Getting started: Analysis of Microbial Communities

Transcription

1 Getting started: Analysis of Microbial Communities June 12, 2015 CLC bio, a QIAGEN Company Silkeborgvej 2 Prismet 8000 Aarhus C Denmark Telephone: Fax: support@clcbio.com

2 Contents Getting started: Analysis of Microbial Communities Prerequisites Results Additional statistical analyses

3 CONTENTS 3 Getting started: Analysis of Microbial Communities Mr. X is a suspect in a murder. The body was found on site 1, but Mr. X claims he was never there because he spent his entire week-end on site 2 and 3. Investigators have found a pair of rubber boots and a pair of hiking shoes at Mr. X s house. Both were dirty with soil on the soles. They took 3 samples of soil from each pair of shoes, and 2 samples of soil from each site: the crime scene (site 1) but also the 2 sites Mr. X claimed he was at (site 2 and 3). This tutorial will guide you through the different tools you can use to transform NGS data into forensic evidence. Indeed, each soil sample is characterized by a specific microbial community. To identify species present in soil samples, DNA is extracted from its microbial community, a region of the 16S gene is PCR amplified, and the resulting amplicon is sequenced using an NGS machine. The Microbial Genomics Module will then assign taxonomy to the reads by clustering them with representative sequences of pseudo-species called Operational Taxonomical Units (OTUs), and tally the occurrences of each OTU. Secondary analyses will further describe microbial communities by estimating alpha and beta diversities in the context of sample metadata. Prerequisites For this tutorial, you must be working with CLC Genomics Workbench 7.5 or higher, or Biomedical Genomics Workbench 2.1 or higher, and you must have installed the Microbial Genomics Module. You will be using a data set containing the sequences and metadata from a round robin trial of several soil types generated in a mock crime investigation as part of the MiSAFE project ( DNA was extracted, and a region of the 16S gene was PCR amplified using standard primers. The resulting amplicon was sequenced on an Illumina MiSeq machine (300 cycles, forward and reverse). The data set includes the following files: Sequence data: 12 data sets (two each for soil from locations 1, 2 and 3, and three each for soil on the suspects rubber boots GT-A and hiking shoes GT-B). The data was generated from the same MiSeq run and is composed of demultiplexed.fastq files. The original files have been down sampled to only contain 1/10th of the reads. While this improves the processing speed of the different analyses performed by the workbench, it will also impact negatively the statistical analysis. Metadata: the spreadsheet MetadataRoundRobin.csv contains metadata information. In this case we have only information about the origin of the samples. Sequencing primer sequences: 16s_primers_round_robin.clc for the 16S primers. Note that several databases are available for download in the Microbial Genomics Module. Database: 16S_97_otus_GG.clc.n Now that the prerequisites have been described, its time to start with the analyses of your samples. 1. Download the sample data from our website: MicrobialAnalysisData.zip. P. 3

4 CONTENTS 4 2. Start the workbench and go to File Import ( ) Illumina ( ) to import the 24 sequence files (ending with "fastq.gz"). Ensure that the import type under Options is set to Paired reads and that the radio button for Paired-end (forward-reverse) is selected. Minimum distance must be set to 200 and Maximum distance to 550. Click on the button labeled Next and select the location where you want to store the imported sequences. We recommend that you create a new folder called Illumina reads for example. You can check that you have now 12 files labeled as "paired". 3. Import the database sequence data by drag-and-drop the 16S_97_otus_GG.clc database and the 16s_primers_round_robin.clc primer sequences into your destination folder in your workbench. Now that all our data has been imported, you can start the workflows from the Microbial Genomics Module. The Data QC and OTU clustering workflow consists of 5 tools being executed sequentially (see a display of the workflow in figure 1). The input necessary to run the workflow are the reads you want to cluster. You can also specify a list of the primers that were used to sequence these reads. 1. Open the workflow Toolbox Workflows Data QC and OTU clustering. 2. Select the 12 sequence files form your folder called Illumina reads. 3. In the Optional Merge Paired Reads window, change the parameters to Mismatch cost 1, Minimum score 40, Gap cost 4 and Maximum unaligned end mismatches to 5 and click Next. 4. In the Trim Sequences window, select the list of primers sequences 16s_primers_round_robin.clc. Leave the other parameters as default and click on the button labeled Next. 5. In the Fixed Length Trimming window, leave the option as default as we want the length of trimming to be automatically detected by the software. 6. In the OTUclustering window, choose from the drop-down menu Reference based OTU clustering and select the file called 16S_97_otus_GG. Click on the button labeled Next. 7. Save your workflow outputs in a new folder called Data QC and OTU clustering. Click on the button labeled Finish. You can follow the progress of the workflow in the Processes bar below the toolbox. When the workflow is done, you will have 3 output files in your folder (figure 2). The file OTU(Table) is the one you will use as input for the Estimate Alpha and Beta Diversities workflow, but as these secondary analyses require metadata you will first use the Add Metadata to OTU Abundance Table tool. You can then run the Estimate Alpha and Beta Diversities workflow which consists of 5 tools and your newly generated OTU(Table) as input file (figure 3). 1. Select Toolbox OTUclustering ( ) Add Metadata to OTU Abundance Table ( ) and choose the OTU(Table) as input. P. 4

5 CONTENTS 5 Figure 1: Layout of the Data QC and OTU clustering workflow. Figure 2: Outputs of the Data QC and OTU clustering workflow. 2. Select the file describing the metadata on your local computer: MetatdataRoundRobin.csv and click on the button labeled Next. 3. Save your result in the Data QC and OTU clustering folder. It is a table that will overwrite the previous OTU(Table) file. The tables are similar to each other, but you have now the option to Aggregate samples based on the headers of the columns of your metadata file. Note: if you had previously open the OTU(Table), close it and reopen it to be able to have the aggregate option on the right side panel of the workbench. 4. Open Toolbox Workflows Estimate Alpha and Beta Diversities and select the OTU (Table). Click on the button labeled Next. P. 5

