Great bit of research showing the amazing adaptation of bat tongues to nectar feeding.
Harper, C., Swartz, S., & Brainerd, E. (2013). Specialized bat tongue is a hemodynamic nectar mop Proceedings of the National Academy of Sciences DOI: 10.1073/pnas.1222726110
A lightning talk or a flash talk is a short presentation, typically anywhere between 1 and 5 minutes. They have been around for over 10 years in programmers’ meetings, and are slowly making inroads into scientific meetings.
The Good: lightning talks give more speakers a chance to present their material to an engaged audience; they cultivate succinct speaking skills. If you don’t like a talk in the session, you don’t have to wait for half an hour for the next one, you only have to wait for five minutes.
The Bad: a long session crammed with lightning talks may cause a jumble in the typical audience member’s brain. Talks that are early or late in the session may receive more attention due to the serial position effect, so that the middle talks are completely lost in the muddle, and the first and last couple of talks are those that are remembered.
Still, suppose you submitted an abstract to a conference, and made the cut for a lightning talk; what now?
Forget most of the skills you were taught for a regular 20 minute conference presentation, or 40 minute seminar. Lightning is a different beast. In a long talk, you teach a bit (background to the field & introduction to the problem at hand), show your stuff (your work), and advertise (show how your work contributed to the field, and how you left it better).
In a lightning talk, you want to get a single message across. And you want it to stand out. So you cannot afford to be too complex, you just don’t have the time.
Do: Prepare five to ten slides. Make sure they are sparse. An image or two per slide. No complex graphs. If you need words, write them big and few.
Do introduce yourself clearly at the beginning (name, affiliation, position, what you do)
Do clearly introduce whatever you are presenting.
Do give the acknowledgement slide at the beginning Although that is common practice in regular talk to give it at the end, in a lightning talk you want your last slide to be something else. See below.
Do speak at your normal pace.
Do make the last slide the impressive one: clear, strong message that will linger a minute longer during Q&A time, impressing itself upon the audience before it is time to move to the next talk. You do not want the acknowledgement slide to be last, as is traditionally done in longer talks.
Do: rehearse, rehearse, rehearse. Even if you are an accomplished speaker who can do a long talk without rehearsals, the lightning talk is a different beast. Waffling costs precious seconds, Moreover, getting back on track you may be tempted to speak faster to make up for lost time. Which is a no.
Don’t cram too many slides or be tempted to speak too fast. Find a way to convey your message at a normal speaking pace. Compressing more words into less time does not increase the information you convey, it actually deceases it. People can only process so much at a given time. Remember that a talk, including a lightning talk is about making people understand something new, not about you maximizing words-per-minute.
Don’t go over the allotted time. If you are not finished by the time the clock buzzes or the session chair signals you to get off, just say “sorry, time’s up. Catch me at the coffee break if you want to hear more.” — and step off the podium .
Ride the lightning!
It has not escaped Twitter’s notice that the Watson & Crick paper is 60 years old today . Sorry, too busy to be really creative, so here is a repost from 2009. Think of it as a transposon.
Short quiz and a movie for DNA day.
1) We celebrate DNA day because:
a) Congress said so
b) Francis Collins said so
c) I said so
2) Who has DNA?
a) CSI Miami
b) James Watson
c) Please, please, PLEASE let the paternity test comes back negative…
3) Nature vs. Nurture: which is more important?
a) Nature
b) Nurture
c) Nurture, but only if your mother was a hamster and your father smelled of elderberries
4) The following movie shows:
a) Replication
b) Application
c) Fumigation
You have until Friday Saturday, April 20th to submit your abstracts to the Automated Function Prediction meeting, an ISMB 2013 Special Interest Group and CAFA: Critical Assessment of Function Annotations.
