Overview

In this tutorial we will perform a metatranscriptomics analysis based on the ASAIM workflow (Batut et al. 2018), using data from Kunath et al. 2018.

comment Note: Two versions of this tutorial

Because this tutorial consists of many steps, we have made two versions of it, one long and one short.

This is the shortened version. Instead of running each tool individually, we will employ workflows to run groups of analysis steps (e.g. data cleaning) at once. If you would like more in-depth discussion of each step, please see the longer version of tutorial

You can also switch between the long and short version at the start of any section.

Introduction

Microbiomes play a critical role in host health, disease, and the environment. The study of microbiota and microbial communities has been facilitated by the evolution of technologies, specifically the sequencing techniques. We can now study the microbiome dynamics by investigating the DNA content (metagenomics), RNA expression (metatranscriptomics), protein expression (metaproteomics) or small molecules (metabolomics):

New generations of sequencing platforms coupled to numerous bioinformatics tools have led to a spectacular technological progress in metagenomics and metatranscriptomics to investigate complex microorganism communities. These techniques are giving insight into taxonomic profiles and genomic components of microbial communities. Metagenomics is packed with information about the present taxonomies in a microbiome, but do not tell much about important functions. That is where metatranscriptomics and metaproteomics play a big part.

In this tutorial, we will focus on metatranscriptomics.

Metatranscriptomics analysis enables understanding of how the microbiome responds to the environment by studying the functional analysis of genes expressed by the microbiome. It can also estimate the taxonomic composition of the microbial population. It provides scientists with the confirmation of predicted open‐reading frames (ORFs) and potential identification of novel sites of transcription and/or translation from microbial genomes. Metatranscriptomics can enable more complete generation of protein sequences databases for metaproteomics.

To illustrate how to analyze metatranscriptomics data, we will use data from time-series analysis of a microbial community inside a bioreactor (Kunath et al. 2018). They generated metatranscriptomics data for 3 replicates over 7 time points. RNAs were enriched by rRNA depletion and treated with DNAse and library was prepared with the TruSeq stranded RNA sample preparation, which included the production of a cDNA library.

In this tutorial, we focus on biological replicate A of the 1st time point. In a follow-up tutorial we will illustrate how compare the results over the different time points and replicates. The input files used here are trimmed version of the original file for the purpose of saving time and resources.

To analyze the data, we will follow the ASaiM workflow and explain it step by step. ASaiM (Batut et al. 2018) is an open-source Galaxy-based workflow that enables microbiome analyses. Its workflow offers a streamlined Galaxy workflow for users to explore metagenomic/metatranscriptomic data in a reproducible and transparent environment. The ASaiM workflow has been updated by the GalaxyP team (University of Minnesota) to perform metatranscriptomics analysis of large microbial datasets.

The workflow described in this tutorial takes in paired-end datasets of raw shotgun sequences (in FastQ format) as an input and proceeds to:

1. Preprocess
2. Extract and analyze the community structure (taxonomic information)
3. Extract and analyze the community functions (functional information)
4. Combine taxonomic and functional information to offer insights into taxonomic contribution to a function or functions expressed by a particular taxonomy.

A graphical representation of the ASaiM workflow which we will be using today is given below:

comment Workflow also applicable to metagenomics data

The approach with the tools described here can also apply to metagenomics data. What will change are the quality control profiles and proportion of rRNA sequences.

Agenda

In this tutorial, we will cover:

1. Overview
2. Data upload
3. Preprocessing
   1. Quality control
   2. Data Cleaning
   3. Ribosomal RNA fragments filtering
   4. Interlace forward and reverse reads
4. Extraction of the community profile
   1. Community structure visualization
5. Extract the functional information
   1. Normalize the abundances
   2. Identify the gene families involved in the pathways
   3. Group abundances into GO slim terms
6. Combine taxonomic and functional information

Data upload

hands_on Hands-on: Data upload

1. Create a new history for this tutorial and give it a proper name

   tip Tip: Creating a new history

   Click the new-history icon at the top of the history panel

   If the new-history is missing:

   1. Click on the galaxy-gear icon (History options) on the top of the history panel
   2. Select the option Create New from the menu

   tip Tip: Renaming a history

   1. Click on Unnamed history (or the current name of the history) (Click to rename history) at the top of your history panel
   2. Type the new name
   3. Press Enter

2. Import T1A_forward and T1A_reverse from Zenodo or from the data library (ask your instructor)

   https://zenodo.org/record/3362849/files/T1A_forward.fastqsanger
   https://zenodo.org/record/3362849/files/T1A_reverse.fastqsanger
   

   tip Tip: Importing data via links

   • Copy the link location

   • Open the Galaxy Upload Manager (galaxy-upload on the top-right of the tool panel)

   • Select Paste/Fetch Data

   • Paste the link into the text field

   • Press Start

   • Close the window

   By default, Galaxy uses the URL as the name, so rename the files with a more useful name.

   tip Tip: Importing data from a data library

   As an alternative to uploading the data from a URL or your computer, the files may also have been made available from a shared data library:

   • Go into Shared data (top panel) then Data libraries

   • Find the correct folder (ask your instructor)

   • Select the desired files
   • Click on the To History button near the top and select as Datasets from the dropdown menu
   • In the pop-up window, select the history you want to import the files to (or create a new one)
   • Click on Import

   As default, Galaxy takes the link as name, so rename them.

