DriverML integrates the Rao’s score test and supervised machine learning to identify cancer driver genes. Rigorous and unbiased benchmark analysis and comparisons of DriverML with other existing tools in 31 independent datasets from The Cancer Genome Atlas (TCGA) show that DriverML is robust and powerful among various datasets and outperforms the other tools with a better balance of precision and recall.
DriverML is free for non-commerical use only.
unzip DriverML-master.zip
cd DriverML-master
tar jxvf training.tar.bz2
chmod +x *.pl *.r *.sh
Requirements: Perl and R are required on user’s Linux environment.
Usage: path/run.driverml.sh -w <AbsolutePath/DriverML-master> –i <AbsolutePath/mutation.txt> -f <AbsolutePath/reference file for input data> -r <AbsolutePath/reference file for training data> [options]
Required arguments:
| Alias | Description |
|---|---|
| -w/--path | The absolute path to the DriverML_v1.0.0. |
| -i/--input | The list of tumor mutations to be analyzed. It should be put in the DriverML-Master/ directory. |
| -f/--reference_genome | The reference genome for input(-i) data. |
| -r/--reference_training | The human reference genome file for training data. Deafuat: hg19 reference file |
Options:
| Alias | Description |
|---|---|
| -g/--mutation_table | The predefined gene mutation table which could be found in the package of this application. It could be either hg19_refGene.exp or hg38_refGene.exp according to -i input file. Default: hg38_refGene.exp |
| -y/--tumor_type | Training mutation data.Default: Pan-cancer |
| -m/--multicore | Set the number of parallel instances to be run concurrently. Default: 4 |
| -t/--simulation_time | Set the number for Monte Carlo Simulation. Default: 2500 |
| -o/--output | Set the prefix of the output file. Default: summary. |
| -c/--cluster_number | Set the upper limit of cluster number for computing BMR. Default: 1 |
| -p/--prior | Set the prior information. Default: Non-TCGA genes from DriverDB and IntOGen databases. |
| -n/--interpolation_number | Set the upper limit of interpolation number for making gene clusters. Default: 100 |
| -d/--indel_ratio | The ratio of point mutation to indel in the background. Default: 0.05 |
| -h/--help | Help information. |
| -v/--version | Software version. |
The mutation file needs to be MAF format. Eight columns named Hugo_Symbol, Chromosome, Start_Position, Variant_Classification, Variant_Type, Reference_Allele, Tumor_Seq_Allele2 and Tumor_Sample_Barcode are required. The first row in the mutation file must be the header including column names above (case sensitive).
Eight required columns are as below:
- Tumor sample barcode is the sample ID in which this mutation occurs.
- Hugo symbol requires HUGO symbols of genes.
- Chromosome that contains the gene.
- Start position requires the lowest numeric position of the reported variant on the genomic reference sequence (1-based coordinate system).
- Reference allele is the plus strand reference allele at this position. Include the sequence deleted for a deletion, or "-" for an insertion.
- Tumor allele2 is tumor sequencing (discovery) allele 2.
- Variant classification describes the translational effect of a variant allele. It is one of Silent, Missense_Mutation, Nonsense_Mutation, Splice_Site, Frame_Shift_Del, Frame_Shift_Ins, In_Frame_Del, and In_Frame_Ins. Other mutation types will not be analyzed.
- Variant type is Type of mutation, such as SNP, DNP, TNP, INS, and DEL.
The mutation file needs to be Detailed information about the MAF format could be found at https://wiki.nci.nih.gov/display/TCGA/Mutation+Annotation+Format+%28MAF%29+Specification. An example file could be seen in /yourpath/DriverML_v1.0.0/example_data/UVM.txt. - If the tumor allele2 is not mutated, it should be replaced by the tumor allele1.
DriverML was developed on a high-performance computing clusters which had nearly 2T memory. The pan-cancer mutation dataset (default) requires much more memory than cancer-specific datasets in the training process. The actual memory usage also depends on the size of the input dataset. Most of the running errors are due to insufficiency of memory.
ExInAtor was designed for the detection of driver genes (principally lncRNAs, but also protein-coding genes) using whole-genome sequencing (WGS) mutation data. According to a recent discussion with the developer of ExInAtor, it is worth noting that the result of ExInAtor in the DriverML paper didn’t represent its optimal performance, due to the use of whole-exome sequencing (WES) mutation data. If anyone want to know the optimal performance of ExInAtor, please refer to the ExInAtor manuscript.
The output of DriverML is a summary of putative driver genes, including the numbers of each mutation type, the value of the statistic, p-value, and FDR adjusted p-value. The genes with negative LRT values (the statistic values) shoule be ignored.
There are 11 columns in the output file. The first column is the gene symbol. The second to eighth columns are numbers of mutations. The ninth column(LRT) is values of the statistics. The tenth and eleventh columns are the P-values and adjusted P-values(Benjamini-Hochberg Procedure). The bigger the statistics, the more likely that gene is a driver. You could refer to the adjusted P-values for possibilities. Find the largest p-adj that is smaller than 0.05(or another value). All values above it (those with lower P-values) are considered significant. The negative values of LRT represent they are not likely to be drivers and you could ignore them.
nohup /AbsolutePath/DriverML-master/run.driverml.sh -w /AbsolutePath/DriverML-master -i example/UVM.txt -f /AbsolutePath/GRCh38.fa -r /AbsolutePath/hg19.fa -m 10 -o UVM-summary.txt > UVM-nohup.out
An example on Google Colab online
Yi Han, Juze Yang, Xinyi Qian, Wei-Chung Cheng, Shu-Hsuan Liu, Xing Hua, Liyuan Zhou, Yaning Yang, Qingbiao Wu, Pengyuan Liu, Yan Lu, DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies, Nucleic Acids Research, Volume 47, Issue 8, 07 May 2019, Page e45, https://doi.org/10.1093/nar/gkz096
If you have any questions, please do not hesitate to contact us.
- The email of the developer is yihan@zju.edu.cn & 250147506@qq.com.
Monday February 2, 2021