SLUPipe: A (S)omatic Ana(L)ysis (U)mbrella (Pipe)line

Description
Features
Requirements
Express Installation - Anaconda
Running SLUPipe
Usage - Sample Entry/Output
Usage - JSON file Configuration
Usage - Example Workflow
Usage - Customizing SLUPipe Variant Callers
Usage - SLUPipe Configuration for High Performance Computing - SLURM
Manual Installation - Anaconda

Description

SLUPipe is a DNA Sequencing Variant Calling Pipeline based on the National Cancer Institute's GDC guidelines. SLUPipe focuses towards automating, merging and parallelizing the following GDC's DNA-Sequence Analysis workflows to increase sample analysis throughput :

Somatic Variant Calling
Variant Annotation
Aggregated Somatic Mutation

For further information on GDC Guidelines visit: https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/DNA_Seq_Variant_Calling_Pipeline/

Features

SLUPipe provides variant calling for paired (Normal & Tumor) and non-paired (Tumor Only) aligned samples at the request of the research group:

Variant Callers

Paired Sample Variant Callers (Normal Mode):

MuSE (1.0.rc)
Mutect2 (GATK3.8.1)
Somatic Sniper (1.0.5.0)
Varscan (2.4.3.2)
Strelka 2 (2.9.10)

Non-paired Sample Variant Callers (Tumor Only):

Pindel (0.2.5b9)
Platypus (1.0.3)
Mutect2

Variants callers can be toggled on/off as requested by the user (config file).

Variant Annotation

Raw VCF files are annotated using Ensembl VEP (v95). The following databases are used for VCF Annotation:

GENCODE v.22
sift v.5.2.2
ESP v.20141103
polyphen v.2.2.2
dbSNP v.146
Ensembl genebuild v.2014-07
Ensembl regbuild v.13.0
HGMD public v.20154
ClinVar v.201601

Requirements

Requirements:

Python 3+

Pandas

Glob

Linux Compatible Computer

CPU Processors with AVX Instruction Support

Express Installation - Anaconda

For convenience, SLUPipe has been configured to run in Anaconda Environments

Please Note: If problems have appeared installing SLUPipe, please head to the manual installation.

1. Clone Github Repository

$ git clone https://github.com/BioHPC/SLUPipe.git

2. Download & Install Anacaonda 4.5+

https://www.anaconda.com/distribution/

3. Automate creation of Anaconda environment.

$ cd SLUPipe
$ conda env create -f environment.yml (environment.yml can be found in SLUPipe root directory)

IMPORTANT : $SLUIPipePATH is your current working directory (user can check this by typing "pwd" in terminal)

Please Note: Environment creation will take around 30-45 minutes to complete.

4. Configure Ensembl VEP For Variant Annotation & MAF Conversion (Local Cache Installation): Create .vep directory at your home directory ($HOME) to store offline cache.

$ cd ~ (Takes you to your home directory. You can also use cd $HOME as well)
$ mkdir .vep
$ cd .vep
$ curl -O -C - ftp://ftp.ensembl.org/pub/release-95/variation/indexed_vep_cache/homo_sapiens_vep_95_GRCh38.tar.gz
$ tar xzf homo_sapiens_vep_96_GRCh38.tar.gz

Please Note: Download time will vary depending on time of day (1 Hr+)

5. Copying Strelka 2 Configuration File to SLUPipe Working Directory:

Locate "configureStrelkaSomaticWorkflow.py" found in conda env bin directory (~/.conda/envs/SLUPipe/bin)
Copy file into SLUPipe working directory ($SLUPipe/src)

$ cp ~/.conda/envs/SLUPipe/bin/configureStrelkaSomaticWorkflow.py $SLUPipe/src/

Tip: If unable to locate ./conda/envs/SLUPipe/bin directory, please run the following two commands to locate path:

$ source activate SLUPipe (start SLUPipe environment)
$ which python (prints full path related to SLUPipe conda environment)

Please Note: If problems have appeared installing SLUPipe, please head to the manual installation.

Running SLUPipe

Activate Anaconda Environment

$ source activate SLUPipe

Execute Pipeline Workflow

$ python3 slupipe.py <config.json>

Version Summary & Execution Description

$ python3 slupipe.py

Check Latest Software Release

$ python3 slupipe.py --update

Usage - Sample Entry/Output

SLUPipe processes and stores results using the following directories found within SLUPipe/src/:

Reference Files (referenceFiles): Place reference .fasta, .fai, dnSNP, normal panels files within this directory.

