Project Management Resources

One of the places that I get a lot of my project management information from is here from the Columbia University Project Reference Site. This is a great site that has a lot of resources.  You could learn to be a PM even without that PMP!

12207_plan_wbs  Here is a IEEE 12207 spreadsheet that I have used in the past a as a primer for projects.  I didn’t make it but I am passing it on!

 

AFCEA and The National Defense Magazine Resources

These organizations have valuable resources that allow you to search over thousands of companies. If you are looking for a job or a capability check them out.
Mega Directory http://nationaldefensemegadirectory.com/
Source Book http://www.afcea.org/sourcebook/search.jsp?dir=S

Great Posts from http://practicalanalyst.com/

More specifically, I like his requirements model Also he linked his Visio Here

Cloud Computing Professional

Subject: Affiliation Discount for Cloud Technology Professional Training Workshop

CloudSchool.com and Arcitura Education are pleased to offer an exclusive discount for the Cloud Technology Professional Training Certification Workshop during September 12-14, 2011 in New York, NY, USA. If three or more individuals register from the same organization, all three registrants are entitled to a further 20% discount on the workshop registration fees. At the time of registration, use the promotion code Howard Cohen to receive this discount or contact info@cloudschool.com directly.

Register via the online form: http://www.cloudschool.com/workshops/newyork0911

The Cloud Technology Professional Training Certification Workshop consists of three one-day course modules covering a range of topics pertaining to cloud computing, cloud platforms, cloud technologies and mechanisms. These courses correspond to the exam requirements for the industry Cloud Technology Professional Certification. Registrants are further entitled to significant discounts on Self-Study Kits and Prometric exam vouchers. For more information about this and other workshops scheduled throughout Europe, visit: http://www.cloudworkshops.com.

This workshop is comprised of course modules that are part of the Cloud Certified Professional (CCP™) program, a curriculum dedicated to excellence in the fields of cloud computing technology, architecture, security, and governance. Vendor-neutral CCP courses are made available through public and private instructor-led workshops offered by Certified Cloud Trainers. These courses are also available via self-study kits which allows for self-paced remote study for anyone in the world. Each course module has a corresponding Prometric exam that can be taken at Prometric testing centers throughout the world. To learn more, visit: http://www.cloudschool.com, http://www.prometric.com/arcitura/ and http://www.arcitura.com

DoD Problems.. Challenge… How to..

 

As a disclaimer, my views do not represent in any way the view of Booz Allen Hamilton.   

I am just one person but I think it is pretty interesting that I can walk into any defense industry space and talk about the same issues and concerns and get exactly the same response.   What is the number one problem in the Department of Defense ?  Is it the war efforts?  Is it the lack of technology?  Is it our efforts in cyberspace?  Is it our loss of troops?  Is it a systematic and holistic failure to meet the many missions of the Department of Defense?   The number one problem that most people I encounter is acquisition.   From civilian senior leadership, military leaders, middle managers down to the staff and action officer level most if not all people identify the business management and acquisition process as being the number one problem in the Department of Defense .

As an Analyst I ask questions that are by design aimed to root out the “real” problem.   Most problems have a tendency to be people or situation based.   In other words, problems are normally masked by symptoms.  We have a tendency to address symptoms because it is what we see and normally because we can work on symptoms with some form of technology.    If you are still reading this I am most likely preaching to the choir and since this is a blog entry, I am not going to go into analogies.  Simply put we generally address the wrong areas when trying to accomplish our goals.   So, I am moving back to what I started talking about which addresses acquisition being one of if not the most important problem to deal with first.   In actuality, acquisition itself is a symptom of the real problem.

In the past our government was an economic juggernaut and could handle wasteful spending and inefficient behavior.  I recently read an article that Apple computer has more money than the US Treasury Department http://tech.fortune.cnn.com/2011/07/29/the-u-s-treasury-has-less-cash-on-hand-than-apple-inc/.    This isn’t an “apples to apples” comparison but it does tell us that as a country we are fiscally and economically challenged and failing to properly manage our money.   This is nothing new here right?  I am simply telling you something that you most probably already know.

That is my point.  If you know this and I know this and they being the government leadership and people that can make a difference by addressing policy, governance and enforcement know this,  why isn’t anything being done?   So, what is the problem we actually need to solve?  The people of this country need to address the behavior that is causing this failure.  I don’t think politicians will make a difference. Calling your Senator won’t help us here.   Congress and the President are busy; just give them a call to see how accessible they are.

There are various missions in the DoD that tackle the many challenges our war fighters, military leaders and civilian leaders face.   From my experience the mission, vision and scope of work is well justified by program leaders.   When I say “well justified” it doesn’t mean I personally agree with it, just that these things are scripted to be important.

