# google/deepvariant **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/google-deepvariant).** 3,729 stars · 791 forks · Python · BSD-3-Clause ## Links - GitHub: https://github.com/google/deepvariant - awesome-repositories: https://awesome-repositories.com/repository/google-deepvariant.md ## Description DeepVariant is a deep learning genotyping tool and DNA sequence analysis pipeline used to identify single nucleotide polymorphisms and indels from next-generation sequencing data. It functions as a convolutional neural network genetic variant caller that treats genomic read alignments as multi-channel image tensors to determine genotypes. The system supports specialized analysis workflows including long-read variant calling for circular consensus sequencing and trio-based variant calling to identify inherited or de novo mutations. It enables model optimization for new species or genome contexts through variant model retraining and the use of pre-trained checkpoints. The project covers a broad range of genomic analysis capabilities, including multiallelic variant resolution, genomic error correction, and haploid genotype adjustment for sex-linked chromosomes. It also provides utilities for consensus sequence determination and haplotype-aware read sorting to increase detection precision. ## Tags ### Artificial Intelligence & ML - [Deep Learning Genotyping Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-genotyping-tools.md) — Provides a neural network based system for calling single nucleotide polymorphisms and insertions or deletions in genomic data. - [Genomic Variant Classifiers](https://awesome-repositories.com/f/artificial-intelligence-ml/image-classification/cnn-architectures/genomic-variant-classifiers.md) — Uses convolutional neural networks to classify genomic variants by treating read alignments as multi-channel image tensors. - [Genomic Input Encoding](https://awesome-repositories.com/f/artificial-intelligence-ml/channel-processing/multi-channel-processors/multi-channel-image-conversion/genomic-input-encoding.md) — Encodes base qualities and sequence identities into separate image channels for processing by deep neural networks. - [Genomic Model Checkpoints](https://awesome-repositories.com/f/artificial-intelligence-ml/large-scale-model-training/vision-transformer-pre-training/pre-trained-model-checkpoints/genomic-model-checkpoints.md) — Provides pre-trained model checkpoints to initialize variant calling for new species via weight fine-tuning. - [Genomic](https://awesome-repositories.com/f/artificial-intelligence-ml/on-device-models/model-retraining/genomic.md) — Optimizes models for new genome contexts or species using specific data and silver standard callsets to improve accuracy. ([source](https://google.github.io/deepvariant/posts/2018-12-05-improved-non-human-variant-calling-using-species-specific-deepvariant-models/)) - [Sequence Model Training](https://awesome-repositories.com/f/artificial-intelligence-ml/sequence-learning-models/sequence-model-training.md) — Implements training procedures to adjust model weights using labeled examples for optimizing variant calling across different data types. ([source](https://google.github.io/deepvariant/posts/2019-01-14-highly-accurate-snp-and-indel-calling-on-pacbio-ccs-with-deepvariant/)) - [Genomic](https://awesome-repositories.com/f/artificial-intelligence-ml/sequence-learning-models/sequence-model-training/genomic.md) — Optimizes neural network weights for specific species or genome contexts using labeled sequencing data. ### Scientific & Mathematical Computing - [DNA Sequence Analysis Pipelines](https://awesome-repositories.com/f/scientific-mathematical-computing/dna-sequence-analysis-pipelines.md) — Provides a computational workflow for processing DNA sequencing reads to determine consensus sequences and genotype mutations. - [Long-Read Variant Callers](https://awesome-repositories.com/f/scientific-mathematical-computing/long-read-variant-callers.md) — Provides a specialized analysis tool for identifying genetic mutations from circular consensus sequencing and other long-read technologies. - [Tensor-Based Genotype Representation](https://awesome-repositories.com/f/scientific-mathematical-computing/tensor-based-genotype-representation.md) — Represents overlapping DNA reads as structured multidimensional arrays to allow deep learning models to analyze spatial patterns. - [Trio Variant Callers](https://awesome-repositories.com/f/scientific-mathematical-computing/trio-variant-callers.md) — Provides a genomic analysis tool that predicts inherited and de novo mutations by comparing related sample sets. - [Consensus Sequence Determiners](https://awesome-repositories.com/f/scientific-mathematical-computing/consensus-sequence-determiners.md) — Identifies correct DNA bases by analyzing matrices of normalized base counts from overlapping reads. ([source](https://google.github.io/deepvariant/posts/2019-01-31-using-nucleus-and-tensorflow-for-dna-sequencing-error-correction/)) - [Genomic Error Correction](https://awesome-repositories.com/f/scientific-mathematical-computing/genomic-error-correction.md) — Predicts and fixes missing or low-quality bases in DNA sequences by leveraging reference genome patterns. - [Genomic Read Error Correctors](https://awesome-repositories.com/f/scientific-mathematical-computing/genomic-read-error-correctors.md) — Predicts missing or low-quality bases in DNA sequences by training neural networks on reference genome patterns. ([source](https://google.github.io/deepvariant/posts/2019-01-31-using-nucleus-and-tensorflow-for-dna-sequencing-error-correction/)) - [Haplotype-Aware Read Sorting](https://awesome-repositories.com/f/scientific-mathematical-computing/haplotype-aware-read-sorting.md) — Organizes genomic reads by their shared ancestral origin to provide cleaner contextual signals for indel detection. ### Part of an Awesome List - [Trio Genomic Analysis](https://awesome-repositories.com/f/awesome-lists/data/genomic-data-analysis/trio-genomic-analysis.md) — Predicts inherited or de novo mutations by calling variants across related family samples. - [Multiallelic](https://awesome-repositories.com/f/awesome-lists/data/variant-calling/multiallelic.md) — Resolves complex multiallelic genotypes by processing tensors for each possible combination of alternate alleles. ([source](https://google.github.io/deepvariant/posts/2020-02-20-looking-through-deepvariants-eyes/)) - [Phased Haplotype Calling](https://awesome-repositories.com/f/awesome-lists/data/variant-calling/phased-haplotype-calling.md) — Increases precision for indel detection by sorting reads according to their haplotype to provide better context. ([source](https://google.github.io/deepvariant/posts/2019-01-14-highly-accurate-snp-and-indel-calling-on-pacbio-ccs-with-deepvariant/)) - [Trio](https://awesome-repositories.com/f/awesome-lists/data/variant-calling/trio.md) — Predicts genomic variants across related samples to identify inherited or de novo mutations. ([source](https://cdn.jsdelivr.net/gh/google/deepvariant@main/README.md)) - [Biological Analysis](https://awesome-repositories.com/f/awesome-lists/ai/biological-analysis.md) — Identifies genetic variations from DNA sequencing data. - [Variant Calling](https://awesome-repositories.com/f/awesome-lists/data/variant-calling.md) — Deep learning-based variant caller.