3. Rename galaxy-pencil the files to T1A_forward and T1A_reverse

   tip Tip: Renaming a dataset

   • Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
   • In the central panel, change the Name field
   • Click the Save button

4. Check that the datatype is fastqsanger (e.g. not fastq). If it is not, please change the datatype to fastqsanger.

   tip Tip: Changing the datatype

   • Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
   • In the central panel, click on the galaxy-chart-select-data Datatypes tab on the top
   • Select fastqsanger
   • Click the Change datatype button

Preprocessing

exchange Switch to extended tutorial

Before starting any analysis, it is always a good idea to assess the quality of your input data and improve it where possible by trimming and filtering reads.

In this section we will run a workflow that performs the following tasks:

1. Assess read quality using FastQC tool and MultiQC tool
2. Filter reads by length and quality using Cutadapt tool
3. Remove ribosomal RNA (rRNA) using SortMeRNA tool
4. Combine the high-quality reads into a single interlaced FastQ file for downstream analysis using FastQ interlacer tool

We will run all these steps using a single workflow, then discuss each step and the results in more detail.

hands_on Hands-on: Pretreatments

1. Import the workflow into Galaxy
   • Copy the URL (e.g. via right-click) of this workflow or download it to your computer.
   • Import the workflow into Galaxy

   tip Tip: Importing a workflow

   • Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows.
   • Click on the upload icon galaxy-upload at the top-right of the screen
   • Provide your workflow
     • Option 1: Paste the URL of the workflow into the box labelled “Archived Workflow URL”
     • Option 2: Upload the workflow file in the box labelled “Archived Workflow File”
   • Click the Import workflow button

2. Run Workflow 1: Preprocessing workflow using the following parameters:
   • “Send results to a new history”: No
   • param-file “1: Forward FastQ file”: T1A_forward
   • param-file “2: Reverse FastQ file”: T1A_reverse

   tip Tip: Running a workflow

   • Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows.
   • Click on the workflow-run (Run workflow) button next to your workflow
   • Configure the workflow as needed
   • Click the Run Workflow button at the top-right of the screen
   • You may have to refresh your history to see the queued jobs

The workflow will take a little while to complete. Once tools have completed, the results will be available in your history for viewing. Note that only the most important outputs will he visible; intermediate files are hidden by default.

While you wait for the workflow to complete, please continue reading, in the next section(s) we will go into a bit more detail about what happens in each step of this workflow and examine the results.

Quality control

During sequencing, errors are introduced, such as incorrect nucleotides being called. These are due to the technical limitations of each sequencing platform. Sequencing errors might bias the analysis and can lead to a misinterpretation of the data.

Sequence quality control is therefore an essential first step in your analysis. In this tutorial we use similar tools as described in the tutorial “Quality control”:

• FastQC generates a web report that will aid you in assessing the quality of your data
• MultiQC combines multiple FastQC reports into a single overview report
• Cutadapt for trimming and filtering

For more information about how to interpret the plots generated by FastQC and MultiQC, please see this section in our dedicated Quality Control Tutorial.

question Questions

Inspect the webpage output from MultiQC

1. How many sequences does each file have?
2. How is the quality score over the reads? And the mean score?
3. Is there any bias in base content?
4. How is the GC content?
5. Are there any unindentified bases?
6. Are there duplicated sequences?
7. Are there over-represented sequences?
8. Are there still some adapters left?
9. What should we do next?

solution Solution

1. Both files have 260,554 sequences

2. The “Per base sequence quality” is globally good: the quality stays around 40 over the reads, with just a slight decrease at the end (but still higher than 35)

   

   The reverse reads have a slight worst quality than the forward, a usual case in Illumina sequencing.

   The distribution of the mean quality score is almost at the maximum for the forward and reverse reads:

   

3. For both forward and reverse reads, the percentage of A, T, C, G over sequence length is biased. As for any RNA-seq data or more generally libraries produced by priming using random hexamers, the first 10-12 bases have an intrinsic bias.

   

   

   We could also see that after these first bases the distinction between C-G and A-T groups is not clear as expected. It explains the error raised by FastQC.

4. With sequences from random position of a genome, we expect a normal distribution of the %GC of reads around the mean %GC of the genome. Here, we have RNA reads from various genomes. We do not expect a normal distribution of the %GC. Indeed, for the forward reads, the distribution shows with several peaks: maybe corresponding to mean %GC of different organisms.

   

5. Almost no N were found in the reads: so almost no unindentified bases

   

6. The forward reads seem to have more duplicated reads than the reverse reads with a rate of duplication up to 60% and some reads identified over 10 times.

   

   In data from RNA (metatranscriptomics data), duplicated reads are expected. The low rate of duplication in reverse reads could be due to bad quality: some nucleotides may have been wrongly identified, altering the reads and reducing the duplication.

7. The high rate of overrepresented sequences in the forward reads is linked to the high rate of duplication.

   

8. Illumina universal adapters are still present in the reads, especially at the 3’ end.

   

9. After checking what is wrong, we should think about the errors reported by FastQC: they may come from the type of sequencing or what we sequenced (check the “Quality control” training: FastQC for more details): some like the duplication rate or the base content biases are due to the RNA sequencing. However, despite these challenges, we can still get slightly better sequences for the downstream analyses.

Data Cleaning

Even though our data is already of pretty high quality, we can improve it even more by:

1. Trimming reads to remove bases that were sequenced with low certainty (= low-quality bases) at the ends of the reads
2. Removing reads of overall bad quality.
3. Removing reads that are too short to be informative in downstream analysis

There are several tools out there that can perform theses steps, but in this analysis we use Cutadapt (Martin 2011).