So far, I have discussed a symptom of a significant problem (the handling of money and our business practices), my perception of the real problem (behavior) and the fact that people can justify their current behavior.   I have also identified the people that should be able to help solve this problem but for one reason or another don’t.

What’s next?

Well.. I believe in Americans.  I believe that when we see various challenges that we individually step up and out to deal with them.   We have put our faith and trust in leadership and leadership has been pounded with more work than they can handle (yes, I am being nice).    That being said, it is up to us individually to lead where we are.   We must individually work to change our own behavior and look to influence others by leading from where we are.   If I am a Janitor, then I look for ways to be efficient in cleaning and thrifty in spending for supplies, or find ways to reuse supplies.   If you are an Executive Assistant, find ways to make a difference in the office.   If you are a Technical Strategist, teach everyone everything you know about Service Orientation and Trusted Computing and technical reuse models.   It doesn’t matter who you are, it matters what you do.   Our jobs do not define us holistically.   In recent days I have seen civilian leaders (you know who you are) step up to the plate and take risks in order to share their ideas on how to create a more effective and efficient acquisition solutions.   It isn’t only up to them.  We will find more success together by working to change these behaviors and tackling the challenges we can see one person and one problem at a time.

It is difficult to understand how we affect each other and how our behavior can become viral.  People can become rock stars overnight or become known for many reasons good or bad.  It doesn’t matter why, it just matter that it is possible and we see it every day.   It can be you, it can be me, and we can make a positive difference.

ToOls

 

 

 

 

 

From time to time I like to share tools I find while working and searching the interwebs. http://pencil.evolus.vn/en-US/Home.aspx

“The Pencil Project’s unique mission is to build a free and opensource tool for making diagrams and GUI prototyping that everyone can use.”

Here are a few more

The following tools make use of the GO ontologies or the gene associations provided by Consortium members. Being listed on this page does not represent an endorsement by the GO Consortium, nor has the Consortium tested the tool or found that it uses the Consortium information accurately. This page is provided to promote an exchange of information between users and software developers.

Key
Use tool online
web-based tool
Download tool
downloadable tool
Windows compatible Mac OS X compatible
Unix compatible linux compatible
compatible OSs (for downloadable tools)

Unless stated otherwise, tools are free for academic use.

Download tool

Windows compatibleMac OS X compatiblelinux compatible

Avadis

Strand Genomics
No publication

Avadis is a data analysis and visualization tool for gene expression data. Avadis has a built-in Gene Ontology browser to view ontology hierarchies. There are common ontology paths for multiple genes. Genes can be clustered based on ontology terms to identify functional signatures in gene expression clusters.

Note that Avadis is proprietary software.

Download tool

Windows compatibleMac OS X compatibleUnix compatiblelinux compatible

BiNGO

Department of Plant Systems Biology, VIB/Ghent University
[Publication abstract]

BiNGO is an open-source Java tool to determine which GO categories are statistically over-represented in a set of genes. BiNGO is implemented as a plugin for Cytoscape, which is a software platform for data integration and visualization of molecular interaction networks. BiNGO maps the predominant functional themes of a given gene set on the GO hierarchy, and outputs this mapping as a Cytoscape graph.

Use tool online

CLASSIFI

Department of Pathology, UT Southwestern Medical Center
[Publication abstract]

CLASSIFI (Cluster Assignment for Biological Inference) is a data-mining tool that can be used to identify significant co-clustering of genes with similar functional properties (e.g. cellular response to DNA damage). Briefly, CLASSIFI uses the Gene Ontology gene annotation scheme to define the functional properties of all genes/probes in a microarray data set, and then applies a cumulative hypergeometric distribution analysis to determine if any statistically significant gene ontology co-clustering has occurred.

Download tool

Windows compatiblelinux compatible

CLENCH

Stanford Center for Biomedical Informatics Research
[Publication abstract]

CLENCH (CLuster ENriCHment) allows A. thaliana researchers to perform automated retrieval of GO annotations from TAIR and calculate enrichment of GO terms in gene group with respect to a reference set. Before calculating enrichment, CLENCH allows mapping of the returned annotations to arbitrary coarse levels using GO slim term lists (which can be edited by the user) and a local installation of GO.

Download tool

Windows compatibleMac OS X compatibleUnix compatiblelinux compatible

ClueGO

INSERM U872 Integrative Cancer Immunology Team 15, Cordelier Research Center, Paris, France
[Publication abstract]

ClueGO is a Cytoscape plug-in that visualizes the non-redundant biological terms for large clusters of genes in a functionally grouped network. It can be used in combination with GOlorize. The identifiers can be uploaded from a text file or interactively from a network of Cytoscape. The type of identifiers supported can be easily extended by the user. ClueGO performs single cluster analysis and comparison of clusters. From the ontology sources used, the terms are selected by different filter criteria. The related terms which share similar associated genes can be combined to reduce redundancy. The ClueGO network is created with kappa statistics and reflects the relationships between the terms based on the similarity of their associated genes. On the network, the node colour can be switched between functional groups and clusters distribution. ClueGO charts are underlying the specificity and the common aspects of the biological role. The significance of the terms and groups is automatically calculated. ClueGO is easy updatable with the newest files from Gene Ontology and KEGG.

