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Several studies indicated that Vav2 and Vav3 Genes polymorphism was closely correlated with primary angle-closure glaucomas (PACG) susceptibility, while the results still remain controversial. We performed a meta-analysis to assess whether Vav2 and Vav3 Genes confer PACG risk. At the same time, bioinformatics was used to study the biological mechanism of Vav2 and Vav3 gene polymorphism and PACG.

The function of Angipoietin-2 (Agn2) in osteosarcoma has not been fully explored and exists controversial. Therefore, we conducted a meta-analysis to investigate the role of Agn2 in the prognosis of osteosarcoma. In addition, bioinformatics analysis was carried out to reveal the mechanism and related pathways of Agn2 in osteosarcoma.

It is now known that there are several patient-related factors that seem to influence the bone formation/regeneration process and consequently the osseointegration of dental implants, such as smoking habits, poor oral hygiene, infectious processes, systemic diseases (osteoporosis, diabetes mellitus), and medications that affect bone metabolism. On the other hand, it is not only factors associated with the patient that influence the biological process of osseointegration, and therefore the success of the implant, but also factors related to the surgical stage, prosthetics as well as inherent characteristics of the implant itself, such as wettability, porosity, roughness, are described as influencing the osseointegration process. Today, there are artificial intelligence algorithms capable of providing a powerful diagnostic tool, from the ability to identify dental implants, through radiographic images, to supporting implant prognosis, identifying and even predicting possible clinical conditions such as early bone loss, mucositis or peri-implantitis. Currently, through methodologies based on advanced neural networks - machine learning - it is possible, besides the previous examples, to anticipate the degree of complexity and the potential risk involved in a given rehabilitative case with an implant. However, all scientific evidence as well as the risk tools used today by practitioners are based on clinical and radiographic parameters that provide limited therapeutic guidance due to the multifactorial complexity of implant supported rehabilitation. Moreover, from the point of view of diagnosis and staging of peri-implant diseases, they are methods that document only the pre-existing state and not the current disease, not considering the progression of the clinical picture. Moreover, they do not take into account systemic conditions, lifestyle, hormonal changes, aging, and many other factors associated with inflammatory processes and that consequently influence the local immune response, whether around a tooth - periodontitis - or around a dental implant - peri-implantitis. Ultimately, the "immunophenotype" is considered to play an important role in the severity of oral inflammatory diseases, as individuals are considered to have a hyper-reactive genetic predisposition and therefore overreact to even small amounts of bacterial biofilms. In considering all these facts and recognizing the multifactorial complexity of oral inflammatory pathology, the ability to provide accurate diagnoses based on a standard clinical diagnosis requires biomarker-based diagnostics. Biomarkers are molecules commonly used in medicine to objectively determine disease status, or responses to a therapeutic intervention. The discovery of biomarkers associated with a particular health or disease profile has been increasing over the past few years. Omics technologies have emerged as powerful tools to investigate different molecular mechanisms between health and disease states, discovering molecules that can be the target of new therapies. This methodology is the key to the successful implementation of precision medicine in dentistry, particularly in RO. Precision medicine is a developing area where diagnosis relies on the combination of clinical and biological parameters from biomarkers, often genetic, allowing: reliable prediction of disease susceptibility, early diagnosis, prognosis and planning of the most effective and safe treatment, meeting the individual patient's needs. Currently, much research is emerging in various health fields, using genomics, proteomics and metabolomics studies to make it possible to provide an accurate diagnosis and a precise treatment plan designed directly for each patient, according to all the individualities that characterize them. The concept of precision medicine does not literally mean the creation of drugs or medical devices unique to a patient, but rather the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease or treatment. Making it possible to distinguish, by clarifying the reason, why a certain individual develops peri-implantitis, while others with similar clinical presentations do not, is the basis of precision dentistry. The goal is to reduce diagnostic errors, develop outcomes, and avoid unnecessary side effects. The aim of this scoping review is to analyze how bioinformatics is being applied in the field of oral implantology as a predictive tool to implant success. The aim is not only to summarize the available scientific evidence that characterizes how artificial intelligence supports the area of implant-supported oral rehabilitation, but also whether there are already studies whose diagnostic, planning or RO control methodology integrates omics technology as a clinical support tool.

