Why Every Leader Should Build a RetroPi

IMG_1991Strategy

It is estimated that the number of mobile phone users in the world is expected to pass the five billion mark by 2019.   The level of interaction with technology creates an illusion of understanding.   Expert users are not expert technologists.

Many senior leaders don’t claim to be expert technologists but when it comes to technology decisions it has become much easier to have an informed opinion on technology whether for design or desired outcome.   This has put a great strain on IT because technology while magical is not magic.   It takes hard work,  a great deal of thought and planning.   Even if agile methodologies are applied,  requirements matter.

In this world of 140 characters or less,  it is safe to say that some folks reading this blog if uninterested would have disengaged about eight sentences ago.    The expectation today is that everything will happen quickly and it will be exactly what was expected or we disengage and find something else.   This is one of many reasons that IT is suffering in large companies today.  CIO’s are fighting subscription or licensed services for solution delivery.   It is true that cloud services and licensed subscription models have great benefit but acquisition of these services still require strategic thinking.

A simple exercise in building a small and usable gaming system will demonstrate end to end implementation of easy to acquire technology and show how important strategic planning and understanding of technology is relative to projects small, medium or large.

Many leaders in industry are walking away from IT and looking to make IT decisions on their own.   They can use their own budget and solve their own problem without IT.  In some instances where risk is low, they can do this with very little impact.  In most cases, they find a need to bring IT in at either the end of the purchase or when they need to actually implement.  This creates contention and can impact their ability to get the software or services they wanted in the first place.   The underlying question is “Why can’t I do here what I do at home”?   Anyone feel free to comment on this .. but the various reasons should be addressed in another blog at some point.   

If industry  / business leaders want to make decisions in technology, they should become familiar with what it takes to compose and implement solutions.  I am not saying they should become IT experts but my contention is leaders should know their desired outcome relative to the environment they are in.    An info-graphic on AI can drive me to put Alexa in my home but this is very different from me deploying Alexa in my company.   As a leader, I need to understand the differences to make an informed and economically sound and viable decision.

Making it Real

The Raspberry Pi is a small computer that you can purchase for as low as $10.00.

The Raspberry Pi has free open source operating systems that you can download for many uses.   One of the operating systems available has software called RetroPi.  With RetroPi you can build a system that plays thousands of games from many of the older popular gaming systems.

You can get as fancy as you like depending on your desired result.

picade

or you can keep it simple

img_2074

Either way,  it is learning experience that incorporates some very basic skills using technology to accomplish a relatively simple goal.

Leadership that seeks to use technologies to enable and grow their business should have a tacit understanding of this kind of project.

Some factors to consider here are

  1. Instructions are readily available both written and video (Build a Pi)
  2. The operating system is no cost and easy to install
  3. The software is pre-configured and highly intuitive
  4. The estimated time to completion is 10 minutes (That’s fast)

Results May Vary

Here we have a simple project that should take 10 minutes to employ.  It will result in countless hours of fun and entertainment for the family and for those who enjoy retro games, it will save hundreds of dollars in purchasing a retro console.  It meets most of the criteria asked from technology companies and IT today.  It has the makings of a perfect simple technology implementation.

Side note, below is my machine that I built..

cogames

LOST -Line of Sight Tasking and Result

ImageThe last thing you did may have been the first thought in someone’s head and the last thing on your big list of things to do.

“If you thought you were busy now, just wait.”

What the management books or leadership books have a hard time conveying is something beyond a process.  The tacit knowledge that makes successful people actually successful is where a lot of the magic lives.   Reading most of these books gives the reader some good ideas on the process but not always the methods employed to create that success.   Enough on this..  back to the point.

LOST – Line of sight tasking causes stress, anxiety and can make some feel overwhelmed.   Managers can forget what tasks they put out and the result is a loss of tasking and accountability.   Recently, I have thought of this as in relation to a math or science problem.   If a manager tasks 15 people by line of sight, he or she can achieve their ultimate goal.   The manager can still have a mission, vision, goal and objective, critical success factors and an end state in mind.   This manager can be successful but there is a cost.   Lets look at this from the perspective of a math problem.

