Sunday, July 13, 2014

Make the link between specimens and genetic products as transparent as possible. #collectingisessential


I think the biggest issue molecular systematists will face in the future will be the pervasiveness of many genetic sequences from misidentified organisms. Our inability to track down the actual animal, plant, or fungus that many of our published genetic sequences come from is already a huge problem. There are plenty of sequences on GenBank and other sources from which we blindly download sequences trusting that this COI sequence from Coryphaena hippurus is actually from a dolphinfish and not an actual dolphin. Although there are lots of checks and balances to make sure that sequence of COI are actually from COI, and that these sequences lack stop codons, there isn’t much to check the accurate identification of the source specimen (a.k.a the voucher). Very few of the millions of sequences on GenBank can be tracked back to an organism deposited in a collection. If you download that sequence of dolphinfish and put it in your phylogeny and it falls out somewhere really weird then you can do a blast search and it will tell you that it is actually probably from a dolphin (if that is the case). But most of us are working at a much finer phylogenetic scale. We are building phylogenies of genera, families, or orders. At this finer scale it is very easy for someone to misidentify a species. (Google images of species from your favorite genus and see how similar congeners are in appearance.) Sequences generated from a misidentified specimen can easily end up on GenBank and often in the phylogeny of the day, and they do.

A wise mentor of mine used to say that every museum label has a question mark after the identification. I would say the same about the identifications on GenBank sequences except the nice thing about the museum specimens is that an expert on the group can one day come and positively identify the species based on the specimens: Not so with most GenBank data. The sequence is out there and if you have the right genus and not the right species it is highly unlikely that someone will catch that error anytime soon. It is quite likely in fact that many others will continue using those sequences, inadvertently perpetuating the error.

My colleagues and I want to put the power of identification back in the hands of taxonomists. I think it is safe to say that most people uploading sequences to GenBank are not experts in the organisms they are studying. They are trusting someone else to do positive identifications for them or they have collected some tissue samples from something they think they know very well but they may not. I see studies all the time where the samples were collected, tissue samples taken and the animals, and the carcasses of those animals were discarded (rather than kept in a permanent collection). That’s really not good, and a waste. First of all names change all the time. What if the study species is later split into two species; which one was the one you collected? What is needed is an unbroken and transparent link between the voucher and the genetic data.

Based on our observations that few researchers provide a link between their genetic data and collections data, either on GenBank or in their publications, a few of us [Carole Baldwin (Curator at the Smithsonian), Larry Page (Curator at the Florida Museum of Natural History) and my student and I] got together and created a nomenclature that should help remind people of the importance of making the link between specimens and genetic products as transparent as possible. The nomenclature is called GenSeq and it works something like this: The quality (trustworthiness if you prefer) of any given genetic sequence is based on how likely the identification of the voucher specimen is correct.  In our nomenclature the highest ranked, best sequences come from primary type specimens, like a holotype. A holotype cannot be misidentified because it is the main specimen chosen to represent the species when it was first described. Sequences from the primary types are ranked as genseq-1, the highest ranking in our system, because those sequences are from specimens with the highest likelihood of being correct identified. Sequences from secondary types (other specimens used in the original description of the species and designated as paratypes or other secondary types) are ranked second, genseq-2. This is followed by specimens from the type locality (same locality as where those primary type specimens were collected), genseq-3. Specimens that are vouchered but not from the type locality or type series as above are in genseq-4. Most sequences from specimens positively identified and deposited in permanent collections will be in that fourth category. The last category, genseq-5, is for photo vouchers. Although not ideal it is sometimes necessary to release an organism that you have taken a tissue sample from (think of something really big, or very rare). Also photo vouchers are necessary in cases where specimens are so small that the body of the organism is destroyed in the process of sampling its DNA. In these rare cases a photo is the best you can do to keep a record for identifying the species in the future. (Read more about photo vouchers versus actual specimens on Twitter using #collectingisessential or (see our paper here); you almost always need more than a photo to positively identify most creatures.

