Letteratura scientifica selezionata sul tema "Third Generation Sequencing (TGS)"

Third generation sequencing (TGS) relates to long reads but with relatively high error rates. Quality of TGS is a hot topic, dealing with errors. This paper combines and investigates three quality related metrics. They are basecalling accuracy, Phred Quality Scores, and GC content. For basecalling accuracy, a deep neural network is adopted. The measured loss does not exceed 5.42.

Thalassemia is one of the most heterogeneous diseases, with more than a thousand mutation types recorded worldwide. Molecular diagnosis of thalassemia by conventional PCR-based DNA analysis is time- and resource-consuming owing to the phenotype variability, disease complexity, and molecular diagnostic test limitations. Moreover, genetic counseling must be backed-up by an extensive diagnosis of the thalassemia-causing phenotype and the possible genetic modifiers. Data coming from advanced molecular techniques such as targeted sequencing by next-generation sequencing (NGS) and third-generation sequencing (TGS) are more appropriate and valuable for DNA analysis of thalassemia. While NGS is superior at variant calling to TGS thanks to its lower error rates, the longer reads nature of the TGS permits haplotype-phasing that is superior for variant discovery on the homologous genes and CNV calling. The emergence of many cutting-edge machine learning-based bioinformatics tools has improved the accuracy of variant and CNV calling. Constant improvement of these sequencing and bioinformatics will enable precise thalassemia detections, especially for the CNV and the homologous HBA and HBG genes. In conclusion, laboratory transiting from conventional DNA analysis to NGS or TGS and following the guidelines towards a single assay will contribute to a better diagnostics approach of thalassemia.

Background and Aims: In in vitro fertilization (IVF) cycles, some patients suffering with vaginosis showed poor reproductive outcome even transferring with good quality embryos. The aim of this pilot study was to evaluate whether the species of Lactobacillus could be detected by next generation sequencing (NGS) and Third generation sequencing (TGS). Method: 18 Patients aged 32–48 years-old who visited our fertility center from February 2021 to June 2022 with previous failure of transfer cycle were included. Exclusion criteria was antibiotic treatment within 3 months prior to enrolment. Genomic DNA of cervical microbiota taken from Cervico vaginal swabs in all patients were extracted and amplified. NGS was performed following the protocol of Ion 16S Metagenomics Kit by detecting V2–4–8 and V3–6, 7–9 regions of 16S. Amplicons were sequenced with Ion GeneStudio S5 Prime System. For TGS, full length of 16S was sequenced with Single Molecule, Real-Time (SMRT) Sequencing (Pacific Biosciences) and analyzed. Results: All of the 18 cervical samples could be amplified with V2–4–8 and V3–6, 7–9 primers and the genus could be 100% identified by NGS. However, most of the Lactobacillus includes L. crispatus, L. jensenii. and L. gasseri showed indistinguishable except of L. iners. On the other hand, TGS clearly identified all Lactobacillus. For Gardnerella, Atopobium and Prevotella, TGS and NGS showed equivalent sensitivity in genus level. However, due to the sequence similarity, Escherichia/Shigella could not be identified from the Lactobacillus. Conclusion: In this report, we aim to compare the sensitivity of detecting bacteria by 16S amplification method. Preliminary results suggest that NGS can distinguish bacterium causes vaginosis in genus level, but there might be misleading if the existence of pathogen such as Escherichia/Shigella. Till now, TGS still exhibits the best sensitivity for distinguish Lactobacillus in species level.

The understanding of the human genome has been greatly improved by the advent of next-generation sequencing technologies (NGS). Despite the undeniable advantages responsible for their widespread diffusion, these methods have some constraints, mainly related to short read length and the need for PCR amplification. As a consequence, long-read sequencers, called third-generation sequencing (TGS), have been developed, promising to overcome NGS. Starting from the first prototype, TGS has progressively ameliorated its chemistries by improving both read length and base-calling accuracy, as well as simultaneously reducing the costs/base. Based on these premises, TGS is showing its potential in many fields, including the analysis of difficult-to-sequence genomic regions, structural variations detection, RNA expression profiling, DNA methylation study, and metagenomic analyses. Protocol standardization and the development of easy-to-use pipelines for data analysis will enhance TGS use, also opening the way for their routine applications in diagnostic contexts.

We propose LongQC as an easy and automated quality control tool for genomic datasets generated by third generation sequencing (TGS) technologies such as Oxford Nanopore technologies (ONT) and SMRT sequencing from Pacific Bioscience (PacBio). Key statistics were optimized for long read data, and LongQC covers all major TGS platforms. LongQC processes and visualizes those statistics automatically and quickly.

Although next-generation sequencing (NGS) technology revolutionized sequencing, offering a tremendous sequencing capacity with groundbreaking depth and accuracy, it continues to demonstrate serious limitations. In the early 2010s, the introduction of a novel set of sequencing methodologies, presented by two platforms, Pacific Biosciences (PacBio) and Oxford Nanopore Sequencing (ONT), gave birth to third-generation sequencing (TGS). The innovative long-read technologies turn genome sequencing into an ease-of-handle procedure by greatly reducing the average time of library construction workflows and simplifying the process of de novo genome assembly due to the generation of long reads. Long sequencing reads produced by both TGS methodologies have already facilitated the decipherment of transcriptional profiling since they enable the identification of full-length transcripts without the need for assembly or the use of sophisticated bioinformatics tools. Long-read technologies have also provided new insights into the field of epitranscriptomics, by allowing the direct detection of RNA modifications on native RNA molecules. This review highlights the advantageous features of the newly introduced TGS technologies, discusses their limitations and provides an in-depth comparison regarding their scientific background and available protocols as well as their potential utility in research and clinical applications.

