MosaicForecast

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A mosaic detecting software based on phasing and random forest

MosaicForecast 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# MosaicForecast
A machine learning method that leverages read-based phasing and read-level features to accurately detect mosaic SNVs (SNPs, small indels) from NGS data. It builds on existing algorithms to achieve a multifold increase in specificity.

![MF_pipeline](https://user-images.githubusercontent.com/8002850/55032948-61016880-4fe8-11e9-8cf8-343fd3cdd26e.png)

## Dependency:
### Required Interpreter Versions:
* Python version 3.6+
* R version 3.5+
### Installation of Dependencies:
1. We have created a docker image with all dependencies installed:  
	https://hub.docker.com/r/yanmei/mosaicforecast  
	Usage:  
		docker image pull yanmei/mosaicforecast:0.0.1  
		docker run -v ${your_local_directory}:/MF --rm -it yanmei/mosaicforecast:0.0.1 /bin/bash  
		gunzip hs37d5.fa.gz  
		Phase.py /MF/demo/ /MF/demo/phasing hs37d5.fa /MF/demo/test.input 20 k24.umap.wg.bw 4 
```
Please note that "${your_local_directory}:/MF" is the absolute path of your local mosaicforecast directory. After attaching your local MF directory to the docker image, you would be able to read and write from that directory in your docker image. The attached directory in the docker image would be "/MF".
```
		
	 
2. You could also install conda first (https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh), and then create an environment using conda through this command:  
conda env create --name MF --file environment.yaml  
The environment 'MF' could be activated through this command:  
conda activate MF  
Other dependencies and resources could be downloaded though running:  
bash downloads.sh

### Python packages (set up the environment mannually, not recommended):
* collections
* itertools
* subprocess
* multiprocessing
* regex
* uuid
* math 
* numpy (1.16.1)
* pandas (0.25.0)
* pyfaidx (0.5.3)
* pysam (0.15.2)
* pysamstats (1.1.2)
* scipy (1.2.1)
### R packages (set up the environment mannually, not recommended):
* caret (6.0-80)
* e1071 (1.7-0)
* glmnet (2.0-16)
* nnet (7.3-12)
* mlr (2.13)
* RColorBrewer (1.1.2)
* ggplot2 (2.3.0.0)
### Other softwares:
* samtools (1.9): https://sourceforge.net/projects/samtools/files/samtools/1.9/
* wigToBigWig (v4):  
wget http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/wigToBigWig  
chmod +x wigToBigWig
* bigWigAverageOverBed (v2):  
wget http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/bigWigAverageOverBed  
chmod +x bigWigAverageOverBed  
* fetchChromSizes:  
wget http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/fetchChromSizes  
chmod +x fetchChromSizes

## Resources:
#### Human reference genome: 
* GRCh37/hg19: 
``` 
wget ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz   
wget ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz.fai   
```
* GRCh38/hg38: 
```
wget ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/technical/reference/GRCh38_reference_genome/GRCh38_full_analysis_set_plus_decoy_hla.fa 
wget ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/technical/reference/GRCh38_reference_genome/GRCh38_full_analysis_set_plus_decoy_hla.fa.fai 
```
#### Mappability score: 
`The wigToBigWig command line below take ~30GB memory, please make sure you requires enought memory before running.`

* Umap score (k=24, GRCh37/hg19):   
```
wget https://bismap.hoffmanlab.org/raw/hg19.umap.tar.gz  
tar -zxvf hg19.umap.tar.gz  
cd hg19  
fetchChromSizes hg19> hg19.chrom.sizes  
wigToBigWig <(zcat k24.umap.wg.gz) hg19.chrom.sizes k24.umap.wg.bw  
```
  
* Umap score (k=24, GRCh38/hg38):   
```
wget https://bismap.hoffmanlab.org/raw/hg38.umap.tar.gz  
tar -zxvf hg38.umap.tar.gz  
cd hg38  
fetchChromSizes hg38> hg38.chrom.sizes  
wigToBigWig <(zcat k24.umap.wg.gz) hg38.chrom.sizes k24.umap.wg.bw  
```

#### Regions to filter out:
* GRCh37/hg19:

Segmental Duplication regions and regions enriched for SNVs with >=3 haploptypes (should be removed before calling all kinds of mosaics):  
```
wget https://github.com/parklab/MosaicForecast/blob/master/resources/SegDup_and_clustered.GRCh37.bed
```

