Tag Archives: rRNA

Mendeley groups on environmental PCR, metagenomics, and microbial eukaryotes

As part of my NSF Research Coordination Network grant (RCN EukHiTS), I am currently managing a number of Mendeley groups that amalgamate relevant journal articles on different topics related to environmental PCR, metagenomics, and microbial eukaryotes. These groups are open (anyone can join with a Mendeley account), and I’m trying to keep them regularly updated with new articles (Mendeley members can also add articles, which I strongly encourage!):

  • Eukaryotic HTP Studies – Publications relevant to high-throughput environmental sequencing approaches focused on microbial eukaryotes. Articles will include any type of -Omic methods (marker gene amplicons, metagenomics, metatranscriptomics, etc.), eukaryote-focused tools/pipelines, and review/opinion pieces.
  • rRNA in Eukaryotes – Literature related to the ribosomal repeat array in eukaryotic genomes – variation in rRNA gene copy number, intragenomic polymorphisms, concerted evolution, transposable elements and their evolutionary and ecological implications.
  • Environmental PCRs – primer sets and bias – Literature related to primer set usage and bias across all taxonomic groups (bacteria, archaea, fungi and microbial eukaryotes) – includes primer sets and methods focused on 16S, 18S, ITS, other rRNA, COI, and other marker genes used for environmental sequencing.
  • eDNA in aquatic ecosystems – This group focuses on environmental DNA (eDNA) applications in aquatic ecosystems, include use of eDNA in bioassessment and environmental monitoring. Literature collection covers methods, analytical tools, and empirical studies (both basic and applied science).

Open source software tool for the week: AXIOME: automated exploration of microbial diversity

Just found this paper via Google Scholar Updates:
GigaScience | Abstract | AXIOME: automated exploration of microbial diversity

From Josh Neufeld’s lab this paper describes a series of tools for automation of microbial diversity analyses.

Abstract:
BackgroundAlthough high-throughput sequencing of small subunit rRNA genes has revolutionized our understanding of microbial ecosystems, these technologies generate data at depths that benefit from automated analysis. Here we present AXIOME (Automation, eXtension, and Integration Of Microbial Ecology), a highly flexible and extensible management tool for popular microbial ecology analysis packages that promotes reproducibility and customization in microbial research.
FindingsAXIOME streamlines and manages analysis of small subunit (SSU) rRNA marker data in QIIME and mothur. AXIOME also implements features including the PAired-eND Assembler for Illumina sequences (PANDAseq), non-negative matrix factorization (NMF), multi-response permutation procedures (MRPP), exploring and recovering phylogenetic novelty (SSUnique) and indicator species analysis. AXIOME has a companion graphical user interface (GUI) and is designed to be easily extended to facilitate customized research workflows.
ConclusionsAXIOME is an actively developed, open source project written in Vala and available from GitHub (http://neufeld.github.com/axiome) and as a Debian package. Axiometic, a GUI companion tool is also freely available (http://neufeld.github.com/axiometic). Given that data analysis has become an important bottleneck for microbial ecology studies, the development of user-friendly computational tools remains a high priority. AXIOME represents an important step in this direction by automating multi-step bioinformatic analyses and enabling the customization of procedures to suit the diverse research needs of the microbial ecology community.

Workflows like this are what many many people need.  I note – I have not used this yet but it looks promising.  It has some parallels to the WATERS workflow package that came from my lab a few years ago (see more about it here: https://phylogenomics.wordpress.com/software/waters/.)  Alas WATERS is no longer being actively developed.  Anyway – AXIOME has additional features and certainly seems like it would be useful to many people.

Guest post from Ashley Bateman on "Full contact microbes" – Roller Derby

A special guest post from Ashley Bateman.

 

Roller derby players share their skin microbes during play
Single-celled organisms are intimately associated with multicellular organisms across the tree of life, and human beings are no exception. Making up 90% of our cellular composition, these invisible passengers (our microbiome) contribute to our health and well-being in crucial ways, including aiding our digestion, the education of our immune system, and resistance to pathogens. Despite this importance, we still lack a fundamental understanding of where our host-associated microbes actually come from. We know that infants are born practically sterile; early-life events such as birth mode can contribute to the types of microbial species found on an individual, but these events cannot adequately explain the majority of spatiotemporal variation observed over a host’s lifetime. To be able to accurately describe the processes that drive host-associated microbial community dynamics, we must have an informed understanding of the role of dispersal in structuring host-associated microbial communities.
Where do they come from? How do they get there? Do these changes (if any) last?
The Green Lab at the University of Oregon-Eugene attempted to answer some of these questions in our latest publication “Significant changes in the skin microbiome mediated by the sport of Roller Derby”, released today by PeerJWe decided to use Roller Derby as a model system to investigate the role of contact in dispersing skin microbial communities between hosts. We have known for a long time that pathogens can be transmitted via direct contact; could not our commensal microbial communities be shared in this way?