The problem to solve is: (3+4i)+(8-11i)

The answer is: 11-7i

as opposed to 

The problem to solve is (3+4i)+(8-11i) and.. Remember, with complex variables, keep like terms involving i together….

Multiply i and 4

Multiply i and 1

The i just gets copied along.

The answer is i

i

4*i evaluates to 4i

3+4*i evaluates to 3+4i

Multiply i and 11

Multiply i and 1

The i just gets copied along.

The answer is i

i

11*i evaluates to 11i

8-11*i evaluates to 8-11i

To add the polynomials 3+4i and 8-11i we try to add or combine terms in one polynomialwith any like terms in the other polynomial.

3 + 8 = 11

4i + -11i = -7i

The answer is 11-7i

(3+4*i)+(8-11*i) evaluates to 11-7i


The final answer (almost!) is

11-7i

Now, let’s simplify the i‘s to get our final answer:
The i in -7i cannot be simplified, so just leave it as is.


The final answer is

11-7i

It is pretty ironic that I am using this as an example since I consider myself Dyscalculia.   Getting to the answer or knowing the answer or getting the result is only PART OF THE PROBLEM.   We have to be able to understand how we got to the answer and further what steps = tasks we took to get there.

What if I were to say to solve this problem (3+4i)+(8-11i) but the “order of operations” is by line of sight management.  “I need you to start with 3+8.”  Could you get to the right answer?  Sure, I bet you could.. might take longer.  How do you know you have the right answer?  How could someone help validate your logic?  How could people duplicate your efforts?

If we take a logical and thought out iterative approach to project and task management there is time to check our work.   Line of sight tasking creates memory loss.  No one really knows what happened last or what is coming next.

The thing about feeling overwhelmed in project management and feeling stressed is to get a hold of the big picture. The problem is that anxiety and stress related to line of sight tasking doesn’t really come from the person that is creating the tasks. It comes from the people that are on the receiving end. Somehow people that are creating the tasks are of the mindset that if you believe it, you can achieve it.

Consider this when you are working on your next project.

DoD isn’t ready for Cloud Computing

If you read the Cloud Computing strategy paper there are a lot of great ideas but in our environment today they aren’t realistic.   Without getting into great detail or even attacking the strategy point by point we can totally dismantle the strategy with one concept, THE SLA / OLA.

How will you hold the DISA accountable when cloud services fail?   Where has DISA been successful with Cloud Computing today…? (be honest now).

Who will advocate for customers when DISA fails?  How will IT acquisition practices and programmatics change to accommodate the cloud?   I could really go on and on.

Bottom line is that DISA and the CIO don’t even know where all of the IT services are in the DoD holistically.   In other words, there is no “list” of DoD services.   I have been asking and looking for years for a list of even cloud like capabilities.    It is solving the wrong problems precisely to add cloud services to the mix and assume that you are going to force people to your services.

I would say that it will flat out fail until you start changing behavior not technology.

They talk about fostering adoption with the stick of governance but anyone who understands anything about “The elephant and the rider”  knows that in our world (DoD) we cannot DEMAND AND FORCE CHANGE.

I wasn’t clear.. sorry..  YOU CANNOT FORCE CHANGE IN THE DOD.

You can lead change.  You can inspire change.  There are a lot of things you CAN do but forcing people to go to cloud solutions on DISA isn’t going to be one of them.

Most people who I know have strong feelings about DISA..  I wouldn’t use the word.. LOVE .

The DoD tried to consolidate services and broker services with technology before, anyone heard of SOA?  It failed because they put technology first.

“If I give you this cool THING, you will LOVE me and you WILL give me your DATA.”  That is bologna.    If you put governance in place that will do it? NOPE.   Governance in the DoD from the highest levels is like a big fat tiger with NO TEETH.  It looks scary but the bite just tickles.