Regions for indels (simple repeats included, should be removed before calling mosaic INDELS):  
```
wget https://github.com/parklab/MosaicForecast/blob/master/resources/allrepeats_forindel.GRCh37.bed 
```
* GRCh38/hg38 (please note that our model is trained under GRCh37 and the file "predictedhap3ormore_cluster.GRCh38.bed" is simply a liftover from the GRCh37 file above, hence could be un-optimized for GRCh38):

Segmental Duplication regions and regions enriched for SNVs with >=3 haploptypes (should be removed before calling all kinds of mosaics):
```
wget https://github.com/parklab/MosaicForecast/blob/master/resources/SegDup_and_clustered.GRCh38.bed
```

Regions for indels (should be removed before calling mosaic INDELS):  
```
wget https://github.com/parklab/MosaicForecast/blob/master/resources/allrepeats_forindel.GRCh38.bed
```
#### Population allele frequency
* gnomAD datasets (recommend to remove variants with population MAF>0.001%):  
https://gnomad.broadinstitute.org/downloads 
* from GATK bundle:
```
ftp://gsapubftp-anonymous@ftp.broadinstitute.org/bundle/
```

## How to run Mutect2-PON:
* Please refer to FAQ.md, we also have a Snakemake pipeline.

# Usage:
## Phasing:
**Usage:**

python Phase.py bam_dir output_dir ref_fasta  input_positions min_dp_inforSNPs Umap_mappability(bigWig file,k=24) n_threads_parallel sequencing_file_format(bam/cram)

**Note:**

1. Name of bam files should be "sample.bam" under the bam\_dir, and there should be index files under the same directory (samtools index sample.bam).   
2. There should be a fai file under the same dir of the fasta file (samtools faidx input.fa).
3. File format of the input\_positions: chr pos-1 pos ref alt sample, sep=\t 
4. The "min\_dp\_inforSNPs" is the minimum depth of coverage of trustworthy neaby het SNPs, can be set to 20.
5. The program to extract mappability score: "bigWigAverageOverBed" should be downloaded and installed, and its path should be added to the PATH environment variable.

**Demo:**
```
python Phase.py demo demo/phasing ${human_g1k_v37_decoy.fasta} demo/test.input 20 ${k24.umap.wg.bw} 2
```
**Output:**
```
output_dir/all.phasing
```
| sample | chr | pos | ref | alt | phasing | conflicting_reads | mappability | variant_type |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| test | 12 | 52644508 | C | T | hap=3 | 0 | 1.0 | SNP |
| test | 15 | 75918044 | G | A | hap=3 | 0 | 1.0 | SNP |
| test | 1 | 1004865 | G | C | hap=3 | 0 | 1.0 | SNP |
| test | 1 | 2591769 | AG | A | hap>3 | 1 | 0.0 | DEL |
| test | 1 | 33801576 | TTTGTTG | T | hap=2 | 0 | 0.583333 | DEL |

```
hap=2: likely het variants
hap=3: likely mosaic variants
hap>3: likely cnv/repeat
conflicting_reads: number of read pairs supporting both ref and alt alleles.

Intermediate files:
1. output_dir/all.merged.inforSNPs.pos: all nearby inforSNPs of candidate mosaics.
2. output_dir/all_2x2table: 2x2 tables by all nearby inforSNPs.
3. output_dir/all.phasing_2by2: Phasing results of mosaics and all nearby inforSNPs (2x2 table).
4. output_dir/multiple_inforSNPs.log: Phasing results of different pairs of inforSNPs.

```

## Extraction of read-level features:
**Usage:**

python ReadLevel_Features_extraction.py input.bed output_features bam_dir ref.fa Umap_mappability(bigWig file,k=24) n_jobs_parallel sequencing_file_format(bam/cram)

**Note:**
1. Names of bam files should be "sample.bam" under the bam_dir, and there should be index files under the same directory (samtools index sample.bam). Cram files are also supported.
2. There should be a fai file under the same dir of the fasta file (samtools faidx input.fa)
3. File format of the input.bed: chr pos-1 pos ref alt sample, sep=\t 
4. We did not use gnomad population AF as an feature (instead we use it to filter), but you can use it to train your model if you have interest in common variants
5. The program to extract mappability score: "bigWigAverageOverBed" should be downloaded and installed, and its path should be added to the PATH environment variable.

