170,182 research outputs found

    Bioinformatics resources for cancer research with an emphasis on gene function and structure prediction tools

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    The immensely popular fields of cancer research and bioinformatics overlap in many different areas, e.g. large data repositories that allow for users to analyze data from many experiments (data handling, databases), pattern mining, microarray data analysis, and interpretation of proteomics data. There are many newly available resources in these areas that may be unfamiliar to most cancer researchers wanting to incorporate bioinformatics tools and analyses into their work, and also to bioinformaticians looking for real data to develop and test algorithms. This review reveals the interdependence of cancer research and bioinformatics, and highlight the most appropriate and useful resources available to cancer researchers. These include not only public databases, but general and specific bioinformatics tools which can be useful to the cancer researcher. The primary foci are function and structure prediction tools of protein genes. The result is a useful reference to cancer researchers and bioinformaticians studying cancer alike

    Review of Bioinformatics Tools and Techniques to Accelerate Ovarian Cancer Research

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    Since the history of humans there was no definitive cure for cancer. The rapid development in the field of bioinformatics has resulted in acceleration of advancement of cancer research. As computing and IT technology improves over time the use and importance of bioinformatics will also rise. The bulk of biological data created by biomedical researchers has increased over the years, and it has become difficult to store and analyze that data. Faster computer processors and advancement in quantum computing will solve the conventional problem of slow data processing and will make the use of bioinformatics even attractive for scientists and researchers across the globe. The success of potential drug candidates and vaccines were identified and credit goes to bioinformatics gene simulation sequencing, simulation and fast data processing. The results were development of a vaccine in record time all thanks to bioinformatics approaches. This paper explores the contribution that bioinformatics has been able to make in the field of ovarian cancer and how the use of DNA sequencing and simulation helped in developing targeted drugs such as PARP inhibitors. It also elucidates the impact bioinformatics can make in developing effective therapies in times to come. Genome sequencing has paved the way in understanding the disease, possible treatment options analyze mutations and further predict the drug target. In this review we will highlight different aspects of bioinformatics tools and techniques that have accelerated the ovarian cancer research

    Chemoresistance acquisition induces a global shift of expression of aniogenesis-associated genes and increased pro-angogenic activity in neuroblastoma cells

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    BACKGROUND: Chemoresistance acquisition may influence cancer cell biology. Here, bioinformatics analysis of gene expression data was used to identify chemoresistance-associated changes in neuroblastoma biology. RESULTS: Bioinformatics analysis of gene expression data revealed that expression of angiogenesis-associated genes significantly differs between chemosensitive and chemoresistant neuroblastoma cells. A subsequent systematic analysis of a panel of 14 chemosensitive and chemoresistant neuroblastoma cell lines in vitro and in animal experiments indicated a consistent shift to a more pro-angiogenic phenotype in chemoresistant neuroblastoma cells. The molecular mechanims underlying increased pro-angiogenic activity of neuroblastoma cells are individual and differ between the investigated chemoresistant cell lines. Treatment of animals carrying doxorubicin-resistant neuroblastoma xenografts with doxorubicin, a cytotoxic drug known to exert anti-angiogenic activity, resulted in decreased tumour vessel formation and growth indicating chemoresistance-associated enhanced pro-angiogenic activity to be relevant for tumour progression and to represent a potential therapeutic target. CONCLUSION: A bioinformatics approach allowed to identify a relevant chemoresistance-associated shift in neuroblastoma cell biology. The chemoresistance-associated enhanced pro-angiogenic activity observed in neuroblastoma cells is relevant for tumour progression and represents a potential therapeutic target

    Microarray Data Analysis and Classification of Cancers

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    When it comes to cancer, there is no standardized approach for identifying new cancer classes nor is there a standardized approach for assigning cancer tumors to existing classes. These two ideas are known as class discovery and class prediction. For a cancer patient to receive proper treatment, it is important that the type of cancer be accurately identified. For my Senior Honors Project, I would like to use this opportunity to research a topic in bioinformatics. Bioinformatics incorporates a few different subjects into one including biology, computer science and statistics. An intricate method for class discovery and class prediction is to use microarray data analysis techniques with gene expression data. I would like to use this Senior Honors Project as an opportunity to do some research within bioinformatics. Bioinformatics has intrigued me because it takes two of my favorite subjects which are computer science and statistics and blends that with biology. This project also gives me the chance to investigate the use of some interesting algorithms. I will use my research of the different classifiers for microarray data analysis using gene expression to form my own opinion on the most accurate method for class discovery and class prediction

    Databases and QSAR for Cancer Research

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    In this review, we take a survey of bioinformatics databases and quantitative structure-activity relationship studies reported in published literature. Databases from the most general to special cancer-related ones have been included. Most commonly used methods of structure-based analysis of molecules have been reviewed, along with some case studies where they have been used in cancer research. This article is expected to be of use for general bioinformatics researchers interested in cancer and will also provide an update to those who have been actively pursuing this field of research

    The RNA-binding protein hnRNPA2 regulates β-catenin protein expression and is overexpressed in prostate cancer

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    The RNA-binding protein hnRNPA2 (HNRNPA2B1) is upregulated in cancer, where it controls alternative pre-mRNA splicing of cancer-relevant genes. Cytoplasmic hnRNPA2 is reported in aggressive cancers, but is functionally uncharacterized. We explored the role of hnRNPA2 in prostate cancer (PCa). Methods: hnRNPA2 function/localization/expression in PCa was determined using biochemical approaches (colony forming/proliferation/luciferase reporter assays/flow cytometry/immunohistocytochemistry). Binding of hnRNPA2 within cancer-relevant 3′-UTR mRNAs was identified by bioinformatics. Results: RNAi-mediated knockdown of hnRNPA2 reduced colony forming and proliferation, while hnRNPA2 overexpression increased proliferation of PCa cells. Nuclear hnRNPA2 is overexpressed in high-grade clinical PCa, and is also observed in the cytoplasm in some cases. Ectopic expression of a predominantly cytoplasmic variant hnRNPA2-ΔRGG also increased PCa cell proliferation, suggesting that cytoplasmic hnRNPA2 may also be functionally relevant in PCa. Consistent with its known cytoplasmic roles, hnRNPA2 was associated with 3′-UTR mRNAs of several cancer-relevant mRNAs including β-catenin (CTNNB1). Both wild-type hnRNPA2 and hnRNPA2-ΔRGG act on CTNNB1 3′-UTR mRNA, increasing endogenous CTNNB1 mRNA expression and β-catenin protein expression and nuclear localization. Conclusion: Nuclear and cytoplasmic hnRNPA2 are present in PCa and appear to be functionally important. Cytoplasmic hnRNPA2 may affect the cancer cell phenotype through 3′-UTR mRNA-mediated regulation of β-catenin expression and other cancer-relevant genes

    Development of Integrative Bioinformatics Applications using Cloud Computing resources and Knowledge Organization Systems (KOS).

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    Use of semantic web abstractions, in particular of domain neural Knowledge Organization Systems (KOS), to manage distributed, cloud based, integrative bioinformatics infrastructure. This presentation derives from recent publication:

Almeida JS, Deus HF, Maass W. (2010) S3DB core: a framework for RDF generation and management in bioinformatics infrastructures. BMC Bioinformatics. 2010 Jul 20;11(1):387. [PMID 20646315].

These PowerPoint slides were presented at Semantic Web Applications and Tools for Life Sciences December 10th, 2010, Berlin, Germany (http://www.swat4ls.org/2010/progr.php), keynote 9-10 am
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