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Alan Rubin https://linux.conf.au/schedule/30300/view_talk http://afrubin.github.io/miniconf/

In the rapidly evolving domain of next generation sequencing and bioinformatics analysis, data generation is one aspect that is increasing at a concomitant rate. The burden associated with processing large amounts of sequencing data has emphasised the need to allocate sufficient computing resources to complete analyses in the shortest possible time with manageable and predictable costs. A novel method for predicting time to completion for a popular bioinformatics software (QIIME), was developed using key variables characteristic of the input data assumed to impact processing time. Multiple Linear Regression models were developed to determine run time for two denoising algorithms and a general bioinformatics pipeline. The models were able to accurately predict clock time for denoising sequences from a naturally assembled community dataset, but not an artificial community. Speedup and efficiency tests for AmpliconNoise also highlighted that caution was needed when allocating resources for parallel processing of data. Accurate modelling of computational processing time using easily measurable predictors can assist NGS analysts in determining resource requirements for bioinformatics software and pipelines. Whilst demonstrated on a specific group of scripts, the methodology can be extended to encompass other packages running on multiple architectures, either in parallel or sequentially.

In the rapidly evolving domain of next generation sequencing and bioinformatics analysis, data generation is one aspect that is increasing at a concomitant rate. The burden associated with processing large amounts of sequencing data has emphasised the need to allocate sufficient computing resources to complete analyses in the shortest possible time with manageable and predictable costs. A novel method for predicting time to completion for a popular bioinformatics software (QIIME), was developed using key variables characteristic of the input data assumed to impact processing time. Multiple Linear Regression models were developed to determine run time for two denoising algorithms and a general bioinformatics pipeline. The models were able to accurately predict clock time for denoising sequences from a naturally assembled community dataset, but not an artificial community. Speedup and efficiency tests for AmpliconNoise also highlighted that caution was needed when allocating resources for parallel processing of data. Accurate modelling of computational processing time using easily measurable predictors can assist NGS analysts in determining resource requirements for bioinformatics software and pipelines. Whilst demonstrated on a specific group of scripts, the methodology can be extended to encompass other packages running on multiple architectures, either in parallel or sequentially.

Bioinformatics Books 2005 看看 生信算法讲义 CN

HLA-E gene from MHC (Major Histocompatibility Complex) system has been recently discovered, in our laboratory, in Invertebrates. Blasts were performed against different protein species to find or not homologies. Results were given in the precedent communication. 

We introduce design and optimization considerations for the 'khmer' package.

Tri 1 2018/2019

Introduction [1] : High blood pressure is a dangerous risk factor for cardio-vascular disease, including coronary artery disease and strokes. In the human body, a system called renin-angiotensin system regulates blood pressure, in which the angiotensin converting enzyme ACE plays an important role in increasing blood pressure . Angiotensin I as a converting enzyme (ACE) catalyzes the conversion of angiotensin I to vasoconstrictor angiotensin II, and also inactivates the antihypertensive vasodilator bradykinin. Inhibition of ACE mainly results in an overall antihypertensive effect. Peptides derived from food proteins can have angiotensin converting enzyme (ACE) inhibiting properties. Casein protein in milk or other dairy products such as cheese is a rich source of bioactive peptides . Bioactive peptides are inactive in the main protein sequence and are released in during milk digestion or milk fermentation by proteolytic bacteria or hydrolysis by proteolytic enzymes. Many of these peptides have several biological activities. Casein-derived peptides, such as opioid peptides, antihypertensive peptides, casein phosphopeptides and glycomacropeptides have various physiological roles, including adjusting and lowering blood pressure by inhibiting angiotensin converting enzyme (ACE). Caseocinin peptide has been derived from the casein Alpha S1 protein It has an ACE-inhibiting enzyme inhibitor and low blood pressure. The purpose of this study was to identify the alpha S1 protein casein and bioactive peptides of the angiotensin converting enzyme (ACE) inhibitor in human milk and compare it with different species of mammals.   Materials and Methods : At first, genomic and protein data for eight different species of mammals (cattle, sheep, camels, horses, humans, ewes and pigs) was collected from the National Center for Bioinformatics Information (NCBI). Physico-chemical properties analysis (atomic state, isoelectric point, half-life, hydrophobicity hydrophilicity , barometric and pH) and - Multiple sequence alignment of   of alpha-s1 casein and the bioactive peptides of Casokinin in 8 species of mammals was done using CLC Main Workbench 5 software. Then, the prediction of bioactive peptide alpha s1 casein and its three-dimensional structure was done with the help of the online software ACCLUSTER Server, I-TASSER and GalaxyWEB. The simulation of the Molecular interaction (docking) of alpha-s1 casein and the bioactive peptides of Casokinin with angiotensin converting enzyme ACE in the cell was done using ClusPro 2.0 software online.   Results and Discussion: the results of bioinformatics analysis of human milk protein with other mammals showed that camel milk has the most similar physicochemical properties of milk to humans and can be a good alternative to human milk in feeding children. The highest and least percentage of amino acid sequence amino acids in alpha-sec1 casein in different mammals with humans respectively is related to camel and Goat. The results of the determination of the position and energy of the connection show that the most suitable binding location with maximum energy is related to human and camel milk. Among the bioactive peptides identified and predicted in eight species of mammals, due to having more proline amino acid in Caseocinin peptide, the camel milk is more resistant than other species. Therefore, the antihypertensive effect in camel milk is greater than other mammals. According to the results, it can be predicted that three-dimensional structure of protein and peptides will have a great effect on antihypertensive properties and it causes a good interaction with the active site of the angiotensin converting enzyme (ACE) and thus inhibits the ACE enzyme and lowers blood pressure. Investigating the performance of alpha S1 protein casein and Caseocinin peptide identified for eight different species of mammals And comparing its results with human milk in inhibiting ACE enzyme With Molecular interaction (Docking) Protein's Bioinformatics Software showed that milk of camels after human milk has the highest performance in Reducing the risk factors for cardiovascular disease such as inhibition of angiotensin converting enzyme (ACE) and blood pressure in preventing the development and progression of cardiovascular disease.   Conclusion: the alpha S1 protein casein and Caseocinin peptide have many biological properties and are very important in the health of the body.It can be said that camel milk is the most similar to human-like physico-chemical properties and It can be a good alternative to human milk in feeding children. The bioactive Caseocinin peptide has pharmaceutical compounds that can be used to treat high blood pressure and heart disease. Therefore, this peptide can be used as superbenefit and natural additives, an appropriate replacement to antihypertensive drugs.