Cutadapt also helps find and remove adapter sequences, primers, poly-A tails and/or other unwanted sequences from the input FASTQ files. It trims the input reads by finding the adapter or primer sequences in an error-tolerant way. Additional features include modifying and filtering reads.

Cutadapt tool outputs a report file containing some information about the trimming and filtering it performed.

question Questions

View the output report from Cutadapt tool.

1. How many basepairs has been removed from the forwards reads because of bad quality? And from the reverse reads?
2. How many sequence pairs have been removed because at least one read was shorter than the length cutoff?

solution Solution

1. 203,654 bp has been trimmed for the forward read (read 1) and 569,653 bp bp on the reverse (read 2). It is not a surprise: we saw that at the end of the sequences the quality was dropping more for the reverse reads than for the forward reads.
2. 27,677 (10.6%) reads were too short after trimming and then filtered.

Ribosomal RNA fragments filtering

Metatranscriptomics sequencing targets any RNA in a pool of micro-organisms. The highest proportion of the RNA sequences in any organism will be ribosomal RNAs. These rRNAs are useful for the taxonomic assignment (i.e. which organisms are found) but they do not provide any functional information, (i.e. which genes are expressed) To make the downstream functional annotation faster, we will sort the rRNA sequences using SortMeRNA (Kopylova et al. 2012). It can handle large RNA databases and sort out all fragments matching to the database with high accuracy and specificity:

SortMeRNA tool removes any reads identified as rRNA from our dataset, and outputs a log file with more information about this filtering.

question Questions

View the log file output from SortMeRNA tool, and scroll down to the Results section.

1. How many reads have been processed?
2. How many reads have been identified as rRNA given the log file?
3. Which type of rRNA are identified? Which organisms are we then expected to identify?

solution Solution

1. 465,754 reads are processed: 232,877 for forward and 232,877 for reverse (given the Cutadapt report)
2. Out of the 465,754 reads, 119,646 (26%) have passed the e-value threshold and are identified as rRNA. The proportion of rRNA sequences is then quite high (around 40%), compared to metagenomics data where usually they represent < 1% of the sequences. Indeed there are only few copies of rRNA genes in genomes, but they are expressed a lot for the cells. Some of the aligned reads are forward (resp. reverse) reads but the corresponding reverse (resp. forward) reads are not aligned. As we choose “If one of the paired-end reads aligns and the other one does not”: Output both reads to rejected file (--paired_out), if one read in a pair does not align, both go to unaligned.
3. The 20.56% rRNA reads are 23S bacterial rRNA, 2.34% 16S bacterial rRNA and 1.74% 18S eukaryotic rRNA. We then expect to identify mostly bacteria but also probably some archae (18S eukaryotic rRNA).

Interlace forward and reverse reads

Tools for taxonomic and functional annotations need a single file as input, even with paired-end data. We need to join the two separate files (forward and reverse) to create a single interleaced file, using FASTQ interlacer tool, in which the forward reads have /1 in their id and reverse reads /2. The join is performed using sequence identifiers (headers), allowing the two files to contain differing ordering. If a sequence identifier does not appear in both files, it is output in a separate file named singles.

We use FASTQ interlacer tool on the outputs of Cutadapt tool and on the unaligned (non-rRNA) reads from SortMeRNA tool to prepare for downstream analysis

Extraction of the community profile

exchange Switch to extended tutorial

The first important information to get from microbiome data is the community structure: which organisms are present and in which abundance. This is called taxonomic profiling. Different approaches can be used:

• Identification and classification of OTUs, as used in amplicon data

  Such an approach first requires sequence sorting to extract only the 16S and 18S sequences, then again using the same tools as for amplicon data. However, because rRNA sequences represent less than 50% of the raw sequences, this approach is not the most statistically supported

• Assignment of taxonomy on the whole sequences using databases with marker genes

In this tutorial, we follow second approach using MetaPhlAn2 (Truong et al. 2015). This tool uses a database of ~1M unique clade-specific marker genes (not only the rRNA genes) identified from ~17,000 reference (bacterial, archeal, viral and eukaryotic) genomes. We will use the Interlaced QC controlled reads file with all reads (not only the non rRNAs) because the rRNAs reads are good marker genes.

hands_on Hands-on: Community Profile

1. Import the workflow into Galaxy
   • Copy the URL (e.g. via right-click) of this workflow or download it to your computer.
   • Import the workflow into Galaxy

   tip Tip: Importing a workflow

   • Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows.
   • Click on the upload icon galaxy-upload at the top-right of the screen
   • Provide your workflow
     • Option 1: Paste the URL of the workflow into the box labelled “Archived Workflow URL”
     • Option 2: Upload the workflow file in the box labelled “Archived Workflow File”
   • Click the Import workflow button

2. Run Workflow 2: Community Profile workflow using the following parameters:
   • “Send results to a new history”: No
   • param-file “1: Interlaced QC controlled reads”: Interlaced QC controlled reads output from the first workflow

   tip Tip: Running a workflow

   • Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows.
   • Click on the workflow-run (Run workflow) button next to your workflow
   • Configure the workflow as needed
   • Click the Run Workflow button at the top-right of the screen
   • You may have to refresh your history to see the queued jobs

3 files are generated by MetPhlAn2 tool:

• A SAM file with the results of the sequence mapping via the reference database.
• A BIOM file with the same information as the previous file but in BIOM format BIOM format is quite common in microbiomics. This is standard, for example, as the input for tools like mothur or QIIME.

