===================
Method description
===================

Target Genome Analysis
~~~~~~~~~~~~~~~~~~~~~~

BALSAMIC :superscript:`1` (**version** = 15.0.0) was used to analyze the data from raw FASTQ files.
We first quality controlled FASTQ files using FastQC v0.11.9 :superscript:`2`.
Adapter sequences and low-quality bases were trimmed using fastp v0.23.2 :superscript:`3`.
Trimmed reads were mapped to the reference genome hg19 using sentieon-tools 202010.02 :superscript:`15`.
Duplicated reads were marked using Dedup from sentieon-tools 202010.02 :superscript:`15`.
The final BAM is promptly quality controlled using CollectHsMetrics, CollectInsertSizeMetrics and CollectAlignmentSummaryMetrics functionalities from Picard tools v2.27.1 :superscript:`6`.
Results of the quality controlled steps were summarized by MultiQC v1.12 :superscript:`7`.
Small somatic mutations (SNVs and INDELs) were called for each sample using VarDict v1.8.2 :superscript:`8`.
Apart from the Vardict filters to report the variants, the called-variants were also further second filtered using the criteria
(*MQ >= 40, DP >= 100, VD >= 5, Minimum AF >= 0.007, Maximum AF < 1, GNOMADAF_popmax <= 0.005, swegen AF < 0.01*).
Only those variants that fulfilled the filtering criteria and scored as `PASS` in the VCF file were reported.
Structural variants (SV) were called using Manta v1.6.0 :superscript:`9` and Dellyv1.0.3 :superscript:`10`.
Copy number variations (CNV) were called using CNVkit v0.9.9 :superscript:`11`.
The variant calls from CNVkit, Manta and Delly were merged using SVDB v2.8.1 :superscript:`12`.
The clinical set of SNV and SV is also annotated and filtered against loqusDB curated frequency of observed variants (frequency < 0.01) from non-cancer cases and only annotated using frequency of observed variants from cancer cases (somatic and germline).
All variants were annotated using Ensembl VEP v104.3 :superscript:`13`. We used vcfanno v0.3.3 :superscript:`14`
to annotate somatic variants for their population allele frequency from gnomAD v2.1.1 :superscript:`18`, CADD v1.6 :superscript:`24`, SweGen :superscript:`22` and frequency of observed variants in normal samples.

Whole Genome Analysis
~~~~~~~~~~~~~~~~~~~~~

BALSAMIC :superscript:`1` (**version** = 15.0.0) was used to analyze the data from raw FASTQ files.
We first quality controlled FASTQ files using FastQC v0.11.9 :superscript:`2`.
Adapter sequences and low-quality bases were trimmed using fastp v0.23.2 :superscript:`3`.
Trimmed reads were mapped to the reference genome hg19 using sentieon-tools 202010.02 :superscript:`15`.
Duplicated reads were marked using Dedup from sentieon-tools 202010.02 :superscript:`15`.
The BAM file was then realigned using Realign from sentieon-tools 202010.02 :superscript:`15` and common population InDels.
The final BAM is quality controlled using WgsMetricsAlgo and CoverageMetrics from sentieon-tools 202010.02 :superscript:`15` and CollectWgsMetrics, CollectMultipleMetrics, CollectGcBiasMetrics, and CollectHsMetrics functionalities from Picard tools v2.27.1 :superscript:`6`.
Results of the quality controlled steps were summarized by MultiQC v1.12 :superscript:`7`.
Small somatic mutations (SNVs and INDELs) were called for each sample using Sentieon TNscope :superscript:`16`.
The called-variants were also further second filtered using the criteria (DP(tumor,normal) >= 10; AD(tumor) >= 3; AF(tumor) >= 0.05, Maximum AF(tumor < 1;  GNOMADAF_popmax <= 0.001; normalized base quality scores >= 20, read_counts of alt,ref alle > 0).
Structural variants were called using Manta v1.6.0 :superscript:`9`, Delly v1.0.3 :superscript:`10` and TIDDIT v3.3.2 :superscript:`12`.
Copy number variations (CNV) were called using ascatNgs v4.5.0 :superscript:`17` (tumor-normal), Delly v1.0.3 :superscript:`10` and CNVpytor v1.3.1 :superscript:`22` (tumor-only) and converted from CNV to deletions (DEL) and duplications (DUP).