Introduction [1] : High blood pressure is a dangerous risk factor for cardio-vascular disease, including coronary artery disease and strokes. In the human body, a system called renin-angiotensin system regulates blood pressure, in which the angiotensin converting enzyme ACE plays an important role in increasing blood pressure . Angiotensin I as a converting enzyme (ACE) catalyzes the conversion of angiotensin I to vasoconstrictor angiotensin II, and also inactivates the antihypertensive vasodilator bradykinin. Inhibition of ACE mainly results in an overall antihypertensive effect. Peptides derived from food proteins can have angiotensin converting enzyme (ACE) inhibiting properties. Casein protein in milk or other dairy products such as cheese is a rich source of bioactive peptides . Bioactive peptides are inactive in the main protein sequence and are released in during milk digestion or milk fermentation by proteolytic bacteria or hydrolysis by proteolytic enzymes. Many of these peptides have several biological activities. Casein-derived peptides, such as opioid peptides, antihypertensive peptides, casein phosphopeptides and glycomacropeptides have various physiological roles, including adjusting and lowering blood pressure by inhibiting angiotensin converting enzyme (ACE). Caseocinin peptide has been derived from the casein Alpha S1 protein It has an ACE-inhibiting enzyme inhibitor and low blood pressure. The purpose of this study was to identify the alpha S1 protein casein and bioactive peptides of the angiotensin converting enzyme (ACE) inhibitor in human milk and compare it with different species of mammals.   Materials and Methods : At first, genomic and protein data for eight different species of mammals (cattle, sheep, camels, horses, humans, ewes and pigs) was collected from the National Center for Bioinformatics Information (NCBI). Physico-chemical properties analysis (atomic state, isoelectric point, half-life, hydrophobicity hydrophilicity , barometric and pH) and - Multiple sequence alignment of   of alpha-s1 casein and the bioactive peptides of Casokinin in 8 species of mammals was done using CLC Main Workbench 5 software. Then, the prediction of bioactive peptide alpha s1 casein and its three-dimensional structure was done with the help of the online software ACCLUSTER Server, I-TASSER and GalaxyWEB. The simulation of the Molecular interaction (docking) of alpha-s1 casein and the bioactive peptides of Casokinin with angiotensin converting enzyme ACE in the cell was done using ClusPro 2.0 software online.   Results and Discussion: the results of bioinformatics analysis of human milk protein with other mammals showed that camel milk has the most similar physicochemical properties of milk to humans and can be a good alternative to human milk in feeding children. The highest and least percentage of amino acid sequence amino acids in alpha-sec1 casein in different mammals with humans respectively is related to camel and Goat. The results of the determination of the position and energy of the connection show that the most suitable binding location with maximum energy is related to human and camel milk. Among the bioactive peptides identified and predicted in eight species of mammals, due to having more proline amino acid in Caseocinin peptide, the camel milk is more resistant than other species. Therefore, the antihypertensive effect in camel milk is greater than other mammals. According to the results, it can be predicted that three-dimensional structure of protein and peptides will have a great effect on antihypertensive properties and it causes a good interaction with the active site of the angiotensin converting enzyme (ACE) and thus inhibits the ACE enzyme and lowers blood pressure. Investigating the performance of alpha S1 protein casein and Caseocinin peptide identified for eight different species of mammals And comparing its results with human milk in inhibiting ACE enzyme With Molecular interaction (Docking) Protein's Bioinformatics Software showed that milk of camels after human milk has the highest performance in Reducing the risk factors for cardiovascular disease such as inhibition of angiotensin converting enzyme (ACE) and blood pressure in preventing the development and progression of cardiovascular disease.   Conclusion: the alpha S1 protein casein and Caseocinin peptide have many biological properties and are very important in the health of the body.It can be said that camel milk is the most similar to human-like physico-chemical properties and It can be a good alternative to human milk in feeding children. The bioactive Caseocinin peptide has pharmaceutical compounds that can be used to treat high blood pressure and heart disease. Therefore, this peptide can be used as superbenefit and natural additives, an appropriate replacement to antihypertensive drugs.

