A Primer on the Great BAMM Controversy

Update, 26 August 2016, 2:30PM. A number of readers brought my attention to a series of blog posts by Moore et al. responding to Rabosky’s rebuttal of their published critique of BAMM. I’ve included links to the posts and summarized their contents below. 

A fundamental problem in evolutionary biology is detecting patterns of variation in rates of lineage diversification and working to understand their causes. One recent statistical method to detect if and where diversification rates have changed across the branches of a phylogeny is known as BAMM, an acronym for Bayesian Analysis of Macroevolutionary Mixtures. Over the course of its short life time, BAMM has proven extraordinarily popular — Google Scholar shows 182 citations for Rabosky’s 2014 paper describing the method alone. BAMM works by using a Bayesian statistical framework and MCMC implementation to identify the number and location of diversification-rate shifts across the branches of a tree and the associated diversification-rate parameters (speciation, extinction, and time dependence) on each branch. In doing so, it provides a number of improvements over earlier software: it is based on an explicit model of of how diversification rates shift, it features a complex and realistic model of branching, and it quantifies statistical uncertainty (rather than only providing fixed point estimates of parameters).

bamm

However, beginning with a heavily-attended talk at this year’s Evolution Meetings in Austin, TX, Brian Moore and colleagues have raised a number of important questions about the performance and reliability of BAMM, begining a dialogue with Rabosky and the other BAMM developers. What follows is a slimmed-down (although admittedly still complex!) summary of both sides’ major points, drawing on Moore et al.’s recent PNAS paper and rebuttals posted in the BAMM documentation.

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The trouble with PCR duplicates

The sequencing center just sent your lane of Illumina data. You’re excited. Life is great. You begin to process the data. You align the data. You check for PCR duplicates. 50 percent. Half of your data is garbage. Everything is horrible. Life is horrible. How did this happen!?!

PCR duplicates are a headache… if a headache were costing you hundreds/thousands of dollars in wasted sequencing. However, they’re an inevitable part of life when using PCR during Illumina library prep. We can define a PCR duplicate as any two reads that came from the same original DNA fragment. These are a problem because they falsely increase homozygosity.  I’ve recently spent way too much time thinking about how these duplicates arise, how we might minimize them, and generally trying to understand what the heck is going on during library prep and sequencing.

In this post I’ll be walking through some RAD data I’ve recently generated (some of these findings could apply to whole genome sequencing, though most of the issues would be far less likely). I’ll focus on the original RAD approach and will assume everyone is somewhat familiar with this method, but see Andrews et al., 2016 for an overview. Hopefully some of this will be useful to others.

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How Molecular Ecologists Work: Katerina Guschanski on running shoes and the boost of a closed door

Welcome to the next installment of How Molecular Ecologists Work!

KONICA MINOLTA DIGITAL CAMERAThis entry is from Dr. Katerina Guschanski, assistant professor at Uppsala University. Katerina is a widely-trained molecular ecologist who most often works with non-human primates. I’ve learned that it often involves poop. Continue reading

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Of microbes and men: Testing the neutral theory with the human microbiome

There is no doubt that one of the hottest current topics in microbiology revolves around the human microbiome. There have been a suite of recent studies we’ve highlighted, on organisms ranging from bees and mice, to humans. A quick google scholar search identifies over 12,000 studies on the human microbiome from 2016 alone.

Figure 2. Human Microbiome Project Consortium, 2012

There are various efforts to characterize the human microbiome, such as the one run by the NIH, established in 2008, with various ambitious goals including determining how disease affects our microbial fauna and the development of a microbial reference genome data set. They also published a nice summary back in the day on the structure and diversity of a healthy human microbiome.

Figure 4. Human Microbiome Project Consortium, 2012.

Although, interestingly enough – it’s not just scientific journals that are focusing on these bacterial communities. I finally ordered my copy of I Contain Multitudes, by Ed Yong (which you’ve likely either read, plan to read, or heard about), that discusses how our microbiome is a big part of who we are.

Microbiomes associated with select organisms represent model systems that will allow us to ultimately unravel the complex interactions among microbes in the environment. If you’re interested in keeping up with the Joneses concerning microbiome studies, you might want to check out Elisabeth Bik’s blog on the topic. It has proven to be (quite understandably) interesting and difficult to figure out how microbes interact in their natural habitats, understanding microbial community ecology is important, but definitely not easy.

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How Molecular Ecologists Work: Sarah Hird on resenting Adobe, letting yourself off the hook, and starting with the hard work

Welcome to the next installment of How Molecular Ecologists Work!

Hird_coffeeThis entry is from Dr. Sarah Hird, postdoc at the University of California, Davis Genome Center and (new!) assistant professor at the University of Connecticut come this fall. Sarah has worked on phylogeography, microbial genomics, and the development of bioinformatics tools.

Sarah was the winner of the 2014 best presentation at the Festival of Bad Ad Hoc Hypotheses (BAHFest), proving that you can be a productive scientist and a funny person at the same time. Continue reading

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Microbes can rapidly evolve host-protective traits

One of the coolest studies I’ve come across so far this year is the fascinating story about microbe-mediated protection in worms by Kayla King et al.

The bacterium Enterococcus faecalis normally causes mild disease in worms (Caenorhabditis elegans). After a week with this bacterial infection, fewer than one in a hundred worms dies. In contrast, Staphylococcus aureus is a highly dangerous bacteria, killing over half of the worms within a day. Interestingly, when you mix the two pathogenic bacteria together, E. faecalis protects the worms from the more virulent competitor, reducing the worms’ mortality rate from 52% to only 18%.

King et al. wanted to investigate if it was possible to select for this mutualistic defensive trait exhibited by E. faecalis, so they continually harvested the bacterium from the worms for 15 worm-generations. The worms and the virulent S. aureus, however, were derived every time from the same genetically identical stock to make sure only E. faecalis could evolve.

King et al.

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Understanding the pieces of all those meeces: characterizing mice gut microbiota

tom and jerry

Image from Google image commons

In an age where a tremendous amount of data is generated, this week has seen some moves towards providing open access to extensive data sets. These attempts have been in the realm of chemistry as well as microbiology, where in a recent paper by Lagkouvardos and colleagues, access was provided to a set of isolates and their respective genomes, characterizing the microbial diversity of mice intestines.

It’s evident that human microbiomes are linked to both physical and mental health, and it’s also essential to understand how gut fauna might also affect mice. Since mice models are used extensively to predict how drugs might impact humans, it’s only logical we characterize the diversity and overlap humans share with the microbiomes of our furry little friends.

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