Antibiotic resistance detection

Authors: Willem de Koning avatar Willem de Koningorcid logoSaskia Hiltemann avatar Saskia Hiltemann
Overview
Creative Commons License: CC-BY Questions:

  • How do I assemble a genome with Nanopore data?

  • How do I get more information about the structure of the genomes?

  • How do I get more information about the antimicrobial resistance genes?

Objectives:

  • Perform Quality control on your reads

  • Assemble a genome with Minimap2/Miniasm/Racon

  • Determine the structure of the genome(s)

  • Scan for antimicrobial resistance genes with Staramr

Requirements:
Time estimation: 3 hours
Supporting Materials:
Published: Jun 25, 2019
Last modification: Mar 14, 2024
License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License. The GTN Framework is licensed under MIT
PURL: https://gxy.io/GTN:T00394
Rating: 5.0 (1 recent ratings, 9 all time)
Revision: 3

Overview

Pervasive use (and misuse) of antibiotics for human disease treatment, as well as for various agricultural purposes, has resulted in the evolution of multi-drug resistant (MDR) pathogenic bacteria. The Center for Disease Control estimates that in the U.S. alone, every year at least 2 million people get an antibiotic-resistant infection, and at least 23,000 people die. Antibiotic resistance poses a major public health challenge, and its causes and mitigations are widely studied.

Plasmids are small DNA molecules within a cell which are physically separated from chromosomal DNA and can replicate independently. They are most commonly found as small circular, double-stranded DNA molecules in bacteria.

Depiction of plasmids.

Plasmids are considered a major vector facilitating the transmission of drug resistant genes among bacteria via horizontal transfer (Beatson and Walker 2014, Smillie et al. 2010). Careful characterization of plasmids and other MDR mobile genetic elements is vital for understanding their evolution and transmission and adaptation to new hosts.

Illustration of transformation of a bacteria to drug resistance.

Due to the high prevalence of repeat sequences and inserts in plasmids, using traditional NGS short-read sequencing to assemble plasmid sequences is difficult and time-consuming. With the advent of third-generation single-molecule long-read sequencing technologies, full assembly of plasmid sequences is now possible.

In this tutorial we will recreate the analysis described in the paper by Li et al. 2018 entitled Efficient generation of complete sequences of MDR-encoding plasmids by rapid assembly of MinION barcoding sequencing data. We will use data sequenced by the Nanopore MinION sequencer.

The assembly will be performed with Minimap2 (Li 2018), Miniasm (Li et al. 2015), Racon (Vaser et al. 2017) and Unicycler (Wick et al. 2017). The downstream analysis will use Nanoplot (Coster et al. 2018), Bandage (Wick et al. 2015), PlasFlow (Krawczyk et al. 2018) and starmr (GitHub).

A schematic view of the workflow we will perform in this tutorial is given below:

Workflow representation of this tutorial.

Agenda

In this tutorial, we will cover:

  1. Overview
  2. Obtaining and preparing data
    1. Importing the data into Galaxy
  3. Quality Control
    1. NanoPlot to explore data
  4. De-novo Assembly
    1. Pairwise alignment using Minimap2
    2. Ultrafast de novo assembly using Miniasm
    3. Remapping using Minimap2
    4. Ultrafast consensus module using Racon
    5. Visualize assemblies using Bandage
    6. Optimizing assemblies using Unicycler
  5. Species and plasmids
    1. Prediction of plasmid sequences and classes using PlasFlow
  6. Antibiotic Resistance
    1. Scan genome contigs for antimicrobial resistance genes
    2. CARD database
  7. Conclusion
Comment: Results may vary

Your results may be slightly different from the ones presented in this tutorial due to differing versions of tools, reference data, external databases, or because of stochastic processes in the algorithms.

In this tutorial we use metagenomic Nanopore data, but similar pipelines can be used for other types of datasets or other long-read sequencing platforms.