We swabbed the upper arms (a frequent contact point between players during a bout) of players belonging to 3 geographically distinct derby teams and characterized the skin microbiome of each player using 16s rRNA gene Illumina sequencing. We found that each team’s upper arm microbiome was significantly different from one another before play, and that this difference decreased after bouts were played. Not only did teams’ skin microbiomes become less different from one another after play, but the differences were driven in part “by the presence of unique indicator taxa that are commonly associated with human skin, gut, mouth, and respiratory tract.” There were also environmental bacteria associated with soil and plants found in the skin samples.

Although we weren’t able to show a direct link between contact and transfer of specific microbial taxa, the best explanation of the data seems to be that contact between these players during a one-hour bout effectively resulted in homogenization of their upper arm skin microbiomes.
So much yet to explore! As a 2nd year graduate student in the Green Lab I hope to address some of the questions that the Roller Derby paper has brought to our attention. My dissertation research is gearing up to understand the role of dispersal on our skin microbiome. Are some skin sites more amenable to changes than others? Can we pick up host-associated microbes not just from other individuals, but from objects that other individuals have touched? Can we pick up non-host-associated microbes? If we can pick them up, how long do they stick around? How do they participate in the functions attributed to the skin microbiome?
Hope to keep up the fantastic momentum that has been launched by this latest publication – if you have any thoughts or comments, feel free to contact me at abateman@uoregon.edu, or via Twitter: @microbesrock
And you can check out a stop-motion video I made on the skin microbiome here:

Interesting paper on strategy to use PCR to simultaneously characterize eukaryotic, bacterial and archaeal microbes

Interesting new paper in PLOS One: PLOS ONE: Simultaneous Amplicon Sequencing to Explore Co-Occurrence Patterns of Bacterial, Archaeal and Eukaryotic Microorganisms in Rumen Microbial Communities

Full citation:  Kittelmann S, Seedorf H, Walters WA, Clemente JC, Knight R, et al. (2013) Simultaneous Amplicon Sequencing to Explore Co-Occurrence Patterns of Bacterial, Archaeal and Eukaryotic Microorganisms in Rumen Microbial Communities. PLoS ONE 8(2): e47879. doi:10.1371/journal.pone.0047879

Basically, the paper describes the development and use of a PCR strategy to simultaneously characterize eukaryotic, bacterial and archaeal microbes from samples.

Primers used are summarized in Table 2

The strategy they employ attempt to correct for differences in amplification differences between the different amplicons which should therefore allow better normalization of relative abundance estimates.  See results in Figure 2.

Definitely worth a look.

Correcting for rRNA copy # in qPCR experiments

Asked this question on Twitter and thought I would share answers here via Storify.  I am putting it below the fold to allow people to avoid the Storify embed if they want to.
//storify.com/phylogenomics/correcting-for-rrna-copy-in-qpcr-data.js[View the story “Correcting for rRNA copy # in qPCR data” on Storify]

Interesting new #PLOS One paper on study design in rRNA surveys

Interesting new paper in PLoS One:  PLOS ONE: Taxonomic Classification of Bacterial 16S rRNA Genes Using Short Sequencing Reads: Evaluation of Effective Study Designs

Abstract: Massively parallel high throughput sequencing technologies allow us to interrogate the microbial composition of biological samples at unprecedented resolution. The typical approach is to perform high-throughout sequencing of 16S rRNA genes, which are then taxonomically classified based on similarity to known sequences in existing databases. Current technologies cause a predicament though, because although they enable deep coverage of samples, they are limited in the length of sequence they can produce. As a result, high-throughout studies of microbial communities often do not sequence the entire 16S rRNA gene. The challenge is to obtain reliable representation of bacterial communities through taxonomic classification of short 16S rRNA gene sequences. In this study we explored properties of different study designs and developed specific recommendations for effective use of short-read sequencing technologies for the purpose of interrogating bacterial communities, with a focus on classification using naïve Bayesian classifiers. To assess precision and coverage of each design, we used a collection of ~8,500 manually curated 16S rRNA gene sequences from cultured bacteria and a set of over one million bacterial 16S rRNA gene sequences retrieved from environmental samples, respectively. We also tested different configurations of taxonomic classification approaches using short read sequencing data, and provide recommendations for optimal choice of the relevant parameters. We conclude that with a judicious selection of the sequenced region and the corresponding choice of a suitable training set for taxonomic classification, it is possible to explore bacterial communities at great depth using current technologies, with only a minimal loss of taxonomic resolution.

