Are we studying the ecology of molecules rather than of microbes?

This week’s journal club paper was the focus of an mBio editorial entitled “In Nature, There Is Only Diversity” that discusses the abstraction of biological concepts such as microbial species and strain by marker gene sequencing and the operational taxonomic unit (OTU). In their article Chase et al. (2017) assess one abundant bacterial OTU within forest communities which that splits into six distinct clades based on gene and protein features from complete genomes. The authors then map the biogeography of these clades across environmental gradients with the use of metagenomics. This paper highlights that when we use marker genes as a proxy for phylogenetic relatedness, the traits that we care about must also track with differences in the marker genes used, which is often not the case. These concepts will be discussed at 3pm on Friday March 23rd in HSC 3N10A. Specifically:

  1. How each phenotype mapped with phylogeny
  2. How phylogeny was measured and compared to OTU diversity
  3. What OTU diversity can and cannot tell us in this example
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Human Microbiome working group 2018

The Microbiome Working Group has moved back to Tuesdays! This is a weekly, drop-in, work/discussion hour that aims to bring microbiome researchers together –  independent of sample type, laboratory, or skill level. This isn’t a meeting and there are no presentations. This is about getting research and analysis done. Bring your laptop and use the presence of like-minded others as motivation.

Everyone is welcome, no need to sign up!

Winter 2018: Tuesdays 3:30-4:30 pm HSC 1J9A

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2in1: Assembly benchmarking and evaluation

The numbers of metagenomics studies are exponentially increasing in recent years. Because we lack comprehensive microbial genome databases for most environments, the microbiome field is moving towards de novo metagenomics, using reference-independent techniques to assemble putative genes and in silico refinement of draft genomes from short shotgun sequencing reads. The challenges with this technique are to critically assess the quality of assembly and to avoid chimeric contigs, which can result from sequencing or assembly errors.

On Friday, November 24 at 3pm in 3N10A, at the human microbiome journal club, we will review the performance of popular assembly algorithms using two benchmarking studies: Van der Walt et. al. (2017) that evaluated the metagenomics assembly using defined microbial communities and Greenwald et. al. (2017) which proposed a workflow to select metagenomic assembler based on the research question, the computational resources available and the bioinformatic expertise of the researcher.

More specifically, we will discuss
1. What are the criteria to critically assess the quality of a metagenomic assembly?
2. Why is it necessary to compare the performance of multiple algorithms for a given dataset?
3. What is the required computational power to conduct de novo assembly?

Hopefully, by the end of this week’s journal club, we’ll excite you enough that you will join us to continue our discussion over a beer at the Bioinformatics and Beers meet up at The Phoenix.

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Can the development of the gut microbiome of infants impact their future health?

Researchers from the University of Alberta have investigated factors which contribute to the deviation of a normal infant gut microbiome and how these deviated gut microbiomes may have the ability to predict future health including food allergies. Significance analysis of microarrays (SAM) was used to quantify the gut microbial abundance of infants over time, specifically exploring factors of birth mode (cesarean section or vaginal delivery), feeding status (breastfed or formula-fed), and antibiotic exposure.

Please join on Friday October 27, 2017 from 3:00 to 4:00PM in HSC 3N10A as we will discuss:
1. What is perceived as a normal infant gut microbiome?
2. What is SAM and what are the strengths and weaknesses of using SAM in analysis?
3. Could other mechanism be used to study microbial community development over time?

Yasmin, F. et. al. (2017). Cesarean Section, Formula Feeding, and Infant Antibiotic Exposure: Separate and Combined Impacts on Gut Microbial Changes in Later Infancy. Frontiers in Pediatrics, 5, 200.

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Reminder: HMJC This Week!

Just a little reminder that the Human Microbiome Journal Club is meeting, unusually, this week!

We’ll be discussing Gloor & Reid and compositional data analysis in HSC 3N10A at 3 p.m. on Friday, September 15. Check out the original post here.

Because of the unusual week, the monthly Bioinformatics and Beers meetup at the Phoenix is immediately afterwards at 4. Come join us for both!

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September journal club moved to Friday, September 15

Please note that the September Human Microbiome Journal Club has been rescheduled to Friday, September 15 at 3 p.m.

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September 15: Gloor & Reid’s Compositional Data Analysis

This month’s Human Microbiome Journal Club has been cancelled due to an outbreak of vacationitis, but we will resume next month on Friday, September 15 at 3 p.m. in HSC 3N10A.

At the end of September, we’ll be discussing Gloor and Reid’s 2016 paper, Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data. Gloor and Reid claim that all next-generation sequencing (NGS) data sets are compositional, and that existing ad-hoc statistical tools invented to analyze RNASeq, ChIP-Seq, microbiome, and other NGS data sets should be replaced with more standard tools that account for the data sets’ compositional nature.

Failing to account for the compositionality of a dataset can lead to spurious apparent differential abundances, badly mismeasured correlations among features, and mis-clustering of samples. Gloor and Reid discuss these effects, and propose as a solution the centre-log ratio transformation. ALDEx2 and ANCOM are two R packages discussed in the paper that implement this transformation, and we’ll discuss what they do similarly, what they do differently, and how they compare to DESeq2.