Introduction [1] : High blood pressure is a dangerous risk factor for cardio-vascular disease, including coronary artery disease and strokes. In the human body, a system called renin-angiotensin system regulates blood pressure, in which the angiotensin converting enzyme ACE plays an important role in increasing blood pressure . Angiotensin I as a converting enzyme (ACE) catalyzes the conversion of angiotensin I to vasoconstrictor angiotensin II, and also inactivates the antihypertensive vasodilator bradykinin. Inhibition of ACE mainly results in an overall antihypertensive effect. Peptides derived from food proteins can have angiotensin converting enzyme (ACE) inhibiting properties. Casein protein in milk or other dairy products such as cheese is a rich source of bioactive peptides . Bioactive peptides are inactive in the main protein sequence and are released in during milk digestion or milk fermentation by proteolytic bacteria or hydrolysis by proteolytic enzymes. Many of these peptides have several biological activities. Casein-derived peptides, such as opioid peptides, antihypertensive peptides, casein phosphopeptides and glycomacropeptides have various physiological roles, including adjusting and lowering blood pressure by inhibiting angiotensin converting enzyme (ACE). Caseocinin peptide has been derived from the casein Alpha S1 protein It has an ACE-inhibiting enzyme inhibitor and low blood pressure. The purpose of this study was to identify the alpha S1 protein casein and bioactive peptides of the angiotensin converting enzyme (ACE) inhibitor in human milk and compare it with different species of mammals.   Materials and Methods : At first, genomic and protein data for eight different species of mammals (cattle, sheep, camels, horses, humans, ewes and pigs) was collected from the National Center for Bioinformatics Information (NCBI). Physico-chemical properties analysis (atomic state, isoelectric point, half-life, hydrophobicity hydrophilicity , barometric and pH) and - Multiple sequence alignment of   of alpha-s1 casein and the bioactive peptides of Casokinin in 8 species of mammals was done using CLC Main Workbench 5 software. Then, the prediction of bioactive peptide alpha s1 casein and its three-dimensional structure was done with the help of the online software ACCLUSTER Server, I-TASSER and GalaxyWEB. The simulation of the Molecular interaction (docking) of alpha-s1 casein and the bioactive peptides of Casokinin with angiotensin converting enzyme ACE in the cell was done using ClusPro 2.0 software online.   Results and Discussion: the results of bioinformatics analysis of human milk protein with other mammals showed that camel milk has the most similar physicochemical properties of milk to humans and can be a good alternative to human milk in feeding children. The highest and least percentage of amino acid sequence amino acids in alpha-sec1 casein in different mammals with humans respectively is related to camel and Goat. The results of the determination of the position and energy of the connection show that the most suitable binding location with maximum energy is related to human and camel milk. Among the bioactive peptides identified and predicted in eight species of mammals, due to having more proline amino acid in Caseocinin peptide, the camel milk is more resistant than other species. Therefore, the antihypertensive effect in camel milk is greater than other mammals. According to the results, it can be predicted that three-dimensional structure of protein and peptides will have a great effect on antihypertensive properties and it causes a good interaction with the active site of the angiotensin converting enzyme (ACE) and thus inhibits the ACE enzyme and lowers blood pressure. Investigating the performance of alpha S1 protein casein and Caseocinin peptide identified for eight different species of mammals And comparing its results with human milk in inhibiting ACE enzyme With Molecular interaction (Docking) Protein's Bioinformatics Software showed that milk of camels after human milk has the highest performance in Reducing the risk factors for cardiovascular disease such as inhibition of angiotensin converting enzyme (ACE) and blood pressure in preventing the development and progression of cardiovascular disease.   Conclusion: the alpha S1 protein casein and Caseocinin peptide have many biological properties and are very important in the health of the body.It can be said that camel milk is the most similar to human-like physico-chemical properties and It can be a good alternative to human milk in feeding children. The bioactive Caseocinin peptide has pharmaceutical compounds that can be used to treat high blood pressure and heart disease. Therefore, this peptide can be used as superbenefit and natural additives, an appropriate replacement to antihypertensive drugs.

Event Date: 27 March 2014 Room B01 Clore Management Centre Birkbeck, University of London Malet Street London WC1E 7HX The Department of Biological Sciences at Birkbeck presents: The 2014 Bernal Lecture Professor Dame Janet Thornton…

Tri 2 2022/2023 (Term Id 2215)

The molecular mechanism of Glioma is still unclear, and there are few early diagnostic markers. Therefore, it is urgent to figure out effective preventive measures, active diagnostic methods and rapid treatment measures. In recent years, relevant studies have revealed that long non-coding RNA (lncRNA) is associated with the prognosis of Glioma. However, these results have not been supported by any evidence. Therefore, this study carried out a meta-analysis method to analyze the relationship between lncRNA and the prognosis of Glioma. In addition, bioinformatics analysis was conducted to investigate the mechanism and related pathways of lncRNAs in Glioma.

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.

