The microbiome doesn't always explain everything.

Posted on 27 Jan, 2015 by Noah Snyder-Mackler


Microbiome research is sexy. Just look at the Google Trends graph. Anyone and everyone is studying the gut, nasal, vaginal, skin, oral, aural, any-other-body-part microbiome. This means that a lot of research is getting published saying what constitutes a “healthy” vs. “unhealthy” microbiome (hint: it’s not binary or that simple)
So don’t blame me for the fact that I read this new study with a healthy bit of skepticism. In fact, I was pointed there by a press release that, in my opinion, overstated their conclusions.
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Incorporating phenotype and genotype in model-based species delimitation

Posted on 26 Jan, 2015 by Melissa DeBiasse

 

caption caption

Figure by Jeremy Yoder showing gene tree species tree discordance. This phenomenon complicates species delimitation efforts using genetic data.


Species are the fundamental unit of biology but identifying them is a challenging task that receives a lot of theoretical and empirical attention. In a recent Evolution paper, Solís‐Lemus et al. (2015) introduce a new model-based method that integrates phenotypic and genetic data in the delimitation of species boundaries. The method also accommodates divergence with gene flow and selectively driven divergence.

The goal of our work is to develop a species delimitation method to combine genetic and trait data into a common framework based on an explicit model of evolution. Specifically, we extend the Bayesian program BPP (Bayesian phylogenetics and phylogeography, Yang and Rannala 2010) to combine genetic and quantitative trait data in a single Bayesian framework, which we call iBPP (integrated BPP).

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Posted in methods, speciation, species delimitation, theory | 2 Comments

The paludicolous life: peatmosses and pH

Posted on 23 Jan, 2015 by Stacy Krueger-Hadfield

High dispersal should counteract local adaptation by continuously redistributing genetic variability.  In the bryophyte Sphagnum warnstorfii, the North Atlantic may not be as formidable a barrier as expected.  Spores may traverse the Atlantic Ocean to North America from Europe and vice versa.


Mikulášková et al. (2015) revisit local adaptation in this high disperser in a new paper in Ecology and Evolution. Its broad tolerance to pH and calcium (two major determinants in species distribution in fens) could be due to genetically differentiated ecotypes. Indeed, pH was an important determinant in genetic structure, but it was independent of geography.
Alternatively, broad tolerance could be due to the occurrence of cryptic species, highlighting either the problems associated with species boundaries or the occurrence of introgression with phylogenetically allied species. Species definitions are a bit of a sticky subject, but both species ID and hybridization raise intriguing questions with regard to the latter’s role in shaping the genetic structure of species and the former’s influence on patterns we describe. In either case, the addition of a free-living phase, differing in ploidy, adds a complicated twist
Mikulášková E, Hájek E, Veleba A, Johnson MG, Hájek T, Shaw JA (2015) Local adaptations in bryophytes revisited: the genetic structure of the calcium-tolerant peatmoss Sphagnum warnstorfii along geographic and pH gradients. Ecology and Evolution 5: 229-242 DOI: 10.1002/ece3.1351

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LaTeX hacks to save your life (and your co-authors')

Posted on 22 Jan, 2015 by Arun Sethuraman

In light of this recent study by Knauff and Nejasmic (2014) that makes a lot of presumptive leaps on the utility and effectiveness of \LaTeX in scientific writing, my case for the utility of \LaTeX for every equation, reference, table, figure, and revision will hopefully sit well with MS Word loyalists (I used to be one too).

We conclude that even experienced LaTeX users may suffer a loss in productivity when LaTeX is used, relative to other document preparation systems. Individuals, institutions, and journals should carefully consider the ramifications of this finding when choosing document preparation strategies, or requiring them of authors – Knauff and Nejasmic (2014)

At the same time, this blog-post (and scores of other text documents that I have written) was typed in Programmer’s Notepad. I could have very well typed this in Vi/Vim/EMACS/Notepad/WordPad/MS Word or any editor of choice – in fact, I write all my \TeX files (and C/C++) in Programmer’s Notepad/Vi. The fundamental difference between an editor (which MS Word is), and a typesetting language (which \LaTeX is) is often overlooked. While I point you to some very valid arguments laid forth by Claus Wilke here, a breakfast conversation with him over the utility of \LaTeX over MS Word prompted me to come up with a list of cool things that you can do using \LaTeX, which you invariably will have trouble with achieving in MS Word to produce publication quality documents. My objective here is to point out some easy to use \LaTeX hacks, and definitely not to belittle MS Word’s utility – all journals will end up typesetting your text nevertheless in accordance with their requirements. As Claus rightfully points out, there is also no one correct/perfect tool to use.

