Senior Sem Summary #2

Riya Shah

Senior Seminar

Dr.Ko

September 29, 2022

Summary

This week’s presentation was by Mr. Shawn Prince, PhD, a Senior Field Application Scientist. He

works at QIAGEN, Digital Insights, a leading global provider of sample and assay technologies. The

heading of this presentation was how to “Generate Hypotheses and Insights for Grants and Publications

using IPA, Even when you don’t have data”. QIAGEN IPA functions to perform analyses and

interpretations of omics data within the context of various biological systems. It also aids in investigating

biological mechanisms underlying disease pathology, drug treatments and identifying biomarkers and

key regulators. It creates networks to understand interactions.

This site works off something known as findings. Findings are the outcomes and reports found

upon searches conducted on the software. The majority of the findings are expert findings. This means

that they have been assessed by an individual. The findings are weekly curated, so new information and

more findings are posted from week to week.

QIAGEN IPA has two possible approaches, one with a dataset and one without a dataset. When

using a dataset, the IPA helps to find connections within the data, novel biomarkers, identifying key

targets and regulators and discovering novel disease mechanisms. Without a dataset, it helps to search

and explore the QIAGEN Knowledgebase, testing hypotheses in silico and identifying the degree of

novelty in a hypothesis.

In order to better gather results for our purposes, it is a good idea to use the pathway tool. It

builds the networks in between for us to see what is going on. For an example, Mr. Prince chose a

particular gene. He specified that a key component of this process is directionality. In using directionality,

we can frame our hypotheses and focus on further inquisitions. The building tool works on using specific

criteria to help refine the pathways. The legend is very useful as it defines all the markings on the

pathways. It gives meaning to the colors, shapes, lines, edges, and everything used within the networks.

For example, warm colors mean something is activated and cool colors mean something is not activated

or inhibited. You are in charge of what molecules you wish to add or ignore. You can even test your

hypotheses using the MAP (molecule activity predictor). This gives a theoretical measurement to a

molecule, and it helps run predicted algorithms with regard to findings and the reactions act accordingly.

In conclusion, this was a very informative presentation, and this seems like a tool which can be

very useful in research conducted everywhere. I may even try to implement this in my own research lab.

Senior Biology Seminar (BIOL 4197)