This article is from BMC Systems Biology , volume 7 . Abstract Background: In order to develop hypothesis on unknown metabolic pathways, biochemists frequently rely on literature that uses a free-text format to describe functional groups or substructures. In computational chemistry or cheminformatics, molecules are typically represented by chemical descriptors, i.e., vectors that summarize information on its various properties. However, it is difficult to interpret these chemical descriptors since they are not directly linked to the terminology of functional groups or substructures that the biochemists use. Methods: In this study, we used KEGG Chemical Function (KCF) format to computationally describe biochemical substructures in seven attributes that resemble biochemists' way of dealing with substructures. Results: We established KCF-S (KCF-and-Substructures) format as an additional structural information of KCF. Applying KCF-S revealed the specific appearance of substructures from various datasets of molecules that describes the characteristics of the respective datasets. Structure-based clustering of molecules using KCF-S resulted the clusters in which molecular weights and structures were less diverse than those obtained by conventional chemical fingerprints. We further applied KCF-S to find the pairs of molecules that are possibly converted to each other in enzymatic reactions, and KCF-S clearly improved predictive performance than that presented previously. Conclusions: KCF-S defines biochemical substructures with keeping interpretability, suggesting the potential to apply more studies on chemical bioinformatics. KCF and KCF-S can be automatically converted from Molfile format, enabling to deal with molecules from any data sources.

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As an anticancer gene, microRNA-145 (miRNA-145) inhibits the growth, migration and invasion of cancer cells, and inhibits tumorigenesis by targeting various genes that are abnormally expressed in tumors. However, whether miRNA-145 can be applied as a biomarker for potential prognosis of ovarian cancer still remains controversial. Therefore, this study further explored the prognostic value and mechanism of miRNA-145 in ovarian cancer through meta-analysis and bioinformatics analysis.

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.

This article is from PLoS Computational Biology , volume 10 . Abstract The interdisciplinary nature of bioinformatics makes it an ideal framework to develop activities enabling enquiry-based learning. We describe here the development and implementation of a pilot project to use bioinformatics-based research activities in high schools, called “Bioinformatics@school.” It includes web-based research projects that students can pursue alone or under teacher supervision and a teacher training program. The project is organized so as to enable discussion of key results between students and teachers. After successful trials in two high schools, as measured by questionnaires, interviews, and assessment of knowledge acquisition, the project is expanding by the action of the teachers involved, who are helping us develop more content and are recruiting more teachers and schools.

Talk by Professor Victoria Stodden, Department of Statistics, Columbia University. The talk was given at UC Berkeley. Abstract Large-scale sequencing projects paved the way for the adoption of pioneering open data policies, making bioinformatics one the leading fields for data availability and access. In this talk I will trace the history of open data in -omics based research, and discuss how open code as well are data are being addressed today. This will include discussing leading edge tools and computational infrastructure developments intended to facilitate reproducible research through workflow tracking, computational environments, and data and code sharing.

In-stent restenosis (ISR) is one of the most important complications and impacts the long-term effects after percutaneous coronary intervention (PCI) in patients with coronary heart disease (CHD). Related studies have revealed that microRNA (miRNA) can predict ISR in CHD patients. MiRNA-126 may be a potential biomarker for the diagnosis of ISR. However, the accuracy of miRNA-126 in the diagnosis of ISR is still controversial. Therefore, this study carried out meta-analysis to further evaluate the accuracy of miRNA-126 in the diagnosis of ISR. At the same time, bioinformatics is used to predict the target genes and miRNA-126 may be involved in regulation, so as to provide theoretical support for the precise treatment of CHD.

This article is from BMC Developmental Biology , volume 11 . Abstract Background: During Drosophila development, titers of the steroid ecdysone trigger and maintain temporal and tissue specific biological transitions. Decades of evidence reveal that the ecdysone response is both unique to specific tissues and distinct among developmental timepoints. To achieve this diversity in response, the several isoforms of the Ecdysone Receptor, which transduce the hormone signal to the genome level, are believed to interact with tissue specific cofactors. To date, little is known about the identity of these cofactor interactions; therefore, we conducted a bioinformatics informed, RNAi luciferase reporter screen against a subset of putative candidate cofactors identified through an in silico proteome screen. Candidates were chosen based on criteria obtained from bioinformatic consensus of known nuclear receptor cofactors and homologs, including amino acid sequence motif content and context. Results: The bioinformatics pre-screen of the Drosophila melanogaster proteome was successful in identifying an enriched putative candidate gene cohort. Over 80% of the genes tested yielded a positive hit in our reporter screen. We have identified both cell type specific and common cofactors which appear to be necessary for proper ecdysone induced gene regulation. We have determined that certain cofactors act as co-repressors to reduce target gene expression, while others act as co-activators to increase target gene expression. Interestingly, we find that a few of the cofactors shared among cell types have a reversible roles to function as co-repressors in certain cell types while in other cell types they serve as co-activators. Lastly, these proteins are highly conserved, with higher order organism homologs also harboring the LXXLL steroid receptor interaction domains, suggesting a highly conserved mode of steroid cell target specificity. Conclusions: In conclusion, we submit these cofactors as novel components of the ecdysone signaling pathway in order to further elucidate the dynamics of steroid specificity.

