Lars Barquist
@lbarquist.bsky.social
Assistant prof @ UofT, associated scientist @ Helmholtz Institute for RNA-based Infection Research. Pathogen systems biology / informatics / functional genomics. Coastal New Hampshirite. Drink Moxie.
This is all enabled by a robust infrastructure that can currently support ~500 genome-wide experiments, and allows for easy development of new visualization plug-ins
February 4, 2025 at 8:22 PM
This is all enabled by a robust infrastructure that can currently support ~500 genome-wide experiments, and allows for easy development of new visualization plug-ins
This lets you easily create figures like this, showing the expression of genes in the Salmonella SPI-2 pathogenicity island across multiple conditions, as well as fitness effects in a variety of animal models (interactive server session to play with here: micromix.helmholtz-hiri.de/salmonella/?...)
February 4, 2025 at 8:22 PM
This lets you easily create figures like this, showing the expression of genes in the Salmonella SPI-2 pathogenicity island across multiple conditions, as well as fitness effects in a variety of animal models (interactive server session to play with here: micromix.helmholtz-hiri.de/salmonella/?...)
At its core, Micromix is a graphical web interface for Python dataframes, letting you mash together and filter different datasets, then visualize the results.
February 4, 2025 at 8:22 PM
At its core, Micromix is a graphical web interface for Python dataframes, letting you mash together and filter different datasets, then visualize the results.
Even more worryingly, there was one case where a mutant was predicted as cipro resistant before correction, but was actually sensitive! Our correction method recovers the correct phenotype.
November 19, 2024 at 3:39 PM
Even more worryingly, there was one case where a mutant was predicted as cipro resistant before correction, but was actually sensitive! Our correction method recovers the correct phenotype.
But does it matter? Yes! In a test data set, we see a lot of mutants predicted to be cipro resistant before correction, but actually have a WT MIC.
November 19, 2024 at 3:39 PM
But does it matter? Yes! In a test data set, we see a lot of mutants predicted to be cipro resistant before correction, but actually have a WT MIC.
So, we calculate local normalization factors, plug in our offsets, and the origin bias is gone and we still have accurate p-values based on the original counts!
November 19, 2024 at 3:39 PM
So, we calculate local normalization factors, plug in our offsets, and the origin bias is gone and we still have accurate p-values based on the original counts!
But this raises issues in statistical testing. The way standard analysis packages like edgeR and DEseq deal with having few replicates is by sharing information between genes: genes with similar counts are assumed to have similar variances.
November 19, 2024 at 3:39 PM
But this raises issues in statistical testing. The way standard analysis packages like edgeR and DEseq deal with having few replicates is by sharing information between genes: genes with similar counts are assumed to have similar variances.
This is particularly a problem for TraDIS, as we’re using DNA copy number as a proxy for mutant abundance. For RNA-seq, this reflects real differences in RNA content, but may mask regulation or interfere with normalization. We observed this in a range of published datasets.
November 19, 2024 at 3:39 PM
This is particularly a problem for TraDIS, as we’re using DNA copy number as a proxy for mutant abundance. For RNA-seq, this reflects real differences in RNA content, but may mask regulation or interfere with normalization. We observed this in a range of published datasets.
The problem for sequencing based assays occurs when you treat with something that stalls replication; the bacteria keep initiating replication, leading to a local increased chromosomal copy number in the vicinity of the ori
November 19, 2024 at 3:39 PM
The problem for sequencing based assays occurs when you treat with something that stalls replication; the bacteria keep initiating replication, leading to a local increased chromosomal copy number in the vicinity of the ori
You see, bacteria have a problem during exponential growth: cell division takes ~20 minutes, but chromosomal replication can take up to 60 min to complete. They solve this by simultaneously producing multiple copies of their genome.
November 19, 2024 at 3:39 PM
You see, bacteria have a problem during exponential growth: cell division takes ~20 minutes, but chromosomal replication can take up to 60 min to complete. They solve this by simultaneously producing multiple copies of their genome.
So, this all started when Amy's student Geri noticed a weird pattern in her data when applying TraDIS to look at E. coli genes involved in ciprofloxacin sensitivity: we saw a huge peak of extreme log fold-changes around the origin of replication!