John Lees-Miller and I collaboratively editing the \LaTeX version of this post on www.overleaf.com


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Posted in howto, science publishing | Tagged , , | 6 Comments

The imitation game: simulating the genetics of large populations

Posted on 21 Jan, 2015 by Rob Denton

The most adorable of simulations. Credit to Liza Gross


Computational simulations of genetic data are such a powerful and flexible tool for carrying out studies in molecular ecology.
Do you want to know how much explanatory power your data provides? Simulate it!
Predict the future response of species to hybridization, climate change, or translocation? Simulate it!
Do you want to know what it is like to run a city, drive a city bus, or be a goat? Ehhh, that’s not really what I’m talking about.
Many of the programs for simulating genetic data rely on constructing simulations based on individuals. Simulating individuals makes a lot of sense: easy to interpret and flexible for many evolutionary scenarios. However, the biggest limitation to individual-based simulators is that the computing power needed to simulate large numbers of individuals can be unwieldy. And if you are really trying to simulate biological phenomenon, large number of individuals is likely a requirement.
There are other types of models for simulation (analytical models) that focus more intently on a handful of genetic parameters of interest. These simulators obtain more accurate estimations of parameters of interest by sacrificing the complexity that may be more representative of those real-world large populations.
MetaPopGen, a new simulation package from Marco Andrello and Stéphanie Manel, offers a new approach to combine the strengths of these methods and simulate complex evolutionary scenarios in large populations. To do this, they ignore individuals and use genotypes as the basic unit of simulation.  This allows for the user to simulate huge sets of “individuals” and opens up a whole range of demographic and genetic complexity.
Sound too good to be true? The trade-off inherent in these simulations is a limitation to a single locus, making MetaPopGen inappropriate for multilocus investigations:

The strengths and weaknesses of MetaPopGen with respect to other forward-time simulators suggests which simulator can be used depending on the evolutionary scenario. While individual- based simulators are well adapted to multilocus systems where the number of individuals is not too large, MetaPopGen is adapted to simulate scenarios with large numbers of individuals but only one locus. The optimal forward-time simulator capable of dealing with multilocus populations of very large size probably does not exist, and the correct practice is to choose the most adapted simulator to the situation of interest.

So, if you are interested in in simulating the effects of complex demographic scenarios across large metapopulations (as the authors do in the example dataset), MetaPopGen might be just what you are looking for.
Additionally, if you aren’t familiar with genetic simulation software, this paper offers a nice entry point to the field. For example, did you know there is a database comparing different types of simulators? If you are just starting to think about simulating some data, the citations and explanations provided by Andrello and Manel could be helpful to you.
Andrello M. & Manel S. (2015). MetaPopGen: an R package to simulate population genetics in large size metapopulations, Molecular Ecology Resources, n/a-n/a. DOI: http://dx.doi.org/10.1111/1755-0998.12371

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New to the genome sequencing $8 menu: Nextera library preps!

Posted on 20 Jan, 2015 by Noah Snyder-Mackler

Illumina/McDonald's dollar menu

Illumina/McDonald’s dollar menu


Researchers are thrifty. We’re always looking for ways to make our expensive supplies and reagents go the extra mile. This shit has been going on for decades – hell, probably even centuries: I remember when I was a kid and my dad paid me $0.10 for every box of pipette tips that I re-filled by hand (attn: Child Services – this is way below minimum wage).
Well, hold onto your britches bargain-whole-genome-sequencers, because there’s a new preprint that’s just for you!
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Posted in genomics, methods, next generation sequencing | Tagged , , , , | 7 Comments

Nature versus nurture in the human immune system

Posted on 19 Jan, 2015 by Melissa DeBiasse

Arnold Schwarzenegger and Danny Devito starred in the 1988 movie Twins. Photo from Wikipedia

Arnold Schwarzenegger and Danny Devito starred in the 1988 movie Twins. Photo from Wikipedia


An organism’s phenotype is the result of its genotype and its environment. Teasing apart the relative importance of these factors in determining phenotype is a difficult task. However, monozygotic (i.e. identical) twins offer a natural experiment to test the contributions of genes (‘nature’) and environment (‘nurture’) to phenotype.
In their 2015 Cell paper Brodin et al. measured 204 immunological parameters  in 210 sets of healthy twins between 8 and 82 years old. They found that variation in immunity between twins was too great to be explained by variation in their genomes. This suggests that the environment plays a larger role than genotype in determining an individual’s immune system phenotype.

Our results show that these functional units of immunity vary across individuals primarily as a consequence of non-heritable factors, with a generally limited influence of heritable ones.

Brodin et al. also found that young twins were more similar to their sibling than old twins were to theirs, suggesting a divergence of immune systems over time as twins are potentially exposed to different environments than their sibling. This also supports the hypothesis that environment influences immunity more than genotype.

the immune system of healthy individuals is very much shaped by the environment and most likely by the many different microbes that an individual encounters in their lifetime.

Brodin P, Jojic V, Gao T, Bhattachatya S, Lopez Angel CJ, Furman D, Shen-Orr S et al. (2015) Variation in the Human Immune System Is Largely Driven by Non-Heritable Influences. Cell (160) 37-47. DOI: 10.1016/j.cell.2014.12.020

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Sex chromosome evolution … in haploids, that is

Posted on 16 Jan, 2015 by Stacy Krueger-Hadfield

In diploid organisms, the rates of mutation and recombination played a pivotal role in the evolution of sex-determining regions and, thus, sex chromosomes.  We know quite a bit theoretically and empirically in XY systems in mammals and ZW systems in birds. But, empirical data on the sex-determining regions in haploid organisms are lacking.

