reform is a python-based command line tool that allows for fast, easy and robust editing of reference genome sequence and annotation files. With the increase in use of genome editing tools such as CRISPR/Cas9, and the use of reference genome based analyses, the ability to edit existing reference genome sequences and annotations to include novel sequences and features (e.g. transgenes, markers) is increasingly necessary. reform provides a fast, easy, reliable, and reproducible solution for creating modified reference genome data for use with standard analysis pipelines and the 10x Cell Ranger pipeline.

The Problem

The Problem

While it is possible to edit reference sequences and annotations manually, the process is time consuming and prone to human error. Data that appears correct to the eye after manual editing may break strict file formatting rules rendering the data unusable during analysis.

The Solution

The Solution

Execution of reform requires a reference sequence (fasta), reference annotation (GFF or GTF), the novel sequences to be added (fasta), and corresponding novel annotations (GFF or GTF). A user provides as arguments the name of the modified chromosome and either the position at which the novel sequence is inserted, or the upstream and downstream sequences flanking the novel sequences. This results in the addition and/or deletion of sequence from the reference in the modified fasta file. In addition to the novel annotations, any changes to the reference annotations that result from deleted or interrupted sequence are incorporated into the modified gff. Importantly, modified gff and fasta files include a record of the modifications.

The position at which the novel sequence is to be inserted can be provided in two different ways:

1) Provide position at which to insert novel sequence.

2) Provide upstream and downstream flanking sequences, resulting in deletion of reference sequence between these two regions and addition of the novel sequence.



reform requires Python3 and Biopython.

On the Prince HPC at NYU, simply load the required module:

module load biopython/intel/python3.6/1.72

Invoke the python script like this:

  --chrom=<chrom> \
  --position=<pos> \ 
  --in_fasta=<in_fasta> \
  --in_gff=<in_gff> \
  --ref_fasta=<ref_fasta> \


chrom ID of the chromsome to modify

position Position in chromosome at which to insert <in_fasta>. Can use -1 to add to end of chromosome. Note: Either position, or upstream AND downstream sequence must be provided.

upsteam_fasta Path to Fasta file with upstream sequence. Note: Either position, or upstream AND downstream sequence must be provided.

downsteam_fasta Path to Fasta file with downstream sequence. Note: Either position, or upstream AND downstream sequence must be provided.

in_fasta Path to new sequence to be inserted into reference genome in fasta format.

in_gff Path to GFF file describing new fasta sequence to be inserted.

ref_fasta Path to reference fasta file.

ref_gff Path to reference gff file.


python3 \
  --chrom="I" \
  --upstream_fasta="data/up.fa" \
  --downstream_fasta="data/down.fa" \
  --in_fasta="data/new.fa" \
  --in_gff="data/new.gff" \
  --ref_fasta="data/Saccharomyces_cerevisiae.R64-1-1.dna.toplevel.fa" \


reformed.fa Modified fasta file.

reformed.gff3 Modified GFF file.

An Example Using 10x Cell Ranger

If you’re using the Cell Ranger pipeline, you’ll need to modify your GTF file with reform and then run cellranger makeref to create the new genome data needed for cellranger count. Here’s an example:

1) Prepare reference data using reform

We’ll need the original reference in fasta format, and a fasta file containing the novel sequence to be incorporated into the reference sequence. We’ll also need the original reference GTF file, and a GTF file with the annotations corresponding to the novel sequence.

Let’s get a reference sequence:


And the reference GTF:


Here’s the novel sequence we’re adding to the reference (new.fa)


Here’s the GTF corresponding to the novel sequence in new.fa (new.gtf):

I    reform    exon    1    240    .    +    0    gene_id "new_gene"; transcript_id "new_gene.1";
I    reform    CDS    1    238    .    +    0    gene_id "new_gene"; transcript_id "new_gene.1";
I    reform    start_codon    1    3    .    +    0    gene_id "new_gene"; transcript_id "new_gene.1";
I    reform    stop_codon    238    240    .    +    0    gene_id "new_gene"; transcript_id "new_gene.1";

In this example, we’ll add this new gene at position 2650 on chromosome “I”:

python3 \
 --chrom="I" \
 --position=2650 \
 --in_fasta="new.fa" \
 --in_gff="new.gff" \
 --ref_fasta="Saccharomyces_cerevisiae.R64-1-1.dna.toplevel.fa" \

We should have two new files reformed.fa and reformed.gtf which we’ll need in the next step.

2) Run Cell Ranger makeref

cellranger mkref \
 --genome=output_genome \
 --fasta=reformed.fa \

This will produce a directory called output_genome which we’ll need to provide for the next step

3) Run Cell Ranger count

cellranger count \
 --id=run_1 \
 --transcriptome=output_genome \
 --fastqs=/path/to/fastq_path/ \

4) Putting it together

Here’s an sbatch script that runs everything together and makes use of resources on the HPC

#SBATCH --verbose
#SBATCH --job-name=reform_ranger
#SBATCH --output=reform_ranger_%j.out
#SBATCH --error=reform_ranger_%j.err
#SBATCH --time=12:00:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=20
#SBATCH --mem=110000
#SBATCH --mail-user=${USER}

module load cellranger/2.1.0
module load biopython/intel/python3.6/1.72

python3 \
 --chrom="I" \
 --position=2650 \
 --in_fasta="new.fa" \
 --in_gff="new.gff" \
 --ref_fasta="Saccharomyces_cerevisiae.R64-1-1.dna.toplevel.fa" \

cellranger mkref \
 --genome=output_genome \
 --fasta=reformed.fa \
 --genes=reformed.gtf \
 --nthreads=20 \

cellranger count \
 --id=run_1 \
 --transcriptome=output_genome \
 --fastqs=/path/to/fastq_path/ \


1 Comment

Sally · 2018-10-16 at 5:57 pm

Works like a charm, thanks!

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