Just a quick follow up to my recent post on How did I miss this? The botrytized wine microbiome … from #UCDavis colleague David Mills. There is a similar paper from the same group also in PLoS One from about the same time: PLOS ONE: Brewhouse-Resident Microbiota Are Responsible for Multi-Stage Fermentation of American Coolship Ale. What a job — microbes, ales and wines, and sequencing. One of the few times when reading a paper where I have said “I wish that was me doing that work.” … must look into getting involved in such studies …
Tag: Misc.
How did I miss this? The botrytized wine microbiome … from #UCDavis colleague David Mills
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| From here. |
Fun use of next generation sequencing in this paper: PLOS ONE: Next-Generation Sequencing Reveals Significant Bacterial Diversity of Botrytized Wine. They used sequencing to characterize the diversity of microbes associated with botrytized wine (wine produced from grapes infected with the mold Botrytis cinerea. They focused in particular on Dolce wine (not 100% sure what this is but I think it is wine from the Dolce winery …). And they focused in particular on the bacteria associated with this wine as it was being produced. Anyway … I am no food/drink microbiologist .. but this seems cool.
Important & neglected aspect of lab studies of animals : effect of habitat change on microbiome
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| By Aaron Logan via Wikipedia |
Very very interesting paper came out recent from some colleagues at UC Davis: PLOS ONE: Routine Habitat Change: A Source of Unrecognized Transient Alteration of Intestinal Microbiota in Laboratory Mice
Abstract: The mammalian intestine harbors a vast, complex and dynamic microbial population, which has profound effects on host nutrition, intestinal function and immune response, as well as influence on physiology outside of the alimentary tract. Imbalance in the composition of the dense colonizing bacterial population can increase susceptibility to various acute and chronic diseases. Valuable insights on the association of the microbiota with disease critically depend on investigation of mouse models. Like in humans, the microbial community in the mouse intestine is relatively stable and resilient, yet can be influenced by environmental factors. An often-overlooked variable in research is basic animal husbandry, which can potentially alter mouse physiology and experimental outcomes. This study examined the effects of common husbandry practices, including food and bedding alterations, as well as facility and cage changes, on the gut microbiota over a short time course of five days using three culture-independent techniques, quantitative PCR, terminal restriction fragment length polymorphism (TRFLP) and next generation sequencing (NGS). This study detected a substantial transient alteration in microbiota after the common practice of a short cross-campus facility transfer, but found no comparable alterations in microbiota within 5 days of switches in common laboratory food or bedding, or following an isolated cage change in mice acclimated to their housing facility. Our results highlight the importance of an acclimation period following even simple transfer of mice between campus facilities, and highlights that occult changes in microbiota should be considered when imposing husbandry variables on laboratory animals.
I personally think that we as a community are going to have to come to grips with the fact that the microbial communities in / on research organisms (of all kinds) may have a profound effect on experimental results. This may explain many of the differences seen in experiments between facilities or over time within a facility. In general, I think either controlling the microbes more carefully in lab experiments (e.g., using defined flora) or at least monitoring them is going to be very important to best interpret studies of plants and animals in the lab (or for that matter – in the field too). Anyway -this paper is a tiny window into one of the ways that controlling for microbiomes may be important in lab studies.
Citation: Ma BW, Bokulich NA, Castillo PA, Kananurak A, Underwood MA, et al. (2012) Routine Habitat Change: A Source of Unrecognized Transient Alteration of Intestinal Microbiota in Laboratory Mice. PLoS ONE 7(10): e47416. doi:10.1371/journal.pone.0047416
Guest post on "CHANCE" ChIP-seq QC and validation software
Guest post by Aaron Diaz from UCSF on a software package called CHANCE which is for ChIP-seq analyses. Aaron wrote to me telling me about the software and asking if I would consider writing about it on my blog. Not really the normal topic of my blog but it is open source and published in an open access journal and is genomicy and bioinformaticy in nature. So I wrote back inviting him to write about it. Here is his post:
CHANCE: A comprehensive and easy-to-use graphical software for ChIP-seq quality control and validation

Our recent paper presents CHANCE a user-friendly software for ChIP-seq QC and protocol optimization. Our user-friendly graphical software quickly estimates the strength and quality of immunoprecipitations, identifies biases, compares the user’s data with ENCODE’s large collection of published datasets, performs multi-sample normalization, checks against qPCR-validated control regions, and produces publication ready graphical reports. CHANCE can be downloaded here.
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An overview of ChIP-seq: cross-
linked chromatin is sheared,
enriched for a transcription factor
or epigenetic mark of interest
using an antibody, purified and
sequenced.
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Chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq) is a powerful tool for constructing genome wide maps of epigenetic modifications and transcription factor binding sites. Although this technology enables the study of transcriptional regulation with unprecedented scale and throughput interpreting the resulting data and knowing when to trust the data can be difficult. Also, when things go wrong it is hard to know where to start when troubleshooting. CHANCE provides a variety of tests to help debug library preparation protocols.