The 16S rRNA amplicon-based sequencing approach represents the most common and cost-effective strategy with great potential for microbiome profiling. The use of second-generation sequencing (NGS) technologies has led to protocols based on the amplification of one or a few hypervariable regions, impacting the outcome of the analysis. Nowadays, comparative studies are necessary to assess different amplicon-based approaches, including the full-locus sequencing currently feasible thanks to third-generation sequencing (TGS) technologies. This study compared three different methods to achieve the deepest microbiome taxonomic characterization: (a) the single-region approach, (b) the multiplex approach, covering several regions of the target gene/region, both based on NGS short reads, and (c) the full-length approach, which analyzes the whole length of the target gene thanks to TGS long reads. Analyses carried out on benchmark microbiome samples, with a known taxonomic composition, highlighted a different classification performance, strongly associated with the type of hypervariable regions and the coverage of the target gene. Indeed, the full-length approach showed the greatest discriminating power, up to species level, also on complex real samples. This study supports the transition from NGS to TGS for the study of the microbiome, even if experimental and bioinformatic improvements are still necessary.

Background Drug-resistant mutations in the ABL1 kinase domain (KD) of BCR::ABL1 are primary mechanisms of resistance to tyrosine kinase inhibitor (TKI) therapy in Ph-positive leukemia. These mutations alter the conformation of ABL1 within BCR::ABL1, impairing TKI and thus diminishing their anti-leukemia efficacy. Our previous studies, presented at the 61st and 62nd ASH Annual Meeting, demonstrated the superiority of next-generation sequencing (NGS) over Sanger sequencing for detecting BCR::ABL1-KD mutations. NGS offers higher sensitivity, accurate mutation frequency determination, and the ability to identify compound or polyclonal mutations within the same amplicon. However, NGS still needs to improve, including its short read length and the disadvantages in identifying in-cis compound mutations, lack of flexibility, and long turnaround time. Leveraging advancements in a novel third-generation sequencing (TGS) technology - characterized by single-molecule sequencing capability, long reads, real-time sequencing, and high accuracy - we have developed an approach for directly full-length in-cis BCR::ABL1-KD mutation screening. Methods The new approach allows for a comprehensive in-cis analysis of resistance mutations in the BCR::ABL1-KD using a novel TGS platform based on single-molecule side synthesis side nanopore sequencing (NSBS), distinct from PacBio or ONT sequencing. We retrospectively analyzed 30 specimens from 30 cases previously tested using NGS, including 15 mutation-positive and 15 mutation-negative specimens under previous investigation. Among them, there were ten single mutations, four double mutations, and one triple mutation, with the variant allele frequency (VAF) ranging from 7.8% to 99.6%. Results The TGS approach can directly analyze in-cis compound BCR::ABL1-KD mutations in a full-length BCR::ABL1-KD sequencing model with high single-base accuracy and superior to the NGS protocol. The novel TGS approach can detect the complete BCR::ABL1 fusion gene sequence (including p190 and p210 types, sequence range 1696~1717bp), which can more accurately obtain the ABL1 sequence in the BCR::ABL1 fusion gene, thus analyzing the veritable resistance mutations in the ABL1 KD of the BCR::ABL1 fusion gene; it has higher detection sensitivity (average sequencing coverage depth of 13086x, range 8226~24152x; Q30 can reach 99.39%; VAF as low as 1%). The TGS approach detected more mutations than NGS (27 vs. 21) and accurately distinguished more complex mutation patterns, including multiple compound or polyclonal mutations. It detected five mutations in the NGS mutation-negative group. The VAF obtained in mutation analysis was inconsistent compared to NGS, indicating that the mutations and mutation frequencies detected by TGS are more in line with the true situation and significant for continuous monitoring. Conclusion Through comparative analysis with the NGS project, the novel TGS approach shows the advantages of longer reads, higher sensitivity and accuracy, and the ability to complete the scheme from library construction to data output within 12 hours. It may provide the clinic with faster and more accurate results of in-cis BCR::ABL1-KD resistance mutation analysis with higher sensitivity and guide the clinic to quickly change treatment plans, thereby improving therapeutic outcomes.

Cette thèse propose des solutions pour améliorer l'assemblage des génomes à partir de lectures de séquençage de troisième génération (lectures longues). Plus précisément, elle se concentre sur l'amélioration de l'assemblage des (méta)génomes contenant plusieurs haplotypes, comme des génomes polyploïdes ou des souches bactériennes proches. Les assembleurs actuels ont du mal à séparer les haplotypes très similaires, et fusionnent généralement des (parties d')haplotypes, ce qui entraîne la perte de polymorphismes et d'hétérozygotie dans l'assemblage final. Ce travail présente une série de méthodes et de logiciels pour obtenir des assemblages contenant des haplotypes bien séparés. Plus précisément, GenomeTailor et HairSplitter transforment un assemblage obtenu avec des lectures longues erronées en un assemblage phasé, améliorant considérablement l'état de l'art lorsque de nombreuses souches sont présentes. Le logiciel Alice propose une nouvelle méthode, basée sur des nouveaux sketchs ``MSR'', pour assembler efficacement plusieurs haplotypes séquencés avec des lectures de haute fidélité. Enfin, cette thèse propose une nouvelle stratégie de scaffolding Hi-C basée sur le démêlage des graphes d'assemblage qui améliore considérablement les assemblages finaux, en particulier lorsque plusieurs haplotypes sont présents
This thesis presents solutions to improve genome assembly from third-generation sequencing reads, with a specific focus on improving the assembly of (meta)genomes containing multiple haplotypes, such as polyploid genomes or close bacterial strains. Current assemblers struggle to separate highly similar haplotypes, often collapsing all or parts of the haplotypes into one, thereby discarding polymorphisms and heterozygosity. This work introduces a series of methods and software tools to achieve haplotype-separated assemblies. Specifically, GenomeTailor and HairSplitter transform a collapsed assembly obtained with erroneous long reads into a phased assembly, significantly improving on the state of the art when numerous strains are present. The software Alice introduces a new method based on the new ``MSR'' sketching technique for efficiently assembling multiple haplotypes sequenced with high-fidelity reads. Additionally, this thesis proposes a new Hi-C scaffolding strategy that involves untangling assembly graphs which significantly improves final assemblies, particularly when several haplotypes are present

