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Analyzing H1N1 swine-origin Influenza A isolates

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H1N1 influenza virus.

The swine-origin Influenza A virus (S-OIV) has spread rapidly around the world with 175 countries now reporting confirmed cases. Here we use mothur to analyze all full-length Hemagglutinin (HA) sequences available at the NCBI Influenza Virus Resource as of August 28, 2009 in order to assess if current sampling efforts are sufficient to capture the diversity of this protein. . This protein is of particular interest as it is responsible for binding to receptor sites on a target cell.

Data Preparation

A total of 599 full-length S-OIV HA sequences were obtained from the NCBI Influenza Virus Resource. These sequences were collected between March 30 to July 28, 2009. Due to the short collection period, sequences are highly similar and possess only a single insertion. A manual alignment of these sequences is possible, but we used MUSCLE version 3.7 with a gap opening penalty of 20 and a gap extension penalty of 0 to produce the desired alignment. Your can download the aligned S-OIV sequences here.

Generating a distance matrix

We will begin by identifying unique sequences in our dataset. This can significantly reduce processing and memory requirements as mothur will perform downstream calculations only on the unique sequences. The following command identifies unique sequences:

mothur > unique.seqs(fasta=HA.fasta)

This will generate two files:

  • HA.unique.fasta: a FASTA file containing the 259 unique sequences in our dataset
  • HA.names: a name file used by mothur to determine which sequences are identical to each other

We can now use the dist.seqs command to generate a distance matrix indicating the uncorrected pairwise distances between our sequence. There are a few sequences in our dataset missing a few initial nucleotides. The parameter countends should be set to false (F) to indicate that missing data at the start or end of a sequence should not be treated as an insertion/deletion. As our sequences are highly similar, we will not consider operational taxonomic units (OTUs) with distances larger than 0.01 in our downstream analysis. In order to save memory, we can tell mothur to not bother saving any distances larger than 0.01 by using the cutoff parameter. The distance matrix is generated with the following command:

mothur > dist.seqs(fasta=HA.unique.fasta, countends=F, cutoff=0.01)

This will generate a single file:

Clustering sequences

We can now use our distance matrix to assign sequences to OTUs. A nice feature of mothur is that it can quickly create OTUs at different distances. For this analysis, we will only consider OTUs at distances up to 0.01. To create OTUs, we need to execute the cluster command after reading in our distance matrix with the read.dist command:

mothur > read.dist(column=HA.unique.dist, name=HA.names, cutoff=0.01, precision=1000)
mothur > cluster(cutoff=0.01, precision=1000)

Notice that we pass the read.dist command our names file using the name parameter. This ensures all our sequences will be clustered into OTUs. The default precision used by mothur is 1e-2. Since we are interested in OTUs between 0 and 0.01 we will increase the precision to 1e-3 by setting the precision parameter to 1000. The cluster command generates three files:

  • HA.unique.fn.list: a list file indicating the sequences that cluster together within an OTU
  • HA.unique.fn.sabund: a species abundance file containing information useful for producing species-abundance plots
  • HA.unique.fn.rabund: a rank-abundance file containing information useful for producing rank-abundance plots

Analyzing global richness and sampling effort

In this section, we will use the single sample analyses commands provided in mothur to examine the richness of collected S-OIV HA proteins and to determine if adequate sampling has been performed to fully capture the global richness.

Richness summary

To begin we will create a summary table indicating the estimated richness of our dataset under different richness calculators and OTU definitions. This can be done by loading in our OTU data using the read.otu command and then using the the summary.single command to generate the summary:

mothur > read.otu(list=HA.unique.fn.list)
mothur > summary.single(calc=nseqs-sobs-chao, label=unique-0.001-0.003-0.005-0.008)

This will generate a single file:

  • HA.unique.fn.summary: a summary file indicating the results of each of the specified calculators for each line in the OTU file

The summary file gives us the following table that indicates that our dataset consists of 599 sequences that form 257 clusters consisting of identical sequences, 117 clusters of sequences within a distance of 0.001, 49 clusters of sequences within a distance of 0.003, 15 clusters of sequences within a distance of 0.005, and 7 clusters of sequences within a distance of 0.008. The richness and 95% confident intervals determined by the Chao calculator are also given.

