l’importance des ET dans la structuration du paysage génomique des arbres, en particulier dans la manière dont ces éléments contribuent à l’évolution des espèces à longue durée de vie. Les recherches futures pourraient étendre ces travaux à d’autres espèces d'arbres et explorer si les schémas observés se retrouvent dans d’autres espèces d’arbres<br>The adaptation of organisms to environmental changes has become a fundamental research question,particularly in the context of climate change. A key area of this research is to identify underlying genetic elements, such as transposable elements (TEs), contributing to this process. TEs are repetitive DNA sequences found across all eukaryotes, possessing the unique ability to move within the genome, a phenomenon known as active transposition. They can cause mutations by generating transposable element insertion polymorphisms (TIPs) between individuals, and even somatic insertions. Generally, TEs remain inactive by epigenetic mechanisms that limit their uncontrolled proliferation. However, they can be reactivated upon various environmental stimuli, making active transposition relatively rare. TE mobility can be detected using extrachromosomal circular DNA (eccDNA) as a marker of transposition. The transpositional landscape of TEs and their recent activity have been documented in model organisms but remain underexplored in perennial species such as trees. This study aims to investigate recent transpositional activity and ongoing mobility of TEs in non-model perennial species, using European beech (Fagus sylvatica) as our model. We sought to study recent TE activity and their continuous mobility byidentifying TE-induced variants within a population and in an individual (at the somatic scale) using whole-genome sequencing (WGS) and mobilome sequencing (eccDNA). We conducted WGS and mobilome sequencing of trees from the Verzy forest, known for its dwarf and tortuous beeches, also referred as "mutants." These trees exhibit unstable phenotypical traits, with some trees developing new normal branches. We identified two TEs belonging to the Miniature Inverted Repeat Transposable Elements (MITEs) type, named SQUIRREL1 and SQUIRREL2, which are actively mobilizing in these trees, producing large amounts of eccDNA and even causing somatic variations.SQUIRREL1 and SQUIRREL2 are also active in beech trees from the Massane forest. Furthermore, in all these trees, several other TEs,mainly MITEs, produce significant amounts of eccDNA, although their activity levels appear to vary depending on the tissues, suggesting that TE activity could be tissue-specific indicating MITE-dominated transposition in beech. Simultaneously, we investigated TIPs in a population of beech trees from the Massane forest, an ancient forest classified as a UNESCO World Heritage site. By sequencing 150 trees, we aimed to understand how TEs contribute to the genetic diversity of the entire population by detecting TIPs generated by Long Terminal Repeat retrotransposons (LTR-RTs) and MITEs using WGS. We detected approximately 30,000 LTR-RT TIPs in each individual, compared to 70,000 MITE TIPs. While most of these TIPs remain at low frequency, many MITE-TIPs are located near functional genes and more conserved within the population. Using these TIPs, we identified several hotspots of variation and conserved regions along the beech genome, providing insights into genome structure in this species. In conclusion, our study highlights the importance of TEs in shaping the genomic landscape of trees, particularly in understanding how these elements contribute to the evolution of long-lived species. Future research could expand this work to other tree species and explore whether the patterns observed in beeches are common in other types of trees

This thesis presents short DNA alignment tools optimization. These short DNA reads are products of next\nobreakdash-generation sequencing technologies. The results produced by existing align\-ment tools can be influenced by various parameters. For this purpose, an optimization framework to find the optimal values of selected parameters was developed. This framework is based on differencial evolution algorithm and its main goal is to maximize the alignment accuracy. The functionality of the framework was tested on both real and generated data sets of short DNA reads. An accurate alignment is crucial for correct prediction of various genetic characteristics.