Use tool online

Database for Annotation, Visualization and Integrated Discovery (DAVID)

National Institute of Allergy and Infectious Diseases
[Publication abstract]

Database for Annotation, Visualization and Integrated Discovery (DAVID) is a web-based tool that provides integrated solutions for the annotation and analysis of genome-scale datasets derived from high-throughput technologies such as microarray and proteomic platforms. Analysis results and graphical displays remain dynamically linked to primary data and external data repositories, thereby furnishing in-depth as well as broad-based data coverage. The functionality provided by DAVID accelerates the analysis of genome-scale datasets by facilitating the transition from data collection to biological meaning.

Download tool

Windows compatible

EASE

National Institute of Allergy and Infectious Diseases
[Publication abstract]

EASE is useful for summarizing the predominant biological “theme” of a given gene list. Given a list of genes resulting from a microarray or other genome-scale experiment, EASE can rapidly calculate over-representation statistics for every possible Gene Ontology term with respect to all genes represented in the data set.

Use tool online

EasyGO

Zhen Su lab at the College of Biological Sciences, China Agricultural University, Beijing, China.
[Publication abstract]

EasyGO is designed for GO term enrichment analysis for agricultural species. It covers gene identifiers and microarray probe IDs for 15 species, including crops and farm animals.

Download tool

Windows compatibleMac OS X compatiblelinux compatible

EGAN: Exploratory Gene Association Networks

UCSF Helen Diller Family Comprehensive Cancer Center Biostatistics and Computational Biology Core
[Publication abstract]

EGAN is a software tool that allows a bench biologist to visualize and interpret the results of high-throughput exploratory assays in an interactive hypergraph of genes, relationships (protein-protein interactions, literature co-occurrence, etc.) and meta-data (annotation, signaling pathways, etc.). EGAN provides comprehensive, automated calculation of meta-data coincidence (over-representation, enrichment) for user- and assay-defined gene lists, and provides direct links to web resources and literature (NCBI Entrez Gene, PubMed, KEGG, Gene Ontology, iHOP, Google, etc.).

Use tool online

eGOn V2.0 (explore Gene Ontology)

Norwegian University of Science and Technology and Norwegian Microarray Consortium
No publication

eGOn V2.0 (explore Gene Ontology) is a web-based tool for mapping microarray data on to the Gene Ontology structure. Several input files may be analyzed simultaneously to compare the distribution of the annotated genes for two or more experiments.

Essential features of eGOn V2.0 are:

  • Visualization: gene annotations are visualized in the GO DAG or as a table view. The granularity of the GO DAG can be edited freely by the user.
  • Filtering: GO annotations can be filtered on evidence codes.
  • Include user defined GO annotations: previously added to the NMC Annotation database.
  • Statistical analysis: Several gene lists are analyzed simultaneously to compare the distribution of the annotated genes over the GO hierarchy. Statistical tests are implemented to allow the user to compute GO annotation dissimilarities within or between gene lists.
  • Connection to Annotation database: Links to the NMC Annotation database, gene and protein information are offered directly from the GO DAG or in exported data.
  • Export: GO DAG information, statistical results and gene and protein information can be exported in Excel, text or XML format.
Use tool onlineDownload tool

Windows compatibleMac OS X compatibleUnix compatiblelinux compatible

ermineJ

Center for Computational Biology and Bioinformatics, Columbia University
[Publication abstract]

ermineJ is a tool for the analysis of gene sets (user defined or those defined by GO terms) in expression data. The software is designed to be used by biologists with little or no informatics background. A command-line interface is available for users who wish to script the use of ermineJ. Several different methods for scoring gene sets are implemented, with a focus on methods that don’t rely on simple “over-representation” measures.

Download tool

Windows compatibleMac OS X compatibleUnix compatiblelinux compatible

Functional Information Viewer and Analyzer (FIVA)

Groningen Biomolecular Sciences and Biotechnology Institute, Haren, the Netherlands
[Publication abstract]

FIVA aids researchers in the prokaryotic community to quickly identify relevant biological processes following transcriptome analysis. Our software is able to assist in functional profiling of large sets of genes and generates a comprehensive overview of affected biological processes.

