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DeepTrio

Overview

DeepTrio is built on top of DeepVariant. It is intended for variant calling of trios or duos. The main advantage of DeepTrio is that genetic inheritance is considered by a neural network for calling variants in trio samples. Also, variant candidates are generated from all samples at once, which ensures a genotype call is made for any position in the trio with a variant. Since DeepTrio is built on top of DeepVariant, general information for DeepVariant also applies to DeepTrio. At the highest level, a user needs to provide the following:

  1. A reference genome in FASTA format and its corresponding .fai index file generated using the samtools faidx command.

  2. An aligned reads files for child and one or two parents in BAM format and its corresponding index file (.bai). The reads must be aligned to the reference genome described above.

The output of DeepTrio is a set of variants in VCF format representing the child and one or two parents.

Similar to DeepVariant, DeepTrio is composed of three stages: make_examples, call_variants, and postprocess_variants. Some of the components ( call_variants, postprocess_variants) are shared with DeepVariant, and make_examples is specialized for DeepTrio. More details about each program are described in detail in the Inputs and outputs section of the DeepVariant documentation.

DeepTrio comes with three models for different types of input data:

  • Illumina whole genome data (WGS).
  • Illumina whole exome data (WES).
  • PacBio HiFi whole genome data (PacBio WGS).

Running DeepTrio

The easiest and recommended way to run DeepTrio is using google/deepvariant:latest-deeptrio docker image. Please refer to the quick start guide for more details on how to run DeepTrio using docker.

Merging VCFs can be done using GLnexus which has been optimized for use with DeepVariant gVCFs. The process is described in the DeepTrio case studies (DeepTrio whole genome sequencing case study and Using DeepTrio for small variant calling from the trio sequenced with PacBio HiFi), and in the manuscript, "Accurate, scalable cohort variant calls using DeepVariant and GLnexus".

Please note that DeepTrio can be run with a run_deeptrio.py script that automates all DeepTrio steps and thus greatly simplifies the inference pipeline. The details of using this script can be found in the section below as well as in the DeepTrio case studies.

Also please note: for the non-PAR regions of the sex chromosomes (X and Y), we recommend running these providing only the parent who contributed the child's chromosome (e.g. for chromosomeX, only the mother and son samples and for chromosomeY only the father and son samples).

If needed, DeepTrio can be built from source. For more details please refer to Building DeeepTrio.

DeepTrio Input assumptions

The reference genome FASTA, passed in using the --ref flag, must be indexed and can either be uncompressed or compressed with bgzip.

All BAM files should be aligned to a "compatible" version of the genome reference provided as the --ref. DeepTrio will only process contigs shared by both the BAM and reference. BAM files must be also sorted and indexed. They must exist on disk, so you cannot pipe them into DeepTrio. Duplicate marking may be performed. In our analyses, there is almost no difference in accuracy with and without duplicate marking except at lower (<20x) coverages. Finally, we recommend that you do not perform BQSR. Running BQSR has a small decrease on accuracy.

If you are providing --regions or other similar arguments, these should refer to contigs present in the reference genome. These arguments accept space-separated lists, so all of the follow examples are valid arguments for --regions or similar arguments:

  • --regions chr20 => only process all of chromosome 20
  • --regions chr20:10,000,000-11,000,000 => only process 10-11mb of chr20
  • --regions "chr20 chr21" => only process chromosomes 20 and 21

Training data

DeepTrio models are trained using the latest publicly avavilable GIAB benchmarks. You can find more details about the training data for each DeepTrio model in the DeepTrio Training Data document.

DeepVariant dependency

DeepTrio is built on top of DeepVariant and they share most of the components. Please see DeepVariant usage guide for a full description of DeepVariant components as well as other consideration for running DeepVariant pipeline.