Reference Files Needed:

GATK Tutorial Data 9183 Somatic Variants: https://drive.google.com/drive/folders/1QdtVEronIzs04L37BFkw29TLjNWcyOpf

1kg_40_m2pon_sitesonly_subset50k.vcf
1kg_40_m2pon_sitesonly_subset50k.vcf.gz
1kg_40_m2pon_sitesonly_subset50k.vcf.idx
1kg_40_m2pon_sitesonly_subset50k.vcf.gz.tbi
dbSNP142_GRCh38_subset50k.vcf.gz
dbSNP142_GRCh38_subset50k.vcf
dbSNP142_GRCh38_subset50k.vcf.idx
dbSNP142_GRCh38_subset50k.vcf.gz.tbi

GATK Resource Bundle: https://software.broadinstitute.org/gatk/download/bundle

Homo_sapiens_assembly38.dict
Homo_sapiens_assembly38.fasta.index
Homo_sapiens_assembly38.fasta.fai
Homo_sapiens_assembly38.fasta

Input Files (Input): Place all .bam files to be processed in here (SLUPipe will automate generation of .bai files within this directory). Sample files for testing can be found here: https://drive.google.com/drive/folders/1QdtVEronIzs04L37BFkw29TLjNWcyOpf

Sample Files:

GATK Tutorial Data 9183 Somatic Variants: https://drive.google.com/drive/folders/1QdtVEronIzs04L37BFkw29TLjNWcyOpf

hcc1143_T_subset50K.bam
hcc1143_T_subset50K.bai
hcc1143_N_subset50K.bam
hcc1143_N_subset50K.bai

Output Files (Output): SLUPipe workflow results will be placed here. Each sample result will have its files organized with the following directory structure:

-Sample_1:
    ->annotated_vcfs:
        ->mutect_output
            -sample_1_muse.annotated.vcf
        ->strelka_output
            -sample_1_strelka.annotated.vcf
    ->mafs:
        -sample_1_muse.maf
        -sample_1_strelka.maf
    ->vcfs
        ->mutect_output
            -sample_1.vcf
        ->strelka_output
            -sample_1.vcf

Important: Users have completely liberty of creating more reference/input/output directories outside of those described here as long as they're specified in the JSON Configuration file.

Usage - JSON file configuration

Users are able to customize SLUPipe workflows to their needs via JSON configuration files. ALL config files must be constructed with the following structure:

Configuration File Structure Format (JSON):

[
  {
    "Pipeline_Mode":"-T",
    "Variant_Callers":["Pindel","Platypus"],
    "Input_Directory":"/student/foo/SLUPipe/src/input",
    "Output_Directory":"student/foo/SLUPipe/src/output",
    "Chromosome_Range": "chr1:16,000,000-215,000,000",
    "vep_ScriptPath": "/student/foo/.conda/envs/SLUPipe/share/ensembl-vep-95.3-0",
    "vep_CachePath": "/student/foo/.vep",
    "reference_directory": "/student/foo/referenceFiles",
    "cpuCores": "8"
  }
]

Pipeline Mode & Variant Callers are indicated in the JSON file as followed:

Non-paired Mode (Tumor Only) = "-T"

Paired Mode (Normal Mode) = "-N"

MuSE = "Muse"
MuTect2 = "Mutect"
Varscan = "Varscan"
Somatic Sniper = "Sniper"
Strelka 2 = "Strelka2"
Pindel = "Pindel"
Platypus "Platypus"

Usage - Example Workflow

Pipeline Workflow Example (Non-paired Mode):

(SLUPipe)$ python3 slupipe.py config.json
TUMOR MODE: DIRECTORY SUMMARY (X to Exit):
--------------------------------------------------------------------------------
NO.               ID               TUMOR
--------------------------------------------------------------------------------
1             tumor2_T         tumor2_T.bam
2             tumor1_T         tumor1_T.bam

IS THIS CORRECT (Y/N): Y
SELECT FILE NUMBERS TO PROCESS (Separate File Numbers By Space): 1