The structural variant (SV) calls from Manta, Delly, TIDDIT, ascatNgs (tumor-normal) and CNVpytor (tumor-only) were merged using SVDB v2.8.1 :superscript:`12`
The clinical set of SNV and SV is also annotated and filtered against loqusDB curated frequency of observed variants (frequency < 0.01) from non-cancer cases and only annotated using frequency of observed variants from cancer cases (somatic and germline).
All variants were annotated using Ensembl VEP v104.3 :superscript:`13`. We used vcfanno v0.3.3 :superscript:`14`
to annotate somatic single nucleotide variants for their population allele frequency from gnomAD v2.1.1 :superscript:`18`, CADD v1.6 :superscript:`24`, SweGen :superscript:`22`  and frequency of observed variants in normal samples.

UMI Data Analysis
~~~~~~~~~~~~~~~~~~~~~

BALSAMIC :superscript:`1` (**version** = 15.0.0) was used to analyze the data from raw FASTQ files.
We first quality controlled FASTQ files using FastQC v0.11.9 :superscript:`2`.
UMI tag extraction and consensus generation were performed using Sentieon tools v202010.02 :superscript:`15`.
Adapter sequences and low-quality bases were trimmed using fastp v0.23.2 :superscript:`3`.
The alignment of UMI extracted and consensus called reads to the human reference genome (hg19) was done by bwa-mem and
samtools using Sentieon utils. Consensus reads were filtered based on the number of minimum reads supporting each UMI tag group.
We applied a criteria filter of minimum reads `3,1,1`. It means that at least three UMI tag groups should be ideally considered from both DNA strands,
where a minimum of at least one UMI tag group should exist in each single-stranded consensus read.
The filtered consensus reads were quality controlled using Picard CollectHsMetrics v2.27.1 :superscript:`5`. Results of the quality controlled steps were summarized by MultiQC v1.12 :superscript:`6`.
For each sample, somatic mutations were called using Sentieon TNscope :superscript:`16`, with non-default parameters for passing the final list of variants
(--min_tumor_allele_frac 0.0005, --filter_t_alt_frac 0.0005, --min_init_tumor_lod 0.5, min_tumor_lod 4, --max_error_per_read 5  --pcr_indel_model NONE, GNOMADAF_popmax <= 0.02).
The clinical set of SNV and SV is also annotated and filtered against loqusDB curated frequency of observed variants (frequency < 0.01) from non-cancer cases and only annotated using frequency of observed variants from cancer cases (somatic and germline).
All variants were annotated using Ensembl VEP v104.3 :superscript:`7`. We used vcfanno v0.3.3 :superscript:`8` to annotate somatic variants for their population allele frequency from gnomAD v2.1.1 :superscript:`18`, CADD v1.6 :superscript:`24`, SweGen :superscript:`22` and frequency of observed variants in normal samples.
For exact parameters used for each software, please refer to  https://github.com/Clinical-Genomics/BALSAMIC.
We used three commercially available products from SeraCare [Material numbers: 0710-067110 :superscript:`19`, 0710-067211 :superscript:`20`, 0710-067312 :superscript:`21`] for validating the efficiency of the UMI workflow in identifying 14 mutation sites at known allelic frequencies.


