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Pyrosequences from deep anoxic cenotes

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Study design

This study describes the analysis of 16S rRNA amplicon barcoded pyrosequences in a multiplex sample run. The purpose of the experiment is to not only explore the diversity of beautiful microbial biomats, which coat the walls of deep, water-filled, cenotes in Northeastern Mexico, but also to see how communities cluster based on the geochemistry of the system. For this study, I ran mothur on a computer with 8 processors, 16 gigs of RAM, and running CentOS linux.

Viewing and trimming sequences

The first thing I like to do is look at my sequence length distribution- here is a histogram from my pyrosequence dataset:

Histogram.png

Work from Mitch Sogin's lab suggests that the sequencing error rate surrounding the normal distribution of sequence length is too high. From this histogram, I choose to trim sequences >300nt and <250nt (270 and 220 trimmed length respectively)

mothur > trim.seqs(fasta=sahl09.fna, oligos=sahl09.oligos, maxambig=0, minlength=220, maxlength=270, allfiles=T, maxhomop=10)

I don't trim by average quality score.

Using Mothur

I used bacterial primers 8F and 357R (which contains the barcode) on 11 separate DNA templates. I first found the unique sequences in the raw fasta file ZAC.tgz:

mothur > unique.seqs(fasta=ZAC.fas)

Alignment

I then attempted to align my sequences to the SILVA sequence space. Specifically, I used Pat's silva reference alignment as my template. The metadata included in the fasta file title line will throw mothur off. It needs to be stripped off so only the name field remains. Here is a script which does this for you: namestripper.zip I then aligned my sequences:

mothur > align.seqs(template=SILVA, candidate=ZAC.unique.fasta, ksize=9, processors=8)

I then checked my alignment in ARB and a recent SILVA database and found that my alignment, which should cover E. coli positions 8-357, was spread out over the length of the 16S rRNA gene. Pat observed that my sequences weren't oriented correctly because my barcode sequence was on the reverse primer. Therefore, I run:

mothur > reverse.seqs(fasta=ZAC.unique.fasta)

Once I aligned the reverse complement of my sequences, the alignment looked great in ARB. I then filtered out empty columns with the filter.seqs command.

mothur > filter.seqs(fasta=ZAC.unique.align, vertical=T)

Distance matrix and clustering

I then created a distance matrix with the dist.seqs command from my aligned sequences. I wasn't interested in sequences less related than 90%. Our server has 8 processors available and I use all of them for the alignment.

mothur > dist.seqs(fasta=ZAC.unique.filter.fasta, cutoff=0.1, calc=eachgap, processors=8)

This step took an hour or so. I then read the distance matrix with the read.dist command, using the "names" file that I generated during the unique.seqs step:

mothur > read.dist(column=ZAC.unique.filter.dist, name=ZAC.names, cutoff=0.1)

I then clustered my sequences with:

mothur > cluster()

I then read the OTUs at 3% distance with:

mothur > read.otu(list=ZAC.unique.filter.list, label=0.03, group=ZAC.groups)

(to see how I make the "group" file, see instructions below)

Part of the study design was to see how the samples cluster- I do this with:

mothur > tree.shared(calc=thetayc)

I also like to look at the diversity stats for each sample. The best way to do this is to run a batch command. Here is an example from one sample:

read.otu(list=ZAC.unique.filter.fn.0Mwater.list)
summary.single(calc=nseqs-sobs-shannon-chao-ace)

I included all of my samples in this batch file.

Results

Alignment

For an OTU based approach using pyrosequences, we want an alignment that works out of the box. While manually editing using ARB is feasible with small Sanger libraries, it becomes almost impossible with hundreds of thousands of sequences. I aligned and processed my dataset with 4 different alignment methods.

  1. The greengenes coreset
  2. Pat's SILVA seed database
  3. An alignment obtained from the comparative rRNA web site
  4. RDP pyrosequencing pipeline, which uses the Infernal aligner

Here are the results of OTUs obtained at a 97% identity level for three libraries. The number of sequences is the same for all 4 method

GG SILVA RDP comprRNA
Sample 1 247 268 249 271
Sample 2 835 831 826 856
Sample 3 2175 2205 2084 2318

For a conservative estimate, I was looking for the alignment which provides the fewest number of OTUs. This may indicate that this method did the best job of aligning the hypervariable V2 region. Based on these results, I chose the RDP alignment. You do need to modify the alignment before you create the distance matrix because the RDP alignment contains internal dots. You need to convert these to dashes, and then not count the terminal gaps in the distance calculation.

Making groups file

This is an alternative method to generate the 'group' file needed by many Mothur applications: it requires ARB

  1. Start ARB
  2. Click 'create and import'
  3. Find either your aligned or unaligned fasta file and import it
  4. Once your sequences are imported, the Search and Query window opens, with all of your sequences marked
  5. Click 'Search species' (on the left side of the window) and 'that are marked' (on the right side), hit the search button
  6. Now click on 'Keep species' (left) 'that match the query' (right), in the search bar, enter specific characters for one environment followed by the wild card (*) in the field below; for example, if you have 100 sequences labeled ENV1, ENV2,....ENV100, type ENV* in the search bar and hit the search button
  7. Now only sequences from one environment are selected, now click 'write to fields of listed'. Pick an unused field, such as 'acc', and type in your environment name, click on write. DO NOT INCLUDE ANY SPACES IN YOUR ENVIRONMENT NAME. Mothur doesn't like those.
  8. If you then go back to the Search and Query window, click on 'Search species' 'that are marked', you should see all of your sequences once again. Repeat this procedure for each environment in your dataset.
  9. Under the 'Tree' heading at the top of the main screen, select 'Select visible info (NDS)'
  10. Make sure only the fields 'name' and 'acc' are selected. Close window.
  11. Click File-> Export -> Export fields using NDS
  12. Name your file, click the 'tab for columns' button, and hit save.
  13. Voila!