It is now known that there are several patient-related factors that seem to influence the bone formation/regeneration process and consequently the osseointegration of dental implants, such as smoking habits, poor oral hygiene, infectious processes, systemic diseases (osteoporosis, diabetes mellitus), and medications that affect bone metabolism. On the other hand, it is not only factors associated with the patient that influence the biological process of osseointegration, and therefore the success of the implant, but also factors related to the surgical stage, prosthetics as well as inherent characteristics of the implant itself, such as wettability, porosity, roughness, are described as influencing the osseointegration process. Today, there are artificial intelligence algorithms capable of providing a powerful diagnostic tool, from the ability to identify dental implants, through radiographic images, to supporting implant prognosis, identifying and even predicting possible clinical conditions such as early bone loss, mucositis or peri-implantitis. Currently, through methodologies based on advanced neural networks - machine learning - it is possible, besides the previous examples, to anticipate the degree of complexity and the potential risk involved in a given rehabilitative case with an implant. However, all scientific evidence as well as the risk tools used today by practitioners are based on clinical and radiographic parameters that provide limited therapeutic guidance due to the multifactorial complexity of implant supported rehabilitation. Moreover, from the point of view of diagnosis and staging of peri-implant diseases, they are methods that document only the pre-existing state and not the current disease, not considering the progression of the clinical picture. Moreover, they do not take into account systemic conditions, lifestyle, hormonal changes, aging, and many other factors associated with inflammatory processes and that consequently influence the local immune response, whether around a tooth - periodontitis - or around a dental implant - peri-implantitis. Ultimately, the "immunophenotype" is considered to play an important role in the severity of oral inflammatory diseases, as individuals are considered to have a hyper-reactive genetic predisposition and therefore overreact to even small amounts of bacterial biofilms. In considering all these facts and recognizing the multifactorial complexity of oral inflammatory pathology, the ability to provide accurate diagnoses based on a standard clinical diagnosis requires biomarker-based diagnostics. Biomarkers are molecules commonly used in medicine to objectively determine disease status, or responses to a therapeutic intervention. The discovery of biomarkers associated with a particular health or disease profile has been increasing over the past few years. Omics technologies have emerged as powerful tools to investigate different molecular mechanisms between health and disease states, discovering molecules that can be the target of new therapies. This methodology is the key to the successful implementation of precision medicine in dentistry, particularly in RO. Precision medicine is a developing area where diagnosis relies on the combination of clinical and biological parameters from biomarkers, often genetic, allowing: reliable prediction of disease susceptibility, early diagnosis, prognosis and planning of the most effective and safe treatment, meeting the individual patient's needs. Currently, much research is emerging in various health fields, using genomics, proteomics and metabolomics studies to make it possible to provide an accurate diagnosis and a precise treatment plan designed directly for each patient, according to all the individualities that characterize them. The concept of precision medicine does not literally mean the creation of drugs or medical devices unique to a patient, but rather the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease or treatment. Making it possible to distinguish, by clarifying the reason, why a certain individual develops peri-implantitis, while others with similar clinical presentations do not, is the basis of precision dentistry. The goal is to reduce diagnostic errors, develop outcomes, and avoid unnecessary side effects. The aim of this scoping review is to analyze how bioinformatics is being applied in the field of oral implantology as a predictive tool to implant success. The aim is not only to summarize the available scientific evidence that characterizes how artificial intelligence supports the area of implant-supported oral rehabilitation, but also whether there are already studies whose diagnostic, planning or RO control methodology integrates omics technology as a clinical support tool.

Bioinformatics dumped with wikiteam3 tools.

Bioinformatics dumped with wikiteam3 tools.

This article is from BMC Bioinformatics , volume 13 . Abstract The BITS2011 meeting, held in Pisa on June 20-22, 2011, brought together more than 120 Italian researchers working in the field of Bioinformatics, as well as students in Bioinformatics, Computational Biology, Biology, Computer Sciences, and Engineering, representing a landscape of Italian bioinformatics research.This preface provides a brief overview of the meeting and introduces the peer-reviewed manuscripts that were accepted for publication in this Supplement.

This article is from BMC Bioinformatics , volume 13 . Abstract The BITS2011 meeting, held in Pisa on June 20-22, 2011, brought together more than 120 Italian researchers working in the field of Bioinformatics, as well as students in Bioinformatics, Computational Biology, Biology, Computer Sciences, and Engineering, representing a landscape of Italian bioinformatics research.This preface provides a brief overview of the meeting and introduces the peer-reviewed manuscripts that were accepted for publication in this Supplement.

This article is from BMC Bioinformatics , volume 13 . Abstract The BITS2011 meeting, held in Pisa on June 20-22, 2011, brought together more than 120 Italian researchers working in the field of Bioinformatics, as well as students in Bioinformatics, Computational Biology, Biology, Computer Sciences, and Engineering, representing a landscape of Italian bioinformatics research.This preface provides a brief overview of the meeting and introduces the peer-reviewed manuscripts that were accepted for publication in this Supplement.