The advent of Next Generation Sequencing (NGS), a little over a decade ago, has led to a vast and rapid increase in the generation of genomic data. The drastically reduced cost has in turn enabled powerful modifications that can be used to investigate not just genetic, but epigenetic, phenomena. Epigenetics refers to the study of mechanisms effecting gene expression other than the genetic code itself and thus, at the transcription level, incorporates DNA methylation, transcription factor binding and histone modifications amongst others. This thesis outlines and tackles two major challenges in the computational analysis of such data using techniques from machine learning. Firstly, I address the problem of testing for differential methylation between groups of bisulfite sequencing data sets. DNA methylation plays an important role in genomic imprinting, X-chromosome inactivation and the repression of repetitive elements, as well as being implicated in numerous diseases, such as cancer. Bisulfite sequencing provides single nucleotide resolution methylation data at the whole genome scale, but a sensitive analysis of such data is difficult. I propose a solution that uses a powerful kernel-based machine learning technique, the Maximum Mean Discrepancy, to leverage well-characterised spatial correlations in DNA methylation, and adapt the method for this particular use. I use this tailored method to analyse a novel data set from a study of ageing in three different tissues in the mouse. This study motivates further modifications to the method and highlights the utility of the underlying measure as an exploratory tool for methylation analysis. Secondly, I address the problem of predictive and explanatory modelling of chromatin immunoprecipitation sequencing data (ChIP-Seq). ChIP-Seq is typically used to assay the binding of a protein of interest, such as a transcription factor or histone, to the DNA, and as such is one of the most widely used sequencing assays. While peak callers are a powerful tool in identifying binding sites of sparse and clean ChIPSeq profiles, more broad signals defy analysis in this framework. Instead, generative models that explain the data in terms of the underlying sequence can help uncover mechanisms that predicting binding or the lack thereof. I explore current problems with ChIP-Seq analysis, such as zero-inflation and the use of the control experiment, known as the input. I then devise a method for representing k-mers that enables the use of longer DNA sub-sequences within a flexible model development framework, such as generalised linear models, without heavy programming requirements. Finally, I use these insights to develop an appropriate Bayesian generative model that predicts ChIP-Seq count data in terms of the underlying DNA sequence, incorporating DNA methylation information where available, fitting the model with the Expectation-Maximization algorithm. The model is tested on simulated data and real data pertaining to the histone mark H3k27me3. This thesis therefore straddles the fields of bioinformatics and machine learning. Bioinformatics is both plagued and blessed by the plethora of different techniques available for gathering data and their continual innovations. Each technique presents a unique challenge, and hence out-of-the-box machine learning techniques have had little success in solving biological problems. While I have focused on NGS data, the methods developed in this thesis are likely to be applicable to future technologies, such as Third Generation Sequencing methods, and the lessons learned in their adaptation will be informative for the next wave of computational challenges.

This diploma thesis deals with taxonomy independent methods for classification of metagenomic signals, aquired by a MinION sequencer. It describes the formation and character of metagenomic data and already existing methods of metagenomic data classification and their development. This thesis also evaluates an impact of the third generation sequencing techniques in the world of metagenomics and further specialises on the function of the Oxford Nanopore MinION sequencing device. Lastly, a custom method for metagenomic data classification, based on data obtained from a MinION sequencer, is proposed and compared with an already existing method of classification.

The present thesis is divided in two sections. The first section outlines the scientific work that I have accomplished during the last year of my graduate studies. The goal was to generate a reference genome for the European barn swallow (Hirundo rustica rustica). The barn swallow (Hirundo rustica) is a migratory bird that has been the focus of a large number of ecological, behavioural and genetic studies. To facilitate further population genetics and genomic studies, I have generated a high-quality genome for the European subspecies (Hirundo rustica rustica) using third-generation Single Molecule Real-Time (SMRT) DNA sequencing from Pacific Biosciences (Menlo Park, California, USA) and optical mapping from Bionano Genomics (San Diego, California, USA). For optical mapping, DNA molecules were labelled both with one of the original Nick, Label, Repair and Stain (NLRS) nickases (enzyme Nb.BssSI) and with the new Direct Label and Stain (DLS) approach (enzyme DLE-1). This allowed to compare and integrate optical maps derived both from NLRS and DLS technologies. The latter was officially released in February 2018 and avoids nicking and subsequent cleavage of DNA molecules upon staining. To my knowledge, this has been the first genome assembly to incorporate DLS data and this approach has more than doubled the assembly N50 with respect to the nickase system. Furthermore, the dual enzyme hybrid scaffold led to a marginal increase in scaffold N50 and an overall increase of confidence in scaffolds. After removal of haplotigs, the final assembly is approximately 1.21 Gbp in size, with a N50 value of over 25.95 Mbp. The high genome contiguity achieved represents an improvement over 650-fold with respect to a previously reported assembly based on paired-end short read data, and it is well in excess of those specified for “Platinum genomes” by the Vertebrate Genomes Project. It can therefore constitute a valuable resource for studies concerning the evolution of avian genomes in general as well as for population genetics and genomics in the barn swallow, with the potential for boosting research on the barn swallow biology and ecology at unprecedented speed. This scientific endeavour culminated in a publication that I authored entitled “SMRT long-read sequencing and Direct Label and Stain optical maps allow the generation of a high-quality genome assembly for the European barn swallow (Hirundo rustica rustica)” published in the peer-reviewed journal Gigascience (IF 7.5, 2016). The second section of this thesis presents the methodological work and the conclusions drawn from my - and other collaborators - work on the study of the evolutionary origins of Huntington’s Disease, a genetic neurodegenerative disorder. The study was conducted in the Laboratory of Stem Cell Biology and Pharmacology of Neurodegenerative Diseases directed by Prof. Elena Cattaneo at the University of Milan where I worked for the first two years of my PhD (and also during my Master Thesis work) and whose research effort is on the phylogenetic and biological investigation of HD causative gene. The goal that I wished to achieve with this study, as part of an on-going effort in the host laboratory aimed at tracing Huntington’s Disease-causing gene throughout evolution, was to reconstruct and understand the evolutionary origins of the CAG repeat embedded into the exon 1 of the Htt gene. This goal could be achieved by collecting DNA sequences from orthologous genes in order to allow a comparative analysis of the differences and similarities between the human sequence and that of other animal species. More specifically, existing sequences could be retrieved from public databases and/or assessed directly by sequencing from biological samples. These samples could be made available from already in place or newly established collaborations. Htt exon 1 sequences could then be aligned to each other in a multiple alignment, resulting in a detailed picture of Htt exon 1 CAG repeats along the tree of life. The multiple alignment, when subjected to a bioinformatics analysis of the selective pressures, could be used to elucidate the evolutionary features of this simple repeat. The study was made possible also thanks to a collaboration between Prof. Cattaneo and my Ph.D. thesis supervisor Prof. Nicola Saino. At the time of writing, a manuscript is in preparation reporting part of the data from this work together with other data obtained in the Cattaneo’s laboratory.