Anything without a clear link to a voucher (specimen or photo) doesn’t get one of these GenSeq tags. We recommend that systematist avoid using genetic data from which the source is unknown or unrecorded (and therefore lacks a GenSeq tag). After all, wouldn’t you prefer to know all the sequences you used were from organisms that were correctly identified by an expert? In an age where there is so much data available from so many species it is time to be picky. If you can download a sequence upload by someone who ‘kind of sorta’ thinks it is from a coelacanth, or the one where the voucher was identified by an expert and the specimen deposited in a permanent collection (where you can check it yourself if you need to) - wouldn’t you always choose the latter.

Right now many systematist, whether they admit it or not, will just not use sequences in a phylogeny if they originally end up in a clearly “wrong” place on the phylogenetic tree. That isn’t very scientific. Knowing it is in the “wrong” place in the first place is subjective. Of course most systematists will do a blast search and try to find alternative sequences, but the point is that the most rigorous approach is to make sure the specimen it came from was positively identified in the first place. We hope this GenSeq nomenclature is a short cut to doing just that.

We have published the GenSeq nomenclature here. And below is a poster we are presenting at some meetings that gives an overview of the idea. We hope people use it (by including a table in their paper linking GenBank #s to voucher #s and adding these GenSeq ranks). Too often people write in their publications something like: “Sequences were uploaded to GenBank and correspond to JK123332-JK678738.” That sentence tells us almost nothing. Not only do we not know what specimen might go with which sequences, we don’t even know which species goes with which sequences. If you then go to GenBank and download those sequences you will have the species - but little else (most people do not report the voucher numbers, if there are any). That’s a shame and something we hope to change. So the take home message is: Always link your genetic data to your voucher’s information! 


 P.S. - the 'Journal of Fish Biology' and' ZooKeys' have added the GenSeq nomenclature recommendations to their Instruction to Authors.

Monday, June 30, 2014

Can’t wait to get back to Kuwait: Fishes from the Arabian Gulf

Bill Ludt, Jim Bishop and me in downtown Kuwait City.
From June 13th to the 22nd my PhD student, Bill Ludt, and I travelled to Kuwait to collect fishes for the LSU Museum of Natural Science ichthyology collection. (This was the 3rd trip that Bill and I have now made to Asia in the last two years.) Over my career I’ve made collections from much of the Indo-West Pacific but am lacking some critical Middle East collections. The Middle East is underrepresented in most fish collections worldwide and because many of the species from the region are poorly known there is a potential that some of them are new to science. When Dr. Jim Bishop invited us to go to Kuwait last year I knew it would be an opportunity we shouldn’t pass up.
            Jim Bishop is familiar to many people in the LSU community; he is an alumnus and a great sponsor of art and research at Louisiana State University. Jim has a wonderful enthusiasm about the work being done at the museum, and he has provided many specimens to our collections in the past. He is also one of the most cultured, kind, and energetic individuals I have ever met. Bill and I were lucky enough to stay with Jim and his wonderful wife Virginia at their home in Kuwait City. We frankly could not have figured our way out of the local airport without them, let alone find the fishes we were targeting. Kuwait is an interesting country with a rich and ancient history that was transformed by the unimaginable wealth that came about through the oil industry. All throughout the places we visited you can see this dichotomy between an ancient desert civilization and a transitioning modern society. The temperature was rarely below 100 degrees even at night, and frequently much higher - this was the desert after all, but the roadways were green with introduced shrubbery that was irrigated with desalinated seawater. Locals wore traditional Islamic garbs (abbeys for women, dishdashas for men) but there were also many expats from India, Syria and the West. It wasn’t always clear when we were seeing the real Kuwait or just the veneer around it.
Bill Ludt rocking the guitarfish.
            Our first days were spent going through the many collections Jim had already obtained for us. Through his work at KISR (Kuwait Institute for Scientific Research) he was able to arrange for specimens of notable material to be held for our visit. These specimens were collected by boat trawls in and around the Arabian Gulf (what we call the Persian Gulf) over the past few years. By the end of the first day we had already gone through hundreds of samples that were a very good representation of Kuwait’s ichthyofauna. Jim had meticulous notes for these materials and much of it was preserved in alcohol (rather than fixed in formalin) so we were able to take DNA samples as well. The majority of the materials that we brought back to LSU are these collections from KISR. We also made substantial collections from the local fish markets, which included material from Iran, Saudi Arabia and Iraq. The local fishmongers sold an amazing variety of species on a daily basis. Although the exact localities of this material will remain unknown it was nice to get additional material from around region. We also did some collecting of samples from the oceans ourselves going out at low tide to collect blennies, gobies and toadfishes from the sandy intertidal zone.
Mudskipper: a fish that doesn't mind being out of water.
We were also able to catch a few mudskippers on one of our last collecting days; these fishes are among my favorite animals to catch, and my least favorite fish to anesthetize. These fishes, which spend most of their time out of the water, are incredibly cute, with big bulgy eyes at the top of their heads and an expressive “face” that makes them look like muppets. They live in muddy areas and keep water trapped in their gills while they scurry about the surface building territories and escape routes. The mud they live in is hard to traverse and the larger individuals were in waist deep mud that made it impossible for us to catch them. We stuck mostly to the shoreline trying to catch smaller individuals. Even these small ones are amazingly adept at getting away. We saw hundreds of these mudskippers and ended up catching only about ten. We were covered in mud by the end of the day and the incredible heat made the conditions rather harsh but ‘mudskippering’ is always great fun.
            All in all we brought back close to 80 species from Kuwait and the Arabian Gulf and roughly 300 new tissue samples and close to 500 specimens. We also built an important relationship with the folks at KISR. Jim invited me to give a presentation there and my talk, “What We Learned from the 2010 Gulf of Mexico Oil Spill: and other projects from Louisiana State University” was well received.  We have also started some collaborative projects with researchers at KISR. We are trying to arrange making a short course in ichthyology that Bill and I would teach sometime in the spring at KISR, and hopefully we will try to sample from other parts of the Middle East as well. In fact this article is hopefully just the first of many we will produce from these trips to the birthplace of civilization.
A stonefish, the most venomous of all fishes.