• A tabular file with the *community profile

    #SampleID	Metaphlan2_Analysis
    k__Bacteria	99.73011
    k__Archaea	0.26989
    k__Bacteria|p__Firmicutes	99.68722
    k__Archaea|p__Euryarchaeota	0.26989
    k__Bacteria|p__Proteobacteria	0.04289
  

  Each line contains a taxa and its relative abundance found for our sample. The file starts with high level taxa (kingdom: k__) and go to more precise taxa.

  question Questions

  Inspect the Community profile file output by MetaPhlAn2 tool

  1. How many taxons have been identified?
  2. What are the different taxonomic levels we have access to with MetaPhlAn2?
  3. What genus and species are found in our sample?
  4. Has only bacteria been identified in our sample?

  solution Solution

  1. The file has 28 lines, including an header. Therefore, 27 taxons of different levels have been identified
  2. We have access: kingdom (k__), phylum (p__), class (c__), order (o__), family (f__), genus (g__), species (s__), strain (t__)
  3. In our sample, we identified:
     • 4 genera (Clostridium, Coprothermobacter, Methanothermobacter, Escherichia) and 1 genus unclassified (Thermodesulfobiaceae unclassified)
     • 4 species (Clostridium thermocellum, Coprothermobacter proteolyticus, Methanothermobacter thermautotrophicus) and 1 unclassified species (Escherichia unclassified)
  4. As expected from the rRNA sorting, we have some archaea, Methanobacteria, in our sample.

comment Note: Analyzing an isolated metatranscriptome

We are analyzing our RNA reads as we would do for DNA reads. This approach has one main caveat. In MetaPhlAn2, the species are quantified based on the recruitment of reads to species-specific marker genes. In metagenomic data, each genome copy is assumed to donate ~1 copy of each marker. But the same assumption cannot be made for RNA data: markers may be transcribed more or less within a given species in this sample compared to the average transcription rate. A species will still be detected in the metatranscriptomic data as long as a non-trivial fraction of the species’ markers is expressed.

We should then carefully interpret the species relative abundance. These values reflect species’ relative contributions to the pool of species-specific transcripts and not the overall transcript pool.

Community structure visualization

Even if the output of MetaPhlAn2 can be easy to parse, we want to visualize and explore the community structure. 2 tools can be used there:

• Krona for an interactive HTML output
• Graphlan for a publication ready visualization

Krona Ondov et al. 2011 renders results of a metagenomic profiling as a zoomable pie chart. It allows hierarchical data, here taxonomic levels, to be explored with zooming, multi-layered pie charts

question Questions

Inspect the output from Krona tool. (The interactive plot is also shown below)

1. What are the abundances of 2 kingdoms identified here?
2. When zooming on bacteria, what are the 2 subclasses identified?

solution Solution

1. Archaea represents 0.3% so bacteria are 99.7% of the organisms identified in our sample
2. 0.02% of bacteria are Enterobacteriales and the rest Clostridia.

GraPhlAn is another software tool for producing high-quality circular representations of taxonomic and phylogenetic trees.

Extract the functional information

exchange Switch to extended tutorial

We would now like to answer the question “What are the micro-organisms doing?” or “Which functions are performed by the micro-organisms in the environment?”.

In the metatranscriptomics data, we have access to the genes that are expressed by the community. We can use that to identify genes, their functions, and build pathways, etc., to investigate their contribution to the community using HUMAnN2 (Franzosa et al. 2018). HUMAnN2 is a pipeline developed for efficiently and accurately profiling the presence/absence and abundance of microbial pathways in a community from metagenomic or metatranscriptomic sequencing data.

To identify the functions made by the community, we do not need the rRNA sequences, specially because they had noise and will slow the run. We will then use the output of SortMeRNA, but also the identified community profile from MetaPhlAn2. This will help HUMAnN2 to focus on the know sequences for the identified organisms.

hands_on Hands-on: Functional Information

1. Import the workflow into Galaxy
   • Copy the URL (e.g. via right-click) of this workflow or download it to your computer.
   • Import the workflow into Galaxy

   tip Tip: Importing a workflow

   • Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows.
   • Click on the upload icon galaxy-upload at the top-right of the screen
   • Provide your workflow
     • Option 1: Paste the URL of the workflow into the box labelled “Archived Workflow URL”
     • Option 2: Upload the workflow file in the box labelled “Archived Workflow File”
   • Click the Import workflow button

2. Run Workflow 3: Functional Information workflow using the following parameters:
   • “Send results to a new history”: No
   • param-file “1: Interlaced non-rRNA reads”: Interlaced non-rRNA reads output from the first workflow
   • param-file “2: Community Profile”: MetaPhlAn2 Community Profile output from the second workflow

   tip Tip: Running a workflow

   • Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows.
   • Click on the workflow-run (Run workflow) button next to your workflow
   • Configure the workflow as needed
   • Click the Run Workflow button at the top-right of the screen
   • You may have to refresh your history to see the queued jobs

tip Tip: Running low on time? Use this faster approach

The first step of this workflow (HUMAnN2 tool) may take quite a bit of time to complete (> 45 min). If you would like to run through this tutorial a bit faster, you can download the output of this step first, and then run the rest of the workflow. Instructions are given below:

1. Import the workflow into Galaxy
   • Copy the URL (e.g. via right-click) of this workflow or download it to your computer.
   • Import the workflow into Galaxy

2. Import the following 2 files (these are the outputs from HUMAnN2 tool):

   https://zenodo.org/record/3362849/files/T1A_humann2_gene_family_abundances.tsv
   https://zenodo.org/record/3362849/files/T1A_humann2_pathway_abundances.tsv
   

