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This work is a showcase for the integration of systems biology and bioinformatics tools, algorithms and models for deciphering biological phenomena. More specifically, it integrates (i) prediction algorithms for identifying and characterizing molecular interactions, (ii) structural modelling of molecule complexes, (iii) network analysis approaches, and (iv) mathematical modelling and simulation. Two comprehensive workflows are implemented for the analysis of collective target gene regulation by microRNAs and for the prediction of cooperating microRNA pairs and their mutual target genes. In two case studies mechanisms of fine-tuned target gene regulation are revealed for different cellular processes and the phenomenon of cooperative target regulation is identified as frequent mechanism of gene regulation in humans.
This edited reflects the current state of knowledge about the role of microRNAs in the formation and progression of solid tumours. The main focus lies on computational methods and applications, together with cutting edge experimental techniques that are used to approach all aspects of microRNA regulation in cancer. We are sure that the emergence of high-throughput quantitative techniques will make this integrative approach absolutely necessary in the near future. This book will be a resource for researchers starting out with cancer microRNA research, but is also intended for the experienced researcher who wants to incorporate concepts and tools from systems biology and bioinformatics into his work. Bioinformaticians and modellers are provided with a general perspective on microRNA biology in cancer, and the state-of-the-art in computational microRNA biology.
This detailed volume provides a collection of protocols for the study of miRNA functions in plants. Beginning with coverage of miRNA function, biogenesis, activity, and evolution in plants, the book continues by guiding readers through methods on the identification and detection of plant miRNAs, bioinformatic analyses, and strategies for functional analyses of miRNAs. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Plant MicroRNAs: Method and Protocols aims to ensure successful results in the further study of this vital area of plant science.
MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression and play an essential role in phenotype development. The regulation mechanism behind miRNA reveals insight into gene expression and gene regulation. Transcription Start Site(TSS) is the key to studying gene expression. However, the TSSs of miRNAs can be thousands of nucleotides away from the precursor miRNAs, which makes it hard to be detected by conventional RNA-Seq experiments. Some previous methods tried to take advantage of sequencing data using sequence features or integrated epigenetic markers, but resulted in either not condition-specific or low-resolution prediction. Furthermore, the availability of a large amount of Single-Cell RNA-Seq(scRNA-Seq) data provides remarkable opportunities for studying gene regulatory mechanisms at single-cell resolution. Incorporating the gene regulatory mechanisms can assist with cell type identification and state discovery from scRNA-Seq data. In this dissertation, we studied computational modeling of gene transcription initialization and expression, including two novel approaches to identify TSSs with various type of conditions and one case study at the single-cell level. Firstly, we studied how TSS can be identified based on Cap Analysis Gene Expression (CAGE) experiments data using the thriving Deep Learning Neural Network. We used a control model to study the Deepbind binding score features that the protein binding motif model can improve overall prediction performance. Furthermore, comparing data from unseen cell lines showed better performance than existing tools. Secondly, to better predict the TSSs of miRNA in a condition-specific manner, we built D-miRT, a two-steam convolutional neural network based on integrated low-resolution epigenetic features and high-resolution sequence features. D-miRT outperformed all baseline models and demonstrated high accuracy for miRNA TSS prediction tasks. Compared with the most recent approaches on cell-specific miRNA TSS identification using cell lines that were unseen to the model training processes, D-miRT also showed superior performance. Thirdly, to study gene transcription initialization and regulation from single-cell perspective, we developed INSISTC, an unsupervised machine learning-based approach that incorporated network structure information for single-cell type classification. In contrast to other clustering algorithms, we showed that INSISTC with the SC3 algorithm provides cluster number estimation. Future studies on gene expression and regulation will benefit from INSISTC's adaptability with regard to the kinds of biological networks that can be used.
One of the fundamental questions in molecular biology is how genes and the control of their expression give rise to so many diverse phenotypes in nature. The mRNA molecule plays a key role in this process as it directs the spatial and temporal expression of genetic information contained in the DNA molecule to precisely instruct biological processes in living organisms. The region located between the STOP codon and the poly(A)-tail of the mature mRNA, known as the 3Untranslated Region (3UTR), is a key modulator of these activities. It contains numerous sequence elements that are targeted by trans-acting factors that dose gene expression, including the repressive small non-coding RNAs, called microRNAs. Recent transcriptome data from yeast, worm, plants, and humans has shown that alternative polyadenylation (APA), a mechanism that enables expression of multiple 3UTR isoforms for the same gene, is widespread in eukaryotic organisms. It is still poorly understood why metazoans require multiple 3UTRs for the same gene, but accumulating evidence suggests that APA is largely regulated at a tissue-specific level. APA may direct combinatorial variation between cis-elements and microRNAs, perhaps to regulate gene expression in a tissue-specific manner. Apart from a few single gene anecdotes, this idea has not been systematically explored. This dissertation research employs a systems biology approach to study the somatic tissue dynamics of APA and its impact on microRNA targeting networks in the small nematode C. elegans. In the first aim, tools were developed and applied to isolate and sequence mRNA from worm intestine and muscle tissues, which revealed pervasive tissue-specific APA correlated with microRNA regulation. The second aim provides genetic evidence that two worm genes use APA to escape repression by microRNAs in the body muscle. Finally, in aim three, mRNA from five additional somatic worm tissues was sequenced and their 3ends mapped, allowing for an integrative study of APA and microRNA targeting dynamics in worms. Together, this work provides evidence that APA is a pervasive mechanism operating in somatic tissues of C. elegans with the potential to significantly rearrange their microRNA regulatory networks and precisely dose their gene expression.
