Which Read Is Barcode on Single Cell Sequencing

Introduction

Barcodes are pocket-sized oligonucleotides that are inserted into the captured sequence at a specific indicate, 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 confronting a reference genome, we can then see which gene locus information technology aligns to and qualitatively assert that, together with the two pieces of data in a higher place, the sequence depicts a transcript from a specific gene that originated from a specific cell.

Barcodes come in a diversity of formats, and in this tutorial we will exist looking at the CEL-Seq2 protocol used in droplet-based single-prison cell RNA-seq.

The CEL-Seq2 Protocol

Dorsum to previous

CEL-Seq2 is a paired-end protocol, meaning that 2 primers bind to contrary ends of a cDNA strand in order to sequence. Each primer has a specific role.

In this case; Read1 contains the barcoding data followed by the polyT tail of the messenger RNA, and Read2 contains the actual sequence. Hither, Read1 is regarded as the 'frontwards' strand and Read2 every bit the 'reverse' strand, though this is more than a convention when dealing with paired-end data rather than an indication of the bodily strand orientation.

Figure one: Read1 encapsulates the barcodes, and Read2 the thouRNA sequence

Agenda

In this tutorial, we will encompass:

1. Agreement Barcodes
2. Mitigating indistinguishable transcript counts with UMIs
   1. Naive Amplification
   2. Distension with UMIs
3. Barcoding Format
   1. Our Four Reads of Interest
   2. Verifying the Barcode Format
4. Uniting Barcodes with Sequence
   1. Coupling our Data Sources

Understanding Barcodes

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

Figure 2: 2 split jail cell barcodes GGG and TCT, added to all read transcripts of two different cells

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

Effigy 3: Random oligonucleotides barcodes added to (prison cell barcoded) reads

At that place are 2 things to take annotation of:

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

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

question Questions

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 information technology against the reference genome enough?

solution Solution

1. If all our reads encode for a Red Cistron (as above), nosotros may want to know which cells express Ruby Gene more than others.
   • e.g. If our Grey cell has ten times more Red Cistron reads than our Green cell, so we know that the Grey cell and Green jail cell differ in their expression of Red Gene - which might be biologically significant.
2. Yep and no!
   • Yes: Nosotros can indeed align our sequence against a reference genome and obtain the name of the factor it aligns against. This sequence will and so contribute to the 'count' of sequences that cistron has, and increase the expression of that cistron.
   • No: We practice not know whether these 'counts' are unique. Many of these counts could exist duplicates equally a result of the amplification process. To explain farther, 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. Equally long as nosotros know that a given read maps to a specific transcript (i.eastward. subsequently mapping information technology to a transcriptome), then we tin assess how unique that read is based on:

1. Jail cell barcode
2. Transcript barcode
3. Mapped location

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

Mitigating duplicate transcript counts with UMIs

Ane of the major bug with sequencing is that the read fragments require distension before they can be sequenced. A gene with a single mRNA transcript will non be detected by almost sequencers, so it needs to exist duplicated 100-1000x times for the sequencer to 'see' information technology.

Amplification is an imprecise process withal, since some reads are amplified more others, and subsequent amplification can atomic number 82 to these over-amplified reads being over-amplified even more, leading to an exponential bias of some reads over others.

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

Naive Amplification

Figure 4: A jail cell with 2 reads from unlike transcripts being amplified unevenly

Consider the to a higher place instance where 2 reads from unlike transcripts are amplified unevenly. The resulting frequency table would yield:

	Reads in Cell i
Cistron Crimson	4
Gene Bluish	0

But the truth is entirely different (i.e. Gene Carmine should have i count, and Gene Blue should likewise take one count). How do we right for this bias?