3. Run Workflow 3: Functional Information (quick) workflow using the following parameters:
   • “Send results to a new history”: No
   • param-file “1: Gene Family abundance”: T1A_humann2_gene_family_abundances.tsv you uploaded
   • param-file “2: Pathway abundance”: T1A_humann2_pathway_abundances.tsv you uploaded

HUMAnN2 tool generates 3 files:

1. A file with the abundance of gene families:

    # Gene Family	humann2_Abundance-RPKs
    UNMAPPED	109359.0000000000
    UniRef50_P62593: Beta-lactamase TEM	51842.4304363200
    UniRef50_P62593: Beta-lactamase TEM|g__Clostridium.s__Clostridium_thermocellum	51842.4304363200
    UniRef50_R5FV61	46966.2923149157
    UniRef50_R5FV61|g__Clostridium.s__Clostridium_thermocellum	46966.2923149157
   

   This file details the abundance of each gene family in the community. Gene families are groups of evolutionarily-related protein-coding sequences that often perform similar functions. Here we used UniRef50 gene families: sequences in a gene families have at least 50% sequence identity.

   Gene family abundance at the community level is stratified to show the contributions from known and unknown species. Individual species’ abundance contributions sum to the community total abundance.

   Gene family abundance is reported in RPK (reads per kilobase) units to normalize for gene length. It reflects the relative gene (or transcript) copy number in the community.

   The “UNMAPPED” value is the total number of reads which remain unmapped after both alignment steps (nucleotide and translated search). Since other gene features in the table are quantified in RPK units, “UNMAPPED” can be interpreted as a single unknown gene of length 1 kilobase recruiting all reads that failed to map to known sequences.

   question Questions

   Inspect the Gene Family Abundances file from HUMAnN2 tool

   1. What is the most abundant family?
   2. Which species is involved in production of this family?
   3. How many gene families have been identified?

   solution Solution

   1. The most abundant family is the first one in the family: UniRef50_P62593, involved in Beta-lactamase TEM, enzymes produced by bacteria that provide multi-resistance to β-lactam antibiotics.
   2. Beta-lactamase TEM seems mostly produced here by Clostridium thermocellum.

   3. There is 6,861 lines in gene family file. But some of the gene families have multiple lines when the involved species are known.

      To know the number of gene families, we need to remove all lines with the species information, i.e. lines with | in them using the tool Select lines that match an expression tool with:

      • “Select lines from”: Gene families and their abundance (output of HUMAnN2)
      • “that”: NOT Matching
      • “the pattern”: \|

      The output file has 3,418 lines, including the header and the UNMAPPED. So 3,416 UniRef50 gene families have been identified for our sample.

2. A file with the abundance of pathways:

    # Pathway	humann2_Abundance
    UNMAPPED	11417.7392070186
    UNINTEGRATED	65807.9719501939
    UNINTEGRATED|g__Clostridium.s__Clostridium_thermocellum	48238.6191078367
    UNINTEGRATED|g__Coprothermobacter.s__Coprothermobacter_proteolyticus	6335.2676240042
    UNINTEGRATED|g__Methanothermobacter.s__Methanothermobacter_thermautotrophicus	80.7884346284
    PWY-6305: putrescine biosynthesis IV	764.5363426532
    PWY-6305: putrescine biosynthesis IV|g__Clostridium.s__Clostridium_thermocellum	751.0036125447
   

   This file shows each pathway and their abundance. Here, we used the MetaCyc Metabolic Pathway Database, a curated database of experimentally elucidated metabolic pathways from all domains of life.

   The abundance of a pathway in the sample is computed as a function of the abundances of the pathway’s component reactions, with each reaction’s abundance computed as the sum over abundances of genes catalyzing the reaction. The abundance is proportional to the number of complete “copies” of the pathway in the community. Indeed, for a simple linear pathway RXN1 --> RXN2 --> RXN3 --> RXN4, if RXN1 is 10 times as abundant as RXNs 2-4, the pathway abundance will be driven by the abundances of RXNs 2-4.

   The pathway abundance is computed once for all species (community level) and again for each species using species gene abundances along the components of the pathway. Unlike gene abundance, a pathway’s abundance at community-level is not necessarily the sum of the abundance values of each species. For example, for the same pathway example as above, if the abundances of RXNs 1-4 are [5, 5, 10, 10] in Species A and [10, 10, 5, 5] in Species B, the pathway abundance would be 5 for Species A and Species B, but 15 at the community level as the reaction totals are [15, 15, 15, 15].

   question Questions

   View the Pathway Abundances output from HUMAnN2 tool

   1. What is the most abundant pathway?
   2. Which species is involved in production of this pathway?
   3. How many gene families have been identified?
   4. What is the “UNINTEGRATED” abundance?

   solution Solution

   1. The most abundant pathway is PWY-6305. It produces the polyamine putrescine that may be involved in interactions with proteins, DNA and RNA molecules.
   2. Like the gene family, this pathway is mostly achieved by Clostridium thermocellum.

   3. There are 146 lines in the pathway file, including the lines with species information. To compute the number of gene families, we need to apply a similar approach as for the gene families by removing the lines with | in them using the tool Select lines that match an expression tool. The output file has 79 lines, including the header, UNMAPPED and UNINTEGRATED. Therefore, 76 UniRef50 pathways have been identified for our sample.”

   4. The “UNINTEGRATED” abundance corresponds to the total abundance of genes in the different levels that do not contribute to any pathways.

