Overview of steps in a typical gene expression microarray experiment.

Topics in blue boxes with solid borders are addressed in the Experimental Design section, those in green boxes with dashed borders are covered in the section on data preparation, and those in purple boxes with dash-dotted borders are discussed in the Data Analysis section of this review.

Reference: Slonim et-al; Getting Started in Gene Expression Microarray Analysis; PLoS Computational Biology

“My sales people are wasting valuable time on Facebook and Linkedin when they should be selling”: Now thats what most sales managers would say. I know thats true , I have irritated fare share of my managers by using social media to create and close sales and in lead generation. And I continue to do it today, thanks to my current orgnaizations forward looking ways , this time around I have the support of my management to these activities.

Society for New Communications Research (SNCR) has oublished a research that shows that company executives are influenced by their online networks.

Here are some key findings from this survey 365 business professionals:

- Professional decision-making is becoming more social – enter the era of Social Media Peer Groups (SMPG)

• Traditional influence cycles are being disrupted by Social Media as decision makers utilize social networks to inform and validate decisions
• Professionals want to be collaborative in the decision-cycle but not be marketed or sold to online; however online marketing is a preferred activity by companies.

– Professional networks are emerging as decision-support tools

• Decision-makers are broadening reach to gather information especially among active users

- Professionals trust online information almost as much as information gotten from in-person

• Information obtained from offline networks still have highest levels of trust with slight advantage over online (offline: 92% – combined strongly/somewhat trust; online: 83% combined strongly/somewhat trust)

- Reliance on web-based professional networks and online communities has increased significantly over the past 3 years

• Three quarters of respondents rely on professional networks to support business decisions
• Reliance has increased for essentially all respondents over the past three years

- Social Media use patterns are not pre-determined by age or organizational affiliation

• Younger (20-35) and older professionals (55+) are more active users of social tools than middle aged professionals.
• There are more people collaborating outside their company wall than within their organizational intranet

I have also found a similarlt interesting  blog . Hosted at Sales20Book.com

“My sales people are wasting valuable time on Facebook and Linkedin when they should be selling”: Now thats what most sales managers would say. But does Social Networking help sales people sell Full Article . I liked that statement especially since it came from Microsoft executive. . As I have also faced the ire of top management for using social media in my career, some time even in Oracle. So this report is an eye opener for opponents of social media for sales.

The complete presentation and arguments are available on http://www.sales20book.com/wp/2009/05/social-networking-in-sales-show-me-the-money/

Microarray techniques produce large amounts of data in many different formats and experiment sizes are growing with more samples analysed in each experiment. Samples are collected over long time and microarray analysis is performed asynchronously and re-analysed as more samples are hybridised. Systematic use of collected data requires tracking of biomaterials, array information, raw data, and assembly of annotations. Particularly for microarray service facilities, where researchers deposit samples for experimentation, information tracking becomes vital for a subsequent data delivery back to the researchers. To meet the information tracking and data analysis challenges involved in microarray experiments BASE has been implemented.

BASE (Bioarray Software Environment) is a comprehensive free web-based database solution for the massive amounts of data generated by microarray analysis. It is a MIAME (Minimum Information About a Microarray Experiment guidelines) compliant application designed for microarray laboratories looking for a single point of storage for all information related to their microarray experimentation. BASE is a multi-user local data repository that features a web browser user interface, laboratory information management system (LIMS) for biomaterials and array production, annotations, hierarchical overview of analysis, and integrates tools like MultiExperiment Viewer (MEV) and GenePattern.

BASE is an annotable microarray data repository and analysis application providing researchers with efficient information management and analysis. BASE stores all microarray experiment related data, biomaterial information, and annotations regardless if analysis tools for specific techniques or data formats are readily available. As new techniques becomes available software applications should be expendable and modifiable to support changed needs. Moreover, it is an open source software and is freely available.

