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

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CA has grown as potential classifier for addressing major problems in bioinformatics. Lot of bioinformatics problems like predicting the protein coding region, finding the promoter region, predicting the structure of protein and many other problems in bioinformatics can be addressed through Cellular Automata. Even though there are some prediction techniques addressing these problems, the approximate accuracy level is very less. An automated procedure was proposed with MACA (Multiple Attractor Cellular Automata) which can address all these problems. The genetic algorithm is also used to find rules with good fitness values. Extensive experiments are conducted for reporting the accuracy of the proposed tool. The average accuracy of MACA when tested with ENCODE, BG570, HMR195, Fickett and Tongue, ASP67 datasets is 78%.

CA has grown as potential classifier for addressing major problems in bioinformatics. Lot of bioinformatics problems like predicting the protein coding region, finding the promoter region, predicting the structure of protein and many other problems in bioinformatics can be addressed through Cellular Automata. Even though there are some prediction techniques addressing these problems, the approximate accuracy level is very less. An automated procedure was proposed with MACA (Multiple Attractor Cellular Automata) which can address all these problems. The genetic algorithm is also used to find rules with good fitness values. Extensive experiments are conducted for reporting the accuracy of the proposed tool. The average accuracy of MACA when tested with ENCODE, BG570, HMR195, Fickett and Tongue, ASP67 datasets is 78%.

The genomes of hundreds of pathogens and their near neighbors are now available and many more are being sequenced. With the availability of this genome information, sequence-based pathogen identification has become an increasingly important tool for clinical diagnostics and environmental monitoring of biological threat agents. Chief among sequence-based identification tools are DNA microarrays, which have the ability to test for thousands of pathogens in a single diagnostic test. The design of microarray diagnostic assays involves the identification of short DNA sequences unique to a pathogen or groups of pathogens where these unique sequences, or ?fingerprints? (also referred to as probes) are used to identify the pathogens. To design pathogen fingerprints, we developed TOFI (Tool for Oligonucleotide Fingerprint Identification), a high performance computing software pipeline that designs microarray probes for multiple related pathogens in a single run. The TOFI pipeline is extremely efficient in designing microarray fingerprints for multiple pathogens. Parallel implementation of computationally expensive specificity analysis of the designed fingerprints drastically reduces the overall execution time of the software. Comprehensive performance analysis shows that TOFI achieves super-linear speedup for up to 74 processors. A Web-based user interface developed using the User Interface Toolkit, provides easy access to the pipeline. Using 74 processors, TOFI took approximately nine hours to design 5,015 in-silico probes for eight Burkholderia genomes with a combined size of more than 50 million base pairs. Experimental validation of these probes with various Burkholderia genomes showed that nearly 80% of the designed fingerprints identify the intended targets.

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 because 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. Last year (first year), I demonstrated that LMO4 can modulate the proliferative response of epithelial cells to TGF signaling and linked LMO4 to a conserved signaling pathway that plays important roles in epithelial homeostasis. This year, I continued to be trained in bioinformatics; I took classes in statistical methods, and received practical training in evaluating large microarray datasets. In addition, I evaluate the regulation by LMO4 of cellular feature of normal breast epithelial cells and cancer cell lines. Both overexpression and knockdown of the LMO4 protein did not have major effects on cell proliferation, migration, invasion and colony formation of primary mammary gland epithelial cell and cancer cell lines (MCF-7, MDA-MB-231 or T47D). However, changes in LMO4 protein level markedly increased apoptosis of mammary normal or cancer cells. Using cDNA microarrays, we screened several LMO4-responsive genes. BMP7 was identified as a key down-stream gene by ChIP assays and promoter reporter assays. Both increase and decrease in LMO4 level can increase BMP7 transcription. BMP7 signaling inhibitor blocked the LMO4 induced apoptosis of MCF-7 cells, indicating that BMP7 mediates LMO4 effects on apoptosis in MCF-7 breast cancer cells. In addition, we 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. We further demonstrated that LMO4 binds to HDAC2 and that they are recruited together to the BMP7 promoter.

In the process of designing and field-testing bioinformatics curriculum materials, we have adopted a three-stage, progressive model that emphasizes collaborative scientific inquiry. The elements of the model include: (1) context setting, (2) introduction to concepts, processes, and tools, and (3) development of competent use of technologically sophisticated tools. A curriculum involving the analysis of HIV sequence data is used to illustrate this framework and provide a context for discussing this student-centered, inquiry-based approach to bioinformatics education and literacy. (Contains 6 figures.)

Lavinia Gordon https://linux.conf.au/schedule/30308/view_talk http://afrubin.github.io/miniconf/

A backup of the "University of Georgia Institute of Bioinformatics Wiki" wiki (database name: ugaiobwiki ) on Miraheze , self-generated by Miraheze itself every month to honor our backups commitment.

