Whole-Genome Sequencing Variant Calling Pipeline — SRR062634

An end-to-end WGS variant calling pipeline built from scratch on a resource-constrained virtual machine: raw reads → QC → trimming → alignment → duplicate marking → variant calling → filtering → functional annotation → visual validation in IGV.

Sample (SRA run)	SRR062634
Source individual	HG00096 (male)
Population	GBR — British in England and Scotland (1000 Genomes Project, EUR super-population)
Sequencing platform	Illumina Genome Analyzer II
Read layout	Paired-end, 2 × 100 bp
Reference genome	GRCh38, Ensembl primary assembly (3,099,750,718 bp)
Mean depth achieved	≈1.48× (low-pass WGS)
Variant caller	FreeBayes
Annotation	Ensembl VEP (web tool)
Environment	Ubuntu MATE 24.04.4 LTS "Noble Numbat" — VirtualBox VM, 16 GB RAM, 4 vCPUs, 100 GB disk

⚠️ Low-coverage demonstration pipeline — not a diagnostic-grade analysis. This dataset was analyzed at ≈1.5× depth (well below the ≥20–30× recommended for clinical genome interpretation) purely to demonstrate the pipeline under hardware constraints. At this depth, allelic dropout is expected — the sequencer may only sample one of the two alleles at a heterozygous site, causing it to be miscalled as homozygous. Every "high-impact" finding below is a screening-level candidate only and would require deep targeted resequencing (Sanger or ≥30× NGS) before any clinical interpretation. See Discussion & Limitations.

At a glance: 1,373,028 variants called → split into 100 HIGH-impact indel calls (83 genes) and 155 HIGH-impact SNP calls (141 genes) after quality filtering and VEP annotation → 2 genes, CYP4B1 and ZNF717, turned up independently in both sets, the strongest signal in the dataset (see Results).

Table of Contents

• Pipeline Overview
• Environment & Tools
• 1. Data Acquisition
• 2. Quality Control — FastQC
• 3. Read Trimming — fastp
• 4. Reference Indexing — BWA
• 5. Alignment — BWA-MEM
• 6. SAM → BAM, Sorting & Indexing
• 7. Alignment QC — flagstat & Qualimap
• 8. Read Groups & Duplicate Marking — Picard
• 9. Variant Calling — FreeBayes
• 10. Variant Statistics — RTG Tools
• 11. Variant Filtering — vcftools
• 12. Functional Annotation — Ensembl VEP
• Results: High-Impact Variants
• Discussion & Limitations
• Project Structure
• References

Pipeline Overview

flowchart TD
    A["Raw FASTQ\nSRR062634_1 / _2"] --> B["FastQC\nQuality control"]
    B --> C["fastp\nAdapter/quality trimming"]
    C --> D["BWA-MEM\nAlignment to GRCh38"]
    D --> E["samtools\nSAM→BAM, sort, index"]
    E --> F["Qualimap + flagstat\nAlignment QC"]
    E --> G["Picard\nRead groups + MarkDuplicates"]
    G --> H["FreeBayes\nVariant calling"]
    H --> I["RTG Tools\nvcfstats"]
    H --> J["vcftools\nSplit & filter SNPs / indels"]
    J --> K["Ensembl VEP (web)\nFunctional annotation"]
    K --> L["IGV\nVisual validation"]

Loading

Environment & Tools

Built inside a local virtual machine (Ubuntu MATE 24.04.4 LTS, 16 GB RAM, 4 vCPUs, 100 GB disk) to keep the whole pipeline reproducible outside the cloud.

# environment.yml
name: wgs_variant_calling
channels:
  - defaults
dependencies:
  - fastqc
  - samtools
  - fastp
  - vcftools
  - bwa
  - igv
  - qualimap
  - picard
  - freebayes
  - rtg-tools

conda env create -f environment.yml

# ...or equivalent, built manually:
conda create -n wgs_variant_calling -c bioconda -c conda-forge \
    fastqc fastp bwa samtools qualimap picard freebayes vcftools igv rtg-tools

Tool	Role
FastQC	Raw read quality control
fastp	Adapter/quality trimming
BWA	Reference indexing + alignment
samtools	SAM/BAM conversion, sorting, indexing, flagstat
Qualimap	Alignment QC report
Picard	Read groups, duplicate marking
FreeBayes	Variant calling
RTG Tools	vcfstats
vcftools	Variant filtering
Ensembl VEP	Web tool (ensembl.org/Tools/VEP) — VCFs were uploaded through the browser, not run locally
IGV	Visual inspection of alignments/variants

Two small reproducibility notes: (1) the defaults channel alone won't resolve most of these bioinformatics packages — add -c bioconda -c conda-forge as in the manual command above, or make sure those channels are in your global ~/.condarc. (2) ensembl-vep isn't in the environment at all because annotation was done through the VEP web interface, not the command-line tool — if you script this step, add the ensembl-vep package or use the REST API instead.

1. Data Acquisition

Raw paired-end reads from the ENA mirror of SRA:

wget https://ftp.sra.ebi.ac.uk/vol1/fastq/SR062/SRR062634/SRR062634_1.fastq.gz
wget https://ftp.sra.ebi.ac.uk/vol1/fastq/SR062/SRR062634/SRR062634_2.fastq.gz

Reference genome (GRCh38, Ensembl):

wget -c ftp://ftp.ensembl.org/pub/current_fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz
gzip -d Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz

This run was originally expected to yield roughly 5× coverage; the actual measured mean depth after alignment came out lower, at ≈1.48× (see §7) — the pipeline was adapted accordingly (--min-coverage 1 in FreeBayes, see §9).

2. Quality Control — FastQC

fastqc *.fastq.gz

Metric	R1	R2
Total sequences	24,476,109	24,476,109
Sequence length	100 bp	100 bp
%GC	40%	40%
Encoding	Sanger / Illumina 1.9	Sanger / Illumina 1.9
Poor-quality sequences	0	0
Overrepresented sequences	None found	None found
Per-base quality	✅ Pass	✅ Pass
Per-tile sequence quality	❌ Fail	✅ Pass
Per-sequence quality scores	✅ Pass	✅ Pass
Per-base sequence content	✅ Pass	⚠️ Warn
Per-sequence GC content	⚠️ Warn	⚠️ Warn
Sequence duplication levels	✅ Pass	✅ Pass
Adapter content	✅ Pass	✅ Pass

Findings:

• Base quality drops below Phred Q32 from around position 72–73 bp onward, though it stays within FastQC's passing range overall.
• R1 flags a per-tile sequence quality fail — a localized, flow-cell-position-specific quality dip rather than a global problem; worth a glance at the per-tile heatmap if repeating this analysis, but not something adapter/quality trimming can fix.
• Both reads warn on per-sequence GC content (expected — the theoretical GC distribution FastQC compares against assumes a generic genome, and real human WGS libraries commonly deviate from it slightly); R2 additionally warns on per-base sequence content, typical of the first few cycles of Illumina reads.
• No overrepresented sequences in either file, and adapter content passes outright — consistent with a library that was reasonably clean going in.
• A Q20 trimming threshold was chosen to remove low-quality bases while preserving read length — reads are only 100 bp long, so overly aggressive trimming was avoided.

