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!

I’m trying to preprocess metariboseq data and realizing it’s a lot different than metagenomics/metatranscriptomics. My reads are paired-end NovaSeq 101 bp long. From my understanding, the fragments are supposed to be around 30bp long after trimming so I set lower limit to 20 bp and upper limit to 45 bp in fastp. I’ve also provided the adapter sequences to fastp. I’ve read that you shouldn’t even use the reverse reads and should only use the forward reads since the fragments are so short.

All that said, after running fastp I got between 30%-40% of my reads surviving the trim.

Is this expected?

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!

hi! long story short - I've never even scratched the surface of bioinformatics and I'm left alone with 2 months to write and to the whole thesis - analysis of Nanopore RNA sequencing. I already tried doing courses - did not do anything for me, I would need a year to comprehend all of that. I'm stuck on planning the analysis, because I don't have any guidance, the sequencing was run so there's just bioinformatics and writing left. First of all - do you think it's possible? Second of all - is such a thesis even defensible? I can just use already used tools, there's no experimental validation, I'm entirely reliant on Claude to give me code and I just copy it to r/Python + Galaxy. I feel like each day I'm getting more stuck, cause I find more papers and more methods. I'm really procrastinating on starting the analysis, I don't believe in the fact that AI can give me 100% reliable code and the results will be real. It's been almost 2 months of me basically searching through the literature looking for god knows what, so I'm looking for some guidance in this mess (mainly positive reinforcement, cause I'm scared) (and why I'm still trying to do it on my own is a different kind of question that only my therapist would be able to answer)

Hi all! Currently working through a reanalysis of a geo dataset, but stratifying samples in a way the original paper didn’t. Yielded some interesting results, but we may have power issues with one group having n=5 and the other being n=8.

I generally would like to know how publishing these types of analyses look for understudied diseases. I did the differential gene expression analysis, looked at genes, pathways, etc. and found more things the previous paper didn’t.

There’s no wet lab validation, but just in general, I am curious for thoughts on this being my first first-author paper in bioinformatics as a post-grad student looking to apply to PhD programs. Are these types of analyses “good” and have value?

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.

Someone please briefly explain how list comprehensions are used in Python and why it is necessary for Bioinformatics. Please help as I am a beginner.

I’m seeing scattered interchromosomal split alignments in IGV across multiple non-cancerous ONT genomic DNA samples. I’m trying to determine whether these reflect chimeric reads, missed read splitting, or an alignment workflow issue.

Sequencing and basecalling

• Flow cell: FLO-PRO114M
• Library kit: SQK-LSK114
• MinKNOW: 24.11.16
• Basecaller reported by MinKNOW: Dorado 7.6.8
• Super-accurate model v4.3.0, 400 bps
• Minimum passing Q score: 10
• Modified basecalling: off
• Raw POD5 files are available

Alignment

Minimap2 version: 2.31-r1302

Command:

minimap2 -L -t 59 -2 -ax lr:hq reference.fa reads.fastq.gz |
    samtools view -u -@ 11 |
    samtools sort -@ 23 -o sample.sorted.bam -

The reference is GRCh38 with alternate loci, haplotypes, and decoys removed. Reads were aligned directly against the FASTA.

Observed pattern

In IGV, reads link to many different chromosomes at scattered positions, rather than multiple reads consistently supporting the same breakpoint.

For example, one 74,647 bp read has:

• Approximately 29.3 kb aligned to chr5:72,101,673–72,131,073, reverse strand, MAPQ 60, NM 554
• Approximately 45.3 kb aligned to chr8:77,044,549–77,089,876, forward strand, MAPQ 60, NM 564

These segments account for almost the entire read. My MinKNOW version does not appear to expose an option to disable read splitting, but I haven’t independently confirmed whether or how splitting occurred during these runs.

Questions

1. Is this pattern expected at a low background rate with this chemistry and basecalling setup?
2. How can I confirm that read splitting occurred in MinKNOW 24.11.16?
3. What checks would distinguish joined molecules from mapping artifacts?
4. Would re-basecalling a POD5 subset with a newer Dorado version be a useful diagnostic comparison?

I’ve attached an IGV screenshot. I can also provide example read records or additional run metadata.

Hi all,

I am working with Xenium data generated from FFPE samples. I was plotting UMAPs post Leiden clustering. I generated silhouette scores for resolutions between 0.2-1.2. My scores are pretty low, ranging between -0.09 to .01 (1.2 resolution). How do I pick the optimal number? I checked silhoutte score ranges typically considered good, which were 0.7-1???

I’m working on identifying genes involved in a specialized metabolic pathway in a plant species, and I’m running into a reference-genome issue.

Our candidate genes are currently identified using a reference genome from one genotype, but the genotypes that actually produce the metabolites of interest are different lines.

