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Quickstart Hail (Eng)

CloudFormation stack preparation

1. Prepare the AWS credentials and apply them in the terminal. 

export AWS_DEFAULT_REGION="us-east-1"
export AWS_ACCESS_KEY_ID="{ACCESS_KEY}"
export AWS_SECRET_ACCESS_KEY="{SECRET_ACCESS_KEY}"
export AWS_SESSION_TOKEN="{SESSION_TOKEN}"

2. Create an S3 bucket in the region where you want to launch this CloudFormation stack.

At this time, I recommend creating a bucket name using your own initial. As you know, the bucket already exists, it cannot be created.

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aws s3 mb s3://{bucket name}-{region} --region {region}

Download and unzip the content from this repository, then place the downloaded content into the S3 bucket you created earlier.

export AWS_BUCKET={bucket name}-{region}
git clone https://github.com/hmkim/quickstart-hail.git
cd quickstart-hail
aws s3 sync . s3://$AWS_BUCKET/quickstart-hail/ --exclude ".git/*"

3. Connect to the Amazon S3 console and check the bucket and directory.

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Run the CloudFormation stack

1. Go to the CloudFormation console.

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2. Creates a new stack. At this time, select With new resources (standard).

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3. Go to the Amazon S3 console, select hail-launcher.template.yaml in the template directory you uploaded earlier, and click Copy URL. The path is as follows:

{bucket name} > quickstart-hail > templates > hail-launcher.template.yaml

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When creating a CloudFormation stack, enter this URL and create the stack.

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4. Proceed with entering information to create a stack.

Type an name for the stack.

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Select a VPC. Select one subnet within the same VPC. For this exercise, select public.

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Let's set it up to create additional buckets as needed.

Enter the name of the existing bucket where the quickstart-hail folder was uploaded, and check the region.

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5. Finally, press the Next button to create the stack.

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6. Check stack creation in CloudFormation.

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If the following portfolio appears in the output along with the CREATE_COMPLETE message in the top stack, you can confirm that it was executed correctly.

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Create an AMI for Hail and VEP

Pre-downloading VEP Data and Storing in Bucket

For VEP, you can pre-download the data and store it in the bucket created or specified through the stack (using the bucketHail value from CloudFormation's Outputs).

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Download VEP data using the wget command:

wget ftp://ftp.ensembl.org/pub/release-112/variation/vep/homo_sapiens_vep_112_GRCh37.tar.gz

Upload the downloaded file to your bucket: (I emphasize that this is the value for the BucketHail key confirmed in CloudFormation Outputs)

aws s3 cp homo_sapiens_vep_112_GRCh37.tar.gz s3://{defined Hail S3 bucket}/vep/cache/

AMI Build

1. Access the CodeBuild console and initiate the build process for each new AMI. Select Start build > Start with overrides.

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2. In the Environment section, expand Additional configuration and input the required values.

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HAIL_VERSION 0.2.105
HTSLIB_VERSION 1.20
SAMTOOLS_VERSION 1.20

* For building with hail-vep option (includes VEP installation):

HAIL_VERSION 0.2.105
HTSLIB_VERSION 1.20
SAMTOOLS_VERSION 1.20
VEP_VERSION  
RODA_BUCKET <VEP download bucket name>

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3. Check the build status

Hail (without VEP): The Hail image build completes in about 20 minutes.

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Hail (VEP): The VEP version build takes approximately 1 hour and 38 minutes to complete.

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You can find the **AMI results** in either the AMI menu of Amazon EC2 console or CodeBuild logs.

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EMR Cluster Setup and Jupyter Environment Configuration

EMR Cluster Setup

1. In the CloudFormation service console's Outputs tab, click the portfolio link.

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2. In the portfolio, locate the relevant Product, click the Access tab, then select Grant access.

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3. Add permissions. Check the user that suits you and grant them access to Hail Products. Search for it, select it, and click Grant access.

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4. After confirming access permissions, navigate to the Product in the Provisioning menu.

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5. With permissions granted, you should now see 2 Products listed.

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6. Select the Hail EMR Cluster product and click Launch product.

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7. Enter the required launch information.

Either enter a name manually or click Generate name.

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Specify the Hail AMI you created earlier. You can find the AMI ID in the EC2 service's AMIs section (as previously described).

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Input the Cluster name and Hail AMI ID. You can leave all other settings at their default values.

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8. Click Launch product at the bottom of the page.

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SageMaker Notebook 실행

  1. Product 메뉴에서 마찬가지로 Launch product를 클릭합니다.

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  1. Hail notebook을 위한 인스턴스의 이름을 입력하고 나머지는 모두 기본값입니다.

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  1. 맨 아래의 Launch product를 클릭합니다.

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참고로 제품(Product)의 실행 과정은 CloudFormation을 통해서도 확인할 수 있습니다.

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GWAS 실습 (Hail)

  1. 노트북을 실행합니다. 이 때 CloudFormation의 Outputs탭에서 url을 확인할 수 있습니다. 클릭하면 Amazon SageMaker의 해당 노트북 인스턴스로 자동 연결됩니다.

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  1. Open JupyterLab을 클릭하여 노트북을 실행합니다.

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  1. 이제 노트북에서 각각 2개의 노트북을 가지고 실습해봅니다.
  • common-notebooks/plotting-tutorail.ipynb
  • common-notebooks/GWAS-tutorial.ipynb

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앞에서 만든 EMR 클러스터를 조회한 뒤 Cluster Name을 2번째 셀에서 수정해줍니다.