Full interactive reports: SRR062634_1_fastqc.html, SRR062634_2_fastqc.html — best source for the visual modules (per-tile heatmap, GC distribution, etc.) referenced above.

3. Read Trimming — fastp

fastp -i SRR062634_1.fastq.gz -I SRR062634_2.fastq.gz \
      -o out1_clean.fq.gz -O out2_clean.fq.gz \
      --detect_adapter_for_pe \
      --trim_poly_x --trim_poly_g \
      --cut_front 20 --cut_tail 20 --cut_mean_quality 20 \
      -h out_FastP.html

fastp v1.3.6 auto-detected different adapters on each mate — AGATCGGAAGAGCGGTTCAGCAGGAATGCCGAG (TruSeq2 PE, read 1) and AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT (Illumina TruSeq Adapter Read 2) — both variants of the standard Illumina adapter, and trimmed them along with poly-G/poly-X runs and any base under Q20 in a 20 bp sliding window from each end.

	Before filtering	After filtering
Total reads	48,952,218	48,340,094
Total bases	4.895 Gbp	4.613 Gbp
Mean length (R1, R2)	100 bp, 100 bp	94 bp, 95 bp
Q20 bases	94.98%	99.03%
Q30 bases	90.47%	94.47%
GC content	40.73%	40.15%

Reads removed (612,124 total, 1.25% of input):

Reason	Reads	% of input
Too short after trimming	579,462	1.184%
Too many N bases	26,484	0.054%
Low quality	5,012	0.010%
Adapter dimer	1,166	0.002%

Estimated duplication rate at this pre-alignment, read-level stage: 0.27% (fastp's k-mer-based estimate — see §8 for the post-alignment, coordinate-based figure from Picard, which is a different methodology and not expected to match exactly). Insert size peak: 169 bp, consistent with the library insert sizes seen later in Qualimap.

Full interactive report: out_FastP.html — has the visual before/after quality curves behind the numbers above.

4. Reference Indexing — BWA

bwa index Homo_sapiens.GRCh38.dna.primary_assembly.fa

Finished constructing BWT in 688 iterations
1759.91 seconds elapse.
Update BWT... 10.89 sec
Pack forward-only FASTA 10.81 sec
Construct SA from BWT and Occ... 1243.52 sec
Version: 0.7.19-r1273
CMD: bwa index Homo_sapiens.GRCh38.dna.primary_assembly.fa
Real time: 3051.642 sec; CPU: 3047.239 sec

Indexing the full GRCh38 primary assembly took ≈51 minutes real time on this VM.

5. Alignment — BWA-MEM

bwa mem -a Homo_sapiens.GRCh38.dna.primary_assembly.fa \
        out1_clean.fq.gz out2_clean.fq.gz \
        -o align.sam 2> stderror.out

Run on 2026-07-07 with BWA 0.7.19-r1273. The -a flag reports all valid alignments per read, not just the best one — this is why downstream read counts (§7) look much larger than the 48.3M clean read pairs that went in: secondary and supplementary alignments (common in repetitive regions) get counted too.

6. SAM → BAM, Sorting & Indexing

# SAM to BAM
samtools view -bS align.sam > align.bam

# Sort by coordinate
samtools sort align.bam align.sorted.bam

# Index
samtools index align.sorted.bam

# Stats
samtools flagstat align.sorted.bam

7. Alignment QC — flagstat & Qualimap

63695674 + 0 in total (QC-passed reads + QC-failed reads)
0 + 0 duplicates
63552054 + 0 mapped (99.77%:-nan%)
63695674 + 0 paired in sequencing
31875528 + 0 read1
31820146 + 0 read2
47515736 + 0 properly paired (74.60%:-nan%)
62720026 + 0 with itself and mate mapped
832028 + 0 singletons (1.31%:-nan%)
9714284 + 0 with mate mapped to a different chr
98183 + 0 with mate mapped to a different chr (mapQ>=5)

qualimap bamqc -bam align.sorted.bam -nt 4 --java-mem-size=12G

Metric	Value
Reference size	3,099,750,718 bp
Number of reads	48,340,094
Mapped reads	48,196,474 (99.7%)
Unmapped reads	143,620 (0.3%)
Mapped paired reads	48,196,474 (99.7%)
Mapped reads, first in pair	24,107,251 (49.87%)
Mapped reads, second in pair	24,089,223 (49.83%)
Mapped reads, both in pair	48,132,470 (99.57%)
Mapped reads, singletons	64,004 (0.13%)
Secondary alignments	15,291,951
Supplementary alignments	63,629 (0.13%)
Read length (min/max/mean)	0 / 100 / 95.5 bp
Duplicated reads (estimated)	2,154,752 (4.46%)
Duplication rate	2.76%
Clipped reads	805,233 (1.67%)
Mean coverage	1.482×
Coverage std. deviation	7.9758
Mean mapping quality	43.93
GC percentage	40.16%
General error rate	0.38%
Mismatches	16,331,602
Insertions	365,687 (0.74% of mapped reads)
Deletions	447,220 (0.90% of mapped reads)
Homopolymer indels	43.53%

A homopolymer-indel fraction of 43.53% is high but expected: homopolymer runs are the classic Achilles' heel of Illumina indel calling, which is part of why indels were filtered more strictly than SNPs later on (§11).

Insert size — a real anomaly worth explaining, not a typo:

	Value
Mean	12,303.55 bp
Standard deviation	892,432.46
P25 / Median / P75	169 / 183 / 195 bp

The mean and standard deviation look absurd next to a median of 183 bp — but this is what qualimapReport.html actually reports, not a transcription error. The most likely explanation: Qualimap's naive insert-size calculation includes discordant read pairs (mates mapping to different chromosomes, or extremely far apart), and flagstat shows there are 9,714,284 of exactly those. A relative handful of pairs with a computed "insert size" in the tens or hundreds of millions of bp is enough to blow up a mean and standard deviation while leaving a robust statistic like the median untouched — P25/median/P75 (169/183/195 bp) is the number that actually reflects this library's real insert size distribution, and it lines up well with fastp's independently-estimated insert size peak of 169 bp (§3).