The genome of an organism encompasses the unique set of genetic instructions for every individual in a species. The genome, in totality, guides the course of evolution, development, genetic and epigenetic growth factors of an individual. Genomics, the study of genome, presents an interdisciplinary landscape, with a multistage data analytics pipeline. Understanding the genome involves determining the order of the four constituent nucleotides or bases or genetic alphabets, namely adenine (A), cytosine (C), guanine (G) and thymine (T), within the genome’s DNA sequence, and the process is widely known as sequencing. Next Generation Sequencing (NGS) involves massively parallel sequencing of genetic data with high throughput. NGS offers an unparalleled interrogation of the genome, throwing deeper insight into the functional and structural investigation of genetic data. The deductions from such a study leave a huge impact across fields, including medical diagnostics, therapeutics and drug discovery, and as well form the basis for genomic medicine. Data processing with NGS happens over an elaborated multi-stage data analytics pipeline. During the primary data analysis, the sequencing process produces billions of short fragments, called short reads, of the target genome. This amounts to petabytes of unprocessed genomic raw data. Short read mapping (SRM) is the process of mapping these short reads to their respective positions in the target genome. Due to the sheer volume of data that needs to be handled, SRM serves as a major sequential bottleneck to the NGS data analytics pipeline in genomics, and presents profound technical and computing challenges. Classified as a complex big data engineering problem, SRM thus calls for innovative computational, scientific and statistical approaches towards big data analysis. A strict validation of various algorithms and softwares in an NGS pipeline is essential, to ensure reliable and accurate results. With growing volume of NGS big data, the SRM and subsequent analytic steps de-mand a High Performance Computing (HPC) environment for data storage and analyses. Existing solutions for accelerating SRM provide notable performance, while leveraging heuristics and incurring significant error rates. Given the impact of the results of SRM in subsequent diagnostics and therapeutics, such heuristics and error rates are not affordable. In this context, we need precise, affordable, reliable and actionable results from SRM, to support any application, with uncompromised accuracy and performance.In this work, we present a massively parallel and scalable archetype, for accurate alignment of short reads, at a fine-grained single nucleotide resolution. The significant contributions of this work are presented below: 1. We present a robust and efficient indexing scheme for the reference genome, which is devoid of heuristics. The scheme reports all possible regions of mapping for a short read, inclusive of repeat regions. The lookup scheme efficiently handles the redundancy in reads. Though this leaves the rest of the pipeline with more data for SRM as compared to the heuristic solutions, it provides the end user with reliable and actionable results. 2. We present an efficient parallel implementation of an accurate sequence alignment algorithm based on the Dynamic Programming (DP) method. Our alignment kernels can seamlessly perform the traceback process in hardware simultaneously with the forward scan, thus eliminating the computational and memory bottlenecks associated with such algorithms. These kernels thus report alignment in a minimum deterministic time, which forms the first level of acceleration for SRM. 3. We present AccuRA, a hardware accelerator targeting reconfigurable hardware platforms. The model scales well at multiple levels of granularity, which precisely aligns short reads, at a fine-grained single nucleotide resolution, and offers full coverage of the genome. 4. We present GMAccS, a GPGPU based solution, for the SRM accelerator. This employs a platform independent model, capable of targeting a heterogeneous set of GPU hardware. 5. We present a performance and scalability analysis model for both the archetypes. The results from the prototypes substantiate the scalability of these architectures at multiple levels of granularity. 6. We present the generalization of our solution across applications which exhibit similar data patterns in terms of volume, variety, rate of production and analysis, randomness and uncertainty involved in data, and use Approximate String Matching (ASM) as the fundamental operation for data analytics. 7. We present the various problems within the biological domain, in terms of complexity and quantity of data, to which our solution can be customized and scaled, at various levels of granularity. We have presented the results from various prototype models of both AccuRA and GMAccS. The AccuRA prototype, hosting eight kernel units on a single reconfigurable device, aligns short reads with an alignment performance of 20.48 Giga Cell Updates Per Second (GCUPs). AccuRA can be ported onto devices as diverse as SoCs, ASICs or reconfigurable platform based hardware coprocessors or accelerators. The scalability analysis proved to substantiate the parallel AccuRA architecture, making it a promising target to accelerate the SRM process in the NGS pipeline. The in-house supercomputing platform SahasraT, which is a Cray XC40 system, hosted the prototype for the GMAccS archetype. The GMAccS prototypes align with an optimal performance of 23.69 Million Maps Per Second (MMPS) to 528.69 MMPS, while scaling from a single GPU to 24 GPUs. The performance model for GMAccS, as well as the results from the prototypes, substantiates the scalability of the GMAccS archetype and the subsequent performance enhancement achieved by it. Both AccuRA and GMAccS accommodate the big data of genomics, with uncompromised accuracy, precision and performance, while aligning the smaller archeal, bacterial and fungal genomes, to the much larger mammalian human genomes. These models have successfully handled redundant reads and multiread alignments. The results from AccuRA and GMAccS are available in the Sequence Alignment/Map (SAM) format, making it compatible with the downstream applications in the NGS pipeline. With a basic parameterized SRM model, and the results from its various prototypes for small and large genome benchmarks, we have gained the confidence that this solution can serve the requirements of accurate and scalable alignment of NGS big data. We believe that our model can serve as a reliable candidate in the future of genomics, called the "genomic highway", where data belonging to multiple applications can be streamed in, and can be aligned real time, with minimal memory and storage requirements, on a generalized alignment engine.