10x Genomics
Visium Spatial Gene Expression

Space Ranger1.0, printed on 12/18/2024

Creating a Reference Package with spaceranger mkref

Space Ranger provides pre-built human (hg19, GRCh38), mouse (mm10), and ercc92 reference packages for read alignment and gene expression quantification in spaceranger count.
To create and use a custom reference package, Space Ranger requires a reference genome sequence (FASTA file) and gene annotations (GTF file).

Compatible Use Cases

Space 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.

Making a Reference Package

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.

Example use cases:

• Reference for single species or multiple species.
• Add a gene to an existing reference package.
• Custom reference including pre-mRNAs.
• Steps to create pre-built human (hg19, GRCh38), mouse (mm10), and ercc92 reference packages.

Filter with mkgtf

GTF files downloaded from sites like ENSEMBL and UCSC often contain transcripts and genes which need to be filtered from your final annotation. Space Ranger provides mkgtf, a simple utility to filter genes based on their key-value pairs in the GTF attribute column:

$ spaceranger mkgtf input.gtf output.gtf --attribute=key:allowable_value

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

$ spaceranger mkgtf Homo_sapiens.GRCh38.ensembl.gtf Homo_sapiens.GRCh38.ensembl.filtered.gtf \
                   --attribute=gene_biotype:protein_coding \
                   --attribute=gene_biotype:lincRNA \
                   --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.

Index with spaceranger mkref

To create custom references, use the spaceranger 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.

Basic Usage

$ spaceranger mkref --genome=output_genome --fasta=input.fa --genes=input.gtf

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:

genome_output/
├── fasta
│   └── genome.fa
├── genes
│   └── genes.gtf
├── pickle
│   └── genes.pickle
├── reference.json
└── star # STAR genome index folder

System Requirements

Indexing a typical human 3Gb FASTA file often takes up to 8 core hours and requires 32 GB of memory. We recommend you run the mkref command with --nthreads equal to the number of cores available on your system.

Single Species

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.

$ spaceranger mkref --genome=hg19 --fasta=hg19.fa --genes=hg19-filtered-ensembl.gtf

When possible, 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.

Multiple Species

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. Multiple species references could be useful in the Visium Spatial Gene Expression Solution when the tissue slide contains cells from multiple organisms. For instance, this can happen in a mouse that has been engrafted with human cells.

$ spaceranger mkref --genome=hg19 --fasta=hg19.fa --genes=hg19-filtered-ensembl.gtf \
                   --genome=mm10 --fasta=mm10.fa --genes=mm10-filtered-ensembl.gtf

Adding One or More Genes to Your Reference

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).

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

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

Generating a Space Ranger compatible "pre-mRNA" Reference Package

The Visium Spatial Gene Expression assay captures unspliced pre-mRNA as well as mature mRNA. However, after alignment, spaceranger count only counts reads aligned to exons. Since the pre-mRNA generates intronic reads, it may be useful to create a custom “pre-mRNA” reference package, listing each gene transcript locus as an exon. Thus, these intronic reads are included in the UMI counts for each gene and barcode.

The mkref utility is capable of constructing a pre-mRNA reference by adding the flag --nuclei to the command, like this:

$ spaceranger mkref --genome=hg19_premrna --fasta=hg19.fa --genes=hg19-filtered-ensembl.gtf --nuclei

This flag modifies the reference so that each line in the GTF that has a feature type (column 3) of transcript is converted to exon and modified with the suffix _premrna. This allows the downstream alignment steps to map reads to introns as if they were exons.