My first Shiny app: fitting sigmoid curves

Since the grad school I have been using R for data analysis and (mainly) for preparation of nice plots. As with LaTeX, it was a pretty steep learning curve. But after a year or so I managed to become comfortable with writing a new script for each new experiment. After some time, however, I started feeling that it wasn’t enough. Many experiments were almost identical, so breeding 99%-similar scripts didn’t seem to be an efficient way of handling data. That’s when I first started to think about making ‘reusable’ apps for specific purposes. Continue reading “My first Shiny app: fitting sigmoid curves”

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Skepticism about synaptic nanocolumns

Couple of days ago I’ve come across a recent paper in Nature with quite an eyebrow-raising title “A trans-synaptic nanocolumn aligns neurotransmitter release to receptors“. The title made me think as if the authors have observed hitherto unknown structures in the synaptic cleft. That would be quite a sensation! But then this sentence comes in the abstract:

These presynaptic RIM nanoclusters closely align with concentrated postsynaptic receptors and scaffolding proteins4, 5, 6, suggesting the existence of a trans-synaptic molecular ‘nanocolumn’.

Now it looked like they propose some kind of neuroscience equivalent of dark matter. You know, something that nobody knows what it is, but that certainly must be there, otherwise there’s no explanation to what we see. Continue reading “Skepticism about synaptic nanocolumns”

Research parasites

It’s really entertaining to watch the (over)reaction of Twitter on the controversial editorial in NEJM about data sharing and open science. As usual, it’s pretty hysteric but has a potential to cause some real-world consequences. The problem is that the authors were reckless enough to use term “research parasites” for those scientists who use the data from other labs without conducting their own experiments. Continue reading “Research parasites”

The database of databases

Today chemical biology generates new high-throughput methods of studying biomolecules almost as quickly as organic chemists report total syntheses. Whole genome, transcriptome, proteome, lipidome, glycome etc. analyses are flourishing and delivering vast amounts of data. Bioinformaticist are trying to cope with the data flow by archiving them in various databases. This has led to a situation when the number and diversity of databases became incomprehensible for a human being. Continue reading “The database of databases”