Starting in January 2018, we will begin hosting monthly webinars to showcase the powerful tools of Opus23. These webinars are completely FREE and will cover the basics of Opus23, discuss what sets us apart from other genomic analysis programs and provide a live demo of both Opus23 and Utopia. The first webinar will be held on January 9th at 11:00AM ET.
The webinars are hosted by Dr. Robert Boyd, one of developers of Opus23, and chief resident at the Center of Excellence in Generative Medicine.
These introductory webinars are best suited for individuals who are interested in learning about Opus23, but have not yet taken the training webinar. They will also serve as a great refresher for any users who have completed the Opus training in the past.
A special discount offer will be provided to all attendees for an upcoming training webinar or client upload.
Thank you for this brilliant creation. I am honored to be a part of this game-changing program. –Robert Lang, MD
Opus 23 Pro is designed to be as intuitive to use as it is powerful. However, mastery requires understanding and fully utilizing all the nuances of the many available apps in the program. For those who specialize in genomic testing and focus a large part of their practice on nutrigenomics, we are developing a weekend seminar intensive on Opus 23 mastery. Participants will work directly with Dr. Peter D’Adamo and his team of teaching assistants in a hands-on environment, using Opus 23 Pro to develop fully formatted and curated genomic reports. Attendees successfully completing the seminar will be certified as official Opus 23 consultants.
The tentative dates for the next seminar are 8/20 – 8/21 at the Center of Excellence in Generative Medicine at the University of Bridgeport, in Bridgeport Connecticut.
Because the seminar is designed to be experiential, seating is extremely limited. Please check back here for further details. Seating is on a first come, first served basis. You can soft-reserve seating (no obligation) by completing the form below. This seminar will not be recorded or available as a webinar. The total cost of the seminar is $850 USD and includes lunch on Saturday. All participants completing the seminar will have three (3) complimentary client licenses assigned to their account for their own future use ($210 USD value).
Note: This seminar is open to all licensed professionals.
International Professionals: we plan to do a similar seminar in London (UK) area sometime later in the year.
I’m interested in attending the next Opus 23 Training Seminar.
This has been a wonderful month for attending training and conferences and for reconnecting with so many friends and colleagues. The Opus Pro new software that Dr. D’Adamo designed is phenomenal! The software is cutting edge and user friendly. Those of us that attended the CT training now have access to software that analyzes SNPs (genetic variations) and helps identify important patterns for current and predisposed health conditions. Wow!!!
This is a current list of the multi-snp algorithms currently available in Opus 23. A few titles repeat, for the simple reason that two different algorithms, using different snps or genes, can result in a similar conclusion. Algorithms can be quite complex: it is not uncommon for one algorithm to call another algorithm as part of its execution. Algorithms are discussed in the blog post describing the Opus 23 LUMEN app.
AB blood group
Blood Type O
A blood group (genotype AO)
A blood group (genotype AA)
A1/A2 blood group
B blood group (genotype BO)
B blood group (genotype BB)
Rhesus (Rh) blood group
Slight PTC Taster
Kell K/k blood group carrier
Duffy blood group positive (Fy+/+) / higher total WBC count
Possible Yu-Zhi (YZ) constitution
Odor perception for β-ionone
Prediction of enhanced hippocampal volume
APOE E3/E3 genotype
Probably APOE E2/E4 genotype, Possibly E1/E3
APOE E2/E4 Genotype
APOE E4/E4 genotype
APOE E4/- genotype
APOE E3/E4 genotype
Bi-carrier of the minor alleles of rs1049296 and rs1800562
APOE E1/E1 Genotype
APOE E2/E2 Genotype
APOE E2/E3 Genotype
GAB2 rs2373115 (CC) and rs1800562 with APOE E4
GAB2 rs75932628(TT) Substitution
One short form 5-HTTLPR
Two long form 5-HTTLPR
Increased risk of Parkinson’s Disease
Decreased risk of Parkinson’s Disease
Increased risk of Parkinson’s Disease
Risk of autism related syndrome
Risk of autism related syndrome
Increased risk of autism/ social communication issues
Risk of autism related syndrome
Risk of autism related syndrome
Increased risk of Alzheimer’s Disease
Decreased risk of Alzheimer’s disease
Early-onset Alzheimer’s Disease risk
Elevated brain and cerebrospinal fluid concentrations of kynurenic acid
Lower levels of Indoleamine 2,3-dioxygenase (IDO1) activity
Dopamine beta hydroxylase levels
Heightened placebo effect
Oxytocin ‘loner/ social empath’ polymorphism
Decreased risk of Alzheimer’s disease
Increased risk of bipolar disorder
Impaired motor skills learning
Increased risk of high cortisol levels when under stress
Lower levels of platelet MAO
D2 receptor DRD2 A1/A2 polymorphism
For Hispanics, 9 times more likely to develop heroin addiction ; risk of bi-polar issues
Increased risk for panic disorder (1.7x more likely)
Risk and intensity of functional somatic syndromes (CFS, FM)
Normal pain sensitivity
Variance in ‘self-referential’ processing in the brain
Higher ‘g’ score (general cognitive ability)
Increased ‘process reward’ from staring at smiling faces
MTHFR Polymorphisms Affecting Enzyme Activity
Risk of asthma, ADHD and Parkinson’s disease from high histamine
Epistatic subtle variants in COMT activity and pain sensitivity
Higher B12 Levels
Approximately 50% reduction in tetrahydrobiopterin (BH4) production and total biopterins
Risk of methyl trapping from high-dose/ premature methylation supplementation
‘Outside-in’ versus ‘inside-out’ genesis of atopic dermatitis.