This article is from PLoS ONE , volume 9 . Abstract We discuss the applicability of the Microsoft cloud computing platform, Azure, for bioinformatics. We focus on the usability of the resource rather than its performance. We provide an example of how R can be used on Azure to analyse a large amount of microarray expression data deposited at the public database ArrayExpress. We provide a walk through to demonstrate explicitly how Azure can be used to perform these analyses in Appendix S1 and we offer a comparison with a local computation. We note that the use of the Platform as a Service (PaaS) offering of Azure can represent a steep learning curve for bioinformatics developers who will usually have a Linux and scripting language background. On the other hand, the presence of an additional set of libraries makes it easier to deploy software in a parallel (scalable) fashion and explicitly manage such a production run with only a few hundred lines of code, most of which can be incorporated from a template. We propose that this environment is best suited for running stable bioinformatics software by users not involved with its development.

This article is from PLoS ONE , volume 9 . Abstract Background: Bacterial Dsb enzymes are involved in the oxidative folding of many proteins, through the formation of disulfide bonds between their cysteine residues. The Dsb protein network has been well characterized in cells of the model microorganism Escherichia coli. To gain insight into the functioning of the Dsb system in epsilon-Proteobacteria, where it plays an important role in the colonization process, we studied two homologs of the main Escherichia coli Dsb oxidase (EcDsbA) that are present in the cells of the enteric pathogen Campylobacter jejuni, the most frequently reported bacterial cause of human enteritis in the world. Methods and Results: Phylogenetic analysis suggests the horizontal transfer of the epsilon-Proteobacterial DsbAs from a common ancestor to gamma-Proteobacteria, which then gave rise to the DsbL lineage. Phenotype and enzymatic assays suggest that the two C. jejuni DsbAs play different roles in bacterial cells and have divergent substrate spectra. CjDsbA1 is essential for the motility and autoagglutination phenotypes, while CjDsbA2 has no impact on those processes. CjDsbA1 plays a critical role in the oxidative folding that ensures the activity of alkaline phosphatase CjPhoX, whereas CjDsbA2 is crucial for the activity of arylsulfotransferase CjAstA, encoded within the dsbA2-dsbB-astA operon. Conclusions: Our results show that CjDsbA1 is the primary thiol-oxidoreductase affecting life processes associated with bacterial spread and host colonization, as well as ensuring the oxidative folding of particular protein substrates. In contrast, CjDsbA2 activity does not affect the same processes and so far its oxidative folding activity has been demonstrated for one substrate, arylsulfotransferase CjAstA. The results suggest the cooperation between CjDsbA2 and CjDsbB. In the case of the CjDsbA1, this cooperation is not exclusive and there is probably another protein to be identified in C. jejuni cells that acts to re-oxidize CjDsbA1. Altogether the data presented here constitute the considerable insight to the Epsilonproteobacterial Dsb systems, which have been poorly understood so far.

Protocol for a scoping review and usability evaluation of Workflow Management Systems (WMS) for bioinformatics. This is the first version of the protocol.

x, 58 pages :

Over 200 risk factors for pressure ulcer and deep tissue injury (PU/DTI) development have been reported spanning multiple domains Veterans with chronic spinal cord injury (SCI) have incidence rates as high as 62-80 and 34 will require at least three hospitalizations. PU/DTI may lead to other serious medical complications, such as osteomyelitis, sepsis and even death. Clinical practice guidelines (CPGs) aid clinicians in primary PU/DTI prevention through evidence based practice and expert opinion. However, there are many factors to consider and limited guidance on how to prioritize for individuals. Correction of all PU/DTI risk factors can be both overwhelming and impractical to implement in clinical practice. The relative importance of risk factors has not yet been investigated, limiting care planning and prioritization of interventions. The need to develop effective clinical tools to prioritize the multiple recommendations of CPG has been identified by experts in the field. We will use bioinformatics to enable data extraction, storage, and analysis to support clinical decision support and user-interface development for complex clinical challenges. Our central hypothesis is that the individuals risk factor profile can provide the basis for adaptive personalized PU prevention care planning based on CPG prioritization. The overall objective is to provide weighted systemic insight to PU risk in persons with SCI to support personalized care plans for primary and secondary PU prevention. The SCIPUD Resource will be developed using data sets extracted from VINCI together with cross-sectional study of tissue health profiles and validated using an observation cohort study.

This article is from Biology Direct , volume 7 . Abstract Abstract: As advances in life sciences and information technology bring profound influences on bioinformatics due to its interdisciplinary nature, bioinformatics is experiencing a new leap-forward from in-house computing infrastructure into utility-supplied cloud computing delivered over the Internet, in order to handle the vast quantities of biological data generated by high-throughput experimental technologies. Albeit relatively new, cloud computing promises to address big data storage and analysis issues in the bioinformatics field. Here we review extant cloud-based services in bioinformatics, classify them into Data as a Service (DaaS), Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), and present our perspectives on the adoption of cloud computing in bioinformatics. Reviewers: This article was reviewed by Frank Eisenhaber, Igor Zhulin, and Sandor Pongor.

Indexing massive data sets is extremely expensive for large scale problems. In many fields, huge amounts of data are currently generated, however extracting meaningful information from voluminous data sets, such as computing similarity between elements, is far from being trivial. It remains nonetheless a fundamental need. This work proposes a probabilistic data structure based on a minimal perfect hash function for indexing large sets of keys. Our structure out-compete the hash table for construction, query times and for memory usage, in the case of the indexation of a static set. To illustrate the impact of algorithms performances, we provide two applications based on similarity computation between collections of sequences, and for which this calculation is an expensive but required operation. In particular, we show a practical case in which other bioinformatics tools fail to scale up the tested data set or provide lower recall quality results.

