After Spring Break


okay – hopefully you all managed to work your way through Qiime in some form or another.  If so, you will have generated a file within your data folder called “otus” and within that folder there will be a file called “otu_table.txt”.  You can open that table in excel and see that you have the # of reads for each sequence by sample and the last column in that sheet shows the identification of the sequence.  If you haven’t been able to get this far – you can go to our class folder (go to “assignments” tab and click on “class library” link) and download an excel spreadsheet that you will find there in Qiime Folder/duke454_wang_080511/otus/Organized_OTU_Table.  Here I have already organized the data in order of sample ID and on the 2nd tab have counted the unique OTUs by sample ID.

As your assignment for our first class after spring break, you should explore this data together with potential environmental correlates (see “Qiime Folder/duke454_wang_080511/Environmental Data from Streans”) of samples JW1-JW35 to answer a question.  You can either devise your own question OR by default, find out if there is any correlation between the extent of watershed development and the OTU richness (=# unique OTUs) across these 35 streams.

Good luck!


Class February 21

In class today you are looking for variation in antimicrobial resistance across your pure cultures.  Everyone chooses a bacteria and does the following.

First, prepare a suspension of your culture in 500 μL sterile water.

Plate 100 μL of this pure culture suspension onto a fresh culture plate using sterile technique, spread with glass spreading rod.

When plate is dry dispense antibiotic disks onto the new culture plate.  Cover .

Then – prepare a 96 well plate by adding 100 μL culture broth to all wells.

Add an additional 100 μL to lanes 1 and 2 only

In rows 1-3 prepare a dilution sequence of 3 different antibiotics.  Add 10 μL of your culture suspension to each prepared well

Incubate for 24-48 hours

Finally – start a broth culture by putting a sterile loop sample of your pure culture into culture broth.  Label.

On Thursday you will each give us a 5 minute presentation on a bacteria from our set of class cultures.  Pick your bacteria and tell us about its morphology, phylogeny and ecology.  Please send your powerpoints to Emily ( by noon on Thursday.


Qiime cheat-sheet!

Greg Bonito kindly prepared a Qiime cheat sheet that you can use in addition to the Qiime tutorial that’s on line….

Microbial Ecology & Evolution   2/16/12

Analyzing 454 Data with Macqiime

Before we get started, there are some things you should know…
QIIME and other bioinformatic programs work with through Unix command line interface. This may sound intimidating, but if you take a few hours to learn some basic operations in your Unix/Linux/Terminal, it will save you *LOTS* of time and frustration in the future. There are plenty of great free online tutorials to help you out.  Here’s one the first of ~6,000,000 google hits for “unix tutorial”:

Also, you will want to have a good text editor to view, edit, and check your files. TextWrangler is a good free editor: If you have any troubles with input files with Qiime, check to make sure that files are saved with Unix (LF) Line breaks and Encoding with Unicode (UTF-8), and that there are no hidden endings on your file.

Getting Started

Open your Terminal (find in your Utilities folder in your Applications). In your terminal, navigate to your “qiime_tutorial-v1.4.0” folder. On my machine I type:

gregorybonito$ cd /Users/gregorybonito/macqiime/qiime_tutorial-v1.4.0

(My computer is named “gregorybonito” and the “$” indicates the UNIX prompt, so I don’t actually type those. The UNIX command “cd” stands for “change directory”.  You can save yourself the trouble of typing the entire path by locating the file in your “finder” or windows directory and dragging it into your terminal.  The path, as if by magic, appears.)

Now that we’re inside the right directory, we’re read to go!  You can type “ls” to view the files in your directory. Included should be the files that you will call with the following “macqiime”commands. The program will always look for input files starting from the directory that you are in and output files will be written to this directory, so if the program can’t find files that you KNOW are there, you should make sure that you are working from the correct directory. Files you will need to get started are a mapping file “Fasting_Map.txt”, a fasta format file of sequences “Fasting_Example.fna”, and a sequence quality file “Fasting_Example.qual”.Qiime Tutorial

Now run through the Qiime 16S data analysis pipeline on your own. Refer to the online tutorial for more background and further options: You can copy and paste the following commands in your terminal prompt to run through this tutorial.

1) “Check mapping file”

macqiime -m Fasting_Map.txt -o mapping_output -v

2) “Demultiplexing”

macqiime -m Fasting_Map.txt -f Fasting_Example.fna -q Fasting_Example.qual -o split_library_output

3) “Pick OTUs through OTU table”

macqiime -i split_library_output/seqs.fna -o otus

4) “OTU Heatmap”

macqiime -i otus/otu_table.txt -o otus/OTU_Heatmap/

5) “OTU Network”

macqiime -m Fasting_Map.txt -i otus/otu_table.txt -o otus/OTU_Network

6) “Make Taxa Summary Charts“

macqiime -i otus/otu_table.txt -o wf_taxa_summary -m Fasting_Map.txt

You will need to create a .txt file for the following rarefaction script. To do this, open up TextWrangler and type:

alpha_diversity:metrics shannon,PD_whole_tree,chao1,observed_species

In the same directory save this file as: alpha_params.txt

[with Unix (LF) Line breaks and Encoding with Unicode (UTF-8)]