Not sure I like everything in the paper.  For example, they focus on naive Bayesian classification methods … when (of course) I prefer phylogenetic methods.  But that is a small issue.  Overall there is a lot of useful detail in here about rRNA based taxonomic studies.  I note – some of this probably applies to metagenomic studies as well … perhaps this group will do a comparable analysis of metagenomics next?

Mizrahi-Man O, Davenport ER, Gilad Y (2013) Taxonomic Classification of Bacterial 16S rRNA Genes Using Short Sequencing Reads: Evaluation of Effective Study Designs. PLoS ONE 8(1): e53608. doi:10.1371/journal.pone.0053608

I note – if you want to catch up / learn / research metagenomics and phylogeny or classification check out the Mendeley group I started on the topic:

http://www.mendeley.com/groups/1152921/phylogenetic-and-related-analyses-of-metagenomic-data/widget/29/3/

RIP Carl Woese: Collecting posts / notes / other information about my main science hero here

My tribute to Carl Woese 12/30/12

Sadly, Carl Woese has passed away.  I am collecting some links and posts about him here in his memory.  He was without a doubt the person who most influenced my career as a scientist.

News stories about Woese’s passing

Some of my posts about Woese

Woese Tree of Life pumpkin (by J. Eisen)

Storification of Tweets and other posts about his passing //storify.com/phylogenomics/rip-carl-woese.js?template=slideshow[View the story “RIP Carl Woese” on Storify]

Other posts worth reading about Woese’s passing

Some videos with Woese 





Miscellaneous

My graduate student Russell Neches used a laser to etch a picture of Carl Woese on a piece of toast.

http://www.mendeley.com/groups/2940711/papers-by-carl-woese/widget/21/3/

People not Projects: the Moore Foundation continues to revolutionize marine microbiology w/ its Investigator program

People not Projects.

It is such a simple concept.  But it is so powerful.  I first became aware of this idea as it relates to funding scientific research in regard to the Howard Hughes Medical Institute’s Investigator program.  Their approach (along with a decent chunk of money) has helped revolutionize biomedical science.  And thus I was personally thrilled to see the introduction of this concept in the area of Marine Microbiology a few years back with the Gordon and Betty Moore Foundation’s “Marine Microbiology Initiative Investigator” program.  Launched in 2004 it helped revolutionize marine microbiology studies in the same way HHMI’s investigator program revolutionized biomedical studies.

The first GBMF MMI Investigator program ran from 2004 -2012. And the people supported were pretty darn special:

Now I am I suppose a little biased in this because at the same time GBMF launched this program they also put a bunch of money into the general area of Marine Microbiology and I have been the recipient of some of that money.  For example, I got a small amount of money as part of the GBMF Funded work at the J. Craig Venter Institute on the Sargasso Sea and Global Ocean Sampling metagenomic sequencing projects and also had a subcontract from UCSD/JCVI to do some work as part of the “CAMERA” metagenomic database project.  I ended up being a coauthor on a diverse collection of papers associated with these projects including Sargasso metagenome and this review, and GOS1GOS2 and my stalking the 4th domain paper.

I am also a bit biased in that I have worked with many of the people on the initial MMI Investigator list some before, some after the awards including papers with Jen Martiny, Ed Delong, Alex Worden and Ginger Armbrust, and Mary Ann Moran.

But perhaps most relevant in terms of possible bias towards the Gordon and Betty Moore Foundation is that in 2007 my lab received funds through the MMI program for a collaborative project with Jessica Green and Katie Pollard for our “iSEEM” project on “Integrating Statistical, Ecological and Evolutionary analyses of Metagenomic Data” (see http://iseem.org) which was one of the most successful collaborations in which I have ever been involved.  This project produced something like a dozen papers and many major new developments in analyses of metagenomic data including 16S copy correction, sifting families, microbeDB, PD of metagenomes, WATERs, BioTorrents, AMPHORA. and STAP.  This project just ended but Katie Pollard and I just got additional funds from GBMF to continue related work.

So sure – I am biased.  But the program is simply great.  In the eight years since the initial grants the Gordon and Betty Moore Foundation has helped revolutionize marine microbiology.  And a lot of this came from the Investigator program and it’s emphasis on people not projects.  I note – the Moore Foundation has clearly decided that this “people not projects” concept is a good one.  A few years ago they partnered with HHMI to launch a Plant Sciences Investigator Program  which I wrote about here.