Perl 2008 Mastering. Perl.for. Bioinformatics EN

This article is from BMC Medical Genomics , volume 7 . Abstract None

Rex A. Dwyer Genomic Perl From Bioinformatics Basics To Working Code Cambridge University Press ( 2002) 2

This article is from Viruses , volume 2 . Abstract The drastic increase in the number of coronaviruses discovered and coronavirus genomes being sequenced have given us an unprecedented opportunity to perform genomics and bioinformatics analysis on this family of viruses. Coronaviruses possess the largest genomes (26.4 to 31.7 kb) among all known RNA viruses, with G + C contents varying from 32% to 43%. Variable numbers of small ORFs are present between the various conserved genes (ORF1ab, spike, envelope, membrane and nucleocapsid) and downstream to nucleocapsid gene in different coronavirus lineages. Phylogenetically, three genera, Alphacoronavirus, Betacoronavirus and Gammacoronavirus, with Betacoronavirus consisting of subgroups A, B, C and D, exist. A fourth genus, Deltacoronavirus, which includes bulbul coronavirus HKU11, thrush coronavirus HKU12 and munia coronavirus HKU13, is emerging. Molecular clock analysis using various gene loci revealed that the time of most recent common ancestor of human/civet SARS related coronavirus to be 1999–2002, with estimated substitution rate of 4×10−4 to 2×10−2 substitutions per site per year. Recombination in coronaviruses was most notable between different strains of murine hepatitis virus (MHV), between different strains of infectious bronchitis virus, between MHV and bovine coronavirus, between feline coronavirus (FCoV) type I and canine coronavirus generating FCoV type II, and between the three genotypes of human coronavirus HKU1 (HCoV-HKU1). Codon usage bias in coronaviruses were observed, with HCoV-HKU1 showing the most extreme bias, and cytosine deamination and selection of CpG suppressed clones are the two major independent biological forces that shape such codon usage bias in coronaviruses.

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Bioinformatics pipelines enable hfr scientists to effectively analyze biological data through automated multi-step processes constructed by individual programs and databases. The huge amount of data and time consuming computations require effectively parallelized pipehnes to provide results within a reasonable time. To reduce researchers' programming burden for pipeline creation and parallelization, we developed the Bioinformatics Pipeline Generation and Parallelization Toolkit (B io Gent). A user needs only to create a pipehne definition file that describes the data processing sequence and input/output files. A program termed schedpipe in the BioGent toolkit takes the definition file and executes the designed procedure. Schedpipe automatically parallelizes the pipeline execution by performing independent data processing steps on muliple CPUs, and by decomposing big datasets into small chunks and processing them in parallel. Schedpipe controls program execution on multiple CPUs through a simple application programming interface (API) of the Parallel Job Manager (PJM) library. As a part of the BioGent toolkit, PJM was developed to effectively launch and monitor programs on multiple CPUs using a Message Passing Interface (MPI) protocol. The PJMAPI can also be used to parallelize other serial programs. A demonstration using PJM for parallelization shows 10% to 50% savings in time compared to an indigenous parallelization through a batch queuing system.

Bioinformatics pipelines enable hfr scientists to effectively analyze biological data through automated multi-step processes constructed by individual programs and databases. The huge amount of data and time consuming computations require effectively parallelized pipehnes to provide results within a reasonable time. To reduce researchers' programming burden for pipeline creation and parallelization, we developed the Bioinformatics Pipeline Generation and Parallelization Toolkit (B io Gent). A user needs only to create a pipehne definition file that describes the data processing sequence and input/output files. A program termed schedpipe in the BioGent toolkit takes the definition file and executes the designed procedure. Schedpipe automatically parallelizes the pipeline execution by performing independent data processing steps on muliple CPUs, and by decomposing big datasets into small chunks and processing them in parallel. Schedpipe controls program execution on multiple CPUs through a simple application programming interface (API) of the Parallel Job Manager (PJM) library. As a part of the BioGent toolkit, PJM was developed to effectively launch and monitor programs on multiple CPUs using a Message Passing Interface (MPI) protocol. The PJMAPI can also be used to parallelize other serial programs. A demonstration using PJM for parallelization shows 10% to 50% savings in time compared to an indigenous parallelization through a batch queuing system.

This article is from G3: Genes|Genomes|Genetics , volume 4 . Abstract Messenger RNA 3′-end formation is an essential posttranscriptional processing step for most eukaryotic genes. Different from plants and animals where AAUAAA and its variants routinely are found as the main poly(A) signal, Chlamydomonas reinhardtii uses UGUAA as the major poly(A) signal. The advance of sequencing technology provides an enormous amount of sequencing data for us to explore the variations of poly(A) signals, alternative polyadenylation (APA), and its relationship with splicing in this algal species. Through genome-wide analysis of poly(A) sites in C. reinhardtii, we identified a large number of poly(A) sites: 21,041 from Sanger expressed sequence tags, 88,184 from 454, and 195,266 from Illumina sequence reads. In comparison with previous collections, more new poly(A) sites are found in coding sequences and intron and intergenic regions by deep-sequencing. Interestingly, G-rich signals are particularly abundant in intron and intergenic regions. The prevalence of different poly(A) signals between coding sequences and a 3′-untranslated region implies potentially different polyadenylation mechanisms. Our data suggest that the APA occurs in about 68% of C. reinhardtii genes. Using Gene Ontolgy analysis, we found most of the APA genes are involved in RNA regulation and metabolic process, protein synthesis, hydrolase, and ligase activities. Moreover, intronic poly(A) sites are more abundant in constitutively spliced introns than retained introns, suggesting an interplay between polyadenylation and splicing. Our results support that APA, as in higher eukaryotes, may play significant roles in increasing transcriptome diversity and gene expression regulation in this algal species. Our datasets also provide useful information for accurate annotation of transcript ends in C. reinhardtii.