A backup of the "University of Georgia Institute of Bioinformatics Wiki" wiki (database name: ugaiobwiki ) on Miraheze , self-generated by Miraheze itself every month to honor our backups commitment.

A backup of the "University of Georgia Institute of Bioinformatics Wiki" wiki (database name: ugaiobwiki ) on Miraheze , self-generated by Miraheze itself every month to honor our backups commitment.

A backup of the "University of Georgia Institute of Bioinformatics Wiki" wiki (database name: ugaiobwiki ) on Miraheze , self-generated by Miraheze itself every month to honor our backups commitment.

A backup of the "University of Georgia Institute of Bioinformatics Wiki" wiki (database name: ugaiobwiki ) on Miraheze , self-generated by Miraheze itself every month to honor our backups commitment.

To facilitate flexible and efficient structural bioinformatics analyses, new functionality for three-dimensional structure processing and analysis has been introduced into PyCogent -- a popular feature-rich framework for sequence-based bioinformatics, but one which has lacked equally powerful tools for handling stuctural/coordinate-based data. Extensible Python modules have been developed, which provide object-oriented abstractions (based on a hierarchical representation of macromolecules), efficient data structures (e.g. kD-trees), fast implementations of common algorithms (e.g. surface-area calculations), read/write support for Protein Data Bank-related file formats and wrappers for external command-line applications (e.g. Stride). Integration of this code into PyCogent is symbiotic, allowing sequence-based work to benefit from structure-derived data and, reciprocally, enabling structural studies to leverage PyCogent's versatile tools for phylogenetic and evolutionary analyses.

Bioinformatics dumped with wikiteam3 tools.

Bioinformatics dumped with wikiteam3 tools.

Bioinformatics dumped with wikiteam3 tools.

Bioinformatics dumped with wikiteam3 tools.

pages cm

pages cm

pages cm

DokuWiki: start – Machine Learning and Bioinformatics Lab Dumped with DokuWiki-Dumper v0.1.26, and uploaded with dokuWikiUploader v0.1.26.

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.

The performance (accuracy and robustness) of several clustering algorithms is studied for linearly dependent random variables in the presence of noise. It turns out that the error percentage quickly increases when the number of observations is less than the number of variables. This situation is common situation in experiments with DNA microarrays. Moreover, an {\it a posteriori} criterion to choose between two discordant clustering algorithm is presented.

The performance (accuracy and robustness) of several clustering algorithms is studied for linearly dependent random variables in the presence of noise. It turns out that the error percentage quickly increases when the number of observations is less than the number of variables. This situation is common situation in experiments with DNA microarrays. Moreover, an {\it a posteriori} criterion to choose between two discordant clustering algorithm is presented.

The performance (accuracy and robustness) of several clustering algorithms is studied for linearly dependent random variables in the presence of noise. It turns out that the error percentage quickly increases when the number of observations is less than the number of variables. This situation is common situation in experiments with DNA microarrays. Moreover, an {\it a posteriori} criterion to choose between two discordant clustering algorithm is presented.

The performance (accuracy and robustness) of several clustering algorithms is studied for linearly dependent random variables in the presence of noise. It turns out that the error percentage quickly increases when the number of observations is less than the number of variables. This situation is common situation in experiments with DNA microarrays. Moreover, an {\it a posteriori} criterion to choose between two discordant clustering algorithm is presented.

The performance (accuracy and robustness) of several clustering algorithms is studied for linearly dependent random variables in the presence of noise. It turns out that the error percentage quickly increases when the number of observations is less than the number of variables. This situation is common situation in experiments with DNA microarrays. Moreover, an {\it a posteriori} criterion to choose between two discordant clustering algorithm is presented.

Data Protection Record of Processing Activities and Privacy Notice generator toolkit by EMBL’s European Bioinformatics Institute

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.

Community structure is a topological property common to many networks. We present in this paper an efficient and accurate approach to detecting a community in a protein-protein interaction network from a given seed protein. Our experimental results show strong structural and functional relationships among member proteins within each of the communities identified by our approach, as verified by MIPS complex catalogue database and annotations.

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.

كتاب المعلوماتية الحيوية

كتاب المعلوماتية الحيوية

كتاب المعلوماتية الحيوية

pages cm

pages cm

pages cm

"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. "

"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. "

"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. "

High-throughput biological assays such as microarrays let us ask very detailed questions about how diseases operate, and promise to let us personalize therapy. Data processing, however, is often not described well enough to allow for exact reproduction of the results, leading to exercises in "forensic bioinformatics" where aspects of raw data and reported results are used to infer what methods must have been employed. Unfortunately, poor documentation can shift from an inconvenience to an active danger when it obscures not just methods but errors. In this report we examine several related papers purporting to use microarray-based signatures of drug sensitivity derived from cell lines to predict patient response. Patients in clinical trials are currently being allocated to treatment arms on the basis of these results. However, we show in five case studies that the results incorporate several simple errors that may be putting patients at risk. One theme that emerges is that the most common errors are simple (e.g., row or column offsets); conversely, it is our experience that the most simple errors are common. We then discuss steps we are taking to avoid such errors in our own investigations.