Use tool online

FuncAssociate

Roth Computational Biology Laboratory, Harvard Medical School
[Publication abstract]

FuncAssociate is a web-based tool that accepts as input a list of genes, and returns a list of GO attributes that are over- (or under-) represented among the genes in the input list. Only those over- (or under-) representations that are statistically significant, after correcting for multiple hypotheses testing, are reported. Currently 10 organisms are supported. In addition to the input list of genes, users may specify a) whether this list should be regarded as ordered or unordered; b) the universe of genes to be considered by FuncAssociate; c) whether to report over-, or under-represented attributes, or both; and d) the p-value cutoff.

new version of FuncAssociate (still at the beta stage!) is now available. This version supports a wider range of naming schemes for input genes, and uses more frequently updated GO associations. However, some features of the original version, such as sorting by LOD or the option to see the gene-attribute table, are not yet implemented.

Use tool online

FuncExpression

Iowa State University
No publication

FuncExpression is a web-based resource for functional interpretation of large scale genomics data. FuncExpression can be used for the functional comparison of plant, animal, and fungal gene name lists generated from genomics and proteomics experiments. Multiple gene lists can be classified, compared and visualized. FuncExpression supports two way-integration of plant gene functional information and the gene expression data, which allows for further cross-validation with plant microarray data from related experiments at BarleyBase.

Download tool

Windows compatibleMac OS X compatibleUnix compatiblelinux compatible

FunCluster, Functional Profiling of Microarray Expression Data

Institut National de la Santé et de la Recherche Medicale (INSERM)Centre de Recherche des Cordeliers, Paris, France
[Publication abstracts 123]

FunCluster is a genomic data analysis tool designed to perform a functional analysis of gene expression data obtained from cDNA microarray experiments. Besides automated functional annotation of gene expression data, FunCluster functional analysis allows to detect co-regulated biological processes (i.e. represented by annotating genomic themes) through a specifically designed co-clustering procedure involving biological annotations and gene expression data. FunCluster’s functional analysis relies on Gene Ontology and KEGG annotations and is currently available for three organisms: Homo sapiensMus musculus andSaccharomyces cerevisiae.

FunCluster is provided as a standalone R package, which can be run on any operating system for which an R environment implementation is available (Windows, Mac OS, various flavors of Linux and Unix). Download it from the FunCluster website, or from the worldwide mirrors of CRAN. FunCluster is provided freely under the GNU General Public License 2.0.

Use tool onlineDownload tool

Windows compatibleMac OS X compatibleUnix compatiblelinux compatible

FunNet: Functional Analysis of Transcriptional Networks

Institut National de la Santé et de la Recherche Medicale (INSERM)Centre de Recherche des Cordeliers, Paris, France
[Publication abstract]

FunNet is designed as an integrative tool for analyzing gene co-expression networks built from microarray expression data. The analytical model implemented in this tool involves two abstraction layers: transcriptional (i.e. gene expression profiles) and functional (i.e. biological themes indicating the roles of the analyzed transcripts). A functional analysis technique, which relies on Gene Ontology and KEGG annotations, is applied to extract a list of relevant biological themes from microarray gene expression data. Afterwards multiple-instance representations are built to relate relevant biological themes to their annotated transcripts. An original non-linear dynamical model is used to quantify the contextual proximity of relevant genomic themes based on their patterns of propagation in the gene co-expression network (i.e. capturing the similarity of the expression profiles of the transcriptional instances of annotating themes). In the end an unsupervised multiple-instance spectral clustering procedure is used to explore the modular architecture of the co-expression network by grouping together biological themes demonstrating a significant relationship in the co-expression network. Functional and transcriptional representations of the co-expression network are provided, together with detailed information on the contextual centrality of related transcripts and genomic themes.

FunNet is provided both as a web-based tool and as a standalone R package. The standalone R implementation can be run on any operating system for which an R environment implementation is available (Windows, Mac OS, various flavors of Linux and Unix) and can be downloaded from the FunNet website, or from the worldwide mirrors of CRAN. Both implementations of the FunNet tool are provided freely under the GNU General Public License 2.0.

Use tool online

G-SESAME

Clemson Bioinformatics Center
[Publication abstract]

G-SESAME contains a set of tools. They are

  1. Tools for measuring the semantic similarity of GO terms.
  2. Tools for measuring the functional similarity of genes.
  3. Tools for clustering genes based on their GO term annotation information.
Use tool online

GARBAN

University of Navarra, Spain
[Publication abstract]

GARBAN is a tool for analysis and rapid functional annotation of data arising from cDNA microarrays and proteomics techniques. GARBAN has been implemented with bioinformatic tools to rapidly compare, classify, and graphically represent multiple sets of data (genes/ESTs, or proteins), with the specific aim of facilitating the identification of molecular markers in pathological and pharmacological studies. GARBAN has links to the major genomic and proteomic databases (Ensembl, GeneBank, UniProt Knowledgebase, InterPro, etc.), and follows the criteria of the Gene Ontology Consortium (GO) for ontological classifications. Source may be shared: e-mail garban@ceit.es.