Opus 23 provides many unique opportunities for data integration and visualization. One app that I’ve just been added to the Opus toolbox is STROBE, a new Opus analytic app that allows you drill-down client genomic data by organ, tissue or cell distribution. To do this Opus mashes up its own internal SNP data with gene tissue expression data derived from a variety of public databases. I then sub-organized the tissue expression data by system (immune, cardiovascular, etc) so that the user could filter by clinical relevance.
Once STROBE is fired up you’re presented with the screen depicted above. It is a typical squarified heat-map rendered in Highcharts. Values are assigned by virtue of the aggregate power factors of client SNP mutations number of genes associated with that tissue. Clicking on any tissue brings up a modal popup with a Manhattan-type gene distribution display.
Y axis shows cumulative power factors of SNPs testing positive for that gene. The X axis is rough approximation of gene locus position. Yellow points indicate genes with exclusively heterozygous mutations. Orange points denote genes that contain homozygous mutations. Drag a rectangle around any area to zoom into that part of the map. Use the ‘Reset Zoom’ to return to full zoom.
Clicking on any data point to bring up the information screen on that gene. Here you can notate, or even move on to examining SNPS, agents and algorithms associated with the gene.
From them main screen you can use the select pull-down menu to limit tissues to specific systems. Here we limit the display to tissues, organs and cells of the immune system:
The Opus 23 PSYCHIC app allows you to search for natural products known to control gene expression. However, unlike a simple search engine, PSYCHIC is able to crawl up and down the molecular ‘Interactome’ (protein-protein interactions and gene expression data) to determine the upstream and downstream genes that interact with the gene you’ve searched for. In addition PSYCHIC allows you to chose which type of natural products (agonists/ antagonists) to include in the upstream and downstream results.
As seen above, when the PSYCHIC screen loads you will be presented with the results of the default search term for the current client, the MTOR gene. The main infographic is comprised of a bar graph divided into two halves. The left half displays the upstream results, while the right half displays the downstream results, based on MTOR’s position in the interactome. The labels along the x-axis display the various natural products and their gene targets PSYCHIC has found that meet the search criteria. The y-axis value of each bar in the graph is determined by the evidence basis and strength of the position in the network for the gene depicted.
At the bottom is a small half-pie chart depicts the SNPs for that gene contained in the Opus 23 Pro database.
You can set filters on each half of the graph to limit results to a specific type (agonism or antagonism) by selecting an option from the pull down menu below. There are four options:
Inhibit/ Drain: This will tell PSYCHIC to return all upstream antagonists and downstream agonists
Inhibit/ Bottleneck: This will tell PSYCHIC to return all upstream antagonists and downstream antagonists
Stimulate/Drain: This will tell PSYCHIC to return all upstream agonists and downstream agonists
Stimulate/ Bottleneck: This will tell PSYCHIC to return all upstream agonists and downstream antagonists
To select a gene to run in PSYCHIC, simply begin typing in its gene symbol in the text input field; PSYCHIC will auto-complete the entry with any genes for which it has data. If multiple options are displayed, simply select the gene you wish to analyze.
When you’re ready, press the ‘Run Psychic’ button to have PSYCHIC run results.
PSYCHIC uses highcharts.js for its data depiction, the CPAN Perl module graph.pm for creating the abstract data structures, the PPI (protein-protein interactions) database, and Opus 23’s own internal agent/gene expression database of PubMed citations.
The Agency app in Opus 23 takes pharmacogenomics to the next level. Relying on the extensive Opus 23 database of published research detailing gene expression data linked to natural products, Agency provides a visual representation of their interactions web. Multiple agents can be displayed, allowing the clinician to synthesis multi-target strategies.
In the image above we see the expression pattern for co-enzyme Q10, a powerful anti-oxidant. Co-enzyme Q10 (COQ10) is the light green node in the center. The tan nodes surrounding it are the genes that interact with COQ10: Red edges (connecting lines) with a ‘T-bar’ indicate that COQ10 inhibits the expression of that gene. Green edges with an arrow indicate that COQ10 enhances the expression of that gene. Genes with a reddish color indicate that they may have compromised function in the current active client in Opus 23. Other agents with especially high abilities to influence the expression of any genes associated with COQ10 are at the periphery of the map and colored gray.
