Sequencing (determining of DNA/RNA nucleotide sequence) is used all over the world for all kinds of analysis. The product of these sequencers are reads, which are sequences of detected nucleotides. Depending on the technique these have specific lengths (30-500bp) or using Oxford Nanopore Technologies sequencing have much longer variable lengths.

Comment: Illumina MiSeq sequencing

Illumina MiSeq sequencing is based on sequencing by synthesis. As the name suggests, fluorescent labels are measured for every base that bind at a specific moment at a specific place on a flow cell. These flow cells are covered with oligos (small single strand DNA strands). In the library preparation the DNA strands are cut into small DNA fragments (differs per kit/device) and specific pieces of DNA (adapters) are added, which are complementary to the oligos. Using bridge amplification large amounts of clusters of these DNA fragments are made. The reverse string is washed away, making the clusters single stranded. Fluorescent bases are added one by one, which emit a specific light for different bases when added. This is happening for whole clusters, so this light can be detected and this data is basecalled (translation from light to a nucleotide) to a nucleotide sequence (Read). For every base a quality score is determined and also saved per read. This process is repeated for the reverse strand on the same place on the flow cell, so the forward and reverse reads are from the same DNA strand. The forward and reversed reads are linked together and should always be processed together!

For more information watch this video from Illumina

Contemporary sequencing technologies are capable of generating an enormous volume of sequence reads in a single run. Nonetheless, each technology has its imperfections, leading to various types and frequencies of errors, such as miscalled nucleotides. These inaccuracies stem from the inherent technical constraints of each sequencing platform. When sequencing bacterial isolates, it is crucial to verify the quality of the data but also to check the expected species or strains are found in the data or if there is any contamination.

To illustrate the process, we take raw data of a bacterial genome (KUN1163 sample) from sequencing data produced in “Complete Genome Sequences of Eight Methicillin-Resistant Staphylococcus aureus Strains Isolated from Patients in Japan” (Hikichi et al. 2019).

Methicillin-resistant Staphylococcus aureus (MRSA) is a major pathogen causing nosocomial infections, and the clinical manifestations of MRSA range from asymptomatic colonization of the nasal mucosa to soft tissue infection to fulminant invasive disease. Here, we report the complete genome sequences of eight MRSA strains isolated from patients in Japan.

Agenda

In this tutorial, we will cover:

1. Galaxy and data preparation
2. Read quality control and improvement
   1. Quality control
   2. Quality improvement
3. Identification of expected species and detection of contamination
   1. Taxonomic profiling
   2. Species identification
   3. Contamination detection
4. Conclusion

Galaxy and data preparation

Any analysis should get its own Galaxy history. So let’s start by creating a new one and get the data (forward and reverse quality-controlled sequences) into it.

Hands-on: Prepare Galaxy and data

1. Create a new history for this analysis

   Tip: Creating a new history

   To create a new history simply click the new-history icon at the top of the history panel:

   

2. Rename the history

   Tip: Renaming a history

   1. Click on galaxy-pencil (Edit) next to the history name (which by default is “Unnamed history”)
   2. Type the new name
   3. Click on Save
   4. To cancel renaming, click the galaxy-undo “Cancel” button

   If you do not have the galaxy-pencil (Edit) next to the history name (which can be the case if you are using an older version of Galaxy) do the following:

   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

Now, we need to import the data: 2 FASTQ files containing the reads from the sequencer.

Hands-on: Import datasets

1. Import the files from Zenodo or from Galaxy shared data libraries:

   https://zenodo.org/record/10669812/files/DRR187559_1.fastqsanger.bz2
   https://zenodo.org/record/10669812/files/DRR187559_2.fastqsanger.bz2
   

   Tip: Importing via links

   • Copy the link location

   • Click galaxy-upload Upload Data at the top of the tool panel

   • Select galaxy-wf-edit Paste/Fetch Data

   • Paste the link(s) into the text field

   • Press Start

   • Close the window

   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:

   1. Go into Data (top panel) then Data libraries
   2. Navigate to the correct folder as indicated by your instructor.
      • On most Galaxies tutorial data will be provided in a folder named GTN - Material –> Topic Name -> Tutorial Name.
   3. Select the desired files
   4. Click on Add to History galaxy-dropdown near the top and select as Datasets from the dropdown menu

   5. In the pop-up window, choose

      • “Select history”: the history you want to import the data to (or create a new one)
   6. Click on Import

2. Rename the datasets to remove .fastqsanger.bz2 and keep only the sequence run ID (DRR187559_1 and DRR187559_2)

   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 to DRR187559_1
   • Click the Save button

3. Tag both datasets #unfiltered

   Tip: Adding a tag

   Datasets can be tagged. This simplifies the tracking of datasets across the Galaxy interface. Tags can contain any combination of letters or numbers but cannot contain spaces.

   To tag a dataset:

   1. Click on the dataset to expand it
   2. Click on Add Tags galaxy-tags
   3. Add tag text. Tags starting with # will be automatically propagated to the outputs of tools using this dataset (see below).
   4. Press Enter
   5. Check that the tag appears below the dataset name

   Tags beginning with # are special!

   They are called Name tags. The unique feature of these tags is that they propagate: if a dataset is labelled with a name tag, all derivatives (children) of this dataset will automatically inherit this tag (see below). The figure below explains why this is so useful. Consider the following analysis (numbers in parenthesis correspond to dataset numbers in the figure below):

   1. a set of forward and reverse reads (datasets 1 and 2) is mapped against a reference using Bowtie2 generating dataset 3;
   2. dataset 3 is used to calculate read coverage using BedTools Genome Coverage separately for + and - strands. This generates two datasets (4 and 5 for plus and minus, respectively);
   3. datasets 4 and 5 are used as inputs to Macs2 broadCall datasets generating datasets 6 and 8;
   4. datasets 6 and 8 are intersected with coordinates of genes (dataset 9) using BedTools Intersect generating datasets 10 and 11.

   

   Now consider that this analysis is done without name tags. This is shown on the left side of the figure. It is hard to trace which datasets contain “plus” data versus “minus” data. For example, does dataset 10 contain “plus” data or “minus” data? Probably “minus” but are you sure? In the case of a small history like the one shown here, it is possible to trace this manually but as the size of a history grows it will become very challenging.

   The right side of the figure shows exactly the same analysis, but using name tags. When the analysis was conducted datasets 4 and 5 were tagged with #plus and #minus, respectively. When they were used as inputs to Macs2 resulting datasets 6 and 8 automatically inherited them and so on… As a result it is straightforward to trace both branches (plus and minus) of this analysis.

   More information is in a dedicated #nametag tutorial.

4. View galaxy-eye the renamed file

The datasets are both FASTQ files.

Question

1. What are the 4 main features of each read in a FASTQ file.
2. What does the _1 and _2 mean in your filenames?

Solution

1. The following:

   • A @ followed by a name and sometimes information of the read
   • A nucleotide sequence
   • A + (optional followed by the name)
   • The quality score per base of nucleotide sequence (Each symbol represents a quality score, which will be explained later)

2. Forward and reverse reads, by convention, are labelled _1 and _2, but they might also be _f/_r or _r1/_r2.

Hands-on: Choose Your Own Tutorial

This is a "Choose Your Own Tutorial" section, where you can select between multiple paths. Click one of the buttons below to select how you want to follow the tutorial

Do you want to go step-by-step or using a workflow?

Step-by-step Workflow

Read quality control and improvement

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. Adapters may also be present if the reads are longer than the fragments sequenced and trimming these may improve the number of reads mapped. Sequence quality control is therefore an essential first step in any analysis.

Assessing the quality by hand would be too much work. That’s why tools like NanoPlot or FastQC are made, as they generate a summary and plots of the data statistics. NanoPlot is mainly used for long-read data, like ONT and PACBIO and FastQC for short read, like Illumina and Sanger. You can read more in our dedicated Quality Control Tutorial.

Before doing any analysis, the first questions we should ask about the input reads include:

• What is the coverage of my genome?
• How good are my reads?
• Do I need to ask/perform for a new sequencing run?
• Is it suitable for the analysis I need to do?