It was thus with great excitement that I saw the call for applications for the second round of the MMI Investigator program.  I certainly pondered applying.  But for many reasons I decided not to.  And today the winners of this competition have been announced and, well, it is an very impressive crew:

Some of the same crowd as the previous round.  Some new people.  Some people not there from the previous round.  All of them are rock stars in their areas especially if one takes into account how senior they are (the more junior people are stars in development).  And all have done groundbreaking work in various areas relating to marine microbiology.  The organisms covered here run the gamut including viruses, bacteria, archaea, and microbial eukaryotes.  The areas of focus covered range from biogeochemistry to ecosystem modeling with everything in between.  It really is an impressive group. Delong pioneered metagenomics and helped launch studies of uncultured microbes in the oceans.  Karl has led the Hawaii Ocean Time series and done other brilliant work.  Sullivan and Rohwer and pushing the frontiers of viral studies in the oceans.  Allen, Armbrust, and Worden are among the leaders in genomic studies of microbial eukaryotes in the marine environment.   Dubilier, Bidle, Fuhrman and Follows Stocker (double listed Follows in original post …) – though they focus on very different aspects of marine microbes – are helping lead the charge in understanding interactions across the domains of life in the marine environment.  Orphan, Saito, Deutsch, Follows and Pearson are on the cutting edge of biogeochemical studies and trying to link experimental studies of microbes to biogeochemistry of oceans.

The great thing about the “people not projects” concept is that the people funded here get to follow their own path.  They are not going to be constrained by the complications and sometime idiocy of the grant review process.  They in essence get to do whatever they want.  Freedom to follow their noses.  Or their guts.  Or whatever.  It is a refreshing concept and as mentioned above has been revolutionary in various areas of science.  There has been a slow but steady spread of the “people not projects” concept to various federal agencies too but it seems to be more of a private foundation type of strategy.  Federal Agencies are so risk averse in funding that this type of concept does not work well there.  I wish there was more.  But I am at least thankful for what HHMI and GBMF and Wellcome and Sloan and other private groups are doing in this regard.  Now – sure – all of these private foundations do not do everything perfectly.  They have blunders here and there like everyone else.  But without a doubt I think we need more of the People not Projects concept.
Oh – and another good thing.  GBMF is quite a big supporter of Open Science in it’s various guises.  So one can expect much of the data, software, and papers from their funding to be widely and openly available.   
It is a grand time to be doing microbiology largely due to revolutions in technology and also to changes in the way we view microbes on the planet.  It is an even grander time to be doing marine microbiology due to the dedication of the Gordon and Betty Moore Foundation to this important topic.  

Referring to 16S surveys as "metagenomics" is misleading and annoying #badomics #OmicMimicry

Aargh.  I am a big fan if of ribosomal RNA based surveys of microbial diversity.  Been doing them for 20+ years and still continue to – even though I have moved on to more genomic/metagenomic based studies.  But it drives me crazy to see rRNA surveys now being called “metagenomics”.

Here are some examples of cases where rRNA surveys are referred to as metagenomics:

I found these examples in about five minutes of googling.  I am sure there are many many more.  
Why does this drive me crazy?  Because rRNA surveys focus on a single gene.  They are not gnomicy in any way.  Thus it is misleading to refer to rRNA surveys as “metagenomics”.  Why do people do this?  I think it is pretty simple.  Genomics and metagenomics are “hot” topics.  To call what one is doing “metagenomics” makes it sound special.  Well, just like adding an “omic” suffix does not make ones work genomics – falsely labeling work as some kind of “omics” also does not make it genomics.
Enough of this.  If you are doing rRNA surveys of microbial communities – great – I love them.  But do not refer to this work as metagenomics.  If you do, you are being misleading, either accidentally or on purpose.    So I think I need a new category of #badomics – “Omic Mimicry” or something like that …
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Note – this post was spurred on by a Twitter conversation – which is captured below (note – I am certain I have complained about this before but cannot find a record of it …)

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Nice review on HiSeq/MiSeq rRNA sequencing from Caporaso et al #microbes

Quick post — nice review worth checking out: The ISME Journal – Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms

from Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G, Knight R.

A key part of the paper, with highlighting from me:-

These observations, in agreement with studies that have addressed this question directly (Kuczynski et al., 2010), suggest that increasing the sequencing depth is not likely to provide additional insight into questions of beta diversity, and we therefore argue that (for questions of beta diversity in particular) the decreased cost of sequencing should be applied to study microbial systems using many more samples, for example, in dense temporal or spatial analyses, rather than with many more sequences per sample.  Of course, if the objective is to identify taxa that are very rare in communities, deeper sequencing will be advantageous. Additionally we note that while as few as 10 sequences per sample may be useful for differentiating very different environment types (for example, soil and feces), as environments become more similar (for example, two soil samples of different pH) more sequences will be required to differentiate them.