Keynote speakers:
Key dates:
Sequence and structure genomics have generated a wealth of data, but extracting meaningful information from genomic information is becoming an increasingly difficult challenge. Both the number and the diversity of discovered sequences are increasing, and the fraction of genes whose function is known is decreasing. In addition, there is a need for annotation which is standardized so that it could be incorporated into function annotation on a large scale. Finally, there is a need to assess the quality of the available function predictionsoftware.
For these reasons and many more, automated protein function prediction is rapidly gaining interest among computational biologists in academia and industry.
The Automated Function Prediction Special Interest Group (AFP SIG) has been part of ISMB since 2005. We call upon all researchers involved in gene and protein functionprediction and annotation, both computational and experimental, to submit an abstract to the AFP meeting. Authors of select abstracts will be invited to give a talk and/or present a poster.
We will also be discussing the upcoming second Critical Assessment of Function Annotations, or CAFA 2. CAFA 1 was a highly successful experiment, engaging 30 groups worldwide, and has resulted in 16 peer-reviewed papers in Nature Methods and BMC Bioinformatics.
We are looking forward to a new and expanded CAFA 2 in 2013-2014, which will include a cellular component prediction track, and a human-specific track.
For further instructions on AFP 2013, please go here: http://BioFunctionPrediction.org
Please submit your abstract now, we are looking forward to seeing you in Berlin.
For continuing information, please subscribe to the following Google Group: https://groups.google.com/forum/?fromgroups#!forum/afp-cafa
Contact: afp.cafa.2013@gmail.com
Organizers:
Steering committee:
Program committee:
I am not inclined to write polemic posts. I generally like to leave that to others, while I take the admittedly easier route of waxing positive over various bits of cool science I find or hear about, and yes, occasionally do myself.
But WSJ editorial from E.O. Wilson has irked me so much, I have decided to go for it. The upset I felt when reading this was on several levels: as a teacher, and a scientist, and as a person concerned for the future of science, and science literacy. In this editorial, Wilson promotes a type of scientific illiteracy that is dangerous if taken to heart by aspiring scientists.
In essence, Wilson draws from his personal experience as a successful scientist who is not only semi-illiterate in math, but proud of it. He claims that, if he succeeded as a math illiterate, so can other scientists, except in ”a few disciplines, such as particle physics, astrophysics and information theory.” (All quotes are from said article, unless noted otherwise.) He claims that “Far more important throughout the rest of science is the ability to form concepts, during which the researcher conjures images and processes by intuition.” He continues to state that: “ The annals of theoretical biology are clogged with mathematical models that either can be safely ignored or, when tested, fail. Possibly no more than 10% have any lasting value.”
Continue reading Terrible advice from a great scientist →
It seems like the forces of light have triumphed somewhere around September 2006:
…as have their evil counterparts, April 2009:
bacteria are neck-in-neck with humans:
But they beat the largest creatures on Earth:
Of course, you can’t beat cats:
Suppose you have a dictionary mydict, with key:value pairs
mydict = {'a':5, 'b':2, 'c':1, 'd':6}
You want to sort the keys by the values, maintaining the keys first in a list of tuples, so that the final list will be:
[('c',1), ('b',2), ('a',5), ('d',6)]
aaaand, the stupid Python trick involves a nested list comprehension:
sorted_list = [(k,v) for v,k in sorted(
[(v,k) for k,v in mydict.items()]
)
]
To get a reverse sorted list:
[('d',6), ('a',5),('b',2),('c',1)]
[(k,v) for v,k in sorted( [(v,k) for k,v in mydict.items()],reverse=True ) ]
Crikey. That’s a stupid python if I ever held one!
Thanks to Mitch Balish for calling my attention to this one.
SR1 bacteria are not exactly a household name, even among microbiologists. They were first discovered in contaminated aquifers, and since then they were found to be also in animal and insect guts, as well as in human mouths. They are even suspected of being a cause of periodontal disease. I should probably say here that SR1 is a whole phylum of bacteria, and not a single genus or species. The reason that they are not that well known is that their discovery was fairly recent.
Also, no one has ever actually seen or grown SR1.