Wednesday, April 23, 2014

Southern Fried for a Day - Collecting organisms to save their species

Check out my guest post for 'Southern Fried Science' - http://www.southernfriedscience.com/?p=16957 - this was in response to the Minteer et al. paper in Science that says we don't need to collect specimens anymore. Ummm... yes we do -

Collecting organisms to save their species

 

Tuesday, February 11, 2014

A Proposal for a New #Altmetric, the Influence Score, to Accompany the H-Index and to Help Evaluate a Scientist’s Impact on Society


The H-index is among the most widely used metrics for evaluating the quality and quantity of a scientist’s publications; but what about their influence on society? Here I introduce the Influence Score that can help an outside reviewer better understand a person’s impact not just among other scientists but among the general public and those outside of academia. If the ultimate goal is to evaluate a person’s true overall role as a scientist, I think we should be considering how they communicate with all people not just other scientists (which is the case with the H-index). The new index can be used to accompany the H-index, but also incorporates it. All elements of the Influence Score can be looked up through simple Google and Twitter/Facebook searches.
            The H-index can be easily calculated in Google Scholar, I prefer it to the Web of Science or Scopus because Google Scholar counts books and other
Fig.1. Google Scholar Profile Page Showing H-Index in Red Box.
non-traditional peer-reviewed publications: and it is free! To calculate the H-index you essentially count down the number of publications and their citations until the numbers no longer overlap. A person with an H-index of 5, has at least 5 papers with 5 citations. Read more
HERE to learn how to calculate the H-index.) Because a scientist’s main role is still to communicate their science among peers (via peer review), the Influence Score multiples their H-index by 100, and down weights the other elements, which are a little easier to accumulate (e.g., # of twitter followers).  I chose the H-index over say, total number of citations (which might be more similar to # of followers), because it is easier to calculate for a given researcher, especially one without a Google Scholar profile (Fig.1). 
            The other measures of the Influence score includes a measure of their visibility with the press (i.e, the Press Index or P-Index for short). Using Google News, one simple puts the person’s name in the search box and counts the number of articles that are found, which Google also does for you (Fig.2). Because
Fig 2. Red box shows Google News P-index.
Google News is only searching through a relatively recent window of time, few scientists will have much more than a few articles about them. Sometimes it is worth googling the person with “science” following their name, as I did for James Watson (e.g., “James Watson science”), to distinguish him from other news articles about people with the same name. This measure largely is to bump up those scientists truly making a social impact as newsmakers. That is without bumping them up too much, I’m trying to avoid giving too much influence to “celebrity scientists” that don’t do much science of their own. Therefore, I suggest that you divide the total number of search results of the Press Index by 100 so that this score is not completely overweighing the person’s academic accomplishments represented by the (albeit crude) measure of the H-index. Folks like James Cameron that are great promoters of science, but are best known for other things, are intentionally excluded here. If someone could separate press about science related activities from all others, they obviously could still be included. This is also the most dynamic element of the score because it can change so rapidly. Jane Goodall can skyrocket to the top of the list with the publication of a new book.