[EN] The work done for this doctorate thesis focuses on error correction of Next Generation Sequencing (NGS) data in the context of High Performance Computing (HPC). Due to the reduction in sequencing cost, the increasing output of the sequencers and the advancements in the biological and medical sciences, the amount of NGS data has increased tremendously. Humans alone are not able to keep pace with this explosion of information, therefore computers must assist them to ease the handle of the deluge of information generated by the sequencing machines. Since NGS is no longer just a research topic (used in clinical routine to detect cancer mutations, for instance), requirements in performance and accuracy are more stringent. For sequencing to be useful outside research, the analysis software must work accurately and fast. This is where HPC comes into play. NGS processing tools should leverage the full potential of multi-core and even distributed computing, as those platforms are extensively available. Moreover, as the performance of the individual core has hit a barrier, current computing tendencies focus on adding more cores and explicitly split the computation to take advantage of them. This thesis starts with a deep analysis of all these problems in a general and comprehensive way (to reach out to a very wide audience), in the form of an exhaustive and objective review of the NGS error correction field. We dedicate a chapter to this topic to introduce the reader gradually and gently into the world of sequencing. It presents real problems and applications of NGS that demonstrate the impact this technology has on science. The review results in the following conclusions: the need of understanding of the specificities of NGS data samples (given the high variety of technologies and features) and the need of flexible, efficient and accurate tools for error correction as a preliminary step of any NGS postprocessing. As a result of the explosion of NGS data, we introduce MuffinInfo. It is a piece of software capable of extracting information from the raw data produced by the sequencer to help the user understand the data. MuffinInfo uses HTML5, therefore it runs in almost any software and hardware environment. It supports custom statistics to mould itself to specific requirements. MuffinInfo can reload the results of a run which are stored in JSON format for easier integration with third party applications. Finally, our application uses threads to perform the calculations, to load the data from the disk and to handle the UI. In continuation to our research and as a result of the single core performance limitation, we leverage the power of multi-core computers to develop a new error correction tool. The error correction of the NGS data is normally the first step of any analysis targeting NGS. As we conclude from the review performed within the frame of this thesis, many projects in different real-life applications have opted for this step before further analysis. In this sense, we propose MuffinEC, a multi-technology (Illumina, Roche 454, Ion Torrent and PacBio -experimental), any-type-of-error handling (mismatches, deletions insertions and unknown values) corrector. It surpasses other similar software by providing higher accuracy (demonstrated by three type of tests) and using less computational resources. It follows a multi-steps approach that starts by grouping all the reads using a k-mers based metric. Next, it employs the powerful Smith-Waterman algorithm to refine the groups and generate Multiple Sequence Alignments (MSAs). These MSAs are corrected by taking each column and looking for the correct base, determined by a user-adjustable percentage. This manuscript is structured in chapters based on material that has been previously published in prestigious journals indexed by the Journal of Citation Reports (on outstanding positions) and relevant congresses.
[ES] El trabajo realizado en el marco de esta tesis doctoral se centra en la corrección de errores en datos provenientes de técnicas NGS utilizando técnicas de computación intensiva. Debido a la reducción de costes y el incremento en las prestaciones de los secuenciadores, la cantidad de datos disponibles en NGS se ha incrementado notablemente. La utilización de computadores en el análisis de estas muestras se hace imprescindible para poder dar respuesta a la avalancha de información generada por estas técnicas. El uso de NGS transciende la investigación con numerosos ejemplos de uso clínico y agronómico, por lo que aparecen nuevas necesidades en cuanto al tiempo de proceso y la fiabilidad de los resultados. Para maximizar su aplicabilidad clínica, las técnicas de proceso de datos de NGS deben acelerarse y producir datos más precisos. En este contexto es en el que las técnicas de comptuación intensiva juegan un papel relevante. En la actualidad, es común disponer de computadores con varios núcleos de proceso e incluso utilizar múltiples computadores mediante técnicas de computación paralela distribuida. Las tendencias actuales hacia arquitecturas con un mayor número de núcleos ponen de manifiesto que es ésta una aproximación relevante. Esta tesis comienza con un análisis de los problemas fundamentales del proceso de datos en NGS de forma general y adaptado para su comprensión por una amplia audiencia, a través de una exhaustiva revisión del estado del arte en la corrección de datos de NGS. Esta revisión introduce gradualmente al lector en las técnicas de secuenciación masiva, presentando problemas y aplicaciones reales de las técnicas de NGS, destacando el impacto de esta tecnología en ciencia. De este estudio se concluyen dos ideas principales: La necesidad de analizar de forma adecuada las características de los datos de NGS, atendiendo a la enorme variedad intrínseca que tienen las diferentes técnicas de NGS; y la necesidad de disponer de una herramienta versátil, eficiente y precisa