Barcodes are small oligonucleotides that are inserted into the captured sequence at a specific point, and provide two pieces of information about the sequence:

1. Which cell the sequence came from
2. Which transcript the sequence came from

When the sequence is mapped against a reference genome, we can then see which gene locus it aligns to and qualitatively assert that, together with the two pieces of information above, the sequence depicts a transcript from a specific gene that originated from a specific cell.

Barcodes come in a variety of formats, and in this tutorial we will be looking at the CEL-Seq2 protocol Hashimshony et al. 2016 used in droplet-based single-cell RNA-seq.

The CEL-Seq2 Protocol

Back to previous

CEL-Seq2 is a paired-end protocol, meaning that two primers bind to opposite ends of a cDNA strand in order to sequence. Each primer has a specific role.

In this case; Read1 contains the barcoding information followed by the polyT tail of the messenger RNA, and Read2 contains the actual sequence. Here, Read1 is regarded as the ‘forward’ strand and Read2 as the ‘reverse’ strand, though this is more a convention when dealing with paired-end data rather than an indication of the actual strand orientation.

Agenda

In this tutorial, we will cover:

1. The CEL-Seq2 Protocol
2. Understanding Barcodes
3. Mitigating duplicate transcript counts with UMIs
   1. Naive Amplification
   2. Amplification with UMIs
4. Barcoding Format
   1. Our Four Reads of Interest
   2. Verifying the Barcode Format
5. Uniting Barcodes with Sequence
   1. Coupling our Data Sources
6. Conclusion

Understanding Barcodes

Cell barcodes are designed primarily for delineating one cell from another, such that read transcripts containing different cell barcodes can be trivially said to be derived from different cells.

Open image in new tab

Transcript barcodes, meanwhile, are random sets of nucleotides added to each transcript.

Open image in new tab

There are two things to take note of:

1. The number of duplicates in the transcript barcodes (left)
2. The number of duplicate read transcripts (right)

Transcript barcodes are often not unique. This becomes evident when you consider that there are approximately 200,000 mRNA’s in a given mammalian cell (Shapiro et al. 2013) which would require barcode lengths of greater than 9 nucleotides to capture, assuming no sequencing errors.

Question

1. Why is it important to know which cell a read came from?
2. Why do we need to barcode a read transcript too? Isn’t mapping it against the reference genome enough?

Solution

1. If all our reads encode for a Red Gene (as above), we may want to know which cells express Red Gene more than others.
   • e.g. If our Grey cell has 10 times more Red Gene reads than our Green cell, then we know that the Grey cell and Green cell differ in their expression of Red Gene - which might be biologically significant.
2. Yes and no!
   • Yes: We can indeed align our sequence against a reference genome and obtain the name of the gene it aligns against. This sequence will then contribute to the ‘count’ of sequences that gene has, and increase the expression of that gene.
   • No: We do not know whether these ‘counts’ are unique. Many of these counts could be duplicates as a result of the amplification process. To explain further, we must look at UMIs and their role in the analysis.

The purpose of transcript barcodes is to reduce the impact of duplicated reads that occur non-linearly during the amplification process.

For this reason, transcript barcodes do not need to be unique. As long as we know that a given read maps to a specific transcript (i.e. after mapping it to a transcriptome), then we can assess how unique that read is based on:

1. Cell barcode
2. Transcript barcode
3. Mapped location

To fully explore the uniqueness of counts, we must discuss the inclusion of UMIs in a single-cell analysis.

Mitigating duplicate transcript counts with UMIs

One of the major issues with sequencing is that the read fragments require amplification before they can be sequenced. A gene with a single mRNA transcript will not be detected by most sequencers, so it needs to be duplicated 100-1000x times for the sequencer to ‘see’ it.

Amplification is an imprecise process however, since some reads are amplified more than others, and subsequent amplification can lead to these over-amplified reads being over-amplified even more, leading to an exponential bias of some reads over others.

