What I did: helped find the SRA datasets for this project. Performed the galaxy workflow analysis for the control and breast cancer sequence data. I created the alpha diversity graph and heat maps. Contributed to writing and organizing the poster. I found the dry lab analysis process to be the most fascinating part of this project. The majority of my lab experience has been wet lab work. Anything dry lab related was minor such as spreadsheets and simple statistics. Working with online sequence manipulation tools such as those found on Galaxy and then subsequent analysis with rStudio has inspired me going forward to dedicate more time and effort to dry lab skills. This work has opened doors in my own extracurricular research that had been previously closed or I was not at all interested in.
How you can help: I think the greatest weakness of this study is a small sample size. The datasets themselves are relatively large, but they cover a very small patient population. I think any meaningful conclusions must be made with more data from a diverse population. Data from other long read whole genome sequencing tools such as PacBio would be great as well as oxford Nanopore is not as accurate as one would hope.
What I did: We analyzed long-read metagenomic sequencing data using a Galaxy-based pipeline to compare gut microbiome diversity across lung, gastric, and breast cancer patients versus a healthy control, finding distinct taxonomic shifts and unexpectedly higher diversity in disease states.
How you can help: Future work should validate these findings by improving genome assembly quality (e.g., CheckM2) and performing functional annotation (e.g., antiSMASH/Prokka) to link microbial composition to metabolic pathways, ideally expanding sample size and controlling for confounders like treatment and diet.
What I did: In this project, my work focused on analyzing pre-treatment lung cancer samples. I used NCBI to search for suitable SRAs and concatenated them together in Galaxy. Additionally, I used the workflow we’ve created to collect data such as the Krona plot, Alpha diversity, and GTDB-Tk for analysis. Finally, we compiled the results of this sample with the output from every dataset in our group to create a series of figures to demonstrate the effect of cancer treatment on the human gut microbiome.
How you can help: I believe more work can be done to improve our project. One thing that could be worked on is to expand the sample sizes of the data that we’ve gathered, and to also look into gathering new datasets from other types of cancers. Moreover, we could also evaluate the quality of our assembled genomes using tools such as CheckM2, and using antiSMASH to investigate potential microbial metabolic pathways associated with cancer.