A sex-chromosome sanity check on the real data: per-chromosome coverage in the full Qualimap report averages ≈1.5× across the autosomes, but chrX sits at 0.7676× and chrY at 0.6716× — both roughly half the autosomal mean, exactly what's expected for a male sample with one copy of each. Mitochondrial coverage (MT) is far higher, at 511.3×, which is also expected: mitochondrial DNA is present in many copies per cell, unlike single-copy nuclear chromosomes. This is a nice independent confirmation that the sample metadata (HG00096, male) and the sequencing data agree with each other.

Full interactive report: align.sorted_stats/qualimapReport.html, with 12 additional plots (coverage across reference, insert-size histogram, GC content, mapping quality, etc.).

8. Read Groups & Duplicate Marking — Picard

Read groups were derived from the read header and SRA/NCBI metadata:

@SRR062634.1 HWI-EAS110_103327062:6:1092:8469/1

Field	Value	Source
Instrument ID (--RGID/--RGPU)	HWI-EAS110_103327062	Read header
Lane (--RGID/--RGPU)	6	Read header
Library ID (--RGLB)	2845856850	NCBI/SRA metadata
Platform (--RGPL)	Illumina Genome Analyzer II	NCBI/SRA metadata

picard AddOrReplaceReadGroups \
    --INPUT align.sorted.bam \
    --OUTPUT align.sorted.rg.bam \
    --RGID HWI-EAS110_103327062.6 \
    --RGLB 2845856850 \
    --RGPL illumina \
    --RGPU HWI-EAS110_103327062 \
    --RGSM SRR062634

picard MarkDuplicates \
    --INPUT align.sorted.rg.bam \
    --OUTPUT align.dedup.bam \
    --METRICS_FILE markDuplicatesMetrics.txt \
    --ASSUME_SORTED True

Run 2026-07-07. Elapsed time for AddOrReplaceReadGroups: 7.42 minutes; for MarkDuplicates: 11.50 minutes.

Real markDuplicatesMetrics.txt output:

Metric	Value
Library	2845856850
Unpaired reads examined	64,004
Read pairs examined	24,066,235
Secondary/supplementary reads	15,355,580
Unmapped reads	143,620
Unpaired read duplicates	4,079
Read pair duplicates	204,174
Optical duplicates	0
Percent duplication	0.8557%
Estimated library size	1,410,324,562

This cross-checks cleanly against Qualimap (§7): unpaired reads examined (64,004) matches Mapped reads, singletons exactly; read pairs examined ×2 (48,132,470) matches Mapped reads, both in pair exactly; and secondary/supplementary reads (15,355,580) matches Secondary + Supplementary alignments (15,291,951 + 63,629) exactly. Three independent tools, three matching numbers — a good sign the pipeline ran cleanly end to end.

Picard's duplication estimate (0.86%) is noticeably lower than both fastp's pre-alignment estimate (0.27%, §3) and Qualimap's (2.76%, §7) — expected, since all three use different methodologies (fastp: k-mer/sequence similarity before alignment; Picard: exact 5′ mapping coordinate + strand after alignment; Qualimap: a statistical estimate from the coverage distribution). None of them is "the" duplication rate; they're three different lenses on the same library.

9. Variant Calling — FreeBayes

samtools index align.dedup.bam

freebayes -f Homo_sapiens.GRCh38.dna.primary_assembly.fa \
          --min-coverage 1 \
          align.dedup.bam > variants.vcf

--min-coverage 1 was necessary given the ≈1.5× depth achieved — at higher default thresholds, most of the genome would have been excluded. FreeBayes's default minimum-alternate-count filter (-C 2, i.e. at least 2 independent reads supporting the alternate allele) was deliberately kept, which meaningfully limits false positives compared to calling on a single read.

10. Variant Statistics — RTG Tools

rtg vcfstats variants.vcf > variants.vcfstats

Location                     : variants.vcf
Failed Filters               : 0
Passed Filters               : 1373028
SNPs                         : 1193841
MNPs                         : 28877
Insertions                   : 47948
Deletions                    : 57874
Indels                       : 7871
Same as reference            : 36617
SNP Transitions/Transversions: 1.92 (1326492/692648)
Total Het/Hom ratio          : 0.44 (407215/929196)
SNP Het/Hom ratio            : 0.45 (368786/825055)
MNP Het/Hom ratio            : 0.88 (13551/15326)
Insertion Het/Hom ratio      : 0.24 (9425/38523)
Deletion Het/Hom ratio       : 0.30 (13310/44564)
Indel Het/Hom ratio          : 0.37 (2143/5728)
Insertion/Deletion ratio     : 0.83 (47948/57874)
Indel/SNP+MNP ratio          : 0.09 (113693/1222718)

(exact output of rtg vcfstats; commas below are added only in prose for readability)

Interpretation: For a low-coverage run (≈1.5×), the raw variant count is large, and a meaningful share are expected FreeBayes false positives from single-read support. Two sanity checks support real biological signal rather than noise:

• Ts/Tv = 1.92, close to the ~2.0–2.1 expected genome-wide for humans — consistent with a real, coherent human variant set rather than technical noise.
• Het/Hom ratio = 0.44 is unusually low (normal WGS is typically ~1.5–2.0). This is expected at this depth: if a position is truly heterozygous (one allele from each parent) but the sequencer only reads it once, it captures only one of the two alleles and the site gets miscalled as homozygous — this is the allelic dropout effect referenced throughout this report.

11. Variant Filtering — vcftools

vcftools --vcf variants.vcf --keep-only-indels --minQ 30 \
         --recode --recode-INFO-all --out variants_indels.vcf

vcftools --vcf variants.vcf --remove-indels --minQ 20 \
         --recode --recode-INFO-all --out variants_snps.vcf

Indels were filtered more strictly (Q30) than SNPs (Q20) because indels — especially in the homopolymer-rich regions flagged in §7 — are more error-prone in short-read alignment.