Amplification with UMIs

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

Effigy 5: A cell with four unique transcripts, two from Gene Carmine and ii from Gene Blue

Here, we come across two unique transcripts from Gene Blood-red and 2 unique transcripts from Gene Blue, each given a (coloured) UMI. Subsequently amplification, Gene Red has more than reads than Gene Blueish. If nosotros were to construct a frequency table equally earlier to count the reads, we would have:

	Reads in Prison cell ane
Gene Red	half dozen
Gene Bluish	three

This information is faux, considering it shows that Red has twice the expression that Blue does. Even so, we can reconstitute the truthful count past considering the UMI data:

	UMI colour	Reads in Jail cell 1
Gene Blood-red	Pink	2
	Blue	4
Gene Bluish	Brown	1
	Green	2

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

	Set of UMIs in Factor	UMIs in Jail cell 1
Gene Red	{Pink, Bluish}	2
Factor Bluish	{Brown, Green}	ii

This then provides u.s.a. with the truthful count of the number of true transcripts for each cistron equally given by our original figure.

	UMIs in Cell 1
Cistron Cherry-red	2
Gene Bluish	ii

question Questions about UMIs

1. Are UMIs non specific to certain genes? Can the aforementioned UMI map to different genes?
2. Can the aforementioned UMI map to dissimilar mRNA molecules of the aforementioned gene?

solution Solution

1. Aye, UMIs are not specific to genes and the aforementioned UMI barcode tin can tag the transcripts of different genes. UMIs are not universal tags, they are but 'added randomness' that help reduce distension bias.
2. Aye, UMIs are not precise but operate probabilistically. In nearly cases, 2 transcripts of the same factor will be tagged by different UMIs. In rarer (simply even so prevalent) cases, the same UMI will capture different transcripts of the aforementioned gene.
   • One helpful fashion to think about how quantification is performed is to detect the following bureaucracy of data Cell Barcode → Gene → UMI

due east.thousand.

BC:Jail 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 take reads that map to both Slx1 and Gh13 in the same cell (AAAT).

Barcoding Format

We now know the role of UMIs and jail cell barcodes, merely how do we handle them in the analysis? Let united states of america look at iv example sequences in our paired-stop FASTQ information.

hands_on Hands-on: Preparing the Information

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 (enquire 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

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

   • Select Paste/Fetch Data

   • Paste the link into the text field

   • Printing Start

   • Shut the window

3. Build a Dataset pair for the 2 FASTQ files
   • Click the Operations on multiple datasets cheque box at the acme of the history panel
   • Cheque the two boxes adjacent to the R1 and R2 scRNA FASTQ samples
   • Click For all selected… and choose Build dataset pair
   • Ensure that the forrard 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 past creating a manifestly 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. Ready the datatype of the file equally tabular

At this point we at present accept a history with 2 items: our paired FASTQ test data, and a tabular file of read names. We volition now apply the tabular file to the FASTQ file and extract only those reads.

hands_on Hands-on: Extracting the Reads

1. Extracting our four reads
   • Filter sequences by ID Tool: toolshed.g2.bx.psu.edu/repos/peterjc/seq_filter_by_id/seq_filter_by_id/0.2.7 with the post-obit parameters:
     • Sequence file to be filtered
       • Click the Dataset Collection icon
       • Select the FastQ drove 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. Modify the datatypes of the output pair to fastqsanger if non already gear up.

   Tip: Changing the datatype

   • Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
   • In the central panel, click on the milky way-nautical chart-select-data Datatypes tab on the elevation
   • Select your desired datatype
   • Click the Salvage push

3. Viewing our 4 reads side-past-side
   • Activate the Scratchbook by clicking on the Enable/Disable Scratchbook icon on the principal elevation toolbar
   • Click on the newly generated FastQ pair ending in "with matched ID" to aggrandize the individual reads
     • Click on the milky way-heart symbol of the forwards read
     • Click somewhere exterior the white box to close the Scratchbook
     • Click on the galaxy-eye symbol of the opposite read
   • Position/Resize the boxes as desired

Our Four Reads of Interest

Permit us examine these 4 reads of interest which we have just sub-selected using their headers:

details Forrard 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 ane:Due north:0:ATCACG CGGCGTGGTAACTTTTTTTTTTTTTTTTCC + AAFFFJJJJJJJFAFFJJJJJJJJF---<F                          

details Contrary Reads:

              @J00182:75:HTKJNBBXX:2:1115:8501:46961 two:North: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:two:2222:13301:35690 2:Due north:0:ATCACG CAATCCTCTCCGTTATCAACTTGCACAATGCTGTCTCCGCAGAATCCCTCCGGATCAGGATCGCTCTCCA + <<A-77--77F<----7AFJ-A--FJJJFAJF-AFAJAJ<JFJ<JJJFFJJJFJJJJJAAFJJJFJJJF-  @J00182:75:HTKJNBBXX:ii:1114:12469:11073 ii:Northward:0:ATCACG ATCCACTTATTGCAAAGCAGAGGACATTGAGTCTCACCTTTTGTCCAGGTCTTCCAATTTCACCCTGCAA + A-77AA-7FF<7FFJFFFJJJJJJJJJJJJJ-AFJJJJJJJFJJJJJJJJJJJJJJJJJJJJJJJJJJJJ                          

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

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

The main source of interest for us is in the (ii) sequences of these reads, which somewhere within encode for 3 crucial pieces of information that we will need to perform quantification:

1. Prison cell Barcode
2. UMI Barcode
3. Reverse-transcribed mRNA sequence

These can be encoded into the sequences of our paired-end information past any ways. In society to know where our barcodes are, we must exist 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 really be seen by examining the distribution of bases in a FastQC plot.

hands_on Hands-on: Confirming the Barcoding

1. FastQC Tool: toolshed.g2.bx.psu.edu/repos/devteam/fastqc/fastqc/0.72+milky way1 with the following parameters:
   • param-collection "Brusque read data from your current history": Paired FastQ (the original paired set) You lot will need to choose 'Dataset collection' to allow this equally an input.
2. Click on FastQC on drove :Webpage
3. Click on the milky way-eye of the Forward read
4. Click on the Per base sequence content header on the side-bar

Figure 6: FastQC plot of Forward read

Here we can see the iii distinct regions forth 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

Nosotros tin 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 Question

Why is this the example? Why is the UMI barcode base distribution smoother than the Jail cell barcode base distribution?

solution Solution

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

Uniting Barcodes with Sequence

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

Read	Cell	UMI	Sequence
@J00182:75:HTKJNBBXX:2:1115:8501:46961	ACCAGA	GGAAGA	GACCTCTGATCTTTACGAAAGGCCAACGCGTTTTCAGTCTGGACACGGTTCAGCTCCTGTTCATTATTCA
@J00182:75:HTKJNBBXX:2:1203:25022:13763	GGTAAC	GTCCCA	GCCACCTAATTTCCGTCATCACACTCCTCTCCGTTTTCAACTTGCACAATGCTGTCTCCGCAGAATCCCT
@J00182:75:HTKJNBBXX:2:2222:13301:35690	GGTAAC	GTCCCA	CAATCCTCTCCGTTATCAACTTGCACAATGCTGTCTCCGCAGAATCCCTCCGGATCAGGATCGCTCTCCA
@J00182:75:HTKJNBBXX:2:1114:12469:11073	GGTAAC	CGGCGT	ATCCACTTATTGCAAAGCAGAGGACATTGAGTCTCACCTTTTGTCCAGGTCTTCCAATTTCACCCTGCAA

question 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 Solution

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

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

                13763:   GCCACCTAATTTCCGTCATCACACTCCTCTCCGTTTTCAACTTGCACAATGCTGTCTCCGCAGAATCCCT 35690:                        CAATCCTCTCCGTTATCAACTTGCACAATGCTGTCTCCGCAGAATCCCTCCGGATCAGGATCGCTCTCCA                              

They draw the aforementioned transcript but take sequences from different reads, and therefore both reads should be counted equally divide reads. Whether or not both these reads are counted every bit a unmarried 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 demand to take the barcode data from the Forward reads, and stick information technology into the header of the Contrary reads. That manner nosotros tin align our sequence to the reference and notwithstanding keep the barcode information associated with the reads.

hands_on Hands-on: Barcode Extraction and Annotation of our iv reads

1. UMI-tools extract Tool: toolshed.g2.bx.psu.edu/repos/iuc/umi_tools_extract/umi_tools_extract/0.v.five.i with the post-obit parameters:
   • "Library type": Paired-terminate Dataset Collection
     • param-drove "Reads in FASTQ format": output (Our paired set up of iv sequences)
     • "Barcode on both reads?": Barcode on kickoff read simply
   • "Use Known Barcodes?": No
   • "Barcode blueprint for start read": NNNNNNCCCCCC
   • "Enable quality filter?": No
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-centre symbol on the Reads2: UMI-tools extract file

