University of Glasgow

Roderic Page

Following on from earlier posts [exploring how to map DNA
barcodes](http://iphylo.blogspot.com/2016/05/notes-on-next-steps-for-million-dna.html)
and [putting barcodes into
GBIF](http://iphylo.blogspot.co.uk/2016/12/ibol-dna-barcodes-in-gbif.html)
it\'s time to think about taking advantage of what makes barcodes
different from typical occurrence data. At present GBIF displays data as
dots on a map (as do I in <http://iphylo.org/~rpage/bold-map/>). But
barcodes come with a lot more information than that. I\'m interested in
exploring how we might measure and visualise biodiversity using just
sequences.

Based on a talk by Zachary Tong ([Going Organic - Genomic sequencing in
Elasticsearch](https://speakerdeck.com/polyfractal/going-organic-genomic-sequencing-in-elasticsearch))
I\'ve started to play with *n*-gram searches on DNA barcodes using
[Elasticsearch](https://www.elastic.co/products/elasticsearch), an open
source search engine. The idea is that we break the DNA sequence into
every possible \"word\" of length *n* (also called a *k*-mer or
*k*-tuple, where *k* = *n*).

For example, for *n* = 5, the sequence GTATCGGTAACGAACTT would look like
this:

    GTATCGGTAACGAACTT

    GTATC
     TATCG
      ATCGG
       TCGGT
        CGGTA
         GGTAA
          GTAAC
           TAACG
            AACGAA
             ACGAAC
              CGAACT
               GAACTT

The sequence GTATCGGTAACGAACTT comes from Hajibabaei and Singer (2009)
who discussed \"Googling\" DNA sequences using search engines (see also
Kuksa and Pavlovic, 2009). If we index sequences in this way then we can
do BLAST-like searches very quickly using Elasticsearch. This means
it\'s feasible to take a DNA barcode and ask \"what sequences look like
this?\" and return an answer qucikly enoigh for a user not to get bored
waiting.

Another nice feature of Elasticsearch is that it supports geospatial
queries, so we can ask for, say, all the barcodes in a particular
region. Having got such a list, what we really want is not a list of
sequences but a phylogenetic tree. Traditionally this can be a time
consuming operation, we have to take the sequences, align them, then
input that alignment into a tree building algorithm. Or do we?

There\'s growing interest in \"alignment-free\" phylogenetics, a phrase
I\'d heard but not really followed up. Yang and Zhang (2008) described
an approach where every sequences is encoded as a vector of all possible
*k*-tuples. For DNA sequences *k* = 5 there are 4^5^ = 1024 possible
combinations of the bases A, C, G, and T, so a sequence is represented
as a vector with 1024 elements, each one is the frequency of the
corresponding 5-tuple. The \"distance\" between two sequences is the
mathematical distance between these vectors for the two sequences. Hence
we no longer need to align the sequences being comapred, we simply chunk
them into all \"words\" of 5 bases in length, and compare the
frequencies of the 1024 different possible \"words\".

In their study Yang and Zhang (2008) found that:

> We compared tuples of diﬀerent sizes and found that tuple size 5
> combines both performance speed and accuracy; tuples of shorter
> lengths contain less information and include more randomness; tuples
> of longer lengths contain more information and less random- ness, but
> the vector size expands exponentially and gets too large and
> computationally ineﬃcient.

So we can use the same word size for both Elasticsearch indexing and for
computing the distance matrix. We still need to create a tree, for which
we could use something quick like neighbour-joining (NJ). This method is
sufficiently quick to be available in Javascript and hence can be
computed by a web browser (e.g.,
[biosustain/neighbor-joining](https://github.com/biosustain/neighbor-joining)).

Putting this all together, I\'ve built a rough-and-ready demo that takes
some DNA barcodes, puts them on a map, then enables you to draw a box on
a map and the demo will retrieve the DNA barcodes in that area, compute
a distance matrix using 5-tuples, then build a NJ tree, all on the fly
in your web browser.