Obtaining and preparing data

We are interested in the reconstruction of full plasmid sequences and determining the presence of any antimicrobial resistance genes. We will use the plasmid dataset created by Li et al. 2018 for their evaluation of the efficiency of MDR plasmid sequencing by MinION platform. In the experiment, 12 MDR plasmid-bearing bacterial strains were selected for plasmid extraction, including E. coli, S. typhimurium, V. parahaemolyticus, and K. pneumoniae.

Comment: Nanopore sequencing

Nanopore sequencing has several properties that make it well-suited for our purposes

  1. Long-read sequencing technology offers simplified and less ambiguous genome assembly
  2. Long-read sequencing gives the ability to span repetitive genomic regions
  3. Long-read sequencing makes it possible to identify large structural variations

How nanopore sequencing works

When using Oxford Nanopore Technologies (ONT) sequencing, the change in electrical current is measured over the membrane of a flow cell. When nucleotides pass the pores in the flow cell the current change is translated (basecalled) to nucleotides by a basecaller. A schematic overview is given in the picture above.

When sequencing using a MinIT or MinION Mk1C, the basecalling software is present on the devices. With basecalling the electrical signals are translated to bases (A,T,G,C) with a quality score per base. The sequenced DNA strand will be basecalled and this will form one read. Multiple reads will be stored in a fastq file.

Importing the data into Galaxy

For this tutorial, in order to speed up the analysis time, we will use 6 of the 12 samples from the original study.

Hands-on: Obtaining our data

  1. Make sure you have an empty analysis history. Give it a name.

    Click the icon at the top of the history panel:

    UI for creating new history

  2. Import Sample Data DOI. 
    https://zenodo.org/record/3247504/files/RB01.fasta
https://zenodo.org/record/3247504/files/RB02.fasta
https://zenodo.org/record/3247504/files/RB04.fasta
https://zenodo.org/record/3247504/files/RB05.fasta
https://zenodo.org/record/3247504/files/RB10.fasta
https://zenodo.org/record/3247504/files/RB12.fasta
    • Copy the link location

    • Click Upload Data at the top of the tool panel

    • Select Paste/Fetch Data

    • Paste the link(s) into the text field

    • Press Start

    • Close the window

  3. Build a list collection containing all fasta files. Name it Plasmids

    • Click on Select Items at the top of the history panel Select Items button
    • Check all the datasets in your history you would like to include

    • Click n of N selected and choose Build Dataset List

      build list collection menu item

    • Enter a name for your collection
    • Click Create List to build your collection
    • Click on the checkmark icon at the top of your history again

    Creating a simple collection

Quality Control

NanoPlot to explore data

The first thing we want to do is to get a feeling for our input data and its quality. This is done using the NanoPlot tool. This will create several plots, a statisical report and an HTML report page.

NanoPlot example.

Hands-on: Plotting scripts for long read sequencing data
  1. Nanoplot ( Galaxy version 1.28.2+galaxy1) with the following parameters
    • “Select multifile mode”: batch
    • “Type of the file(s) to work on”: fasta
    • “files”: The Plasmids dataset collection you just created
    1. Click on Dataset collection in front of the input parameter you want to supply the collection to.
    2. Select the collection you want to use from the list

The HTML report gives an overview of various QC metrics for each sample. For example, it will plot the read length distribution of each sample: NanoPlot Output.

Question

What was the mean read length for this (RB01) sample?

4906.3

This can be determined by looking at the NanoStats or HTML output of NanoPlot RB01.

For more information on the topic of quality control, please see our sequence analysis training materials

De-novo Assembly

Pairwise alignment using Minimap2

In this experiment we used Nanopore sequencing; this means sequencing results with long reads, and significant overlaps between those reads. To find this overlap, Minimap2 is used. Minimap2 is a sequence alignment program that can be used for different purposes, but in this case we’ll use it to find overlaps between long reads with an error rate up to ~15%. Typical other use cases for Minimap2 include:

  1. mapping PacBio or Oxford Nanopore genomic reads to the human genome
  2. splice-aware alignment of PacBio Iso-Seq or Nanopore cDNA or Direct RNA reads against a reference genome
  3. aligning Illumina single- or paired-end reads
  4. assembly-to-assembly alignment
  5. full-genome alignment between two closely related species with divergence below ~15%.

