Space Ranger1.0 (latest), printed on 05/26/2020
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).
Space Ranger supports the use of customer-generated references under the following conditions:
To create a custom reference:
Example use cases:
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.
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.
|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.|
|Path(s) to FASTA file containing your genome reference. Specify multiple genomes by specifying the --fasta argument multiple times.|
|Path(s) to genes GTF file(s) containing annotated genes for your genome reference. Specify multiple genomes by specifying the --genes argument multiple times.|
|(Optional) Number of threads used during STAR genome index generation. Defaults to 1.|
|(Optional) 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.|
|(Optional) Reference version string to include with reference.|
$ spaceranger mkref --genome=output_genome --fasta=input.fa --genes=input.gtf
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! <<<
genome_output/ ├── fasta │ └── genome.fa ├── genes │ └── genes.gtf ├── pickle │ └── genes.pickle ├── reference.json └── star # STAR genome index folder
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.
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:
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
--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
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.
|1||Chromosome||Must refer to a chromosome/contig in the genome fasta.|
|4||Start||Start position on the reference (1-based inclusive).|
|5||End||End position on the reference (1-based inclusive).|
|7||Strand||Strandedness of this feature on the reference:
|9||Attributes||A semicolon-delimited list of key-value pairs of the form
After adding the necessary records to your FASTA file and the additional lines to your GTF file, run
spaceranger mkref as normal.
The Visium Spatial Gene Expression assay captures unspliced pre-mRNA as well as mature
mRNA. However, after
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.
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
exon and modified with the suffix
_premrna. This allows the downstream alignment steps to map reads to
introns as if they were exons.