Emerging Web Tools and Their Applications in Bioinformatics

Bioinformatics is an emerging area of interest for many researchers and scientists. It has unlimited applications in many areas. The most important application of this is to know about genes, et cetera. But nowadays, research has also started in the emerging areas of network security and threats using bioinformatics. In the present scenario, we are highly dependent on Internet. The Web has invited different people from different backgrounds to work together sitting at far places. And to fulfill the needs of the interested and involved people, lots of Web based tools have been developed, and many others are being developed. In this chapter, the area of bioinformatics has been introduced along with its applications, Web, developed Web based tools, and a case study of one such tool.

Data Analysis and Interpretation in Metabolomics

Metabolomics represents the new ‘omics’ approach of the functional genomics era. It consists in the identification and quantification of all small molecules, namely metabolites, in a given biological system. While metabolomics refers to the analysis of any possible biological system, metabonomics is specifically applied to disease and physiopathological situations. The data collected within these approaches is highly integrative of the other higher levels and is hence amenable to be explored with a top-down systems biology point of view. The aim of this chapter is to give a global view of the state of the art in metabolomics describing the two analytical techniques usually used to give rise to this kind of data, nuclear magnetic resonance, NMR, and mass spectrometry. In addition, the author will focus on the different data analysis tools that can be applied to such studies to extract information with special interest at the attempts to integrate metabolomics with other ‘omics’ approaches and its relevance in systems biology modeling.

A Comparative Study of an Unsupervised Word Sense Disambiguation Approach

Word sense disambiguation is the problem of selecting a sense for a word from a set of predefined possibilities. This is a significant problem in the biomedical domain where a single word may be used to describe a gene, protein, or abbreviation. In this paper, we evaluate SENSATIONAL, a novel unsupervised WSD technique, in comparison with two popular learning algorithms: support vector machines (SVM) and K-means. Based on the accuracy measure, our results show that SENSATIONAL outperforms SVM and K-means by 2% and 17%, respectively. In addition, we develop a polysemy-based search engine and an experimental visualization application that utilizes SENSATIONAL’s clustering technique.

BMP Signaling in Regenerative Medicine

More than 40 years after the discovery of Bone Morphogenetic Proteins (BMPs) as bone inducers, a whole protein family of growth factors connected to a wide variety of functions in embryonic development, homeostasis, and regeneration has been characterized. Today, BMP2 and BMP7 are already used in the clinic to promote vertebral fusions and restoration of non-union fractures. Besides describing present clinical applications, the authors review ongoing trials highlighting the future possibilities of BMPs in medicine. Apparently, the physiological roles of BMPs have expanded their range from bone growth induction and connective tissue regeneration to cancer diagnosis/treatment and cardiovascular disease prevention.

Molecular Network Analysis of Target RNAs and Interacting Proteins of TDP-43, a Causative Gene for the Neurodegenerative Diseases ALS/FTLD

TAR DNA-binding protein-43 (TDP-43) is an evolutionarily conserved nuclear protein that regulates gene expression by forming a multimolecular complex with a wide variety of target RNAs and interacting proteins. Abnormally phosphorylated, ubiquitinated, and aggregated TDP-43 proteins constitute a principal component of neuronal and glial cytoplasmic and nuclear inclusions in the brains of patients with amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD), establishing a novel clinical entity designated TDP-43 proteinopathy. Although increasing evidence suggests that the neurodegenerative process underlying ALS and FTLD is attributable to a toxic gain of function or a loss of cellular function of TDP-43, the precise molecular mechanisms remain largely unknown. Recent advances in systems biology enable us to characterize the global molecular network extracted from large-scale data of the genome, transcriptome, and proteome with the pathway analysis tools of bioinformatics endowed with a comprehensive knowledge base. The present study was conducted to characterize the comprehensive molecular network of TDP-43 target RNAs and interacting proteins, recently identified by deep sequencing with next-generation sequencers and mass spectrometric analysis. The results propose the systems biological view that TDP-43 serves as a molecular coordinator of the RNA-dependent regulation of gene transcription and translation pivotal for performing diverse neuronal functions and that the disruption of TDP-43-mediated molecular coordination induces neurodegeneration in ALS and FTLD.