**References**
~~~~~~~~~~~~~~~~

1. Foroughi-Asl, H., Jeggari, A., Maqbool, K., Ivanchuk, V., Elhami, K., & Wirta, V. BALSAMIC: Bioinformatic Analysis pipeLine for SomAtic MutatIons in Cancer (Version v8.2.10) [Computer software]. https://github.com/Clinical-Genomics/BALSAMIC
2. Babraham Bioinformatics - FastQC A Quality Control tool for High Throughput Sequence Data. Accessed June 22, 2020. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
3. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884-i890. https://doi.org/10.1093/bioinformatics/bty560
4. Li H. (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. https://doi.org/10.48550/arXiv.1303.3997
5. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J., Homer N., Marth G., Abecasis G., Durbin R. and 1000 Genome Project Data Processing Subgroup (2009) The Sequence alignment/map (SAM) format and SAMtools. Bioinformatics, 25, 2078-9. https://doi.org/10.1093/bioinformatics/btp352
6. Picard Tools - By Broad Institute. Accessed June 22, 2020. https://broadinstitute.github.io/picard/
7. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32(19):3047-3048. https://doi.org/10.1093/bioinformatics/btw354
8. Lai Z, Markovets A, Ahdesmaki M, Chapman B, Hofmann O, McEwen R, Johnson J, Dougherty B, Barrett JC, and Dry JR. VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research. Nucleic Acids Res. 2016. https://doi.org/10.1093/nar/gkw227
9. Chen, X. et al. (2016) Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics, 32, 1220-1222. https://doi.org/10.1093/bioinformatics/btv710
10. Tobias Rausch, Thomas Zichner, Andreas Schlattl, Adrian M. Stuetz, Vladimir Benes, Jan O. Korbel. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics. 2012 Sep 15;28(18):i333-i339. https://doi.org/10.1093/bioinformatics/bts378
11. Talevich, E, Shain, A.H, Botton, T, & Bastian, B.C. CNVkit: Genome-wide copy number detection and visualization from targeted sequencing. PLOS Computational Biology. 2016, 12(4):e1004873. https://doi.org/10.1371/journal.pcbi.1004873
12. Jesper Eisfeldt et.al. TIDDIT, an efficient and comprehensive structural variant caller for massive parallel sequencing data. F1000 research. 2017. https://doi.org/10.12688/f1000research.11168.2
13. McLaren W, Gil L, Hunt SE, et al. The Ensembl Variant Effect Predictor. Genome Biology. 2016;17(1):122. https://doi.org/10.1186/s13059-016-0974-4
14. Pedersen BS, Layer RM, Quinlan AR. Vcfanno: fast, flexible annotation of genetic variants. Genome Biology. 2016;17(1):118. https://doi.org/10.1186/s13059-016-0973-5
15. Donald Freed, Rafael Aldana, Jessica A. Weber, Jeremy S. Edwards. The Sentieon Genomics Tools - A fast and accurate solution to variant calling from next-generation sequence data. Bioinformatics. 2016, Volume 32,Issue 8. https://doi.org/10.1093/bioinformatics/btv710
16. Donald Freed, Renke Pan, Rafael Aldana. TNscope: Accurate Detection of Somatic Mutations with Haplotype-based Variant Candidate Detection and Machine Learning Filtering. bioRvix. https://doi.org/10.1101/250647
17. Keiran MR, Peter VL, David CW, David J, Andrew M, Adam PB , Jon WT, Patrick T, Serena Nik-Zainal, Peter J C. ascatNgs: Identifying Somatically Acquired Copy-Number Alterations from Whole-Genome Sequencing Data. Curr Protoc Bioinformatics. 2016. https://doi.org/10.1002/cpbi.17
18. Karczewski, K.J., Francioli, L.C., Tiao, G. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020). https://doi.org/10.1038/s41586-020-2308-7
19. Seraseq ctDNA Complete Reference Material AF 1%. https://www.seracare.com/Seraseq-ctDNA-Complete-Reference-Material-AF1-0710-0671/
20. Seraseq ctDNA Complete Reference Material AF 0.5%. https://www.seracare.com/Seraseq-ctDNA-Complete-Reference-Material-AF05-0710-0672/
21. Seraseq ctDNA Complete Reference Material AF 0.1%. https://www.seracare.com/Seraseq-ctDNA-Complete-Reference-Material-AF01-0710-0673/
22. Ameur, A., Dahlberg, J., Olason, P. et al. SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population. Eur J Hum Genet 25, 1253–1260 (2017). https://doi.org/10.1038/ejhg.2017.130
23. Milovan Suvakov, Arijit Panda, Colin Diesh, Ian Holmes, Alexej Abyzov, CNVpytor: a tool for copy number variation detection and analysis from read depth and allele imbalance in whole-genome sequencing, GigaScience, Volume 10, Issue 11, November 2021, giab074, https://doi.org/10.1093/gigascience/giab074
24. Rentzsch P., Witten D., Cooper G.M., Shendure J., Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2018. https://doi.org/10.1093/nar/gky1016. PubMed PMID: 30371827.