The enormous complexity of biological systems at the molecular level must be answered with powerful computational methods. Computational biology is a young field, but has seen rapid growth and advancement over the past few decades. Surveying the progress made in this multidisciplinary field, the Handbook of Computational Molecular Biology of
MicroRNAs (miRNAs) are single-stranded, small, usually non-coding RNAs of about 22 nucleotides in length, that control gene expression at the posttranscriptional level through translational inhibition, degradation, adenylation, or destabilization of their target mRNAs. Although hundreds of miRNAs have been identified in various species, many more may still remain unknown. Therefore, the discovery of new miRNA genes is an important step for understanding miRNA mediated post transcriptional regulation mechanisms. First attempts for the identification of novel miRNA genes were almost exclusively based on directional cloning of endogenous small RNAs and high-throughput sequencing of large numbers of cDNA clones. However, conventional forward genetic screening is known to be biased towards abundantly and/or ubiquitously expressed miRNAs that can dominate the cloned products. Hence, such biological approaches might be limited in their ability to detect rare miRNAs, and restricted to the tissues and the developmental stage of the organism under examination. These limitations have led to the development of sophisticated computational approaches attempting to identify possible miRNAs in silico. Nevertheless, the programs designed to predict possible miRNAs in a genome are not sensitive or accurate enough to warrant sufficient confidence for validating all their predictions experimentally. With this study, we aim to solve these problems by developing a new and sensitive machine learning based approach to predict potential miRNAs in the human genome.
In Clinical Bioinformatics, Second Edition, leading experts in the field provide a series of articles focusing on software applications used to translate information into outcomes of clinical relevance. Recent developments in omics, such as increasingly sophisticated analytic platforms allowing changes in diagnostic strategies from the traditional focus on single or small number of analytes to what might be possible when large numbers or all analytes are measured, are now impacting patient care. Covering such topics as gene discovery, gene function (microarrays), DNA sequencing, online approaches and resources, and informatics in clinical practice, this volume concisely yet thoroughly explores this cutting-edge subject. Written in the successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible protocols, and notes on troubleshooting and avoiding known pitfalls. Authoritative and easily accessible, Clinical Bioinformatics, Second Edition serves as an ideal guide for scientists and health professionals working in genetics and genomics.
"Applied Computational Genomics" focuses on an in-depth review of statistical development and application in the area of human genomics including candidate gene mapping, linkage analysis, population-based, genome-wide association, exon sequencing and whole genome sequencing analysis. The authors are extremely experienced in the area of statistical genomics and will give a detailed introduction of the evolution in the field and critical evaluations of the advantages and disadvantages of the statistical models proposed. They will also share their views on a future shift toward translational biology. The book will be of value to human geneticists, medical doctors, health educators, policy makers, and graduate students majoring in biology, biostatistics, and bioinformatics. Dr. Yin Yao Shugart is investigator in the Intramural Research Program at the National Institute of Mental Health, Bethesda, Maryland USA. ​
Introduces readers to the state of the art of omics platforms and all aspects of omics approaches for clinical applications This book presents different high throughput omics platforms used to analyze tissue, plasma, and urine. The reader is introduced to state of the art analytical approaches (sample preparation and instrumentation) related to proteomics, peptidomics, transcriptomics, and metabolomics. In addition, the book highlights innovative approaches using bioinformatics, urine miRNAs, and MALDI tissue imaging in the context of clinical applications. Particular emphasis is put on integration of data generated from these different platforms in order to uncover the molecular landscape of diseases. The relevance of each approach to the clinical setting is explained and future applications for patient monitoring or treatment are discussed. Integration of omics Approaches and Systems Biology for Clinical Applications presents an overview of state of the art omics techniques. These methods are employed in order to obtain the comprehensive molecular profile of biological specimens. In addition, computational tools are used for organizing and integrating these multi-source data towards developing molecular models that reflect the pathophysiology of diseases. Investigation of chronic kidney disease (CKD) and bladder cancer are used as test cases. These represent multi-factorial, highly heterogeneous diseases, and are among the most significant health issues in developed countries with a rapidly aging population. The book presents novel insights on CKD and bladder cancer obtained by omics data integration as an example of the application of systems biology in the clinical setting. Describes a range of state of the art omics analytical platforms Covers all aspects of the systems biology approach—from sample preparation to data integration and bioinformatics analysis Contains specific examples of omics methods applied in the investigation of human diseases (Chronic Kidney Disease, Bladder Cancer) Integration of omics Approaches and Systems Biology for Clinical Applications will appeal to a wide spectrum of scientists including biologists, biotechnologists, biochemists, biophysicists, and bioinformaticians working on the different molecular platforms. It is also an excellent text for students interested in these fields.