November 19, 2024 at 3:39 PM
So, this all started when Amy's student Geri noticed a weird pattern in her data when applying TraDIS to look at E. coli genes involved in ciprofloxacin sensitivity: we saw a huge peak of extreme log fold-changes around the origin of replication!
Finally, we looked at the relationship between transcript stability and RBP binding — we find that at least half of the 500 most stable transcript are bound by RBPs, and they tend to be destabilized by RBP deletion.
November 16, 2024 at 6:41 PM
Finally, we looked at the relationship between transcript stability and RBP binding — we find that at least half of the 500 most stable transcript are bound by RBPs, and they tend to be destabilized by RBP deletion.
… and we were able to show that this translated into a sensitivity to hydrogen peroxide stress in a ProQ deletion strain
November 16, 2024 at 6:41 PM
… and we were able to show that this translated into a sensitivity to hydrogen peroxide stress in a ProQ deletion strain
We were also able to connect changes in stability to phenotypes — doing some GO term analysis of destabilized transcripts showed enrichment for transcripts involved in the oxidative stress response…
November 16, 2024 at 6:41 PM
We were also able to connect changes in stability to phenotypes — doing some GO term analysis of destabilized transcripts showed enrichment for transcripts involved in the oxidative stress response…
We were able to combine our stability analysis with CLIP-seq to see which RBP binding sites do lead to destabilization — for ProQ, which was known to bind 3’ UTRs, only a minority of bound transcripts are destabilized
November 16, 2024 at 6:41 PM
We were able to combine our stability analysis with CLIP-seq to see which RBP binding sites do lead to destabilization — for ProQ, which was known to bind 3’ UTRs, only a minority of bound transcripts are destabilized
Some striking findings: differences in stability are not correlated with differences detected by simple RNA-seq. We think this is because deletion of these global regulators leads to downstream effects on the transcriptome that don’t have much to do with direct regulation
November 16, 2024 at 6:41 PM
Some striking findings: differences in stability are not correlated with differences detected by simple RNA-seq. We think this is because deletion of these global regulators leads to downstream effects on the transcriptome that don’t have much to do with direct regulation
Now that we had a model, we could go back to our original question — what transcripts are differentially destabilized when global RBPs are deleted. Here’s an example for cspE, a known target of the RBP ProQ.
November 16, 2024 at 6:41 PM
Now that we had a model, we could go back to our original question — what transcripts are differentially destabilized when global RBPs are deleted. Here’s an example for cspE, a known target of the RBP ProQ.
Does this make a difference in inferring half-lives? Yes! By properly fitting the data, we found that bacterial half-lives are about 3 times shorter than previously thought — on average less than 1 minute!
November 16, 2024 at 6:41 PM
Does this make a difference in inferring half-lives? Yes! By properly fitting the data, we found that bacterial half-lives are about 3 times shorter than previously thought — on average less than 1 minute!
This wasn’t just an artifact of our sequencing protocol — you can see this in radioactive probing data as well, and it’s all over the place in the literature once you know what to look for.
November 16, 2024 at 6:41 PM
This wasn’t just an artifact of our sequencing protocol — you can see this in radioactive probing data as well, and it’s all over the place in the literature once you know what to look for.
To our surprise, during model development we found that transcripts don’t keep decaying indefinitely — they hit some sort of baseline, meaning that the simple linear or piece-wise linear models that have been used before don’t really fit the data very well.
November 16, 2024 at 6:41 PM
To our surprise, during model development we found that transcripts don’t keep decaying indefinitely — they hit some sort of baseline, meaning that the simple linear or piece-wise linear models that have been used before don’t really fit the data very well.
To investigate this, we adapted a protocol where we treat with an RNA polymerase inhibition, then watch transcripts decay over time. To analyze the data my postdoc Laura Jenniches began building a Bayesian model in Stan, a probabilistic programming language
November 16, 2024 at 6:41 PM
To investigate this, we adapted a protocol where we treat with an RNA polymerase inhibition, then watch transcripts decay over time. To analyze the data my postdoc Laura Jenniches began building a Bayesian model in Stan, a probabilistic programming language