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BLAZE (Barcode identification from Long reads for AnalyZing single-cell gene Expression)

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Important Notes: This repo is actively being updated. Please make sure you have the latest release.

Keywords:

Oxford Nanopore sequencing, Demultiplexing, Single Cell, Barcode.

Overview

Combining single-cell RNA sequencing with Nanopore long-read sequencing enables isoform-level analysis in single cells. However, due to the higher error rate in Nanopore reads, the demultiplexing of cell barcodes and Unique molecular Identifiers (UMIs) can be challenging. This tool enables the accurate identification of barcodes and UMIs solely from Nanopore reads. The output of BLAZE is a barcode whitelist and a fastq of demultiplexed reads with barcodes and UMIs identified, which can be utilised by downstream tools such as FLAMES to quantify genes and isoforms in single cells. For a detailed description of how BLAZE works and its performance across different datasets, please see our Genome Biology paper.

What’s New

Feature added

  • v2.0:

    • Add a final step to perform the read-to-whitelist assignment: A putative barcode (16nt) will first be extended to include flanking bases from both sides. Then we scan through the whitelist and find the one with the lowest subsequence edit distance (ED: defined as the minimum edits required to make a shorter sequence a subsequence of the longer one).

    • Identifies the putative UMI sequences for each read The end position of the barcode, which is also the start position of the UMI sequence, will be corrected by taking into account the insertion and deletion errors in the putative barcode. The 10 (for 10x v2 kit) or 12nt (for 10x v3 kit) sequence immediately downstream will be used as UMI.

    • Significant runtime improvement (~5-10 times faster)

    • Add more information to the putative barcode table:

      • Putative UMI
      • Flanking bases before barcode and after UMI (for correction of insertion and deletion within the putative barcode and UMIs)
  • v2.2:
    • Trim the bases before and included in UMI from the demultiplexed reads: The output format will be in fastq or fastq.gz. The header with be @<16 nt BC>_<12 nt UMI>#read_id_<strand>
    • Add more information to the putative barcode table:
      • UMI end position (used for later trimming the adaptor-UMI sequence from each read)
  • v2.4
    • Adding more supported 10X kit. The option --10x-kit-version (or --kit-version) can take '3v4', '3v3'(default), '3v2', '3v1' for 10X 3' GEX kit v4 to v2 respectively, and '5v3', '5v2' for 10X 5' GEX kit v3 and v2
  • v2.5
    • Restrand the final demultiplexed reads into the transcript strand. This can be turned off by add --no-restrand.

Changes

  • From v2.0:
    • --emptydrop option in v1.x is on by default and is no longer user-specified.
  • From v2.5:
    • New read name format in the demultiplexed FASTQ: @<barcode>_<UMI>_<original read id>_<strand ('+' or '-') CB:Z:<barcode> UB:Z:<UMI>. The CB and UB tag can be pass to the bam file if using minimap2 with option -y.
    • In the final demultiplexed FASTQ, the strand definition has been updated: ‘+’ now represents the transcript strand, while the previous definition associated it with the strand with forward sequence of the cell barcode, which is the reverse strand of the transcript.

Installation

pip3 install blaze2

Dependencies

  • pandas
  • numpy
  • tqdm
  • matplotlib
  • fast-edit-distance

Running BLAZE

Required Input:

  • Long-read fastq files
  • Expected number of cells: The expected number of cells is a required input (specified by --expect-cells=xx). Note that the output is robust to the specified number, but a rough number is needed to determine the count threshold to output the barcode list.
  • Barcode whitelist (optional): A file containing all possible barcodes (more details). Note: there is no need to specify the file if you are using 10x Single Cell gene expression chemistry. The corresponding whitelists are included with BLAZE. By default, BLAZE will assume the use of 3' v3 chemistry and automatically choose the corresponding 10X whitelist. You may specify --kit-version if the data were generated using a different chemistry (run blaze -h for more information). You can also provide your own whitelist by specifying --full-white-list=<filename> (e.g., if you used customised barcodes).

Running BLAZE:

Running the whole pipeline:

blaze --expect-cells <int> --output-prefix <prefix> --threads <int>  <path to the fastq(s)>

The details of the pipeline and output can be found here. Please run blaze -h for more options.

Understanding the BLAZE pipeline and output

BLAZE performs the following steps:

Step 1: Locate the putative barcode and UMI sequence in each read: BLAZE first searches for putative barcodes (i.e. non-error corrected sequence at the expected barcode position) by locating the 10X adapter and polyT in each read. Both the sequenced strand and reverse complement strand are considered for each read. These putative barcodes and their quality scores (minQ) are recorded in putative_bc.csv. The IDs of reads where no putative barcode can be located are also listed in the file but without any putative barcode or minQ score. Note that the putative barcodes and UMIs identified at this step were NOT error-corrected. BLAZE will perform error correction at step 3.

  • Output 1. Putative barcode and UMI sequence in each read, default filename: putative_bc.csv. It contains 7 columns:

    • col1: read id
    • col2: putative barcode (i.e. the basecalled barcode segment in each read, specifically the 16nt sequence after the identified 10X adaptor within each read without correction for any basecalling errors)
    • col3: minimum Phred score of the bases in the putative barcode
    • col4: putative_umi (i.e. the UMI segment in each read, specifically the 10 (for 10x v2 kits) or 12nt (for 10x v3 and v4 kits) sequence after the identified putative barcode without correction for any basecalling errors)
    • col5: 0-based UMI end position in each read, a positive value indicates that the barcode and UMI were found at the forward strand of the read, and a negative value indicates the barcode and UMI were extracted (including the flanking sequencing in col 6 & 7) from the reverse strand.
    • col6: flanking sequence immediately upstream to the barcode in the reads
    • col7: flanking sequence immediately downstream to the UMI in the reads

    Note: col 2 to 7 will be empty if no barcode is found within a read.