This article is from BMC Genomics , volume 13 . Abstract Background: MicroRNA (miRNA) is a class of functional non-coding small RNA with 19-25 nucleotides in length while Amur grape (Vitis amurensis Rupr.) is an important wild fruit crop with the strongest cold resistance among the Vitis species, is used as an excellent breeding parent for grapevine, and has elicited growing interest in wine production. To date, there is a relatively large number of grapevine miRNAs (vv-miRNAs) from cultivated grapevine varieties such as Vitis vinifera L. and hybrids of V. vinifera and V. labrusca, but there is no report on miRNAs from Vitis amurensis Rupr, a wild grapevine species. Results: A small RNA library from Amur grape was constructed and Solexa technology used to perform deep sequencing of the library followed by subsequent bioinformatics analysis to identify new miRNAs. In total, 126 conserved miRNAs belonging to 27 miRNA families were identified, and 34 known but non-conserved miRNAs were also found. Significantly, 72 new potential Amur grape-specific miRNAs were discovered. The sequences of these new potential va-miRNAs were further validated through miR-RACE, and accumulation of 18 new va-miRNAs in seven tissues of grapevines confirmed by real time RT-PCR (qRT-PCR) analysis. The expression levels of va-miRNAs in flowers and berries were found to be basically consistent in identity to those from deep sequenced sRNAs libraries of combined corresponding tissues. We also describe the conservation and variation of va-miRNAs using miR-SNPs and miR-LDs during plant evolution based on comparison of orthologous sequences, and further reveal that the number and sites of miR-SNP in diverse miRNA families exhibit distinct divergence. Finally, 346 target genes for the new miRNAs were predicted and they include a number of Amur grape stress tolerance genes and many genes regulating anthocyanin synthesis and sugar metabolism. Conclusions: Deep sequencing of short RNAs from Amur grape flowers and berries identified 72 new potential miRNAs and 34 known but non-conserved miRNAs, indicating that specific miRNAs exist in Amur grape. These results show that a number of regulatory miRNAs exist in Amur grape and play an important role in Amur grape growth, development, and response to abiotic or biotic stress.

This article is from Journal of Biomedical Research , volume 25 . Abstract Visceral leishmaniasis or kala-azar is a potent parasitic infection causing death of thousands of people each year. Medicinal compounds currently available for the treatment of kala-azar have serious side effects and decreased efficacy owing to the emergence of resistant strains. The type of immune reaction is also to be considered in patients infected with Leishmania donovani (L. donovani). For complete eradication of this disease, a high level modern research is currently being applied both at the molecular level as well as at the field level. The computational approaches like remote sensing, geographical information system (GIS) and bioinformatics are the key resources for the detection and distribution of vectors, patterns, ecological and environmental factors and genomic and proteomic analysis. Novel approaches like GIS and bioinformatics have been more appropriately utilized in determining the cause of visearal leishmaniasis and in designing strategies for preventing the disease from spreading from one region to another.

This article is from BMC Bioinformatics , volume 13 . Abstract Background: Multidimensional protein identification technology (MudPIT)-based shot-gun proteomics has been proven to be an effective platform for functional proteomics. In particular, the various sample preparation methods and bioinformatics tools can be integrated to improve the proteomics platform for applications like target organelle proteomics. We have recently integrated a rapid sample preparation method and bioinformatics classification system for comparative analysis of plant responses to two plant hormones, zeatin and brassinosteroid (BR). These hormones belong to two distinct classes of plant growth regulators, yet both can promote cell elongation and growth. An understanding of the differences and the cross-talk between the two types of hormone responses will allow us to better understand the molecular mechanisms and to identify new candidate genes for plant engineering. Results: As compared to traditional organelle proteomics, the organelle-enrichment method both simplifies the sample preparation and increases the number of proteins identified in the targeted organelle as well as the entire sample. Both zeatin and BR induce dramatic changes in signaling and metabolism. Their shared-regulated protein components indicate that both hormones may down-regulate some key components in auxin responses. However, they have shown distinct induction and suppression of metabolic pathways in mitochondria and chloroplast. For zeatin, the metabolic pathways in sucrose and starch biosynthesis and utilization were significantly changed, yet the lipid biosynthesis remained unchanged. For BR, lipid biosynthesis and β-oxidation were both down-regulated, yet the changes in sucrose and starch metabolism were minor. Conclusions: We present a rapid sample preparation method and bioinformatics classification for effective proteomics analysis of plant hormone responses. The study highlighted the largely differing response to zeatin and brassinosteroid by the metabolic pathways in chloroplast and mitochondria.