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.

"The book provides an overview of various bioinformatics tools and applications across several fields, including agriculture, corals, structural bioinformatics, data-mining, text-mining, medicinal plants, antibiotics, protein structure prediction, drug design, gene expression, micro-arrays, proteomics, molecular phylogenic location of the Indian Liver Fluke, rough sets to predict protein structural class, artificial neural networks for prediction of amino acids levels, plant systems biology, molecular modeling, homology modeling, bio-efficacy of indigenous bacillus through in-silico approach, fresh aquaculture and fisheries, pesticides and insecticides, and databases and tools development in the relevant area. This book would be particularly valuable for individuals working in the fields of agricultural biotechnology, bioinformatics, computer science, and applied statistics. "

Bioinformatics in Ayurveda has the potential to revolutionize the field by providing scientific validation for Ayurvedic practices, help researchers in understanding disease mechanisms that cause degenerative disorders. which include diseases such as Alzheimers, Parkinsons, Huntingtons, osteoarthritis, and other conditions characterized by the progressive degeneration of tissues, cells, or organs. By leveraging computational tools and data driven insights, bioinformatics aids in understanding the molecular mechanisms of these diseases, identifying potential therapeutic targets, and accelerating the development of effective treatments. It is the key to future commercialization of sequence data from human genome project will be to develop informatics technology that transforms this data into information that is useful for diagnosis and therapy. In this new wave of revolution and commercialization our traditional system of ayurveda needs renovation. This work mainly provides information regarding how bioinformatics plays a role in drug development and understanding the disease mechanism in degenerative disorders. Dr. G. Meghana | Dr. K. Madhusudana Rao | Dr. R. Yamini Diwakar | Dr. T. Leela Rani "Analytical Approach on Application of Bioinformatics in Drug Development in the Management of Degenerative Disorders" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-2 , April 2025, URL: https://www.ijtsrd.com/papers/ijtsrd78489.pdf Paper Url: https://www.ijtsrd.com/medicine/ayurvedic/78489/analytical-approach-on-application-of-bioinformatics-in-drug-development-in-the-management-of-degenerative-disorders/dr-g-meghana

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.

The 14-base pair hypervariable region in mitochondrial DNA (mtDNA) of Asian populations, specifically Japanese and Chinese students at Plattsburgh State University, was examined. Previous research on this 14-base pair region showed it to be susceptible to mutations and as a result indicated direct correlation with specific ethnic populations. Earlier studies provided the 14-base pair region sequence analysis for Asians in general. This inquiry-based project was generated in a junior/senior college general genetics course. The project produced sequences of the 14-base pair hypervariable region for Japanese and Chinese populations, specifically. The investigation examined 28 Japanese and 27 Chinese international students. A Control group, consisting of 20 random students was also sampled. The 14-base pair hypervariable region for each sample was analyzed through comparison using bioinformatics. Analyzing the samples showed that, overall, 46% of the Japanese samples, 59% of the Chinese samples, and 75% of the Control samples had the same 14-base pair sequence. In addition to this, it was observed that the Japanese, Chinese, and Control group all had their own specific mutations within the 14-base pair hypervariable region. (Contains 3 figures and 2 tables.)

BLAST is most popular sequence alignment tool used to align bioinformatics patterns. It uses local alignment process in which instead comparing whole query sequence with database sequence it breaks query sequence into small words and these words are used to align patterns. it uses heuristic method which make it faster than earlier smith-waterman algorithm. But due small query sequence used for align in case of very large database with complex queries it may perform poor. To remove this draw back we suggest by using MSA tools which can filter database in by removing unnecessary sequences from data. This sorted data set then applies to BLAST which can then indentify relationship among them i.e. HOMOLOGS, ORTHOLOGS, PARALOGS. The proposed system can be further use to find relation among two persons or used to create family tree. Ortholog is interesting for a wide range of bioinformatics analyses, including functional annotation, phylogenetic inference, or genome evolution. This system describes and motivates the algorithm for predicting orthologous relationships among complete genomes. The algorithm takes a pairwise approach, thus neither requiring tree reconstruction nor reconciliation

As the most common type of cerebrovascular disease, ischemic stroke is the disturbance of cerebrovascular circulation caused by various factors, with complex pathogenesis. At present, the molecular mechanism of ischemic stroke is still unclear, and there lacks early diagnostic markers. Therefore, there is an urgent need to find effective preventive measures, active diagnostic methods and rapid treatment measures. In recent years, related studies have displayed that Long non-coding RNAs (lncRNAs) is related to the prognosis of ischemic stroke. However, the results are not supported by some evidence. Therefore, in this study, meta-analysis was used to analyze the relationship between lncRNAs and the prognosis of ischemic stroke. In addition, we carried out bioinformatics analysis to study the action mechanism and related pathways of lncRNAs in ischemic stroke.