Compression for downstream tools:

bgzip variants_snps.vcf.recode.vcf
bgzip variants_indels.vcf.recode.vcf

12. Functional Annotation — Ensembl VEP

The two filtered, bgzipped VCFs were uploaded through the Ensembl VEP web tool (not run locally — see the note in Environment & Tools), and the annotated VCFs it returned were downloaded and parsed with awk to extract HIGH-impact, high-confidence calls into a flat, readable format.

	Indels	SNPs
Variants processed	112,555	1,007,252
Variants filtered out	0	0
Existing / novel variants	36,417 (32.4%) existing	953,853 (94.7%) existing
Overlapped genes	34,929	67,773
Overlapped transcripts	357,518	588,050
Overlapped regulatory features	3,558	34,244
Top coding consequence	frameshift_variant (19%)	synonymous_variant (56%)

These "variants processed" counts are a nice coherence check against §10/§11: variants.vcf held 113,693 raw indel-type calls and 1,222,718 raw SNP/MNP-type calls; after the Q30/Q20 filters, VEP actually processed 112,555 indels (99.0% survived) and 1,007,252 SNPs (82.4% survived) — the much bigger drop for SNPs is consistent with a large fraction of single-read, lower-confidence SNP calls at this coverage getting caught by the stricter-in-relative-terms Q20 cutoff. The lower "existing variant" rate for indels (32.4% vs. 94.7% for SNPs) also matches expectations — population variant databases are far less complete for indels than for SNPs.

VEP web summary for the indels VCF.

VEP web summary for the SNPs VCF.

Indels — HIGH-impact frameshift / stop-gained / stop-lost variants:

awk -F '\t' '!/^#/{
    if ($8 ~ /CSQ=/) {
        # Isolate the CSQ block
        split($8, t1, "CSQ=");
        split(t1[2], cb, ";");

        # Separate the different transcripts (comma-separated)
        n = split(cb[1], tr, ",");
        # Save already-printed combinations to avoid duplicates at the same position
        p = "";
        for (i = 1; i <= n; i++) {
            # Separate the internal VEP fields (pipe-separated): Allele|Consequence|Impact|Gene...
            split(tr[i], f, "|");
            cons = f[2];
            imp  = f[3];
            gen  = f[4];

            if (gen == "") gen = "Unknown";
            # Filter consequences of interest
            if (imp == "HIGH" && cons ~ /frameshift_variant|stop_gained|stop_lost|missense_variant/) {
                # Unique key per position-consequence-gene to avoid cluttering the file
                key = $1 "_" $2 "_" cons "_" gen;
                if (p !~ key) {
                    print $1 "\t" $2 "\t" cons "\t" imp "\t" gen;
                    p = p " " key
                }
            }
        }
    }
}' results_indels.vcf | sort -V -u > results_indels.txt

SNPs — HIGH-impact stop-gained / stop-lost variants:

awk -F '\t' '!/^#/{
    if ($8 ~ /CSQ=/) {
        split($8, t1, "CSQ=");
        split(t1[2], cb, ";");
        n = split(cb[1], tr, ",");
        p = "";
        for (i = 1; i <= n; i++) {
            f_n = split(tr[i], f, "|");
            cons = f[2];
            imp  = f[3];
            gen  = f[4];

            if (gen == "") gen = "Unknown";
            if (imp == "HIGH" && cons ~ /stop_gained|stop_lost/) {
                key = $1 "_" $2 "_" cons "_" gen;
                if (p !~ key) {
                    print $1 "\t" $2 "\t" cons "\t" imp "\t" gen;
                    p = p " " key
                }
            }
        }
    }
}' results_snps.vcf | sort -V -u > results_snps.txt

Both scripts print an imp (impact) column — the working copy of the indels script only printed 4 fields (no impact column), but the real results_indels.txt output has 5 columns identical in shape to the SNPs file, so the version above reflects what was actually run.

Cross-referencing genes that appear in both lists:

comm -12 <(awk -F'\t' '{print $5}' results_snps.txt | sort -u) \
         <(awk -F'\t' '{print $5}' results_indels.txt | sort -u)
# → CYP4B1, ZNF717

(independently re-verified directly against the two result files for this report: of 83 unique indel genes and 141 unique SNP genes, exactly two — CYP4B1 and ZNF717 — appear in both.)

Finally, IGV was launched with extra heap space (the default wasn't enough for a full genome + two VCF tracks) and pointed at the coordinates found above:

_JAVA_OPTIONS='-Xmx10g' igv

# Files loaded:
#   variants_indels.vcf.recode.vcf.gz
#   variants_snps.vcf.recode.vcf.gz
#   align.sorted.bam
#   align.sorted.bam.bai

Results: High-Impact Variants

VEP annotation and filtering (§12) narrowed the raw call set down to 100 HIGH-impact indel calls across 83 genes and 155 HIGH-impact SNP calls across 141 genes. The tables below highlight the entries with the clearest biological narrative; the full lists are in the collapsible sections further down and in results_indels.txt / results_snps.txt.

Highlighted indels

Gene	Locus	Consequence	Notes
EWSR1	chr22:29,281,368	Stop-lost + NMD transcript variant	Tumor suppressor gene; losing the stop codon here would abnormally elongate the protein. EWSR1 is classically associated with Ewing sarcoma, though its best-established oncogenic mechanism is a somatic EWSR1–FLI1 gene fusion, not a germline stop-loss — worth keeping in mind when interpreting this specific call.
MYT1L	chr2:1,830,817	Frameshift variant	Transcription factor essential for neuronal maturation and development. Loss-of-function variants are linked to an autosomal dominant intellectual disability syndrome with speech delay and hyperactivity/obesity.
POT1	chr7:124,866,305	Frameshift + NMD transcript variant	Protects telomere integrity, preventing cellular aging and malignant transformation. Mutations raise the risk of hereditary cutaneous melanoma and certain leukemias.
CLN5	chr13:77,017,068	Frameshift variant	Linked to a rare, devastating lysosomal disorder: Neuronal Ceroid Lipofuscinosis type 5, causing progressive neurodegeneration with vision loss, epilepsy, and motor decline in childhood.
SETBP1	chr18:44,876,705	Frameshift variant	Central regulator of embryonic and cognitive development. Mutations cause SETBP1 haploinsufficiency disorder, marked by severe psychomotor delay and speech apraxia.