Quality control

FastQC combines quality statistics from all separate reads and combines them in plots. An important plot is the Per base sequence quality.

DRR187559_1	DRR187559_2

Here you have the reads sequence length on the x-axes against the quality score (Phred-score) on the y-axis. The y-axis is divided in three sections:

• Green = good quality,
• Orange = mediocre quality, and
• Red = bad quality.

For each position, a boxplot is drawn with:

• the median value, represented by the central red line
• the inter-quartile range (25-75%), represented by the yellow box
• the 10% and 90% values in the upper and lower whiskers
• the mean quality, represented by the blue line

Question

How does the mean quality score change along the sequence?

Solution

The mean quality score (blue line) decreases at the sequences end. It is common for the mean quality to drop towards the end of the sequences, as the sequencers are incorporating more incorrect nucleotides at the end. For Illumina data it is normal that the first few bases are of some lower quality and how longer the reads get the worse the quality becomes. This is often due to signal decay or phasing during the sequencing run.

Quality improvement

Depending on the analysis it could be possible that a certain quality or length is needed. In this case we are going to trim the data using fastp (Chen et al. 2018):

• Trim the start and end of the reads if those fall below a quality score of 20

  Different trimming tools have different algorithms for deciding when to cut but trimmomatic will cut based on the quality score of one base alone. Trimmomatic starts from each end, and as long as the base is below 20, it will be cut until it reaches one greater than 20. A sliding window trimming will be performed where if the average quality of 4 bases drops below 20, the read will be truncated there.

• Filter for reads to keep only reads with at least 30 bases: Anything shorter will complicate the assembly

fastp generates also a report, similar to FastQC, useful to compare the impact of the trimming and filtering.

Question

Looking at fastp HTML report

1. How did the average read length change before and after filtering?
2. Did trimming improve the mean quality scores?
3. Did trimming affect the GC content?
4. Is this data ok to assemble? Do we need to re-sequence it?

Solution

1. Read lengths went down more significantly:
   • Before filtering: 190bp, 221bp
   • After filtering: 189bp, 219bp
2. It increased the percentage of Q20 and Q30 bases (bases with quality score above 20 and 30 respectively)
3. No, it did not. If it had, that would be unexpected.
4. This data looks OK. The number of short reads in R1 is not optimal but assembly should partially work but not the entire, closed genome.

Identification of expected species and detection of contamination

When working with bacterial isolates, it is crucial to verify whether the expected species or strains are present in the data and to identify any potential contamination. Ensuring the presence of the intended species is essential for the accuracy and reliability of the research, as deviations could lead to erroneous conclusions. Additionally, detecting contamination is vital to maintain the integrity of the samples and to avoid misleading results that could compromise subsequent analyses and applications.

Taxonomic profiling

To find out which microorganisms are present, we will compare the filtered reads of the sample to a reference database, i.e. sequences of known microorganisms stored in a database, using Kraken2 (Wood et al. 2019).

Details: Kraken2 and the k-mer approach for taxonomy classification

In the \(k\)-mer approach for taxonomy classification, we use a database containing DNA sequences of genomes whose taxonomy we already know. On a computer, the genome sequences are broken into short pieces of length \(k\) (called \(k\)-mers), usually 30bp.

Kraken examines the \(k\)-mers within the query sequence, searches for them in the database, looks for where these are placed within the taxonomy tree inside the database, makes the classification with the most probable position, then maps \(k\)-mers to the lowest common ancestor (LCA) of all genomes known to contain the given \(k\)-mer.

Kraken2 uses a compact hash table, a probabilistic data structure that allows for faster queries and lower memory requirements. It applies a spaced seed mask of s spaces to the minimizer and calculates a compact hash code, which is then used as a search query in its compact hash table; the lowest common ancestor (LCA) taxon associated with the compact hash code is then assigned to the k-mer.

You can find more information about the Kraken2 algorithm in the paper Improved metagenomic analysis with Kraken 2.

For this tutorial, we will use the PlusPF database which contains the RefSeq Standard (archaea, bacteria, viral, plasmid, human, UniVec_Core), protozoa and fungi data.

