
GluonTS
GluonTS is a Python toolkit for probabilistic time series modeling, focused on deep learning.

A deep learning pipeline for variant calling from next-generation DNA sequencing data.

DeepVariant is a deep learning-based variant caller that leverages convolutional neural networks to identify genetic variants from DNA sequencing data. It processes aligned reads (BAM/CRAM), transforms them into pileup image tensors, and classifies these tensors using a CNN. The final output is reported in standard VCF or gVCF files. DeepVariant supports germline variant-calling in diploid organisms and has been adapted for somatic calling via DeepSomatic. It's designed for NGS data (Illumina, Element), PacBio HiFi, and Oxford Nanopore. The tool can be used with various case studies, including whole genome/exome sequencing and RNA sequencing. DeepTrio extends DeepVariant for trio/duo analysis. Using Docker is the recommended deployment method, enabling a consistent and reproducible environment. Experimental GPU support accelerates the call_variants stage.
DeepVariant is a deep learning-based variant caller that leverages convolutional neural networks to identify genetic variants from DNA sequencing data.
Explore all tools that specialize in deep learning. This domain focus ensures DeepVariant delivers optimized results for this specific requirement.
Uses a convolutional neural network to classify variants, improving accuracy over traditional methods.
Supports data from Illumina, PacBio, Oxford Nanopore, and Complete Genomics platforms.
Capable of calling both germline variants (DeepVariant) and somatic variants (DeepSomatic).
Enables variant calling using pangenome graphs, improving accuracy in diverse populations.
Extends DeepVariant to utilize trio information (child and parents) for improved variant calling.
Supports GPU acceleration for the call_variants stage, significantly reducing processing time.
Install Docker.
Download the DeepVariant Docker image: `docker pull google/deepvariant:latest`.
Prepare input files (BAM/CRAM, reference genome).
Create input and output directories.
Run DeepVariant using the `docker run` command, specifying input and output directories, model type, and other parameters.
Inspect the resulting VCF/gVCF files for variant calls.
All Set
Ready to go
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"DeepVariant is highly regarded for its accuracy and robustness in variant calling, especially in challenging datasets."
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GluonTS is a Python toolkit for probabilistic time series modeling, focused on deep learning.

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