All we know is that they are called SR1
A new journal, Molecular Metabolism has the following policies: one week for reviews, and three possible outcomes only: Reject, Accept, or Minor Revision. Good for them on both decisions. Bonus: your editors are Mr. Blonde, Mr. Blue, Mr. Brown, Mr. Orange and Mr. Pink. And they are professionals (although they may not tip).
The Automated Function Prediction, an ISMB 2013 Special Interest Group meeting and CAFA: Critical Assessment of Function Annotations. July 20, 2013, Berlin
Keynote speakers
Sequence and structure genomics have generated a wealth of data, but extracting meaningful information from genomic information is becoming an increasingly difficult challenge. Both the number and the diversity of discovered sequences are increasing, and the fraction of genes whose function is known is decreasing. In addition, there is a need for annotation which is standardized so that it could be incorporated into function annotation on a large scale. Finally, there is a need to assess the quality of the available function prediction software.
For these reasons and many more, automated protein function prediction is rapidly gaining interest among computational biologists in academia and industry.
The Automated Function Prediction Special Interest Group (AFP SIG) has been part of ISMB since 2005. We call upon all researchers involved in gene and protein function prediction and annotation, both computational and experimental, to submit an abstract to the AFP meeting. Authors of select abstracts will be invited to give a talk and/or present a poster.
We will also be discussing the upcoming second Critical Assessment of Function Annotations, or CAFA. CAFA 1 was a highly successful experiment, engaging 30 groups worldwide, and has resulted in 16 peer-reviewed papers in Nature Methods and BMC Bioinformatics:
http://www.nature.com/nmeth/
http://www.biomedcentral.com/
We are looking forward to a new and expanded CAFA 2 in 2013-2014, which will include a cellular component prediction track, and a human-specific track.
For further instructions on AFP 2013, please go here: http://BioFunctionPrediction.
We are looking forward to seeing you in Berlin!
Iddo Friedberg, co-chair, on behalf of the AFP 2013 organizing committee
For continuing information, please subscribe to the following Google Group: https://groups.google.com/
Contact: afp.cafa.2013@gmail.com
Advertisomics: environmental sequencing aimed at obtaining popular press coverage with little or no scientific value. Samples obtained from an environment otherwise not of microbiological interest. “Hey, did you hear they swabbed the car wheels in the building’s parking lot and found that the microbes all cluster by tire brand name?”
Celebromics: sequencing the genome or microbiome of a celebrity. Generally the sequence is not even published, but just the act of sequencing it provides publicity for the lucky lab, the celeb, and maybe even a microbial species or two. “They sequenced the genome of Keith Richards, and found a duplicated set of multiple drug resistance genes.”
Contaminomics: sequencing results published prematurely, and later discovered that the major finding is the result of a contamination.
DuhOmics: unsurprising results from a genomic study. Usually confirming common knowledge that did not require a genomic study in the first place.
Lazarusomics: sequencing the genome of an extinct animal, including hominids, with the implicit or explicit promise that we will be able, very soon, to reverse the extinction.
Shockomics: related to advertisomics. Sequencing for shock value and pop publicity. Usually involving human or animal bodily secretions or parts you’d rather not have known about.
TooMuchInformationOmics: A result of the personal genomics and microbiome industry. No, I am not interested in that heel spur gene that you got from your grandmother, nor am I interested in the novelty of the chlamydia strain they found in your partner’s microbiome.
ZZZomics: an omics paper that makes you fall asleep half way through the introduction.
From the organizers of CAGI 2013. I have been to the Critical Assessment in 2010 and 2011, and even participated as an assessor. It’s a fun meeting, and if your work involves prediction of phenotypes from genotypes, there is still time (just about) to accept some of the challenges.
