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

Knowledge Management Approaches for predicting Biomarker and Assessing its Impact on Clinical Trials

The recent success of companion diagnostics along with the increasing regulatory pressure for better identification of the target population has created an unprecedented incentive for the drug discovery companies to invest into novel strategies for stratified biomarker discovery. Catching with this trend, trials with stratified biomarker in drug development have quadrupled in the last decade but represent a small part of all Interventional trials reflecting multiple co-developmental challenges of therapeutic compounds and companion diagnostics. To overcome the challenge, varied knowledge management and system biology approaches are adopted in the clinics to analyze/interpret an ever increasing collection of OMICS data. By semi-automatic screening of more than 150,000 trials, we filtered trials with stratified biomarker to analyse their therapeutic focus, major drivers and elucidated the impact of stratified biomarker programs on trial duration and completion. The analysis clearly shows that cancer is the major focus for trials with stratified biomarker. But targeted therapies in cancer require more accurate stratification of patient population. This can be augmented by a fresh approach of selecting a new class of biomolecules i.e. miRNA as candidate stratification biomarker. miRNA plays an important role in tumorgenesis in regulating expression of oncogenes and tumor suppressors; thus affecting cell proliferation, differentiation, apoptosis, invasion, angiogenesis. miRNAs are potential biomarkers in different cancer. However, the relationship between response of cancer patients towards targeted therapy and resulting modifications of the miRNA transcriptome in pathway regulation is poorly understood. With ever-increasing pathways and miRNA-mRNA interaction databases, freely available mRNA and miRNA expression data in multiple cancer therapy have created an unprecedented opportunity to decipher the role of miRNAs in early prediction of therapeutic efficacy in diseases. We present a novel SMARTmiR algorithm to predict the role of miRNA as therapeutic biomarker for an anti-EGFR monoclonal antibody i.e. cetuximab treatment in colorectal cancer. The application of an optimised and fully automated version of the algorithm has the potential to be used as clinical decision support tool. Moreover this research will also provide a comprehensive and valuable knowledge map demonstrating functional bimolecular interactions in colorectal cancer to scientific community. This research also detected seven miRNA i.e. hsa-miR-145, has-miR-27a, has- miR-155, hsa-miR-182, hsa-miR-15a, hsa-miR-96 and hsa-miR-106a as top stratified biomarker candidate for cetuximab therapy in CRC which were not reported previously. Finally a prospective plan on future scenario of biomarker research in cancer drug development has been drawn focusing to reduce the risk of most expensive phase III drug failures

Novel computational approaches to research longitudinal microRNA-mRNA expression datasets

Ph. D. Thesis.microRNAs (miRNAs) regulate many biological processes and are used as biomarkers for the classification of diseases, conditions and developmental stages. miRNAs function by targeting and negatively regulating specific mRNAs. One limitation of utilising miRNAs in experimental work is the complex and often redundant behaviour of miRNA-mRNA interactions; as a single miRNA can regulate many mRNAs and one mRNA can be regulated by multiple miRNAs. This complexity stifles the potential of miRNAs. However, miRNAmRNA expression datasets are becoming generated more frequently and they can help to garner greater understanding of how miRNAs regulate biological systems. Furthermore, researchers are generating longitudinal datasets as these can elude to greater understanding of how biological conditions change over time. Thus there is a rise of longitudinal miRNA-mRNA expression datasets. However, extracting useful information from increasingly sophisticated datasets is a challenge in biological research. Exploration of such datasets using computational techniques, such as big data bioinformatics, kinetic modelling and machine learning could help in identifying interesting miRNA-mRNA interactions. During this PhD I asked if these methodologies can be used to gain insights from a range of longitudinal miRNA-mRNA expression datasets. Hence, I developed an R/Bioconductor tool called TimiRGeN to integrate, analyse and generate small networks from longitudinal miRNA-mRNA datasets. Datasets from kidney fibrosis, chondrogenesis dataset, breast cancer and Huntington’s disease (HD) were analysed with TimiRGeN. Results from the chondrogenesis dataset analysis were taken forward to generate a multimiRNA kinetic model. With help from my collaborators this model was validated and predictions were made. Using the HD dataset, machine learning (ML) techniques trained models to detect if samples have disease or wild type conditions. Overall, I have developed and used multiple computational techniques to increase knowledge gained from longitudinal miRNA-mRNA datasets, and I believe the results show these techniques can contribute to miRNA research

Development of a text mining approach to disease network discovery

Scientific literature is one of the major sources of knowledge for systems biology, in the form of papers, patents and other types of written reports. Text mining methods aim at automatically extracting relevant information from the literature. The hypothesis of this thesis was that biological systems could be elucidated by the development of text mining solutions that can automatically extract relevant information from documents. The first objective consisted in developing software components to recognize biomedical entities in text, which is the first step to generate a network about a biological system. To this end, a machine learning solution was developed, which can be trained for specific biological entities using an annotated dataset, obtaining high-quality results. Additionally, a rule-based solution was developed, which can be easily adapted to various types of entities. The second objective consisted in developing an automatic approach to link the recognized entities to a reference knowledge base. A solution based on the PageRank algorithm was developed in order to match the entities to the concepts that most contribute to the overall coherence. The third objective consisted in automatically extracting relations between entities, to generate knowledge graphs about biological systems. Due to the lack of annotated datasets available for this task, distant supervision was employed to train a relation classifier on a corpus of documents and a knowledge base. The applicability of this approach was demonstrated in two case studies: microRNAgene relations for cystic fibrosis, obtaining a network of 27 relations using the abstracts of 51 recently published papers; and cell-cytokine relations for tolerogenic cell therapies, obtaining a network of 647 relations from 3264 abstracts. Through a manual evaluation, the information contained in these networks was determined to be relevant. Additionally, a solution combining deep learning techniques with ontology information was developed, to take advantage of the domain knowledge provided by ontologies. This thesis contributed with several solutions that demonstrate the usefulness of text mining methods to systems biology by extracting domain-specific information from the literature. These solutions make it easier to integrate various areas of research, leading to a better understanding of biological systems

