First for 2011:
Proteomic and electron microscopy survey of large assemblies in macrophage cytoplasm.
Maco, B., Ross I.L., Landsberg, M., Mouradov, D., Saunders, N.F.W., Hankamer, B. and Kobe, B. (2011)
Molecular & Cellular Proteomics, in press, doi:10.1074/mcp.M111.008763
This is an in-press article which is freely-available just now (although strangely, the supplemental data are not). I’m pleased to note that we also made the raw data available in Proteome Commons. In fact, it was a condition of publication.
Lots of hard work went into this one. My contribution was quite minor: some bioinformatic analysis and hacking away at PyMsXML to make it work with newer versions of vendor formats. I’d like to thank Brad Chapman with respect to PyMsXML, who provided invaluable advice via BioStar.
This is really interesting. I’m reading it at work so I can’t tell you if it’s behind the paywall, but I sincerely hope not; it deserves to be read widely:
Edwards, A.M. et al. (2011)
Too many roads not taken.
Nature 470: 163–165
Most protein research focuses on those known before the human genome was mapped. Work on the slew discovered since, urge Aled M. Edwards and his colleagues.
The article includes some nicely-done bibliometric analysis. I’ve lifted a few quotes that illustrate some of the key points.
- More than 75% of protein research still focuses on the 10% of proteins that were known before the genome was mapped
- Around 65% of the 20,000 kinase papers published in 2009 focused on the 50 proteins that were the ‘hottest’ in the early 1990s
- Similarly, 75% of the research activity on nuclear hormone receptors in 2009 focused on the 6 (of 48) receptors that were most studied in the mid 1990s
- A common assumption is that previous research efforts have preferentially identified the most important proteins – the evidence doesn’t support this
- Why the reluctance to work on the unknown? […] scientists are wont to “fondle their problems”
- Funding and peer-review systems are risk-averse
- The availability of chemical probes for a given receptor dictates the level of research interest in it; the development of these tools is not driven by the importance of the protein
I love the phrase “fondle their problems.”
I’ve long felt that academic research has increasingly little to do with “advancing knowledge” and is more concerned with churning out “more of the same” to consolidate individual careers. However, that’s just me being opinionated and anecdotal. What do you think?
This is the story of how an open source project and a science communication tool combined to save the day.
Read the rest…
We find ourselves wondering why codon adaptation index (CAI) is used as a measure of protein expression level in this article.
One answer is that CAI does correlate well with protein expression in many proteomics studies; but surely these same studies contain raw data with protein expression level? On reflection, I bet the answer is that it’s too difficult and laborious to access this type of data. There are plenty of papers that describe large-scale analysis of protein expression using proteomics, but the data are locked up in the articles or as inappropriate supplementary files.
Note to self: look into open-source software and standard data formats for proteomic data.
Every so often, a new issue of the under-rated Briefings in Bioinformatics appears in my feed reader.
The latest is a special issue on computational proteomics. High-throughput proteomics is all the rage in academic, clinical and industrial settings just now, so this is well worth a read.
Bioinformaticians looking for ways to help out with the management and analysis of proteomic data should look in particular at: