tutorial.md

layout	title	zenodo_link	questions	objectives	time_estimation	key_points	contributors	tags
tutorial_hands_on	16S Microbial analysis with Nanopore data	https://zenodo.org/record/4274812	How can we analyse the health status of the soil? How do plants modify the composition of microbial communities?	Use Nanopore data for studying soil metagenomics Analyze and preprocess Nanopore reads Use Kraken2 to assign a taxonomic labels	2H	We learned to use MinION Nanopore data for analyzing the health status of the soil We preprocessed Nanopore sequences in order to improve their quality	gallardoalba	nanopore

Introduction

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Healthy soils are an essential element in maintaining the planet’s ecological balance. That is why their protection must be considered a priority in order to guarantee the well-being of humanity. The alteration of microbial populations often precedes changes in the physical and chemical properties of soils, so monitoring their condition can serve to predict their future evolution, allowing to develop strategies to mitigate ecosystem damage.

Advances in sequencing technologies have opened up the possibility of using the study of taxa present in bacterial communities as indicators of soil condition. For such an approach to be possible, variations in microbial populations need to be less affected by spatial factors than by human-derived alterations, which has been confirmed by various investigations ({% cite Hermans2016 %}, {% cite Fierer2006 %}).

Among the environmental variables that have shown the greatest correlation with alterations in the composition of bacterial communities are soil pH, the carbon-nitrogen ratio (C:N), and the co-concentrations of Olsen P (a measure of plant available phosphorus), aluminium and copper (Fig. 1).

On the other hand, many species of microorganisms establish complex symbiotic relationships with plant organisms. The fraction of the substrate that is directly influenced by root secretions and associated microorganism is known as rhizosphere. Thus, for example, bacteria of the genus Bacillus, Pseudomonas or Burkholderia appear associated with the plant roots, protecting them from pathogenic microorganisms.

In this tutorial, we will use sequencing data obtained through the MinION sequencer (Oxford Nanopore Technologies) with two objectives: 1) evaluate the health status of soil samples and 2) study how microbial populations are modified by their interaction with plant roots. This example was inspired by {% cite Brown2017 %}.

Agenda

In this tutorial, we will cover:

1. TOC {:toc}

{: .agenda}

Background on data

Next, we will introduce some details about the datasets that we are going to use to perform the analysis.

The datasets

In this example, we will use a dataset originally hosted in the NCBI SRA database, with the accession number SRP194577. To obtain the samples, the DNA was extracted using the Zymo Research Kit, followed by PCR amplification of 16S rRNA genes. Sequencing was carried out on a Nanopore Minion device. We are going to use four datasets, corresponding to two experimental conditions:

• Soil:
  • Surface sample (0-5 cm): bulk_top.fastq.gz
  • Deep sample (10-15 cm): bulk_bottom.fastq.gz
• Rhizosphere:
  • Surface sample (0-5 cm): rhizosphere_top.fastq.gz
  • Deep sample (10-15 cm): rhizosphere_bottom.fastq.gz

{% icon question %} Question

Why do we sequence the 16S rRNA genes for analyzing microbial communities?

{% icon solution %} Solution

There are several reasons to use these genes as taxonomic markers. First, they are constituent parts of ribosomes, a piece of highly-conserved biological machinery, which makes them universally distributed. On the other hand, these genes present regions with varying degrees of sequence variability, which allows them to be used as a species-specific signature. {: .solution} {: .question}

Get data

{% icon hands_on %} Hands-on: Data upload

1. Create a new history for this tutorial

2. Import the files from Zenodo:

   • Open the file {% icon galaxy-upload %} upload menu
   • Click on Collection tab
   • Click of the Paste/Fetch button
   • Paste the Zenodo links and press Start and Build

   https://zenodo.org/record/4274812/files/bulk_bottom.fastq.gz
   https://zenodo.org/record/4274812/files/bulk_top.fastq.gz
   https://zenodo.org/record/4274812/files/rhizosphere_bottom.fastq.gz
   https://zenodo.org/record/4274812/files/rhizosphere_top.fastq.gz
   

   • Assign a name to the new collection: soil collection

   {% snippet faqs/galaxy/datasets_import_via_link.md %} {% snippet faqs/galaxy/datasets_import_from_data_library.md %}

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Assess datasets quality

Before starting to work on our datasets, it is necessary to assess their quality. This is an essential step if we aim to obtain a meaningful downstream analysis.

{% icon details %} FASTQ sequence quality format

FASTQ format is a text-based format for storing both a biological sequence and its corresponding quality scores. Both the sequence letter and the quality score are encoded with a single ASCII character for brevity. You can find more info in the Wikipedia article.

{: .details}

Quality control using FastQC and MultiQC

FastQC is one of the most widely used tools to check the quality of the samples generated by High Throughput Sequencing (HTS) technologies.

{% icon hands_on %} Hands-on: Quality check

1. {% tool FastQC %} with the following parameters:

   • {% icon param-collection %} "Dataset collection": soil collection

2. Rename the outputs as FastQC unprocessed: Raw and FastQC unprocessed: Web

3. {% tool MultiQC %} with the following parameters:

   • In "Results":

     • "Which tool was used generate logs?": FastQC

     • {% icon param-collection %} "Dataset collection":

     • FastQC unprocessed: Raw

4. Click on the {% icon galaxy-eye %} (eye) icon and inspect the generated HTML file

{: .hands_on}

FastQC provides information on various parameters, such as the range of quality values across all bases at each position. MultiQC allows summarizing the output of different outputs from FastQC.

In the first place, we are going to analyse the sequence length distribution of the different datasets.

{% icon question %} Question

1. Can you explain the sequence length distribution plot?

{% icon solution %} Solution

The main peak, around 1700 bp, corresponds approximately to the length of the gene coding for 16S rRNA. There is also a secondary peak around 200 bp, which may be due to truncated amplifications, or as a result of non-specific hybridization of primers used for PCR.

{: .solution} {: .question}

If we examine figure 3, we can see that up to 3000 bp the quality of our sequencing data is around a Phred score of 12, which is a relatively low value compared to other sequencing technologies. This is explained because Nanopore reads poses high error rates in the basecalled reads (10% as compared to 1% for Illumina). However, Nanopore sequencing generates very long reads (in theory only limited by the mechanisms of extraction of the genetic material), enabling the sequencing of the complete 16S rRNA gene, which makes it possible to identify bacterial taxa at higher resolution.

Let's have a look at figure 4, which shows the content of the GC by sequence.

{% icon question %} Questions

What can cause GC content to show a bimodal peak?

{% icon solution %} Solution

There are several possible causes for the presence of twin peaks in the GC content. One possible explanation is the presence of adapters in the sequence. Another possible cause is some kind of contamination, such as chimeras.

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{% icon comment %} Comments

For more information on the topic of quality control, please see our training materials here. {: .comment}

Improve the dataset quality

In order to improve the quality of our data, we will use two tools presented above, porechop and fastp.

Adapter and chimera removal with porechop

Nanopore sequencing technology requires to ligate adapters to both ends of genomic material to facilitate the strand capture and loading of a processive enzyme at the 5'end, boosting the effectiveness of the sequencing process. Adapter sequences should be removed because they can interfere with aligment of reads to 16S rRNA gene reference database, for which we will use the porechop tool ({% cite rrwick2017 %}).

{% icon comment %} Comment

If you are interested in Nanopore sequecing technology, you can find more information in {% cite Jain2016 %}.

{: .comment}

On the other hand, chimeric sequences are considered a contaminant and should be removed because they can result in artificial inflation of the microbial diversity. With Porechop you can eliminate them.

{% icon hands_on %} Hands-on: Remove adapters with porechop

1. {% tool Porechop %} with the following parameters:
   • "Input FASTA/FASTQ": {% icon param-collection %} soil collection
   • "Output format for the reads": fastq
2. Rename the output as soil collection trimmed

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Filter sequences with fastp

To increase the specificity of the analysis, we will select the reads with lengths between 1000 bp and 2000 bp, which are more informative from a taxonomic point of view, because they include both preserved and hypervariable regions of the 16S rRNA gene. In addition, sequences will be filtered on a minimum average read quality score of 9, according to the recommendations from {% cite Nygaard2020 %}. This stage will be carried out through the use of fastp ({% cite Chen_2018 %}), an open-source tool designed to process FASTQ files.

{% icon hands_on %} Hands-on: Filter sequence with fastp

1. {% tool fastp %} with the following parameters:

   • "Single-end or paired reads": Single-end - {% icon param-collection %} "Dataset collection": soil collection trimmed
     • In "Adapter Trimming Options":
       • "Disable adapter trimming": Yes
   • In "Filter Options":
     • In "Quality filtering options":
       • "Qualified quality phred": 9
     • In "Length filtering options":
       • "Length required": 1000
       • "Maximum length": 2000
   • In "Read Modification Options":
     • "PolyG tail trimming": Disable polyG tail trimming

2. Rename the output as soil collection processed

{: .hands_on}

Re-evaluate datasets quality

After processing the sequences, we are going to analyze them again using FastQC and MultiQC to see if we have managed to correct the anomalies that we had detected.

{% icon hands_on %} Hands-on: Quality check

1. {% tool FastQC %} with the following parameters:

   • {% icon param-collection %} "Dataset collection": soil collection processed

2. Rename the outputs as FastQC processed: Raw and FastQC processed: Web

3. {% tool MultiQC %} with the following parameters:

   • In "Results":

     • "Which tool was used generate logs?": FastQC

     • {% icon param-collection %} "Dataset collection":

     • FastQC processed: Raw

4. Click on the {% icon galaxy-eye %} (eye) icon and inspect the generated HTML file

{: .hands_on}

We can verify that after processing the samples, the GC content presents a unimodal distribution, which indicates that the anomalies in the sequences have been successfully eliminated (Figure 6).

