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.

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.

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 tool (Li 2018), Miniasm tool (Li 2016), Racon tool (Vaser et al. 2017) and Unicycler tool (Wick et al. 2017). The downstream analysis will use Nanoplot tool (Coster et al. 2018), Bandage tool (Wick et al. 2015), PlasFlow tool (Krawczyk et al. 2018) and starmr tool (GitHub).

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

Agenda

In this tutorial, we will cover:

1. Obtaining and preparing data
   1. Importing the data into Galaxy
2. Quality Control
   1. NanoPlot to explore data
3. 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
4. Species and plasmids
   1. Prediction of plasmid sequences and classes using PlasFlow
5. Antibiotic Resistance
   1. Scan genome contigs for antimicrobial resistance genes
   2. CARD database
6. Conclusion

comment Note: 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 Background: 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

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 Hands-on: Obtaining our data

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

2. Import Sample Data

   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
   

   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.

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

   tip Tip: Creating a dataset collection

   • Click on Operations on multiple datasets (check box icon) at the top of the history panel
   • Check all the datasets in your history you would like to include

   • Click For all selected.. and choose Build dataset list

     

   • 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

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.

hands_on Hands-on: Plotting scripts for long read sequencing data

1. NanoPlot tool 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

   tip Tip: Selecting a dataset collection as input

   1. Click on param-collection 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:

question Question

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

solution Solution

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 training materials here

De-novo Assembly

Pairwise alignment using Minimap2

In this experiment we used Nanopore sequencing; this means that sequencing results in long reads with overlap. 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).

hands_on Hands-on: Pairwise sequence alignment

1. Map with minimap2 tool 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”: Created dataset collection (Plasmids)
   • “Single or Paired-end reads”: Single
   • “Select fastq dataset”: The Plasmids dataset collection
   • “Select analysis mode (sets default)”: Oxford Nanopore all-vs--all overlap mapping
   • In the section Set advanced output options:
     • “Select an output format”: paf

   tip Tip: Selecting a dataset collection as input

   1. Click on param-collection 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:

View the output of Minimap2 tool 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 tool (Li 2016). 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 tool) 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.

hands_on Hands-on: De novo assembly

1. miniasm tool with the following parameters
   • “Sequence Reads”: The Plasmids dataset collection
   • “PAF file”: Output Minimap dataset collection created by Minimap2 tool

   tip Tip: Selecting a dataset collection as input

   1. Click on param-collection 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

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

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 tool for consensus construction.

hands_on Hands-on: Pairwise sequence alignment

1. GFA to Fasta tool with the following parameters
   • “Input GFA file”: the Assembly Graph (collection) created by Miniasm tool
2. Map with minimap2 tool 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 collection created by GFA to Fasta tool
   • “Single or Paired-end reads”: single
   • “Select fastq dataset”: The Plasmids collection
   • “Select analysis mode (sets default)”: PacBio/Oxford Nanopore read to reference mapping (-Hk19)
   • In the section Set advanced output options:
     • “Select an output format”: paf

   tip Tip: Selecting a dataset collection as input

   1. Click on param-collection 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

   question Question

   How many contigs do we have for the RB05 sample after the use of Minimap2 tool and Miniasm tool?
   
   Hint: run Nanoplot tool on the output of GFA to Fasta tool

   solution Solution

   22

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

Ultrafast consensus module using Racon

The mapped reads can be improved even more using Racon tool (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.

hands_on Hands-on: Consensus module

1. Racon tool with the following parameters
   • “Sequences”: The Plasmids dataset collection
   • “Overlaps”: the latest PAF file collection created by Minimap2 tool
   • “Target sequences”: the FASTA file collection created by GFA to Fasta tool

The Racon tool 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 tool can give a clear view of the assembly.