Fig.3. Twitter and Facebook Fan pages showing # of followers.
            The third part of the Influence Score considers your sway in social media (i.e, the Social Media Index or SM-index for short), specifically Twitter or Facebook. For someone on Twitter you get 1pt for every follower. For someone not on Twitter but that has either a Facebook “Fan” Page or Facebook “followers,” you get 1pt per fan or follower. You don’t get points for regular old Facebook “friends” because that isn’t necessarily measuring your scientific influence. If they have both a Facebook Fan page and a Twitter handle you only get points for whichever is the higher value. To learn more about the role of twitter for outreach read David Shiffman’s @whysharksmatter excellent article HERE).  As with the Press-Index you divide the total number by 100; again this is to allow the more academic H-index to still have some weight. The reason being that someone with 40,000 Twitter followers and an H-index of 0 might not really be more influential than a scientist with an H-index of 40 and only 4000 twitter followers.
            The Influence Score is then the total of your (H-Index X 100) + (Press index/100) + (Social Media-Index/100). I would round this to the nearest integer. All three can be discovered relatively easily through searches (e.g. GoogleScholar, GoogleNews, and a Facebook/Twitter search). Below I’ve compiled a list of some of the most well known scientists and have calculated their Influence Score. I’ve also added folks randomly that I admire that might not be the most famous folks but that I hope will be one day, I think adopting the Influence Score might help them get the recognition they deserve for the impact they have in society. This metric is imperfect: but I hope it is a good start.

I would like to thank Paige Brown @FromTheLabBench and her class #manship4002 for helping me figure out a more user-friendly way to compile this Influence Score. Also would like to thank Joshua Drew (@Drew_Lab) and David Shiffman (@WhySharksMatter) for their comments and advice.


Scientist
H-Index x 100
Press Index /100 (total articles according to GoogleNews)
Social Media Index
/100
(Twitter/or Facebook followers)
Influence Score
Stephen Hawking
8400
781
9,591
18,772
Neil deGrasse Tyson
600
14
16,100
16,714
Richard Dawkins
4000
36
8,870
12,906
Bill Nye
100
140
11,500
11,740
Jared Diamond
10800
104
208
11,112
J. Craig Venter
9600
20
186
9,806
E.O. Wilson
8800
64
12
8,876
Steven Chu
8200
17
212
8,429
Buzz Aldrin
0
10
8,150
8,160
Sean B. Carroll
7500
.01
0
7,500
Jane Goodall
4500
21
1,650
6,171
Ed Yong
0
.12
4,660
4,660
Jane Lubchenco
4600
.71
0
4,601
Jack Horner
3600
.16
18
3,618
Carl Zimmer
1700
3.83
1700
3,404
Amanda Vincent
3200
0
3
3,203
Neil Shubin
3100
3.94
9
3,113
David Attenborough
1500
45
1,423
2,968
James Watson
2600
18
0
2,618
Hope Jahren
2200
0.01
24
2,224
Eugenie Clark
2200
0.01
0
2,200
Eugenie Scott
1800
0.06
71
1,871
David Shiffman
200
0.04
1,470
1,670
Sylvia Earle
1400
1
213
1,614

Note: As always I would like feedback on this post and if people have suggestions for changes or additions to the metric. Please e-mail me at prosanta@lsu.edu