Use tool online

GENECODIS

National Center of Biotechnology (CNB-CSIC): and Universidad Complutense de Madrid
[Publication abstract]

GENECODIS is a web-based tool for the functional analysis of gene lists. It integrates different sources of information to search for annotations that frequently co-occur in a set of genes and rank them by their statistical significance. It allows the analysis of annotations from different databases such as GO, KEGG or SwissProt.

Use tool onlineDownload tool

Windows compatibleMac OS X compatibleUnix compatiblelinux compatible

GeneMerge

Harvard University
[Publication abstract]

GeneMerge returns functional genomic information for a given set of genes and provides statistical rank scores for over-representation of particular functions or categories in the dataset. All GO species are represented in addition to other species and functional genomic data.

Use tool online

GFINDer: Genome Function INtegrated Discoverer

Bio-Medical Informatics Laboratory at the Politecnico di Milano
No publication

GFINDer: Genome Function INtegrated Discoverer is a multi-database system providing large-scale lists of user-classified sequence identifiers with genome-scale biological information and functional profiles biologically characterizing the different gene classes in the list. GFINDer automatically retrieves updated annotations of several functional categories from different sources, identifies the categories enriched in each class of a user-classified gene list, and calculates statistical significance values for each category. Moreover, GFINDer enables to functionally classify genes according to mined functional categories and to statistically analyse the obtained classifications, aiding in better interpreting microarray experiment results.

Download tool

WindowsMac OS Xlinux

GOALIE (Generalized Ontological Algorithmic Logical Invariants Extractor)

NYU Bioinformatics Group
No publication

GOALIE (Generalized Ontological Algorithmic Logical Invariants Extractor) is a tool for the construction of time-course dependent enrichments. Requires an ODBC connection to an instance of the GO database.

Download tool

Windows compatibleMac OS X compatibleUnix compatiblelinux compatible

GOdist

The Hebrew University of Jerusalem
[Publication abstract]

GOdist is a Matlab program that analyzes Affymetrix microarray expression data implementing Kolmogorov-Smirnov (KS) continuous statistics approach. It also implements the discrete approach using Fisher exact test employing a two-tailed hypergeometric distribution. GOdist enables detection of both kinds of changes within specific GO terms represented on the array in relation to different populations: the global array population, the direct parents of the analyzed GO term and the global parent of it (e.g. biological process, molecular function or cellular component).

Use tool online

Gene Ontology Enrichment Analysis Software Toolkit (GOEAST)

Institute of Genetics and Developmental Biology, Chinese Academy of Sciences
[Publication abstract]

Gene Ontology Enrichment Analysis Software Toolkit (GOEAST) is a web based software toolkit providing easy to use, visualizable, comprehensive and unbiased Gene Ontology (GO) analysis for high-throughput experimental results, especially for results from microarray hybridization experiments. The main function of GOEAST is to identify significantly enriched GO terms among give lists of genes using accurate statistical methods.

Download tool

Windows compatible

Gene Ontology Explorer (GOEx)

Systems Engineering and Computer Science Program at the Federal University of Rio de Janeiro, Brazil, and the Yates Lab at the Scripps Research Institute, La Jolla, California.
[Publication abstract]

Gene Ontology Explorer (GOEx) combines data from protein fold changes with GO over-representation statistics to help draw conclusions in proteomic experiments. It is tightly integrated within the PatternLab for Proteomics project and, thus, lies within a complete computational environment that provides parsers and pattern recognition tools designed for spectral counting. GOEx offers three independent methods to query data: an interactive directed acyclic graph, a specialist mode where key words can be searched, and an automatic search. A details description of these methods is provided in the publication.

Download tool

Windows compatibleMac OS X compatibleUnix compatiblelinux compatible

GOHyperGAll

University of California, Riverside
No publication

To test a sample population of genes for overrepresentation of GO terms, the R/BioC function GOHyperGAll computes for all GO nodes a hypergeometric distribution test and returns the corresponding p-values. A subsequent filter function performs a GO Slim analysis using default or custom GO Slim categories. Basic knowledge about R and BioConductor is required for using this tool.

Download tool

Windows compatibleMac OS X compatibleUnix compatiblelinux compatible

High-Throughput GoMiner

Genomics and Bioinformatics Group of LMP, NCI, NIH and Medical Informatics and Bioimaging group of BME, Georgia Tech/Emory University
[Publication abstract]

High-Throughput GoMiner is an ‘industrial-strength’ integrative Gene Ontology tool for interpretation of multiple-microarray experiments. GoMiner is a Java-based program package that organizes lists of ‘interesting’ genes (e.g., up- and down-regulated genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. GoMiner provides quantitative and statistical output files and two useful visualizations: (i) a tree-like structure analogous to that in the AmiGO browser and (ii) a compact, dynamically interactive DAG. Genes displayed in GoMiner are linked to major public bioinformatics resources. A companion tool, MatchMiner, can be used as a preprocessor to obtain gene names for input to GoMiner or other GO tools. For users running under a Unix-based operating system, there is an automated script for easy installation of the local database.