Minimap2 is faster and more accurate than mainstream long-read mappers such as BLASR, BWA-MEM, NGMLR and GMAP and therefore widely used for Nanopore alignment. Detailed evaluations of Minimap2 are available in the Minimap2 publication (Li 2018).

Pairwise alignment.

Hands-on: Pairwise sequence alignment
  1. Map with minimap2 ( Galaxy version 2.17+galaxy2) with the following parameters
    • “Will you select a reference genome from your history or use a built-in index?”: Use a genome from history and build index
      • “Use the following data collection as the reference sequence”: Plasmids dataset collection we just created
    • “Single or Paired-end reads”: Single
      • “Select fastq dataset”: The Plasmids dataset collection
      • “Select a profile of preset options”: Oxford Nanopore all-vs-all overlap mapping
    • In the section Set advanced output options:
      • “Select an output format”: paf
    1. Click on Dataset collection in front of the input parameter you want to supply the collection to.
    2. Select the collection you want to use from the list

This step maps the Nanopore sequence reads against itself to find overlaps. The result is a PAF file. PAF is a text format describing the approximate mapping positions between two set of sequences. PAF is TAB-delimited with each line consisting of the following predefined fields:

Col Type Description
1 string Query sequence name
2 int Query sequence length
3 int Query start (0-based)
4 int Query end (0-based)
5 char Relative strand: “+” or “-“
6 string Target sequence name
7 int Target sequence length
8 int Target start on original strand (0-based)
9 int Target end on original strand (0-based)
10 int Number of residue matches
11 int Alignment block length
12 int Mapping quality (0-255; 255 for missing)

View the output of Minimap2 of the collection against RB12, it should look something like this:

channel_100_69f2ea89-01c5-45f4-8e1b-55a09acdb3f5_template	4518	114	2613	+	channel_139_250c7e7b-f063-4313-8564-d3efbfa7e38d_template	3657	206	2732	273	2605	0	tp:A:S	cm:i:29	s1:i:240	dv:f:0.2016	rl:i:1516
channel_100_69f2ea89-01c5-45f4-8e1b-55a09acdb3f5_template	4518	148	1212	+	channel_313_35f447cb-7e4b-4c3d-977e-dc0de2717a4d_template	3776	2433	3450	218	1064	0	tp:A:S	cm:i:31	s1:i:210	dv:f:0.1291	rl:i:1516
channel_100_69f2ea89-01c5-45f4-8e1b-55a09acdb3f5_template	4518	251	1328	+	channel_313_a83f7257-52db-46e4-8e2a-1776500c7363_template	3699	2327	3382	208	1082	0	tp:A:S	cm:i:29	s1:i:203	dv:f:0.1363	rl:i:1516

Ultrafast de novo assembly using Miniasm

The mapped reads are ready to be assembled with Miniasm (Li et al. 2015). Miniasm is a very fast Overlap Layout Consensus based de-novo assembler for noisy long reads. It takes all-vs-all read self-mappings (typically by Minimap2 ) as input and outputs an assembly graph in the GFA format.

Different from mainstream assemblers, miniasm does not have a consensus step. It simply concatenates pieces of read sequences to generate the final sequences. The optimal case would be to recreate a complete chromosome or plasmid. Thus the per-base error rate is similar to the raw input reads.

Pairwise alignment. Open image in new tab

Figure 1: Sequencing and Assembly Schematic. A longer sequence is constructed from many smaller, overlapping fragments. These are aligned and reconstructed during assembly to produce the best guess assembly.
Hands-on: De novo assembly
  1. miniasm ( Galaxy version 0.3+galaxy0) with the following parameters
    • “Sequence Reads”: The Plasmids dataset collection
    • “PAF file”: Output Minimap dataset collection created by Minimap2
    1. Click on Dataset collection in front of the input parameter you want to supply the collection to.
    2. Select the collection you want to use from the list

The Assembly Graph output file gives information about the steps taken in the assembly.

The output should look like:

S	utg000001l	GAAATCATCAGGCGTTTTTCACGATATGGACGGGAAGATGCGGAAATAGGCAGGAGGACATAGAA [..]
a	utg000001l	0	channel_364_204a2254-2b6f-4f10-9ec5-6d40f0b870e4_template:101-4457	+	4357

Remapping using Minimap2

Remapping is done with the original reads, using the Miniasm assembly as a reference, in order to improve the consensus base call per position. This is used by Racon for consensus construction. This is done as some reads which might not have mapped well during the consensus calling, will now map to your scaffold.

The Assembly graph created can be used for mapping again with Minimap2, but first the graph should be transformed to FASTA format.

Hands-on: Pairwise sequence alignment
  1. GFA to Fasta ( Galaxy version 0.1.1) with the following parameters
    • “Input GFA file”: the Assembly Graph (collection) created by Miniasm
    Question

    How many contigs do we have for the RB05 sample after de novo assembly?

    Hint: run Nanoplot on the output of GFA to Fasta

    25

    This can be determined by looking at the NanoStats output of NanoPlot.

  2. Map with minimap2 ( Galaxy version 2.17+galaxy2) with the following parameters
    • “Will you select a reference genome from your history or use a built-in index?”: Use a genome from history and build index
      • “Use the following dataset as the reference sequence”: FASTA file output from GFA to Fasta (collection)
    • “Single or Paired-end reads”: single
      • “Select fastq dataset”: The Plasmids collection
      • “Select a profile of preset options”:: PacBio/Oxford Nanopore read to reference mapping (-Hk19)
    • In the section Set advanced output options:
      • “Select an output format”: paf
    1. Click on Dataset collection in front of the input parameter you want to supply the collection to.
    2. Select the collection you want to use from the list

Ultrafast consensus module using Racon

The mapped reads can be improved even more using Racon (Vaser et al. 2017) to find a consensus sequence. Racon is a standalone consensus module to correct raw contigs generated by rapid assembly methods which do not include a consensus step. It supports data produced by both Pacific Biosciences and Oxford Nanopore Technologies.

Consensus Module.

Hands-on: Consensus module
  1. Racon ( Galaxy version 1.4.13) with the following parameters
    • “Sequences”: The Plasmids dataset collection
    • “Overlaps”: the latest PAF file collection created by Minimap2
    • “Target sequences”: the FASTA file collection created by GFA to Fasta

The Racon output file gives the final contigs.

The output of RB04 should look something like:

>utg000001c LN:i:4653 RC:i:11 XC:f:0.888889
AATGCAGCTATGGCGCGTGCGGTGCCAAGAAAGCCCGCAGATATTCCGCTTCCTCGCTCATT [..]

Visualize assemblies using Bandage

To get a sense of how well our data was assembled, and to determine whether the contigs are chomosomal or plasmid DNA (the former being linear sequences while plasmids are circular molecules), Bandage can give a clear view of the assembly.

Bandage (Wick et al. 2015) (a Bioinformatics Application for Navigating De novo Assembly Graphs Easily), is a program that creates visualisations of assembly graphs. Sequence assembler programs (such as Miniasm (Li et al. 2015), Velvet (Zerbino and Birney 2008), SPAdes (Bankevich et al. 2012), Trinity (Grabherr et al. 2011) and MEGAHIT Li et al. 2015) carry out assembly by building a graph, from which contigs are generated.

By visualizing these assembly graphs, Bandage allows users to better understand, troubleshoot, and improve the assemblies.

Bandage GUI.

Hands-on: Visualising de novo assembly graphs
  1. Bandage image ( Galaxy version 0.8.1+galaxy2) with the following parameters
    • “Graphical Fragment Assembly”: the Assembly graph collection created by Miniasm
  2. Explore the output images
Question

In how many samples were the full plasmid sequences assembled?

