deepvariant | analysis pipeline that uses a deep neural network | Genomics library

 by   google Python Version: v1.5.0 License: BSD-3-Clause

kandi X-RAY | deepvariant Summary

kandi X-RAY | deepvariant Summary

deepvariant is a Python library typically used in Healthcare, Pharma, Life Sciences, Artificial Intelligence, Genomics applications. deepvariant has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However deepvariant build file is not available. You can download it from GitHub.

DeepVariant is a deep learning-based variant caller that takes aligned reads (in BAM or CRAM format), produces pileup image tensors from them, classifies each tensor using a convolutional neural network, and finally reports the results in a standard VCF or gVCF file. DeepVariant supports germline variant-calling in diploid organisms.

            kandi-support Support

              deepvariant has a medium active ecosystem.
              It has 2819 star(s) with 689 fork(s). There are 165 watchers for this library.
              It had no major release in the last 12 months.
              There are 3 open issues and 595 have been closed. On average issues are closed in 13 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of deepvariant is v1.5.0

            kandi-Quality Quality

              deepvariant has 0 bugs and 0 code smells.

            kandi-Security Security

              deepvariant has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              deepvariant code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              deepvariant is licensed under the BSD-3-Clause License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              deepvariant releases are available to install and integrate.
              deepvariant has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              deepvariant saves you 17750 person hours of effort in developing the same functionality from scratch.
              It has 35284 lines of code, 2024 functions and 176 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed deepvariant and discovered the below as its top functions. This is intended to give you an instant insight into deepvariant implemented functionality, and help decide if they suit your requirements.
            • Generate examples
            • Create an estimator
            • Create warm start settings
            • Copy data from training info
            • Create an example runner
            • Write protos
            • Close all streams
            • Writes the runtime stats
            • Compute the metrics for the given predictions
            • Draw a deep variant pileup
            • Check if a variant is a variant call
            • Create realigner configuration
            • Return a list of Allelelemismatches for each alternative
            • Transform call_variants output into a single variant
            • The model function
            • Normalizes log10 probabilities
            • Create a list of commands to run
            • Attention V3
            • Call variant variants
            • Run evaluation loop
            • Resolve filespec
            • Create all command files and log files
            • Trim a read to a region
            • Parse and run TF_CONFIG
            • Returns the model function
            • Check options
            Get all kandi verified functions for this library.

            deepvariant Key Features

            No Key Features are available at this moment for deepvariant.

            deepvariant Examples and Code Snippets

            Cdot img1Lines of Code : 35dot img1License : Permissive (MIT)
            copy iconCopy
            docker pull kishwars/pepper_deepvariant:r0.4
            docker run kishwars/pepper_deepvariant:r0.4 margin phase -h
            docker run \
                -v `pwd`:/data \
                kishwars/pepper_deepvariant:r0.4 \
                margin phase \
                /data/$YOUR_ALIGNMENT_HERE.bam \
            copy iconCopy
            cd /mnt/fs_shared
            sudo singularity pull  docker://dancooke/octopus
            gcloud dataproc jobs submit pyspark --region=us-central1 --cluster=cluster-555  --properties=spark.pyspark.python=/usr/bin/python3.6,spark.pyspark.driver.python=/usr/bin/python3.6,sp  
            copy iconCopy
            mkdir -p dipasm
            cd dipasm
            git clone
            cd DipAsm/docker
            docker build -t dipasm .
            cd ../../..
            docker run -it --rm -v $PWD/dipasm/DipAsm:/wd/dipasm/DipAsm/ -e HOSTWD=$PWD/dipasm/DipAsm -v /var/run/docker.sock:/var/  

            Community Discussions


            String formatting of wildcards within output strings
            Asked 2020-Jul-14 at 08:33

            I am relatively new to snakemake, and I am having some trouble adapting a scatter-gather DeepVariant workflow into snakemake rules.

            In the original Snakefile, I would like to scatter the first step across a cluster. DeepVariant uses a *.00001-of-00256.* format to track the shard number in an intermediate file format, so I need to use string formatting to supply both the shard number and the total number of shards within input, output, and shell fields, and I provide the shard number as a wildcard in the params of the scatter rule. The expand() function in the input field of the gather rule is correctly generating the expected filenames, but it is unable to find the input file paths that would be generated by the scatter step.

            I have generated a minimal reproducible example below, as well as the output of running this example (lightly redacted to remove some path information).



            Answered 2020-Jul-14 at 08:33

            This is how I would do it:



            Snakemake with Singularity
            Asked 2020-Feb-03 at 22:49

            I'm trying to use Singularity within one of my Snakemake rules. This works as expected when running my Snakemake pipeline locally. However, when I try to submit using sbatch onto my computing cluster, I run into errors. I'm wondering if you have any suggestions about how to translate the local pipeline to one that can work on the cluster. Thank you in advance!

            The rule which causes errors uses Singularity to call variants with DeepVariant:



            Answered 2020-Feb-03 at 22:49

            As seen in the resolved command of error message where semi-colon separates two lines of shell: instead of whitespace, this error is due to string formatting in shell:.

            You could use triple-quoted format:


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install deepvariant

            You can download it from GitHub.
            You can use deepvariant like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            Please open a pull request if you wish to contribute to DeepVariant. Note, we have not set up the infrastructure to merge pull requests externally. If you agree, we will test and submit the changes internally and mention your contributions in our release notes. We apologize for any inconvenience. If you have any difficulty using DeepVariant, feel free to open an issue. If you have general questions not specific to DeepVariant, we recommend that you post on a community discussion forum such as BioStars.
            Find more information at:

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