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GOrilla

Technion – Laboratory of Computational Biology and Agilent Labs Tel-Aviv
[Publication abstract]

GOrilla is a web-based application that identifies enriched GO terms in ranked lists of genes, without requiring the user to provide explicit target and background sets. These are determined in a data driven manner. GOrilla employs a flexible threshold statistical approach to discover GO terms that are significantly enriched at the top of a ranked gene list. The tool supports several input formats: gene symbol, gene and protein RefSeq, Uniprot, Unigene and Ensembl. Supported organisms include: human, mouse, rat, yeast, D. melanogasterC. elegans and A. thaliana. The input to GOrilla is either a ranked gene list or target and background sets. The graphical output shows the results in the context of the GO DAG.

Use tool online

GOstat

Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
[Publication abstract]

GOstat, is an easy to use web tool to determine statistically significant over- or under-represented GO categories within lists of genes. Data files are updating monthly.

Download tool

Windows compatible

GoSurfer

Harvard School of Public Health
[Paper (PDF format)]

GoSurfer uses Gene Ontology information in the analysis of gene sets obtained from genome-wide computations, microarray analysis or any other highly parallel method. It includes rigorous statistical testing, interactive graphics and automated updating of the annotation available for common gene identifiers (UniGene, LocusLink) or Affymetrix probe sets.

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Windows compatibleMac OS X compatibleUnix compatiblelinux compatible

GO Term Finder

Saccharomyces Genome Database
[Publication abstract]

The GO Term Finder searches for significant shared GO terms, or parents of the GO terms, used to annotate gene products in a given list. A web-based GO Term Finder at Saccharomyces Genome Database searches annotations of budding yeast gene products. A generic GO Term Finder has been created by Stanford Microarray Database and can be downloaded fromCPAN. This code has been used to implement a web-based generic GO Term Finder by the Princeton genomics group; this implementation provides analysis, via a web tool, of genes from any species (including human) for which there are GO annotations publicly available through the GO web site.

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GOTM (Gene Ontology Tree Machine)

University of Tennessee Genome Science and Technology and Oak Ridge National Laboratory (ORNL)
[Publication abstract]

GOTM is a web-based tool for the analysis and visualization of sets of interesting genes based on Gene Ontology hierarchies. This tool provides user friendly data navigation and visualization. It generates expandable tree for browsing the GO hierarchy, fixed tree as HTML output for archive and Bar charts at different annotation levels for publication. GOTM provides statistical analysis to indicate GO categories with relatively enriched gene numbers and suggest biological areas that warrant further study. Enriched GO categories can be visualized in Sub-trees or DAGs. Subset of genes can be retrieved by GO term or keyword searching. Detailed information for each gene can be retrieved directly from our a local database GeneKeyDB.

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GOToolBox

Developmental Biology Institute of Marseille
[Publication abstract]

GOToolBox is a series of web-based programs allowing the identification of statistically over- or under-represented terms in a gene dataset relative to a reference gene set; the clustering of functionally related genes within a set; and the retrieval of genes sharing annotations with a query gene. GO annotations can also be constrained to a slim hierarchy or a given level of the ontology and terms can be filtered on evidence codes. Updated monthly with GO and gene association files.

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GraphWeb

BIIT Group, Institute of Computer Science, University of Tartu
[Publication abstract]

GraphWeb allows the detection of modules from biological, heterogeneous and multi-species networks, and the interpretation of detected modules using Gene Ontology, cis-regulatory motifs and biological pathways.

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GREAT

Contact GREAT users mailing list
The Bejerano Lab at Stanford University
[Publication abstract]

We developed the Genomic Regions Enrichment of Annotations Tool (GREAT) to analyze the functional significance of cis-regulatory regions identified by localized measurements of DNA binding events across an entire genome. Whereas previous methods took into account only binding proximal to genes, GREAT is able to properly incorporate distal binding sites and control for false positives using a binomial test over the input genomic regions. GREAT incorporates annotations from 20 ontologies and is available as a web application. The utility of GREAT extends to data generated for transcription-associated factors, open chromatin, localized epigenomic markers and similar functional data sets, and comparative genomics sets.

Tool submission date: May 2010

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Windows compatibleMac OS X compatibleUnix compatiblelinux compatible

L2L

Department of Biochemistry, University of Washington
[Publication abstract]

L2L is a simple but powerful tool for discovering the hidden biological significance in microarray data. Through an easy-to-use web interface, L2L will mine a list of up- or down-regulated genes for Gene Ontology terms that are significantly enriched. L2L can also compare the list of genes to a database of hundreds of published microarray experiments, in order to identify common patterns of gene regulation. A downloadable command-line version can run customized and batch analyses.

