10x Genomics Support/Cell Ranger/Tutorials/

Building a Custom Reference for 3' Single Cell Gene Expression

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Cell Ranger provides pre-built human, mouse, and barnyard (human & mouse) reference packages for read alignment and gene expression quantification in cellranger count. Build notes are available here.

Cell Ranger allows users to create a custom reference package using cellranger mkref. To make a custom reference, you will need a reference genome sequence (FASTA file) and gene annotations (GTF file). A tutorial, Build a Custom Reference (cellranger mkref), is available to walk you through the steps.

Custom references built with previous versions of cellranger mkref can be used with the latest versions of cellranger count or cellranger multi. However, references built with the latest cellranger mkref may not be compatible with all older versions of the pipelines.

Cell Ranger supports the use of customer-generated references under the following conditions:

  • Your reference should have only a small number of overlapping gene annotations. Reads aligning non-uniquely to multiple genes cause the pipeline to detect fewer molecules.
  • Your FASTA and GTF files must be compatible with the open source splicing-aware RNA seq aligner, STAR. To be considered for transcriptome alignment, genes must have annotations with feature type 'exon' (column 3) in the GTF file.

Example use cases:

To create a custom reference:

  1. Filter GTF file with mkgtf to contain only genes of interest
  2. Index the FASTA and GTF files with mkref

GTF files downloaded from sites like ENSEMBL and UCSC often contain transcripts and genes which need to be filtered from your final annotation. Cell Ranger provides mkgtf, a simple utility to filter genes based on their key-value pairs in the GTF attribute column. The command syntax requires input and output GTF file names and --attribute values specifying gene biotypes to filter from the GTF file:

cellranger mkgtf input.gtf output.gtf --attribute=key:allowable_value

In the command above, the allowable_value can be any of the accepted biotypes listed below:

  • protein_coding
  • lncRNA
  • antisense
  • IG_C_gene
  • IG_D_gene
  • IG_J_gene
  • IG_LV_gene
  • IG_V_gene
  • IG_V_pseudogene
  • IG_J_pseudogene
  • IG_C_pseudogene
  • TR_C_gene
  • TR_D_gene
  • TR_J_gene
  • TR_V_gene
  • TR_V_pseudogene
  • TR_J_pseudogene

For example, the following filtering was applied to generate the GTF file for the GRCh38 Cell Ranger reference package:

cellranger mkgtf Homo_sapiens.GRCh38.ensembl.gtf Homo_sapiens.GRCh38.ensembl.filtered.gtf \ --attribute=gene_biotype:protein_coding \ --attribute=gene_biotype:lncRNA \ --attribute=gene_biotype:antisense \ --attribute=gene_biotype:IG_LV_gene \ --attribute=gene_biotype:IG_V_gene \ --attribute=gene_biotype:IG_V_pseudogene \ --attribute=gene_biotype:IG_D_gene \ --attribute=gene_biotype:IG_J_gene \ --attribute=gene_biotype:IG_J_pseudogene \ --attribute=gene_biotype:IG_C_gene \ --attribute=gene_biotype:IG_C_pseudogene \ --attribute=gene_biotype:TR_V_gene \ --attribute=gene_biotype:TR_V_pseudogene \ --attribute=gene_biotype:TR_D_gene \ --attribute=gene_biotype:TR_J_gene \ --attribute=gene_biotype:TR_J_pseudogene \ --attribute=gene_biotype:TR_C_gene

This generated a filtered GTF file Homo_sapiens.GRCh38.ensembl.filtered.gtf from the original unfiltered GTF file Homo_sapiens.GRCh38.ensembl.gtf. In the output file, other biotypes such as gene_biotype:pseudogene are excluded from the GTF annotation.

To create custom references, use the cellranger mkref command, passing it one or more matching sets of FASTA and GTF files. This utility copies your FASTA and GTF, indexes these in several formats, and outputs a folder with the name you pass to --genome. Input GTF files are typically filtered with mkgtf prior to mkref.

ArgumentDescription
--genomeRequired. Unique genome name(s), used to name output folder. Should contain only alphanumeric characters and optionally period, hyphen, and underscore characters [a-zA-Z0-9_-]+. Specify multiple genomes by specifying the --genome argument multiple times.
--fastaRequired. Path(s) to FASTA file containing your genome reference. Specify multiple genomes by specifying the --fasta argument multiple times.
--genesRequired. Path(s) to GTF file(s) containing annotated genes for your genome reference. Specify multiple genomes with the --genes argument for each genome.
--memgbOptional. Maximum memory (GB) used during STAR genome index generation. Defaults to 16. Please note, the amount of memory specified must be greater than the number of gigabases in the input reference FASTA file.
--ref-versionOptional. Reference version string to include with reference.
--nthreadsOptional. Number of threads used during STAR genome index generation. Defaults to 1.
--help or -hOptional. Show list of all arguments and options.
--versionOptional. Show version.

Basic usage

cellranger mkref --genome=output_genome --fasta=input.fa --genes=input.gtf

System requirements

Indexing a typical human 3Gb FASTA file often takes up to 8 core hours and requires 32 GB of memory.