**Demo:**
```
python ReadLevel_Features_extraction.py demo/test.input demo/test.features demo ${ref.fa} ${k24.umap.wg.bw} 2 bam  
```
**Output:**
```
A list of read-level features for each input site.
```

| id | dp_p | conflict_num | mappability | type | length | GCcontent | ref_softclip | alt_softclip | querypos_p | leftpos_p | seqpos_p | mapq_p | baseq_p | baseq_t | ref_baseq1b_p | ref_baseq1b_t | alt_baseq1b_p | alt_baseq1b_t | sb_p | context | major_mismatches_mean | minor_mismatches_mean | mismatches_p | AF | dp | mosaic_likelihood | het_likelihood | refhom_likelihood | althom_likelihood | mapq_difference | sb_read12_p | dp_diff |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| test\~11\~40316580\~C\~T | 0.3183008162818 | 0 | 1 | SNP | 0 | 0.476190476190476 | 0.0240384615384615 | 0 | 0.1582 | 0.16521 | 0.68821 | NA | 0.91657 | -0.57364 | 0.98911 | 0.21893 | 0.67576 | -0.8528 | 0.69934 | GGA | 0.00878 | 0.0144466666666667 | 0.29396 | 0.028 | 214 | 0.999414559235067 | 3.90999117967593e-49 | 0.000585440764932926 | 0 | 0 | 0.69142 | -12.8571 |
| test\~12\~52644508\~C\~T | 0.197545792452075 | 0 | 1 | SNP | 0 | 0.571428571428571 | 0.0208333333333333 | 0 | 0.19325 | 0.20057 | 0.88251 | NA | 0.11764 | -0.95448 | 0.31536 | 0.6827 | 0.31601 | 0.58756 | 0.13401 | CGC | 0.01236 | 0.0127266666666667 | 0.17424 | 0.054 | 203 | 0.999999999985368 | 5.11687178601205e-39 | 1.46319954019795e-11 | 0 | 0 | 0.36124 | -12.8571 |

```
1. id: uniq ID of the input candidate sites.
2. mappability: UMAP mappability score at the candidate site (k=24).
3. type: type of the candidate mutation (SNP, MNP, INS or DEL).
4. length: difference of base pair lengh of ref and alt allele for candidate sites.
5. GCcontent: 20-bp local GCcontent.
6. ref_softclip: proportion of soft-clipped reads for ref reads.
7. alt_softclip: proportion of soft-clipped reads for alt reads.
8. querypos_p: p-value or effect size by wilcoxon's rank sum test of base query positions of ref and alt alleles.
9. leftpos_p: p-value or effect size by wilcoxon's rank sum test of left-most positions of ref and alt reads.
10. seqpos_p: p-value or effect size by wilcoxon's rank sum test of base sequencing cycles of ref and alt alleles.
11. baseq_p: p-value or effect size by Wilcoxon's rank sum test of base qualities of ref and alt alleles.
12. baseq_t: The test statistic under the large-sample approximation that the rank sum statistic is normally distributed (wilcox rank sum test of base qualites of alt alleles vs. ref alleles).
13. ref_baseq1b_p: p-value or effect size by Wilcoxon's rank sum test of base qualities from ref reads at mutant position, compared with base qualities from ref reads at 1bp downtream of the mutant position.
14. ref_baseq1b_t: The test statistic under the large-sample approximation that the rank sum statistic is normally distributed (wilcox rank sum test of base qualities from ref reads at mutant position, compared with base qualities from ref reads at 1bp downtream of the mutant position).
15. alt_baseq1b_p: p-value or effect size by Wilcoxon's rank sum test of base qualities from alt reads at mutant position, compared with base qualities from alt reads at 1bp downtream of the mutant position.
16. alt_baseq1b_t: The test statistic under the large-sample approximation that the rank sum statistic is normally distributed (wilcox rank sum test of base qualities from alt reads at mutant position, compared with base qualities from alt reads at 1bp downtream of the mutant position).
17. context: three-nucleotide base context on the reads surrounding the mutant position.
18. major_mismatches_mean: average mismatches per ref reads.
19. minor_mismatches_mean: average mismatches per alt reads.
20. mismatches_p: p-value or effect size by Wilcoxon's rank sum test of mismatches per ref reads vs. mismatches per alt reads.
21. sb_p: p-value or effect size by Fisher's exact test of strand bias for ref and alt alleles.
22. sb_read12_p: p-value or effect size by Fisher's exact test of read1/read2 bias for ref and alt alleles.
23. mosaic_likelihood: mosaic genotype likelihood calculated (assuming uniform distribution of mosaics allele fraction from 0-1).
24. het_likelihood: Genotype likelihood of the variant being germline heterozygous.
25. refhom_likelihood: reference-homozygous genotype likelihood.
26. mapq_p: p-value or effect size by Wilcoxon's rank sum test of mapping qualities of ref and alt reads.
27. mapq_difference: difference of average map quality per alt reads vs. average map quality per ref reads.
28. AF: variant allele fraction.
29. dp: read depth at mutant position.
30. dp_diff: difference of average read depths of local (<200bp) and distant (>2kb) regions.
31. dp_p: p-value or effect size by Wilcoxon's rank sum test of read depths sampled within 200bp window surrounding the mutant position vs. read depths sampled in distant regions from the mutant position (>2kb).
32. conflict_num: number of read pairs supporting both ref and alt alleles.
```