This article is from BMC Bioinformatics , volume 13 . Abstract Background: Multidimensional protein identification technology (MudPIT)-based shot-gun proteomics has been proven to be an effective platform for functional proteomics. In particular, the various sample preparation methods and bioinformatics tools can be integrated to improve the proteomics platform for applications like target organelle proteomics. We have recently integrated a rapid sample preparation method and bioinformatics classification system for comparative analysis of plant responses to two plant hormones, zeatin and brassinosteroid (BR). These hormones belong to two distinct classes of plant growth regulators, yet both can promote cell elongation and growth. An understanding of the differences and the cross-talk between the two types of hormone responses will allow us to better understand the molecular mechanisms and to identify new candidate genes for plant engineering. Results: As compared to traditional organelle proteomics, the organelle-enrichment method both simplifies the sample preparation and increases the number of proteins identified in the targeted organelle as well as the entire sample. Both zeatin and BR induce dramatic changes in signaling and metabolism. Their shared-regulated protein components indicate that both hormones may down-regulate some key components in auxin responses. However, they have shown distinct induction and suppression of metabolic pathways in mitochondria and chloroplast. For zeatin, the metabolic pathways in sucrose and starch biosynthesis and utilization were significantly changed, yet the lipid biosynthesis remained unchanged. For BR, lipid biosynthesis and β-oxidation were both down-regulated, yet the changes in sucrose and starch metabolism were minor. Conclusions: We present a rapid sample preparation method and bioinformatics classification for effective proteomics analysis of plant hormone responses. The study highlighted the largely differing response to zeatin and brassinosteroid by the metabolic pathways in chloroplast and mitochondria.

The comparison of computer generated protein structural models is an important element of protein structure prediction. It has many uses including model quality evaluation, selection of the final models from a large set of candidates or optimisation of parameters of energy functions used in template-free modelling and refinement. Although many protein comparison methods are available online on numerous web servers, they are not well suited for large scale model comparison: (1) they operate with methods designed to compare actual proteins, not the models of the same protein, (2) majority of them offer only a single pairwise structural comparison and are unable to scale up to a required order of thousands of comparisons. To bridge the gap between the protein and model structure comparison we have developed the Protein Models Comparator (pm-cmp). To be able to deliver the scalability on demand and handle large comparison experiments the pm-cmp was implemented "in the cloud". Protein Models Comparator is a scalable web application for a fast distributed comparison of protein models with RMSD, GDT TS, TM-score and Q-score measures. It runs on the Google App Engine (GAE) cloud platform and is a showcase of how the emerging PaaS (Platform as a Service) technology could be used to simplify the development of scalable bioinformatics services. The functionality of pm-cmp is accessible through API which allows a full automation of the experiment submission and results retrieval. Protein Models Comparator is free software released on the Affero GNU Public Licence and is available with its source code at: http://www.infobiotics.org/pm-cmp This article presents a new web application addressing the need for a large-scale model-specific protein structure comparison and provides an insight into the GAE (Google App Engine) platform and its usefulness in scientific computing.

The Microsoft Biology Initiative (MBI) is an effort in Microsoft Research to bring new technology and tools to the area of bioinformatics and biology. This initiative is comprised of two primary components, the Microsoft Biology Foundation (MBF) and the Microsoft Biology Tools (MBT). The Microsoft Biology Foundation (MBF) is a language-neutral bioinformatics toolkit built as an extension to the Microsoft .NET Framework, initially aimed at the area of Genomics research. Currently, it implements a range of parsers for common bioinformatics file formats; a range of algorithms for manipulating DNA, RNA, and protein sequences; and a set of connectors to biological web services such as NCBI BLAST. MBF is available under an open source license, and executables, source code, demo applications, and documentation are freely downloadable. The Microsoft Biology Tools (MBT) are a collection of tools targeted at helping the biology and bioinformatics researcher be more productive in making scientific discoveries. The tools provided here take advantage of the capabilities provided in the Microsoft Biology Foundation, and are good examples of how MBF can be used to create other tools. This tutorial will provide an overview of the library, details about how to extend and re-use the library, and demonstrations of the tools released that use the library: The MSR Biology Extension for Excel and the MSR Sequence Assembler. ©2010 Microsoft Corporation. All rights reserved.