############################
COMMENCING PIPELINE WORKFLOW
############################

Pindel: Calling Variants -> tumor2_T
Pindel: Calling Variants Complete -> tumor2_T
Platypus: Calling Variants -> tumor2_T
Platypus: Calling Variants Complete -> tumor2_T
MuTect2: Calling Variants -> tumor2_T
Mutect2: Calling Variants Complete -> tumor2_T
Ensembl VEP: Annotating Variants -> tumor2_T-pindel
Ensembl VEP: Annotating Variants Complete -> tumor2_T-pindel
Ensembl VEP: Annotating Variants -> tumor2_T-platypus
Ensembl VEP: Annotating Variants Complete -> tumor2_T-platypus
Ensembl VEP: Annotating Variants -> tumor2_T-mutect2
Ensembl VEP: Annotating Variants Complete -> tumor2_T-mutect2
VCF2MAF: Converting VCF to MAF -> tumor2_T-pindel
VCF2MAF: VCF to MAF Conversion Complete -> tumor2_T-pindel
VCF2MAF: Converting VCF to MAF -> tumor2_T-platypus
VCF2MAF: VCF to MAF Conversion Complete -> tumor2_T-platypus
VCF2MAF: Converting VCF to MAF -> tumor2_T-mutect2
VCF2MAF: VCF to MAF Conversion Complete -> tumor2_T-mutect2
Merging MAF: Saving Merged MAFs -> ./output/maf/tumor2_T.final.maf

---------------------- MERGED MAF SUMMARY ---------------------------

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1619 entries, 0 to 1618
Columns: 134 entries, Hugo_Symbol to variant_caller
dtypes: float64(70), int64(5), object(59)
memory usage: 1.7+ MB
None
count     1619
unique       1
top        maf
freq      1619
Name: variant_caller, dtype: object

---------------------------------------------------------------------

############################
 PIPELINE WORKFLOW COMPLETE
############################

Pipeline Workflow Result (Non-paired Mode MAF File):

GNU nano 2.3.1                                                     File: tumor2_T.final.maf                                                                                                                 

Hugo_Symbol     Entrez_Gene_Id  Center  NCBI_Build	Chromosome	Start_Position  End_Position    Strand  Variant_Classification  Variant_Type    Reference_Allele        Tumor_Seq_Allele1	Tumor_Seq_Allele2	dbSNP_RS        dbSNP_Val_Status        Tumor_Sample_Barcode    Matched_Norm_Sample_Barcode     Match_Norm_Seq_Allele1  Match_Norm_Seq_Allele2  Tumor_Validation_Allele1        Tumor_Validation_Allele2        Match_Norm_Validation_Allele1   Match_Norm_Validation_Allele2   Verification_Status     Validation_Status	Mutation_Status Sequencing_Phase        Sequence_Source Validation_Method	Score   BAM_File        Sequencer	Tumor_Sample_UUID	Matched_Norm_Sample_UUID        HGVSc   HGVSp   HGVSp_Short     Transcript_ID   Exon_Number     t_depth t_ref_count     t_alt_count     n_depth n_ref_count     n_alt_count     all_effects     Allele  Gene    Feature Feature_type    Consequence     cDNA_position   CDS_position    Protein_position        Amino_acids     Codons  Existing_variation	ALLELE_NUM	DISTANCE        STRAND_VEP	SYMBOL  SYMBOL_SOURCE   HGNC_ID BIOTYPE CANONICAL	CCDS    ENSP    SWISSPROT	TREMBL  UNIPARC RefSeq  SIFT    PolyPhen        EXON    INTRON  DOMAINS AF	AFR_AF  AMR_AF  ASN_AF  EAS_AF  EUR_AF  SAS_AF  AA_AF   EA_AF   CLIN_SIG        SOMATIC PUBMED  MOTIF_NAME	MOTIF_POS	HIGH_INF_POS    MOTIF_SCORE_CHANGE	IMPAC$
FAM131C 0	.	GRCh38  chr1    16063558        16063558        +	Missense_Mutation	SNP     C	C	T	rs755896471             TUMOR   NORMAL  C	C                                                                                                                               c.101G>A        p.Arg34His	p.R34H  ENST00000375662 2/7     42	38	4	50	50	0	FAM131C,missense_variant,p.Arg34His,ENST00000375662,NM_182623.2;FAM131C,intron_variant,,ENST00000494078,;	T	ENSG00000185519 ENST00000375662 Transcript	missense_variant        285/1695        101/843 34/280  R/H     cGc/cAc rs755896471,COSM6378897 1               -1	FAM131C HGNC    HGNC:26717	protein_coding  YES     CCDS41270.1     ENSP00000364814 Q96AQ9          UPI000022B016   NM_182623.2     tolerated(1)    benign(0)	2/7             hmmpanther:PTHR15736:SF2,hmmpanther:PTHR15736                                                                                   0,1                                             MODERATE        1	SNV     1               0,1                                                                                     Tier5   GCG     .	.                                                                                               2.442e-05               2.979e-05               5.806e-05                               0.00013000000$
MRPS15  0	.	GRCh38  chr1    36455626        36455626        +	3'Flank SNP     A	A	G	rs2275479               TUMOR   NORMAL  A	A                                                                                                                                                       ENST00000373116         13	11	2	13	13	0	MRPS15,downstream_gene_variant,,ENST00000373116,NM_031280.3;MRPS15,downstream_gene_variant,,ENST00000462067,;MRPS15,downstream_gene_variant,,ENST00000477040,;MRPS15,downstream_gene_variant,,ENST00000488606,; G	ENSG00000116898 ENST00000373116 Transcript	downstream_gene_variant                                         rs2275479	1	92.0    -1	MRPS15  HGNC    HGNC:14504	protein_coding  YES     CCDS411.1	ENSP00000362208 P82914          UPI0000135287   NM_031280.3                                             0.1358  0.0802  0.1297          0.3065  0.0905  0.0859                                                                          MODIFIER        1	SNV     1                                                                                                       Tier5   TAA     .	.                                                                                                                                                                       36455626        maf
CENPF   0	.	GRCh38  chr1    214608652	214608652	+	Intron  SNP     G	G	A	rs1482929177            TUMOR   NORMAL  G	G                                                                                                                               c.-41-5062G>A                   ENST00000366955         57	45	11	19	19	0	CENPF,intron_variant,,ENST00000366955,NM_016343.3;CENPF,intron_variant,,ENST00000464322,;CENPF,intron_variant,,ENST00000495259,;,regulatory_region_variant,,ENSR00000386218,;ABHD17AP3,non_coding_transcript_exon_variant,,ENST00000503096,;UBE2V1P13,downstream_gene_variant,,ENST00000436983,;        A	ENSG00000117724 ENST00000366955 Transcript	intron_variant                                          rs1482929177    1               1	CENPF   HGNC    HGNC:1857	protein_coding  YES     CCDS31023.1     ENSP00000355922 P49454          UPI00001AE985   NM_016343.3                             1/19                                                                                                                                            MODIFIER        1	SNV     1                                                                                               1.0     PASS    CGG     .	.                                                                                                                    $