The Critical Assessment of Genome Interpretation (CAGI) is a community
experiment to assess computational methods for predicting the
phenotypic impacts of genomic variation. The current CAGI experiment
has eight open challenges, available on the CAGI website:
https://genomeinterpretation.org/In the CAGI experiment, participants are provided genetic variants and
make predictions of resulting phenotypes. Independent assessors then
evaluate these predictions against experimental characterizations.
The primary goals of the experiment are to establish the current
state of the art, identify bottlenecks in genome interpretation,
inform critical areas of future research, and connect researchers
from diverse disciplines whose expertise is essential for advancing
methods for interpreting genomic variation.The deadline for current CAGI predictions is 28 March 2013.
Anonymous submissions, with limitations, are allowed this year.
https://genomeinterpretation.org/content/anonymity-policy
We encourage use of both established methods and experimental
approaches, and we welcome predictors of all backgrounds.The current CAGI experiment will culminate in a conference in Berlin,
on 17-18 July 2013, immediately before the ISMB SIGs. An NHGRI R13
grant will help support travel and participation in the meeting.
https://genomeinterpretation.org/content/cagi-2012-conferencePrevious CAGI experiments have highlighted striking breakthroughs
as well as disappointing failures. Publications from the previous
CAGI are underway; slides and posters presentations about CAGI may
be found at:
https://genomeinterpretation.org/content/cagi-presentations
The results from the current CAGI challenge will be published as well.The currently open CAGI challenges are:
+ Seventy-seven PGP genomes (provided by George Church).
Challenge: Predict clinical phenotypes from genome data, and match
individuals to their health records.
https://genomeinterpretation.org/content/PGP2012+ Exomes of Crohn’s disease patients and healthy individuals (provided
by Andre Franke). Challenge: predict which individuals have Crohn’s.
https://genomeinterpretation.org/content/new-crohns-dataset+ Exomes from two families with lipid metabolism disorders (provided
by John Kane and Pui-Yan Kwok). Challenge: predict lipid profiles
and a causative variant.
https://genomeinterpretation.org/content/FCH
https://genomeinterpretation.org/content/HA+ Variants in DNA double-strand break repair genes (provided by Sean
Tavtigan). Challenge: predict probability of each variant occurring
in a breast cancer case versus healthy control.
https://genomeinterpretation.org/content/MRN+ Mutations in p53 gene exons affecting mRNA splicing (provided by
Jeremy Sanford). Challenge: predict how variants impact splicing.
https://genomeinterpretation.org/content/Splicing-2012+ Variants of a p16 tumor suppressor protein (provided by Silvio
Tosatto). Challenge: predict how well variants inhibit cell
proliferation.
https://genomeinterpretation.org/content/p16_2012+ Shewanella oneidensis MR-1 gene disruptions (provided by Adam Arkin).
Challenge: Predict impact of microbial gene disruptions on cell
growth under stress conditions
https://genomeinterpretation.org/content/MR-1_2012+ riskSNPs disease-associated loci (provided by John Moult). Challenge:
identify potential causative SNPs.
https://genomeinterpretation.org/content/risksnps2012We are also soliciting challenges for the next CAGI. Please contact us
at cagi@genomeinterpretation.org with proposals for suitable datasets.In order to access the current challenges and submit predictions for CAGI,
please register at https://genomeinterpretation.org/.Registered users also have access to presentations from the previous
CAGI conferences, as well as posters and talk slides that summarize
the results.
Sincerely,Daniel Barsky, CAGI 2012 Organizer
Steven E. Brenner, CAGI Chair
John Moult, CAGI Chaircagi ‘at’ genomeinterpretation ‘dot’ org
A problem I encountered now, when revising a paper and adding a supplement per the editor’s request. How do I number my tables and figures as Table S1, S2 etc.? A solution was provided in Stackexchange, but the syntax was not good for my version of LaTeX, and I don’t like \makeatletter (here’s why). Here is a working solution to supplementary figure and table numbering. Place this bit in your document preamble:
\newcommand{\beginsupplement}{%
\setcounter{table}{0}
\renewcommand{\thetable}{S\arabic{table}}%
\setcounter{figure}{0}
\renewcommand{\thefigure}{S\arabic{figure}}%
}
Then, when your supplement starts, just add the line:
\beginsupplement
Voila! Instant “Table S1″ and “Figure S1″. Enjoy.
