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Following the first Banbury workshop in March 2003, Jesse Ausubel and I wrote a “Draft Scientific Rationale and Strategy” that described DNA barcoding as ““Google” for Life Forms” (with the name in quotes in case readers didn’t get the reference, hard to imagine today!). One year and a second Banbury workshop later the Consortium for the Barcode of Life (CBOL) was inaugurated at Smithsonian Institution, National Museum of Natural History, Washington, DC.

This week the Google Foundation announced a $3 million Global Impact Award to CBOL to enable a DNA barcode reference library for endangered species (and their close relatives) as a tool to prevent illegal wildlife trafficking.  As in 2003, this is a wonderfully natural pairing of organizations and a cause for the entire barcoding community to celebrate.

In the language of today, we can see the DNA Barcoding/Google for Life Forms is a kind of “open access” to taxonomic knowledge.  It may turn out that the ability to identify species, like the ability to search the internet, will have wider consequences than we currently forsee. In The Viral Storm: The Dawn of a New Pandemic Age (2011), author Nathan Wolfe cites the 2008 high school student DNA barcoding ‘Sushi-gate’ project as “one of the first notable examples of nonscientists “reading” genetic information.” As a Cassandra, Wolfe envisions this as a first step towards DIY bioterrorists but I imagine it is more likely a first step towards DIY biologists sequencing everything in sight, helping monitor the health of the environment, including tracking spread of human and animal diseases.

Last week’s post looked at amino acid variation among avian BARCODEs (11,000 sequences, 2,700 bird species). The findings were that common variants (present in >0.1% of sequences) are few and restricted in terms of types of amino acid substitutions, while rare variants (present in <0.1% of sequences) are many and diverse, the latter consistent with our published observation (PLoS ONE 2012 e:43992) that most rare variants in this dataset are sequencing errors.

Here I follow-up on this observation to look more closely at the same dataset, this time asking what is the relationship between variant frequency and number? For this analysis I separated probable biological rare variants (found in 2 or more individuals of a species) from those that were likely sequencing errors or contained in pseudogenes (more details in PLoS ONE paper).

As shown in figure below, this analysis gave what looks like a surprisingly simple relationship between variant number and frequency, which presumably reflects some evolutionary principle assuming it is not an accidental feature of this particular dataset. It may be of interest to analyze amino acid variant frequency and number among BARCODE datasets from other taxonomic groups.

A larger version of this figure is available here.

 

In PLoS ONE 2012 e:43992 Kevin Kerr and I reported that most of what appeared to be rare nucleotide and amino acid variants in avian BARCODEs were in fact sequencing errors, based on finding these were strongly concentrated at the ends of the amplified barcode segment. Here I look at the nature of common and rare amino acid substitutions in this same dataset of 11,333 avian BARCODEs from 2,709 species. Do these support our inference that rare variants are mostly errors?  I believe the large figure below says yes.

The more common variants (present in >0.1% sequences) are shown at top and the rare variants (present in <0.1% sequences) at bottom. The left shows variants at each of the 216 amino acid positions, sorted according to the mode amino acid (shown in gray) and grouped by codon 2nd position nucleotide. At right, the proportion of substitutions for each amino acid is shown, weighted according to the modal amino acid frequency.

The main observation is that common variants are relatively few in number (69) and type (mostly isoleucine (I) <–> valine(V)), suggesting strong biological constraints on allowable variation.  On the other hand, rare variants are many (377) and diverse, which is what one would expect if these are largely sequencing errors.

A larger version of the figure is here, and the Excel file used to generate the figure is here.

I think there is potentially more of interest here in terms of allowable substitutions. For example, Breen et al Nature 2012 490:535 recently demonstrated that molecular evolution is highly constrained by epistasis, such that most mutations are not allowed in a given context, which is presumably what underlies the restricted variation in avian COI. (Breen and colleagues calculations were based on alignments of 2 nuclear and 14 organellar genes, the latter including COI.) In a general way this makes sense–birds can have different kinds of feathers but none have scales like fish. It might be of interest to compare COI amino acid variation in birds to other barcode datasets such as fish or lepidoptera.

Happy Thanksgiving!