1.Introduction

Whole genome re-sequencing (WGS) reveals the complete DNA make-up of an individual with complete reference genome, enabling us to better understand variations both within and between individuals or populations.By Aligned to reference genome, we could find plenty of variants like SNP,InDel,SV and CNV in an individual's sequencing data,which could be applied to clinical research, population genetics, association studies, evolutionary biology and many other related fields.

2.Project Information

2.1. Sample information

Species name：Human

Latin name：Homo sapiens

2.2. Disease information

Disease：DEMO

2.3. Database

Database	link	web_link
Genome	hg19	ftp://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/chromFa.tar.gz

2.4. Bioinformatics software

Analysis item	Software	Version/date
Quality control	fastp	0.19.4
Alignment	BWA	0.7.12
	SAMtools	1.9
	Picard	2.17.3
SNP/INDEL calling	GATK	4.0.4.0
SNP/INDEL annotation	VEP

3.Materials and Methods

3.1. DNA extraction and whole genome sequencing

Total DNA was isolated from whole blood collected in tubes with EDTA or tissue samples by using a standard DNA extraction protocol. The quantity of DNA was measured by reading A260/280 ratios by spectrophotometer. When A260/280 ratios located range 1.8 to 2.0, DNA was available.

The genomic DNA (gDNA, 1ug) was sheared using Covaris Series sonicator to achieve target peak of 200 to 300bp. Size selection after fragmentation was performed using Ampure XP beads,and blunt-end DNA fragments were generated by a combination of fill-in reaction and exonuclease activity. An A-base is then added to the blunt ends of each strand, preparing them for ligation to the T-tail sequencing adapters. 5 cycles of PCR were performed for amplification of the ligation products to generate gDNA library . At last, we performed the 150bp paired-end sequencing on an Illumina NovaSeq 6000 following the vendor's recommended protocol.

Workflow of WGS

3.2. Bioinformatics analysis

We sequenced generating a total of DEMO paired-end reads of 150bp length. This yielded DEMO G of sequence.Prior to alignment, the low quality reads (1, reads containing sequencing adaptors; 2, nucleotide with q quality score lower than 20) were removed. After that, a total of DEMO G bp of cleaned, paired-end reads were produced.

For the alignment step, BWA is utilized to perform reference genome alignment with the reads contained in paired FASTQ files. And as first post-alignment processing step, Picard tools is utilized to identified and mark duplicate reads from BAM file.

In the second post-alignment processing step, local read realignment is performed to correct for potential alignment errors around indels. Mapping of reads around the edges of indels often results in misaligned bases creating false positive SNP calls. Local realignment uses these mismatching bases to determine if a site should be realigned, and applies a computationally intensive algorithm to determine the most consistent placement of the reads with respect to the indel and remove misalignment artifacts.

Each base of each read has an associated quality score, corresponding to the probability of a sequencing error. Due to the Systematic biases, the reported quality scores are known to be inaccurate and as such must be recalibrated prior to genotyping. After recalibration, the recalibrated quality score in the output BAM will more closely correspond to the probability of a sequencing error.

Variant calls can be generated with GATK HaplotypeCaller or UnifiedGenotyper, which Examine the evidence for variation from reference via Bayesian inference.

A Gaussian mixture model is fit to assigning accurate confidence score to each putative mutation call and evaluating new potential variants.

Biological functional annotation is a crucial step in finding the links between genetic variation and disease. SnpEff is utilized to add biological information to a set of variants.

Bioinformatics pipeline for WGS

4. Overview

4.1. Sample information

document location:summary/1_RawData/sample_info_mendelian.xlsx

4.2. Statistics of Sequencing Quality

document location:summary/1_RawData/ReadsQC.xlsx

4.3. Sequencing depth

Fig:Reads Depth Coverage

document location:summary/2_MappedData/ReadsDepthCoverage.png

Depth of coverage on each chromosome

Depth of coverage=covered total length/total length of each chromosome

document location:summary/2_MappedData/DepthCoverageByChr.png

4.4. Statistics of mapped reads

document location:summary/2_MappedData/MappedStatistics.xlsx

Table Description：

Term	Description
Total Reads	Number of total reads
Valid Reads	Number of Valid reads
Mapped Reads	Number of mapped reads on reference genome
Duplicate Reads	Number of duplicate reads
Valid Rate %	ratio:Valid reads/Total Reads
Mapped Rate %	ratio:Mapped Reads/Valid Reads
Duplicate Rate %	ratio:Duplicate Reads/Mapped Reads
MEAN_READ_LENGTH	Average length of mapped reads
MEAN_DEPTH	Mean dapth
PCT_TARGET_BASES_1X	percentage of base>=1x
PCT_TARGET_BASES_2X	percentage of base>=2x
PCT_TARGET_BASES_10X	percentage of base>=10x
PCT_TARGET_BASES_20X	percentage of base>=20x
PCT_TARGET_BASES_30X	percentage of base>=30x

Fig:　Depth of coverage on each sample

5. Variant calling

A single-nucleotide polymorphism, often abbreviated to SNP, is a variation in a single nucleotide that occurs at a specific position in the genome including transition and transversion, where each variation is present to some appreciable degree within a population (e.g. > 1%).