SO.. here is my challenge to YOU reader.  Ask everyone you know what cloud services exist in the DoD.   Ask them where they are and how much they cost.   Ask them what the SLA(service level agreement) looks like.  Ask them what the OLA (operational level agreement) looks like.   Ask them who they currently service .   Ask them for a list of technical capabilities.

LAST BUT NOT LEAST.. Ask them to be honest about what they have and who they are serving and the maturity of their environment.

Prove me wrong, but I am willing to bet that you won’t find a list unless you make it yourself.   You will find services all over the place funded in all sorts of ways.   You will find that the CIO doesn’t know what is in her enterprise. **sorry** is that effort underway?  I didn’t see a discovery phase in the strategy but maybe I overlooked it.

Let me know what you come up with…

 

 

 

 

http://blogs.federaltimes.com/federal-times-blog/2012/07/12/dod-releases-cloud-computing-strategy/

Agile DoD Part 2

I changed the title because the requirements changed…. Really.

I woke up this morning and grabbed that first cup of java, it was really good as usual.   My wife makes great coffee, she does it every day without fail.   We don’t even need to meet about it.   On that note, I grabbed the paper and read and article tied to meetings adversely impacting IQ( http://news.menshealth.com/how-meetings-make-you-stupid/2012/02/13/ ).

Still reading? Good because I will get to my point.   Agile is good BUT agile is good if there is balance as with everything else.  Let’s take apart the next piece of the Agile Principles.  Remember that my focus and context is related to the DoD.  Where you may have the ability to execute YOUR way 100% my opinions may not apply.

Principles behind the Agile Manifesto

We follow these principles:Our highest priority is to satisfy the customer
through early and continuous delivery
of valuable software.

Number 1 priority is customer satisfaction.  GREAT! Early delivery of software does not compute.  How about “on time” software delivery?  How about “highest quality” software delivery?  In the DoD, they don’t need early and often.  They need on time and stable.   Think not?  Continuous delivery of software means continuous software updates.   See http://diacap.org/ if you are interested in the implementation process for DoD Information Systems for certification and accreditation (C&A).   The most common answer I get from people who practice agile is… “oh that.. that has to change”  

Welcome changing requirements, even late in development.

Agile processes harness change for

the customer’s competitive advantage.

Competitive advantage has to do with beating someone in a commercial market position.   War is long, war is cold and war for the DoD is not a business.  I didn’t say war is not a business, I said for the DoD it isn’t a business.  Not in the same sense that you would talk about Apple or Microsoft, Google or Facebook.   Sure, there are places in the DoD that need this kind of aggressive process where you can change late in the game (dynamically) but for the most part, building software and systems in and for the DoD is a long process with reason.   When I was on board the USS Mount Whitney, they would push back ships movement for electronic updates and changes.   The reason is that software right out of the gate hardly ever does what it needs to do, it takes time and planning.  Even then there are great challenges, the problem here is that we need to be an agile defense force and that means we shouldn’t be held back because some developer forgot a line of code and needs to just do a quick update.   Remember early and often in the DoD results in confusion and delay.   Imagine for a minute that you plan a trip and that you are going to fly to your destination.  While aboard the plane the pilot informs you that you will be arriving early due to some great tail winds.    Even without any other changes, how many passengers were just adversely impacted?  Getting somewhere early isn’t always the right answer.   Changing up software or altering requirements late in the game need significant considerations when dealing with defense systems. 

Deliver working software frequently, from a
couple of weeks to a couple of months, with a
preference to the shorter timescale.

I addressed this earlier with the DIACAP but what I can say is that this process can be made to work if we aren’t delivering software on that same schedule.  In other words, if the developers are way ahead in a development cycle, this can provide testers an opportunity to really put the software through very comprehensive and stringent testing.   That would work, as long as the production cycle was consistent. 

Business people and developers must work
together daily throughout the project.