Kraken2 generates 2 outputs:

• Classification: tabular files with one line for each sequence classified by Kraken and 5 columns:

  1. C/U: a one letter indicating if the sequence classified or unclassified
  2. Sequence ID as in the input file
  3. NCBI taxonomy ID assigned to the sequence, or 0 if unclassified
  4. Length of sequence in bp (read1|read2 for paired reads)

  5. A space-delimited list indicating the lowest common ancestor (LCA) mapping of each k-mer in the sequence

     For example, 562:13 561:4 A:31 0:1 562:3 would indicate that:

     1. The first 13 k-mers mapped to taxonomy ID #562
     2. The next 4 k-mers mapped to taxonomy ID #561
     3. The next 31 k-mers contained an ambiguous nucleotide
     4. The next k-mer was not in the database
     5. The last 3 k-mers mapped to taxonomy ID #562

     |:| indicates end of first read, start of second read for paired reads

   Column 1	Column 2	Column 3	Column 4	Column 5
   C	DRR187559.1	1280	164|85	0:1 1279:1 0:41 1279:10 0:5 1280:5 1279:1 0:1 1279:1 0:7 1279:5 0:2 1279:6 0:12 1279:5 0:19 1279:2 0:6 |:| 0:39 1279:2 0:6 1279:3 0:1
   C	DRR187559.2	1280	70|198	0:2 1279:5 0:29 |:| 0:52 1279:5 0:13 1279:3 0:23 1279:2 0:45 1280:1 0:9 1280:3 A:8
   C	DRR187559.3	1279	106|73	0:4 1279:3 0:36 1279:4 0:10 1279:5 0:3 1279:5 0:2 |:| 0:39
   C	DRR187559.4	1279	121|189	1279:6 0:17 1279:4 0:28 1279:2 0:30 |:| 0:7 1279:5 0:19 1279:5 0:20 1279:1 0:8 1279:1 0:1 1279:6 0:25 1279:2 0:44 A:11
   C	DRR187559.5	1279	68|150	1279:2 0:20 1279:3 0:9 |:| 0:10 1279:3 0:24 1279:2 0:9 1279:5 0:21 1279:5 0:20 1279:5 0:9 1279:1 0:2
   C	DRR187559.6	1280	137|246	0:2 1280:5 0:28 1279:1 0:28 1279:2 0:8 1279:2 0:23 1279:1 0:3 |:| 1279:1 0:2 1279:3 0:61 1279:2 0:14 1279:2 0:97 A:30 
  

  Question

  1. Is the first sequence in the file classified or unclassified?
  2. What is the taxonomy ID assigned to the first classified sequence?
  3. What is the corresponding taxon?

  Solution

  1. classified
  2. 1280
  3. 1280 corresponds to Staphylococcus aureus .

• Report: tabular files with one line per taxon and 6 columns or fields

  1. Percentage of fragments covered by the clade rooted at this taxon
  2. Number of fragments covered by the clade rooted at this taxon
  3. Number of fragments assigned directly to this taxon
  4. A rank code, indicating
     • (U)nclassified
     • (R)oot
     • (D)omain
     • (K)ingdom
     • (P)hylum
     • (C)lass
     • (O)rder
     • (F)amily
     • (G)enus, or
     • (S)pecies

     Taxa that are not at any of these 10 ranks have a rank code that is formed by using the rank code of the closest ancestor rank with a number indicating the distance from that rank. E.g., G2 is a rank code indicating a taxon is between genus and species and the grandparent taxon is at the genus rank.