7)  “Alpha rarefaction”

macqiime -i otus/otu_table.txt -m Fasting_Map.txt -o wf_arare/ -p alpha_params.txt -t otus/rep_set.tre -a

8) “Beta diversity and plots”

macqiime -i otus/otu_table.txt -m Fasting_Map.txt -o wf_bdiv_even146/ -t otus/rep_set.tre -e 146 -a

9) “Jackknifed beta diversity”

macqiime -i otus/otu_table.txt -t otus/rep_set.tre -m Fasting_Map.txt -o wf_jack -e 110 -a

10) “Make Bootstrapped Tree”

macqiime -m

11) “Make Bi-Plots”

macqiime -i wf_bdiv_even146/unweighted_unifrac_pc.txt -m Fasting_Map.txt -t wf_taxa_summary/otu_table_L3.txt –n_taxa_keep 5 -o 3d_biplot

* The above is modified from Anthony Amends MOTHUR Tutorial for Fungal Community Analysis and Qiimes overview tutorial on 16S data.


In class on February 14th

Today we are investing our time along 2 trajectories

(1) lets phenotype your pure cultures

Gram staining of bacteria:  We want to classify your bacteria as gram positive or gram negative using gram staining.  To do this put 2 very small drops of water on a microscope slide (you may want to use your sterile loop to move a water droplet.  Now take your sterile loop and remove a sample from your bacterial colony.  Use your loop to place the sample in a water droplet and then use your loop to distribute the sample into the water droplet.  Now heat your slide so that the water evaporates off and the cells adhere to the slide surface.  Place your slide on the staining tray over the sink.  Add crystal violet dye for 30 seconds.  Then treat with an iodine solution which helps fix the crystal violet to the peptidoglycan in the cell wall.  Then, flood the slide with ethanol as a decolorizing agent.  Your goal here is to get rid of all the dye that isn’t bound to membranes. Finally stain with safranin for 30 seconds and then blot the slides to dry.  Now stick them under a microscope and see if it is gram positive (a thick double cell wall which binds the crystal violet and will be purple) or gram negative which doesn’t bind the crystal violet and so these will just be pink from the final dye step.

Fungi:  We want to mount tissue samples of our fungal samples on slides in a drop of 3% KOH under a coverslip.  Examine at 100X and characterize their morphology.  Refer to manuals and attempt to identify based on morphology.

(2) Set up new colony PCRs for bacteria (16S) and fungi (ITS) – this is your last chance so if you really had poor quality in your earlier runs then this is a good chance to try again.

Extract DNA –> Colony PCR –> Check on gel –> exoap –> BIG DYE –> Submit for sequencing

You will be giving a symposium on the culture diversity of our samples next week so it would be good to have sequence data.

3) QIIME Workshop with Greg Bonito.




Before class on February 14

Your sequence files are available for download under the “Urban Microbe Foray” tab. Please…

— Manually edit your sequences. If you have a Mac, you can download 4Peaks (click on “Features” for some basic instructions) to your own computer. You can also use Sequencher (online tutorials) on either of the computers in the teaching lab, or most other Biology Dept computers.

— Export your sequences to a FASTA-format file and identify your cultures by running a BLAST search against the NCBI database.

— Greg Bonito will be giving us a demo on analyzing 454 data and has provided a handout, which is linked to in the syllabus. Download QIIME to your computer and look through the tutorial before class.

— Remember to bring your laptops with you on Tuesday!

In class on February 9th

In class on the 9th we:

1- set up our Winogradsky column experiments by table

Table 1 – 6 homogenized soil cores assigned to 1 of 3 saltwater treatments (fresh, 1/2 strength seawater, full strength seawater) and 1 of 2 light treatments (full spectrum, infrared)

Table 2 – collected 6 intact soil cores from 3 distances away from a road (near, far, farther) and assigned 1 in each pair to a high NO3 enrichment treatment)

Table 3 – used soils from 4 restored wetlands varying in soil organic matter content to prepare 8 cores (a pair from each wetland).  One core in each wetland received supplemental C as potassium acetate.

THEN – we checked out how to identify our cultured specimens from sequence data.  We learned how to decide whether a sequence read is useful or not and how to trim it to improve its quality.  We then learned how to submit a sequence to BLAST (Basic Local Alignment Search Tool, the wikipedia of sequence data) or to use a more rigorous database system for bacterial DNA – the Ribosomal Database Project.  We used 2 of your sequences, but [HOMEWORK]  you guys are supposed to now figure out the identity (or likely identity) of all the sequences run by your table.

Finally – we discussed 3 papers selected by Alyse that examined bigoeographic variation in microbial diversity across the worlds oceans (Fuhrman et al. 2008); the response of microbial community structure to experimental manipulations of redox (Pett-Ridge & Fireston 2005); and an experimental test of whether there is a “home team advantage” whereby microbial communities are best able to degrade the organic matter from which they are isolated or whether microbial communities are sufficiently diverse that they are functionally redundant (Strickland et al. 2009).