Engineering Gene Control Circuits with Allosteric Ribozymes in Human Cells as a Medicine of the Future

Systems and synthetic biology promise to develop new approaches for analysis and design of complex gene expression regulatory networks in living cells with many practical applications to the pharmaceutical and biotech industries. In this chapter the development of novel universal strategies for exogenous control of gene expression is discussed. They are based on designer allosteric ribozymes that can function in the cell. The synthetic riboswitches are obtained by a patented computational procedure that provides fast and accurate modular designs with various Boolean logic functions. The riboswitches can be designed to sense in the cell either the presence or the absence of disease indicative RNA(s) or small molecules, and to switch on or off the gene expression of any exogenous protein. In addition, the riboswitches can be engineered to induce RNA interference or microRNA pathways that can conditionally down regulate the expression of key proteins in the cell. That can prevent a disease’s development. Therefore, the presented synthetic riboswitches can be used as truly universal cellular biosensors. Nowadays, disease indicative RNA(s) can be precisely identified by employing next-generation sequencing technologies with high accuracy . The methods can be employed not only for exogenous control of gene expression but also for re-programming the cell fate, anticancer, and antiviral gene therapies. Such approaches may be employed as potent molecular medicines of the future.

A Two-Layer Learning Architecture for Multi-Class Protein Folds Classification

Classification of protein folds plays a very important role in the protein structure discovery process, especially when traditional sequence alignment methods fail to yield convincing structural homologies. In this chapter, we have developed a two-layer learning architecture, named TLLA, for multi-class protein folds classification. In the first layer, OET-KNN (Optimized Evidence-Theoretic K Nearest Neighbors) is used as the component classifier to find the most probable K-folds of the query protein. In the second layer, we use support vector machine (SVM) to build the multi-class classifier just on the K-folds, generated in the first layer, rather than on all the 27 folds. For multi-feature combination, ensemble strategy based on voting is selected to give the final classification result. The standard percentage accuracy of our method at ~63% is achieved on the independent testing dataset, where most of the proteins have <25% sequence identity with those in the training dataset. The experimental evaluation based on a widely used benchmark dataset has shown that our approach outperforms the competing methods, implying our approach might become a useful vehicle in the literature.

Modules in Biological Networks

One of the most prominent properties of networks representing complex systems is modularity. Network-based module identification has captured the attention of a diverse group of scientists from various domains and a variety of methods have been developed. The ability to decompose complex biological systems into modules allows the use of modules rather than individual genes as units in biological studies. A modular view is shaping research methods in biology. Module-based approaches have found broad applications in protein complex identification, protein function prediction, protein expression prediction, as well as disease studies. Compared to single gene-level analyses, module-level analyses offer higher robustness and sensitivity. More importantly, module-level analyses can lead to a better understanding of the design and organization of complex biological systems.

FPGA-Based Accelerators for Bioinformatics Applications

The recent and astonishing advances in Molecular Biology, which led to the sequencing of an unprecedented number of genomes, including the human, would not have been possible without the help of Bioinformatics. Bioinformatics can be defined as a research area where computational tools and algorithms are developed to help biologists in the task of understanding the organisms. Some Bioinformatics applications, such as pairwise and sequence-profile comparison, require a huge amount of computing power and, therefore, are excellent candidates to run in FPGA platforms. This chapter discusses in detail several recent proposals on FPGA-based accelerators for these two Bioinformatics applications, highlighting the similarities and differences among them. At the end of the chapter, research tendencies and open questions are presented.

Biclustering of DNA Microarray Data

In this chapter, different methods and applications of biclustering algorithms to DNA microarray data analysis that have been developed in recent years are discussed and compared. Identification of biological significant clusters of genes from microarray experimental data is a very daunting task that emerged, especially with the development of high throughput technologies. Various computational and evaluation methods based on diverse principles were introduced to identify new similarities among genes. Mathematical aspects of the models are highlighted, and applications to solve biological problems are discussed.