  5. NCBI taxonomic ID number
  6. Indented scientific name

   Column 1	Column 2	Column 3	Column 4	Column 5	Column 6
   0.24 	1065 	1065 	U 	0 	unclassified
   99.76 	450716 	14873 	R 	1 	root
   96.44 	435695 	2 	R1 	131567 	cellular organisms
   96.44 	435675 	3889 	D 	2 	Bacteria
   95.56 	431709 	78 	D1 	1783272 	Terrabacteria group
   95.53 	431578 	163 	P 	1239 	Firmicutes
   95.49 	431390 	4625 	C 	91061 	Bacilli
   94.38 	426383 	1436 	O 	1385 	Bacillales
   94.04 	424874 	2689 	F 	90964 	Staphylococcaceae
   93.44 	422124 	234829 	G 	1279 	Staphylococcus 
  

  Question

  1. How many taxa have been found?
  2. What are the percentage on unclassified?
  3. What are the domains found?

  Solution

  1. 627, as the number of lines
  2. 0.24%
  3. Only Bacteria

Species identification

In Kraken output, there are quite a lot of identified taxa with different levels. To obtain a more accurate and detailed understanding at the species level, we will use Bracken.

Bracken refines the Kraken results by re-estimating the abundances of species in metagenomic samples, providing a more precise and reliable identification of species, which is crucial for downstream analysis and interpretation.

Bracken (Bayesian Reestimation of Abundance after Classification with Kraken) is a “simple and worthwile addition to Kraken for better abundance estimates” (Ye et al. 2019). Instead of only using proportions of classified reads, it takes a probabilistic approach to generate final abundance profiles. It works by re-distributing reads in the taxonomic tree: “Reads assigned to nodes above the species level are distributed down to the species nodes, while reads assigned at the strain level are re-distributed upward to their parent species” (Lu et al. 2017).

Bracken generates 2 outputs:

• Kraken style report: tabular files with one line per taxon and 6 columns or fields. Same configuration as the Report output of Kraken:

  1. Percentage of fragments covered by the clade rooted at this taxon
  2. Number of fragments covered by the clade rooted at this taxon
  3. Number of fragments assigned directly to this taxon
  4. A rank code, indicating
     • (U)nclassified
     • (R)oot
     • (D)omain
     • (K)ingdom
     • (P)hylum
     • (C)lass
     • (O)rder
     • (F)amily
     • (G)enus, or
     • (S)pecies
  5. NCBI taxonomic ID number
  6. Indented scientific name

   Column 1	Column 2	Column 3	Column 4	Column 5	Column 6
   100.00 	450408 	0 	R 	1 	root
   99.14 	446538 	0 	R1 	131567 	cellular organisms
   99.14 	446524 	0 	D 	2 	Bacteria
   99.14 	446524 	0 	D1 	1783272 	Terrabacteria group
   99.13 	446491 	0 	P 	1239 	Firmicutes
   99.13 	446491 	0 	C 	91061 	Bacilli
   99.06 	446152 	0 	O 	1385 	Bacillales
   99.06 	446152 	0 	F 	90964 	Staphylococcaceae
   99.04 	446101 	0 	G 	1279 	Staphylococcus
   95.62 	430661 	430661 	S 	1280 	Staphylococcus aureus 
  

  Question

  1. How many taxa have been found?
  2. What is the family found?

  Solution

  1. 119, as the number of lines
  2. Staphylococcus, with 99.04%!

• Report: tabular files with one line per taxon and 7 columns or fields

  1. Taxon name
  2. Taxonomy ID
  3. Level ID (S=Species, G=Genus, O=Order, F=Family, P=Phylum, K=Kingdom)
  4. Kraken assigned reads
  5. Added reads with abundance re-estimation
  6. Total reads after abundance re-estimation
  7. Fraction of total reads

   Column 1	Column 2	Column 3	Column 4	Column 5	Column 6	Column 7
   Staphylococcus aureus 	1280 	S 	182995 	247666 	430661 	0.95616
   Staphylococcus roterodami 	2699836 	S 	698 	48 	746 	0.00166
   Staphylococcus epidermidis 	1282 	S 	587 	13 	600 	0.00133
   Staphylococcus lugdunensis 	28035 	S 	511 	3 	514 	0.00114
   Staphylococcus schweitzeri 	1654388 	S 	409 	26 	435 	0.00097
   Staphylococcus simiae 	308354 	S 	398 	2 	400 	0.00089 
  

Question

1. How many species have been found?
2. Which the species has been the most identified? And in which proportion?
3. What are the other species?

Solution

1. 51 (52 lines including 1 line with header)
2. Staphylococcus aureus with 95.6% of the reads.
3. Most of the other species are from Staphylococcus genus, so same as Staphylococcus aureus. The other species in really low proportion.

