Update README.md

README.md

@@ -69,21 +69,70 @@ Which provide greater control of prediction parameters including (but not limite
   
 An example prediction for SNVs from a VCF can be found in the methods of the publication and below:
 
-python vcf_predict.py --artifact_path {10X $MODEL} \ CHROMOSOME HOLDOUT MODELS TO ENSEMBLE
---vcf_file ${VCF} \ VARIANTS OF INTEREST
---fasta_file ${FASTA} \ REFERENCE GENOME
---output ${OUTPUT} \ OUTPUT PATH
---relative_start 9 \ START WINDOW
---relative_end 180 \ END WINDOW
---step_size 10 \ N WINDOWS
---strand_reduction mean \ REDUCTION METHOD OF FWD/REV STRAND PREDICTIONS
---window_reduction mean \ REDUCTION METHOD OF PREDICTION WINDOWS
-
-And include specialized prediction methods for:
-  small indels (<= 10bp recommended) (vcf_predict_indel.py)
-  haplotypes (vcf_predict_haplotype.py)
-
-Which can be run in the same way as vcf_predict.py but with modifications to handle sequence padding (vcf_predict_indel.py) or windowing (vcf_predict_haplotype.py)
+To generate MPAC predictions for SNVs from the command line, first define your variants in a VCF-like TSV (no VCF header required) with the following tab-separated columns:
+
+| Column | Description |
+| --- | --- |
+| `CHROM` | Chromosome |
+| `POS` | Position |
+| `ID` | Variant ID (can be blank or `.` if none) |
+| `REF` | Reference allele |
+| `ALT` | Alternate allele |
+| `QUAL` | Quality (can be `.` if null) |
+| `FILTER` | Filter status (can be `.` if null) |
+| `INFO` | Can be `.` if null; predictions populate this column in the output |
+
+### SNVs
+
+SNV predictions are handled with `vcf_predict.py`:
+
+```bash
+python vcf_predict.py \
+    --artifact_path {10X $MODEL} \
+    --vcf_file ${VCF} \
+    --fasta_file ${FASTA} \
+    --output ${OUTPUT} \
+    --relative_start 9 \
+    --relative_end 180 \
+    --step_size 10 \
+    --strand_reduction mean \
+    --window_reduction mean \
+    --feature_ids K562 HepG2 SKNSH
+```
+
+| Argument | Description |
+| --- | --- |
+| `--artifact_path` | Chromosome holdout models to ensemble |
+| `--vcf_file` | Variants of interest |
+| `--fasta_file` | Reference genome |
+| `--output` | Output path |
+| `--relative_start` | First window variant position |
+| `--relative_end` | Final window variant position |
+| `--step_size` | Window step size |
+| `--strand_reduction` | Reduction method for fwd/rev strand predictions |
+| `--window_reduction` | Reduction method for window predictions |
+| `--feature_ids` | Labels for predictions in the output |
+
+Additonal arguments can be found in vcf_predict.py
+
+### Small indels
+
+Small indels (≤ 10 bp recommended) are handled the same way with `vcf_predict_indel.py`, with modification to the plasmid sequence padding loop.
+
+### Haplotypes
+
+Haplotype predictions are handled similarly with `vcf_predict_haplotype.py`, with modification to the windowing loop to ensure all windows contain the desired variants.
+
+### Output
+
+Output is a VCF-like TSV with predictions occupying the `INFO` column, see below:
+
+### Example output
+
+```
+CHROM   POS         ID              REF  ALT  INFO
+chr22   11121724    COSV106573183   A    G    K562__ref=0.3535322;HepG2__ref=0.29076257;...
+```
 ## Citation
 
 ```bibtex