Highlighted SNPs

Gene	Locus	Consequence	Notes
MTOR	chr1:11,247,580	Stop-gained	Core cell-biology gene; the mTOR pathway regulates cell growth, proliferation and survival. Loss-of-function variants are linked to neurodevelopmental disorders and play a key role in oncology.
PINK1	chr1:20,649,670	Stop-gained	Encodes a mitochondrial kinase that protects neurons from cellular stress. Mutations cause autosomal recessive, early-onset Parkinson's disease.
NF1	chr17:31,378,929	Stop-lost + NMD transcript variant	Neurofibromin 1, a tumor suppressor. Mutations cause Neurofibromatosis Type 1, a dominant disorder causing benign nerve tumors and skin pigmentation changes.
STAT1	chr2:190,970,704	Stop-lost (+ NMD transcript variant)	Core immune-system gene. Mutations cause Combined Immunodeficiency, leaving patients highly vulnerable to infection.
FTO	chr16:54,118,489	Stop-lost + NMD transcript variant	The "obesity gene," regulating energy homeostasis and body fat mass. Variants are associated with elevated BMI susceptibility and type 2 diabetes.
CLOCK	chr4:55,476,019	Stop-gained	Core circadian-clock transcription factor. Disruption is linked in the literature to circadian rhythm and metabolic disturbances.

Cross-referenced genes: CYP4B1 & ZNF717

Two genes turned up independently in both the SNP and indel call sets — a pattern much less likely to arise from a technical artifact than an isolated call would be:

Gene	Locus	Variant	Ref → Alt	Qual
CYP4B1	chr1:46,815,074–46,815,077	Indel — frameshift (+ splice-region, + NMD in some transcripts)	GATG → GG	77,614
CYP4B1	chr1:46,815,187	SNP — stop-gained (+ NMD transcript variant)	G → A	50,641
ZNF717	chr3:75,732,061–75,732,065	Indel — frameshift	TCCTG → TCTG	51,834
ZNF717	chr3:75,737,024	SNP — stop-gained	C → A	41,069
ZNF717	chr3:75,741,687	SNP — stop-gained (+ NMD transcript variant)	—	—

(Ref/Alt/Qual for the two ZNF717 SNPs and the CYP4B1/ZNF717 indels are read directly off the IGV screenshots below; the third ZNF717 SNP at 75,741,687 wasn't in the captured views.)

CYP4B1 is a cytochrome P450 family member; two consecutive high-impact mutations here raise a candidate pharmacogenomic finding that could alter drug metabolism. ZNF717 is a zinc-finger gene that, beyond its regulatory role, sits in a segmentally duplicated, highly repetitive region of chromosome 3 — a genomic context notorious for short-read alignment ambiguity, making it a useful methodological control for interpreting any variant call in a homologous gene family with caution. All four QUAL scores above (41,069–77,614) are high for a ≈1.5× dataset, for the straightforward reason that FreeBayes QUAL scales with supporting evidence and these positions happen to sit under locally higher coverage — worth remembering that QUAL here reflects local read support, not independent biological validation.

IGV view of chr1:46,815,074–46,815,187 (GRCh38/hg38): the CYP4B1 indel and SNP, with align.sorted.bam coverage/reads beneath.

IGV view of chr3:75,733,485–75,735,591 (GRCh38/hg38): the ZNF717 indel and SNP calls in context.

Full result sets

All 100 HIGH-impact indel calls (83 genes) — click to expand

Chr	Position	Consequence	Gene
1	11,857,563	frameshift_variant	NPPB
1	12,879,725	frameshift_variant	PRAMEF4
1	16,045,018	stop_lost + 3_prime_UTR_variant + NMD_transcript_variant	CLCNKB
1	20,652,814	frameshift_variant	DDOST
1	46,815,074	frameshift_variant	CYP4B1
1	46,815,074	frameshift_variant + splice_region_variant	CYP4B1
1	46,815,074	frameshift_variant + splice_region_variant + NMD_transcript_variant	CYP4B1
1	78,662,885	frameshift_variant	IFI44
1	236,736,599	frameshift_variant	ACTN2
2	1,830,817	frameshift_variant	MYT1L
2	9,412,254	frameshift_variant + NMD_transcript_variant	ITGB1BP1
2	24,179,258	frameshift_variant + NMD_transcript_variant	FAM228A
2	43,947,357	frameshift_variant	LRPPRC
2	43,947,357	frameshift_variant + NMD_transcript_variant	LRPPRC
2	72,465,143	frameshift_variant	EXOC6B
2	95,951,363	stop_gained	ANKRD36C
2	95,951,363	stop_gained + NMD_transcript_variant	ANKRD36C