Hint: what shape do you expect plasmid molecules to be?

Ideally, we want to see circular assemblies, indicating the full plasmid sequence was resolved. This is not the case for most of the samples, but we will improve our assemblies in the next section!

For example, the assembly for sample RB01 looks something like this (your assembly will look a bit different due to randomness in several of the tools):

Bandage output for sample RB01. Open image in new tab

Figure 2: Bandage output for sample RB01. Large fragments were assembled, but not the full circular plasmid molecules.

As you can see from these Bandage outputs, we were able to assemble our data into fairly large fragments, but were not quite successful in assembling the full (circular) plasmid sequences.

However, all the tools we used to do the assembly have many different parameters that we did not explore, and multiple rounds of mapping and cleaning could improve our data as well. Choosing these parameters carefully could potentially improve our assembly, but this is also a lot of work and not an easy task. This is where Unicycler (Wick et al. 2017) can help us out.

Optimizing assemblies using Unicycler

The assembly tools we used in this tutorial are all implemented in Unicycler , which will repeatedly run these tools on your data using different parameter settings, in order to find the optimal assembly.

Unicycler has a couple of advantages over running the tools separately:

  1. The first modification is to help circular replicons assemble into circular string graphs.
  2. Racon polishing is carried out in multiple rounds to improve the sequence accuracy. It will polish until the assembly stops improving, as measured by the agreement between the reads and the assembly.
  3. Circular replicons are ‘rotated’ (have their starting position shifted) between rounds of polishing to ensure that no part of the sequence is left unpolished.

Let’s try it on our data!

Hands-on: Unicycler assembly
  1. Create assemblies with Unicycler ( Galaxy version 0.4.8.0) with the following parameters
    • “Paired or Single end data”: None
    • “Select long reads. If there are no long reads, leave this empty”: The Plasmids dataset collection
  2. Bandage image ( Galaxy version 0.8.1+galaxy2) with the following parameters
    • “Graphical Fragment Assembly”: the Final Assembly Graph collection created by Unicycler

  3. Examine the output images again

  4. Use the Scratchbook to compare the two assemblies for sample RB01
    • Compare the Bandage images for our two assemblies:
      1. The assembly we got from running minimap2, miniasm, racon (first time we ran bandage)
      2. The assembly obtained with Unicycler
    • Tip: Search your history for the term bandage to easily find the outputs from our two bandage runs

    If you would like to view two or more datasets at once, you can use the Window Manager feature in Galaxy:

    1. Click on the Window Manager icon on the top menu bar.
      • You should see a little checkmark on the icon now
    2. View a dataset by clicking on the eye icon to view the output
      • You should see the output in a window overlayed over Galaxy
      • You can resize this window by dragging the bottom-right corner
    3. Click outside the file to exit the Window Manager
    4. View a second dataset from your history
      • You should now see a second window with the new dataset
      • This makes it easier to compare the two outputs
    5. Repeat this for as many files as you would like to compare
    6. You can turn off the Window Manager by clicking on the icon again

    To make it easier to find datasets in large histories, you can filter your history by keywords as follows:

    1. Click on the search datasets box at the top of the history panel.

      history search box

    2. Type a search term in this box
      • For example a tool name, or sample name
    3. To undo the filtering and show your full history again, press on the clear search button next to the search box

  5. Repeat this comparison for the other samples.

    Question

    For which samples has the plasmid assembly improved?

    Exploring the outputs for all the samples reveals that many now display circular assemblies, indicating the full plasmids sequence was resolved.

The Assembly graph image of the RB01 assembly with miniasm shows one unclear hypothetical plasmid, where the output of Unicycler shows two clear plasmids, as also shown by Li et al. 2018.

Bandage output.