We should now be able to come across the post-obit reads:

details Forward Reads:

              @J00182:75:HTKJNBBXX:ii:1115:8501:46961_ACCAGA_GGAAGA ane:N:0:ATCACG TTTTTTTTTTTTTTTTTT + FFFJJJJJJJJJJJJJJJ  @J00182:75:HTKJNBBXX:two:1203:25022:13763_GGTAAC_GTCCCA 1:N:0:ATCACG TTTTTTTTTTTTTTTTTT + JFFJJJJJJJJJFJ<FF-  @J00182:75:HTKJNBBXX:two:2222:13301:35690_GGTAAC_GTCCCA 1:North: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:ii:1115:8501:46961_ACCAGA_GGAAGA two:Due north: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:Northward: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                          

Observe 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 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 Frontwards reads useful at all?

solution Solution

1. Comparison:
   • Forward:
     • Sequence: The cell and umi sections of the sequence accept been removed, leaving behind merely the PolyT tail.
     • Header: The prison cell and umi sections of the sequence have been added equally cell_umi barcode in the header
   • Opposite:
     • Sequence: Has not changed.
     • Header: The cell and umi sections of the sequence from the Forward (annotation: Non Reverse) reads have been added to the header.
2. With the inclusion of the prison cell and UMI barcodes into the header of our sequence information, nosotros at present accept all our useful information in the Reverse reads. Nosotros can now finer throw away our Forward reads, as they have no more useful data inside them.

Nosotros have now successfully multiplexed data from several dissimilar (prison cell) sources into a single file that will greatly simplify the mapping/alignment procedure downstream.

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

Conclusion

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

This tutorial is function of the https://singlecell.usemilky way.eu portal (Tekman et al. 2020).

Key points

• Verifying the distribution of barcodes via a FASTQC plot

• Relocating barcodes into headers

• Removing unwanted barcodes

Frequently Asked Questions

Have questions nearly this tutorial? Bank check out the tutorial FAQ page or the FAQ page for the Transcriptomics topic to see if your question is listed there. If not, delight ask your question on the GTN Gitter Channel or the Galaxy Help Forum

Useful literature

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

References

1. Tekman, M., B. Batut, A. Ostrovsky, C. Antoniewski, D. Clements et al., 2020 A unmarried-cell RNA-sequencing training and analysis suite using the Galaxy framework. GigaScience 9: giaa102.

Feedback

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Citing this Tutorial

1. Mehmet Tekman, 2021 Agreement Barcodes (Galaxy Preparation Materials). https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/scrna-umis/tutorial.html Online; accessed TODAY
2. Batut et al., 2018 Community-Driven Data Analysis Grooming for Biological science Cell Systems 10.1016/j.cels.2018.05.012

details BibTeX

              @misc{transcriptomics-scrna-umis, writer = "Mehmet Tekman", title = "Understanding Barcodes (Galaxy Training Materials)", year = "2021", month = "03", 24-hour interval = "23" url = "\url{https://training.galaxyproject.org/training-fabric/topics/transcriptomics/tutorials/scrna-umis/tutorial.html}", annotation = "[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,     calendar month = {jun},     publisher = {Elsevier {BV}},     volume = {6},     number = {6},     pages = {752--758.e1},     writer = {B{\'{eastward}}r{\'{due east}}overnice 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{\`{east}}re and Gildas Le Corguill{\'{eastward}} 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},     championship = {Community-Driven Information Assay Training for Biological science},     journal = {Cell Systems} }            

Congratulations on successfully completing this tutorial!

Do you want to extend your noesis? Follow ane of our recommended follow-upwards trainings:

• Transcriptomics
  • Plates, Batches, and Barcodes: slides slides

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Source: https://galaxyproject.github.io/training-material/topics/transcriptomics/tutorials/scrna-umis/tutorial.html

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