2	200,921,327	frameshift_variant	ORC2
2	227,535,065	frameshift_variant	AGFG1
3	15,430,021	stop_lost	EAF1
3	32,146,756	frameshift_variant	GPD1L
3	44,499,299	frameshift_variant + splice_region_variant	ZNF852
3	52,925,997	frameshift_variant	SFMBT1
3	53,186,747	frameshift_variant + NMD_transcript_variant	PRKCD
3	75,732,061	frameshift_variant	ZNF717
3	98,391,562	frameshift_variant	OR5K3
3	130,471,876	frameshift_variant	COL6A5
3	130,471,876	frameshift_variant + NMD_transcript_variant	COL6A5
3	179,242,978	frameshift_variant	KCNMB3
3	179,242,978	frameshift_variant + NMD_transcript_variant	KCNMB3
4	154,323,249	frameshift_variant	DCHS2
5	55,968,342	frameshift_variant	IL6ST
5	55,968,342	frameshift_variant + NMD_transcript_variant	IL6ST
5	69,356,411	frameshift_variant + NMD_transcript_variant	AK6
5	75,029,442	frameshift_variant	GCNT4
5	109,344,169	frameshift_variant	PJA2
5	109,699,601	frameshift_variant	MAN2A1
5	139,455,079	frameshift_variant + NMD_transcript_variant	ECSCR
5	172,770,422	frameshift_variant + NMD_transcript_variant	DUSP1
6	32,638,940	frameshift_variant + NMD_transcript_variant	HLA-DQA1
6	32,638,953	frameshift_variant + NMD_transcript_variant	HLA-DQA1
6	111,390,029	frameshift_variant	REV3L
7	44,229,646	frameshift_variant + NMD_transcript_variant	CAMK2B
7	75,987,499	frameshift_variant + NMD_transcript_variant	TMEM120A
7	124,866,305	frameshift_variant + NMD_transcript_variant	POT1
7	151,217,750	frameshift_variant	ABCF2
7	151,217,750	frameshift_variant + splice_region_variant + NMD_transcript_variant	ABCF2
9	33,618,339	frameshift_variant	TRBV20OR9-2
9	35,682,767	frameshift_variant	TPM2
9	35,682,767	frameshift_variant + NMD_transcript_variant	TPM2
9	107,331,689	frameshift_variant	RAD23B
9	123,169,873	frameshift_variant	STRBP
9	128,694,106	stop_lost	SET
10	5,768,081	frameshift_variant + NMD_transcript_variant	GDI2
11	5,727,045	stop_gained + frameshift_variant	OR52P1
11	31,426,361	frameshift_variant	DNAJC24
11	31,426,361	frameshift_variant + NMD_transcript_variant	DNAJC24
11	45,923,443	frameshift_variant	LARGE2
11	56,820,024	frameshift_variant	OR5G3
11	56,820,174	frameshift_variant	OR5G3
11	119,027,725	frameshift_variant + splice_region_variant	SLC37A4
11	126,271,239	frameshift_variant + NMD_transcript_variant	FOXRED1
12	26,681,871	stop_gained + frameshift_variant + splice_region_variant	ITPR2
12	85,244,867	frameshift_variant	LRRIQ1
13	25,097,068	stop_gained	PABPC3
13	47,965,494	frameshift_variant	SUCLA2
13	77,017,068	frameshift_variant	CLN5
13	101,191,895	frameshift_variant	NALCN
14	20,002,693	frameshift_variant	OR4Q2
14	20,198,016	frameshift_variant	OR11G2
14	24,212,554	frameshift_variant	CHMP4A
14	24,441,111	frameshift_variant	SDR39U1
14	37,521,239	frameshift_variant	MIPOL1
14	49,584,702	frameshift_variant + NMD_transcript_variant	RPS29
14	95,452,211	frameshift_variant + NMD_transcript_variant	SYNE3
15	30,373,077	frameshift_variant	CHRFAM7A
15	41,380,243	frameshift_variant	NUSAP1
16	19,581,668	frameshift_variant	VPS35L
16	58,277,627	frameshift_variant	CFAP263
17	41,835,615	frameshift_variant + NMD_transcript_variant	NT5C3B
17	75,130,651	frameshift_variant	NT5C
17	75,517,334	frameshift_variant	TSEN54
17	76,754,133	frameshift_variant	MFSD11
17	76,754,133	frameshift_variant + NMD_transcript_variant	MFSD11
18	44,876,705	frameshift_variant	SETBP1
19	9,341,350	frameshift_variant	ZNF559
19	18,220,547	frameshift_variant + NMD_transcript_variant	PDE4C
19	22,757,390	frameshift_variant	ZNF99
19	51,501,537	frameshift_variant	SIGLEC12
19	51,501,537	frameshift_variant + NMD_transcript_variant	SIGLEC12
19	54,452,636	frameshift_variant + NMD_transcript_variant	LENG8
19	58,286,217	frameshift_variant	ZNF8
20	20,616,156	frameshift_variant	RALGAPA2
20	20,616,156	frameshift_variant + NMD_transcript_variant	RALGAPA2
21	33,549,183	frameshift_variant	SON
21	33,576,378	frameshift_variant	SON
21	33,576,390	frameshift_variant	SON
22	24,511,780	frameshift_variant	UPB1
22	29,281,368	stop_lost + NMD_transcript_variant	EWSR1
22	31,458,448	frameshift_variant	EIF4ENIF1