Outputs

A successful mkref run should conclude with a message similar to this:

Creating new reference folder at output_genome ...done Writing genome FASTA file into reference folder... ...done Computing hash of genome FASTA file... ...done Writing genes GTF file into reference folder... WARNING: The following transcripts appear on multiple chromosomes in the GTF: This can indicate a problem with the reference or annotations. Only the first chromosome will be counted. ...done Computing hash of genes GTF file.. ...done Writing genes index file into reference folder (may take over 10 minutes for a 3Gb genome).. ...done Writing genome metadata JSON file into reference folder... ...done Generating STAR genome index (may take over 8 core hours for a 3Gb genome)... ...done. >>> Reference successfully created! <<<

Output listing:

── output_genome ├── fasta │ ├── genome.fa │ └── genome.fa.fai ├── genes │ └── genes.gtf.gz ├── reference.json └── star ├── chrLength.txt ├── chrNameLength.txt ├── chrName.txt ├── chrStart.txt ├── exonGeTrInfo.tab ├── exonInfo.tab ├── geneInfo.tab ├── Genome ├── genomeParameters.txt ├── SA ├── SAindex ├── sjdbInfo.txt ├── sjdbList.fromGTF.out.tab ├── sjdbList.out.tab └── transcriptInfo.tab

The most common use case is to create a reference for only one species. In this case, there is one set of matched FASTA and GTF files typically obtained from Ensembl, NCBI, or UCSC.

cellranger mkref --genome=GRCh38 --fasta=GRCh38.fa --genes=GRCh38-filtered-ensembl.gtf

When possible, please obtain genome sequence (FASTA) and gene annotations (GTF) from the same source: Use Ensembl FASTA files with Ensembl GTF files. Chromosome or sequence names in the FASTA file must match the chromosome or sequence names in the GTF file.

As noted in the STAR manual, the most comprehensive genome sequence and annotations are recommended:

  • For the genome sequence, include all major chromosomes, unplaced and unlocalized scaffolds, but do not include patches and alternative haplotypes.

    • In Ensembl, the recommended genome file to download is annotated as "primary assembly."
    • In NCBI, it is "no alternative - analysis set."
  • For the GTF file, genes must be annotated with feature type "exon" (column 3).

    • Prior to mkref, GTF annotation files from Ensembl and NCBI are typically filtered with mkgtf to include only a subset of the annotated gene biotypes.

To create a reference for multiple species, run the mkref command with multiple FASTA and GTF files. This is similar to the single species case above, but note that the order of the arguments matters. The arguments are grouped by the order they appear; for instance, the first --genome option listed corresponds to the first --fasta and --genes options listed. Please use or create this type of reference when analyzing barnyard validation experiments for estimating multiplet rates.

cellranger mkref --genome=GRCh38 --fasta=GRCh38.fa --genes=GRCh38-filtered-ensembl.gtf \ --genome=mm10 --fasta=mm10.fa --genes=mm10-filtered-ensembl.gtf

Provided that you follow the format described above, it is fairly simple to add custom gene definitions to an existing reference. First, add the additional FASTA sequence records to the fasta/genome.fa file. Next, update the GTF file, genes/genes.gtf, with the gene annotation record(s). An example is described in the cellranger mkref tutorial for adding a marker gene to the FASTA and GTF files.

Depending on your experimental set-up, consider including UTR sequence, and in particular the 3' UTR, to the marker gene. Since 10x Genomics gene expression assays capture transcripts by poly-A and 3' gene expression assays utilize the 3' ends of transcripts to create sequencing library inserts, reads are expected to align towards the 3' end of a transcript, including into the UTR.\n\nIf the UTR sequence is not unique (i.e., recapitulates a sequence in another transcript), it should be included to discount all reads aligning to it and the other locus. Otherwise, in this scenario, the counts at the other locus could be artificially inflated, while reducing counts for the marker gene (more information here).

The GTF file format is essentially a list of records, one per line, each comprising nine tab-delimited non-empty fields.

ColumnNameDescription
1ChromosomeMust refer to a chromosome/contig in the genome fasta.
2SourceUnused.
3Featurecellranger count only uses rows where this line is exon.
4StartStart position on the reference (1-based inclusive).
5EndEnd position on the reference (1-based inclusive).
6ScoreUnused.
7StrandStrandedness of this feature on the reference: + or -.
8FrameUnused.
9AttributesA semicolon-delimited list of key-value pairs of the form key "value". The attribute keys transcript_id and gene_id are required; gene_name is optional and may be non-unique, but if present will be preferentially displayed in reports.

After adding the necessary records to your FASTA file and the additional lines to your GTF file, run cellranger mkref .

The single-nuclei RNA-seq assay captures unspliced pre-mRNA as well as mature mRNA. However, after alignmentcellranger count only counts reads aligned to exons. Since the pre-mRNA will generate intronic reads, it may be useful to count these reads as well. Previously, it was recommended to create a custom “pre-mRNA” reference package, listing each gene transcript locus as an exon, in order to count intronic reads. In Cell Ranger v5.0, there is a new include-introns option for counting intronic reads that should be used instead, and the usage of pre-mRNA references is deprecated.

A read may align to multiple transcripts and genes, but Cell Ranger only considers a read confidently mapped to the transcriptome if it is mapped to a single gene (after converting the xf tag value to binary, 1-bit means the read is confidently mapped to the transcriptome).

To assess whether reads mapped to multiple genes, examine the GX or GN tags in the output BAM file, which are generated by Cell Ranger after alignment with STAR. Uniquely mapped reads will have one gene ID for GX and one gene name for GN , while multi-mapped reads will list multiple gene IDs and names.

If you encounter a crash while running cellranger mkref, upload the tarball (with the extension .mri.tgz) in your output directory. Customize the code with your email:

cellranger upload [email protected] genome_id.mri.tgz

Where genome_id is what you input into the --genome option of mkfref. This tarball contains numerous diagnostic logs that 10x Genomics support can use for debugging. You will receive an automated email from 10x Genomics. If not, email [email protected]. For the fastest service, respond with the following:

  • The exact cellranger command you used
  • The sample sheet that you used
  • The RunInfo.xml and runParameters.xml files from your BCL directory
  • The kind of libraries you are demultiplexing (including chemistry)