3. A file with the coverage of pathways:

   Pathway coverage provides an alternative description of the presence (1) and absence (0) of pathways in a community, independent of their quantitative abundance.

comment Note: Analyzing an isolated metatranscriptome

As we already mentioned above, we are analyzing our RNA reads as we would do for DNA reads and therefore we should be careful when interpreting the results. We already mentioned the analysis of the species’ relative abundance from MetaPhlAn2, but there is another aspect we should be careful about.

From a lone metatranscriptomic dataset, the transcript abundance can be confounded with the underlying gene copy number. For example, transcript X may be more abundant in sample A relative to sample B because the underlying X gene (same number in both samples) is more highly expressed in sample A relative to sample B; or there are more copies of gene X in sample A relative to sample B (all of which are equally expressed). This is a general challenge in analyzing isolated metatranscriptomes.

The best approach would be to combine the metatranscriptomic analysis with a metagenomic analysis. In this case, rather than running MetaPhlAn2 on the metatranscriptomic data, we run it on the metagenomic data and use the taxonomic profile as input to HUMAnN2. RNA reads are then mapped to any species’ pangenomes detected in the metagenome. Then we run HUMAnN2 on both metagenomics and metatranscriptomic data. We can use both outputs to normalize the RNA-level outputs (e.g. transcript family abundance) by corresponding DNA-level outputs to the quantification of microbial expression independent of gene copy number.

Here we do not have a metagenomic dataset to combine with and need to be careful in our interpretation

Normalize the abundances

Gene family and pathway abundances are in RPKs (reads per kilobase), accounting for gene length but not sample sequencing depth. While there are some applications, e.g. strain profiling, where RPK units are superior to depth-normalized units, most of the time we need to renormalize our samples prior to downstream analysis.

question Questions

Inspect galaxy-eye the Normalized gene families file

1. What percentage of sequences has not been assigned to a gene family?
2. What is the relative abundance of the most abundant gene family?

solution Solution

1. 14% (0.140345 x 100) of the sequences have not be assigned to a gene family
2. The Beta-lactamase TEM gene family represents 6% of the reads.

question Questions

Examine galaxy-eye the Normalized pathways file.

1. What is the UNMAPPED percentage?
2. What percentage of reads assigned to a gene family has not be assigned to a pathway?
3. What is the relative abundance of the most abundant gene family?

solution Solution

1. UNMAPPED, here 14% of the reads, corresponds to the percentage of reads not assigned to gene families. It is the same value as in the normalized gene family file.
2. 81% (UNINTEGRATED) of reads assigned to a gene family have not be assigned to a pathway
3. The PWY-6305 pathway represents 0.9% of the reads.

Identify the gene families involved in the pathways

We would like to know which gene families are involved in our most abundant pathways and which species. For this, we use the tool Unpack pathway abundances to show genes included tool

This tool unpacks the pathways to show the genes for each. It adds another level of stratification to the pathway abundance table by including the gene family abundances:

# Pathway	humann2_Abundance
ANAGLYCOLYSIS-PWY: glycolysis III (from glucose)	46.9853692906
ANAGLYCOLYSIS-PWY|g__Coprothermobacter.s__Coprothermobacter_proteolyticus	23.6863932121
ANAGLYCOLYSIS-PWY|g__Coprothermobacter.s__Coprothermobacter_proteolyticus|UniRef50_B5Y8V1: 6-phosphofructokinase 1 (Phosphofructokinase 1)(Phosphohexokinase 1) (ATP-PFK)	12.4204627049
ANAGLYCOLYSIS-PWY|g__Coprothermobacter.s__Coprothermobacter_proteolyticus|UniRef50_B5Y8V2: 6-phosphofructokinase (Phosphofructokinase)(Phosphohexokinase)	24.9672323561
ANAGLYCOLYSIS-PWY|g__Coprothermobacter.s__Coprothermobacter_proteolyticus|UniRef50_B5Y8I1: Triosephosphate isomerase

question Questions

View galaxy-eye the output from Unpack pathway abundances to show genes included tool

1. Which gene families are involved in the PWY-6305 pathway? And which species?

solution Solution

1. If we search the generated file for (using CTRF):

PWY-6305: putrescine biosynthesis IV	0.00939758
PWY-6305|g__Clostridium.s__Clostridium_thermocellum	0.00923124
PWY-6305|g__Clostridium.s__Clostridium_thermocellum|UniRef50_Q814Q2: Agmatinase	0.00189827
PWY-6305|g__Clostridium.s__Clostridium_thermocellum|UniRef50_D4KNP6: Arginine decarboxylase	8.60858e-05
PWY-6305|g__Clostridium.s__Clostridium_thermocellum|UniRef50_G8LSV4: Arginine/lysine/ornithine decarboxylase	0.000559771

The gene families UniRef50_Q814Q2, UniRef50_D4KNP6 and UniRef50_G8LSV4 are identified, for Clostridium thermocellum.

Group abundances into GO slim terms

The gene families can be a long list of ids and going through the gene families one by one to identify the interesting ones can be cumbersome. To help constuct a big picture, we could identify and use categories of genes using the gene families. Gene Ontology (GO) analysis is widely used to reduce complexity and highlight biological processes in genome-wide expression studies. There is a dedicated tool which groups and converts UniRef50 gene family abundances generated with HUMAnN2 into GO slim terms.

3 outputs are generated from executing this tool: the abundances of GO slim terms grouped in 3 groups (molecular functions, biological processes and cellular components). Each file is a tabular file with 3 columns: GO slim term id, name and abundance.

question Questions

View galaxy-eye the 3 outputs of this tool (Group abundances ..)