All 155 HIGH-impact SNP calls (141 genes) — click to expand

Chr	Position	Consequence	Gene
1	7,810,084	stop_lost	PER3
1	11,247,580	stop_gained	MTOR
1	16,313,577	stop_gained + NMD_transcript_variant	FBXO42
1	20,649,670	stop_gained	PINK1
1	43,319,285	stop_lost + NMD_transcript_variant	TIE1
1	46,406,314	stop_lost + NMD_transcript_variant	FAAH
1	46,815,187	stop_gained	CYP4B1
1	46,815,187	stop_gained + NMD_transcript_variant	CYP4B1
1	86,578,931	stop_lost + NMD_transcript_variant	CLCA4
1	156,595,130	stop_gained	GPATCH4
1	159,940,547	stop_gained	IGSF9
1	179,097,936	stop_gained	TOR3A
1	186,128,104	stop_gained	HMCN1
1	197,103,757	stop_gained	ASPM
1	241,889,511	stop_gained	EXO1
1	247,256,207	stop_lost	VN1R5
2	17,718,215	stop_lost + NMD_transcript_variant	SMC6
2	31,388,163	stop_gained	XDH
2	36,581,589	stop_lost + splice_region_variant + NMD_transcript_variant	FEZ2
2	69,841,503	stop_lost + NMD_transcript_variant	GMCL1
2	85,322,745	stop_lost	TGOLN2
2	86,136,732	stop_lost	PTCD3
2	102,932,971	stop_gained	TMEM182
2	165,139,597	stop_gained + NMD_transcript_variant	SCN3A
2	170,382,125	stop_lost	MYO3B
2	190,970,704	stop_lost	STAT1
2	190,970,704	stop_lost + NMD_transcript_variant	STAT1
2	195,858,719	stop_gained	DNAH7
2	218,574,094	stop_lost + NMD_transcript_variant	CNOT9
2	227,348,325	stop_gained + splice_region_variant	MFF
3	75,737,024	stop_gained	ZNF717
3	75,741,687	stop_gained	ZNF717
3	75,741,687	stop_gained + NMD_transcript_variant	ZNF717
3	98,007,903	stop_gained + splice_region_variant	GABRR3
3	112,580,892	stop_gained	SLC35A5
3	151,425,659	stop_gained	IGSF10
3	161,496,988	stop_gained	OTOL1
3	193,354,168	stop_gained	ATP13A5
4	6,696,979	stop_lost	S100P
4	7,025,314	stop_gained	TBC1D14
4	17,625,671	stop_lost + NMD_transcript_variant	MED28
4	55,476,019	stop_gained	CLOCK
4	78,910,637	stop_gained	BMP2K
4	101,918,205	stop_gained	BANK1
4	106,247,274	stop_gained + NMD_transcript_variant	TBCK
4	177,344,597	stop_lost + NMD_transcript_variant	NEIL3
5	10,258,533	stop_gained + splice_region_variant	CCT5
5	10,258,533	stop_gained + splice_region_variant + NMD_transcript_variant	CCT5
5	33,751,349	stop_gained	ADAMTS12
5	33,994,011	stop_lost + NMD_transcript_variant	AMACR
5	65,572,113	stop_gained	PPWD1
5	65,572,113	stop_gained + NMD_transcript_variant	PPWD1
5	72,223,727	stop_gained	MRPS27
5	72,443,757	stop_lost	ZNF366
6	16,290,530	stop_gained	GMPR
6	29,417,995	stop_lost	OR12D1
6	31,157,072	stop_gained	CCHCR1
6	31,557,542	stop_gained + NMD_transcript_variant	NFKBIL1
6	99,377,472	stop_lost + NMD_transcript_variant	COQ3
6	125,258,620	stop_gained	TPD52L1
6	132,751,453	stop_gained	VNN2
7	45,177,162	stop_gained + NMD_transcript_variant	RAMP3
7	66,130,230	stop_gained + NMD_transcript_variant	CRCP
7	93,994,230	stop_lost	BET1
7	100,463,647	stop_gained	SPACDR
7	128,675,534	stop_lost + NMD_transcript_variant	GARIN1A
8	99,121,478	stop_gained	VPS13B
9	20,717,415	stop_gained	FOCAD
9	98,081,135	stop_lost	NANS
9	99,961,606	stop_gained	STX17
9	113,043,345	stop_gained	ZFP37
9	115,078,087	stop_gained	TNC
9	115,078,087	stop_gained + NMD_transcript_variant	TNC
9	136,946,108	stop_gained + NMD_transcript_variant	C8G
10	1,096,268	stop_lost	WDR37
10	7,599,799	stop_gained	ITIH5
10	26,096,625	stop_gained	MYO3A
10	26,096,625	stop_gained + NMD_transcript_variant	MYO3A
10	27,398,296	stop_lost	PTCHD3
10	97,583,213	stop_gained	ANKRD2
10	100,199,101	stop_lost + NMD_transcript_variant	CHUK
10	116,366,676	stop_lost	CCDC172
10	124,509,562	stop_lost	LHPP
10	133,283,930	stop_lost + NMD_transcript_variant	TUBGCP2
11	5,489,631	stop_gained	OR52D1
11	19,187,410	stop_gained	CSRP3
11	56,663,740	stop_gained	OR5AR1
11	60,389,693	stop_gained	MS4A7
11	74,267,198	stop_gained	P4HA3
11	76,544,475	stop_gained	EMSY
11	124,186,025	stop_gained	OR10D3
11	125,461,743	stop_lost	FEZ1
11	125,461,743	stop_lost + NMD_transcript_variant	FEZ1
12	6,529,922	stop_lost + NMD_transcript_variant	NCAPD2
12	7,089,703	stop_gained + NMD_transcript_variant	C1R
12	11,021,672	stop_lost	TAS2R19
12	11,030,436	stop_gained	TAS2R31
12	12,504,579	stop_lost + NMD_transcript_variant	DUSP16
12	21,167,963	stop_lost + NMD_transcript_variant	SLCO1B1
12	40,486,279	stop_gained	MUC19
12	51,006,214	stop_lost + NMD_transcript_variant	SLC11A2
12	55,247,471	stop_gained	OR6C74
12	95,242,193	stop_gained	VEZT
12	101,714,892	stop_gained + NMD_transcript_variant	CHPT1
12	120,700,547	stop_gained	MLEC
12	123,932,637	stop_lost + NMD_transcript_variant	DNAH10
13	40,799,269	stop_gained	SLC25A15
13	40,799,269	stop_gained + NMD_transcript_variant	SLC25A15
13	40,799,281	stop_gained	SLC25A15
13	40,799,281	stop_gained + NMD_transcript_variant	SLC25A15
13	45,529,803	stop_lost + NMD_transcript_variant	COG3
14	23,182,893	stop_gained	SLC7A8
14	23,182,893	stop_gained + NMD_transcript_variant	SLC7A8
14	31,483,548	stop_gained	GPR33
14	34,624,708	stop_gained + NMD_transcript_variant	SNX6
14	53,950,804	stop_lost	BMP4
14	55,005,169	stop_lost	WDHD1
14	68,925,078	stop_gained	ACTN1
14	103,438,074	stop_lost + NMD_transcript_variant	MARK3
14	103,531,188	stop_lost + NMD_transcript_variant	TRMT61A
15	55,430,684	stop_gained	DNAAF4
15	66,501,339	stop_gained + NMD_transcript_variant	RPL4
15	88,841,848	stop_gained	ACAN
16	24,571,807	stop_gained	RBBP6
16	31,060,401	stop_gained	ZNF668
16	53,490,138	stop_gained	RBL2
16	54,118,489	stop_lost + NMD_transcript_variant	FTO
16	69,456,172	stop_lost + NMD_transcript_variant	CYB5B
16	71,933,983	stop_gained	PKD1L3
16	89,535,485	stop_gained	SPG7
17	19,962,946	stop_gained	AKAP10
17	29,530,680	stop_gained	TAOK1
17	31,378,929	stop_lost + NMD_transcript_variant	NF1
17	48,922,889	stop_gained	UBE2Z
17	49,619,957	stop_gained + NMD_transcript_variant	SPOP
17	78,098,496	stop_gained	TNRC6C
18	28,036,487	stop_gained	CDH2
19	20,548,129	stop_gained	ZNF737
19	35,171,700	stop_gained	FXYD5
19	43,203,386	stop_gained	PSG4
19	49,866,849	stop_lost + NMD_transcript_variant	PNKP
19	53,573,530	stop_gained	ZNF331
19	55,388,036	stop_gained	RPL28
19	57,131,414	stop_gained	USP29
19	57,850,470	stop_lost	ZNF814
20	9,478,943	stop_lost	PLCB4
20	62,387,999	stop_lost	RPS21
21	30,371,809	stop_gained	KRTAP13-2
21	46,153,782	stop_gained	FTCD
22	22,353,380	stop_lost	IGLV5-48
22	26,466,075	stop_gained	HPS4
22	26,466,075	stop_gained + NMD_transcript_variant	HPS4
22	41,940,168	stop_gained	CENPM
X	48,991,174	stop_gained	GRIPAP1
X	119,471,038	stop_gained	SLC25A5

Discussion & Limitations

The full bioinformatics pipeline for whole-genome sequencing sample SRR062634 ran successfully end to end, from raw-read acquisition at the NCBI/EBI SRA mirror through to clinically-framed variant interpretation via Ensembl VEP.

Despite the inherent limitations of low-pass sequencing depth (≈1.48×), the pipeline held up methodologically. Global alignment quality was strong, and the transition/transversion ratio (Ts/Tv = 1.92) sits right where the human genome standard predicts. Numbers cross-checked cleanly across independent tools at every stage — flagstat, Qualimap, and Picard all agree to the read on singleton, paired, and secondary/supplementary counts (§8) — which is a good sign the pipeline ran correctly rather than accumulating silent errors.