BASE website: http://base.thep.lu.se

Here are some useful and handy bioinformatics links which would aid in study of bioinformatics and various related fields:

http://www.cellbiol.com/

http://www.expasy.org/links.html

http://www.biochemweb.org/databases.shtml

http://bioinformatics.byu.edu/

http://www-personal.umich.edu/~lpt/chemlinks.htm

http://www.sciencegateway.org/tools/index.html

http://molbiol-tools.ca/

http://dorakmt.tripod.com/mtd/biomed.html

http://www.bio.ku.dk/mundy/links.htm

http://users.breathe.com/hachen/mol_biol_sites.html

http://www.whitney.ufl.edu/resources/molecular-links.htm

http://bioinformatics.ws/index.php/Bioinformatics_tools_and_algorithms

http://fruitfly4.aecom.yu.edu/molbio.html

http://www.biologie.uni-erlangen.de/mpp/pages/tools_prot.html

http://staff.umt.edu.my/~cha_ts/Cha%20Bioinfo.html

According to information from the The Contract Research Organization Union China (CROU) under the China National Pharmaceutical Technology Market Association, it is developing the first industry standard for the Chinese CRO sector, Guidelines for Clinical Trial Services of Contract Research Organizations. Currently drafting of the document is already completed and it is likely to be introduced before the end of this year.

The Guideline was formulated with references to relevant WHO documents, ICH-GCP, the Drug Administration Law of China, Provisions for Approval of Drugs, and Guidelines for Quality Control of Clinical Trials (Chinese GCP), according to Gong Yanhua, Secretary General of CROU.

Members of the technical work group are mostly experts from leading clinical CRO such as Quintiles and Pharmanet, while those of the academic advisory group are mostly representatives of MNC and leading local pharma companies. As its next step, CROU hopes to establish a technical committee for standardization of clinical trial services of Chinese CROs soon

in preclinical research service, Chinese CROs possess better service capabilities than Indian CROs; whereas in clinical research service, it is just opposite. In process R&D and scale-up synthesis, both countries possess similar capabilities. However, Indian companies possess better skills and capabilities than Chinese companies in formulation, manufacturing and marketing of generic drugs

The pharma outsourcing industries in both countries have grown rapidly in the recent few years. They are currently valued at about $1.42 B in China and $1.77 B in India, respectively; each occupying only about 2% share in the global pharma outsourcing market. On the other hand, both markets are posed to still grow rapidly in the future as they are driven by a number of positive factors. However, China appears to have higher future growth potential than India as it has fewer growth resistors. It will very likely catch and even surpass India after 2010.

At present, India is better than China in small molecule drug R&D and manufacturing. But China is superior over India in biotechnologies including the R&D and manufacturing of macro compounds. India offers better product quality but China has more cost reduction advantage. In terms of investment opportunities, China seems to present more attractions than India as its industry infrastructure and biotechnologies are more advanced.

Indian Council of Medical Research (ICMR) has decided to upgrade the  Clinical Trial Registry of India (CTRI) on par with international standards as per the norms set by the WHO.

Neil de Crescenzo, SVP Oracle Health Sciences http://tinyurl.com/yjklays
Oracle in Health Sciences Industry http://tinyurl.com/yly257v

Oracle Health Sciences Global Business Videos
1. Neil de Crescenzo, SVP and General Manager, Oracle Health Sciences http://tinyurl.com/yjklays
2. Oracle in Health Sciences Industry http://tinyurl.com/yly257v

MNC pharma companies to control 8% of $20bn worth Indian medicines market by 2015. According to a FICCI study more than 60% of trials in India is conducted by Global Big Pharma companies.  Indian Clinical Research arena is often compared to the Indian outsourcing success and hte wave of BPO industry success in India.

Multi national companies that launched their own captive BPO centres India have now sold them to Indian vendors,  that trend has grown, the likes of , GE, Citi Bank, all have sold their captive centres to Indian vendors

In curent wave or Indian CRO success in clinical research is compared to such trends. There may be a possiblity that the likes of Novartis, Pfizer, Lilly, Sanofi, GSK can sell their captive centres that focus on clinical research to Indian CRO’s.

