Just DNA-Seq

Just DNA-Seq is a set of open-source libraries and pipelines designed to help you with:

  • realigning your raw genome reads and doing variant calling using the latest tools and Bioinformatic pipelines
  • annotating your own genome using longevity OakVar plugin with:
    • known longevity, cancer risks
    • known drug interactions
    • cardio hereditary diseases risks
    • coronary artery disease risks variations
  • getting a comprehensive and transparent open-source report, inclusive of all previously mentioned information as well as polygenic health risk predictions and personal recommendations related to longevity:

(also you can see an example of the full report here)

You can test the project right now! Click the link to access our online portal agingkills.eu and explore it.

You can use our demo account to see already generated reports (login: jdnaseq@gmail.com password: 123456)

Why Just DNA-Seq?

First human genome sequencing project concluded Apr 14, 2003 after more than 12 years of labor and costing around 3,000,000,000$. Today it turned from a great human endeavor to something widely accessible. Sequencing is commercially available at a price of roughly 400-800$. You get both sequencing and interpretation of the results for the price. In fact, the sequencing job is often served as a side dish to predictions.

So, why bother with the DIY approach when there is such a variety of commercical options?

The lack of transparency, privacy and, most importantly, room for customization are the main drivers for us, Just DNA-Seq team, to develop a DIY toolbox.

Reports contents are typically decided by the service provider, your choice is limited with their product line. Results you get from such services are based on proprietary databases and algorithms. While having large R&D departments and a lot of medical data to base conclusions upon, their methods are not transparent at all, in many cases, you have no idea why they made this or that prediction. Verifying or comparing their results is therefore troublesome, especially, when you get totally different predictions from different companies.

More to that, what if we want to see how well our genes align with the prospect of longevity? On one hand, it is known that surviving up to 90 years is more about lifestyle choices. On the other hand living longer depends more on the genetics. People with exceptional longevity are not distinct in terms of lifestyle factors from the general population [PMID: 21812767]. Unfortunately, there are simply no commercial offerings at all to address the demand for longevity-related predictions.

At the same time, all the information necessary to do your own genomics research is out there, generously shared by the scientific community and openly available.

So, why not to use existing open-source solutions then? Well, no luck here either! We’ve looked through the available solutions and found out there are no tools to cover one topic very close to our hearts: genetics of longevity.

That’s when Just DNA-Seq comes into play!

How it works

The Just DNA-Seq platform consists of OakVar (Open-source Genomic Variant Analysis Platform) modules, bioinformatics pipelines, and additional libraries. One can realign and annotate the genome of interest employing Just DNA-Seq tools to to retrieve polygenic risk scores (PRS), information about variants associated with age-related diseases or major life threatening risks and longevity-associated variants present in genome.

At the moment we have the following modules in our report:

Longevity variants report is based on 1900 variants from LongevityMap and other data sources which are scored and prioritized according to multiple criteria. It also depends on ClinGene, dbSNP and ClinVar modules. To help you better understand the role your genes play in longevity, we have categorized them into 11 distinct groups.
Github repository: just_longevitymap
Longevity PRS report is based on existing longevity polygenic scores. At the moment, our best performing PRS is implements the score presented in [PMC8087277] and comprises 330 variants. This PRS was shown to be significantly associated with cognitively healthy aging and with prolonged survival. While it is not enough to have “centenarian” genes to become one, they are still needed to cut down major health risks and therefore gain longevity escape velocity.
Github repository: just_prs
Of the major life threatening risks, Hereditary Cancers are the ones reliably predictable based on genetic variants data. Certain genetic variants determine a higher-than-average risk of developing a specific kind of cancer. The Oncological risks report includes about 300 genes, which are related to cancer predisposition, cancer progression and tumor cell motility.
Github repository: just_cancer
Aging is commonly associated with deteriorating health. As a result, plenty of drugs are commonly prescribed at an older age. Moreover, some of these drugs are now known to have geroprotective action in addition to their primary indications for medicine use (e.g. statins, metformin, rapamycin, etc.). To a large extent Drug metabolism is highly dependent on a person’s genetic polymorphisms because these variations affect the activity of xenobiotics-transforming enzymes. As a result, individual dose correction or even medication replacement is frequently required in order to avoid adverse effects. Longevity-drugs report is mostly based on data from PharmGKB database (https://www.pharmgkb.org/) and DrugAge and provides insight into one’s drug metabolism dispositions for a number of widely used drugs.
Github repository: just_drugs
We keep expanding our “Major risks” module with information about other common diseases which are among leading causes of death worldwide and are major contributors to decreased longevity. This part contains the information about hereditary cardio desisease and coronary artery disease (CAD). Timely detection and diagnosis of heart disorders can lead to enhanced treatment options, help to prevent sudden cardiac death, and improve prognosis. Our report analyzes the most relevant genes for arrhythmias, congenital heart disease, and cardiomyopathies. Analyzed gene abnormalities may cause the following syndromes: Long and short QT, Brugada syndrome, catecholaminergic polymorphic ventricular tachycardia, cardiomyopathies dilated and hypertrophic, and congenital heart defects. In addition, this panel includes vascular abnormalities, such as dolichoectasia and hereditary hemorrhagic telangiectasia.Identifying individuals with a genetic predisposition to CAD allows for earlier detection and intervention, which can prevent serious complications and improve outcomes. And understanding a person’s genetic makeup can help tailor treatment plans to their specific needs. For example, if a person has a genetic variant that increases their risk of CAD, their healthcare provider may recommend more aggressive treatment or lifestyle changes.
Github repositories: just_cardio and just_coronary
Thrombophilia is a condition in which an individual has an increased tendency to develop blood clots. This can be caused by genetic or acquired factors, and the risk of developing thrombophilia increases with age. This report contains data of blood clot or deep vein thrombosis (PGS000931), and the list with genes, associated with an increased risk of thrombophilia.
Github repository: just_thrombophilia
Genetics plays an important role in lipid metabolism and the development of heart disease. Genes control the production, transport, and metabolism of cholesterol and other lipids. Lipid metabolism plays an important role in the development of heart disease, and this risk increases with age. This report contains data of the PRS of Coronary atherosclerosis development (PGS001839), and the list with genes, associated with an increased risk of CAD.
Github repository: just_lipidmetabolism

How to work with personal genome

Detailed user documentation is still in the making and is available at https://just-dna-seq.readthedocs.io

Meanwhile we recommend watching our workshop! It demonstrates and explains common use-cases are with the examples that you can reproduce.

You can check out an example of the full report here.