I’ll take us through the paper, and we’ll discuss

  1. The effects of compositionality apparent structure
  2. The function and motivation of log-ratio transformations and
  3. The assumptions this normalization technique implicitly makes about the data, and whether it actually helps us answer the biological questions we’re interested in.

Come join the discussion Friday, September 15 at 3 p.m. in HSC 3N10A.

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Working Group is Moving!

The microbiome working group is a drop in space to support anyone working with microbiome data who feels that they could use some advice or instruction. The intent is for people to help each other and everyone who uses the group is encouraged to attend weekly so that they can pass on what they’ve learned to the new attendees. In the past we’ve gathered on Tuesdays but starting next week working group will move to Mondays from 3:30 – 4:30 pm in HSC 4N52A.  I hope to see you all there!

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The Microbiome and Longevity: How gut microbes may impact lifespan

In recent years, the microbiome has been recognized as a major factor affecting the functioning of host organisms, influencing both health and disease. At the same time, aging research has provided many possible interventional strategies aimed at extending the human lifespan. Several studies have shown a link between the human gut microbiota and aging, as well as how the aging process can affect the structure of the microbiota and its homeostasis with the host immune system. Over the past few decades, the nematode C. elegans has become an important organism for studying aging but more recently, it has been used as a model for microbiome research. C. elegans’ short and easily observable lifespan as well as its defined microbiota can be used as a model to further understand the interactions between microbes and host aging.

In their recent paper, Han et al. used a C. elegans model to identify specific mechanisms by which gut bacteria may influence mitochondrial dynamics and aging. They screened for bacterial mutants that affected aging in their C. elegans model, and identified a specific link between a bacterial polysaccharide and worm mitochondrial dynamics. Their screen was able to identify 29 bacterial mutants out of 3983 that led to an extended lifespan in the worms. Two bacterial mutants which increase production of the polysaccharide colanic acid were chosen for further study because they acted independently of certain pathways associated with longevity and aging. The findings of this paper indicate that the makeup of the microbiota may influence aging in the host organism. This raises the possibility of identifying and using bacterial variants associated with healthy aging with the goal of enhancing human longevity.

On July 28th at 3pm in HSC 3N10A I hope to examine the findings and methods presented in this paper as well as discuss a few key points:

  • The advantages and disadvantages of the C. elegans model for microbiome research
  • Microbiome modulation of the aging process
  • The influence of the microbiome on mitochondrial function


Paper Citation: Han, B., Sivaramakrishnan, P., Lin, C. C. J., Neve, I. A., He, J., Tay, L. W. R., … & Herman, C. (2017). Microbial Genetic Composition Tunes Host Longevity. Cell169(7), 1249-1262.

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2 for 1 deal: a response to Waste Not, Want Not and the benchmarking of ANCOM

This week’s microbiome journal club will look at the recent paper by Rob Knight’s group (Weiss et al., 2017) that simultaneously responded to “Waste Not, Want Not” and confirmed ANCOM as an effective differential abundance analysis technique.  More details about these two contributions below.

Questions to be answered and discussed:

  • When to rarefy 16S data
  • The assumptions of various normalisation techniques (DeSeq2, EdgeR, cumulative sum scaling [metagenomeSeq], relative abundance)
  • Is ANCOM an effective tool?
  • Can we trust these simulations?
  • ANCOM versus other tools that adjust for compositional effects: ALDEx2 (Greg Gloor’s group)
  • Potential conflicts of interest in tool development (more users = more funding? – perhaps true of all research outcomes as more citations tends to get more funding)

Come to HSC 3N10A from 3-4pm on Friday, June 23rd to join in the discussion. I will do my best to make this session entertaining so that everyone can have enough energy to actually come to the Phoenix afterwards.

The paper can be found here.

More details:

A response by Rob Knight’s group to “Waste Not, Want Not” finally gets published, three years* after that paper put the very common practice of rarefying (subsampling to common sequencing depth) in microbiome studies into controversial territory. The impact of “Waste Not, Want Not” was very large and created a divide between two groups that develop competitive microbiome analysis pipelines, those behind QIIME (Rob Knight’s group) and those behind the phyloseq R package (Paul McMurdie and Susan Holmes, authors of “Waste Not, Want Not“). McMurdie and Holmes criticise QIIME as a “one size fits all” pipeline, since “it is often encouraged to rarefy counts as a normalizing transformation prior to any/all analysis” (phyloseq FAQ). Weiss et al, 2017 clears up most confusion about the utility of rarefying, suggesting it is the best method to correct for sequencing depth effects when using presence/absence beta diversity metrics such as unweighted UniFrac and binary Jaccard. Weighted metrics such as Bray-Curtis and weighted UniFrac are not affected as much by differences in sequencing depth and can be used after normalizing by relative abundance. Any effect of sequencing depth can be seen by rarefying many times and doing multiple rarefaction PCoA, which is the best way to “Waste Not” and still fix sequencing depth bias.

*This response was originally submitted in October, 2015

The impact of this paper does not stop there, as Weiss and colleagues also benchmarked ANCOM against many other popular differential abundance analysis techniques (DeSeq2, EdgeR, metagenomeSeq, etc.). ANCOM was shown to be the only technique examined to control false discovery rate in the presence of spurious differences associated with the compositional nature of 16S microbiome data. Can we trust their simulations?


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