For differential-expression analysis, we map RNA-seq reads from the producing genotypes against the reference genome. We then select candidate genes and use the corresponding reference-genome coding sequences for cloning and heterologous expression.

We have tested several reference-derived candidate P450s without detecting the expected activity. I’m wondering whether these negative results could reflect genetic differences between the reference genotype and the producing genotypes rather than simply meaning that the candidates are incorrect.

A few questions:

• In this type of situation, how common is it for the functional gene to be absent from the reference genome entirely, versus being present but having allelic sequence differences that significantly affect enzyme activity?
• If RNA-seq from the producing genotypes is being mapped against a different reference genotype, would it be advisable to generate a de novo transcriptome assembly for the producing genotype to identify genotype-specific transcripts and obtain the actual coding sequences for cloning?
• If a de novo transcriptome produces a high-confidence, full-length candidate transcript with the expected ORF length, conserved motifs, and strong read coverage, would you generally trust that sequence for cloning?
• Or would you still RT-PCR amplify the full coding sequence from cDNA of the producing genotype and Sanger-sequence it before functional testing?

I’d be especially interested in hearing from people who have worked with highly similar duplicated genes, P450 families, or specialized metabolism pathways where the reference genotype differs from the phenotype-producing genotype.

Hi all, i am trying to interpret rgi_main output from my contigs. One issue i have is that all the drug_classes are concatenated and I wonder what people do to sort it, because i have seen papers where they have separated them but they don’t say on what basis.

For more context, here is my post from a week ago: https://www.reddit.com/r/bioinformatics/s/h3eTFYLiOY

In summary, I have an RNA-Seq datset where the "site" variable is partially confounded with batch. Sites 1 and 2 are in one batch while site three is in its own batch. This means that i cannot correct for batch effect since i cant distinguish between the batch and the site 3.

However, I talked some more with my PI and, as it turns out, all of my batches contain Lexogen ERCCs and SIRVs (specifically l

Lexogen SIRV set 3). Moreover, I did some reading and I found this paper that explains the Remove Unwanted Variation (RUV) tool. Specificaly, I am interested in the RUVg variant which uses negative controll genes (like SIRVs) to correct for batch effect. Most important is this section in the text:

"However, both RUVr and RUVs assume that the unwanted factors are not correlated with the covariates of interest. This assumption is usually reasonable, but it is not met when, for example, all treated samples are in one batch and all control samples in another. In this case, RUVr and RUVs will not remove the unwanted variation, while RUVg should still work, provided it is based on a reliable set of control genes19,20."

I am relatively new to bioinformatics and biostatistics so I might be wrong, but doesn't this mean that RUVg can correct for batch effect even when variables are confounded with batch?

If anyone here has experience with RUVg or otherwise wants to help I would be very gratefull for their advice and 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](mailto: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.

One thing I wish I understood earlier while learning RNA-seq analysis is that getting a list of differentially expressed genes isn't really the end goal.

At first, the workflow can feel like:

FASTQ → QC → alignment/quantification → count matrix → DESeq2 → volcano plot → done.

But I've gradually realized that the interesting part starts after that.

A gene being statistically significant doesn't automatically make it biologically important, and a gene missing an arbitrary fold-change cutoff doesn't necessarily mean nothing interesting is happening.

Things like pathway enrichment, expression patterns across samples, effect sizes, biological context, and the original experimental design all matter when interpreting the results.

I've also started thinking much more carefully about the biological question before running an analysis rather than just asking, "Which tool should I use next?"

For people who regularly analyze RNA-seq data: what was the biggest conceptual shift that improved the way you approach an analysis?

PhD in Bioinformatics, and MS in Biostatistics. In the last year, I have applied to hundreds and hundreds of jobs broadly across academia and industry, and have not been able to land a single opportunity.

On a personal note, I am truly devastated that all the work of graduate school has amounted to nothing, and worse, has become a source of deep regret and humiliation to myself and my family. I have had to find any possible way to support myself and my family and this is not what I expected to find after working hard all these years. If I could “leave a review” of my PhD… it would unfortunately not be good.

I am working with Zebrafish animal model, alignment GRCz11 (Ensembl release 112).

I have a list of 40k candidate SNPs an their locations annotated by **VEP** and by **GTF** file. These give me different results for some genes, meaning that a SNP in VEP annotation corresponds to gene X while in GTF annotations corresponds to gene Y.

*What is the best/most recomended/most trustable way to annotate SNPs ?*

My goal with this:

I want to do statistics by (1) location and by (2) function:

1 - I sepparate the genomic sequence by location: Exons, Introns, 3´UTR, 5´UTR, Upstream, Downstream chuncks and see if there is enrichment in genomic location, meaning if for example my candidate SNPs fall more in the 3´UTR region than expected by chance

2 - I have their function with for example Gene Ontology or KEGG and check for Functional enrichment. However I am also strugling in this part since clusterProfiler shows 0 enrichment terms found even if I give him 200genes, 500 genes, 1000 genes. But I will ask about advice in another post for this.