This article is from BMC Bioinformatics , volume 14 . Abstract User-centred design (UCD) is a type of user interface design in which the needs and desires of users are taken into account at each stage of the design process for a service or product; often for software applications and websites. Its goal is to facilitate the design of software that is both useful and easy to use. To achieve this, you must characterise users’ requirements, design suitable interactions to meet their needs, and test your designs using prototypes and real life scenarios.For bioinformatics, there is little practical information available regarding how to carry out UCD in practice. To address this we describe a complete, multi-stage UCD process used for creating a new bioinformatics resource for integrating enzyme information, called the Enzyme Portal (http://www.ebi.ac.uk/enzymeportal). This freely-available service mines and displays data about proteins with enzymatic activity from public repositories via a single search, and includes biochemical reactions, biological pathways, small molecule chemistry, disease information, 3D protein structures and relevant scientific literature.We employed several UCD techniques, including: persona development, interviews, ‘canvas sort’ card sorting, user workflows, usability testing and others. Our hope is that this case study will motivate the reader to apply similar UCD approaches to their own software design for bioinformatics. Indeed, we found the benefits included more effective decision-making for design ideas and technologies; enhanced team-working and communication; cost effectiveness; and ultimately a service that more closely meets the needs of our target audience.

This article is from Journal of Extracellular Vesicles , volume 2 . Abstract Extracellular vesicles (EVs) are the collective term for the various vesicles that are released by cells into the extracellular space. Such vesicles include exosomes and microvesicles, which vary by their size and/or protein and genetic cargo. With the discovery that EVs contain genetic material in the form of RNA (evRNA) has come the increased interest in these vesicles for their potential use as sources of disease biomarkers and potential therapeutic agents. Rapid developments in the availability of deep sequencing technologies have enabled the study of EV-related RNA in detail. In October 2012, the International Society for Extracellular Vesicles (ISEV) held a workshop on “evRNA analysis and bioinformatics.” Here, we report the conclusions of one of the roundtable discussions where we discussed evRNA analysis technologies and provide some guidelines to researchers in the field to consider when performing such analysis.

This article is from Source Code for Biology and Medicine , volume 9 . Abstract Background: A multi-million dollar research initiative involving the National Institutes of Health (NIH), Wellcome Trust and African scientists has been launched. The initiative, referred to as H3Africa, is an acronym that stands for Human Heredity and Health in Africa. Here, we outline what this initiative is set to achieve and the latest commitments of the key players as at October 2013. Findings: The initiative has so far been awarded over $74 million in research grants. During the first set of awards announced in 2012, the NIH granted $5 million a year for a period of five years, while the Wellcome Trust doled out at least $12 million over the period to the research consortium. This was in addition to Wellcome Trust’s provision of administrative support, scientific consultation and advanced training, all in collaboration with the African Society for Human Genetics. In addition, during the second set of awards announced in October 2013, the NIH awarded to the laudable initiative 10 new grants of up to $17 million over the next four years. Conclusions: H3Africa is poised to transform the face of research in genomics, bioinformatics and health in Africa. The capacity of African scientists will be enhanced through training and the better research facilities that will be acquired. Research collaborations between Africa and the West will grow and all stakeholders, including the funding partners, African scientists, scientists across the globe, physicians and patients will be the eventual winners.

This article is from Oncology Letters , volume 8 . Abstract MicroRNAs (miRNAs) are important in the regulation of cell growth, differentiation, apoptosis and carcinogenesis. The overexpression of oncogenic miRNAs or the underexpression of tumor suppressor miRNAs exhibits a critical function in the tumorigenesis of oral cancer. The aim of the present study was to identify differentially expressed miRNAs (DE-miRNAs), which may differentiate oral cancer from normal tissues, as well as the molecular signatures that differ in tumor histology. The miRNA expression profiles of GSE28100 [the Gene Expression Omnibus (GEO) accession number] were downloaded from the GEO database and an independent sample t-test was used to identify statistical differences between the DE-miRNAs of the oral cancer patients and the healthy control subjects. The target genes of DE-miRNA were retrieved from the miRecords database. Furthermore, a protein-protein interaction network was constructed using the Search Tools for the Retrieval of Interacting Genes database and Cytoscape software. A total of 15 DE-miRNAs were identified and among them, hsa-miR-15a drew specific attention. Gene Ontology analysis revealed that the target genes of fibroblast growth factor (FGF)2 are involved in the progression of oral cancer. Furthermore, functional analysis indicated that the FGF-receptor signaling pathway was significantly upregulated in oral cancer. hsa-miR-15a is important in the regulation of oral cancer and thus, may present a potential biomarker for the prediction of oral cancer progression.

