Prediction of secondary structures for large RNA molecules

The prediction of correct secondary structures of large RNAs is one of the unsolved challenges of computational molecular biology. Among the major obstacles is the fact that accurate calculations scale as O(n⁴), so the computational requirements become prohibitive as the length increases. We present a new parallel multicore and scalable program called GTfold, which is one to two orders of magnitude faster than the de facto standard programs mfold and RNAfold for folding large RNA viral sequences and achieves comparable accuracy of prediction. We analyze the algorithm's concurrency and describe the parallelism for a shared memory environment such as a symmetric multiprocessor or multicore chip. We are seeing a paradigm shift to multicore chips and parallelism must be explicitly addressed to continue gaining performance with each new generation of systems. We provide a rigorous proof of correctness of an optimized algorithm for internal loop calculations called internal loop speedup algorithm (ILSA), which reduces the time complexity of internal loop computations from O(n⁴) to O(n³) and show that the exact algorithms such as ILSA are executed with our method in affordable amount of time. The proof gives insight into solving these kinds of combinatorial problems. We have documented detailed pseudocode of the algorithm for predicting minimum free energy secondary structures which provides a base to implement future algorithmic improvements and improved thermodynamic model in GTfold. GTfold is written in C/C++ and freely available as open source from our website.M.S.Committee Chair: Bader, David; Committee Co-Chair: Heitsch, Christine; Committee Member: Harvey, Stephen; Committee Member: Vuduc, Richar

A Survey of Parallel Data Mining

With the fast, continuous increase in the number and size of databases, parallel data mining is a natural and cost-effective approach to tackle the problem of scalability in data mining. Recently there has been a considerable research on parallel data mining. However, most projects focus on the parallelization of a single kind of data mining algorithm/paradigm. This paper surveys parallel data mining with a broader perspective. More precisely, we discuss the parallelization of data mining algorithms of four knowledge discovery paradigms, namely rule induction, instance-based learning, genetic algorithms and neural networks. Using the lessons learned from this discussion, we also derive a set of heuristic principles for designing efficient parallel data mining algorithms

The International Virus Bioinformatics Meeting 2023

The 2023 International Virus Bioinformatics Meeting was held in Valencia, Spain, from 24–26 May 2023, attracting approximately 180 participants worldwide. The primary objective of the conference was to establish a dynamic scientific environment conducive to discussion, collaboration, and the generation of novel research ideas. As the first in-person event following the SARS-CoV-2 pandemic, the meeting facilitated highly interactive exchanges among attendees. It served as a pivotal gathering for gaining insights into the current status of virus bioinformatics research and engaging with leading researchers and emerging scientists. The event comprised eight invited talks, 19 contributed talks, and 74 poster presentations across eleven sessions spanning three days. Topics covered included machine learning, bacteriophages, virus discovery, virus classification, virus visualization, viral infection, viromics, molecular epidemiology, phylodynamic analysis, RNA viruses, viral sequence analysis, viral surveillance, and metagenomics. This report provides rewritten abstracts of the presentations, a summary of the key research findings, and highlights shared during the meeting

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

[Abstract] Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028

Algorithms for Analysis of Heterogeneous Cancer and Viral Populations Using High-Throughput Sequencing Data

Next-generation sequencing (NGS) technologies experienced giant leaps in recent years. Short read samples reach millions of reads, and the number of samples has been growing enormously in the wake of the COVID-19 pandemic. This data can expose essential aspects of disease transmission and development and reveal the key to its treatment. At the same time, single-cell sequencing saw the progress of getting from dozens to tens of thousands of cells per sample. These technological advances bring new challenges for computational biology and require the development of scalable, robust methods to deal with a wide range of problems varying from epidemiology to cancer studies. The first part of this work is focused on processing virus NGS data. It proposes algorithms that can facilitate the initial data analysis steps by filtering genetically related sequencing and the tool investigating intra-host virus diversity vital for biomedical research and epidemiology. The second part addresses single-cell data in cancer studies. It develops evolutionary cancer models involving new quantitative parameters of cancer subclones to understand the underlying processes of cancer development better