노트북 셀을 한번에 실행하기 위해서 시작하고자 하는 셀에 커서를 놓은 뒤 일괄 실행할 수 있습니다.

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최종적으로 튜토리얼로 주어진 코드가 정상적으로 실행할 수 있었다면 아래와 같은 결과들을 확인할 수 있습니다.

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기타

VEP configuration

S3 버킷에서 해당 json파일 객체를 선택하고 Copy S3 URI를 클릭합니다.

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예시) vep-configuration-GRCh37.json

{
        "command": [
                "/opt/ensembl-vep/vep",
                "--format", "vcf",
                "--dir_plugins", "/opt/vep/plugins",
                "--dir_cache", "/opt/vep/cache",
                "--json",
                "--everything",
                "--allele_number",
                "--no_stats",
                "--cache", "--offline",
                "--minimal",
                "--assembly", "GRCh37",
                "--plugin", "LoF,human_ancestor_fa:/opt/vep/loftee_data/human_ancestor.fa.gz,filter_position:0.05,min_intron_size:15,conservation_file:/opt/vep/loftee_data/phylocsf_gerp.sql,gerp_file:/opt/vep/loftee_data/GERP_scores.final.sorted.txt.gz",
                "-o", "STDOUT"
        ],
        "env": {
                "PERL5LIB": "/opt/vep"
        },
    "vep_json_schema": "Struct{assembly_name:String,allele_string:String,ancestral:String,colocated_variants:Array[Struct{aa_allele:String,aa_maf:Float64,afr_allele:String,afr_maf:Float64,allele_string:String,amr_allele:String,amr_maf:Float64,clin_sig:Array[String],end:Int32,eas_allele:String,eas_maf:Float64,ea_allele:String,ea_maf:Float64,eur_allele:String,eur_maf:Float64,exac_adj_allele:String,exac_adj_maf:Float64,exac_allele:String,exac_afr_allele:String,exac_afr_maf:Float64,exac_amr_allele:String,exac_amr_maf:Float64,exac_eas_allele:String,exac_eas_maf:Float64,exac_fin_allele:String,exac_fin_maf:Float64,exac_maf:Float64,exac_nfe_allele:String,exac_nfe_maf:Float64,exac_oth_allele:String,exac_oth_maf:Float64,exac_sas_allele:String,exac_sas_maf:Float64,id:String,minor_allele:String,minor_allele_freq:Float64,phenotype_or_disease:Int32,pubmed:Array[Int32],sas_allele:String,sas_maf:Float64,somatic:Int32,start:Int32,strand:Int32}],context:String,end:Int32,id:String,input:String,intergenic_consequences:Array[Struct{allele_num:Int32,consequence_terms:Array[String],impact:String,minimised:Int32,variant_allele:String}],most_severe_consequence:String,motif_feature_consequences:Array[Struct{allele_num:Int32,consequence_terms:Array[String],high_inf_pos:String,impact:String,minimised:Int32,motif_feature_id:String,motif_name:String,motif_pos:Int32,motif_score_change:Float64,strand:Int32,variant_allele:String}],regulatory_feature_consequences:Array[Struct{allele_num:Int32,biotype:String,consequence_terms:Array[String],impact:String,minimised:Int32,regulatory_feature_id:String,variant_allele:String}],seq_region_name:String,start:Int32,strand:Int32,transcript_consequences:Array[Struct{allele_num:Int32,amino_acids:String,appris:String,biotype:String,canonical:Int32,ccds:String,cdna_start:Int32,cdna_end:Int32,cds_end:Int32,cds_start:Int32,codons:String,consequence_terms:Array[String],distance:Int32,domains:Array[Struct{db:String,name:String}],exon:String,gene_id:String,gene_pheno:Int32,gene_symbol:String,gene_symbol_source:String,hgnc_id:String,hgvsc:String,hgvsp:String,hgvs_offset:Int32,impact:String,intron:String,lof:String,lof_flags:String,lof_filter:String,lof_info:String,minimised:Int32,polyphen_prediction:String,polyphen_score:Float64,protein_end:Int32,protein_start:Int32,protein_id:String,sift_prediction:String,sift_score:Float64,strand:Int32,swissprot:String,transcript_id:String,trembl:String,tsl:Int32,uniparc:String,variant_allele:String}],variant_class:String}"
}

vep-tutorial 코드에서 아래 내용에서 위에서 복사한 S3 객체 URI로 수정하여 사용할 수 있습니다.

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VEP 플러그인 설치

만일 VEP의 플러그인 설치에 변경사항 (추가 등)이 있다면 AMI를 다시 만들어야 합니다. 이 때 AMI를 만들 데 사용되는 VEP 설치에 관한 코드는 vep_install.sh 입니다. 해당 코드를 수정 후 다시 AMI를 빌드합니다.

다음을 참고하여 커스텀하게 Hail, VEP 툴을 설치 및 AMI를 빌드할 수 있습니다.

EMR 클러스터 EBS (HDFS) 동적 볼륨 늘리기

- 데이터가 클 경우 사전에 클러스터상에 구성된 볼륨의 용량이 부족할 수 있습니다. 아래 블로그 내용을 참고하여, EBS 볼륨의 부족분을 동적으로 늘릴 수 있습니다.

https://aws.amazon.com/ko/blogs/big-data/dynamically-scale-up-storage-on-amazon-emr-clusters/

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