As the most common type of cerebrovascular disease, ischemic stroke is the disturbance of cerebrovascular circulation caused by various factors, with complex pathogenesis. At present, the molecular mechanism of ischemic stroke is still unclear, and there lacks early diagnostic markers. Therefore, there is an urgent need to find effective preventive measures, active diagnostic methods and rapid treatment measures. In recent years, related studies have displayed that Long non-coding RNAs (lncRNAs) is related to the prognosis of ischemic stroke. However, the results are not supported by some evidence. Therefore, in this study, meta-analysis was used to analyze the relationship between lncRNAs and the prognosis of ischemic stroke. In addition, we carried out bioinformatics analysis to study the action mechanism and related pathways of lncRNAs in ischemic stroke.

As the most common type of cerebrovascular disease, ischemic stroke is the disturbance of cerebrovascular circulation caused by various factors, with complex pathogenesis. At present, the molecular mechanism of ischemic stroke is still unclear, and there lacks early diagnostic markers. Therefore, there is an urgent need to find effective preventive measures, active diagnostic methods and rapid treatment measures. In recent years, related studies have displayed that Long non-coding RNAs (lncRNAs) is related to the prognosis of ischemic stroke. However, the results are not supported by some evidence. Therefore, in this study, meta-analysis was used to analyze the relationship between lncRNAs and the prognosis of ischemic stroke. In addition, we carried out bioinformatics analysis to study the action mechanism and related pathways of lncRNAs in ischemic stroke.

As the most common type of cerebrovascular disease, ischemic stroke is the disturbance of cerebrovascular circulation caused by various factors, with complex pathogenesis. At present, the molecular mechanism of ischemic stroke is still unclear, and there lacks early diagnostic markers. Therefore, there is an urgent need to find effective preventive measures, active diagnostic methods and rapid treatment measures. In recent years, related studies have displayed that Long non-coding RNAs (lncRNAs) is related to the prognosis of ischemic stroke. However, the results are not supported by some evidence. Therefore, in this study, meta-analysis was used to analyze the relationship between lncRNAs and the prognosis of ischemic stroke. In addition, we carried out bioinformatics analysis to study the action mechanism and related pathways of lncRNAs in ischemic stroke.

As the most common type of cerebrovascular disease, ischemic stroke is the disturbance of cerebrovascular circulation caused by various factors, with complex pathogenesis. At present, the molecular mechanism of ischemic stroke is still unclear, and there lacks early diagnostic markers. Therefore, there is an urgent need to find effective preventive measures, active diagnostic methods and rapid treatment measures. In recent years, related studies have displayed that Long non-coding RNAs (lncRNAs) is related to the prognosis of ischemic stroke. However, the results are not supported by some evidence. Therefore, in this study, meta-analysis was used to analyze the relationship between lncRNAs and the prognosis of ischemic stroke. In addition, we carried out bioinformatics analysis to study the action mechanism and related pathways of lncRNAs in ischemic stroke.

As the most common type of cerebrovascular disease, ischemic stroke is the disturbance of cerebrovascular circulation caused by various factors, with complex pathogenesis. At present, the molecular mechanism of ischemic stroke is still unclear, and there lacks early diagnostic markers. Therefore, there is an urgent need to find effective preventive measures, active diagnostic methods and rapid treatment measures. In recent years, related studies have displayed that Long non-coding RNAs (lncRNAs) is related to the prognosis of ischemic stroke. However, the results are not supported by some evidence. Therefore, in this study, meta-analysis was used to analyze the relationship between lncRNAs and the prognosis of ischemic stroke. In addition, we carried out bioinformatics analysis to study the action mechanism and related pathways of lncRNAs in ischemic stroke.

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.

In response to the potential use of viruses as biological weapons, we have established the Viral Biological-threat Bioinformatics Resource (VBBR) that collects, catalogs, annotates, and analyzes genetic information related to potential viral threats. This work expands upon available knowledge of virus replication, pathogenicity, and virus-host interactions on the basis of individual protein domains, individual genes, and whole genomes. To date, we have constructed a genome and gene sequence database that has been populated with the sequence information for viruses currently listed on the NIH and CDC priority pathogen list. We have also developed a variety of analytical and visualization tools that aid in the analysis of the genomic information coded for by these viruses. Finally, the information developed as a result of this work has been made available to the scientific community through a (currently access-controlled) web site (http://vbbr.genome.uab.edu) that supports research efforts to develop environmental detectors, diagnostics, antiviral compounds, new vaccines, and animal models in support of biodefense research goals.

In response to the potential use of viruses as biological weapons, we have established the Viral Biological-threat Bioinformatics Resource (VBBR) that collects, catalogs, annotates, and analyzes genetic information related to potential viral threats. This work expands upon available knowledge of virus replication, pathogenicity, and virus-host interactions on the basis of individual protein domains, individual genes, and whole genomes. To date, we have constructed a genome and gene sequence database that has been populated with the sequence information for viruses currently listed on the NIH and CDC priority pathogen list. We have also developed a variety of analytical and visualization tools that aid in the analysis of the genomic information coded for by these viruses. Finally, the information developed as a result of this work has been made available to the scientific community through a (currently access-controlled) web site (http://vbbr.genome.uab.edu) that supports research efforts to develop environmental detectors, diagnostics, antiviral compounds, new vaccines, and animal models in support of biodefense research goals.