Yes and no, see my earlier comment on meetings.  I have been involved with agile project management in the DoD one way or another for a few years now, I haven’t seen a 10-15 minute meeting situation that is consistent.   Anyone that tells you otherwise, get me his or her number, we need to learn from them.  Also, who are business people?  Are developers just a bunch of hipsters hanging out smoking doobs and drinking energy drinks?  Are they spending 20 minutes coding and then heading to the gym for hours?  Developers ( a lot of them) are business people.    I think it is fair to say that   agile can learn a little from SOA concepts where they are more specific about who is working together on what and for what purpose. 

Build projects around motivated individuals.
Give them the environment and support they need,
and trust them to get the job done.

I LOVE this principle and it should be number 1!  TRUST is key.  

Trust is the foundation of every aspect of business any business end to end.  There is no other single more important factor.   Of course there are other things that are needed but without trust work cannot and will not succeed.   

The most efficient and effective method of
conveying information to and within a development
team is face-to-face conversation.

That is so 2001.  Quote me. 

The most efficient and effective method of conveying information to and within a development team is to develop, facilitate, nurture, maintain trusted relationships.  Provide the methods and tools required to access your trusted agents, and have faith in them.  Be available to them and provide them with clear “COMMANDERS INTENT”!

The paradox of war in the Information Age is one of managing massive amounts of information and resisting the temptation to overcontrol it. The competitive advantage is nullified when you try to run decisions up and down the chain of command. All platoons and tank crews have real-time information on what is going on around them, the location of the enemy, and the nature and targeting of the enemy’s weapons system. Once the commander’s intent is understood, decisions must be devolved to the lowest possible level to allow these front line soldiers to exploit the opportunities that develop. —General Gordon Sullivan, quoted in ‘Delivering Results’ by David Ulrich

You might say ….. WAIT A SECOND HOWIE!!!! What are you saying?  First you talk about all this DoD requirement mumbo jumbo then you say “Commanders Intent”!!!  Isn’t that an oxymoron?

Nope.  Commanders intent gives us the overall mission, vision, scope, objective and timelines.   It gives product owners, developers, management, leaders the ability to execute at their level.   Commanders intent isn’t a free for all but what it does is provide enough information to give groups leeway to make decisions as they need to in order to accomplish the greater goal with less frequent communication.  I can provide examples if needed but just off the hip look at Boeing manufacturing the 787.

Working software is the primary measure of progress.

Successful implementation and usage is the primary measure of progress. 

Agile processes promote sustainable development.
The sponsors, developers, and users should be able
to maintain a constant pace indefinitely.

While running a marathon or any other race for that matter is it usual that some fall back and cluster in groups and some pull ahead.   The thought process that everyone can keep up with everyone is mind-boggling.   I can barely keep up with my children! 

Continuous attention to technical excellence
and good design enhances agility.

Love it.. 

Simplicity–the art of maximizing the amount
of work not done–is essential.

Yeah I like maximizing stuff I haven’t done.  Called the “honey do list”  for some reason it continues to grow no matter what I do to attack it.  Wonder why? 

The best architectures, requirements, and designs
emerge from self-organizing teams.

What happens when the team is forced to do something because a dumb ass is working in it?  Everyone works around Billy dee dumb ass? Not realistic.   The best architectures, requirements and designs emerge from good leadership, good listening, clear direction, good followership  and people working together to fight “the dumb”! 

At regular intervals, the team reflects on how
to become more effective, then tunes and adjusts
its behavior accordingly.

At the beach! So.. I meet on Monday-Friday with you and both of us haven’t changed our behavior through any of those meetings but on some other regular interval we are going to come together and adjust?  How about constant feedback?  How about open honest dialogue?  How about trust?  

I am arguing that agile must be tempered by context.   I am arguing that it isn’t a panacea but a practice.  You know your doctor “practices medicine” and we still have the common cold.  

Software development and systems engineering include systems integration and goal oriented objectives.   Oh yeah, it is about PEOPLE !

Be agile and be realistic.

Cloud Computing Professional

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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

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.

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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.
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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.

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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.

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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.

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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.

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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.

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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).

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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.

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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.

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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.

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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.

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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.

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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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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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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.

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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.

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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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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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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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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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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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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