6 CONTENTS 6 Figure 3: Layout of the Alpha and Beta Diversities workflow. 5. In the Alpha analysis window, deselect everything but Number of OTUs. 6. In the Beta analysis window select D_0.5 UniFrac but deselect Bray-Curtis and Jaccard measures, unweighted and weighted UniFrac. 7. Save the data in a new folder called Estimate Alpha and Beta Diversities. Running this workflow will give at least 4 outputs (figure 4): an alignment of the OTUs, a phylogenetic tree of the OTUs, a diversity report for the alpha diversity and a PCoA for the beta diversity. Figure 4: Outputs of the Alpha and Beta Diversities workflow. Results The primary output of your analysis is an OTU abundance table annotated with the metadata. In this investigation, the metadata defines the origin of the different soil samples and allows the aggregation of the results to improve visualization of the results. In addition, the module offers several ways to look at your newly generated OTU clusters: the table itself, but also Stacked Bar Charts and Stacked Area Charts ( ) as well as Zoomable Sunbursts ( ). P. 6

7 CONTENTS 7 Open the OTU(Table) file from your Data QC and OTU clustering folder and click on the Stacked Bar Chart icon in the lower part of the workbench. In the right side panel, choose to aggregate samples by Type (figure 5). We observe a striking similarity between the GT-A profile found on the suspects rubber boots and the profile of the soil from Site 1, indicating that Mr. X was most likely lying when he pretended he had never been on Site 1. Figure 5: Aggregate samples based on metadata information. Now open the results of the alpha diversity analysis, called OTU(Table)(Filtered)(Alpha diversity): the plot contains the rarefaction results of the specified alpha diversity measure while each line corresponds to one sample. The coloring scheme can be set by double clicking on a graph and changing the color for lines one sample at a time. Note: you need to set different shades of a particular color to the different samples as the samples cannot have the exact same color. In the following graph (figure 6) we have chosen shades of blue for GT-A and shades of green for GT-B. The lines do not plateau, indicating that we would need more samples to reach a definite conclusion, but GT-A samples seem to have similar measures of alpha diversity as the sites 1, 2, and 3 while GT-B samples are clearly apart. We suspect that Mr.X was not wearing his hiking boots on any of the sites sampled. Figure 6: Results of the alpha diversity analysis measured using Number of OTUs as parameters. Finally, beta diversity estimates differences in species diversity between samples. The beta diversity analysis tool performs a Principal Component Analysis PCoA ( ) on the UniFrac P. 7

8 CONTENTS 8 distances (figure 7). Figure 7: Result of the beta diversity analysis. In a PCoA of the beta diversities, the soil samples cluster according to their origin. In this case, selecting PCoA as X axis and PCoA1 as Y axis allows for a better visualization of Site 1 and GT-A being clustered together, confirming a similarity between the 2 soils and thus confirming our suspicion that Mr. X was on site 1 with his rubber boots. Additional statistical analyses As a tool for assessing similarity between samples, a heat map and dendrogram can be helpful. In order to perform additional statistical tests, you can use the Convert to Experiment tool to assign categories to the samples from your OTU abundance table. 1. Select Toolbox OTUclustering ( ) Convert to Experiment ( ) 2. Choose OTU(Table) from the Data QC and OTU clustering folder as input, and which Metadata group to consider as factors (here Type) before saving the resulting experiment in a new folder you can call Statistics. The file is called OTU(Table)(experiment). 3. Select Toolbox Transcriptomics Analysis ( ) in CLC Genomics Workbench and Expression Analysis ( ) in Biomedical Genomics Workbench Quality control ( ) Hierarchical Clustering of Samples ( ) 4. Choose the OTU(Table)(experiment) as input. Click on the button labeled Next. 5. Select Euclidian distance and Average linkage and click on the button labeled Next. 6. Save your result in the Statistics folder. It will overwrite the OTU(Table)(experiment) file. If the OTU(Table)(experiment) table was previously opened in your workbench, close it and open it again. The Experiment table now displays a button at the bottom with a heat map icon. Selecting this view will display the heat map (figure 8). We can see that GT-A is again nested together with Site 1, confirming once more that the soil found on the rubber boots is extremely similar to the one sampled from site 1. Finally, you can assess the robustness of your results by running a PERMANOVA analysis on your samples. PERMANOVA can be used to measure effect size and significance of beta diversity. P. 8

9 CONTENTS 9 Figure 8: Heat map of the Hierarchical Clustering of Samples analysis. 1. Select Toolbox OTUclustering ( ) PERMANOVA ( ). 2. Choose OTU(Table) from the Data QC and OTU clustering folder as input and select Type as Metadata group. 3. Deselect Bray-Curtis and Jaccard measures and specify the phylogenetic tree (OTU(Table)(Filtered)align from the Estimate Alpha and Beta Diversities folder. Select D_0.5 UniFrac, deselect weighted and unweighted UniFrac, and leave the number of permutations to 99,999. Click on the button labeled Next. 4. Save the report in the Statistics folder. The result of the PERMANOVA analysis is a table (figure 9). The PERMANOVA confirms that the clusters are significant (p=0,00008), but with only two to three replicates for each sample or group, the clustering is not significant on pair-wise comparisons of the Types. The investigators will need more samples - and in particular from the rubber boots solesand site 1 - to transform this analysis into actual evidence! P. 9

10 CONTENTS 10 Figure 9: Result of the PERMANOVA analysis. P. 10