Reflections on 2023 and outlook

It has been an amazing year of research. I've been at Burnet Institute since August 2023 as Head of Bioinformatics and I've really enjoyed the challenge of serving the many and varied 'omics projects at the Institute and loved discussing new project ideas with everyone here. I'm still active at Deakin in student supervision and project collaborations and this is ongoing.

In terms of research directions, my group has been focused on our three themes: 

1. Bioinformatics collaborative analysis

2. Building better software tools for omics analysis

3. Reproducibility and research rigour

Some of the long-running projects have been completed including methylation analysis of type-1 diabetes complications, which has been about 10 years in the making [1]. The number of collaborative projects has dipped, which is normal when changing institutes and I hope this will lift in the coming years as Burnet work gets completed.

In terms of research directions for 2024, there are many. One thing I hope to share with you soon is our approach to applying functional class scoring (like GSEA) to Infinium array data. This has been tricky since each gene can have many probes, and sometimes probes can be annotated to more than one gene. But I think we have a solution. It will be interesting to apply this to some of the larger EWAS datasets and prospective studies to see if it has any potential for methylation signatures to preceed disease onset. In doing this work we have learned a bit more about the differences between over-representation and functional class scoring tests which I hope to bring back to the transcriptome world.

Reproducibility has become an ever bigger part of my portfolio of projects since last year's paper "Urgent need for consistent standards for functional enrichment analysis" [2], which was very successful and probably my most important work of my career. Since then we have been conducting reproducibility surveys with my new PhD student Anusuiya Bora and we have learned a great deal about what it takes to make bioinformatics research reproducible and reliable. We also put together a review called "The five pillars of computational reproducibility: Bioinformatics and beyond" [3] which outlines the principles of reproducibility that we should be adopting. Along these lines, Anusuiya and I have written a protocol that outlines how to do enrichment analysis using these five pillar principles [4]. Next year we will be going even harder on reproducibility and enrichment analysis. We will have pieces on the utility of web-servers for bioinformatics and how we can make them more robust. We will also be examining some tools that are used by the microRNA community and in particular whether functional enrichment analysis of microRNA targets is something that is useful or not. 

This work on reproducibility is very satisfying as it directly educates emerging researchers on best practices, but the challenging part with this reproducibility agenda is that focused funding in this area is difficult to obtain as our work doesn't seem to fit in any existing grant scheme. We could do a lot more and better work in this direction if the funding issue were resolved.

1. Khurana I, Kaipananickal H, Maxwell S, Birkelund S, Syreeni A, Forsblom C, Okabe J, Ziemann M, Kaspi A, Rafehi H, Jørgensen A, Al-Hasani K, Thomas MC, Jiang G, Luk AO, Lee HM, Huang Y, Thewjitcharoen Y, Nakasatien S, Himathongkam T, Fogarty C, Njeim R, Eid A, Hansen TW, Tofte N, Ottesen EC, Ma RC, Chan JC, Cooper ME, Rossing P, Groop PH, El-Osta A. Reduced methylation correlates with diabetic nephropathy risk in type 1 diabetes. J Clin Invest. 2023 Feb 15;133(4):e160959. doi: 10.1172/JCI160959. PMID: 36633903; PMCID: PMC9927943.

2. Wijesooriya K, Jadaan SA, Perera KL, Kaur T, Ziemann M. Urgent need for consistent standards in functional enrichment analysis. PLoS Comput Biol. 2022 Mar 9;18(3):e1009935. doi: 10.1371/journal.pcbi.1009935. PMID: 35263338; PMCID: PMC8936487.

3. Ziemann M, Poulain P, Bora A. The five pillars of computational reproducibility: bioinformatics and beyond. Brief Bioinform. 2023 Sep 22;24(6):bbad375. doi: 10.1093/bib/bbad375. PMID: 37870287; PMCID: PMC10591307.

4. Ziemann M, Bora A. A recipe for extremely reproducible enrichment analysis. DOI: dx.doi.org/10.17504/protocols.io.j8nlkwpdxl5r/v2


Popular posts from this blog

Mass download from google drive using R

Google drive is great for sharing documents and other small files but it's definitely not suited to moving  many large files around. For example I just received 170 fastq files that are about 200 MB in size. If you use the browser to download the whole folder, the web app will zip the contents for you which will take a LOOONG time. Alternatively you can download each and every one of those files one by one, which is annoying and prone to human error. You can insist to your collaborators to transfer in a different way, but there are not that many user-friendly and economic approaches. Your biologist collaborators probably won't be able to use rsync  to get the data to you safely. And fast convenient tools for moving around large files like Hightail cost a lot of money. A good solution to this problem is to use the R package googledrive  which enables command line automation of tasks that might take a long time manually. The package vignette has a good overview of the main comma
Read more

Data analysis step 8: Pathway analysis with GSEA

Image
In our RNA-seq series so far we've performed differential analysis and generated some pretty graphs, showing thousands of differentially expressed genes after azacitidine treatment. In order to understand the biology underlying the differential gene expression profile, we need to perform pathway analysis. We use Gene Set Enrichment Analysis ( GSEA ) because it can detect pathway changes more sensitively and robustly than some methods. A 2013 paper compared a bunch of gene set analyses software with microarrays and is worth a look. Generate a rank file The rank file is a list of detected genes and a rank metric score. At the top of the list are genes with the "strongest" up-regulation, at the bottom of the list are the genes with the "strongest" down-regulation and the genes not changing are in the middle. The metric score I like to use is the sign of the fold change multiplied by the inverse of the p-value, although there may be better methods out there
Read more

Generating a custom gmt file for gene set analysis

Image
Pathway and gene set analysis is a common procedure for interpretation of RNA-seq or other genome-wide expression assays. Most of the time, we use GSEA to tell us whether our gene sets of interest are up- or down-regulated. We can use gene sets from KEGG , Reactome , GO ,  MSigDB  and other sources, but you can also generate your own gene sets. The format used for GSEA is gmt . I'm going to take you through two examples of generating custom gene sets: Generate gene sets from published data sets using GEO2R Let's say you're interested in the transcription factor STAT1. I found a dataset in GEO called "Knockdown of STAT1 in SCC61 tumor xenografts leads to alterations in the expression of energy metabolic pathways", which has a paper in BMC Med.  Most uploaded array data sets can be reanalysed with GEO2R, which runs the array analysis tool Limma but this is embedded in the webpage and has a GUI which makes it very accessible for biologists. Click this link
Read more