Custom awk-based filtering isolated intergenic background noise and sequencing artifacts in hypermutable regions (such as the SON or ZNF gene families, both well represented in the full result tables above) with precision, concentrating the analysis on a refined panel of 100 indel and 155 SNP HIGH-impact variants. The most methodologically and biologically robust finding of the project is that severe variants in CYP4B1 and ZNF717 appear independently and simultaneously in both the SNP and indel call sets — sharply reducing the odds that these particular calls are technical artifacts (see Results). Beyond these two, the pipeline also retained unique HIGH-impact variants of interest: frameshifts in neurodevelopmental genes (MYT1L, SETBP1, CLN5) and a telomere-protective gene (POT1), alongside stop-gain/stop-loss variants in tumor suppressors (NF1, EWSR1), a mitochondrial-quality-control kinase linked to Parkinson's disease (PINK1), a circadian-rhythm regulator (CLOCK), a body-fat-mass regulator (FTO), and a core growth-signaling gene (MTOR).

None of this implies the sequenced individual has or will develop any of these conditions. At 1.48× coverage, the data suffer from a severe technical phenomenon called allelic dropout, where the sequencer only reads one of the two alleles present at a given position. Since most of the severe conditions discussed here (PINK1-linked neurodegeneration in particular) follow autosomal recessive inheritance, in the worst realistic scenario the individual would be an asymptomatic, healthy carrier.

Relaxing the minimum-coverage requirement (--min-coverage 1) allowed the entire available genome to be explored, while keeping FreeBayes's default minimum alternate-count filter (-C 2) ensured every variant was supported by at least two independent reads — meaningfully reducing the false-positive rate compared to single-read calling. Accordingly, this pipeline serves an exclusively high-priority screening and candidate-identification function. Deep diagnostic sequencing (>30×) or targeted Sanger validation would be mandatory to confirm true genotypes and determine zygosity for critical calls such as EWSR1 and PINK1 before any clinically conclusive judgment could be made.

Project Structure

.
├── 1.Data
│   ├── Raw
│   │   ├── SRR062634_1.fastq
│   │   ├── SRR062634_1_fastqc.zip
│   │   ├── SRR062634_1.fastq.gz
│   │   ├── SRR062634_2.fastq
│   │   ├── SRR062634_2_fastqc.zip
│   │   └── SRR062634_2.fastq.gz
│   └── Reference
│       ├── Homo_sapiens.GRCh38.dna.primary_assembly.fa
│       ├── Homo_sapiens.GRCh38.dna.primary_assembly.fa.amb
│       ├── Homo_sapiens.GRCh38.dna.primary_assembly.fa.ann
│       ├── Homo_sapiens.GRCh38.dna.primary_assembly.fa.bwt
│       ├── Homo_sapiens.GRCh38.dna.primary_assembly.fa.fai
│       ├── Homo_sapiens.GRCh38.dna.primary_assembly.fa.pac
│       └── Homo_sapiens.GRCh38.dna.primary_assembly.fa.sa
├── 2.QC
│   ├── SRR062634_1_fastqc.html
│   └── SRR062634_2_fastqc.html
├── 3.Clean
│   ├── fastp.json
│   ├── out1_clean.fq.gz
│   ├── out2_clean.fq.gz
│   └── out_FastP.html
├── 4.Alignment
│   ├── align.bam
│   ├── align.sam
│   ├── align.sorted.bam
│   ├── align.sorted.bam.bai
│   ├── align.sorted_stats
│   │   ├── css/                                (Qualimap report assets)
│   │   ├── genome_results.txt
│   │   ├── images_qualimapReport/               (12 plots: coverage, GC, insert size, mapping quality, etc.)
│   │   ├── qualimapReport.html
│   │   └── raw_data_qualimapReport/              (12 raw-data text files backing the plots)
│   └── stderror.out
├── 5.MarkDuplicates
│   ├── align.dedup.bam
│   ├── align.dedup.bam.bai
│   ├── align.sorted.rg.bam
│   └── markDuplicatesMetrics.txt
├── 6.Variant_calling
│   ├── variants_indels.vcf.log
│   ├── variants_indels.vcf.recode.vcf.gz
│   ├── variants_snps.vcf.log
│   ├── variants_snps.vcf.recode.vcf.gz
│   ├── variants.vcf
│   ├── variants.vcf.idx
│   └── variants.vcfstats
└── 7.VEP_results
    ├── results_indels.txt
    ├── results_indels.vcf
    ├── results_snps.txt
    └── results_snps.vcf

14 directories, 89 files

References

1. Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Software]. Babraham Bioinformatics. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
2. Chen, S., Zhou, Y., Chen, Y., & Gu, J. (2018). fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 34(17), i884–i890. https://doi.org/10.1093/bioinformatics/bty560
3. Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics, 25(14), 1754–1760. https://doi.org/10.1093/bioinformatics/btp324
4. Danecek, P., Bonfield, J. K., Liddle, J., Marshall, J., Ohan, V., Pollard, M. O., Whitwham, A., Keane, T., McCarthy, S. A., Davies, R. M., & Li, H. (2021). Twelve years of SAMtools and BCFtools. GigaScience, 10(2), giab008. https://doi.org/10.1093/gigascience/giab008
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6. "Picard Toolkit." (2019). Broad Institute, GitHub Repository. https://broadinstitute.github.io/picard/
7. Garrison, E., & Marth, G. (2012). Haplotype-based variant detection from short-read sequencing. arXiv preprint, arXiv:1207.3907. https://arxiv.org/abs/1207.3907
8. Cleary, J. G., Braithwaite, R., Gaastra, K., Hilbush, B. S., Inglis, S., Irvine, S. A., Jackson, A., Littin, R., Nohzadeh-Malakshah, S., Rathod, M., Ware, D., Trigg, L., & De La Vega, F. M. (2014). Joint Variant and De Novo Mutation Identification on Pedigrees from High-Throughput Sequencing Data. Journal of Computational Biology, 21(6), 405–419. https://doi.org/10.1089/cmb.2014.0029
9. McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R. S., Thormann, A., Flicek, P., & Cunningham, F. (2016). The Ensembl Variant Effect Predictor. Genome Biology, 17(1), 122. https://doi.org/10.1186/s13059-016-0974-4
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