As expected Staphylococcus aureus represents most of the reads in the data.

Contamination detection

To explore Kraken report and specially to detect more reliably minority organisms or contamination, we will use Recentrifuge (Martı́ Jose Manuel 2019).

Recentrifuge enhances analysis by reanalyzing metagenomic classifications with interactive charts that highlight confidence levels. It supports 48 taxonomic ranks of the NCBI Taxonomy, including strains, and generates plots for shared and exclusive taxa, facilitating robust comparative analysis.

Recentrifuge includes a novel contamination removal algorithm, useful when negative control samples are available, ensuring data integrity with control-subtracted plots. It also excels in detecting minority organisms in complex datasets, crucial for sensitive applications such as clinical and environmental studies.

Recentrifuge generates 3 outputs:

• A statistic table with general statistics about assignations

  Question

  1. How many sequences have been used?
  2. How many sequences have been classified?

  Solution

  1. 451,780
  2. 450,715

• A data table with a report for each taxa

  Question

  1. How many taxa have been kept?
  2. What is the lowest level in the data?

  Solution

  1. 187 (190 lines including 3 header lines)
  2. The lowest level is strain.

• A HTML report with a Krona chart

  Question

  1. What is the percentage of classified sequences?
  2. When clicking on Staphylococcus aureus, what can we say about the strains?
  3. Is there any contamination?

  Solution

  1. 99.8%
  2. 99% of sequences assigned to Staphylococcus aureus are not assigned to any strains, probably because they are too similar to several strains. Staphylococcus aureus subsp. aureus JKD6159 is the strain with the most classified sequences, but only 0.3% of the sequences assigned to Staphylococcus aureus.
  3. There is no sign of a possible contamination. Most sequences are classified to taxon on the Staphylococcus aureus taxonomy. Only 3% of the sequences are not classified to Staphylococcus.

Once we have identified contamination, if any is present, the next step is to remove the contaminated sequences from the dataset to ensure the integrity of the remaining data. This can be done using bioinformatics tools designed to filter out unwanted sequences, such as BBduk (Bushnell et al. 2017).

BBduk tool is a member of the BBTools package, where ‘Duk’ stands for Decontamination Using Kmers. BBduk filters or trims reads for adapters and contaminants using k-mers, effectively removing unwanted sequences and improving the quality of the dataset.

Additionally, it’s important to document and report the contamination findings to maintain transparency and guide any necessary adjustments in sample collection or processing protocols.

Conclusion

In this tutorial, we inspected the quality of the bacterial isolate sequencing data and checked the expected species and potential contamination. Prepared short reads can be used in downstream analysis, like Genome Assembly. Even after the assembly, you can identify contamination using a tool like CheckM (Parks et al. 2015). CheckM tool can be used for screening the ‘cleanness’ of bacterial assemblies.

To learn more about data quality control, you can follow this tutorial: Quality Control.

You've Finished the Tutorial

Please also consider filling out the Feedback Form as well!

Key points

• Conduct quality control on every dataset before performing any other bioinformatics analysis