But for the time being such ambitions by Indian CRO will remain as pipe dream untill they will move  away from “I can do it cheaper and faster than in west”- sales pitch. And gains credibility and resources to offer value added service to Global Pharma

For example Indian CRO can offer backend integration with Indian Medicinal chemistry experts and companies to provide drug re-licencing /re-positioning services. They need to think about offering these value add services. Following are some of the areas Indian CRO’s can look

1. what happens to failed Clinical Trial and how can Indian CRO help Global Pharma to Drug repositioning/Re-profiling of drugs

2. How can Indian CRO provide Pharmacogenomics  services with clinical trial

3. How can Indian CRO help in personalized medicine initiatives

4. Even though it serves the vested interests of Global Pharma how can Indian CRO help in Extending patents of existing drugs with ANDA and NDDS

5. Pharmacovigilance and Post -Market Surveillance for Risk Assesement and Risk Mitigation

6. Data Warehousing and Data Mining by integrating clinical and non clinical data from multiple studies

7. Drug Life cycle management services

8. Generating Key opinion leaders and KOL platform by using data from multiple trials and resources

9. Premarketing Clinical Drug Safety and Risk Assessment

10. Designing Pharmacoepidemiology and Pharmacoecconomics stratgey and Aiding Evidence based pharmacotherapy

Sinnce I am out of the pitch due to fever , I am going to be on my twitter for a while. May be head gone crazy or its the fever, But I was thinking why cant we use the tinyurl.com in Adverse event reporting, i mean the concept . I mean if I dont have to fill every column and row in a ADR report every time and instead just click on one single button which would fill out all entries from a previous matching database and I just have to add what is anything has changed from that.

DBMS Consulting has received an Honorable Mention in the Life Sciences & Health Care Industry Solution category at the Oracle Open World 2009 Partner North America Alliances and Channels Titan Award Ceremony. DBMS Consulting was chosen from among several candidate

details

New Momentum, a leading provider of SaaS‐based anti‐counterfeiting and channel integrity solutions, is working with Oracle to help pharmaceutical companies combat the escalating problem of counterfeit drugs and meet new regulatory compliance requirements.

According to the Center for Medicine in the Public Interest, worldwide pharmaceutical counterfeits are expected to increase by 13% annually nearly twice the pace of legitimate drugs. This means that counterfeit drugs could become a $75B industry by 2010. As these bad drugs flood the market, patient wellness is at risk and so are manufacturers’ revenues and brand reputation. This growing counterfeit problem, combined with the expected federal regulations for serialized drug products and electronic pedigrees, creates significant challenges for pharmaceutical companies.

New Momentum’s CEO, Stuart Clifton, commented, “The best way to meet these challenges is to incorporate internal enterprise and supply chain data with external data on counterfeit suspects and activity. That’s why we’re working closely with Oracle’s Life Sciences team to provide the first solution to offer pharmaceutical companies the ability to track units through the supply chain as well as quickly identify and find counterfeits.”
Oracle is focusing on helping manufacturers with a total solution that includes the ability to serialize each sellable unit and then track that unit through the supply chain via electronic pedigree. By integrating its 24/7 real‐time view of counterfeit suspects, New Momentum expects to help pharmaceutical customers be proactive in their anti‐counterfeiting efforts and comply with serialization and pedigree mandates. Prototypes of this solution will be demonstrated at Oracle OpenWorld, New Momentum Booth #2619A and Oracle Booth #S‐082 from October 11‐15 in San Francisco.

Array-CGH (Comparative Genomic Hybridasation) is becoming a common method used for analysis of patients’ genomes. Array-CGH works by taking a reference genome covering the whole human genome sequence, cutting it into thousands of pieces and orderly attach them to a chip. These pieces are called probes and are usually on the range of 500-2000 DNA bases long. A saliva or blood sample is then taken from the patient and its DNA is also chopped into thousands of pieces in suspension with a solvent. The array is then washed with the suspension containing the patient’s DNA.