Assign taxonomic classifications

One of the key steps in metagenomic data analysis is to identify the taxon to which the individual reads belong. Taxonomic classification tools are based on microbial genome databases to identify the origin of each sequence.

Taxonomic classification with Kraken2

To perform the taxonomic classification we will use Kraken2 ({% cite Wood_2019 %}). This tool uses the minimizer method to sample the k-mers (all the read's subsequences of length k) in a deterministic fashion in order to reduce memory constumption and processing time. In addition, it masks low-complexity sequences from reference sequences by using dustmasker.

{% icon comment %} Comments

Kraken2 uses a compact hash table, a probabilistic data structure that allows for faster queries and lower memory requirements. It applies a spaced seed mask of s spaces to the minimizer and calculates a compact hash code, which is then used as a search query in its compact hash table; the lowest common ancestor (LCA) taxon associated with the compact hash code is then assigned to the k-mer. You can find more information about the Kraken2 algorithm in the paper Improved metagenomic analysis with Kraken 2. {: .comment}

For this tutorial, we will use the SILVA database ({% cite Quast2012 %}). It includes over 3.2 million 16S rRNA sequences from the Bacteria, Archaea and Eukaryota domains.

{% icon hands_on %} Hands-on: Assign taxonomic labels with Kraken2

1. {% tool Kraken2 %} with the following parameters:

   • "Single or paired reads": Single
   • {% icon param-collection %} "Dataset collection": soil collection processed
   • "Print scientific names instead of just taxids": Yes
   • "Confidence": 0.1
   • In "Create Report":
     • "Print a report with aggregrate counts/clade to file": Yes
     • "Format report output like Kraken 1's kraken-mpa-report": Yes
   • "Select a Kraken2 database": Silva (Created: 2020-06-24T164526Z, kmer-len=35, minimizer-len=31, minimizer-spaces=6)

   {% icon comment %} Comment

   A confidence score of 0.1 means that at least 10% of the k-mers should match entries in the database. This value can be reduced if a less restrictive taxonomic assignation is desired. {: .comment}

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Analyze taxonomic assigment

Once we have assigned the corresponding taxa to each sequence, the next step is to properly visualize the data, for which we will use the Krona pie chart tool ({% cite Ondov_2011 %}). But before that, we need to adjust the format of the data output from Kraken2.

{% icon hands_on %} Hands-on: Adjust dataset format

1. {% tool Reverse %} with the following parameters:

   • {% icon param-collection %} "Dataset collection": Report: Kraken2 on collection

2. {% tool Replace Text %} with the following parameters:

   • {% icon param-collection %} "Dataset collection": Reverse on collection
   • In "Replacement":
     • {% icon param-repeat %} "Insert Replacement"
       • "Find pattern": \|
       • "Replace with": \t

3. {% tool Remove beginning %} with the following parameters:

   • {% icon param-collection %} "Dataset collection": Replace Text on collection

{: .hands_on}

Visualize the taxonomical classification with Krona

Krona allows hierarchical data to be explored with zooming, multi-layered pie charts. With this tool, we can easily visualize the composition of the bacterial communities and compare how the populations of microorganisms are modified according to the conditions of the environment.

{% icon hands_on %} Hands-on: Visualize metagenomics analysis results

1. {% tool Krona pie chart %} with the following parameters:
   • "What is the type of your input data": Tabular
   • {% icon param-collection %} "Dataset collection": Remove beginning on collection
   • "Provide a name for the basal rank": Bacteria

{: .hands_on}

Let's take a look at the result. Using the search bar we can check if certain taxa are present.

<iframe id="krona" src="krona_all.html" frameBorder="0" width="100%" height="900px">  </iframe>

{% icon question %} Questions

1. What can we say about the health status of the soil samples?
2. Are there significant differences in the bacterial population structure between soil samples and rhizosphere samples?

{% icon solution %} Solution

1. The low presence of Alphaproteobacteria (including members of the order Rhizobiales), as well as the abundance of Bacteroidetes and Gammaproteobacteria, suggest that the soil is highly exposed to phosphorus. This mineral is strongly associated with the anthropogenic activity as it is an important component of agrochemicals.
2. We can observe that there are important differences in the structure of the bacterial populations between the soil and rhizosphere samples. Particularly significant is the increase in phylum Plactomycetes, which are usually abundant in the rhizosphere.

{: .solution}

{: .question}

Conclusion

In this tutorial we used MinION Nanopore sequencing data to study the health status of soil samples and the structure of bacterial populations. The results indicate that the soil suffers some degree of erosion as a result of their exposure to agrochemicals. We have also been able to study how the composition of microbial communities is modified in presence of plant organisms.