“Well, in our country,” said Alice, still panting a little, “you’d generally get to somewhere else — if you run very fast for a long time, as we’ve been doing.”
“A slow sort of country!” said the Queen. “Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!”
Through the Looking Glass and what Alice Found There Lewis Carroll
The Red Queen hypothesis is well-accepted in evolutionary biology. Organisms evolve and adapt not to gain an evolutionary advantage, but simply to not fall behind competing organisms that evolve and adapt. Hence, everyone has to “run as fast as they can” (evolve) to “stay in the same place” (reproduce). It’s a nice hypothesis, and has been shown to be fairly descriptive when dealing with close competitors, such as host-parasite or predator-prey relationships.
Which is why the title of this paper published in mBio has piqued my interest: “The Black Queen Hypothesis: Evolution of Dependencies through Adaptive Gene Loss”. What is the Black Queen hypothesis?
The Red Queen in Alice was a chess piece. (And not, as the authors say in the paper, a card). The Black Queen is from a card game: namely, the Queen of Spades in the game of Hearts. Hearts is a three to five player card game, and the idea is to avoid taking tricks containing certain cards. Anything in hearts suite is bad, with one penalty point per card. But the Queen of Spades is particularly horrible, with 13 penalty points. Thus, the idea is to avoid taking hearts or the Queen of Spades.
Are you kidding? I spent three weeks at Camp Winiwinaia on Lake George the summer I was twelve. YMCA camp — poor kids’ camp my mother called it. It rained practically every day, and all we did was play Hearts and hunt The Bitch.
Hearts in Atlantis Stephen King
The authors of the paper use Hearts to set a model explaining reductive evolution in bacteria. Why would some bacterial lineages of free-living bacteria lose genes? How does an evasion trick card game tie into evolution?
In the context of evolution, the BQH (Black Queen Hypothesis IF) posits that certain genes, or more broadly, biological functions, are analogous to the queen of spades. Such functions are costly and therefore undesirable, leading to a selective advantage for organisms that stop performing them. At the same time, the function must provide an indispensable public good, necessitating its retention by at least a subset of the individuals in the community—after all, one cannot play Hearts without a queen of spades.
One such Black Queen card is the catalase-peroxidase gene, katG. katG protects against hydrogen peroxide (H2O2), a toxic byproduct of marine photosynthesis. The catalase-peroxidase protein is iron dependent, and its synthesis can be expensive, especially in an iron-poor environment. Two common marine cyanobacteria are Synenchococcus and Prochlorococcus, which typically are found in the same communities. Most Prochlorococcus lack the katG gene in their genome, while Synechococcus do have it. It seems that in ocean-surface communities, Synechococcus is holding the katG Black Queen gene in the game, while Procholorococcus elegantly avoided taking that costly card. Synechococcus is the workhorse of reducing the toxic H2O2, while the katG-deficient bacteria enjoy the common benefits to all. So it is best to be a member of a lineage that avoids having katG, while living in close proximity to the lineages that have katG. The figure below shows that the entire Prochlorococcus clade (green) lacks katG, but (presumably), living in a community with Synechococcus, allows it to benefit from the katG gene carried by the latter.
Comparison between the phylogenies of the catalase-peroxidase and small subunit rRNA genes for cyanobacteria with sequenced genomes. Although there are some differences in branching order between the two trees, the marine Synechococcus KatG proteins form a well-supported monophyletic clade, implying that this protein was present in the clade’s ancestor and was subsequently lost in several lineages (indicated by red dots on the rRNA tree), including Prochlorococcus. Green, representatives of the Prochlorococcus clade; orange, marine Synechococcus clade; cyan, other Cyanobacteria. Bootstrap values less than 75% are omitted. Only the tree topologies are shown; branch lengths do not represent genetic distances.