Bandage tool (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 tool (Li 2016), Velvet tool (Zerbino and Birney 2008), SPAdes tool (Bankevich et al. 2012), Trinity tool (Grabherr et al. 2011) and MEGAHIT tool 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.

hands_on Hands-on: Visualising de novo assembly graphs

1. Bandage image tool with the following parameters
   • “Graphical Fragment Assembly”: the Assembly graph collection created by Miniasm tool
2. Explore galaxy-eye the output images

question Question

In how many samples were the full plasmid sequences assembled?

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

solution Solution

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):

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 tool (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 tool, which will repeatedly run these tools on your data using different parameter settings, in order to find the optimal assembly.

Unicycler tool 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 tool 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 Hands-on: Unicycler assembly

1. Create assemblies with Unicycler tool 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 tool with the following parameters
   • “Graphical Fragment Assembly”: the Final Assembly Graph collection created by Unicycler tool
3. Examine galaxy-eye the output images again

question Question

For which samples has the plasmid assembly improved?

solution Solution

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 tool shows one unclear hypothetical plasmid, where the output of Unicycler tool shows two clear plasmids, as also shown by Li et al. 2018.

Species and plasmids

Prediction of plasmid sequences and classes using PlasFlow

To automatically determine whether the contigs represent chromosomal or plasmid DNA, PlasFlow tool (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 tool 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%.

hands_on Hands-on: Prediction of plasmid sequences

1. PlasFlow tool with the following parameters
   • “Sequence Reads”: the Final Assembly collection created by Unicycler tool

question Question

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

solution Solution

plasmid.Proteobacteria

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

The most important output of PlasFlow tool 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 tool 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.

hands_on Hands-on: Prediction of AMR genes

1. staramr tool with the following parameters
   • “genomes”: the Final Assembly collection created by Unicycler

question Question

Which samples contained the resistance gene: dfrA17?

Hint: Check the resfinder.tsv created by staramr

solution Solution

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 tool:

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)

question Question

What is the resistance mechanism of the dfrA17 gene?

solution Solution

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:

keypoints Key points

• Minimap2, Miniasm, and Racon can be used for quickly assembling Nanopore data

• Unicycler can be used to optimize settings of assembly tools

• Nanopore sequencing is useful for reconstruction of genomes

• Antimicrobial resistance genes are detectable after fast assembly

• The CARD database is a useful resource describing antibiotic resistance genes

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. Munita, J. M., and C. A. Arias Mechanisms of Antibiotic Resistance, pp. 481–511 in Virulence Mechanisms of Bacterial Pathogens, Fifth Edition, American Society of Microbiology. 10.1128/microbiolspec.vmbf-0016-2015
2. Zerbino, D. R., and E. Birney, 2008 Velvet: Algorithms for de novo short read assembly using de Bruijn graphs. Genome Research 18: 821–829. 10.1101/gr.074492.107
3. Smillie, C., M. P. Garcillan-Barcia, M. V. Francia, E. P. C. Rocha, and F. de la Cruz, 2010 Mobility of Plasmids. Microbiology and Molecular Biology Reviews 74: 434–452. 10.1128/mmbr.00020-10
4. Grabherr, M. G., B. J. Haas, M. Yassour, J. Z. Levin, D. A. Thompson et al., 2011 Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotechnology 29: 644–652. 10.1038/nbt.1883
5. Bankevich, A., S. Nurk, D. Antipov, A. A. Gurevich, M. Dvorkin et al., 2012 SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing. Journal of Computational Biology 19: 455–477. 10.1089/cmb.2012.0021
6. Zankari, E., H. Hasman, S. Cosentino, M. Vestergaard, S. Rasmussen et al., 2012 Identification of acquired antimicrobial resistance genes. Journal of Antimicrobial Chemotherapy 67: 2640–2644. 10.1093/jac/dks261
7. Carattoli, A., E. Zankari, Garcı́a-Fernández Aurora, M. V. Larsen, O. Lund et al., 2014 In SilicoDetection and Typing of Plasmids using PlasmidFinder and Plasmid Multilocus Sequence Typing. Antimicrobial Agents and Chemotherapy 58: 3895–3903. 10.1128/aac.02412-14
8. Beatson, S. A., and M. J. Walker, 2014 Tracking antibiotic resistance. Science 345: 1454–1455. 10.1126/science.1260471
9. Li, D., C.-M. Liu, R. Luo, K. Sadakane, and T.-W. Lam, 2015 MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31: 1674–1676. 10.1093/bioinformatics/btv033
10. Wick, R. R., M. B. Schultz, J. Zobel, and K. E. Holt, 2015 Bandage: interactive visualization ofde novogenome assemblies: Fig. 1. Bioinformatics 31: 3350–3352. 10.1093/bioinformatics/btv383
11. Li, H., 2016 Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences. Bioinformatics 32: 2103–2110. 10.1093/bioinformatics/btw152
12. Jia, B., A. R. Raphenya, B. Alcock, N. Waglechner, P. Guo et al., 2016 CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Research 45: D566–D573. 10.1093/nar/gkw1004
13. Vaser, R., I. Sović, N. Nagarajan, and M. Šikić, 2017 Fast and accurate de novo genome assembly from long uncorrected reads. Genome Research 27: 737–746. 10.1101/gr.214270.116
14. Wick, R. R., L. M. Judd, C. L. Gorrie, and K. E. Holt, 2017 Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. PLOS Computational Biology 13: 1–22. 10.1371/journal.pcbi.1005595
15. Zankari, E., R. Allesøe, K. G. Joensen, L. M. Cavaco, O. Lund et al., 2017 PointFinder: a novel web tool for WGS-based detection of antimicrobial resistance associated with chromosomal point mutations in bacterial pathogens. Journal of Antimicrobial Chemotherapy 72: 2764–2768. 10.1093/jac/dkx217
16. Jayakumar, V., and Y. Sakakibara, 2017 Comprehensive evaluation of non-hybrid genome assembly tools for third-generation PacBio long-read sequence data. Briefings in Bioinformatics 20: 866–876. 10.1093/bib/bbx147
17. Krawczyk, P. S., L. Lipinski, and A. Dziembowski, 2018 PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures. Nucleic Acids Research 46: e35–e35. 10.1093/nar/gkx1321
18. Li, R., M. Xie, N. Dong, D. Lin, X. Yang et al., 2018 Efficient generation of complete sequences of MDR-encoding plasmids by rapid assembly of MinION barcoding sequencing data. GigaScience 7: 10.1093/gigascience/gix132
19. Coster, W. D., S. D’Hert, D. T. Schultz, M. Cruts, and C. V. Broeckhoven, 2018 NanoPack: visualizing and processing long-read sequencing data (B. Berger, Ed.). Bioinformatics 34: 2666–2669. 10.1093/bioinformatics/bty149
20. Li, H., 2018 Minimap2: pairwise alignment for nucleotide sequences (I. Birol, Ed.). Bioinformatics 34: 3094–3100. 10.1093/bioinformatics/bty191
21. Maio, N. D., L. P. Shaw, A. Hubbard, S. George, N. Sanderson et al., 2019 Comparison of long-read sequencing technologies in the hybrid assembly of complex bacterial genomes. 10.1101/530824

Citing this Tutorial

1. Willem de Koning, Saskia Hiltemann, 2020 Antibiotic resistance detection (Galaxy Training Materials). /training-material/topics/metagenomics/tutorials/plasmid-metagenomics-nanopore/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-plasmid-metagenomics-nanopore,
    author = "Willem de Koning and Saskia Hiltemann",
    title = "Antibiotic resistance detection (Galaxy Training Materials)",
    year = "2020",
    month = "01",
    day = "16"
    url = "\url{/training-material/topics/metagenomics/tutorials/plasmid-metagenomics-nanopore/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}
}