Usage - Customizing SLUPipe Variant Callers

SLUPipe's variant callers by default run GDC Guideline arguments. However, each variant caller can be customized to tailor to a user's need. This is accomplished by providing a JSON file during SLUPipe's execution:

$ python3 slupipe.py config.json <muse.json> <mutect.json> <varscan.json> <sniper.json> ...

Please Note: You're not required to provide a JSON file for variant callers you don't intend to run custom arguments. SLUPipe will keep running those variant callers using the base configuration.

Creating Custom Variant Caller Files

Custom variant caller arguments must abide to the following formats:

Muse:

[
  {
    "call": {
      "-f": "./referenceFiles/Homo_sapiens_assembly38.fasta",
      "-r": "chr1:16,000,000-215,000,000",
      "-O":  "./muse_output/"

    },

    "sump": {
      "-I": "./muse_output/muse_call.MuSE.txt",
      "-E": "",
      "-D": "./referenceFiles/dbSNP142_GRCh38_subset50k.vcf.gz",
      "-O": "./muse_output/"

    }

  }
]

IMPORTANT: Name config file "muse.json" otherwise SLUPipe won't detect it.

Mutect:

[
  {
    "mutect2": {
      "-nct": "8"
    }
  }
]

IMPORTANT: Name config file "mutect.json" otherwise SLUPipe won't detect it.

Varscan: [ {

    "samtools": {
      "-q": "1"
    },

    "varscan_somatic": {
      "--mpileup": "1",
      "--min-coverage": "8",
      "--min-coverage-normal": "8",
      "--min-coverage-tumor": "6",
      "--min-var-freq": "0.10",
      "--min-freq-for-hom":"0.75",
      "--normal-purity": "1.0",
      "--tumor-purity":"1.00",
      "--p-value": "0.99",
      "--somatic-p-value":"0.05",
      "--strand-filter": "0"



    },

    "varscan_processSomatic": {
      "--min-tumor-freq": "0.10",
      "--max-normal-freq": "0.05",
      "--p-value": "0.07"

    }
  }

]

IMPORTANT: Name config file "varscan.json" otherwise SLUPipe won't detect it.