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[](https://player.vimeo.com/video/209886913)

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Play

![Vimeo](https://f.vimeocdn.com/p/images/crawler_logo.png){.logo}
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[Phylodiversity on the fly](https://vimeo.com/209886913) from [Roderic
Page](https://vimeo.com/rdmpage) on [Vimeo](https://vimeo.com).
:::::

This is all very crude, and I need to explore scalability (at the moment
I limit the results to the first 200 DNA sequences found), but it\'s
encouraging. I like the idea that, in principle, we could go to any part
of the globe, ask \"what\'s there?\" and get back a phylogenetic tree
for the DNA barcodes in that area.

This also means that we could start exploring phylogenetic diversity
using DNA barcodes, as Faith & Baker (2006) wanted a decade ago:

> \...PD has been advocated as a way to make the best-possible use of
> the wealth of new data expected from large-scale DNA "barcoding"
> programs. This prospect raises interesting bio-informatics issues
> (discussed below), including how to link multiple sources of evidence
> for phylogenetic inference, and how to create a web-based linking of
> PD assessments to the barcode--of-life database (BoLD).

The phylogenetic diversity of an area is essentially the length of the
tree of DNA barcodes, so if we build a tree we have a measure of
diversity. Note that this contrasts with other approaches, such as
Miraldo et al.\'s \"An Anthropocene map of genetic diversity\" which
measured genetic diversity within species but not between (!).

#### Practical issues

There are a bunch of practical issues to work through, such as how
scalable it is to compute phylogenies using Javascript on the fly. For
example, could we do something like generate a one degree by one degree
grid of the Earth, take all the barcodes in each cell and compute a
phylogeny for each cell? Could we do this in CouchDB? What about
sampling, should we be taking a finite, random sample of sequences so
that we try and avoid sampling bias?

There are also data management issues. I\'m exploring downloading DNA
barcodes, creating a Darwin Core Archive file using the [Global Genome
Biodiversity Network (GGBN) data
standard](http://wiki.ggbn.org/ggbn/Main_Page), then converting the
Darwin Core Archive into JSON and sending that to Elasticsearch. The
reason for the intermediate step of creating the archive is so that we
can edit the data, add missing geospatial informations, etc. I envisage
having a set of archives, hosted say on GitHub. These archives could
also be directly imported into GBIF, ready for the time that GBIF can
handle genomic data.

#### References

- Faith, D. P., & Baker, A. M. (2006). Phylogenetic diversity (PD) and
  biodiversity conservation: some bioinformatics challenges. Evol
  Bioinform Online. 2006; 2: 121--128.
  [PMC2674678](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2674678/)

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- Hajibabaei, M., & Singer, G. A. (2009). Googling DNA sequences on the
  World Wide Web. BMC Bioinformatics. Springer Nature.
  <https://doi.org/10.1186/1471-2105-10-s14-s4>

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- Kuksa, P., & Pavlovic, V. (2009). Efficient alignment-free DNA barcode
  analytics. BMC Bioinformatics. Springer Nature.
  <https://doi.org/10.1186/1471-2105-10-s14-s9>

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- Miraldo, A., Li, S., Borregaard, M. K., Florez-Rodriguez, A.,
  Gopalakrishnan, S., Rizvanovic, M., ... Nogues-Bravo, D. (2016,
  September 29). An Anthropocene map of genetic diversity. Science.
  American Association for the Advancement of Science (AAAS).
  <https://doi.org/10.1126/science.aaf4381>

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- Yang, K., & Zhang, L. (2008, January 10). Performance comparison
  between k-tuple distance and four model-based distances in
  phylogenetic tree reconstruction. Nucleic Acids Research. Oxford
  University Press (OUP). <https://doi.org/10.1093/nar/gkn075>

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Googling DNA sequences on the World Wide Web

Efficient alignment-free DNA barcode analytics

An Anthropocene map of genetic diversity

Performance comparison between k-tuple distance and four model-based distances in phylogenetic tree reconstruction

Following on from earlier posts exploring how to map DNA barcodes and putting barcodes into GBIF it's time to think about taking advantage of what makes barcodes different from typical occurrence data. At present GBIF displays data as dots on a map (as do I in http://iphylo.org/~rpage/bold-map/). But barcodes come with a lot more information than that.

Publicaciones de Rogue Scholar

This is what phylodiversity looks like