A few examples that can be gleaned from the map:
The ability of COQ10 to increase superoxide dismutase (SOD1) might be enhanced by concurrent administration of Silymarin and Pycnogenol
The ability of COQ10 to antagonize interleukin 6 (IL6) might be enhanced by concurrent administration of omega 3 fatty acids
The effect of COQ10 to antagonize vascular endothelial growth factor A (VEGFA) might be enhanced by concurrent administration of wogonin (Scutellaria biacalensis), honokiol (Magnolia officinalis) and matrine (Sophora flavenscens). Notice that this gene is compromised in the current client.
Like virtually every data depiction in Opus 23, clicking on any node brings up the information popup for that entity: clicking on an agent node brings up its expression data (with links to PubMed citations) while clickin on any gene brings up its genomic data any relevant SNP data associated with that gene.
In addition to its network (web) depiction, any natural product can have its gene expression data depicted as a polar chart.
The polar chart format display the gene expression data as orange if the evidence is suggestive of an antagonistic effect, or green if the effect is agonistic. The strength of the evidence is computed from the sum total of the evidence, scaled by the type of experimental subject (in vitro, animal or human study).
SuperMogadon is a highly flexible search and sort tool that allows you to easily compare the client’s genotype with results from Genome Wide Association Studies* (GWAS) through the Opus 23 Pro database.
As you can see there are over 50+ pages of GWAS data in SuperMogadon, which would make grinding through the data rather impractical. Like most data displays in Opus 23 that deal with large amounts of data, SuperMogadon features a ‘filterable’ display. Type full or partial search terms into the search box at the upper left hand corner and SuperMogadon immediately displays only those results.
Click on the graph icon to display SNP distribution for that pathology or trait as a Manhattan Plot. Click on any column title to sort by that column.
Clicking on the blue graph icon in the ‘Show Plot’ column will launch the SuperMogadon Manhattan plotter for that disease/trait.
TheSuperMogadon Viewer is a GWAS Manhattan plotter, a type of scatter chart used to display data with a large number of data-points. Genomic SNP coordinates (marked by chromosome) are displayed along the X-axis, with the negative logarithm of association P-value for the disease or pathology displayed on the Y-axis. Because the strongest associations have the smallest P-values (e.g., 10 −15), their negative logarithms will be the greatest (e.g., 15). In the example above, we see the graph for ‘Type II diabetes’ as a GWAS Manhattan plot.
Client SNP genotypes results are shape and color-coded:
Gray-colored points denote client SNPs that do not contain the risk allele
Orange-colored points denote that the client is homozygous for the risk allele
Yellow-colored points denote that the client is heterozygous for the risk allele
Square-shaped points signify that the SNP is in the GWAS and Opus 23 Pro databases and when clicked will trigger an information pop-up
Circle-shaped points signify that the SNP is not in the Opus 23 Pro database but is in the GWAS database and is reported by 23andMe. When clicked these SNPs will bring up its GWAS PubMed reference article
Drag-select to zoom section or use the scroller at the bottom. Hover over any point to learn more. Clicking on any point triggers a full-information popup window. Like any other element in Opus 23 Pro, you can notate the SNP in SuperMogadon Viewer by clicking on the link to bring up the information pop-up, then clicking the ‘Add/Edit Note’ button at the top of the pop-up screen. You can also send any popup element directly to curation (so that it shows up in the Client Report.)
Opus 23 Pro subscribes to the philosophy of ‘TMTOWTDI’ (There’s more than one way to do it, pronounced ‘Tim Toady’.) The program was designed with this idea in mind, in that it ‘doesn’t try to tell the physician how to parse the data.’ Rather, it presents many different frameworks and cross-sections of the available client data, using a myriad of infographic treatments. This (and our ability as a species to excel at pattern recognition) dramatically increases the odds that a noteworthy finding will not go undiscovered.
* In genetic epidemiology, a genome-wide association study (GWA study, or GWAS), also known as whole genome association study (WGA study, or WGAS), is an examination of many common genetic variants in different individuals to see if any variant is associated with a trait. GWASs typically focus on associations between single-nucleotide polymorphisms (SNPs) and traits like major diseases.
MoboCaster is an Opus 23 Pro informatics app that performs scenario-specific genomic analysis since, as clinicians, that’s pretty much how we think about genomics.
