Reading an interesting post at Genomes Unzipped, “Human genetics is microbial genomics“, which states:
Only 10% of cells on your “human” body are human anyway, the rest are microbial.
Have you read a sentence like that before? So have I. So has a reader who left a comment:
I was wondering if you have a source for “Only 10% of cells on your “human” body are human anyway, the rest are microbial”
It’s a good question. Everyone quotes this figure, almost no-one provides a reference. Let’s go in search of one.
Read the rest…
You can guarantee that when scientists publish a study titled:
Determining the Presence of Periodontopathic Virulence Factors in Short-Term Postmortem Alzheimer’s Disease Brain Tissue
a newspaper will publish a story titled:
Poor dental health and gum disease may cause Alzheimer’s
Without access to the paper, it’s difficult to assess the evidence. I suggest you read Jonathan Eisen’s analysis of the abstract. Essentially, it makes two claims:
- that cultured astrocytes (a type of brain cell) can adsorb and internalize lipopolysaccharide (LPS) from Porphyromonas gingivalis, a bacterium found in the mouth
- that LPS was also detected in brain tissue from 4/10 Alzheimer’s disease (AD) cases, but not in tissue from 10 matched normal brains
Regardless of the biochemistry – which does not sound especially convincing to me – how about the statistics?
Read the rest…
Just how many (bad) -omics are there anyway? Let’s find out.
Update: code and data now at Github
Read the rest…
Here’s a tip. When you write an article about your software, the title of which indicates that open-source is important:
A universal open-source Electronic Laboratory Notebook
but you then:
- provide almost no details in the abstract
- do not provide a link to a website or repository from which your “free” software can be obtained
- choose not to make the article open access
- and put the installation instructions in a supplementary data file which is also not open access
Don’t be surprised when no-one uses your software.
Or is the publication more important to you than the product?
File under “interesting articles that I don’t have time to write about at length.”
- Archaea and Fungi of the Human Gut Microbiome: Correlations with Diet and Bacterial Residents
Long ago, before metagenomics and NGS, I did a little work on detection of Archaea in human microbiomes. There’s a blog post in the pipeline about that but until then, enjoy this article in PLoS ONE.
- Mutational heterogeneity in cancer and the search for new cancer-associated genes
This article is getting a lot of attention on Twitter this week. Brief summary: cancer cells are really messed up in all sorts of ways, most of which are not causal with respect to the cancer. Anyone who has ever looked at microarray data knows that it’s not uncommon for 50% or more of genes to show differential expression in a cancer/normal comparison, so this is hardly a new concept. I think we need to move away from ever-more detailed characterizations of the ways in which cancer cells are “messed up.” We know that they are and that doesn’t provide much insight, in my opinion.
- The vast majority of statistical analysis is not performed by statisticians
Interesting post by Jeff Leek, summarized very well by its title. It points out that many more people are now interested in data analysis, many of them are not trained professionally as statisticians (I’m in this category myself) and we need to recognize and plan for that.
Bonus post doing the rounds of social media: Using Metadata to Find Paul Revere. Social network analysis, 18th-century style. Amusing, informative and topical.
My journey from bench scientist to bioinformatician began with archaeal genomes. So I was somewhat startled to read The catalytic mechanism for aerobic formation of methane by bacteria, in which we learn about the “ocean-dwelling bacterium Nitrosopumilus maritimus“.
So was Jonathan Eisen of course and you should go and read why. Every top hit in a Web search for that organism tells us that Nitrosopumilus maritimus is an archaeon.
Looking forward to a rapid correction and apology from Nature.
Title edited from “phylogeny” to “taxonomy” at the insistence of @BioinfoTools ;)
Retraction Watch reports a study of microarray data sharing. The article, published in Clinical Chemistry, is itself behind a paywall despite trumpeting the virtues of open data. So straight to the Open Access Irony Award group at CiteULike it goes.
I was not surprised to learn that the rate of public deposition of data is low, nor that most deposited data ignores standards and much of it is low quality. What did catch my eye though, was a retraction notice for one of the articles from the study, in which the authors explain the reason for retraction.
Read the rest…
Floating by in the Twitter stream, this from @leonidkruglyak. It leads to a light-hearted opinion(ated) piece by Sydney Brenner in Current Biology, 1996.
In 1996, you may recall, the Web was just a few years old. Amusingly (sadly?), it seems that Brenner predicted many of the topics in science publishing that we’re still discussing in 2013. It’s just that he thought they would be implemented in no time at all.
For example, open refereeing:
It is incidents such as this that have led me to question whether the anonymity of referees needs to be guarded so closely
Self-publishing/archiving and post-publication peer review:
The electronic pre-print with open discussion (not refereeing) will soon become commonplace; in fact, labs could go into the publication business by themselves
Demise of the journal impact factor, publishing economics and altmetrics:
We will need something to substitute for the present ratings given to papers appearing in ‘superior, peer-reviewed publications’ (and commercial publishers will find ways of making people pay for this)
Perhaps we should have a readership index; it should not be beyond the wit of man to devise a way of recording whenever a paper is read, hard-copied or cited
As Ethan said:
This week in Retraction Watch: Hypertension retracts paper over data glitch.
The retraction notice describes the “data glitch” in question (bold emphasis added by me):
…the authors discovered an error in the code for analyzing the data. The National Health and Nutrition
Examination Survey (NHANES) medication data file had multiple observations per participant and
was merged incorrectly with the demographic and other data files. Consequently, the sample size was
twice as large as it should have been (24989 instead of 10198). Therefore, the corrected estimates of
the total number of US adults with hypertension, uncontrolled hypertension, and so on, are significantly
different and the percentages are slightly different.
Let’s leave aside the observation that 24989 is not 2 x 10198. I tweeted:
Not that simple though, is it? Read on for the Twitter discussion.
Read the rest…
Update: as pointed out in the comments, the amusing error in this article has been “corrected” (or at least, “edited away”). Thanks for your interest.
Update: I note that this article is now “Highly Accessed” ;)
An integrative analysis of DNA methylation and RNA-Seq data for human heart, kidney and liver
BMC Systems Biology 2011, 5(Suppl 3):S4
(insert statistical method here). No, really.
With thanks to Simon J Greenhill and Dave Winter.