Download tool

Windows compatible

MAPPFinder

Gladstone Institutes, University of California
[Publication abstract]

MAPPFinder is an accessory program for GenMAPP. This program allows users to query any existing GenMAPP Expression Dataset Criterion against GO gene associations and GenMAPP MAPPs (microarray pathway profiles). The resulting analysis provides the user with results that can be viewed directly upon the Gene Ontology hierarchy and within GenMAPP, by selecting terms or MAPPs of interest.

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Meta Gene Profiler (MetaGP)

Institute of Statistical Mathematics, Research Organization of Information and Systems, Japan
[Publication details]

Meta Gene Profiler (MetaGP) is a web application tool for discovering differentially expressed gene sets (meta genes) from the gene set library registered in our database. Once user submits gene expression profiles which are categorized into subtypes of conditioned experiments, or a list of genes with the valid pvalues, MetaGP assigns the integrated p-value to each gene set by combining the statistical evidences of genes that are obtained from gene-level analysis of significance. The current version supports the nine Affymetrix GeneChip arrays for the three organisms (human, mouse and rat). The significances of GO terms are graphically mapped onto the directed acyclic graph (DAG). The navigation systems of GO hierarchy enable us to summarize the significance of interesting sub-graphs on the web browser.

Download tool

Windows compatibleMac OS X compatiblelinux compatible

MultiExperiment Viewer (MeV)

DFCI, JCVI, and the University of Washington
[Publication details]

(MeV) is a versatile microarray data analysis tool, incorporating sophisticated algorithms for clustering, visualization, classification, statistical analysis and biological theme discovery. Analyze gene expression or CGH microarray data and with MeV’s many clustering, statistical analysis and graphical display tools. MeV generates informative and interrelated displays of expression and annotation data from single or multiple experiments.

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Onto-Compare

Intelligent Systems and Bioinformatics Laboratory, Wayne State University
[Publication abstracts 12]

Onto-Compare is a web based tool that permits comparison of commercial microarrays based on GO. Onto-Compare allows the user to assess the functional bias associated with each array and helps determine the best microarray for a given biological phenomenon described using GO terms.

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Windows compatibleMac OS X compatibleUnix compatiblelinux compatible

Onto-Design

Intelligent Systems and Bioinformatics Laboratory, Wayne State University
[Publication abstract]

Onto-Design allows the user to design custom microarrays by selecting a set of UniGene cluster IDs that represent a given subset of biological processes described using GO terms.

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Onto-Express

Intelligent Systems and Bioinformatics Laboratory, Wayne State University
[Publication abstracts 123]

Onto-Express searches the public databases and returns tables that correlate expression profiles with the cytogenetic gene locations, the biochemical and molecular functions, the biological processes, cellular components and cellular roles of the translated proteins.

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Onto-Miner

Intelligent Systems and Bioinformatics Laboratory, Wayne State University
[Publication abstract]

Onto-Miner allows searching of various public bioinformatics databases via clone ID, UniGene gene symbol, LocusLink ID, accession number etc. and can carry out batch mode queries using entire lists of genes. The site can be used as a resource by third party developers who would like to provide detailed gene information for arbitrary lists of genes.

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Onto-Translate

Intelligent Systems and Bioinformatics Laboratory, Wayne State University
[Publication abstract]

Onto-Translate is a web based tool that allows the user to quickly translate lists of accession IDs, UniGene cluster IDs and Affymetrix probe IDs from one to another. Onto-Translate helps identifying the same information across various databases and reduce the redundancy in arbitrary lists of genes.

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OntoGate

Max Planck Institute for Molecular Genetics
[Publication abstract]

OntoGate provides access to GenomeMatrix (GM) entries from Ontology terms and external datasets which have been associated with ontology terms, to find genes from different species in the GM, which have been mapped to the ontology terms. OntoGate includes a BLAST search of amino acid sequences corresponding to annotated genes.

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Windows compatibleMac OS X compatibleUnix compatiblelinux compatible

The Ontologizer

Charité University Hospital, Germany
[Publication abstracts 12]

The Ontologizer The Ontologizer is a Java webstart application for GO term enrichment analysis that provides browsing and graph visualization capabilities. The Ontologizer allows users to analyze data with the standard Fisher exact test and also the parent-child method and topology methods.

The tool can be started directly from the web using Java webstart. For graph visualizations, users need to install the GraphViz library. The tool is freely available to all, and source code is available at SourceForge.

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Probe Explorer

Centro de Investigacion del CancerUniversidad de Salamanca (Spain)
No publication

Probe Explorer is an open access web-based bioinformatics application designed to show the association between microarray oligonucleotide probes and transcripts in the genomic context, but flexible enough to serve as a simplified genome and transcriptome browser. Coordinates and sequences of the genomic entities (loci, exons, transcripts), including vector graphics outputs, are provided for fifteen metazoa organisms and two yeasts. Alignment tools are used to built the associations between Affymetrix microarrays probe sequences and the transcriptomes (for human, mouse, rat and yeasts). Search by keywords is available and user searches and alignments on the genomes can also be done using any DNA or protein sequence query.

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ProfCom, Profiling of Complex Functionality

The Institute of Bioinformatics and Systems Biology (IBIS), Helmholtz Zentrum München, Munich, Germany
[Publication abstract]

ProfCom is a web-based tool for the functional interpretation of a gene list that was identified to be related by experiments. A trait which makes ProfCom a unique tool is an ability to profile enrichments of not only available Gene Ontology (GO) terms but also of complex function. A complex function is constructed as Boolean combination of available GO terms. The complex functions inferred by ProfCom are more specific in comparison to single terms and describe more accurately the functional role of genes.

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Windows compatible

SeqExpress

SeqExpress
[Publication abstract]

SeqExpress is a comprehensive analysis and visualisation package for gene expression experiments. GO is used to assign functional enrichment scores to clusters, using a combination of specially developed techniques and general statistical methods. These results can be explored using the in built ontology browsing tool or through the generated web pages. SeqExpress also supports numerous data transformation, projection, visualisation, file export/import, searching, integration (with R), and clustering options.

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SerbGO

Statistics and Bioinformatics Unit, Institut de Recerca Hospital Universitari Vall d’Hebron and Statistics and Bioinformatics Research Group, University of Barcelona
[Publication abstract]

SerbGO is a web-based tool intended to assist researchers determine which microarray tools for gene expression analysis which make use of the GO ontologies are best suited to their projects. SerbGO is a bidirectional application. The user can ask for some features by checking on the Query Form to get the appropriate tools for their interests. The user can also compare tools to check which features are implemented in each one.

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SOURCE

Stanford Microarray Database
[Publication abstract]

SOURCE compiles information from several publicly accessible databases, including UniGene, dbEST, UniProt Knowledgebase, GeneMap99, RHdb, GeneCards and LocusLink. GO terms associated with LocusLink entries appear in SOURCE.

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Windows compatibleMac OS X compatibleUnix compatiblelinux compatible

Short Time-series Expression Miner (STEM)

Carnegie Mellon University
[Publication abstracts 12]

The Short Time-series Expression Miner (STEM) is a Java program for clustering, comparing, and visualizing short time series gene expression data (8 time points or less). STEM allows researchers to identify significant temporal expression profiles and the genes associated with these profiles and to compare the behavior of these genes across multiple conditions. STEM is fully integrated with the Gene Ontology (GO) database and supports GO category gene enrichment analyses for sets of genes having the same temporal expression pattern. STEM also supports the ability to easily determine and visualize the behavior of genes belonging to a given GO category, identifying which temporal expression profiles were enriched for these genes.

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T-Profiler

Columbia University and University of Amsterdam
[Publication abstract]

T-Profiler uses the t-test to score changes in the average activity of pre-defined groups of genes. The gene groups are defined based on Gene Ontology categorization, ChIP-chip experiments, upstream matches to a consensus transcription factor binding motif, and location on the same chromosome, respectively. A jack-knife procedure is used to make calculations more robust against outliers. T-profiler makes it possible to interpret microarray data in a way that is both intuitive and statistically rigorous, without the need to combine experiments or choose parameters.

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Windows compatibleMac OS X compatibleUnix compatiblelinux compatible

Tools for High-throughput Experiments Analysis (THEA)

Virtual Biology Lab at the Institute of Signaling, Developmental Biology and Cancer Research
[Publication abstract]

THEA (Tools for High-throughput Experiments Analysis) is an integrated information processing system dedicated to the analysis of post-genomic data. It allows automatic annotation of data issued from classification systems with selected biological information (including the Gene Ontology). Users can either manually search and browse through these annotations, or automatically generate meaningful generalizations according to statistical criteria (data mining).

I copied this list 🙂

from here.. http://www.geneontology.org/GO.tools.microarray.shtml

 

 

Did you know?

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Now don’t believe the last line because it isn’t true, I made it up. That is the point of this discussion.   We deal with information on a minute to minute basis.  We rely on information to make decisions and we put our trust in words that others have written.  How do you know what is right or wrong?  How do you validate the facts from the fiction?  Clearly it is easy to see that we can put false information into a data thread with true information and cause problems.    This applies to any information we deal with.   This is something that we need to think about as we move forward in dealing with data and moving to the cloud!