para la corrección de errores. En el contexto del análisis de datos, la tesis presenta MuffinInfo. La herramienta MuffinInfo es una aplicación software implementada mediante HTML5. MuffinInfo obtiene información relevante de datos crudos de NGS para favorecer el entendimiento de sus características y la aplicación de técnicas de corrección de errores, soportando además la extensión mediante funciones que implementen estadísticos definidos por el usuario. MuffinInfo almacena los resultados del proceso en ficheros JSON. Al usar HTML5, MuffinInfo puede funcionar en casi cualquier entorno hardware y software. La herramienta está implementada aprovechando múltiples hilos de ejecución por la gestión del interfaz. La segunda conclusión del análisis del estado del arte nos lleva a la oportunidad de aplicar de forma extensiva técnicas de computación de altas prestaciones en la corrección de errores para desarrollar una herramienta que soporte múltiples tecnologías (Illumina, Roche 454, Ion Torrent y experimentalmente PacBio). La herramienta propuesta (MuffinEC), soporta diferentes tipos de errores (sustituciones, indels y valores desconocidos). MuffinEC supera los resultados obtenidos por las herramientas existentes en este ámbito. Ofrece una mejor tasa de corrección, en un tiempo muy inferior y utilizando menos recursos, lo que facilita además su aplicación en muestras de mayor tamaño en computadores convencionales. MuffinEC utiliza una aproximación basada en etapas multiples. Primero agrupa todas las secuencias utilizando la métrica de los k-mers. En segundo lugar realiza un refinamiento de los grupos mediante el alineamiento con Smith-Waterman, generando contigs. Estos contigs resultan de la corrección por columnas de atendiendo a la frecuencia individual de cada base. La tesis se estructura por capítulos cuya base ha sido previamente publicada en revistas indexadas en posiciones dest
[CAT] El treball realitzat en el marc d'aquesta tesi doctoral se centra en la correcció d'errors en dades provinents de tècniques de NGS utilitzant tècniques de computació intensiva. A causa de la reducció de costos i l'increment en les prestacions dels seqüenciadors, la quantitat de dades disponibles a NGS s'ha incrementat notablement. La utilització de computadors en l'anàlisi d'aquestes mostres es fa imprescindible per poder donar resposta a l'allau d'informació generada per aquestes tècniques. L'ús de NGS transcendeix la investigació amb nombrosos exemples d'ús clínic i agronòmic, per la qual cosa apareixen noves necessitats quant al temps de procés i la fiabilitat dels resultats. Per a maximitzar la seua aplicabilitat clínica, les tècniques de procés de dades de NGS han d'accelerar-se i produir dades més precises. En este context és en el que les tècniques de comptuación intensiva juguen un paper rellevant. En l'actualitat, és comú disposar de computadors amb diversos nuclis de procés i inclús utilitzar múltiples computadors per mitjà de tècniques de computació paral·lela distribuïda. Les tendències actuals cap a arquitectures amb un nombre més gran de nuclis posen de manifest que és esta una aproximació rellevant. Aquesta tesi comença amb una anàlisi dels problemes fonamentals del procés de dades en NGS de forma general i adaptat per a la seua comprensió per una àmplia audiència, a través d'una exhaustiva revisió de l'estat de l'art en la correcció de dades de NGS. Esta revisió introduïx gradualment al lector en les tècniques de seqüenciació massiva, presentant problemes i aplicacions reals de les tècniques de NGS, destacant l'impacte d'esta tecnologia en ciència. D'este estudi es conclouen dos idees principals: La necessitat d'analitzar de forma adequada les característiques de les dades de NGS, atenent a l'enorme varietat intrínseca que tenen les diferents tècniques de NGS; i la necessitat de disposar d'una ferramenta versàtil, eficient i precisa per a la correcció d'errors. En el context de l'anàlisi de dades, la tesi presenta MuffinInfo. La ferramenta MuffinInfo és una aplicació programari implementada per mitjà de HTML5. MuffinInfo obté informació rellevant de dades crues de NGS per a afavorir l'enteniment de les seues característiques i l'aplicació de tècniques de correcció d'errors, suportant a més l'extensió per mitjà de funcions que implementen estadístics definits per l'usuari. MuffinInfo emmagatzema els resultats del procés en fitxers JSON. A l'usar HTML5, MuffinInfo pot funcionar en gairebé qualsevol entorn maquinari i programari. La ferramenta està implementada aprofitant múltiples fils d'execució per la gestió de l'interfície. La segona conclusió de l'anàlisi de l'estat de l'art ens porta a l'oportunitat d'aplicar de forma extensiva tècniques de computació d'altes prestacions en la correcció d'errors per a desenrotllar una ferramenta que suport múltiples tecnologies (Illumina, Roche 454, Ió Torrent i experimentalment PacBio). La ferramenta proposada (MuffinEC), suporta diferents tipus d'errors (substitucions, indels i valors desconeguts). MuffinEC supera els resultats obtinguts per les ferramentes existents en este àmbit. Oferix una millor taxa de correcció, en un temps molt inferior i utilitzant menys recursos, la qual cosa facilita a més la seua aplicació en mostres més gran en computadors convencionals. MuffinEC utilitza una aproximació basada en etapes multiples. Primer agrupa totes les seqüències utilitzant la mètrica dels k-mers. En segon lloc realitza un refinament dels grups per mitjà de l'alineament amb Smith-Waterman, generant contigs. Estos contigs resulten de la correcció per columnes d'atenent a la freqüència individual de cada base. La tesi s'estructura per capítols la base de la qual ha sigut prèviament publicada en revistes indexades en posicions destacades de l'índex del Journal of Citation Repor
Alic, AS. (2016). Improved Error Correction of NGS Data [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/67630
TESIS