1. Which of the GO terms related to molecular functions is the most abundant?

solution Solution

1. The GO terms in the Molecular function abundance file are not sorted by abundance:

GO id	GO name	Abundance
GO:0000150	recombinase activity	123.345
GO:0000150|g__Clostridium.s__Clostridium_thermocellum	recombinase activity|g__Clostridium.s__Clostridium_thermocellum	123.345
GO:0000166	nucleotide binding	51078.796
GO:0000166|g__Clostridium.s__Clostridium_thermocellum	nucleotide binding|g__Clostridium.s__Clostridium_thermocellum	43316.679
GO:0000166|g__Coprothermobacter.s__Coprothermobacter_proteolyticus	nucleotide binding|g__Coprothermobacter.s__Coprothermobacter_proteolyticus	7609.601
GO:0000166|g__Methanothermobacter.s__Methanothermobacter_thermautotrophicus	nucleotide binding|g__Methanothermobacter.s__Methanothermobacter_thermautotrophicus	152.517

So to identify the most abundant GO terms, we first need to sort the file using the Sort data in ascending or descending order tool tool:

GO:0016491	oxidoreductase activity	58558.589
GO:0016787	hydrolase activity	58119.165
GO:0016787|g__Clostridium.s__Clostridium_thermocellum	hydrolase activity|g__Clostridium.s__Clostridium_thermocellum	57390.482
GO:0016491|g__Clostridium.s__Clostridium_thermocellum	oxidoreductase activity|g__Clostridium.s__Clostridium_thermocellum	54813.645
GO:0000166	nucleotide binding	51078.796
GO:0043167	ion binding	50385.168
GO:0003735	structural constituent of ribosome	46442.807

The most abundant GO terms related to molecular functions seem to be linked to oxidoreductase or hydrolase activity, but also to nucleotide, ion, nucleic acid, metal ion binding.

Combine taxonomic and functional information

With MetaPhlAn2 and HUMAnN2, we investigated “Which micro-organims are present in my sample?” and “What functions are performed by the micro-organisms in my sample?”. We can go further in these analyses, for example using a combination of functional and taxonomic results. Although we did not detail that in this tutorial you can find more methods of analysis in our tutorials on shotgun metagenomic data analysis.

Although gene families and pathways, and their abundance may be related to a species, in the HUMAnN2 output, relative abundance of the species is not indicated. Therefore, for each gene family/pathway and the corresponding taxonomic stratification, we will now extract the relative abundance of this gene family/pathway and the relative abundance of the corresponding species and genus.

hands_on Hands-on: Combine taxonomic and functional information

1. Combine MetaPhlAn2 and HUMAnN2 outputs tool with the following parameters:
   • param-file “Input file corresponding to MetaPhlAN2 output”: Community profile (output of MetaPhlAn2)
   • param-file “Input file corresponding HUMAnN2 output”: Normalized gene families
   • “Type of characteristics in HUMAnN2 file”: Gene families
2. Inspect the generated file

The generated file is a table with 7 columns:

1. genus
2. abundance of the genus (percentage)
3. species
4. abundance of the species (percentage)
5. gene family id
6. gene family name
7. gene family abundance (percentage)

genus	genus_abundance	species	species_abundance	gene_families_id	gene_families_name	gene_families_abundance
Clostridium	76.65512	Clostridium_thermocellum	76.65512	UniRef50_P62593	Beta lactamase TEM	6.65317186667
Clostridium	76.65512	Clostridium_thermocellum	76.65512	UniRef50_R5FV61		6.0274016911
Clostridium	76.65512	Clostridium_thermocellum	76.65512	UniRef50_P80579	Thioredoxin	5.34305149909
Clostridium	76.65512	Clostridium_thermocellum	76.65512	UniRef50_A3DC67		4.56309128026

question Questions

1. Are there gene families associated with each genus identified with MetaPhlAn2?
2. How many gene families are associated to each genus?
3. Are there gene families associated to each species identified with MetaPhlAn2?
4. How many gene families are associated to each species?

solution Solution

1. To answer the questions, we need to group the contents of the output of Combine MetaPhlAn2 and HUMAnN2 outputs by 1st column and count the number of occurrences of gene families. We do that using Group data by a column tool:

   hands_on Hands-on: Group by genus and count gene families

   1. Group data by a column tool
      • “Select data”: output of Combine MetaPhlAn2 and HUMAnN2 outputs
      • “Group by column”: Column:1
      • “Operation”:
        • Click on param-repeat “Insert Operation”
          • “Type”: Count
          • “On column”: Column:5

   With MetaPhlAn2, we identified 4 genus (Clostridium, Coprothermobacter, Methanothermobacter, Escherichia). But in the output of Combine MetaPhlAn2 and HUMAnN2 outputs, we have only gene families for Clostridium, Coprothermobacter and Methanothermobacter. The abundance of Escherichia is probably too low to correctly identify correctly some gene families.

2. 2323 gene families are associated to Clostridium, 918 to Coprothermobacter and 202 to Methanothermobacter. Given a genus abundance of 76.65512 for Clostridium, 20.75226 for Coprothermobacter and 0.26989 for Methanothermobacter, the ratio between number of gene families and genus abundance is really high for Methanothermobacter (748.45) compare to Methanothermobacter (44.26) and Coprothermobacter (30.30).

3. For this question, we should group on the 3rd column:

   hands_on Hands-on: Group by species and count gene families

   1. Group data by a column tool
      • “Select data”: output of Combine MetaPhlAn2 and HUMAnN2 outputs
      • “Group by column”: Column:3
      • “Operation”:
        • Click on param-repeat “Insert Operation”
          • “Type”: Count
          • “On column”: Column:5

   The 3 species (Clostridium thermocellum, Coprothermobacter proteolyticus, Methanothermobacter thermautotrophicus) identified by MetaPhlAn2 are associated to gene families.