DNA is a double chain of nucleotide bases where one chain complements the other. Knowing one chain of the DNA, it is possible to know the other chain. In its natural state, a single DNA chain will tend to bind to its complementary chain. Thus, by washing the patient’s suspension with the array probes will make the patient’s DNA pieces bind to its complementary DNA in the array.

Array-CGH can be used to detect whether a patient has a region of the genome missing or duplicated. Probes attached to the chip emit a different color depending on their state of binding. Once the array is washed, most of probe spots will appear yellow, that is, all different probes of the reference genome are bound to the patient’s DNA. If a DNA region is missing in the patient, the complementary spots in the array appear in red. These changes appear in sequential order mapping to the reference genome missing in the patient. Depending on the genes that overlap to the deletion, different symptoms may appear in the patient.

The same happens if the array shows a series of green spots, indicating that a duplicated region of the genome has been found in the DNA of the patient. Because the gene content will be altered in the duplicated region, this may cause disease as a consequence of the over-expression of genes included in the duplicated regions.

Thus, using array techniques, we are now able to find deletions or duplications in the genome of a patient beyond the microscopic level, i.e. changes not directly observable. We are all familiar with the features of a patient with Down’s Syndrome. This syndrome is caused because there is an extra copy of chromosome 21 in the affected patient, due to a duplication of one of the two usual copies (Trisomy 21).

Most of the chromosomal deletions and duplications occur at the molecular level [1], not identifiable with microscopic techniques, as in the case of Down’s Syndrome. Up until recently most of the patients suspected of suffering from genomic diseases, i.e. diseases caused by pathogenic deletions or duplications, went undiagnosed because techniques did not allow detection beyond big chromosomal changes (like whole chromosomes). Techniques such as array-CGH now allow detection of chromosomal changes a thousand times smaller in length.

For a price of about £100 per array one can have one’s genome screened for chromosomal changes. In fact, it seems that most of the genetic changes between any two people (in terms of number of DNA bases) is dependent on the level of micro- deletions and duplications (called Copy Number Variations) [2], just the level we are now starting to handle with current analysis techniques. Next generation sequencing technologies are fast arriving that will allow the base-by-base complete sequencing of the DNA of people at price of $1000 in a short period of time [3].

[1] H.V. Firth, S.M. Richards, A.P. Bevan, S. Clayton, M. Corpas, D. Rajan, S. Van Vooren, Y. Moreau, R.M. Pettett, N.P. Carter (2009). DECIPHER: DatabasE of Chromosomal Imbalance and Phenotype using Ensembl Resources. The American Journal of Human Genetics.

[2] J. R. Lupski (2009). Genomic disorders ten years on.  Genome Medicine

unSAP the Lifescience Industry with Oracle’s Healthy Lead in Lifescience Vericals. According to the latest IDC Insight Report
In the Oracle vs. SAP life sciences battle, Oracle gains a healthy lead. read the full report at

http://searchoracle.techtarget.com/news/article/0,289142,sid41_gci1368762,00.html

After genome sequencing, DNA microarray analysis has become the most widely used source of genome scale data in the life sciences. Microarray expression studies are producing massive qunatities of gene expression and other functional genomics data, which priomise to provide an insight into gene function and inetractions within and across metabolic pathways. Unlike genome sequence data, however, which have standard formats for presentation and widely used tools and databases, much of the microarray daa generated so far remain inaccessible.

To make this information accesible in a proper format MIAME (Minimum Information About a Microarray Experiment) was introduced. MIAME format was introduced to address the ned for comprehensive annotation necessary to interpret the results of microarray data. It is platform independent but includes essential evidence about how the gene expression level measurements have been obtained.

Although the goal of MIAME is to specify only the content of the information and not the technical format, MIAME includes recommendations for which parts of the information should be provided as controlled vocabularies. MIAME includes a description of the six sections which need to be included:

1. Experimental Design
2. Array Design
3. Samples
4. Hybridizations
5. Measurements
6. Normalization controls

This specific format would really make it easy to interpret microarray data obtained from various experiments.
To read more on MIAME click here-  http://www.mged.org/Workgroups/MIAME/miame.html

 
After genome sequencing, DNA microarray analysis has become the most widely used source of genome scale data in the life sciences. Microarray expression studies are producing massive qunatities of gene expression and other functional genomics data, which priomise to provide an insight into gene function and inetractions within and across metabolic pathways. Unlike genome sequence data, however, which have standard formats for presentation and widely used tools and databases, much of the microarray daa generated so far remain inaccessible.