The functional correlation of missense mutations which cause disease remains a challenge to understanding the basis of genetic diseases. This is particularly true for proteins related to diseases for which there are no available three dimensional structures. One such disease is Shwachman Diamond syndrome SDS OMIM 260400, a multi system disease arising from loss of functional mutations. The Homo sapiens Shwachman Bodian Diamond Syndrome gene hSBDS is responsible for SDS. hSBDS is expressed in all tissues and encodes a protein of 250 amino acids SwissProt accession code Q9Y3A5. Sequence analysis of disease associated alleles has identified more than 20 different mutations in affected individuals. While a number of these mutations have been described as leading to the loss of protein function due to truncation, translation or surface epitope association, the structural basis for these mutations has yet to be determined due to the lack of a three-dimensional structure for SBDS.

The functional correlation of missense mutations which cause disease remains a challenge to understanding the basis of genetic diseases. This is particularly true for proteins related to diseases for which there are no available three dimensional structures. One such disease is Shwachman Diamond syndrome SDS OMIM 260400, a multi system disease arising from loss of functional mutations. The Homo sapiens Shwachman Bodian Diamond Syndrome gene hSBDS is responsible for SDS. hSBDS is expressed in all tissues and encodes a protein of 250 amino acids SwissProt accession code Q9Y3A5. Sequence analysis of disease associated alleles has identified more than 20 different mutations in affected individuals. While a number of these mutations have been described as leading to the loss of protein function due to truncation, translation or surface epitope association, the structural basis for these mutations has yet to be determined due to the lack of a three-dimensional structure for SBDS.

One of the greatest benefits of escience—the use of distributed computing and data resources for scientific discovery—is the opportunity for scientists to begin working with data sets that would have been too large to work with otherwise and, consequently, ask questions that would have not been possible. There are many obvious challenges escience faces because of its distributed nature, but other challenges that, while not uniquely escientific, remain sufficiently domain-sensitive that solutions do not seem easily shareable. One particularly difficult problem is integration—how to coherently bring together disparate, massive data sets. Focus has been generally placed on the physical layer, borrowing from the three layers of data modeling, where details of implementation predominate. This problem will likely continue, though there is some hope leveraging “smart” architectures like smart clients. Logical integration—how to meaningfully bring together massive, disparate data sets—from the scientists’ perspective is even more challenging. Another challenge of escience is creating meaningful, interactive visualizations of massive data sets. A direct benefit of this kind of visualization is allowing the scientist to freely explore in a setting that is more familiar and intuitive. In this presentation will we discuss three ongoing projects, CATPA (Curation and Alignment Tool for Protein Analysis), INGeNE (Integrated, Gene Network Explorer), and SNPEx (SNP Explorer) that address the challenges of integration and visualization. CATPA is a smart client application that allows for the curation of protein families at the residue level, including deletions. Interaction is done visually. INGeNE is an application that allows for functional genomic discovery by building networks of relationships where an edge is a determined by a combination of microarray data, protein-protein data, gene-gene interaction data, and phenotypic expression data. SNPEx is an application that includes a novel algorithm to find the most informative set of tagging SNPs. Additionally, we decided to implement SNPEx in both Java/MySQL and C#/SQLServer 2000 to compare performance of the two systems and found the later to be superior in our suite of tests. ©2005 Microsoft Corporation. All rights reserved.

Git bundles for all non-empty code and wiki repositories under https://github.com/LANL-Bioinformatics as of 2025-03-04 20:39 UTC. Retrieved with Git 2.30.2 using git clone --mirror $REPOURL $TMPCLONEDIR and GIT_DIR=$TMPCLONEDIR git bundle create $BUNDLENAME --all