• Review the quality metrics and, if necessary, improve the quality of your data

• Check the impact of the quality control

• Detect witch microorganisms are present and extract the species level

• Check possible contamination in a bacterial isolate

Frequently Asked Questions

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. Wood, D. E., and S. L. Salzberg, 2014 Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biology 15: R46. 10.1186/gb-2014-15-3-r46
2. Parks, D. H., M. Imelfort, C. T. Skennerton, P. Hugenholtz, and G. W. Tyson, 2015 CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Research 25: 1043–1055. 10.1101/gr.186072.114
3. Bushnell, B., J. Rood, and E. Singer, 2017 BBMerge - Accurate paired shotgun read merging via overlap (P. J. Biggs, Ed.). 12: e0185056. 10.1371/journal.pone.0185056
4. Lu, J., F. P. Breitwieser, P. Thielen, and S. L. Salzberg, 2017 Bracken: estimating species abundance in metagenomics data. PeerJ Computer Science 3: e104. 10.7717/peerj-cs.104
5. Chen, S., Y. Zhou, Y. Chen, and J. Gu, 2018 fastp: an ultra-fast all-in-one FASTQ preprocessor. 10.1093/bioinformatics/bty560
6. Hikichi, M., M. Nagao, K. Murase, C. Aikawa, T. Nozawa et al., 2019 Complete Genome Sequences of Eight Methicillin-Resistant Staphylococcus aureus Strains Isolated from Patients in Japan (I. L. G. Newton, Ed.). Microbiology Resource Announcements 8: 10.1128/mra.01212-19
7. Martı́ Jose Manuel, 2019 Recentrifuge: Robust comparative analysis and contamination removal for metagenomics. PLoS computational biology 15: e1006967. 10.1371/journal.pcbi.100696
8. Wood, D. E., J. Lu, and B. Langmead, 2019 Improved metagenomic analysis with Kraken 2. Genome biology 20: 1–13. 10.1186/s13059-019-1891-0
9. Ye, S. H., K. J. Siddle, D. J. Park, and P. C. Sabeti, 2019 Benchmarking Metagenomics Tools for Taxonomic Classification. Cell 178: 779–794. 10.1016/j.cell.2019.07.010

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Citing this Tutorial

1. Bérénice Batut, Clea Siguret, Quality and contamination control in bacterial isolate using Illumina MiSeq Data (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/quality-contamination-control/tutorial.html Online; accessed TODAY
2. Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
3. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

BibTeX

@misc{sequence-analysis-quality-contamination-control,
author = "Bérénice Batut and Clea Siguret",
	title = "Quality and contamination control in bacterial isolate using Illumina MiSeq Data (Galaxy Training Materials)",
	year = "",
	month = "",
	day = ""
	url = "\url{https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/quality-contamination-control/tutorial.html}",
	note = "[Online; accessed TODAY]"
}
@article{Hiltemann_2023,
	doi = {10.1371/journal.pcbi.1010752},
	url = {https://doi.org/10.1371%2Fjournal.pcbi.1010752},
	year = 2023,
	month = {jan},
	publisher = {Public Library of Science ({PLoS})},
	volume = {19},
	number = {1},
	pages = {e1010752},
	author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
	editor = {Francis Ouellette},
	title = {Galaxy Training: A powerful framework for teaching!},
	journal = {PLoS Comput Biol} Computational Biology}
}

                   

Funding

These individuals or organisations provided funding support for the development of this resource

ABRomics

Galaxy Administrators: Install the missing tools

You can use Ephemeris's shed-tools install command to install the tools used in this tutorial.

shed-tools install [-g GALAXY] [-a API_KEY] -t <(curl https://training.galaxyproject.org/training-material/api/topics/sequence-analysis/tutorials/quality-contamination-control/tutorial.json | jq .admin_install_yaml -r)

Alternatively you can copy and paste the following YAML

---
install_tool_dependencies: true
install_repository_dependencies: true
install_resolver_dependencies: true
tools:
- name: fastqc
  owner: devteam
  revisions: 5ec9f6bceaee
  tool_panel_section_label: FASTA/FASTQ
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: bracken
  owner: iuc
  revisions: 978ae4147c29
  tool_panel_section_label: Metagenomic Analysis
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: bracken
  owner: iuc
  revisions: 1d4bd12f01cf
  tool_panel_section_label: Metagenomic Analysis
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: fastp
  owner: iuc
  revisions: 65b93b623c77
  tool_panel_section_label: FASTA/FASTQ
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: fastp
  owner: iuc
  revisions: c59d48774d03
  tool_panel_section_label: FASTA/FASTQ
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: kraken2
  owner: iuc
  revisions: 20e2f64aa1fe
  tool_panel_section_label: Metagenomic Analysis
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: kraken2
  owner: iuc
  revisions: cdee7158adf3
  tool_panel_section_label: Metagenomic Analysis
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: recentrifuge
  owner: iuc
  revisions: b5407cc2bf51
  tool_panel_section_label: Metagenomic Analysis
  tool_shed_url: https://toolshed.g2.bx.psu.edu/

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