In class February 7th


1. Colony PCRs:  complete Big Dye sequencing reactions started last week from your colony PCR.  Each table work as a group to make the 2 Cycle Sequencing Master Mixes (fungal or bacterial) using primers for fungi (ITS1) or bacteria (bactF).  Use 1 microliter of your ExoAP solution.

2. Pure cultures: check your streak plates, and re-streak any cultures that aren’t pure yet.  Once cleaned up, transfer to a clean MEA or TSA plate.  Keep tabs on which cultures have been PCR/sequenced, and which ones haven’t.  You’ll have a chance to re-PCR any cultures you missed later.

3. Discussion:  of general methods for analysis of microbial communities

4.  We’ll learn on Thursday how to work with individual sequence data and then next week we’ll talk about how to work with complex community data (like we will get soon from our pyrosequencing run).  Please download DNAStar from DUke OIT here

5. We had a good discussion comparing 3 papers that used similar statistical analyses to describe microbial community data but very different laboratory analyses to acquire that data.

Fang et al. 2001 used FAME (a PLFA approach) and BIOLOG plates in an attempt to compare the carbon degradation diversity (from BIOLOG plates) and the diversity of fatty acids (from FAME)  between soils immediately adjacent to the roots of several different plants.  No DNA at all here folks.

Braker et al. 2001 used T-RFLP of fragment lengths associated with 16s rRNA and nirS genes to examine differences in the community composition of bacteria from marine sediments (here composition = similarity in the abundance and presence of fragment lengths between communities).  They linked their fragment length data to preexisting clone library data to figure out something about identity. No sequence information here folks.

Gotelli et al 2011 used high throughput sequencing to compare the metagenome of bacteria and fungi between endophytic (inside root) and rhizosphere (near root) microbial communities of a small # of poplar trees.


Plans for Class on February 7th

On Tuesday we’re going to talk about how you work with sequence data, we’re going to get a chance to work with an example dataset and we’re going to have a discussion, led by Kris Voss about how we analyze community datasets.  This is your chance to learn a whole lot of acroynyms: PLFA, T-RFLPS, DGGE, phylochip, NMS, CCA, PCA – woo hoo!

In preparation for class all of you should read 2 papers that you can download here

Everyone Reads: Cao et al. Community Analysis Based Methods.  Chapter 11 in C. Hagedorn et al. (eds.), Microbial Source Tracking: Methods, Applications, 251pp

Table 1:  (that’s Kris, Alyse, Charlotte, Maria & Gillian) should read the Fang et al. 2001 paper about soil microbial community analysis using FAME and BIOLOG

Table 2: (that’s Auriel, Matt, Michael, Harrison & Katy) should read Braker et al. 2001 about sediment community analysis along redox gradients using T-RFLP analysis of 16s rRNA and nirS genese.

Table 3: (that’s Scott, Jeremy, Ryan, Lisa, and Kylie) should read Gotelli et al. 2011 which examines soil rhizosphere communities using 454 sequencing of 16s rRNA region for bacteria and D1/D2 region for fungi

Our goal for Tuesday will be to understand the kinds of information that can be generated about microbial communities using these different approaches and the ways in which community data can be analyzed and presented.  Think hard about the pros and cons of the approaches you are reading about in your table specific paper.  Come to class prepared to present your paper to peers.

Class on February 2nd

Today we have several goals:

1) we’re getting an overview and tour of the sequencing facility

2) we’re discussing the importance and limitations of culturing

3) we’re assessing our PCR by running PCR products on gels

Class Goals January 31st

Today in class our goal is to determine how successful our culturing effort has been so far

1. Pull out your culture plates.

2. Count and record the # of colonies per plate.  Use a marker to dot each colony as you count it to keep track.

3. Identify colony types (bacteria, fungi, yeast) using a microscope

4. Each person will perform a PCR of ~8 colonies

5. Select a few of your colonies for pure culture isolation – move isolates to new plates – you can do this for ~4 colonies

***KEEP CAREFUL TRACK of source sample, initial plate ID, &  new plate ID in your notes***

Hints (this is Emily’s summary of Rytas’s geshtalt)

If you are looking at circular colonies:

Bacteria: Each distinct circular colony should represent an individual bacterial cell or group that has divided repeatedly. Being kept in one place, the resulting cells have accumulated to form a visible patch. Most bacterial colonies appear white, cream, or yellow in color, and fairly circular in shape.

Yeasts: Yeast colonies generally look similar to bacterial colonies. Some species, such as Candida, can grow as white patches with a glossy surface.

bacteria vs. yeast – bacterial cells are smaller — you can’t see the individual cells of a bacterial colony at 100X – you can see them if they are yeast colonies

Molds: Molds are actually fungi, and they often appear whitish grey, with fuzzy edges. They usually turn into a different color, from the center outwards.

 If you are looking at filamentous colonies

Bacteria: things like Bacillus grown in dendritic patterns but you won’t be able to see distinguishable cells

Filamentous fungi – filamentous hyphae

**for some this may be hard unless you actually look at them under higher magnification**


Most colonies

Most colors

Biggest single colony