## Genotype Prediction:

**Usage:**

Rscript Prediction.R input\_file(feature\_list) model\_trained model\_type(Phase|Refine) output\_file(predictions)

**Note:**
1. The "input\_file" is a list of read-level features obtained in the last step.
2. The "model\_trained" is the pre-trained RF model to predict genotypes.
3. If you trained model with refined-genotypes (mosaic, het, refhom, repeat), then the "model\_type" is "Refine"; otherwise if you trained model with Phasing (hap=2, hap=3, hap>3), then the "model\_type" is "Phase". 
4. We also added annotations of additional filtrations: Predicted mosaics with extra-high read depths (>=2X), sites with >=1.5X read depths and >=20% AF were marked as "low-confidence"; predicted mosaics with only one alt allele and <1% AF were marked as "cautious".

> You may use our models trained with brain WGS data for SNPs (paired-end read at 50-250X read depths, we train our models based on Mutect2-PON callings. To our experience, the models were pretty robust across different depths, but the best strategy would be using a model with similar depth with your data):
>
> * models\_trained/50xRFmodel\_addRMSK\_Refine.rds
> * models\_trained/100xRFmodel\_addRMSK\_Refine.rds
> * models\_trained/150xRFmodel\_addRMSK\_Refine.rds
> * models\_trained/200xRFmodel\_addRMSK\_Refine.rds
> * models\_trained/250xRFmodel\_addRMSK\_Refine.rds
>
> We also pre-trained a model for mosaic deletions (using paired-end read at 250X, with phasing information):
> * models\_trained/deletions\_250x.RF.rds

**Demo:**
```
Rscript Prediction.R demo/test.SNP.features models_trained/250xRFmodel_addRMSK_Refine.rds Refine demo/test.SNP.predictions   
Rscript Prediction.R demo/test.DEL.features models_trained/deletions_250x.RF.rds Phase demo/test.DEL.predictions
```
**Output:**
```
Genotype predictions for all input sites.
```

| id | AF | dp | prediction | het | mosaic | refhom | repeat |
| --- | --- | --- | --- | --- | --- | --- | --- |
| test\~11\~40316580\~C\~T | 0.028 | 214 | mosaic | 0.002 | 0.958 | 0 | 0.04 |
| test\~12\~52644508\~C\~T | 0.054 | 203 | mosaic | 0.002 | 0.982 | 0 | 0.016 |
| test\~15\~75918044\~G\~A | 0.036 | 193 | mosaic | 0.006 | 0.812 | 0 | 0.182 |
| test\~1\~1004865\~G\~C | 0.085 | 212 | mosaic | 0.006 | 0.988 | 0 | 0.006 |

```
1. prediction: genotype predictions including refhom, het, mosaic and repeat.
2. het/mosaic/refhom/repeat: genotyping probabilities for each genotype.
```

## You could also train RF models using your own data:
**Usage:**

Rscript Train_RFmodel.R input(trainset) output(prediction_model) type_model(Phase|Refine) type_variant(SNP|INS|DEL)

**Note:**

1. You could choose to train your model based on Phasing (hap=2, hap=3, hap>3, type in "Phase") or Refined genotypes ("mosaic","het","refhom","repeat", type in "Refine").
2. The input file should be a list of pre-generated read-level features, adding a column termed "phase" (Phase model) or "phase\_model\_corrected" (Refined genotypes model). 
3. We strongly recommend using Refined genotypes instead of Phasing genotypes, since ~50% of hap=3 sites were validated as "repeat" variants in our dataset:  
![website_pie](https://user-images.githubusercontent.com/8002850/57800167-be7b8100-771e-11e9-9773-0e39249698a6.png)