To make this information accesible in a proper format MIAME (Minimum Information About a Microarray Experiment) was introduced. MIAME format was introduced to address the ned for comprehensive annotation necessary to interpret the results of microarray data. It is platform independent but includes essential evidence about how the gene expression level measurements have been obtained.

Although the goal of MIAME is to specify only the content of the information and not the technical format, MIAME includes recommendations for which parts of the information should be provided as controlled vocabularies. MIAME includes a description of the six sections which need to be included:

1. Experimental Design
2. Array Design
3. Samples
4. Hybridizations
5. Measurements
6. Normalization controls

This specific format would really make it easy to interpret microarray data obtained from various experiments.
To read more on MIAME click here-  http://www.mged.org/Workgroups/MIAME/miame.html

 
After genome sequencing, DNA microarray analysis has become the most widely used source of genome scale data in the life sciences. Microarray expression studies are producing massive qunatities of gene expression and other functional genomics data, which priomise to provide an insight into gene function and inetractions within and across metabolic pathways. Unlike genome sequence data, however, which have standard formats for presentation and widely used tools and databases, much of the microarray daa generated so far remain inaccessible.

To make this information accesible in a proper format MIAME (Minimum Information About a Microarray Experiment) was introduced. MIAME format was introduced to address the ned for comprehensive annotation necessary to interpret the results of microarray data. It is platform independent but includes essential evidence about how the gene expression level measurements have been obtained.

Although the goal of MIAME is to specify only the content of the information and not the technical format, MIAME includes recommendations for which parts of the information should be provided as controlled vocabularies. MIAME includes a description of the six sections which need to be included:

1. Experimental Design
2. Array Design
3. Samples
4. Hybridizations
5. Measurements
6. Normalization controls

This specific format would really make it easy to interpret microarray data obtained from various experiments.
To read more on MIAME click here-  http://www.mged.org/Workgroups/MIAME/miame.html

‘Translational Research: From Bench to Bedside’ Capitol Hill Breakfast Briefing Hosted by the Council for American Medical Innovation

The Briefing is the second in a three-part series on “Achieving Recovery Through Discovery”

This is the second in a three-part briefing series examining the role of public policy in promoting medical innovation to help our nation recover from health and economic crises.

For details on other briefings, visit: www.americanmedicalinnovation.org. WHO:

Remarks by:

Amy Comstock Rick, CEO of the Parkinson’s Action Network –The Honorable Patrick Kennedy, U.S. Representative (D-RI) (invited) — Debra Lappin, President of the Council for American Medical Innovation — Alan Leshner, Ph.D., President of the American Association for the Advancement of Science (AAAS) — Lesa Mitchell, Vice President of the Ewing Marion Kauffman Foundation