Bam Somatic Sniper

[
  {

    "bam_somatic_sniper": {
      "-q": "1",
      "-L": "-G",
      "-Q": "15",
      "-s": "0.01",
      "-T": "0.85",
      "-N": "2",
      "-r": "0.001",
      "-n": "NORMAL",
      "-t": "TUMOR",
      "-F": "vcf"
    }

  }

]

IMPORTANT: Name config file "sniper.json" otherwise SLUPipe won't detect it.

Strelka 2

[
  {
    "strelka_config": {
      "--outputCallableRegions": "",
      "--exome": ""
    },

    "strelka_run": {
      "-m": "local",
      "-j": "4"
    }
  }
]

IMPORTANT: Name config file "strelka.json" otherwise SLUPipe won't detect it.

Pindel

[
  {

    "pindel_read": {
      "-T": "4"
    },

    "pindel2vcf": {
      "-R": "Homo_Sapiens_Assembly38",
      "-d": "20101123"
    }
  }

]

IMPORTANT: Name config file "strelka.json" otherwise SLUPipe won't detect it.

Platypus

[
  {
    "call_Variants": {
      "--nCPU=": "4"
    }
  }
]

IMPORTANT: Name config file "platypus.json" otherwise SLUPipe won't detect it.

Usage - SLUPipe Configuration for High Performance Computing - SLURM

SLUPipe has been developed to be compatible with High Performance Computing (HPC) and SLURM Job Scheduling.

SLUPipe Execution:

Users will construct and provide a base JSON configuration file providing same arguments as before with the inclusion of two new key values:

Number of Nodes : Nodes used during HPC Workflow
Node Samples : Samples processed per node during HPC Workflow

IMPORTANT: SLUPipe HPC mode will process ALL samples found within the input directory.

HPC Base Configuration File Example

[
  {
    "Pipeline_Mode":"-T",
    "Variant_Callers":["Pindel","Platypus"],
    "Input_Directory":"/student/foo/SLUPipe/src/input",
    "Output_Directory":"student/foo/SLUPipe/src/output",
    "Chromosome_Range": "chr1:16,000,000-215,000,000",
    "vep_ScriptPath": "/student/foo/.conda/envs/SLUPipe/share/ensembl-vep-95.3-0",
    "vep_CachePath": "/student/foo/.vep",
    "reference_directory": "/student/foo/referenceFiles",
    "nodes": "2",
    "node_samples": [] <- Must always be empty list
  }
]

Once the base configuration file has been constructed, users must then execute the following script to adapt workload for SLURM compatibility:

$ python3 gen_batches.py <base_configuration_file>

This scripts divides all the samples found in the input directory into smaller jobs by generating new JSON files, each representing a portion of a the total workload:

Input Directory:
    -> Demo1_T.bam
    -> Demo1_N.bam
    -> Demo2_T.bam
    -> Demo2_N.bam


2 Samples / 2 Nodes = 1 Sample Per Job: 

Auto Generated JSON 1:
[
  {
    "Pipeline_Mode":"-T",
    "Variant_Callers":["Pindel","Platypus"],
    "Input_Directory":"/student/foo/SLUPipe/src/input",
    "Output_Directory":"student/foo/SLUPipe/src/output",
    "Chromosome_Range": "chr1:16,000,000-215,000,000",
    "vep_ScriptPath": "/student/foo/.conda/envs/SLUPipe/share/ensembl-vep-95.3-0",
    "vep_CachePath": "/student/foo/.vep",
    "reference_directory": "/student/foo/referenceFiles",
    "nodes": "2",
    "node_samples:["Demo1_T.bam","Demo1_N.bam"]
  }
]

Auto Generated JSON 2:
[
  {
    "Pipeline_Mode":"-T",
    "Variant_Callers":["Pindel","Platypus"],
    "Input_Directory":"/student/foo/SLUPipe/src/input",
    "Output_Directory":"student/foo/SLUPipe/src/output",
    "Chromosome_Range": "chr1:16,000,000-215,000,000",
    "vep_ScriptPath": "/student/foo/.conda/envs/SLUPipe/share/ensembl-vep-95.3-0",
    "vep_CachePath": "/student/foo/.vep",
    "reference_directory": "/student/foo/referenceFiles",
    "nodes": "2",
    "node_samples:["Demo2_T.bam","Demo2_N.bam"]
  }
]