Reads unique 0.001 0.003 0.005 0.008
OTU Chao1 OTU Chao1 OTU Chao1 OTU Chao1 OTU Chao1
599 257 1362 (933, 2064) 117 259 (190, 392) 49 104 (67, 210) 15 22 (16, 57) 7 13 (7, 44)

These results clearly show that our sequences are highly similar and indicate that downstream analysis should consider OTUs defined by distances of 0.003 or less.

Generating a heatmap

To investigate how many sequences fall within each OTU we can generate a heatmap using the heatmap.bin command:

mothur > heatmap.bin(scale=linear, label=unique-0.001-0.003)

By default, this command will produce a heatmap for each OTU distance defined in our list file. We can use the label parameter to specify that we are only interested in OTUs defined at a distance of 0.0 (unique), 0.001, and 0.003. The scale parameter is used to indicate if the heatmap coloring should use log10 (default), log2, or linear scaling. This command will generate three SVG files:

  • HA.unique.fn.unique.heatmap.bin.svg: a heatmap showing the number of sequences where OTU clusters contain identical sequences
  • HA.unique.fn.0.001.heatmap.bin.svg: a heatmap showing the number of sequences where OTU cluster contains sequences within 0.001
  • HA.unique.fn.0.003.heatmap.bin.svg: a heatmap showing the number of sequences where OTU cluster contains sequences within 0.003

The heatmap is given below. Black lines indicate OTUs containing a single sequence whereas pure red lines indicate OTUs with 151 (unique), 207 (d=0.001), or 49 (d=0.003) sequences.

Heatmaps indicating number of sequences in each OTU. Heatmap goes from black to red.

Generating a rarefaction curve

Here we will generate a rarefaction curve in order to determine if a sufficient number of S-OIV HA sequences have been collected:

mothur > rarefaction.single(calc=sobs-chao, label=unique-0.001-0.003, freq=10)

The freq parameter indicates that we want to check how the number of identified OTUs changes as we continually increase our sampling effort by 10 samples. This command will generate two files:

We can use R to plot the data contained in these files. The following commands will generate the rarefaction curves shown below:

data<-read.table(file="HA.unique.fn.rarefaction", header=T)
plot(x=data$numsampled, y=data$unique, xlab="Number of Sequences Sampled",ylab="OTUs", type="l", col="red", font.lab=3)
lines(x=data$numsampled, y=data$X0.001, type="l", col="green", font.lab=3)
lines(x=data$numsampled, y=data$X0.003, type="l", col="blue", font.lab=3)
legend(x=1, y=257, c("unique", "0.001", "0.003"), c("red", "green", "blue"))

The rarefaction curve for clusters of unique sequences indicates that the number of OTUs observed is increasing almost linearly with sampling effort. This indicates that more sampling is required if we wish to fully capture the global diversity of the S-OIV HA protein. However, when OTUs are defined at a distance of 0.003 we observe that the rarefaction curve is approaching a horizontal line indicating that current sampling efforts are sufficient.

Rarefaction curve for S-OIV HA sequences.

Analyzing sampling effort in New York state

A 197 of the 599 sequences in our dataset were collected in New Yorkstate . Here we will examine the sampling effort within New York state by taking advantage of mothur's notion of a group. A group file specifies which sample a sequence belongs to. This HA groups file indicates which samples were collected in New York state or elsewhere. In order to create a rarefaction curve for just the New York sequences we need to create an OTU file for this group:

mothur > read.otu(list=HA.unique.fn.list, group=HA.groups)

This will generate three files:

We can now create a rarefaction curve for the New York sequences:

mothur > read.otu(rabund=HA.unique.fn.New_York.rabund)
mothur > rarefaction.single(calc=sobs-chao, label=unique-0.001-0.003, freq=10)

This will generate two files:

Plotting of this data using R is described above. Similar to our results on the full dataset, we see that sampling effort needs to be increased if we wish to use OTUs defined by clusters of unique sequences, but appears to be adequate if downstream analyses use OTUs defined at a distance of 0.003 or greater.

Rarefaction curve for S-OIV HA sequences collected in New York.

Conclusions

Our results suggest that current sampling efforts are insufficient to capture the complete global diversity of the S-OIV HA protein or the diversity within New York state. As such, it is recommended that downstream analyses use OTUs define at a distance of 0.003 or greater where current sampling efforts appear sufficient.