The browser screen (above) in MoboCaster lists an overview of several genomic scenarios, such as the HPA Axis, Oxalate Genomics, Phase I Detoxification, etc. that display a heat map of colored boxes representing potentially problematic genes for the current client in this scenario. The size and color of the box indicates the significance of the gene in relation to others in this scenario: The larger and darker the box, the more problematic a gene may be for this client. The genes with curated SNPs with the highest power factor assigned by Opus 23 Pro editors will show up in a darker blue color and have a larger sized box. A bar below each scenario gives a key to the relative value assigned to each color. Hovering your mouse cursor over a box will move the arrow along color key to indicate the aggregated power factor of that gene, as well as the number of SNPs used to calculate this value.
Clicking on any box will open a pop-up with information about the gene and the SNP status for the active client.
If your client has all homozygous negative SNPs for a gene in a MoboCaster scenario, that gene will not appear in the MoboCaster overview. You can display the MoboCaster scenarios as either a heatmap (above) or as a polar chart:
Clicking on the name of the scenario open it up in the MoboCaster.
This links to a page with a description of the scenario and details of all the SNPs and their values in this scenario, listed in alphabetical order of the gene symbols. Clicking on a symbol from the list at the top of the page will take you directly to that gene and its description.
If the gene is also listed in any maps or other Mobocaster scenarios or has associated natural products, these will be listed here with a hyperlink. The description of the gene may be curated to be specific to this scenario: to find out general information about the gene click on the gene symbol link to open the gene pop-up. Clicking on the SNP rs ID link will open a SNP pop-up.
Some scenarios may examine only specific SNPs in a gene, for example, in the Phase I Detoxification scenario CYP1A2 lists only rs762551, as this SNP is known to be a high-inducibility variant. In this situation clicking on the link to CYP1A2 will open a pop-up giving you information about the other CYP1A2 SNPs for the current client.
Initially, I was very excited about the prospect of allowing the import of ancestry.com DNA data into Opus 23. After all, they use the same Illumina technology as 23andMe (although 23andMe apparently have their own unique chip.) Initial testing was promising. Like 23andMe, Ancestry supplies raw data in a basic tab-delineated text file. It’s in a slightly different format, but there as no problem parsing it. In fact Ancestry offered several possible advantages over 23andMe.
It retails for $99 USD, which used to be what 23andMe charged, until they were cleared by the FDA to supply some very basic health insight data, at which time they hiked the price of the test up to an almost extortionate $199 USD, because now they can tell you what color eye you might have and whether your earwax is soft or hard.
Ancestry SNPs are all reported with the ‘rs’ number. In order to cross-reference SNPs in any of the bioinformatics databases, we need their official id number, which is, as per dbSNP, referenced as ‘rs[the id number]’. For example the SNP main SNPs (C677T and A1298C) for the MTHFR gene are rs1801133 and rs1801131. 23andme uses a lot of internal SNP ids, which they prefix with an ‘i’. The internal references usually do indicate SNPs that otherwise have rs id numbers, and if you are dogged enough, you can usually get the proper rs id for an internal SNP, but they don’t make it easy.
Problems arose when it occurred to me that it would be prudent to cross-compare the Ancestry and 23andMe SNPs with the basic Opus 23 curated SNP database. Opus 23 accesses several SNP databases, but its own internal database is the jewel in the crown, hand-curated by our developers with special reference to clinical utilization and nutrigenomics significance. So I wrote a simple Perl script to do the work.
The results were depressing, to say the least. Roughly 35-40% of the SNPs in the Opus curated database that are reported by 23andMe are not reported by Ancestry. And some of these are biggies, like the SNPs that control secretor status on FUT2. This leads me to believe that the Ancestry DNA analysis is skewed towards genealogy determination (perhaps not surprisingly) and not health outcomes.
There has been an explosion of interest in the microbiome. Outfits such as uBiome has made it relatively inexpensive and easy to have your microbiome profiled. These services extract the bacterial DNA out of the sample and identify each of the bacteria that the DNA came from.
There appear to be some limitations with the technology. I’ve been told by sources whom I consider informed that uBiome is not that accurate the deeper into phylogeny. Genera data may only be 40-60% reliable by some estimates. So while the major distinctions such as phylum and class may be reliable, drilling down to the precise distribution amounts of particular species may not be so helpful.
Nonetheless there may be some advantages to importing microbiome data into Opus 23 Pro. From a research perspective we’d have the benefit of cross-comparing genomic data with microbiome data, and the ability to perhaps correlate dietary changes based on genomic analysis with progressive changes in sequential microbiome samples.
One advantage is the ease of working with uBiome raw data. The most basic raw data you can download is a simple JSON data file that usually runs about 30K in size, so we’re not talking about any sort of server stress. This data is straightforward enough to parse. The image above was generated out of some basic uBiome data and ported to a visualization script (D3.js) to produce a sunburst information distribution.
The same data ported to a dendrogram based on taxonomic distribution:
Although this feature will probably not ship with Opus 23 Pro when it hits the pavement in January, I’ll probably add this microbiome tracking ability sometime shortly afterwards.