Step 2: Generate the barcode list. To accurately identify the barcode list, BLAZE first identifies high-quality putative barcodes by choosing putative barcodes that exactly match the 10X barcode whitelist and have minQ >= threshold (Default: 15). Next, BLAZE scans through the list of high-quality putative barcodes and counts the number of appearances of each unique barcode sequence.
Finally, BLAZE generates a cell-associated barcode list by picking unique barcodes whose counts are above a quantile-based threshold. In addition, BLAZE picks those unique barcodes that are likely associated with empty droplets by choosing unique barcodes that are at least a certain edit distance (Default: 5) from the cell-associated barcodes list. The empty-droplet-associated barcodes can be used for estimating the ambient RNA expression.

  • Output 2: Cell-ranger style cell-associated barcode list, the default filename: whitelist.csv.
  • Output 3: "Barcode rank plot" (or "knee plot") using the high-quality putative barcodes.
  • Output 4: list of barcodes associated with empty droplets, default filename: emtpy_bc.csv.

Step 3: Assign reads to the barcodes. With the barcode list generated in step 2, BLAZE assigns reads to cells by comparing the putative barcodes with the barcode list and finding the closest match. Specifically, for each read, the putative barcode has been identified in step 1. Among the barcode list, BLAZE identifies the barcode with the lowest ED from the read. Note that the reads would not be assigned if 1. the lowest ED is larger than a threshold (Default: 2). 2. Multiple barcodes in the list have an equal lowest ED. If a read barcode is successfully assigned to a barcode, the UMI sequence will be also adjusted for the INDEL error in the putative barcode.

  • Output 5: fastq files with modified read name: @<barcode>_<UMI>_<original read id>_<strand ('+' or '-') CB:Z:<barcode> UB:Z:<UMI>. The CB and UB tag can be pass to the bam file if using minimap2 with option -y. For strand, '+' means the read came from the transcript strand and '-' means the reverse strand.

Note: the output fastq can be directly used in FLAMES for downstream steps.

Additional (optional) features

High-sensitivity mode

By default, BLAZE is configured to minimise false-positive barcode detections and is therefore relatively conservative. BLAZE has a high-sensitivity mode for users who prioritise high recall of the barcodes (and cells) present. To use specify --high-sensitivity-mode. Users should be aware that high-sensitivity mode trades higher recall (i.e. more true barcodes) for potentially lower precision (i.e. more non-cell associated barcodes) and therefore we recommended running an empty drops analysis[1] to distinguish cell-associated barcodes and barcodes from empty droplets with an ambient RNA expression profile.

Example code:

Run BLAZE in default mode: the expected number of cells is set to be 1000 and run with 12 threads

blaze --expect-cells=1000 --threads=12 path/to/fastq_pass

Run BLAZE in high-sensitivity mode: the expected number of cells is set to be 1000 and run with 12 threads

blaze --high-sensitivity-mode --expect-cells=1000 --threads=12 path/to/fastq_pass

Rerun blaze or update previous run(s)

By default, BLAZE reuses existing files if they exist. For example, if you need to change some settings and rerun BLAZE after running:

blaze --expect-cells 500 --output-prefix ourdir/ --threads 8  /data

you will need to specify a different prefix or specify --overwrite. Otherwise, the output would NOT be updated.

BLAZE runs the 3 steps above sequentially, if you believe some files in the previous run can be reused, you could keep them to skip corresponding steps. For example, you have run the following code, which generated the output from the 1st and 2nd steps:

blaze --expect-cells 500 --output-prefix ourdir/ --no-demultiplexing --threads 8  /data

Afterwards, if you need the demultiplexing result, you can directly run

blaze --expect-cells 500 --output-prefix ourdir/ --threads 8  /data

and BLAZE will skip the 1st and 2nd steps as the output files were found in outdir/, which is much faster than rerunning the entire pipeline. However, if runtime is not a concern, it's recommended to use --overwrite option which always runs from the beginning and updates all the output files.

More information:

blaze -h

Limitation:

BLAZE is compatible with 10X 3' v4 kit but its performance hasn’t been tested on it yet.

Citing BLAZE

If you find BLAZE useful for your work, please cite our paper:

You, Y., Prawer, Y. D., De Paoli-Iseppi, R., Hunt, C. P., Parish, C. L., Shim, H., & Clark, M. B. (2023) Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE. Genome Biol 24, 66. You et al. 2023

Data availability

The data underlying the article "Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE" are available from ENA under accession PRJEB54718. The processed data and scripts used in this study are available at https://github.com/youyupei/bc_whitelist_analysis/.

References

[1] Lun, A. T., Riesenfeld, S., Andrews, T., Gomes, T., & Marioni, J. C. (2019). EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome biology, 20(1), 1-9.

Test run

The following command runs BLAZE on a test dataset provided in /test/data. The expected output can be found here.

bash test/run_test.sh