Note: Cell barcodes are not shown in any of the below examples, we assume they were added to our transcripts previously.

Naive Amplification

Open image in new tab

Consider the above example where two reads from different transcripts are amplified unevenly. The resulting frequency table would yield:

	Reads in Cell 1
Gene Red	4
Gene Blue	0

But the truth is entirely different (i.e. Gene Red should have 1 count, and Gene Blue should also have 1 count). How do we correct for this bias?

Amplification with UMIs

Unique Molecular Identifiers (or UMIs) constitute the second portion of a barcode, where their role is to uniquely count reads such that amplicons of the same read are only counted once, e.g:

Open image in new tab

Here, we see two unique transcripts from Gene Red and two unique transcripts from Gene Blue, each given a (coloured) UMI. After amplification, Gene Red has more reads than Gene Blue. If we were to construct a frequency table as before to count the reads, we would have:

	Reads in Cell 1
Gene Red	6
Gene Blue	3

This information is false, because it shows that Red has twice the expression that Blue does. However, we can reconstitute the true count by considering the UMI information:

	UMI colour	Reads in Cell 1
Gene Red	Pink	2
	Blue	4
Gene Blue	Pink	1
	Green	2

From this we can then make the decision to ignore the frequencies of these UMIs, and simply count how many unique UMIs we see in each gene:

	Set of UMIs in Gene	UMIs in Cell 1
Gene Red	{Pink, Blue}	2
Gene Blue	{Pink, Green}	2

This then provides us with the true count of the number of true transcripts for each gene as given by our original figure.

	UMIs in Cell 1
Gene Red	2
Gene Blue	2

Question: about UMIs

1. Are UMIs not specific to certain genes? Can the same UMI map to different genes?
2. Can the same UMI map to different mRNA molecules of the same gene?

Solution

1. Yes, UMIs are not specific to genes and the same UMI barcode can tag the transcripts of different genes. UMIs are not universal tags, they are just ‘added randomness’ that help reduce amplification bias.
2. Yes, UMIs are not precise but operate probabilistically. In most cases, two transcripts of the same gene will be tagged by different UMIs. In rarer (but still prevalent) cases, the same UMI will capture different transcripts of the same gene.
   • One helpful way to think about how quantification is performed is to observe the following hierarchy of data Cell Barcode → Gene → UMI

e.g.

BC:Cell	Maps to Gene	BC:UMI
AAAT	Slx1	TCA
AAAT	Slx2	GTG
AAAT	Gh13	TCA
TTAA	Slx1	TCA
TTAA	Atp3	CCC

If UMIs were unique to a gene, then the TCA UMI barcode would not have reads that map to both Slx1 and Gh13 in the same cell (AAAT).

Barcoding Format

We now know the role of UMIs and cell barcodes, but how do we handle them in the analysis? Let us look at 4 example sequences in our paired-end FASTQ data.

Hands-on: Preparing the Data

1. Create a new history and rename it (e.g. ‘Inspecting FastQ Files in scRNA batch data’)
2. Import the following files from Zenodo or from the data library (ask your instructor)

   https://zenodo.org/record/2573177/files/test_barcodes_celseq2_R1.fastq.gz
   https://zenodo.org/record/2573177/files/test_barcodes_celseq2_R2.fastq.gz
   

   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

   
   

3. Build a Dataset pair for the two FASTQ files
   • Click on galaxy-selector “Select Items” at the top of the history panel
   • Check the two boxes next to the R1 and R2 scRNA FASTQ samples
   • Click 2 of N selected and choose Build dataset pair
   • Ensure that the forward read is the R1 sample, and the reverse read is the R2 sample.
     • Click ‘Swap’ otherwise.
   • Set the name of the pair
4. Generate a list of reads to filter by creating a plain tabular file containing the following read names:

   J00182:75:HTKJNBBXX:2:1114:12469:11073
   J00182:75:HTKJNBBXX:2:2222:13301:35690
   J00182:75:HTKJNBBXX:2:1203:25022:13763
   J00182:75:HTKJNBBXX:2:1115:8501:46961
   

5. Set the datatype of the file as tabular

At this point we now have a history with two items: our paired FASTQ test data, and a tabular file of read names. We will now apply the tabular file to the FASTQ file and extract only those reads.