On reconnaît maintenant aux transcrits des implications multiples dans le fonctionnement des cellules eucaryotes. En plus de leur rôle originel de messagers assurant la liaison entre l'ADN et la synthèse protéique, l’usage de transcrits alternatifs apparaît comme un mode de contrôle post-transcriptionnel de l'expression génique. Exemplairement, plusieurs mécanismes distincts de régulation impliquant la production de transcrits matures retenant des introns (IRTs) ont été récemment décrits. Ces observations sont largement tributaires du développement de la seconde génération de séquençage haut-débit de l'ARN (RNA-seq). Cependant, ces données ne permettent pas d’identifier la structure complète des IRTs , dont le répertoire est encore très parcellaire. L’émergence d’une troisième génération de séquençage, à même de lire les transcrits dans leur intégralité, pourrait permettre d’y remédier. Bien que chaque technologie présente des inconvénients propres qui n'autorisent qu'une vision partielle et partiale du transcriptome, elles se complètent sur plusieurs points. Leur association, au moyen de méthodes dites hybrides, offre donc des perspectives intéressantes pour aborder l'étude des isoformes. L'objet de cette thèse est d'examiner ce que ces deux types de données peuvent, seuls ou combinés, apporter plus spécifiquement à l'étude des événements de rétention d'intron (IR). Un nombre croissant de travaux exploitent la profondeur et la largeur de couverture des données de seconde génération pour déceler et quantifier l'IR. Toutefois, il existe encore peu de méthodes informatiques dédiées à leur analyse et l’on fait souvent appel à des méthodes conçues pour d'autres usages comme l'étude de l'expression des gènes ou des exons. En tous les cas, leur capacité à apprécier correctement l'IR ne sont pas garanties. C'est la raison pour laquelle nous mettons en place un plan d'évaluation des méthodes de mesure des niveaux d’IR. Cette analyse révèle un certain nombre de biais, susceptibles de nuire à l'interprétation des résultats et nous amène à proposer une nouvelle méthode d’estimation. Au-delà de la vision centrée sur les variants, les données de longs reads Oxford Nanopore ont le potentiel de révéler la structure complète des IRTs, et ainsi, d’inférer un certain nombre de leurs caractéristiques. Cependant, leur taux d’erreur élevé et la troncation des séquences sont des obstacles incontournables. A large échelle, le traitement informatique de ces données nécessite l’introduction d’heuristiques, qui privilégient certaines formes de transcrits et, en général, occultent les formes rares ou inattendues. Il en résulte une perte importante d’information et de qualité d’interprétation. Pour la réduire, nous développons une méthode hybride de correction des séquences et proposons des stratégies ciblées pour reconstituer et caractériser les IRTs
In eucaryotic cells, the roles of RNA transcripts are known to be varied. Besides their role as messengers, transferring information from DNA to protein synthesis, the usage of alternative transcripts appears as a means to control gene expression in a post-transcriptional manner. Exemplary, the production of mature transcripts retaining introns (IRTs) was recently shown to take part in several distinct regulatory mechanisms. These observations benefited greatly from the development of the second generation of RNA-sequencing (RNA-seq). However, these data do not allow to identify the entire structure of IRTs, whose catalog is still fragmented. The emerging third generation of RNA-seq, apt to read RNA sequences in their full extent, could help achieve this goal. Despite their respective drawbacks and biases, both technologies are, to some extent, complementary. It is therefore appealing to try and combine them through so-called hybrid methods, so as to perform analyses at the isoform level. In the present thesis, we aim to investigate the potential of these two types of data, alone or in combination, in order to study intron retention (IR) events, more specifically. A growing number of studies harness the high coverage depths provided by second generation data to detect and quantify IR. However, there exist few dedicated computational methods, and many studies rely on methods designed for other purposes, such as gene or exon expression analysis. In any case, their ability to accurately measure IR has not been certified. For this reason, we set up a benchmark of the various IR quantification methods. Our study reveals several biases, prone to prejudice the interpretation of results and prompted us to suggest a novel method to estimate IR levels. Beyond event-centered analyses, Oxford Nanopore long read data have the capability to reveal the full-length structure of IRTs, and thereby to allow to infer some of their features. However, their high error rate and truncation events constitute inescapable impediments. Transcriptome-wide, the computational treatment of these data necessitates heuristics which will favor specific transcript forms, and, generally, overlook rare or unexpected ones. This results in a considerable loss of information and precludes meaningful interpretations. To address these issues, we develop a hybrid correction method and suggest specific strategies to recover and characterize IRTs