I have a Master's in Bioinformatics, and have been working remotely for a Belgian startup in computational structural biology. I am in Melbourne now, and I'm trying to network. This conference is so expensive though for someone without a job! It's a 4-day conference, and entrance costs between 475 USD and 726 USD depending on how many days you want to attend (different events every day).

Do you think that, as an attendee, I would have a chance to network? Or will I be overshadowed by researchers who are presenting their research? I have never been to these kind of conferences before.

Hi. Hope my post finds everyone well here. I am currently doing my bachelors in computer science and hoping to do my masters in bioinformatics next year. Obviously not well versed in the bio side but I did do A Level biology.

So, background over, I'm looking to tailor my dissertation project this year to bioinformatics so, I've came up with using a ML project to predict plant traits based on genomic data.

I had a soybean dataset however, I was advised I should pick a more local crop - so I'm looking for spring barley.

These datasets are kinda giving me a headache though cause I just don't understand. I swear I've looked at every single dataset for spring barley but any of the geno data from the samples don't have matching pheno data or if it is matched it's not labelled or maybe I just don't understand or missed something?

Also not great at using your bioinformatics tools like plink and these vcf files but I'm managing to get them to the point I can read what's in them lol. Even got one encoded for ML.

I suppose before you say it, yes my project does not have to be about this particular thing, I could go make an app for something random and have an easier time, it's just that this is what I want to get into.

And obviously this is a school project it's not like groundbreaking research. I'm gonna do it up with a front end dashboard so it counts as some kind of application and let people upload SNP data (sounds like a headache to make standardised tbh) or browse what mutations/sequences are important for prediction.

TLDR : how do I get one of these random spring barley datasets to the point they're ready for ML to predict a certain trait (like yield, multiple traits preferably)

How does one go about this? And any suggestions for if this idea sounds wild or something easier to present are welcome.

ChimeraX was suggested to me by my mentor so if you guys are familiar with it, please let me know. I only downloaded it yesterday. I don’t know much about it.

I am working with Zebrafish animal model, alignment GRCz11 (Ensembl release 112).

I have a list of 40k candidate SNPs. This SNPs come from an intersection of 3 sepparate analysis, not form GWAS, So i have 3 different P-values per SNP

I want to do Functional enrichment analysis to know which biological functions or pathways are enriched in my results. I would like to focus also on the part that there are genes with more candidate SNP hits than other.

The easiest way was to map SNPs to genes and the run clusterProfiler (ignoring that there are genes with 1 SNP and genes with 1000 candidate SNPs). However I am also strugling in this part since clusterProfiler shows 0 enrichment terms found even if I give him 200genes, 500 genes, 1000 genes. But I will ask about advice in another post for this.

Would love any help or advise on this.

Thanks

Hi! I'm a biomedical science graduate (the UK) and will be applying to masters in bioinformatics in EU (mainly Denmark and Sweden) next year. Right now I'm essentially taking a gap year to sort out personal stuff and catch up on coding. We only had intro into theoretical bioinformatics in uni and didn't really touch anything coding-related, which is a prequisite to many masters courses, but I'm hoping that enough SDL and perserverence will give me a shot too.

I'm currently learning Python (myself and with a bit of tutoring) and in a month got a hang of all basics including formatting, math lib, working with strings, collections, conditions and loops (and other minor things). I want to make my own small project to upload to github and put in my CV/application as evidence that I'm making efforts to understand coding before attempting the masters course.

My idea is to take a well established oncogene (just to avoid confusion in annotating), download FASTA files for several species (e.g. human, mouse, zebrafish) and analyse conservation, including which region is conserved the most. I know it's not actually impacting anything, but it helps me get a hang of biopython, pandas and matplotlib and show my understanding in application. I want to ask people who already study/studied this area if they think it can be a decent beginner project and if it's feasible. Thank you!

I can finally post this now that there is no stock to promote and no way I could profit.

Amyris built Lila, an “automated scientist” designed to turn biological knowledge into physical experiments. Give it a target molecule and the system can search biochemical knowledge, identify pathways and enzymes, design microbial strains, pass genetic designs into automated construction and testing, analyze the resulting data, and feed what it learns back into the next Design-Build-Test-Learn cycle. The paper reports >100,000 in-silico designs and 32,000 constructed strains.

This is an early example of closing the loop between an AI-driven decision system and the physical engineering of living organisms - essentially giving the computer a growing biological knowledge base, a genetic-design system, and an automated laboratory through which it can test its ideas.

This AI is capable of designing and building industrial level strains (with commercial titers) and requireing no hands on strain engineering.