Hands-on: Extracting the Reads

1. Extracting our 4 reads
   • Filter sequences by ID ( Galaxy version 0.2.7) with the following parameters:
     • Sequence file to be filtered
       • Click the Dataset Collection icon
       • Select the FastQ collection if not already selected.
     • Filter using the ID list from:tabular file
       • Tabular file containing sequence identifiers:Pasted Entry
     • Column(s) containing sequence identifiers
       • Select/Unselect all:(tick the box)
     • Output positive matches, negative matches, or both?:Just positive matches (ID on list), as a single file

2. Change the datatypes of the output pair to fastqsanger if not already set.

   Tip: Changing the datatype

   • Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
   • In the central panel, click galaxy-chart-select-data Datatypes tab on the top
   • In the galaxy-chart-select-data Assign Datatype, select your desired datatype from “New type” dropdown
     • Tip: you can start typing the datatype into the field to filter the dropdown menu
   • Click the Save button

3. Viewing our 4 reads side-by-side
   • Activate the Scratchbook by clicking on the Enable/Disable Scratchbook icon on the main top toolbar
   • Click on the newly generated FastQ pair ending in “with matched ID” to expand the individual reads
     • Click on the galaxy-eye symbol of the forward read
     • Click somewhere outside the white box to close the Scratchbook
     • Click on the galaxy-eye symbol of the reverse read
   • Position/Resize the boxes as desired

Our Four Reads of Interest

Let us examine these four reads of interest which we have just sub-selected using their headers:

Details: Forward Reads:

@J00182:75:HTKJNBBXX:2:1115:8501:46961 1:N:0:ATCACG
GGAAGAACCAGATTTTTTTTTTTTTTTTTT
+
AAFFFJJJJJJJFFFJJJJJJJJJJJJJJJ

@J00182:75:HTKJNBBXX:2:1203:25022:13763 1:N:0:ATCACG
GTCCCAGGTAACTTTTTTTTTTTTTTTTTT
+
AAFFFJJJJJJJJFFJJJJJJJJJFJ<FF-

@J00182:75:HTKJNBBXX:2:2222:13301:35690 1:N:0:ATCACG
GTCCCAGGTAACTTTTTTTTTTTTTTTTTT
+
AAFFFJJJJJJJ<AFJJJJJFFJJFJJJFF

@J00182:75:HTKJNBBXX:2:1114:12469:11073 1:N:0:ATCACG
CGGCGTGGTAACTTTTTTTTTTTTTTTTCC
+
AAFFFJJJJJJJFAFFJJJJJJJJF---<F

Details: Reverse Reads:

@J00182:75:HTKJNBBXX:2:1115:8501:46961 2:N:0:ATCACG
GACCTCTGATCTTTACGAAAGGCCAACGCGTTTTCAGTCTGGACACGGTTCAGCTCCTGTTCATTATTCA
+
A<<A-777F<AA<AJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJ

@J00182:75:HTKJNBBXX:2:1203:25022:13763 2:N:0:ATCACG
GCCACCTAATTTCCGTCATCACACTCCTCTCCGTTTTCAACTTGCACAATGCTGTCTCCGCAGAATCCCT
+
---<----<A---77-7A-FJ<JJFFJJ<JJAJ7<-FAFFJJFF<FFJJFFAJFA-AFFFJFFFFFJAJJ

@J00182:75:HTKJNBBXX:2:2222:13301:35690 2:N:0:ATCACG
CAATCCTCTCCGTTATCAACTTGCACAATGCTGTCTCCGCAGAATCCCTCCGGATCAGGATCGCTCTCCA
+
<<A-77--77F<----7AFJ-A--FJJJFAJF-AFAJAJ<JFJ<JJJFFJJJFJJJJJAAFJJJFJJJF-

