HRProfiler

Homologous Recombination Profiler (HRProfiler) is a classification tool that predicts HRD status for both whole-genome and exome-sequenced breast and ovarian samples using HRD-specific mutation and copy number features.

Installation

bash pip install HRProfiler

Run HRProfiler

From within a python session, you can run HRProfiler as follows: ```python $ python3

from HRProfiler.scripts import HRProfiler as HR HR.HRProfiler(datamatrix,genome,exome,INDELSDIR,SNVDIR,CNVDIR,RESULTDIR,cnvfiletype,bootstrap,nreplicates,normalize,hrdpropthresh,plotpredictions,organ) ``` The layout of the parameters are as follows: |Parameters |Info | |:-------------|:-------------| | datamatrix | An optional pandas dataframe with the following required feature columns (in the same order):

• N[C>T]G: Proportion of C:G>T:A single base substitutions at 5’-NpCpG-3’ context
• N[C>G]T: Proportion of C:G>G:C single base substitutions at 5’-NpCpT-3’ context
• DEL:5:MH: Proportion (WGS) or counts (WES) of deletions spanning at least 5bp at microhomologies
• LOH:1-40Mb: Proportion of genomic segments with loss of heterozygosity (LOH) with sizes at least 1 megabase
• 2-4:HET:>40Mb: Proportion of heterozygous genomic segments with TCN between 2 and 4 and sizes above 40 megabases
• 3-9:HET:10-40Mb: Proportion of heterozygous genomic segments with TCN between 3 and 9 and sizes between 10 and 40 megabases

| | genome | Genome build of snv and indel input files. Options include: GRCh37 and GRCh38 (default: GRCh38) | | exome | Is the input data exome or not (default: True) | | SNVDIR | Directory path to snv vcf/tab-delimited files (default: None) | | INDELSDIR | Directory path to indel vcf/tab-delimited files. If SNVDIR contains mutation files with both indels and SNVs, then set INDELDIR=None (default: None) | | CNVDIR | Directory path to allele-specific segmentation files (default: None) | | RESULTDIR | Path to the directory where HRProfiler will save the output and the log files (default: None) | | cnvfiletype | File type for CNV files provided. Options include: 'ASCAT' , 'ASCAT-NGS', 'ABSOLUTE','BATTENBERG', 'SEQUENZA' (default: 'ASCAT') | | bootstrap | Simulate features per sample based on the sample-weighted probability (default: False) | | nreplicates | Number of replicates to simulate per sample (default: 20) |
| normalize | Normalize each feature column by pre-defined mean and standard deviation (default: True) | | hrdpropthresh | HRD Probability threshold to classify a sample as HRD (default: 0.5) | | plotpredictions | plot a histogram with the HRD probability values for all samples (default: True) | | organ | Organ type for prediction. Options include 'BREAST' and 'OVARIAN' (default: 'BREAST')

Examples

1. Extract HRD features and predict HRD status for samples with input vcf and copy number files:

To determine HRD status for samples with snvs and indel vcf files and allele-specific copy number calls, we can run the following example code where input files have been provided for 5 breast WGS samples.

```python

Note: cd to parent directory that contains the HRProfiler folder before executing the command

from HRProfiler.scripts import HRProfiler as HR HR.HRProfiler(datamatrix=None, genome='GRCh37', exome=False, INDELSDIR='./HRProfiler/example/input/indels', SNVDIR='./HRProfiler/example/input/mutations/', CNVDIR='./HRProfiler/example/input/copynumber/', RESULTDIR='./HRProfiler/example/outputexample1/', cnvfiletype = 'ASCAT', bootstrap=False, nreplicates=20, normalize=True, hrdprobthresh=0.5, plot_predictions=True, organ='BREAST') ```

2. Predict HRD status for samples with preprocessed HRD features:

To determine the HRD status for samples with pre-defined HRD features: ['NCTG', 'NCGT', 'DEL5MH', 'LOH.1.40Mb', '3-9:HET.10.40Mb','2-4:HET.40Mb'], run the following command:

```python

Note: cd to parent directory that contains the HRProfiler folder before executing the command.

from HRProfiler.scripts import HRProfiler as HR import pandas as pd

datamatrix = pd.readcsv('./HRProfiler/example/input/exampledatamatrix.txt', sep="\t")

HR.HRProfiler(datamatrix=datamatrix, genome='GRCh37', exome=False, INDELSDIR=None, SNVDIR=None, CNVDIR=None, RESULTDIR='./HRProfiler/example/outputforexample2/', cnvfiletype='ASCAT', bootstrap=False, nreplicates=20, normalize=True, hrdprobthresh=0.5, plot_predictions=True, organ='BREAST') ```

HRProfiler Output

HRProfiler generates a histogram with the HRD probabilities per sample and a tab-delimited table with the following columns:
|Columns |Info | |:------------- |:-------------| | samples | sample names | | NCTG | Proportion of C:G>T:A single base substitutions at 5’-NpCpG-3’ context | | NCGT | Proportion of C:G>G:C single base substitutions at 5’-NpCpT-3’ context | | DEL5MH | Proportion or total counts of deletions spanning at least 5bp at microhomologies | | LOH.1.40Mb | Proportion of genomic segments with loss of heterozygosity (LOH) with sizes between 1 and 40 megabases | | 3-9:HET.10.40Mb | Proportion of heterozygous genomic segments with TCN between 2 and 4 and sizes above 40 megabases | | 2-4:HET.40Mb | Proportion of heterozygous genomic segments with TCN between 2 and 4 and sizes above 40 megabases | |hrd.prob | HRD Probability | |prediction | HRD status |

Additional columns provided if bootstrap=True: |Columns |Info | |:------------- |:-------------| |mean.hrd.prob | Average HRD probability across all replicates | |LCI.95.hrd.prob | Lower confidence interval (2.5%) HRD Probability | |UCI.95.hrd.prob | Upper confidence interval (97.5%) HRD Probability |

License

Academic Software License: © 2024 University of California, San Diego (“Institution”). Academic or nonprofit researchers are permitted to use this Software (as defined below) subject to Paragraphs 1-4:

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2. Any distribution of copies of this Software -- including any derivative works made by you thereof -- must include a copy (including the copyright notice above), and be made subject to the terms, of this Academic Software License; failure by you to adhere to the requirements in Paragraphs 1 and 2 will result in immediate termination of the license granted to you pursuant to this Academic Software License effective as of the date you first used the Software.

3. IN NO EVENT WILL INSTITUTION BE LIABLE TO ANY ENTITY OR PERSON FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE USE OF THIS SOFTWARE, EVEN IF INSTITUTION HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. INSTITUTION SPECIFICALLY DISCLAIMS ANY AND ALL WARRANTIES, EXPRESS AND IMPLIED, INCLUDING, BUT NOT LIMITED TO, ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE IS PROVIDED “AS IS.” INSTITUTION HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS OF THIS SOFTWARE.

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