4. As the species found derived directly from the genus (not 2 species for the same genus here), the number of gene families identified are the sames: 2323 for Clostridium thermocellum, 918 for Coprothermobacter proteolyticus and 202 Methanothermobacter thermautotrophicus. The abundances are also the same.

We could now apply the same tool to the pathways and run similar analysis.

Conclusion

In this tutorial, we analyzed one metatranscriptomics sample from raw sequences to community structure, functional profiling. To do that, we:

1. preprocessed the raw data: quality control, trimming and filtering, sequence sorting and formatting

2. extracted and analyzed the community structure (taxonomic information)

   We identified bacteria to the level of strains, but also some archaea.

3. extracted and analyzed the community functions (functional information)

   We extracted gene families, pathways, but also the gene families involved in pathways and aggregated the gene families into GO terms

4. combined taxonomic and functional information to offer insights into taxonomic contribution to a function or functions expressed by a particular taxonomy

The workflow can be represented this way:

The dataset used here was extracted from a time-series analysis of a microbial community inside a bioreactor (Kunath et al. 2018) in which there are 3 replicates over 7 time points. We analyzed here only one single time point for one replicate.

keypoints Key points

• Metatranscriptomics data have the same QC profile that RNA-seq data

• A lot of metatranscriptomics sequences are identified as rRNA sequences

• With shotgun data, we can extract information about the studied community structure and also the functions realised by the community

• Metatranscriptomics data analyses are complex and must be careful done, specially when they are done without combination to metagenomics data analyses

Useful literature

Further information, including links to documentation and original publications, regarding the tools, analysis techniques and the interpretation of results described in this tutorial can be found here.

References

1. Martin, M., 2011 Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. journal 17: 10–12.
2. Ondov, B. D., N. H. Bergman, and A. M. Phillippy, 2011 Interactive metagenomic visualization in a Web browser. BMC bioinformatics 12: 385.
3. Kopylova, E., L. Noé, and H. Touzet, 2012 SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28: 3211–3217.
4. Truong, D. T., E. A. Franzosa, T. L. Tickle, M. Scholz, G. Weingart et al., 2015 MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nature methods 12: 902.
5. Batut, B., K. Gravouil, C. Defois, S. Hiltemann, J.-F. Brugère et al., 2018 ASaiM: a Galaxy-based framework to analyze microbiota data. GigaScience 7: giy057.
6. Franzosa, E. A., L. J. McIver, G. Rahnavard, L. R. Thompson, M. Schirmer et al., 2018 Species-level functional profiling of metagenomes and metatranscriptomes. Nature methods 15: 962.
7. Kunath, B. J., F. Delogu, A. E. Naas, M. Ø. Arntzen, V. G. H. Eijsink et al., 2018 From proteins to polysaccharides: lifestyle and genetic evolution of Coprothermobacter proteolyticus. The ISME journal 1.

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Citing this Tutorial

1. Pratik Jagtap, Subina Mehta, Ray Sajulga, Bérénice Batut, Emma Leith, Praveen Kumar, Saskia Hiltemann, 2020 Metatranscriptomics analysis using microbiome RNA-seq data (short) (Galaxy Training Materials). /training-material/topics/metagenomics/tutorials/metatranscriptomics-short/tutorial.html Online; accessed TODAY
2. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

details BibTeX

@misc{metagenomics-metatranscriptomics-short,
    author = "Pratik Jagtap and Subina Mehta and Ray Sajulga and Bérénice Batut and Emma Leith and Praveen Kumar and Saskia Hiltemann",
    title = "Metatranscriptomics analysis using microbiome RNA-seq data (short) (Galaxy Training Materials)",
    year = "2020",
    month = "02",
    day = "14"
    url = "\url{/training-material/topics/metagenomics/tutorials/metatranscriptomics-short/tutorial.html}",
    note = "[Online; accessed TODAY]"
}
@article{Batut_2018,
        doi = {10.1016/j.cels.2018.05.012},
        url = {https://doi.org/10.1016%2Fj.cels.2018.05.012},
        year = 2018,
        month = {jun},
        publisher = {Elsevier {BV}},
        volume = {6},
        number = {6},
        pages = {752--758.e1},
        author = {B{\'{e}}r{\'{e}}nice Batut and Saskia Hiltemann and Andrea Bagnacani and Dannon Baker and Vivek Bhardwaj and Clemens Blank and Anthony Bretaudeau and Loraine Brillet-Gu{\'{e}}guen and Martin {\v{C}}ech and John Chilton and Dave Clements and Olivia Doppelt-Azeroual and Anika Erxleben and Mallory Ann Freeberg and Simon Gladman and Youri Hoogstrate and Hans-Rudolf Hotz and Torsten Houwaart and Pratik Jagtap and Delphine Larivi{\`{e}}re and Gildas Le Corguill{\'{e}} and Thomas Manke and Fabien Mareuil and Fidel Ram{\'{\i}}rez and Devon Ryan and Florian Christoph Sigloch and Nicola Soranzo and Joachim Wolff and Pavankumar Videm and Markus Wolfien and Aisanjiang Wubuli and Dilmurat Yusuf and James Taylor and Rolf Backofen and Anton Nekrutenko and Björn Grüning},
        title = {Community-Driven Data Analysis Training for Biology},
        journal = {Cell Systems}
}