@J00182:75:HTKJNBBXX:2:1114:12469:11073 2:N:0:ATCACG
ATCCACTTATTGCAAAGCAGAGGACATTGAGTCTCACCTTTTGTCCAGGTCTTCCAATTTCACCCTGCAA
+
A-77AA-7FF<7FFJFFFJJJJJJJJJJJJJ-AFJJJJJJJFJJJJJJJJJJJJJJJJJJJJJJJJJJJJ

What we observe are the standard four lines of any FASTQ file:

1. Read name starting with @
2. Sequence of nucleotide bases
3. Separator +
4. Quality string of the nucleotide bases in ASCII

The main source of interest for us is in the (2) sequences of these reads, which somewhere within encode for three crucial pieces of information that we will need to perform quantification:

1. Cell Barcode
2. UMI Barcode
3. Reverse-transcribed mRNA sequence

These can be encoded into the sequences of our paired-end data by any means. In order to know where our barcodes are, we must be familiar with the sequencing primers used in the analysis:

Verifying the Barcode Format

As shown in CEL-Seq2 protocol, we have the following encoding:

• Forward Read:
  • 01-06bp: UMI Barcode
  • 07-12bp: Cell Barcode
  • 13-30bp: Poly-T tail
• Reverse Read:
  • 01-70bp: mRNA sequence

The encoding of the barcodes on the first read can actually be seen by examining the distribution of bases in a FastQC plot.

Hands-on: Confirming the Barcoding

1. FastQC ( Galaxy version 0.72+galaxy1) with the following parameters:
   • param-collection “Short read data from your current history”: Paired FastQ (the original paired set) You will need to choose ‘Dataset collection’ to allow this as an input.

   Comment

   We are only interested in the distribution of bases on the Forward read, but it is more convenient to process the data as a pair instead of un-hiding the original dataset

2. Click on FastQC on collection :Webpage
3. Click on the galaxy-eye of the Forward read
4. Click on the Per base sequence content header on the side-bar

Open image in new tab

Here we can see the three distinct regions along the x-axis that correspond to our expected CEL-Seq2 Schema:

• 01-06bp: smooth, relatively constant bases.
• 07-12bp: noisy, highly varied distribution of bases.
• 13-30bp: T-dominated region

We can see that the distribution of nucleotides in the 01-06bp range is relatively more stable than the distribution of nucleotides in the 07-12bp range, which seems to exhibit more extreme variation.

Question

Why is this the case? Why is the UMI barcode base distribution smoother than the Cell barcode base distribution?

Solution

There are far more UMIs than cells. Cell barcodes are designed and selected with a specified edit distance, greatly limiting their availability in the data. UMIs are not so well-curated – i.e it is possible to encounter the same UMI in the same cell multiple times. The more extreme variation in the 7-12bp region is simply caused by a fewer number of samples.

Uniting Barcodes with Sequence

In a sense, we have a disparity in our data: the reverse reads contain the sequences we wish to map, but not the barcodes; the forward reads contain the barcode, but not the sequence. For the forward and reverse reads given above, the information that we really want from both can be summarized in this table:

Question

Provided that these reads all map to the same gene:

1. Which of these reads come from the same cell?
2. Which of these reads are PCR duplicates?

Solution

1. Reads:
   • @J00182:75:HTKJNBBXX:2:1203:25022:13763
   • @J00182:75:HTKJNBBXX:2:2222:13301:35690
   • @J00182:75:HTKJNBBXX:2:1114:12469:11073 all have the cell barcode GGTAAC.
2. Reads:
   • @J00182:75:HTKJNBBXX:2:1203:25022:13763
   • @J00182:75:HTKJNBBXX:2:2222:13301:35690 appear to be PCR duplicates, since they both have the same cell barcode and same UMI.

However if we consider their sequences, we can see that they contain different (but overlapping) sequences.