Indel is a molecular biology term for an insertion or deletion of bases in the genome of an organism. It is classified among small genetic variations, measuring from 1 to 10 000 base pairs in length, including insertion and deletion events that may be separated by many years, and may not be related to each other in any way. A microindel is defined as an Indel that results in a net change of 1 to 50 nucleotides.

Meanwhile,Indels can be used as genetic markers in natural populations, especially in phylogenetic studies. It has been shown that genomic regions with multiple Indels can also be used for species-identification procedures.

A copy number variation (CNV) is when the number of copies of a particular gene varies from one individual to the next. Following the completion of the Human Genome Project, it became apparent that the genome experiences gains and losses of genetic material. The extent to which copy number variation contributes to human disease is not yet known. It has long been recognized that some cancers are associated with elevated copy numbers of particular genes.

A single-nucleotide variant (SNV) is a variation in a single nucleotide without any limitations of frequency and may arise in somatic cells. A somatic single nucleotide variation (e.g., caused by cancer) may also be called a single-nucleotide alteration.

5.1. SNP Statistics

document location:summary/3_VariantData/SNP_INDEL_PositionType_VariantsType.xlsx

High-Impact Effects	Moderate-Impact Effects	Low-Impact Effects
SPLICE_SITE_ACCEPTOR	NON_SYNONYMOUS_CODING	SYNONYMOUS_START
SPLICE_SITE_DONOR	CODON_CHANGE	NON_SYNONYMOUS_START
START_LOST	CODON_INSERTION	START_GAINED
EXON_DELETED	CODON_CHANGE_PLUS_CODON_INSERTION	SYNONYMOUS_CODING
FRAME_SHIFT	CODON_DELETION	SYNONYMOUS_STOP
STOP_GAINED	CODON_CHANGE_PLUS_CODON_DELETION	NON_SYNONYMOUS_STOP
STOP_LOST	UTR_5_DELETED	INTRON
FRAME_SHIFT+START_LOST	UTR_3_DELETED	UPSTREAM
FRAME_SHIFT+STOP_GAINED		DOWNSTREAM
		UTR_3_PRIME
		UTR_5_PRIME

document location:summary/3_VariantData/PA/PA_SNV.png

document location:summary/3_VariantData/VariantsType_SNP.xlsx

document location:ssrc/summary/3_VariantData/PA/PA_SNP_VariantsType.png

5.2. VEP Annotation for SNP

All SNPs were annotated by VEP in VCF format: summary/3_VariantData/PA/PA_SNP.annotation.fixed.function.vcf

Table Description：

Term	Description
CHROM	chromosome id
POS	chromosome position
ID	dbSNP ID
REF	reference allele
ALT	alternative allele
QUAL	quality
FILTER	filter
INFO	information
AD	Allelic depths
DP	Approximate read depth
GQ	Genotype Quality
GT	genotype
PL	Phred-scaled likelihoods

5.3. InDel Statistics

document location:summary/3_VariantData/SNP_INDEL_PositionType_VariantsType.xlsx

document location：src/summary/3_VariantData/PA/PA_INDEL.png

document location：src/summary/3_VariantData/VariantsType_INDEL.xlsx

document location：ssrc/summary/3_VariantData/PA/PA_INDEL_VariantsType.png

5.4. VEP Annotation for InDel

All InDels were annotated by VEP in VCF format: summary/3_VariantData/PA/PA_INDEL.annotation.fixed.function.vcf

Term	Description
CHROM	chromosome id
POS	chromosome position
ID	dbSNP ID
REF	reference allele
ALT	alternative allele
QUAL	quality
FILTER	filter
INFO	information
AD	Allelic depths
DP	Approximate read depth
GQ	Genotype Quality
GT	Genotype
PL	Phred-scaled likelihoods

5.5. CNV Statistics

CNVs(copy number variations) on the genome were caculated by Control-Freec.

document location：summary/4_CNV/PA_proband/PA_proband_CNVs.xlsx

PA_proband_makeGraph_Chromosome_10.pngPA_proband_makeGraph_Chromosome_11.pngPA_proband_makeGraph_Chromosome_12.pngPA_proband_makeGraph_Chromosome_13.pngPA_proband_makeGraph_Chromosome_14.pngPA_proband_makeGraph_Chromosome_15.pngPA_proband_makeGraph_Chromosome_16.pngPA_proband_makeGraph_Chromosome_17.pngPA_proband_makeGraph_Chromosome_18.pngPA_proband_makeGraph_Chromosome_19.pngPA_proband_makeGraph_Chromosome_1.pngPA_proband_makeGraph_Chromosome_20.pngPA_proband_makeGraph_Chromosome_21.pngPA_proband_makeGraph_Chromosome_22.pngPA_proband_makeGraph_Chromosome_2.pngPA_proband_makeGraph_Chromosome_3.pngPA_proband_makeGraph_Chromosome_4.pngPA_proband_makeGraph_Chromosome_5.pngPA_proband_makeGraph_Chromosome_6.pngPA_proband_makeGraph_Chromosome_7.pngPA_proband_makeGraph_Chromosome_8.pngPA_proband_makeGraph_Chromosome_9.pngPA_proband_makeGraph_Chromosome_X.png