One of the primary uses of CHANCE is to check the strength of the IP. CHANCE produces a summary statement which will give you an estimate of the percentage of the IP reads which map DNA fragments pulled down by the antibody used for the ChIP. In addition to the size of this signal component within the IP CHANCE reports the fraction of the genome these signal reads cover, as well as the statistical significance of the genome wide percentage enrichment relative to control in the form of a q-value (positive false discovery rate). CHANCE has been trained on CHIP-seq experiments from the ENCODE repository by making over 10,000 Input to IP and Input to replicate Input comparisons. The q-value reported gives then the fraction of comparisons between Input sample techinical replicates that report an enrichment for signal in one sample compared to another equal to the user provided sample or greater. CHANCE identifies insufficient sequencing depth, PCR amplification bias in library preparation, and batch effects.
CHANCE identifies biases in sequence content and quality, as well as cell-type and laboratory-dependent biases in read density. Read-density bias reduces the statistical power to distinguish subtle but real enrichment from background noise. CHANCE visualizes base-call quality and nucleotide frequency with heat maps. Furthermore, efficient techniques borrowed from signal processing uncover biases in read density caused by sonication, chemical digestion, and library preparation.
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| A typical IP enrichment report. |
CHANCE cross-validates enrichment with previous ChIP-qPCR results. Experimentalists frequently use ChIP-qPCR to check the enrichment of positive control regions and the background level of negative control regions in their IP DNA relative to Input DNA. It is thus important to verify whether those select regions originally checked with PCR are captured correctly in the sequencing data. CHANCE’s spot-validation tool provides a fast way to perform this verification. CHANCE also compares enrichment in the user’s experiment with enrichment in a large collection of experiments from public ChIP-seq databases.
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| CHANCE has a user friendly graphical interface. |
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| How CHANCE might be used to provide feedback on protocol optimization. |
Gotta love those DNA extractions :)
Because I was so late to introduce myself, I can also talk about what we’ve been doing for the last few weeks. We’ve gone to the aquariums and collected samples, done some DNA extractions, and done PCR on our samples. Our samples came from salt water and fresh water tanks and include water, sediment, and gunk from the walls of the tanks. Our latest issue has come after PCR, while running the Gel. It seems like our issue might be all the way back in PCR A. I am very excited for the new aquatic systems that we will start sampling in the next couple weeks. We are hoping to start sampling the minute they load the tubs. Our hope is to sample very frequently in the first couple days because we know much of the microbial community will develop in this time. So what’s the point of that, you ask? Well we would love to study the succession of microbial communities in these new aquatic systems. That’s all for now!
Aquarium Project
So the project is finally in full swing! After a couple of weeks of practice sampling and getting used to different protocols for extracting, purifying and amplifying the DNA, we have now moved on to working with our real samples. We have extracted DNA from 18 samples (includes replicates of 3) from the tropical tank and the cold water tank. Funnily, the hardest part of the project so far has been running gels. We initially hit a couple of problems such as thickness of the gel, a loose wire in a gel box and differences in loading buffers and dyes. However, now that Akshay is going to be coming in, hopefully he can give us a couple of hints from his experience from IGEM, cause god knows how many gels he had to make this summer 😛
Aquarium Blog Post 1 (#longoverdue)
So after a long and intense summer and first half of fall quarter, the iGEM international competition came and concluded. So now I get the chance to walk down the lab (towards the gel room!) a few more feet and start work on the aquarium project I am helping with in the Eisen Lab. I can now devote more time to the processes of the project, and not just be involved with going to retrive samples from the aquarium, and trying to figure out where we keep our Taq Polymerase. I will try and come in the lab in the next few days, and figure out the workflow and how to get this project going. I am very excited for the project as well as hoping for some actual success in our process. This is the first of many blog posts as well, so stay tuned for the next segment of our project!
Thank you interwebs: help proving fungi are cool
Well, am teaching three lectures this week on Fungal Diversity for BIS002C at UC Davis. And I decided tonight to ask the internet for help finding cool new stories on fungi. And boy did the internet come through in the clutch. Thanks internet. See Storification of Twitter and Facebook discussions below:
http://storify.com/phylogenomics/fungi-are-cool.js[View the story “Fungi are cool” on Storify]
Fungi are cool
Storified by Jonathan Eisen · Sun, Nov 04 2012 23:13:14
Quick post: nice microbial genomes database: MGBD (hat tip to Google Scholar Updates)
Just discovered this paper: MBGD update 2013: the microbial genome database for exploring the diversity of microbial world. Seems to be a useful microbial genomes database with some nice associated tools. Among the potentially useful features:
General Ortholog Table
Select your own organisms for a Custom Ortholog Table
Add your own genome in My MBGD Mode
And more. Anyway – worth checking out.
I note – I found out about this via Google Scholar Updates:
For more on Scholar Updates see here.
You win some, you lose some
Our project is starting to pick up! After our initial sampling/sequencing period, we realized that there is actual DNA we can work with from the tanks. This past week, we started our actual sample collecting from the tropical tank. We collected 3 sets of samples from the sediment, walls, and water. Throughout the week, we extracted the DNA and ran PCR on all 9 samples (plus one negative control). Today, we completed the gel electrophoresis and got some unpleasant results. Unfortunately, we couldn’t see the primer bands and the DNA bands didn’t show up like we thought they would. This means something went wrong in our PCR, but we don’t know if it was during PCR16SA or PCR16SB. Well, it’s back to the drawing board! Starting next week, we will be re-running the PCR on the 9 samples and possible collecting more samples from other tanks.
Although this week’s results were a bust, we know that there is definitely some DNA present that we can work with. I’m sure we’ll be finding some pretty cool things as we continue sampling and sequencing. 🙂