This article is from PLoS Computational Biology , volume 10 . Abstract None

LMO4 is highly expressed in breast epithelial cells and is related to cell proliferation and/or invasion in vivo. Because these cellular features are associated with breast carcinogenesis and since LMO4 is overexpressed in more than 50% of breast cancer cases, we hypothesize that LMO4 may play roles in oncogenesis of breast epithelial cells by regulating proliferation, invasion and/or other cellular features. Using LMO4 over-expression or shRNA expression system in vitro, I found that LMO4 play crucial roles in the regulation of cell proliferation and apoptosis of normal mammary gland epithelial cells or breast cancer cells. Furthermore, I have also observed that deletion of LMO4 impaired the function and development of mammary gland in LMO4 conditional knockout mice, indicating that LMO4 protein is necessary for maintaining the normal development of mice mammary gland. In addition, I demonstrated that the LMO4 can modulate TGF signaling and regulated the proliferative response of epithelial cells to TGF signaling, and thereby linked LMO4 to a conserved signaling pathway that plays important roles in epithelial homeostasis. Under the support of grant, I received excellent training in bioinformatics. By combining previously described functional methods with bioinformatics approaches, we used DNA microarrays to discover LMO4-responsive genes, and identified BMP7 as a key down-stream gene of LMO4. In addition, we also found a significant correlation between LMO4 and BMP7 transcript levels in a large dataset of human breast cancers, providing additional support that BMP7 is a bona fide target gene of LMO4. Finally, we demonstrated that LMO4 binds to HDAC2 and that they are recruited together to the BMP7 promoter. We also suggested a novel mechanism for LMOs; LMO4, Clim2 and HDAC2 are part of a transcriptional complex, and alterations in LMO4 levels can disrupt the complex, leading to decreased HDAC2 recruitment and increased promoter activity.

Neurofibromatoses (NF) are disorders caused by mutations in NF1/NF2 genes and characterized by fibromatous tumors on nerves, skin, and bones. A method to rapidly and cheaply re-sequence the NF loci would greatly aid in the molecular classification of the disease by forging links between sequence variations and clinical manifestations. We propose to develop a technique to rapidly re-sequence genomic loci using microarray hybridization and bioinformatics. The NF locus is first amplified using a high processivity polymerase and hybridized on a custom microarray containing all possible 10mer combinations of the four deoxy-ribonucleotides. A virtual profile of a hybridization map of the locus is generated using the available sequence information. This map is then compared to an actual hybridization. If the sequences are identical, then the two images would superimpose. Using the differences in the virtual and real hybridization, we propose to map the mutations in the locus.

Perl 2008 James D. Tisdall Mastering Perl For Bioinformatics EN

Perl 2008 James D. Tisdall Mastering Perl For Bioinformatics EN

Perl 2008 James D. Tisdall Mastering Perl For Bioinformatics EN

Perl 2008 James D. Tisdall Mastering Perl For Bioinformatics EN

It is essential to protect Service Members from toxic and hazardous chemicals and materials across the entire range of deployment missions, from humanitarian to theater-level combat. Tools that rapidly and accurately quantify exposure-induced health effects would enable Commanders to make informed decisions on return-to-duty and allow service members to thrive in environments of uncertainty and danger. Currently, U.S. Forces lack a suitable method to rapidly screen and assess risk of toxic end organ injury following chemical exposures in the field. Markers of end-organ injury and toxicity and other health effects markers, particularly those used in a clinical setting, could be integrated into a lab-on-a-chip fieldable detection cartridge for molecular indicators of injury following chemical exposure or for routine surveillance when operating in dangerous environments. We utilized large public repositories of drug toxicity data to infer biomarkers of toxic industrial chemicals exposure. Using a computational and relational approach we prioritized militarily relevant toxic industrial chemicals and their anticipated adverse health effects, to include potential threats related to megacities. Initially, we focused on liver and kidney toxicity because these organs are particularly susceptible to toxic injury, often used in drug toxicity studies with large amounts of publically available data, and in some cases have established FDA-qualified biomarker panels or clinical assays. We mined the literature and databases (e.g., DrugMatrix and the Comparative Toxicogenomics Database) for candidate gene targets to make predictions about biomarkers related to toxic industrial chemicals that cause the same adverse health effects. We identified 78 and 244 gene modules associated with liver and kidney injury, respectively, and qualified some of these targets in independent animal studies.

Preeclampsia has genetic correlation. Many studies have shown that microRNA (miRNA) polymorphism is highly associated with preeclampsia, but the results are inconsistent. The purpose of this study is to systematically evaluate the relationship between miRNA polymorphism and preeclampsia.

This article is from BMC Genomics , volume 15 . Abstract Background: Cysticercosis remains a major neglected tropical disease of humanity in many regions, especially in sub-Saharan Africa, Central America and elsewhere. Owing to the emerging drug resistance and the inability of current drugs to prevent re-infection, identification of novel vaccines and chemotherapeutic agents against Taenia solium and related helminth pathogens is a public health priority. The T. solium genome and the predicted proteome were reported recently, providing a wealth of information from which new interventional targets might be identified. In order to characterize and classify the entire repertoire of protease-encoding genes of T. solium, which act fundamental biological roles in all life processes, we analyzed the predicted proteins of this cestode through a combination of bioinformatics tools. Functional annotation was performed to yield insights into the signaling processes relevant to the complex developmental cycle of this tapeworm and to highlight a suite of the proteases as potential intervention targets. Results: Within the genome of this helminth parasite, we identified 200 open reading frames encoding proteases from five clans, which correspond to 1.68% of the 11,902 protein-encoding genes predicted to be present in its genome. These proteases include calpains, cytosolic, mitochondrial signal peptidases, ubiquitylation related proteins, and others. Many not only show significant similarity to proteases in the Conserved Domain Database but have conserved active sites and catalytic domains. KEGG Automatic Annotation Server (KAAS) analysis indicated that ~60% of these proteases share strong sequence identities with proteins of the KEGG database, which are involved in human disease, metabolic pathways, genetic information processes, cellular processes, environmental information processes and organismal systems. Also, we identified signal peptides and transmembrane helices through comparative analysis with classes of important regulatory proteases. Phylogenetic analysis using Bayes approach provided support for inferring functional divergence among regulatory cysteine and serine proteases. Conclusion: Numerous putative proteases were identified for the first time in T. solium, and important regulatory proteases have been predicted. This comprehensive analysis not only complements the growing knowledge base of proteolytic enzymes, but also provides a platform from which to expand knowledge of cestode proteases and to explore their biochemistry and potential as intervention targets. Electronic supplementary material: The online version of this article (doi: 10.1186/1471-2164-15-428) contains supplementary material, which is available to authorized users.