Leaky Functions
The authors talk about “leaky functions”: functions that provide advantage to the community in a way that is unintentionally altruistic: if an organism has the ability to extracellularly protect against H2O2, and that species lived in a community, others will benefit. However, the BQH model predicts that lineages will continue to lose leaky functions, as long as at least one lineage maintains it, benefiting the community. Should that lineage lose the leaky function, or be removed form the community, the effects could be devastating to the community now lacking that leaky function. In other words, leaky-function species eventually become keystone species of their ecosystem.
My two cents worth: I like the model. Like any good model, it provides us with testable hypotheses, and if it works well it will provide predictive powers to evolutionary changes in microbial communities. It can explain the rarity of some essential functions in a microbial community, and possibly why so many microbes fail to grow in pure culture. Time will tell how well this model will work. My only problem is that I am not sure I agree with the title the authors gave their model. Getting the Queen of Spades in Hearts is devastating to your hand (you basically lose). That would be the genetic equivalent of a cell going apoptotic (killing itself) following a cancer mutation or a viral infection. The BQH model is more subtle, an evolutionary cost-benefit model via the leaky function mechanism. Maybe the “volunteer fire-brigade hypothesis”? Or a generic: “it’s a dirty job but somebody’s got to do it” hypothesis?
Morris, J., Lenski, R., & Zinser, E. (2012). The Black Queen Hypothesis: Evolution of Dependencies through Adaptive Gene Loss mBio, 3 (2) DOI: 10.1128/mBio.00036-12
This is romantic! So listen up!
A 3D heart shape may be drawn using the following implicit function:
Or, in Python:
def heart_3d(x,y,z):
return (x**2+(9/4)*y**2+z**2-1)**3-x**2*z**3-(9/80)*y**2*z**3
Trouble is, there is no direct way of graphing implicit functions in Python. But anything can be found on Stack Overflow.
Putting it all together:
#!/usr/bin/env python
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import matplotlib.pyplot as plt
import numpy as np
def heart_3d(x,y,z):
return (x**2+(9/4)*y**2+z**2-1)**3-x**2*z**3-(9/80)*y**2*z**3
def plot_implicit(fn, bbox=(-1.5,1.5)):
''' create a plot of an implicit function
fn ...implicit function (plot where fn==0)
bbox ..the x,y,and z limits of plotted interval'''
xmin, xmax, ymin, ymax, zmin, zmax = bbox*3
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
A = np.linspace(xmin, xmax, 100) # resolution of the contour
B = np.linspace(xmin, xmax, 40) # number of slices
A1,A2 = np.meshgrid(A,A) # grid on which the contour is plotted
for z in B: # plot contours in the XY plane
X,Y = A1,A2
Z = fn(X,Y,z)
cset = ax.contour(X, Y, Z+z, [z], zdir='z',colors=('r',))
# [z] defines the only level to plot for this contour for this value of z
for y in B: # plot contours in the XZ plane
X,Z = A1,A2
Y = fn(X,y,Z)
cset = ax.contour(X, Y+y, Z, [y], zdir='y',colors=('red',))
for x in B: # plot contours in the YZ plane
Y,Z = A1,A2
X = fn(x,Y,Z)
cset = ax.contour(X+x, Y, Z, [x], zdir='x',colors=('red',))
# must set plot limits because the contour will likely extend
# way beyond the displayed level. Otherwise matplotlib extends the plot limits
# to encompass all values in the contour.
ax.set_zlim3d(zmin,zmax)
ax.set_xlim3d(xmin,xmax)
ax.set_ylim3d(ymin,ymax)
plt.show()
if __name__ == '__main__':
plot_implicit(heart_3d)
Show this to your date on the next Valentine’s Day, because it is too late for this one. Trust me, results are guranteed. Not sure what kind of results though.
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