碩士
國立中正大學
資訊工程研究所
106
The 3rd generation sequencing can produce long reads with fast turnaround time yet also with high error rate. Consequently, errors on the sequencing reads are usually corrected before genome assembly. One of the strategies of error correction is alignment-based method, which requires time-consuming alignment among reads based on dynamic programming (DP). In this thesis, we implement a bit-parallelism algorithm to accelerate DP and compare with traditional banded DP speedup. In addition, the bit-parallelism algorithm is fine tuned for correcting errors specific in third-generation sequencing. The results showed that, though bit-parallelism DP is faster than banded DP, the accuracy is unexpectedly decreased. Further investigation indicated that bit-parallelism DP performs worse in tandem repeat regions, which requires specific algorithms for better accuracy.

碩士
國立中正大學
資訊工程研究所
106
Third-generation sequencing technologies are able to generate longer reads within shorter turnaround time, but they come at the cost of higher sequencing error rates. Therefore, prior to genome assembly, error correction is required to reduce the errors presented in the sequencing reads. The error correction and assembly software that we developed (called FILEC) has improved the speed and contiguity of a leading genome assembler called Canu; however, the assembly accuracy of FILEC is lower than that of Canu. In this thesis, we first investigated the regions FILEC tend to wrongly corrected, and observed that they are regions containing low-coverage repeats and tandem repeats. Subsequently, we develop new methods for identifying and for improving the correction algorithms specifically for these regions. The experimental results indicated that the accuracies can be slightly improved by improving the original alignment-free correction algorithm. But surprisingly, the accuracies can be greatly improved by the slower alignment-based correction using dynamic programming. Our results imply a good balance of alignment-free and alignment-based correction algorithms is crucial for improving both assembly speed and accuracy.

This book presents a comprehensive overview of the foundations of single-molecule studies, based on manipulation of the molecules and observation of these with fluorescent probes. It first discusses the forces present at the single-molecule scale, the methods to manipulate them, and their pros and cons. It goes on to present an introduction to single-molecule fluorescent studies based on a quantum description of absorption and emission of radiation due to Einstein. Various considerations in the study of single molecules are introduced (including signal to noise, non-radiative decay, triplet states, etc.) and some novel super-resolution methods are sketched. The elastic and dynamic properties of polymers, their relation to experiments on DNA and RNA, and the structural transitions observed in those molecules upon stretching, twisting, and unzipping are presented. The use of these single-molecule approaches for the investigation of DNA–protein interactions is highlighted via the study of DNA and RNA polymerases, helicases, and topoisomerases. Beyond the confirmation of expected mechanisms (e.g., the relaxation of DNA torsion by topoisomerases in quantized steps) and the discovery of unexpected ones (e.g., strand-switching by helicases, DNA scrunching by RNA polymerases, and chiral discrimination by bacterial topoII), these approaches have also fostered novel (third generation) sequencing technologies.

AbstractThanks to the revolutionary invention of the polymerase chain reaction and the sequencing of DNA and RNA by means of “Sanger sequencing” in the 1970th and 1980th, it became possible to detect microorganisms in art and cultural assets that do not grow on culture media or that are non-viable. The following generation of sequencing systems (next generation sequencing, NGS) already allowed the detection of microbial communities on objects without the intermediate step of cloning, but still most of the NGS technologies used for the study of microbial communities in objects of art rely on “target sequencing” linked to the selectivity of the primers used for amplification. Today, with the third generation of sequencing technology, whole genome and metagenome sequencing is possible, allowing the detection of taxonomic units of all domains and kingdoms as well as functional genes in the produced metagenome. Currently, Nanopore sequencing technology is a good, affordable, and simple way to characterize microbial communities, especially in the field of Heritage Science. It also has the advantage that a bioinformatic analysis can be performed automatically. In addition to genomics and metagenomics, other “-omics” techniques such as transcriptomics, proteomics, and metabolomics have a great potential for the study of processes in art and cultural heritage, but are still in their infancy as far as their application in this field is concerned.

Chickpea is the third most important pulse crop in the world and ranks first in the Middle East; however, it has been subjected to only limited research in modern genomics. In the first period of this project (US-3034-98R) we constructed two large-insert BAC and BIBAC libraries, developed 325 SSR markers and mapped QTLs controlling ascochyta blight resistance (ABR) and days to first flower (DTF). Nevertheless, the utilities of these tools and results in gene discovery and marker-assisted breeding are limited due to the absence of an essential platform. The goals of this period of the project were to use the resources and tools developed in the first period of the project to develop a BAC/BIBAC physical map for chickpea and using it to identify BAC/BIBACcontigs containing agronomic genes of interest, with an emphasis on ABR and DTF, and develop DNA markers suitable for marker-assisted breeding. Toward these goals, we proposed: 1) Fingerprint ~50,000 (10x) BACs from the BAC and BIBAC libraries, assemble the clones into a genome-wide BAC/BIBAC physical map, and integrate the BAC/BIBAC map with the existing chickpea genetic maps (Zhang, USA); 2) fine-map ABR and DTFQTLs and enhance molecular tools for chickpea genetics and breeding (Shahal, Sherman and DaniShtienberg, Israel; Chen and Muehlbauer; USA); and 3) integrate the BAC/BIBAC map with the existing chickpea genetic maps (Sherman, Israel; Zhang and Chen, USA). For these objectives, a total of $460,000 was requested originally, but a total of $300,000 was awarded to the project. We first developed two new BAC and BIBAC libraries, Chickpea-CME and Chickpea- CHV. The chickpea-CMEBAC library contains 22,272 clones, with an average insert size of 130 kb and equivalent to 4.0 fold of the chickpea genome. The chickpea-CHVBIBAC library contains 38,400 clones, with an average insert size of 140 kb and equivalent to 7.5 fold of the chickpea genome. The two new libraries (11.5 x), along with the two BAC (Chickpea-CHI) and BIBAC (Chickpea-CBV) libraries (7.1 x) constructed in the first period of the project, provide libraries essential for chickpea genome physical mapping and many other genomics researches. Using these four libraries we then developed the proposed BAC/BIBAC physical map of chickpea. A total of 67,584 clones were fingerprinted, and 64,211 (~11.6 x) of the fingerprints validated and used in the physical map assembly. The physical map consists of 1,945 BAC/BIBACcontigs, with each containing an average of 39.2 clones and having an average physical length of 559 kb. The contigs collectively span ~1,088 Mb, being 1.49 fold of the 740- Mb chickpea genome. Third, we integrated the physical map with the two existing chickpea genetic maps using a total of 172 (124 + 48) SSR markers. Fourth, we identified tightly linked markers for ABR-QTL1, increased marker density at ABR-QTL2 and studied the genetic basis of resistance to pod abortion, a major problem in the east Mediterranean, caused by heat stress. Finally, we, using the integrated map, isolated the BAC/BIBACcontigs containing or closely linked to QTL4.1, QTL4.2 and QTL8 for ABR and QTL8 for DTF. The integrated BAC/BIBAC map resulted from the project will provide a powerful platform and tools essential for many aspects of advanced genomics and genetics research of this crop and related species. These includes, but are not limited to, targeted development of SNP, InDel and SSR markers, high-resolution mapping of the chickpea genome and its agronomic genes and QTLs, sequencing and decoding of all genes of the genome using the next-generation sequencing technology, and comparative genome analysis of chickpea versus other legumes. The DNA markers and BAC/BIBACcontigs containing or closely linked to ABR and DTF provide essential tools to develop SSR and SNP markers well-suited for marker-assisted breeding of the traits and clone their corresponding genes. The development of the tools and knowledge will thus promote enhanced and substantial genetic improvement of the crop and related legumes.