This article is from BMC Genomics , volume 15 . Abstract Background: Cysticercosis remains a major neglected tropical disease of humanity in many regions, especially in sub-Saharan Africa, Central America and elsewhere. Owing to the emerging drug resistance and the inability of current drugs to prevent re-infection, identification of novel vaccines and chemotherapeutic agents against Taenia solium and related helminth pathogens is a public health priority. The T. solium genome and the predicted proteome were reported recently, providing a wealth of information from which new interventional targets might be identified. In order to characterize and classify the entire repertoire of protease-encoding genes of T. solium, which act fundamental biological roles in all life processes, we analyzed the predicted proteins of this cestode through a combination of bioinformatics tools. Functional annotation was performed to yield insights into the signaling processes relevant to the complex developmental cycle of this tapeworm and to highlight a suite of the proteases as potential intervention targets. Results: Within the genome of this helminth parasite, we identified 200 open reading frames encoding proteases from five clans, which correspond to 1.68% of the 11,902 protein-encoding genes predicted to be present in its genome. These proteases include calpains, cytosolic, mitochondrial signal peptidases, ubiquitylation related proteins, and others. Many not only show significant similarity to proteases in the Conserved Domain Database but have conserved active sites and catalytic domains. KEGG Automatic Annotation Server (KAAS) analysis indicated that ~60% of these proteases share strong sequence identities with proteins of the KEGG database, which are involved in human disease, metabolic pathways, genetic information processes, cellular processes, environmental information processes and organismal systems. Also, we identified signal peptides and transmembrane helices through comparative analysis with classes of important regulatory proteases. Phylogenetic analysis using Bayes approach provided support for inferring functional divergence among regulatory cysteine and serine proteases. Conclusion: Numerous putative proteases were identified for the first time in T. solium, and important regulatory proteases have been predicted. This comprehensive analysis not only complements the growing knowledge base of proteolytic enzymes, but also provides a platform from which to expand knowledge of cestode proteases and to explore their biochemistry and potential as intervention targets. Electronic supplementary material: The online version of this article (doi: 10.1186/1471-2164-15-428) contains supplementary material, which is available to authorized users.

The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or just within a meta-analysis comparison, instead of one list it is often the case that sets of alternative feature lists (possibly of different lengths) are obtained. Here we introduce a method, based on the algebraic theory of symmetric groups, for studying the variability between lists ("list stability") in the case of lists of unequal length. We provide algorithms evaluating stability for lists embedded in the full feature set or just limited to the features occurring in the partial lists. The method is demonstrated first on synthetic data in a gene filtering task and then for finding gene profiles on a recent prostate cancer dataset.

Bioinformatics Codes dumped with WikiTeam tools.

Bioinformatics Codes dumped with WikiTeam tools.

Bioinformatics Codes dumped with WikiTeam tools.

Bioinformatics Codes dumped with WikiTeam tools.

Bioinformatics Codes dumped with WikiTeam tools.

Its an archive of my neocities site

Its an archive of my neocities site

This article is from Journal of Clinical Bioinformatics , volume 2 . Abstract The 6th Benelux Bioinformatics Conference (BBC11) held in Luxembourg on 12 and 13 December 2011 attracted around 200 participants, including internationally-renowned guest speakers and more than 100 peer-reviewed submissions from 3 continents. Researchers from the public and private sectors convened at BBC11 to discuss advances and challenges in a wide spectrum of application areas. A key theme of the conference was the contribution of bioinformatics to enable and accelerate translational and clinical research. The BBC11 stressed the need for stronger collaborating efforts across disciplines and institutions. The demonstration of the clinical relevance of systems approaches and of next-generation sequencing-based measurement technologies are among the existing opportunities for increasing impact in translational research. Translational bioinformatics will benefit from research models that strike a balance between the importance of protecting intellectual property and the need to openly access scientific and technological advances. The full conference proceedings are freely available at http://www.bbc11.lu.