Theoretical and computational modeling of rna-ligand interactions

Ribonucleic acid (RNA) is a polymeric nucleic acid that plays a variety of critical roles in gene expression and regulation at the level of transcription and translation. Recently, there has been an enormous interest in the development of therapeutic strategies that target RNA molecules. Instead of modifying the product of gene expression, i.e., proteins, RNAtargeted therapeutics aims to modulate the relevant key RNA elements in the disease-related cellular pathways. Such approaches have two significant advantages. First, diseases with related proteins that are difficult or unable to be drugged become druggable by targeting the corresponding messenger RNAs (mRNAs) that encode the amino acid sequences. Second, besides coding mRNAs, the vast majority of the human genome sequences are transcribed to noncoding RNAs (ncRNAs), which serve as enzymatic, structural, and regulatory elements in cellular pathways of most human diseases. Targeting noncoding RNAs would open up remarkable new opportunities for disease treatment. The first step in modeling the RNA-drug interaction is to understand the 3D structure of the given RNA target. With current theoretical models, accurate prediction of 3D structures for large RNAs from sequence remains computationally infeasible. One of the major challenges comes from the flexibility in the RNA molecule, especially in loop/junction regions, and the resulting rugged energy landscape. However, structure probing techniques, such as the “selective 20-hydroxyl acylation analyzed by primer extension” (SHAPE) experiment, enable the quantitative detection of the relative flexibility and hence structure information of RNA structural elements. Therefore, one may incorporate the SHAPE data into RNA 3D structure prediction. In the first project, we investigate the feasibility of using a machine-learning-based approach to predict the SHAPE reactivity from the 3D RNA structure and compare the machine-learning result to that of a physics-based model. In the second project, in order to provide a user-friendly tool for RNA biologists, we developed a fully automated web interface, “SHAPE predictoR” (SHAPER) for predicting SHAPE profile from any given 3D RNA structure. In a cellular environment, various factors, such as metal ions and small molecules, interact with an RNA molecule to modulate RNA cellular activity. RNA is a highly charged polymer with each backbone phosphate group carrying one unit of negative (electronic) charge. In order to fold into a compact functional tertiary structure, it requires metal ions to reduce Coulombic repulsive electrostatic forces by neutralizing the backbone charges. In particular, Mg2+ ion is essential for the folding and stability of RNA tertiary structures. In the third project, we introduce a machine-learning-based model, the “Magnesium convolutional neural network” (MgNet) model, to predict Mg2+ binding site for a given 3D RNA structure, and show the use of the model in investigating the important coordinating RNA atoms and identifying novel Mg2+ binding motifs. Besides Mg2+ ions, small molecules, such as drug molecules, can also bind to an RNA to modulate its activities. Motivated by the tremendous potential of RNA-targeted drug discovery, in the fourth project, we develop a novel approach to predicting RNA-small molecule binding. Specifically, we develop a statistical potential-based scoring/ranking method (SPRank) to identify the native binding mode of the small molecule from a pool of decoys and estimate the binding affinity for the given RNA-small molecule complex. The results tested on a widely used data set suggest that SPRank can achieve (moderately) better performance than the current state-of-art models

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

The evolution of the mammal placenta — a computational approach to the identification and analysis of placenta-specific genes and microRNAs.

The presence of a placenta is an important synapomorphy that defines the mammal clade. From the fossil record we know that the first placental mammal lived approximately 125 million years ago, with the chorioallantoic placenta evolving not long after. In this thesis a set of 22 complete genomes from Eutherian, non-Eutherian and outgroup species are compared, the aim being to identify protein-coding and regulatory alterations that are likely to be implicated in the emergence of mammal placenta in the fossil record. To this end we have examined the roles played by positive selection and miRNA regulation in the evolution of the placenta. We have identified those genes that underwent functional shift uniquely in the ancestral placental mammal lineage and that are also heavily implicated in disorders of the placenta. Carrying out a thorough analysis of non-coding regions of the 22 genomes included in the study we identified a cohort of miRNAs that exist only in placental mammals. Many of the placenta related genes described above have multiple predicted “placenta-specific” miRNA binding sites. Together these results indicate a role for both adaptation in protein-coding regions and emergence of novel noncoding regulators in the origin and evolution of mammal placentation

The 1st International Electronic Conference on Chemical Sensors and Analytical Chemistry

The 1st International Electronic Conference on Chemical Sensors and Analytical Chemistry was held on 1–15 July 2021. The scope of this online conference was to gather experts that are well-known worldwide who are currently working in chemical sensor technologies and to provide an online forum for the presention and discussion of new results. Throughout this event, topics of interest included, but were not limited to, the following: electrochemical devices and sensors; optical chemical sensors; mass-sensitive sensors; materials for chemical sensing; nano- and micro-technologies for sensing; chemical assays and validation; chemical sensor applications; analytical methods; gas sensors and apparatuses; electronic noses; electronic tongues; microfluidic devices; lab-on-a-chip; single-molecule sensing; nanosensors; and medico-diagnostic testing