Tech Videos

Video 2: Prediktr: Prediction Tool for Infectious Disease Surveillance


WholeGenome Integration and Detection Algorithms as a DiagnosticTool for Infectious Agent Detection:

Everyday, millions of nucleotides are being sequenced by high throughput, next generation sequencing (NGS) machines. With increased speed and accuracy of the sequencers coupled by the decreasing costs to perform DNA sequencing, many novel applications of DNA sequencing are being considered. These range from cancer studies to the detection of pathogens from body fluids or environmental samples to detect and monitor diesease outbreaks. We have created 2 algorithms that enable us to utilize the large amounts of high throughput sequencing data from bacteria and rapidly analyze the presence or absence of pathogenic bacterial strains in metagenomic or mixed samples. Based on our simulation tests and statistical results, we have been able to detect the presence of pathogens from metagenomic samples (human gut or oral) even at very small loads of 0.1%. We have also been able to detect the presence and statistically distinguish loads that are close to one another.

The algorithms are used sequentially. The genome integration algorithm is used as a primary tool, and once implemented and deployed can be used sporadically to update the database. The main tool is the detection algorithm that detects the presence of pathogenic bacterium/ bacteria even at very small loads (0.1%) in the sample being tested and can be used by researchers on a regular basis as a diagnostic tool.The biggest advantage of using this technique/ method is that it was developed on laptops with 4GB RAM and it can be run on laptop computers coupled with low-cost, handheld, highthroughput DNA-sequencers to help researchers at target geographical areas to rapidly identify the presence of pathogenic bacterium or bacterial contamination in mixed or metagenomics samples from livings systems, soil or water, that can potentially save lives.

Video 1: See Kubernetes and GCS-fuse in action!

Scenario: Imagine you have a django app running in a pod in kubernetes. You want this application to access some controlled data (say medical or genetic or financial data) stored in a google or amazon bucket but you cannot allow your users to download said data. This video demonstrates how a sidecar pattern would look like in kubernetes (hosted on GCP) with privileges to access the data and pass them off to the application podScenario: Imagine you have a django app running in a pod in kubernetes. You want this application to access some controlled data (say medical or genetic or financial data) stored in a google or amazon bucket but you cannot allow your users to download said data. This video demonstrates how a sidecar pattern would look like in kubernetes (hosted on GCP) with privileges to access the data and pass them off to the application pod.

If you are interested in the code base, email me at contact@coderchick.com. I will also be adding everything here in a few days. Be sure to follow the blog and subscribe for updates!