Once the JSON files have been created, users can then generate a SLURM compatible BASH script to send jobs to SLURM Job Scheduler:

#1/bin/bash

source activate SLUPipe

for FILE in *.json:
    echo ${FILE}; do
    sbatch -n 2 -t 1-00:00 --job-name=SLUPipe --cpus-per-task=10 --partition=medmem --wrap="python3 slupipe_apex.py ${FILE}"
    sleep 1
    
done

Run BASH Script

$ ./run_slupipe_hpc.sh

Each job's results will be placed in the output directory specified in base configuration JSON file.

Installation - Anaconda

1. Clone Github Repository

$ git clone https://github.com/BioHPC/SLUPipe.git

2. Download & Install Anacaonda 4.5+

https://www.anaconda.com/distribution/

3.A Create an Anaconda Environment

$ conda create -n SLUPipe

4. Activate the Anaconda Environment:

$ source activate SLUPipe

5. The SLUPipe will require the following Python packages for it to be functionable

biobambam-2.0.87

$ conda install -c bioconda biobambam

bwa.kit-0.7.15

$ conda install -c bioconda bwakit

ensembl-vep 95.3

$ conda install -c bioconda ensembl-vep=95.3

GenomeAnalysisTK-3.8.0

$ conda install -c bioconda gatk

MuSE 1.0.rc

$ conda install -c bioconda muse

pandas 0.24.2

$ conda install -c anaconda pandas

pindel-0.2.5b9

$ conda install -c bioconda pindel

platypus-opt 1.0.3

$ conda install -c bioconda platypus-variant

psycopg2 - 2.7.6.1

$ conda install -c anaconda psycopg2

samtools-1.9

$ conda install -c bioconda samtools

strelka 2.9.10

$ conda install -c bioconda strelka

somatic-sniper 1.0.5.0

$ conda install -c bioconda somatic-sniper

varscan - 2.4.3.2

$ conda install -c bioconda varscan

vcf2maf - 1.6.16

$ conda install -c bioconda vcf2maf

6. Configuring Ensembl VEP For Variant Annotation & MAF Conversion (Local Cache Installation):

Create .vep directory to store offline cache: mkdir ~/.vep
$ cd $HOME/.vep
$ curl -O -C - ftp://ftp.ensembl.org/pub/release-95/variation/indexed_vep_cache/homo_sapiens_vep_95_GRCh38.tar.gz
$ tar xzf homo_sapiens_vep_96_GRCh38.tar.gz

Please Note: Download time will vary depending on time of day (1 Hr+)

7. Copying Strelka 2 Configuration File to SLUPipe Working Directory:

Locate "configureStrelkaSomaticWorkflow.py" found in SLUPipe conda env bin directory (~/.conda/envs/SLUPipe/bin)
Copy file into SLUPipe working directory ($SLUPipe/src)

$ cp ~/.conda/envs/SLUPipe/bin/configureStrelkaSomaticWorkflow.py $SLUPipe/src/

Please Note: $SLUIPipePATH is your current working directory (user can check this by typing "pwd" in terminal)

Tip: If unable to locate ./conda/envs/SLUPipe/bin directory, please run the following two commands to locate path:

$ source activate SLUPipe (start SLUPipe environment)
$ which python (prints full path related to SLUPipe conda environment)

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SLUPipe: A (S)omatic Ana(L)ysis (U)mbrella (Pipe)line

Table of Contents

Description

Features

Variant Callers

Variant Annotation

Requirements

Express Installation - Anaconda

Running SLUPipe

Usage - Sample Entry/Output

Usage - JSON file configuration

Usage - Example Workflow

Usage - Customizing SLUPipe Variant Callers

Usage - SLUPipe Configuration for High Performance Computing - SLURM

Installation - Anaconda

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SLUPipe: A (S)omatic Ana(L)ysis (U)mbrella (Pipe)line

Table of Contents

Description

Features

Variant Callers

Variant Annotation

Requirements

Express Installation - Anaconda

Running SLUPipe

Usage - Sample Entry/Output

Usage - JSON file configuration

Usage - Example Workflow

Usage - Customizing SLUPipe Variant Callers

Usage - SLUPipe Configuration for High Performance Computing - SLURM

Installation - Anaconda

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