13763:   GCCACCTAATTTCCGTCATCACACTCCTCTCCGTTTTCAACTTGCACAATGCTGTCTCCGCAGAATCCCT
35690:                        CAATCCTCTCCGTTATCAACTTGCACAATGCTGTCTCCGCAGAATCCCTCCGGATCAGGATCGCTCTCCA

They describe the same transcript but have sequences from different reads, and therefore both reads should be counted as separate reads. Whether or not both these reads are counted as a single read due to their identical barcodes, or counted separately due to their differing sequences depends entirely on the deduplication utility they are fed into it.

Coupling our Data Sources

How should we unite these two source of information into a single location without impacting the data content?

For this we need to take the barcode information from the Forward reads, and stick it into the header of the Reverse reads. That way we can align our sequence to the reference and still keep the barcode information associated with the reads.

Hands-on: Barcode Extraction and Annotation of our 4 reads

1. UMI-tools extract ( Galaxy version 0.5.5.1) with the following parameters:
   • “Library type”: Paired-end Dataset Collection
     • param-collection “Reads in FASTQ format”: output (Our paired set of 4 sequences)
     • “Barcode on both reads?”: Barcode on first read only
   • “Use Known Barcodes?”: No
   • “Barcode pattern for first read”: NNNNNNCCCCCC
   • “Enable quality filter?”: No

   Comment

   • Here we specify the format of our barcodes as NNNNNNCCCCCC where the Ns represent UMI bases and the Cs represent the cell barcodes.
   • In some protocols, actual sequence data can be found in between the cell and UMI barcodes, wherein it is neccesary to represent sequence bases using X.
   • e.g. A protocol that starts with a 3bp sequence, followed by a 4bp Cell barcode, followed once again by a 10bp sequence, and then finally a 5bp UMI barcode, would require the following barcode format: XXXCCCCXXXXXXXXXXNNNNN

2. Click the galaxy-eye symbol on the Reads1: UMI-tools extract file
3. Click somewhere outside the white box to close the Scratchbook
4. Click the galaxy-eye symbol on the Reads2: UMI-tools extract file

We should now be able to see the following reads:

Details: Forward Reads:

@J00182:75:HTKJNBBXX:2:1115:8501:46961_ACCAGA_GGAAGA 1:N:0:ATCACG
TTTTTTTTTTTTTTTTTT
+
FFFJJJJJJJJJJJJJJJ

@J00182:75:HTKJNBBXX:2:1203:25022:13763_GGTAAC_GTCCCA 1:N:0:ATCACG
TTTTTTTTTTTTTTTTTT
+
JFFJJJJJJJJJFJ<FF-

@J00182:75:HTKJNBBXX:2:2222:13301:35690_GGTAAC_GTCCCA 1:N:0:ATCACG
TTTTTTTTTTTTTTTTTT
+
<AFJJJJJFFJJFJJJFF

@J00182:75:HTKJNBBXX:2:1114:12469:11073_GGTAAC_CGGCGT 1:N:0:ATCACG
TTTTTTTTTTTTTTTTCC
+
FAFFJJJJJJJJF---<F>

Details: Reverse Reads:

@J00182:75:HTKJNBBXX:2:1115:8501:46961_ACCAGA_GGAAGA 2:N:0:ATCACG
GACCTCTGATCTTTACGAAAGGCCAACGCGTTTTCAGTCTGGACACGGTTCAGCTCCTGTTCATTATTCA
+
A<<A-777F<AA<AJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJ

@J00182:75:HTKJNBBXX:2:1203:25022:13763_GGTAAC_GTCCCA 2:N:0:ATCACG
GCCACCTAATTTCCGTCATCACACTCCTCTCCGTTTTCAACTTGCACAATGCTGTCTCCGCAGAATCCCT
+
---<----<A---77-7A-FJ<JJFFJJ<JJAJ7<-FAFFJJFF<FFJJFFAJFA-AFFFJFFFFFJAJJ

