Notes on finding georeferenced sequences in GenBank

Notes on how many georeferenced DNA sequences there are in GenBank, and how many could potentially be georeferenced.

BCT	Bacterial sequences
PRI	Primate sequences
ROD	Rodent sequences
MAM	Other mammalian sequences
VRT	Other vertebrate sequences
INV	Invertebrate sequences
PLN	Plant and Fungal sequences
VRL	Viral sequences
PHG	Phage sequences
RNA	Structural RNA sequences
SYN	Synthetic and chimeric sequ
UNA	Unannotated sequences

https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi
?db=nucleotide nucleotides
&term=ddbj embl genbank with limits[filt]
NOT transcriptome[All Fields] ignore transcriptome data
NOT mRNA[filt] ignore mRNA data
NOT TSA[All Fields] ignore TSA
NOT scaffold[All Fields] ignore scaffold
AND src lat lon[prop] include records that have source feature "lat_lon"
AND 2010/01/01:2010/12/31[pdat] from this date range
AND gbdiv_pri[PROP] restrict search to PRI division (primates)
AND srcdb_genbank[PROP] Need this if we query by division, see NBK49540

Numbers of nucleotide sequences that have latitude and longitudes in GenBank for each year.

Date	PRI	ROD	MAM	VRT	INV	PLN
2010/01/01	4	127	2552	9551	92692	7174
2011/01/01	371	1204	8160	17657	78494	7968
2012/01/01	6	5803	4214	21696	84060	27314
2013/01/01	297	349	761	10764	70411	23435
2014/01/01	15	2904	4761	14598	68076	14018
2015/01/01	174	527	1983	17843	363538	35501
2016/01/01	58	2615	1263	14898	757893	22813
2017/01/01	938	1758	1017	10712	75066	28180

Numbers of nucleotide sequences that don't have latitude and longitudes in GenBank for each year but do have the country field and hence could be georeferenced.

Date	PRI	ROD	MAM	VRT	INV	PLN
2010/01/01	6660	2654	5534	32666	62577	56692
2011/01/01	3998	3266	6210	33717	74015	98664
2012/01/01	5377	5590	7283	55332	86945	103379
2013/01/01	10928	4805	8013	66373	69719	95817
2014/01/01	9727	3492	6751	59913	77816	135372
2015/01/01	8922	6774	13964	60578	85867	167337
2016/01/01	6430	3384	10860	62238	95711	145111
2017/01/01	11474	3520	4912	41159	91219	109747

Notes on displaying big trees using Google Maps/Leaflet

Notes to self on web map-style tree viewers. The basic idea is to use Google Maps or Leaflet to display a tree. Hence we need to compute tiles. One approach is to use a database that supports spatial queries to store the x,y coordinates of the tree. When we draw a tile we compute the coordinates of that tile, based on position and zoom level, do a spatial query to extract all lines that intersect with the rectangle for that tile, and draw those.

A nice example of this is Lifemap (see also De Vienne, D. M. (2016). Lifemap: Exploring the Entire Tree of Life. PLOS Biology, 14(12), e2001624. doi:10.1371/journal.pbio.2001624).

It occurs to me that for trees that aren't too big we could do this without an external database. For example, what if we used a Javascript implementation of an R-tree, such as imbcmdth/RTree or its fork leaflet-extras/RTree. So, we could compute the coordinates of the nodes in the tree in "geographic" space, store the bounding box for each line/arc in an R-tree, then query that R-tree for lines that intersect with the bounding box of the relevant tile. We could use a clipping algorithm to only draw the bits of the lines that cross the tile itself.

Web maps, at least in my experience, make trips to a tile server to fetch a tile, we would want instead to call a routine within our web page, because all the data would be loaded into that page. So we'd need to modify the tile creating code.

The ultimate goal would be to have a single page web app that accepts a Newick-style tree and converts into a browsable, zoomable visualisation.

Possible project: #itaxonomist, combining taxonomic names, DOIs, and ORCID to measure taxonomic impact

Imagine a web site where researchers can go, log in (easily) and get a list of all the species they have described (with pretty pictures and, say, GBIF map), and a list of all DNA sequences/barcodes (if any) that they've published. Imagine that this is displayed in a colourful way (e.g., badges), and the results tweeted with the hastag #itaxonomist.

Imagine that you are not a taxonomist, but if you have worked with one (e.g., published a paper), you can go to the site, log in, and discover that you “know” a taxonomist. Imagine if you are a researcher who has cited taxonomic work, you can log in and discover that your work depends on a taxonomist (think six degrees of Kevin Bacon).

Imagine that this is run as a campaign (hashtag #itaxonomist), with regular announcements leading up to the release date. Imagine if #itaxonomist trends. Imagine the publicity for the work taxonomists do, and the new found ability for them to quantitatively demonstrate this.

How does it work?

#itaxonomist relies on three things:

1. People having an ORCID
2. People having publications with DOIs (or otherwise easily identifiable) in their ORCID profile
3. A map between DOIs (etc.) and the names in the nomenclators (ION, IPNI, Index Fungorum, ZooBank)

Implementation

Under the hood this builds part of the “biodiversity knowledge graph”, and uses ideas I and others have been playing around with (e.g., see David Shorthouse’s neat proof of concept http://collector.shorthouse.net/agent/0000-0002-7260-0350 and my now defunct Mendeley project http://iphylo.blogspot.co.uk/2011/12/these-are-my-species-finding-taxonomic.html).

For a subset of people and names this we could build this very quickly. Some some taxonomists already have ORCIDs , and some nomenclators have limited numbers of DOIs. I am currently building lists of DOIs for primary taxonomic literature, which could be used to seed the database.

The “i am a taxonomist” query is simply a map between ORCID to DOI to name in nomenclator. The “i know a taxonomist” is a map between ORCID and DOI that you share with a taxonomist, but there are no names associated with that DOI (e.g., a paper you have co-authored with a taxonomist that wasn’t on taxonomy, or at least didn’t describe a new species). The “six degrees of taxonomy” relies on the existence of open citation data, which is trickier, but some is available in PubMed Central and/or could be harvested from Pensoft publications.

Possible project: extract taxonomic classification from tags (folksonomy)

Note to self about a possible project. This PLoS ONE paper:

Tibély, G., Pollner, P., Vicsek, T., & Palla, G. (2013, December 31). Extracting Tag Hierarchies. (P. Csermely, Ed.)PLoS ONE. Public Library of Science (PLoS). http://doi.org/10.1371/journal.pone.0084133

describes a method for inferring a hierarchy from a set of tags (and cites related work that is of interest). I've grabbed the code and data from http://hiertags-beta.elte.hu/home/ and put it on GitHub.

Possible project

Use Tibély et al. method (or others) on taxonomic names extracted from BHL text (or other) and see if we can reconstruct taxonomic classifications. ow do classifications compare to those in databases? Can we enhance existing databases using this technique (e.g., extract classifications from literature for groups pporly represented in existing databases)? Could be part of larger study of what we can learn from co-occurrence of taxonomic names, e.g. Automatically extracting possible taxonomic synonyms from the literature.

Note to anyone reading this: if this project sounds interesting, by all means feel free to do it. These are just notes about things that I think would be fun/interesting/useful to do.