The code version associated with the bioArxiv preprint is tag 1.0

FLASHit

FLASH (Finding Low Abundance Sequences by Hybridization) is a method using the targeted nuclease Cas9 to enrich for an arbitrary set of sequences in a complex DNA library. FLASH-NGS achieves up to 5 orders of magnitude of enrichment, sub-attomolar detection of genes of interest, and minimal background from non-FLASH-derived reads.

FLASHit is open-source software for designing FLASH experiments. It allows users to design guide RNA libraries for any set of target genes. The guides are guaranteed to have good structure, and to avoid cutting a set of off-target genes, such as human DNA in the case of a library designed to target resistant genes in pathogenic bacteria.

The problem of constructing a library efficiently, using the fewest guides necessary to get the desired coverage of all target genes, is solved as a mixed integer program optimization. For details, see the formal description.

The inputs consist of:

• fasta files of genes to be targeted
• (optional) a list of guides to exclude
• (optional) a list of guides to include

The output is:

• a list of RNA guides

Prerequisites

1. Install the Anaconda package manager for python.

2. Clone the FLASH repository from GitHub

   git clone https://github.com/czbiohub/flash.git

   cd flash

3. Install the dependencies with conda.

   conda env create -f environment.yml

   source activate flash

4. License the Gurobi optimizer.

Get a license from Gurobi. Academic licenses are free.

grbgetkey KEYHERE

4. Install GO.

Workflows

This section will cover the most common use cases of creating your own library and creating the AMR library from the paper.

Creating your own library

To generate a FLASH library targeting genes from a single fasta file and avoiding the human genome and a reference E. Coli strain, run

make library TARGETS=[input.fasta] OUTPUT=library.txt.

For example,

make library TARGETS=examples/colistin/genes.fasta OUTPUT=library.txt

To exclude a set of guides,

make library_excluding TARGETS=examples/colistin/genes.fasta OUTPUT=library.txt EXCLUDE=examples/colistin/exclude.txt

To incluce a set of guides,

make library_including TARGETS=examples/colistin/genes.fasta OUTPUT=library.txt INCLUDE=examples/colistin/include.txt

To include padding for input genes provide a yaml as follows:

make library TARGETS=examples/colistin/genes.fasta PADDING=examples/colistin/padding.yml OUTPUT=library.txt

Formatting Genes

Input genes to FLASHit should be placed in one or more fasta files. If multiple fasta files are used, they should be in a single subdirectory of inputs.

The header of each gene can contain arbitrary key:value pairs separated by pipes (|).

Some keys have special meaning in FLASHit.

flash_key: A unique ID for the gene. If unspecified, it will be automatically generated from the fasta header before the first pipe. flash_mutation_ranges: Locations of known mutations in each gene. These sites will be avoided as CRISPR targets, and the optimizer will attempt to generate fragments that contain each of those loci.

Example:

>NCTC_8325|ARO:3003312|Staphyloccocus_aureus_NCTC_8325_parC|flash_key:parC__NCTC_8325__fluoroquinolone_additional|flash_resistance:fluoroquinolone|flash_mutation_ranges:nt240+12
GTGAGTGAAATAATTCAAGATTTATCACTTGAAGATGTTTTAGGTGATCGCTTTGGAAGATATAGTAAATATATTATTCAAGAGCGTGCATTGCCAGATGTTCGTGATGGTTTAAAACCAGTACAACGTCGTATTTTATATGCAATGTATTCAAGTGGTAATACACACGATAAAAATTTCCGTAAAAGTGCGAAAACAGTCGGTGATGTTATTGGTCAATATCATCCACATGGAGAC...

Bases

FLASHit expects each nucleotide to be ATCG or N, if ambiguous. It will automatically convert the other formats for ambiguous bases (UYRSWKMBDHV) to N. If you have RNA data (U instead of T), then you may convert it to to DNA by running the following on the command-line.

sed '/^[^>]/ y/uU/tT/' INPUT_FASTA_RNA.fa > OUTPUT_FASTA_DNA.fa

You would then use the output DNA fasta as the input to FLASHit.

Mutations

The guides may be designed to capture certain mutations present in each gene. Guides that would hybridize to those variable regions are forbidden, and we ensure that each variable region is contained in a fragment. (The default assumption is that reads are paired-end 150. We ensure that the mutation is contained in the first or last 150 bp of the fragment.) This will allow you to identify which allele of a gene present in each sample.

The mutations are encoded in the fasta headers of each gene as follows.

• Amino Acid Change

A50Q indicates that an alanine at position 50 in the amino acid translation of the sequence may be mutated to a glutamine. Only the position (50 here) is used by the optimizer. If you do not know the result of the mutation, you may use an arbitrary letter.

• Nucleic Acid Substitution/Deletion

nt420+4:GGAT indicates that the 4 bases beginning at position 420 (420-423), may be mutated to GGAT. The target is optional (nt420+4 would also work).

• Nucleic Acid Insertion

+nt349:CACTG indicates that CACTG may be inserted in between bases 349 and 350. The target is optional (+nt349 would also work).

These are included in a comma-separated field. For example,

flash_mutation_ranges: A50Q,Y400L,+nt349:CACTG,+nt100,+nt120:CG,nt420+4

The optimizer will return an error if there is a gene for which one of the mutations cannot be captured in a fragment. This happens most often for SNPs near the end of genes, when there is no legal target between the SNP and the end of the gene. If the neighboring genomic context is known, it can be added by hand by pre/post-pending sequence to the gene (and adjusting the locations for the SNPs accordingly).

Visualization

To inspect a given gene--its list of potential CRISPR cut sites, its SNPs, and the cuts made by a given library--run the display_genes.py script.

For example, after running one of the examples for creating a library:

python display_genes.py MCR-1 MCR-2 MCR-3 MCR-4 --library library.txt

Creating a bed file of the guides

To generate a bed file marking the locations a guide library would hybridize in a set of genes run:

python generate_bed_file.py --input-fasta examples/colistin/genes.fasta --library library.txt --output colistin.bed

Additional details about the build stages

These steps are handled by the workflows mentioned above. This section gives additional details about the individual steps.

Build gene files

This step creates a standardized fasta file for each gene, with metadata tags embedded in the header. These files are deposited in generated_files/genes. This is automatic for genes coming from CARD and Resfinder; for information about how to format your own genes appropriately consult the Gene Formatting section below.

make build_gene_files

Compile offtargets

This step extracts and collates all CRISPR targets from the offtarget files. For the human genome, this takes about 2 minutes and produces a ~4 GB txt file.

make generate_offtargets

Build gene indices

This step finds all the CRISPR targets in the input genes, filtered for structure and offtargets.

make build_indices

Run optimizer

This step finds the optimal library of guides for targeting the input genes.

make optimizer

To search for guides including a given library, include that library as an additional input. To ensure that certain guides are not included in your library, include a list of those guides as an additional input.

python optimizer.py --include [library.txt] --exclude [badguides.txt] --output [library.txt]

Extract guides for a certain set of genes

python extract_guides.py [library.txt] [gene_list.txt]

AMR Use Case

The files used to generate the full guide set for AMR has been moved to a separate repository. Currently the repository includes the inputs used for full guide generation, but will be updated to allow for the creation of AMR libraries from CARD and Refinder inputs.