@J00182:75:HTKJNBBXX:2:2222:13301:35690_GGTAAC_GTCCCA 2:N:0:ATCACG
CAATCCTCTCCGTTATCAACTTGCACAATGCTGTCTCCGCAGAATCCCTCCGGATCAGGATCGCTCTCCA
+
<<A-77--77F<----7AFJ-A--FJJJFAJF-AFAJAJ<JFJ<JJJFFJJJFJJJJJAAFJJJFJJJF-

@J00182:75:HTKJNBBXX:2:1114:12469:11073_GGTAAC_CGGCGT 2:N:0:ATCACG
ATCCACTTATTGCAAAGCAGAGGACATTGAGTCTCACCTTTTGTCCAGGTCTTCCAATTTCACCCTGCAA
+
A-77AA-7FF<7FFJFFFJJJJJJJJJJJJJ-AFJJJJJJJFJJJJJJJJJJJJJJJJJJJJJJJJJJJJ

Notice the remaining sequence in each of the reads, and that the reverse reads appear to fully encapsulate all the information that we wanted to capture in our table at the beginning of this section.

Question

1. Compare the Forward/Read1 and Reverse/Read2 reads to those prior the extraction. What has happened to the header and sequence of each read?
2. Are the Forward reads useful at all?

Solution

1. Comparison:
   • Forward:
     • Sequence: The cell and umi sections of the sequence have been removed, leaving behind only the PolyT tail.
     • Header: The cell and umi sections of the sequence have been added as cell_umi barcode in the header
   • Reverse:
     • Sequence: Has not changed.
     • Header: The cell and umi sections of the sequence from the Forward (note: NOT Reverse) reads have been added to the header.
2. With the inclusion of the cell and UMI barcodes into the header of our sequence data, we now have all our useful data in the Reverse reads. We can now effectively throw away our Forward reads, as they have no more useful information within them.

We have now successfully multiplexed data from several different (cell) sources into a single file that will greatly simplify the mapping/alignment process downstream.

We have also now successfully de-multiplexed our data, by decoding each pair of reads into barcoding and sequence parts and making use of the FASTQ format by storing this information within the FASTQ headers and data, respectively.

Conclusion

With this tutorial we have understood the importance of handling FASTQ data from different sources, and extracting the information we need (barcodes (cell and UMI) and sequence) using UMI-tools so that we can perform mapping without losing any context of where the reads are derived from.

This tutorial is part of the https://singlecell.usegalaxy.eu portal (Tekman et al. 2020).

You've Finished the Tutorial

Please also consider filling out the Feedback Form as well!

Key points

• Verifying the distribution of barcodes via a FASTQC plot

• Relocating barcodes into headers

• Removing unwanted barcodes

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. Shapiro, E., T. Biezuner, and S. Linnarsson, 2013 Single-cell sequencing-based technologies will revolutionize whole-organism science. Nature Reviews Genetics 14: 618–630. 10.1038/nrg3542
2. Hashimshony, T., N. Senderovich, G. Avital, A. Klochendler, Y. de Leeuw et al., 2016 CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biology 17: 10.1186/s13059-016-0938-8
3. Tekman, M., B. Batut, A. Ostrovsky, C. Antoniewski, D. Clements et al., 2020 A single-cell RNA-sequencing training and analysis suite using the Galaxy framework. GigaScience 9: giaa102. 10.1093/gigascience/giaa102 https://academic.oup.com/gigascience/article/9/10/giaa102/5931798

Feedback

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Citing this Tutorial

1. Mehmet Tekman, Understanding Barcodes (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-umis/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{single-cell-scrna-umis,
author = "Mehmet Tekman",
	title = "Understanding Barcodes (Galaxy Training Materials)",
	year = "",
	month = "",
	day = "",
	url = "\url{https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-umis/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}
}

                   