This article is from Genomics & Informatics , volume 11 . Abstract We had analyzed 10 exome sequencing data and single nucleotide polymorphism chips for blood cancer provided by the PGM21 (The National Project for Personalized Genomic Medicine) Award program. We had removed sample G06 because the pair is not correct and G10 because of possible contamination. In-house software somatic copy-number and heterozygosity alteration estimation (SCHALE) was used to detect one loss of heterozygosity region in G05. We had discovered 27 functionally important mutations. Network and pathway analyses gave us clues that NPM1, GATA2, and CEBPA were major driver genes. By comparing with previous somatic mutation profiles, we had concluded that the provided data originated from acute myeloid leukemia. Protein structure modeling showed that somatic mutations in IDH2, RASGEF1B, and MSH4 can affect protein structures.

This article is from Philosophical Transactions of the Royal Society B: Biological Sciences , volume 367 . Abstract There has been a huge effort in the advancement of analytical techniques for molecular biological data over the past decade. This has led to many novel algorithms that are specialized to deal with data associated with biological phenomena, such as gene expression and protein interactions. In contrast, ecological data analysis has remained focused to some degree on off-the-shelf statistical techniques though this is starting to change with the adoption of state-of-the-art methods, where few assumptions can be made about the data and a more explorative approach is required, for example, through the use of Bayesian networks. In this paper, some novel bioinformatics tools for microarray data are discussed along with their ‘crossover potential’ with an application to fisheries data. In particular, a focus is made on the development of models that identify functionally equivalent species in different fish communities with the aim of predicting functional collapse.

This article is from BMC Cancer , volume 12 . Abstract Background: With over 20 million formalin-fixed, paraffin-embedded (FFPE) tissue samples archived each year in the United States alone, archival tissues remain a vast and under-utilized resource in the genomic study of cancer. Technologies have recently been introduced for whole-transcriptome amplification and microarray analysis of degraded mRNA fragments from FFPE samples, and studies of these platforms have only recently begun to enter the published literature. Results: The Emerging Technologies for Translational Bioinformatics symposium on gene expression profiling for archival tissues featured presentations of two large-scale FFPE expression profiling studies (each involving over 1,000 samples), overviews of several smaller studies, and representatives from three leading companies in the field (Illumina, Affymetrix, and NuGEN). The meeting highlighted challenges in the analysis of expression data from archival tissues and strategies being developed to overcome them. In particular, speakers reported higher rates of clinical sample failure (from 10% to 70%) than are typical for fresh-frozen tissues, as well as more frequent probe failure for individual samples. The symposium program is available at http://www.hsph.harvard.edu/ffpe. Conclusions: Multiple solutions now exist for whole-genome expression profiling of FFPE tissues, including both microarray- and sequencing-based platforms. Several studies have reported their successful application, but substantial challenges and risks still exist. Symposium speakers presented novel methodology for analysis of FFPE expression data and suggestions for improving data recovery and quality assessment in pre-analytical stages. Research presentations emphasized the need for careful study design, including the use of pilot studies, replication, and randomization of samples among batches, as well as careful attention to data quality control. Regardless of any limitations in quantitave transcriptomics for FFPE tissues, they are often the only biospecimens available for large patient populations with long-term history and clinical follow-up. Current challenges can be expected to remain as RNA sequencing matures, and they will thus motivate ongoing research efforts into noise reduction and identification of robust, translationally relevant biological signals in expression data from FFPE tissues.

This article is from BMC Cancer , volume 12 . Abstract Background: With over 20 million formalin-fixed, paraffin-embedded (FFPE) tissue samples archived each year in the United States alone, archival tissues remain a vast and under-utilized resource in the genomic study of cancer. Technologies have recently been introduced for whole-transcriptome amplification and microarray analysis of degraded mRNA fragments from FFPE samples, and studies of these platforms have only recently begun to enter the published literature. Results: The Emerging Technologies for Translational Bioinformatics symposium on gene expression profiling for archival tissues featured presentations of two large-scale FFPE expression profiling studies (each involving over 1,000 samples), overviews of several smaller studies, and representatives from three leading companies in the field (Illumina, Affymetrix, and NuGEN). The meeting highlighted challenges in the analysis of expression data from archival tissues and strategies being developed to overcome them. In particular, speakers reported higher rates of clinical sample failure (from 10% to 70%) than are typical for fresh-frozen tissues, as well as more frequent probe failure for individual samples. The symposium program is available at http://www.hsph.harvard.edu/ffpe. Conclusions: Multiple solutions now exist for whole-genome expression profiling of FFPE tissues, including both microarray- and sequencing-based platforms. Several studies have reported their successful application, but substantial challenges and risks still exist. Symposium speakers presented novel methodology for analysis of FFPE expression data and suggestions for improving data recovery and quality assessment in pre-analytical stages. Research presentations emphasized the need for careful study design, including the use of pilot studies, replication, and randomization of samples among batches, as well as careful attention to data quality control. Regardless of any limitations in quantitave transcriptomics for FFPE tissues, they are often the only biospecimens available for large patient populations with long-term history and clinical follow-up. Current challenges can be expected to remain as RNA sequencing matures, and they will thus motivate ongoing research efforts into noise reduction and identification of robust, translationally relevant biological signals in expression data from FFPE tissues.

Bioinformatics 1998: Volume 14 , Issue Index. Digitized from IA1586925-06 . Next issue: sim_bioinformatics_1998_14_1 .