r/bioinformatics

I'm working on an independent project using single cell data to study cell-cell communication in breast cancer.

I've been using LIANA to identify ligand receptor communication programs and SHAP/XAI to try to understand which genes are most important in explaining these programs.

I've gotten pretty far into the analysis, but honestly, I feel like I need someone who actually works in this field to talk to before I go any further. I have a bunch of questions about my methodology, whether I'm interpreting things correctly, and how I could validate what I'm finding.

If you work in cancer genomics, computational biology, or anything related to single cell RNA analysis, I'd LOVE to talk to you.

Please comment or DM me if you're open to discussions.

Hello everyone!

I recently got my, first RNAseq as well as proteomics data set. And thankfully a standard bioinformatic analysis with it. Honestly, I am amazed at what you can find out, when you have the Tools to really dig into your data!

Now, I have been trying (successfully) to replicate the RNAseq data analysis Pipeline by the help of vibecoding with AI, and I can interpret and understand all of my analyses. However, I could never write any bit of Code for it... In the end I don't really understand what the Code does, I can just Review my data afterwards.

Long story short: I would really love to get into bioinformatics a little deeper. Maybe a short term goal would be to build my own fully customizable RNAseq pipeline from scratch and then see from there. Are there any resources you guys with experience can recommend for me?

Thanks!

Hi everyone!

I’m looking for raw genomic and transcriptomic datasets to practice and improve my bioinformatics and computational biology skills.

I’m particularly interested in datasets such as:

- Whole Genome Sequencing (WGS): Raw FASTQ files for genome assembly, variant calling, and comparative genomics.

- RNA-Seq: Raw FASTQ files for differential gene expression analysis, transcriptome assembly, and functional enrichment.

- Whole Exome Sequencing (WES): Raw sequencing data for variant identification and annotation.

- Long-Read Sequencing: PacBio or Oxford Nanopore datasets for genome assembly and structural variant analysis.

- Metagenomics: Raw sequencing data for microbial diversity and taxonomic profiling.

If you have any publicly available datasets, research project data, or recommendations for accessing raw sequencing data, please share the links or repository names.

I’m familiar with bioinformatics tools and workflows and would like to work with real-world datasets rather than only tutorial datasets.

Repositories such as NCBI SRA, ENA, and GEO are already on my radar, but I’d also appreciate suggestions for interesting datasets or specific accession numbers that are suitable for independent analysis.

Thanks in advance for your help!

Jenny Chen’s lab at EMBL Rome, in collaboration with my lab (Rompani Lab) is looking for a bioinformatics intern to contribute to multiple projects investigating the molecular mechanisms in the brain underlying rodent behavior. These projects address questions ranging from cognitive decline to the evolution of social behavior across rodents. The intern will be responsible for analyzing bulk- and single-cell RNA-sequencing data collected from rodent brains to identify genetic markers and neuronal cell types that drive behavioral phenotypes. The ideal candidate has a strong foundation in computer programming and statistics, and has experience analyzing neuronal transcriptomic data. Experience working with data from non-model organisms is a plus, but not required.

The position is a full-time (1000€/month, after taxes) on-site position at EMBL Rome, and requires a minimum of a 1-year commitment. The start date is as soon as possible. To apply, please send a cover letter and CV to chenlab.emblrome@gmail.com. Your cover letter should include: 1) what attracts you to the position, 2) how your experience is relevant to the position, 3) confirmation that you are able to commit to a 1-year on-site position at EMBL Rome, and 4) your preferred start date.

Clarifying Edit: We take candidates from anywhere in the world--EMBL is an EU-level intergovernmental organization, so has an easier time with getting people visas than most places.

Clarifying Edit2: We are interviewing on a rolling basis, will put an edit note here when position is filled.

Clarifying Edit3: 1000E/month in the area the lab is in (not Rome city center) is enough for somebody to live alone in a studio apartment (400Eish/month), not needing roomate like in most big cities--Italy is much, much cheaper to live in than American cities, so quality of life is quite a bit higher. Top firms and such in the city center only pay 700-800E/month to their interns.

Hi everyone,

I am working on a cancer RNA-seq analysis project, and I have found two GEO datasets that I would like to use together. I am trying to understand whether there is a scientifically valid way to integrate them rather than simply analyzing them independently.

Dataset 1:

Xenium-based high-throughput RNA in situ hybridization/spatial transcriptomics

Contains normal tumor samples, DCIS, and invasive breast cancer samples

Dataset 2:

Bulk RNA-seq

Samples are classified according to metastasis status and breast cancer molecular subtypes

My original goal is to perform a normal/control vs tumor/disease-type analysis and identify differentially expressed genes and biologically relevant pathways, followed by downstream analysis.

However, the two datasets are clearly different in terms of technology and experimental design. One is spatial transcriptomics and the other is bulk RNA-seq. So, is it scientifically/statistically valid to integrate these two datasets in a single study?

I am particularly interested in knowing what would be considered a methodologically defensible approach for this, rather than simply combining the two matrices because they contain overlapping genes.

Any suggestions regarding an appropriate integration strategy, statistical design, or published examples of similar spatial + bulk RNA-seq analyses would be very helpful.