hello

KO | EN

gitlite — search

#javascript #python #hacktoberfest #golang #rust #deep-learning #nodejs #awesome-list #pytorch #security #ios #mcp #openai #open-source

(★ 12)

DNN-based small variant caller

hello 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# HELLO: A DNN-based small variant caller

HELLO is a Deep Neural Network-based small variant caller that can call variants for Illumina, PacBio, and hybrid Illumina-PacBio settings. HELLO uses customized Deep Neural Networks which provide accurate variant calls with relatively small model size.

# Information regarding HELLO's methodology

The methodologies used in the current version of HELLO are described in our publication at BMC Bioinformatics: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04311-4

Older methodologies used in a prior version of HELLO, with only support for hybrid variant calling, is described at https://www.biorxiv.org/content/10.1101/2020.03.23.004473v1.

The models released in this package (including models for hybrid variant calling) do not follow methodologies described in the bioRxiv article, but follow the methodologies described in the paper published at BMC Bioinformatics, which are significantly different from the bioRxiv version.

# Information regarding HELLO's code and usage

(NEW) PacBio haplotagged model is available in models directory. The source code for running the model is in hello_dev.tar.gz, pending merge.

*NOTE*: By default HELLO calls variants for chromosomes 1-22 only. To change this behavior, please use the --chromosomes option.

The repository contains files for HELLO - a small variant caller that is designed for running standalone and hybrid small variant calling.

The following Docker image may be used with the tool. These images may not be final, and we will update here when the images are updated.

`docker pull oddjobs/hello_image.x86_64`

To build the tool, please run

```
cmake .
make -j 12
```

NOTE: To properly download models, git-lfs needs to be installed. Once installed, please do `git lfs pull` inside the git repo.

To run Illumina variant calling, please use the following command

```
python python/call.py \
    --ibam $bam \
    --ref $REF \
    --network models/illumina_multi_coverage_mapq_threshold_hg002_continue_run16.wrapper.dnn \
    --num_threads $NUM_THREADS \
    --workdir $workdir --mapq_threshold 5
```

To run PacBio variant calling, please use the following command (`--include_hp` to be added for haplotagged PacBio reads).

```
python python/call.py \
    --pbam $bam \
    --ref $REF \
    --network models/pacbio_multi_coverage_mapq_threshold_hg00216.wrapper.dnn \
    --num_threads $NUM_THREADS \
    --workdir $workdir --mapq_threshold 5
```

To run hybrid Illumina-PacBio variant calling, please use the following command

```
python python/call.py \
    --ibam $ibam \
    --pbam $pbam \
    --ref $ref \
    --network models/no_ensemble_multi_coverage_mapq_threshold_hg002_continued17.wrapper.dnn \
    --num_threads $NUM_THREADS \
    --workdir $workdir \
    --mapq_threshold 5 \
    --reconcilement_size 0
```

The output VCF file may be found in `$workdir/results.mean.vcf`.

Errata: Currently, the VCF output by the tool has a syntax error with the INFO field. This may be fixed with the following code
```
fix_vcf() {
    correct_string="##INFO=<ID=MixtureOfExpertPrediction,Type=String,Number=1,Description=\"Mean predictions from experts\">"
    cat $1 | sed "s?##INFO=<ID=MixtureOfExpertPrediction,Description=\"Mean predictions from experts\"?$correct_string?g" > $2
}

fix_vcf $workdir/results.mean.vcf $workdir/results.mean.corrected.vcf
```

hello

hello 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

hello

hello 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README