Development of efficient De Bruijn graph-based algorithms for genome assembly

Programa Oficial de Doutoramento en Computación. 5009V01[Abstract] During the last two decades, thanks to the development of new sequencing techniques, the study of the genome has become very popular in order to discover the genetic variation present in both humans and other organisms. The predominant mode of genome analysis is through the assembly of reads in one or multiple chains for as long as possible. The most traditional way of assembly is the one that involves reads from a single genome. In this field, in the last decade, third-generation readings have emerged with new challenges for which there are no efficient solutions. The first contribution that has been made in this thesis is Compact-Flye, a tool for the efficient assembly of third-generation reads on the Flye algorithm. This tool is based on the ingenious use of compact data structures to improve typical assembly steps such as counting and indexing k-mers. Apart from the assembly of a genome, there are techniques that seek to assemble all the genomes contained in a given sample. This assembly is known as multiple sequence assembly or haplotype reconstruction, a subject also treated in this thesis. Our first approach to solving this has been viaDBG, which is the first solution based on de Bruijn graphs that offers results comparable to current techniques in viral genome assembly while maintaining the efficiency of these graphs. Our second contribution is ViQUF, which is a natural improvement on its predecessor. ViQUF completely changes the algorithm of viaDBG but continues to be based on the same structures, although with some variations that allow it not only to improve results in terms of time and quality, but also to provide additionalinformation such as an estimate of the relative presence of each species in the sample.[Resumen] Durante las últimas dos décadas, gracias al desarrollo de nuevas técnias secuenciación, el estudio del genoma ha ganado mucha popularidad de cara a conocer la variación genética presente tanto seres humanos como otros organismos. El modo predominante de análisis del genoma es a través del ensamblaje de lecturas en una o múltiples cadenas lo más largas posibles. La manera más tradicional de ensamblaje es el que implica lecturas provenientes de un solo genoma. En este campo, en la última década han surgido las lecturas de tercera generación con nuevos retos para los que no existen soluciones eficientes. La primera aportación que se ha realizado en esta tesis es Compact-Flye una herramienta para el ensamblaje eficiente de lecturas de tercera generación sobre el algoritmo Flye. Esta herramienta está basada en el uso igenioso de estructuras compactas de datos para mejorar etapas típicas del ensamblaje como el conteo e indexación de k-mers. Al margen del ensamblaje de un genoma existen técnicas que buscan ensamblar todos los genomas contenidos en una muestra determinada. Este ensamblaje es conocido como ensamblaje múltiple de secuencias o reconstrucción de haplotipos, tema también tratado en esta tesis. Nuestra primera aproximación para la resolución de este ha sido viaDBG, que es la primera solución basada en grafos de de Bruijn que ofrece resultados comparables a las técnicas vigentes en ensamblaje de genomas víricos, mientras que mantiene la eficiencia de estos grafos. Nuestra segunda aportación es ViQUF, que es una mejora natural de su predecesor. ViQUF cambia totalmente la algoritmia de viaDBG, pero sigue cimentándose en las mismas estructuras aunque con alguna variación que le permite no solo mejorar resultados en tiempo y calidad. Sino que además le permite aportar más información como estimaciones relativa de cada especie en la muestra.[Resumo] Durante as dúas últimas décadas, grazas ao desenvolvemento de novas técnicas de secuenciación, o estudo do xenoma fíxose moi popular para descubrir a variación xenética presente tanto nos humanos como noutros organismos. O modo predominante de análise do xenoma é a través da ensamblaxe de lecturas nunha ou varias cadeas o maior tempo posible. A forma máis tradicional de ensamblar é a que implica lecturas dun só xenoma. Neste campo, na última década xurdiron lecturas de terceira xeración con novos retos para os que non existen solucións eficientes. A primeira contribución que se fixo nesta tese é Compact-Flye, unha ferramenta para a montaxe eficiente de lecturas de terceira xeración sobre o algoritmo Flye. Esta ferramenta baséase no uso intelixente de estruturas de datos compactas para mellorar os pasos típicos de montaxe, como contar e indexar k-mers. Ademais da montaxe dun xenoma, existen técnicas que buscan ensamblar todos os xenomas contidos nunha determinada mostra. Este conxunto coñécese como conxunto de secuencias múltiples ou reconstrución de haplotipos, tema tamén tratado nesta tesis. O noso primeiro enfoque para resolver isto foi viaDBG, que é a primeira solución baseada en gráficos de Bruijn que ofrece resultados comparables ás técnicas actuais de ensamblaxe de xenoma viral, mantendo a eficiencia destes gráficos. A nosa segunda incorporación é ViQUF, que é unha mellora natural con respecto ao seu predecesor. ViQUF cambia completamente o algoritmo de viaDBG pero segue baseándose nas mesmas estruturas, aínda que con algunha variación que lle permite non só mellorar os resultados en tempo e calidade. Pero tamén permite achegar máis información como estimacións relativas de cada especie da mostra.Xunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C 2021/53Xunta de Galicia; IG240.2020.1.185Xunta de Galicia; IN852A 2018/14Quiero agradecer al Centro de Investigación de Galicia “CITIC”, financiado por la Xunta de Galicia y la Unión Europea (European Regional Development Fund- Galicia 2014-2020 Program), con la beca ED431G 2019/01. También agradecer a la Xunta de Galicia/FEDER-UE que ha financiado esta tesis a través de las becas [ED431C 2021/53; IG240.2020.1.185; IN852A 2018/14]; al Ministerio de Ciencia e Innovación con las becas [TIN2016- 78011-C4-1-R; FPU17/02742; PID2019-105221RB-C41; PID2020-114635RB-I00]; y a la academia de Finlandia [grants 308030 and 323233 (LS)]

Distributed computational intelligence applied in bioinformatics

Master'sMASTER OF ENGINEERIN

ViennaRNA Package 2.0

<p>Abstract</p> <p>Background</p> <p>Secondary structure forms an important intermediate level of description of nucleic acids that encapsulates the dominating part of the folding energy, is often well conserved in evolution, and is routinely used as a basis to explain experimental findings. Based on carefully measured thermodynamic parameters, exact dynamic programming algorithms can be used to compute ground states, base pairing probabilities, as well as thermodynamic properties.</p> <p>Results</p> <p>The <monospace>ViennaRNA</monospace> Package has been a widely used compilation of RNA secondary structure related computer programs for nearly two decades. Major changes in the structure of the standard energy model, the <it>Turner 2004 </it>parameters, the pervasive use of multi-core CPUs, and an increasing number of algorithmic variants prompted a major technical overhaul of both the underlying <monospace>RNAlib</monospace> and the interactive user programs. New features include an expanded repertoire of tools to assess RNA-RNA interactions and restricted ensembles of structures, additional output information such as <it>centroid </it>structures and <it>maximum expected accuracy </it>structures derived from base pairing probabilities, or <it>z</it>-<it>scores </it>for locally stable secondary structures, and support for input in <monospace>fasta</monospace> format. Updates were implemented without compromising the computational efficiency of the core algorithms and ensuring compatibility with earlier versions.</p> <p>Conclusions</p> <p>The <monospace>ViennaRNA Package 2.0</monospace>, supporting concurrent computations <monospace>via OpenMP</monospace>, can be downloaded from <url>http://www.tbi.univie.ac.at/RNA</url>.</p