Frozen, breaded, ready-to-cook chicken products have been implicated in outbreaks of salmonellosis. Some of these outbreaks can be large. For example, one outbreak of Salmonella Enteritidis involved 193 people in nine countries between 2018 and 2020, of which 122 cases were in the UK. These ready-to-cook products have a browned, cooked external appearance, which may be perceived as ready-to-eat, leading to mishandling or undercooking by consumers. Continuing concerns about these products led FSA to initiate a short-term (four month), cross-sectional surveillance study undertaken in 2021 to determine the prevalence of Salmonella spp., Escherichia coli and antimicrobial resistance (AMR) in frozen, breaded or battered chicken products on retail sale in the UK. This study sought to obtain data on AMR levels in Salmonella and E. coli in these products, in line with a number of other FSA instigated studies of the incidence and nature of AMR in the UK food chain, for example, the systematic review (2016). Between the beginning of April and the end of July 2021, 310 samples of frozen, breaded or battered chicken products containing either raw or partly cooked chicken, were collected using representative sampling of retailers in England, Wales, Scotland and Northern Ireland based on market share data. Samples included domestically produced and imported chicken products and were tested for E. coli (including extended-spectrum beta-lactamase (ESBL)-producing, colistin-resistant and carbapenem-resistant E. coli) and Salmonella spp. One isolate of each bacterial type from each contaminated sample was randomly selected for additional AMR testing to determine the minimum inhibitory concentration (MIC) for a range of antimicrobials. More detailed analysis based on Whole Genome Sequencing (WGS) data was used to further characterise Salmonella spp. isolates and allow the identification of potential links with human isolates. Salmonella spp. were detected in 5 (1.6%) of the 310 samples and identified as Salmonella Infantis (in three samples) and S. Java (in two samples). One of the S. Infantis isolates fell into the same genetic cluster as S. Infantis isolates from three recent human cases of infection; the second fell into another cluster containing two recent cases of infection. Countries of origin recorded on the packaging of the five Salmonella contaminated samples were Hungary (n=1), Ireland (n=2) and the UK (n=2). One S. Infantis isolate was multi-drug resistant (i.e. resistant to three different classes of antimicrobials), while the other Salmonella isolates were each resistant to at least one of the classes of antimicrobials tested. E. coli was detected in 113 samples (36.4%), with counts ranging from <3 to >1100 MPN (Most Probable Number)/g. Almost half of the E. coli isolates (44.5%) were susceptible to all antimicrobials tested. Multi-drug resistance was detected in 20.0% of E. coli isolates. E. coli isolates demonstrating the ESBL (but not AmpC) phenotype were detected in 15 of the 310 samples (4.8%) and the AmpC phenotype alone was detected in two of the 310 samples (0.6%) of chicken samples. Polymerase Chain Reaction (PCR) testing showed that five of the 15 (33.3%) ESBL-producing E. coli carried blaCTX-M genes (CTX-M-1, CTX-M-55 or CTX-M-15), which confer resistance to third generation cephalosporin antimicrobials. One E. coli isolate demonstrated resistance to colistin and was found to possess the mcr-1 gene. The five Salmonella-positive samples recovered from this study, and 20 similar Salmonella-positive samples from a previous UKHSA (2020/2021) study (which had been stored frozen), were subjected to the cooking procedures described on the sample product packaging for fan assisted ovens. No Salmonella were detected in any of these 25 samples after cooking. The current survey provides evidence of the presence of Salmonella in frozen, breaded and battered chicken products in the UK food chain, although at a considerably lower incidence than reported in an earlier (2020/2021) study carried out by PHE/UKHSA as part of an outbreak investigation where Salmonella prevalence was found to be 8.8%. The current survey also provides data on the prevalence of specified AMR bacteria found in the tested chicken products on retail sale in the UK. It will contribute to monitoring trends in AMR prevalence over time within the UK, support comparisons with data from other countries, and provide a baseline against which to monitor the impact of future interventions. While AMR activity was observed in some of the E. coli and Salmonella spp. examined in this study, the risk of acquiring AMR bacteria from consumption of these processed chicken products is low if the products are cooked thoroughly and handled hygienically.