In case you don't have experimentally-evaluated sites, it's ok to manually-check ~100 hap=3 sites with igv, and mark the sites in messy regions as "repeat". Here are some examples of "hap=3" sites experimentally evaluated as "repeat":  
   
![hap3_wrong_examples](https://user-images.githubusercontent.com/8002850/55192738-9094aa00-517b-11e9-8809-e67d548b009f.png)

and here are some examples of "hap=3" sites experimentally evaluated as true positive mosaics:
	
![hap3_right](https://user-images.githubusercontent.com/8002850/55193553-a905c400-517d-11e9-8ce1-a0eaeb30d312.png)

**Demo:**
```
Rscript Train_RFmodel.R demo/trainset demo/Phase_model.rds Phase SNP  
Rscript Train_RFmodel.R demo/trainset demo/Refine_model.rds Refine SNP  
Rscript Train_RFmodel.R demo/deletions_trainset demo/Deletions_Refine_model.rds Phase DEL 
```
**Output:**
```
Random Forest prediction model
```

## Convert phasing to four-category genotypes based on experimental data:

#### (Recommended when you have >=100 orthogonally-evaluated or manually-checked sites with hap=3)
**Usage:**

Rscript PhasingRefine.R input(trainset) output1(model) output2(converted genotypes) read_length(int) pdf(plot) variant_type(SNP|INDEL)

**Note:**

1. The input file should be a list of pre-generated read-level features for all sites including phasable and non-phasable ones, adding a column termed "phase", containing the pre-generated haplotype number for each site (hap=2, hap=3, hap>3, notphased), and a column termed "validation", containing the orthogonally validation results. The un-evalulated sites should be "NA" in the "validation" column.
2. The output1 is the multinomial regression model, the output2 is the extraplolated four-category genotypes for all phasable sites.
3. The "hap=3" sites could contain ~50% FP sites, mostly "repeat" sites. When you don't have experimentally evaluated sites, it's ok to check ~100 hap=3 sites manually, converting the "hap=3" sites present in messy regions as "repeat" and covert "hap=3" sites present in clean regions as "mosaic".

**Demo:**
```
Rscript PhasingRefine.R demo/trainset demo/model_phasingcorrection.rds demo/phasable_sites_convertedgenotypes 150 demo/phasable_sites_Refine.pdf SNP 
```
**Output:**
```
A list of extrapolated genotypes based on Phasing, Readlevel features and orthogonal validations.
```

| id | phase | validation | phase_model_corrected | pc1 | pc2 | pc3 | pc4 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 1465\~7\~64358306\~C\~T | hap=3 | repeat | repeat | 1.76125132511822 | -0.0974980761360579 | 0.040830632773886 | 1.76651681595286 |
| 1465\~2\~10704065\~T\~C | hap=3 | repeat | repeat | -0.0124184653486693 | -0.289637541460141 | -2.01395435019693 | 1.5135587692184 |
| 1465\~10\~42529522\~G\~C | hap=3 | NA | repeat | 0.707159292549739 | -3.91325643368487 | 1.54152147288395 | -1.10846627458624 |
| 1465\~7\~122592074\~G\~T | hap=3 | mosaic | mosaic | -0.104370300644773 | 2.33641117703566 | -0.0299470543274239 | 1.40486521150486 |
| 1465\~X\~61712742\~A\~G | hap=3 | repeat | repeat | 0.574318366975694 | 1.16511088082416 | 1.24290479319458 | 1.24880945486403 |
| 1465\~10\~42544320\~C\~T | hap=3 | NA | repeat | 0.544602308768669 | 1.64954352441225 | 0.28095361817584 | 0.744802179936821 |

```
1. phase_model_corrected: Four-category genotypes extrapolated based on phasing and read-level features.
2. pc1/pc2/pc3/pc4/pc5: the first five PCA components constructed with read-level features.
3. demo/phasable_sites_Refine.pdf: A plot showing the genotype extrapolation from phasing to 4-category genotypes.
```
![phasing_refine](https://user-images.githubusercontent.com/8002850/55196676-7ad8b200-5186-11e9-8324-b0a16f80e6de.png)

## Contact:
If you have any questions please contact us:

Yanmei Dou: douyanmei@westlake.edu.cn, douyanmei@gmail.com  
Peter J Park: peter_park@hms.harvard.edu

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