Sphaerocarpus female thallus ©fernzenmosses.com

Sphaerocarpus female thallus ©fernzenmosses.com


Immler and Otto (2015) provide a model and a brief literature review on the evolution of sex chromosomes in organisms with dioecious haploid stages.  They focus on two aspects of haploid sex chromosome evolution:

reduced recombination and degeneration due to the accumulation of deleterious mutations.

Their modeling predicts that the decay of recombination, for example, is just as much a part of the evolution of haploid sex chromosomes as it is in diploid systems.  However, there are some important differences between diploid and haploid sex determination.  The masking of deleterious mutations does not happen in haploid male and females so degeneration of the haploid sex-determining region should be slower than in diploids.
They, then, provide a literature review ranging from algae to bryophytes to fungi.  Only a few of the studies had reported sequence information about the sex determining region, but they all confirmed Immler and Otto’s models.  In all cases, signs of reduced recombination were found, as well as differentiation in the sex-determining regions of males and females, even in fungi in which anisogamy is limited or absent.
Immler S and Otto SP (2015) The evolution of sex chromosomes in organisms with separate haploid sexes. Evolution 10.1111/evo.12602

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SpaceMix, and a brief history of Spatial Genetics

Posted on 15 Jan, 2015 by Arun Sethuraman

Incorporating spatial data to inform studies of the population demography of a species has a long history of interest. From inferring geographical clines in Principal Components Analyses (Menozzi et al. 1978), using location data as “informative priors” during model-based estimation of admixture (Hubisz et al. 2009), using phylogenetic trees (and other distance based methods) and superimposing them upon geographical distributions to make predictions about what has come to be known as “phylogeography” of a species, measuring the correlation between geographic distance matrices and genetic distance matrices (sensu Peakall and Smouse 1999), estimating spatial autocorrelation (eg. Moran’s I, correllograms – see Brian Epperson’s book for an excellent review, also Sokal and Oden 1991) to discover directional clines in the genetic-spatial distribution of a species, pruning variance-covariance matrices in genetic data using graph-theoretical/network algorithms to discover geographical-genetic structure, detecting differences in allele frequency spectra of populations to detect founder effects and range expansions (see Peter and Slatkin 2013), just to name a few.

SpaceMix

Inferred map of human admixture using SpaceMix from Bradburd et al. (2015).


At the core of all these methods is the variance-covariance structure in the genetics (primarily observed and/or ancestral allele frequency distribution), and the apparent geographical distribution of the species. Continue reading →

Posted in bioinformatics, howto, population genetics, R, software | Tagged , , , | Leave a comment

A current review of modern and ancient eDNA

Posted on 14 Jan, 2015 by Rob Denton


There is something romantic about environmental DNA. The ability to discover the presence of almost any species just by detecting the microscopic bread crumbs they leave behind? That is really just a deerstalker and pipette away from Sherlock-level science.
But if you are anything like me, aside from knowing that folks get excited about, you might not know what exactly is possible using eDNA and metagenomics. No matter what your familiarity with the field is, I’m betting you can learn something from this new review from Mikkel Pedersen and colleagues:
Here were my top three gee-whiz moments:

  1. The eDNA under examination may be hitching a ride.

Natural transformation is a process through which cells take up extracellular DNA from the surroundings and integrate it into their own genomes [46,47]. Many bacteria are known to be agents for natural transformation, as are some archaea and even a eukaryotic group of micro-invertebrates, the bdelloid rotifers [48–51]. The majority of DNA that microbes take up is quickly degraded and re-metabolized in the cell, but some DNA persists for long enough to recombine with the host genome [52].

  1. I knew contamination was one of the most difficult aspects of eDNA studies….but even the reagents?!

…..contaminants can be difficult to distinguish from endogenous DNA. For example, DNA contaminants from various sources are found in reagents [10,21,77–82]. Although most of these are from readily identified domesticated animals or cultivated plants, others such as Salix [83] are not and can be mistaken for genuine environmental diversity.

  1. Ice cores, soil samples, permafrost? Too easy. Let’s go find some whales!

Recently, two studies showed that seawater is also a source of macro-organismal eDNA for detection of whale species [18] and marine fish diversity [17] (figure 2). Importantly, eDNA from fresh and seawater appears to reflect contemporary rather than past diversity, as eDNA decays within a few days or weeks in the water column [16,17,61,196,197].

Pedersen M.W., L. Ermini, C. D. Sarkissian, J. Haile, M. Hellstrom, J. Spens, P. F. Thomsen, K. Bohmann, E. Cappellini & I. B. Schnell & (2014). Ancient and modern environmental DNA, Philosophical Transactions of the Royal Society B: Biological Sciences, 370 (1660) 20130383-20130383. DOI: http://dx.doi.org/10.1098/rstb.2013.0383

Posted in DNA barcoding, genomics, metagenomics, Paleogenomics | Tagged , | 2 Comments