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/single-cell/tutorials/scrna-umis/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: e7b2202befea
  tool_panel_section_label: FASTA/FASTQ
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: umi_tools_extract
  owner: iuc
  revisions: 6a675c3aa610
  tool_panel_section_label: FASTA/FASTQ
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: seq_filter_by_id
  owner: peterjc
  revisions: 141612f8c3e3
  tool_panel_section_label: Filter and Sort
  tool_shed_url: https://toolshed.g2.bx.psu.edu/

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• 5 stars: Liked: yes

May 2024

• 5 stars: Liked: Very interactive. Video helped when I couldn't find certain tools, the explanations were very in-depth. This was precisely the resource that a beginner like me needed (but couldn't find elsewhere) for getting started with pre-processing expression data. I am not in a genomics lab, so a tool like this is absolutely invaluable. Thank you so much! Disliked: A few things. 1) it was difficult to know how to search for the tools (i.e. UMI-tools extract) and I had to look at the video to understand how to access it. 2) I had to double-back from UMIs to understand why we need both cell-barcodes and transcript-barcodes. What made everything click was the statement that there are 200,000 mRNA transcripts in each cell, so one gene will produce k transcripts in the cell, and the goal of transcript-barcodes is to try to uniquely identify k. 3) Figure 1 is still a bit unclear to me. If read 1 and read 2 are performed and sequenced separately, then how does the fastq file know which read 1 is paired with which read 2? Perhaps this information is in an earlier tutorial, and I am doing this out of sequence. 4) I would like to see a bit more calculus on how 6 base pairs is enough. 4^6 = 4096, so does that mean each processing batch can handle up to 4096 cells? But the cell barcodes are designed to be far apart, so maybe it's closer 1024 cells? Also, that implies that we can capture at max 4096 transcript copies per gene, which seems like not a lot? Generally, concrete numbers like these and motivations for why only 6 base pairs are used for cell barcodes and transcript barcodes would be very insightful (maybe some people use more, maybe there are tradeoffs?). 5) Figure 1 was incredibly insightful, but still a bit abstract. I know the simplicity helps broad comprehension, but I personally wanted a single, very concrete example of an RNA sequence (with all 70 bases), because there is some ambiguity. For example, in Figure 1, what is V? Also, the article starts off talking about cDNA, but then refers to figure 1 and mRNA. It might be too tedious or pedagogical, but it would have helped (and still would help!) a complete beginner like me to understand step-by-step even the very beginning of "this is a typical mRNA strand of length 70", "mRNA has a poly-A tail that is specifically targeted by a barcode containing a poly-T tail". Something I still don't understand is how the joint complex gets sequenced. The article mentions 2 promoters (one on each end), but what happens when the mRNA gets replicated from one end, and the polymerase reaches the intersection?

September 2023

• 5 stars: Liked: It is understandable
• 5 stars: Liked: Easy to follow with multiple examples

March 2023

• 3 stars: Disliked: explanation on the different types of barcoding/UMI schemes

December 2022

• 4 stars: Liked: UMI was described in detail and examples were great. Disliked: One example with 4 reads were given. What happens to the R2 file when one of the reads is indeed a duplicate? Would there be only 4 reads instead? It would be nice if there are a couple of more examples like that.

March 2022

• 5 stars: Liked: The fact that there are pictures which helps more the understanding fro each step. Disliked: Nothing
• 5 stars: Liked: the brief exxplanation Disliked: more explicit practical experience

November 2021

• 5 stars: Liked: the qs and answers and clear explanation of each step Disliked: refer to which CLI tool commands can be used for each step

July 2021

• 4 stars: Liked: clear explanation of the barcodes/UMI and where/how to find that info in reads Disliked: the 'plain' tabular file was a bit confusing and took a while to get in the right format

February 2021

• 3 stars: Liked: Handy Disliked: Tutorial video went too fast with no much explanation.
• 1 stars: Disliked: what will the preprocessing be useful for - perhaps this should be explained?
• 0 stars: Liked: I've never done a single cell analysis before! Disliked: It was a little bit fast.