Species and plasmids

Prediction of plasmid sequences and classes using PlasFlow

To automatically determine whether the contigs represent chromosomal or plasmid DNA, PlasFlow (Krawczyk et al. 2018) can be used, also in the case where a full circular plasmid sequence was not assembled. Furthermore, it assigns the contigs to a bacterial class.

PlasFlow is a set of scripts used for prediction of plasmid sequences in metagenomic contigs. It relies on the neural network models trained on full genome and plasmid sequences and is able to differentiate between plasmids and chromosomes with accuracy reaching 96%.

Pairwise alignment.

Hands-on: Prediction of plasmid sequences
  1. PlasFlow ( Galaxy version 1.0) with the following parameters
    • “Sequence Reads”: the Final Assembly collection created by Unicycler
Question

What is the classification of contig_id 0 in RB10? (Hint: Check the probability table created by PlasFlow)

plasmid.Proteobacteria

This can be determined by looking at the 5th column of the probability table.

The most important output of PlasFlow is a tabular file containing all predictions, consisting of several columns including:

contig_id 	contig_name 	contig_length 	id 	label 	...

where:

  • contig_idis an internal id of sequence used for the classification
  • contig_name is a name of contig used in the classification
  • contig_length shows the length of a classified sequence
  • id is an internal id of a produced label (classification)
  • label is the actual classification
  • ... represents additional columns showing probabilities of assignment to each possible class

Additionally, PlasFlow produces FASTA files containing input sequences binned to plasmids, chromosomes and unclassified.

Antibiotic Resistance

Scan genome contigs for antimicrobial resistance genes

To determine whether the contigs contain antimircobial resistance genes (AMR) staramr can be used. Staramr scans bacterial genome contigs against both the ResFinder (Zankari et al. 2012), PointFinder (Zankari et al. 2017), and PlasmidFinder (Carattoli et al. 2014) databases (used by the ResFinder webservice) and compiles a summary report of detected antimicrobial resistance genes.

Pairwise alignment.

Hands-on: Prediction of AMR genes
  1. staramr ( Galaxy version 0.7.1+galaxy2) with the following parameters
    • “genomes”: the Final Assembly collection created by Unicycler
Question

Which samples contained the resistance gene: dfrA17?

Hint: Check the resfinder.tsv created by staramr

RB01, RB02, and RB10

This can be determined by looking at the 2nd column of the resfinder.tsv output (and the first column for the sample names).

There are 5 different output files produced by staramr :

  1. summary.tsv: A summary of all detected AMR genes/mutations in each genome, one genome per line.
  2. resfinder.tsv: A tabular file of each AMR gene and additional BLAST information from the ResFinder database, one gene per line.
  3. pointfinder.tsv: A tabular file of each AMR point mutation and additional BLAST information from the PointFinder database, one gene per line.
  4. settings.txt: The command-line, database versions, and other settings used to run staramr.
  5. results.xlsx: An Excel spreadsheet containing the previous 4 files as separate worksheets.

The summary file is most important and provides all the resistance genes found.

CARD database

To get more information about these antibiotic resistant genes, you can check the CARD database (Comprehensive Antibiotic Resistance Database) (Jia et al. 2016)

Screenshot of the CARD database. Open image in new tab

Figure 3: Screenshot of the CARD database interface. CARD gives information about the antibiotic resistance genes, as well as links to relevant publications.
Question

What is the resistance mechanism of the dfrA17 gene?

antibiotic target replacement

This can be determined by searching for the gene on the CARD database

For more information about antibiotic resistance mechanisms, see Munita and Arias

Conclusion

You have now seen how to perform an assembly on Nanopore sequencing data, and classify the type and species of the sequences, as well as determined the presence of potential antibiotic resistance genes.

As for any analysis, there are many different tools that can do the job, and the tools presented here are just one possible pipeline. Which tools are best for your specific data and research question depends on a number of factors. For more information and comparisons between various tools, review papers such as Maio et al. 2019 and Jayakumar and Sakakibara 2017 may provide further insight.

You have worked your way through the following pipeline:

Workflow representation of this tutorial.