For Regiserting for the next event on October chek the site

http://www.regonline.com/builder/site/Default.aspx?eventid=767604

Affymetrix —  Developing systems to acquire, analyze and manage genetic info.
Agilent Technologies —   Provider of a range of microarrays for different organisms, manufacture the 2100 bioanalyzer.
Asper Biotechnology —   Manufacture coated microarray glass slides.
Axon Instruments —  Design and manufacture of instrumentation for genomics and proteomics.
BioDiscovery —   Providing software solutions for gene expression research.
BioMicro Systems — Providing the MAUI Hybridization system for active mixing of ultra low volumes during microarray hybridization.
BioRobotics —  Design, manufacture, and supply of automated solutions for molecular biology research.
BioSieve –  Provides microarray data analysis package on Java platform.
Cartesian Technologies —   Providing tools for microscale liquid handling and associated automation.
Clondiag Chip Technologies —  Imaging and LIMS software and technologies.
Clontech – Development and production of innovative biological products.
GeneData –  Providing computational solutions for analyisis of large quantities of data.
GeneLogic — Providing a data management platform for large-scale data analysis.
Genemachines – Developing machinery for genomics automation.
Gene Network Sciences – Developing dynamic computer models of living cells and next generation data-mining tools.
Genomic Solutions – Providing a variety of genomic research tools.
Genetix — Providing microarray printers, scanners, reagents and consumables.
Genotypic – A genomics and bioinformatics company, providing microarray products & services.
Genome Explorations Inc. — Providing Gene Expression analysis using the Affymetrix Platform.
Iobion Informatics LLC —   Microarray data management and analysis software.
LION Bioscience – Providing expression data analysis systems.
Molecular Dynamics –  Developing and manufacturing microarray systems.
Motorola Life Sciences — Developing system solutions for high-performance gene espression profiling.
MWG Biotech — Microarray provider of multiple whole genome arrays, custom arrays and other array products
Ocimum Biosolutions — Providing biotechnology software solutions, including Genowiz for micorarray data analysis and management.
Packard BioScience — Producing tools used in genomics and proteomics.
Perkin Elmer – Providing a list of various microarray products.
Rosetta Inpharmatics — Providers of bioinformatics solutions and gene expression analysis systems.
Scanalytics – Providing image analysis software for extracting and visualizing DNA microarray data.
Silicon Genetics – Providing genomic expression data analysis, visualization, mining, and storage products.
SSI Robotics – Robotic automation systems and instrument integration for life science related processes.
Superarray Bioscience – Developing pathway/application specific gene expression tools

Add more to this list…… !!!

Oracle’s 3rd Annual Drug Development and Safety Forum 2009

October 21 – 22, 2009

Oracle invites you to the 2009 Drug Development and Safety Forum

At this forum we will be discussing important topics and issues facing the Indian Pharmaceutical Biotechnology, and CRO industry. This event will be highlighted by presentations from key industry thought leaders who will share their perspective on industry best practices as well as their unique experiences.

The content will cover important topics in clinical development, and safety. You will hear how Oracle solutions have enabled organizations in the industry shorten their time to market, gain operational efficiencies, reduce clinical study costs and accelerate insight into action

As a follow up to the success of last two year’s Clinical Development and Safety Forum this year’s event will prove to be a must attend event.

October 21 – 22, 2009

The Leela Kempinski
Sahar, Mumbai, India

To Register Now , contact Sushma at sushma.bs@oracle.com +91 80 4029 1295 (include your name, company, e-mail address, contact mobile number, arrival/departure – dates/time).

Highlights:
•    Keynote Presentations
•    Networking lunch
•    Networking Dinner on the first day
•    Perspectives from industry strategists and thought leaders
•    Hear about business best practices and lessons learned from leading companies
•    A unique networking opportunity with colleagues, industry and Oracle experts

Cross-platform microarray analysis is an increasingly important research tool, but researchers still lack open source tools for storing, integrating, and analyzing large amounts of microarray data obtained from different array platforms.

An open source integrated microarray database and analysis suite, WebArrayDB (http://www.webarraydb.org), has been developed that features convenient uploading of data for storage in a MIAME (Minimal Information about a Microarray Experiment) compliant fashion, and allows data to be mined with a large variety of R-based tools, including data analysis across multiple platforms. Different methods for probe alignment, normalization and statistical analysis are included to account for systematic bias. Student’s t-test, moderated t-tests, non-parametric tests, and analysis of variance or covariance (ANOVA/ANCOVA) are among the choices of algorithms for differential analysis of data. Users also have the flexibility to define new factors and create new analysis models to fit complex experimental designs. All data can be queried or browsed through a web browser. The computations can be performed in parallel on symmetric multiprocessing (SMP) systems or Linux clusters.
The software package is available for use on a public web server (http://www.webarraydb.org) or can be downloaded.

Check out WebArray at: http://www.webarraydb.org