Hi , I am working on an ML project to predict which genes are associated with specific phenotypes in Arabidopsis thaliana. I'm particularly interested in understanding how environmental conditions influence the relationship between genotype and phenotype.

I’m using 1001 Genomes, AraPheno, and Kawakatsu, but I’m struggling to find data linking genotype accessions to phenotypes measured under specific environmental conditions (e.g., temperature or light).

Does anyone know of a dataset or repository that provides this information, even for a single environmental variable? Multiple linkable datasets would also work.

Any pointers or suggestions would be greatly appreciated. Thank you!

Hello!

As the title says, I'm currently analysing maternal transcriptomics and need to see what is upregulated and downregulated in case group. This will be done longitudinally.

I have no background in bioinformatics, coding, statistics etc. My background is medical. Im doing this to built my research skills. My supervisor is amazing and has patiently taught me everything. I am now analysing the data for a possible publisable outcome.

I have been working on this for 2 weeks. I have understood the terms in my .csv file including NES, leading edge, log2fold, etc. My biggest hurdle is

I have literally tons of core enrichment genes, how do I know which to extract and look up. I did a excel count today and one gene stood out amongst the reactome GSEA Pathway which was AAAS. In the filtered unregulated data, it was present 32 times. Okay. But what to do with it? It's not in the top of the upregulated DEG data.

Additionally, i have browsed reactome.org and understood the heirarchy but I don't get how to organize my pathways in not "manual labor" way. I manually copy pasted the name and noticed that top 20 had singnal transduction, cell cycle and dna repair pathways.

I havent even touched KEGG yet and I'm lost with what to do with all this.

Any explanation or yotube video would be really helpful. Honestly, I feel im wasting my time by going in circles. Currently, i have a code running matching my 1426 reactome pathways to reactome.org API.

But i would appreciate professional help. It feels im lost in the woods. Whatever I do, whatever I read, i dont have the navigational tools just random information in my brain that is serving no purpose.

Thank you.

I ask because when I used it for my dataset from C. elegans, the homologene gene symbols and descriptions are obviously wrong. A lot of C. elegans genes do not have names, and instead use sequence names based of the BAC and YAC positions they were cloned onto. Examples are C30F2.4 and R08F11.4. When I ran such genes through analysis, the homolog instead showed completely different genes: C30F2.4 - C30F2.6, and R08F11.4 - fpn-1.3 (which has the sequence name R08F11.6). What's more, some genes with proper names that are well-annotated, like zipt-2.3, wasn't recognized at all. zipt-2.3 is a member of ZIP family, a family of zinc transporters, but the line for that gene is blank. This resulted in problems in enrichment analyses downstream.

I initially used WormEnrichr for my dataset, but the libraries were outdated (GO term 2018) and redundant GO terms weren't grouped. This made it very hard to make figures. Other tools like WormCat didn't have my zinc transporters annotated, either. Enrichment tool from Wormbase also trimmed the zinc transport GO terms. Under "zinc transport" on Wormbase, there are a list of genes associated with the term. If you paste this list to the Wormbase enrichment tool, you won't get the same GO term back in the results.

Right now I'm completely baffled. What am I supposed to use for my enrichment analyses, and what am I supposed to do with the results I get back from Metascape?