One of the greatest benefits of escience—the use of distributed computing and data resources for scientific discovery—is the opportunity for scientists to begin working with data sets that would have been too large to work with otherwise and, consequently, ask questions that would have not been possible. There are many obvious challenges escience faces because of its distributed nature, but other challenges that, while not uniquely escientific, remain sufficiently domain-sensitive that solutions do not seem easily shareable. One particularly difficult problem is integration—how to coherently bring together disparate, massive data sets. Focus has been generally placed on the physical layer, borrowing from the three layers of data modeling, where details of implementation predominate. This problem will likely continue, though there is some hope leveraging “smart” architectures like smart clients. Logical integration—how to meaningfully bring together massive, disparate data sets—from the scientists’ perspective is even more challenging. Another challenge of escience is creating meaningful, interactive visualizations of massive data sets. A direct benefit of this kind of visualization is allowing the scientist to freely explore in a setting that is more familiar and intuitive. In this presentation will we discuss three ongoing projects, CATPA (Curation and Alignment Tool for Protein Analysis), INGeNE (Integrated, Gene Network Explorer), and SNPEx (SNP Explorer) that address the challenges of integration and visualization. CATPA is a smart client application that allows for the curation of protein families at the residue level, including deletions. Interaction is done visually. INGeNE is an application that allows for functional genomic discovery by building networks of relationships where an edge is a determined by a combination of microarray data, protein-protein data, gene-gene interaction data, and phenotypic expression data. SNPEx is an application that includes a novel algorithm to find the most informative set of tagging SNPs. Additionally, we decided to implement SNPEx in both Java/MySQL and C#/SQLServer 2000 to compare performance of the two systems and found the later to be superior in our suite of tests. ©2005 Microsoft Corporation. All rights reserved.

This article is from BMC Bioinformatics , volume 13 . Abstract Background: MicroRNAs (miRNAs) are noncoding RNAs that direct post-transcriptional regulation of protein coding genes. Recent studies have shown miRNAs are important for controlling many biological processes, including nervous system development, and are highly conserved across species. Given their importance, computational tools are necessary for analysis, interpretation and integration of high-throughput (HTP) miRNA data in an increasing number of model species. The Bioinformatics Resource Manager (BRM) v2.3 is a software environment for data management, mining, integration and functional annotation of HTP biological data. In this study, we report recent updates to BRM for miRNA data analysis and cross-species comparisons across datasets. Results: BRM v2.3 has the capability to query predicted miRNA targets from multiple databases, retrieve potential regulatory miRNAs for known genes, integrate experimentally derived miRNA and mRNA datasets, perform ortholog mapping across species, and retrieve annotation and cross-reference identifiers for an expanded number of species. Here we use BRM to show that developmental exposure of zebrafish to 30 uM nicotine from 6–48 hours post fertilization (hpf) results in behavioral hyperactivity in larval zebrafish and alteration of putative miRNA gene targets in whole embryos at developmental stages that encompass early neurogenesis. We show typical workflows for using BRM to integrate experimental zebrafish miRNA and mRNA microarray datasets with example retrievals for zebrafish, including pathway annotation and mapping to human ortholog. Functional analysis of differentially regulated (p

This article is from Biology , volume 1 . Abstract Forward genetic screens in Drosophila melanogaster using ethyl methanesulfonate (EMS) mutagenesis are a powerful approach for identifying genes that modulate specific biological processes in an in vivo setting. The mapping of genes that contain randomly-induced point mutations has become more efficient in Drosophila thanks to the maturation and availability of many types of genetic tools. However, classic approaches to gene mapping are relatively slow and ultimately require extensive Sanger sequencing of candidate chromosomal loci. With the advent of new high-throughput sequencing techniques, it is increasingly efficient to directly re-sequence the whole genome of model organisms. This approach, in combination with traditional chromosomal mapping, has the potential to greatly simplify and accelerate mutation identification in mutants generated in EMS screens. Here we show that next-generation sequencing (NGS) is an accurate and efficient tool for high-throughput sequencing and mutation discovery in Drosophila melanogaster. As a test case, mutant strains of Drosophila that exhibited long-term survival of severed peripheral axons were identified in a forward EMS mutagenesis. All mutants were recessive and fell into a single lethal complementation group, which suggested that a single gene was responsible for the protective axon degenerative phenotype. Whole genome sequencing of these genomes identified the underlying gene ect4. To improve the process of genome wide mutation identification, we developed Genomes Management Application (GEM.app, https://genomics.med.miami.edu), a graphical online user interface to a custom query framework. Using a custom GEM.app query, we were able to identify that each mutant carried a unique non-sense mutation in the gene ect4 (dSarm), which was recently shown by Osterloh et al. to be essential for the activation of axonal degeneration. Our results demonstrate the current advantages and limitations of NGS in Drosophila and we introduce GEM.app as a simple yet powerful genomics analysis tool for the Drosophila community. At a current cost of

This article is from Genes , volume 1 . Abstract The emergence of next-generation sequencing (NGS) platforms imposes increasing demands on statistical methods and bioinformatic tools for the analysis and the management of the huge amounts of data generated by these technologies. Even at the early stages of their commercial availability, a large number of softwares already exist for analyzing NGS data. These tools can be fit into many general categories including alignment of sequence reads to a reference, base-calling and/or polymorphism detection, de novo assembly from paired or unpaired reads, structural variant detection and genome browsing. This manuscript aims to guide readers in the choice of the available computational tools that can be used to face the several steps of the data analysis workflow.