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• Introduction
  • Contact information
  • System requirements
  • Installing modules
    • Licensing modules
    • Uninstalling modules
  • Installing server extensions
    • Licensing server extensions
• Methods and tools
  • Methods
    • Trimming
    • Readmapping
    • Deduplication
    • Local realignment
    • Structural variant detection
    • UMI grouping
    • Primer trimming
    • Germline variant detection
    • Somatic variant detection
    • Tumor normal variant detection
    • Batching
    • Limitations
  • Tools
    • LightSpeed Fastq to Germline Variants
      • LightSpeed Fastq to Germline Variants outputs
    • LightSpeed Fastq to Somatic Variants
      • LightSpeed Fastq to Somatic Variants outputs
    • LightSpeed Fastq to Somatic Variants Tumor Normal
      • LightSpeed Fastq to Somatic Variants Tumor Normal outputs
    • Report from LightSpeed Fastq to Variants tools
    • Calculate TMB Score (LightSpeed)
    • Copy Number Variant Detection (WGS) (beta)
      • Copy Number Variant Detection (WGS) (beta) outputs
• Template Workflows
  • General Template Workflows
    • Fastq to Annotated Germline Variants
      • Outputs from Fastq to Annotated Germline Variants
    • Fastq to Annotated Germline Variants with Coverage Analysis
      • Outputs from Fastq to Annotated Germline Variants with Coverage Analysis
    • Fastq to Annotated Somatic Variants
      • Outputs from Fastq to Annotated Somatic Variants
    • Fastq to Annotated Somatic Variants with Coverage Analysis
      • Outputs from Fastq to Annotated Somatic Variants with Coverage Analysis
    • Fastq to Annotated Somatic Variants (Tumor Normal)
      • Outputs from Fastq to Annotated Somatic Variants (Tumor Normal)
    • Fastq to Annotated Somatic Variants (Tumor Normal) with Coverage Analysis
      • Outputs from Fastq to Annotated Somatic Variants (Tumor Normal) with Coverage Analysis
    • Fastq to Germline CNV Control
      • Outputs from Fastq to Germline CNV Control
    • Fastq to Somatic CNV Control
      • Outputs from Fastq to Somatic CNV Control
  • Template Workflows - QIAseq Targeted DNA
    • QIAseq Fastq to Annotated Germline Variants
      • Outputs from QIAseq Fastq to Annotated Germline Variants
    • QIAseq Fastq to Annotated Somatic Variants
      • Outputs from QIAseq Fastq to Annotated Somatic Variants
    • QIAseq Fastq to Germline CNV Control
      • Outputs from QIAseq Fastq to Germline CNV Control
    • QIAseq Fastq to Somatic CNV Control
      • Outputs from QIAseq Fastq to Somatic CNV Control
  • Template Workflows - QIAseq Targeted DNA Pro
    • QIAseq Pro Fastq to Annotated Germline Variants
      • Outputs from QIAseq Pro Fastq to Annotated Germline Variants
    • QIAseq Pro Fastq to Annotated Somatic Variants
      • Outputs from QIAseq Pro Fastq to Annotated Somatic Variants
    • QIAseq Pro Fastq to Germline CNV Control
      • Outputs from QIAseq Pro Fastq to Germline CNV Control
    • QIAseq Pro Fastq to Somatic CNV Control
      • Outputs from QIAseq Pro Fastq to Somatic CNV Control
• Bibliography

LightSpeed Fastq to Somatic Variants

The LightSpeed Fastq to Somatic Variants tool is designed to provide variant calls from raw sequencing data within a very short timeframe.

The tool can perform read trimming, mapping, deduplication, local realignment and somatic variant calling. For a description of each step, see LightSpeed Methods.

LightSpeed Fastq to Somatic Variants can only analyze one sample per analysis start. To analyze samples in batch, LightSpeed Fastq to Somatic Variants must be included in a workflow (see Batching). Template workflows for LightSpeed analyses are available (see Template Workflows), but it is also possible to create custom workflows. Read about workflows here http://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Workflows.html.

To run the somatic LightSpeed tool go to:

        Tools | LightSpeed () | LightSpeed Fastq to Somatic Variants ()

If you are connected to a CLC Server via your Workbench, you will be asked where you would like to run the analysis. We recommend that you run the analysis on a CLC Server when possible.

In the first wizard step, specify fastq files and a reference sequence (figure 3.6):

• Input data
  • Reads (fastq) Fastq files for analysis. At least two fastq files representing R1 and R2 reads must be provided.

• References
  • References The reference sequence that reads will be mapped to.

• Reference masking
  • No masking Reads are mapped to the full reference sequence.
  • Exclude annotated Reads are mapped to the full reference sequence except regions specified in the masking track.
  • Include annotated only Reads are only mapped to the regions specified in the masking track.
  • Masking track The track specifying the masking regions.

Figure 3.6: Input fastq files and references, and, optionally, a track for reference masking.

Next, options are available for trimming (figure 3.7):

• Trimming
  • Quality trim Reads are trimmed for low quality nucleotides.
  • Quality trim limit Adjust the quality trim limit for softer or harder trimming. Read more about the quality trim limit here: http://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Quality_trimming.html.
  • Minimum read length after quality trim Trimmed reads shorter than this length are removed.
  • Adapter trim Reads are trimmed for read-through adapter sequence.
  • Minimum read length after adapter trim Trimmed reads shorter than this length are removed.
  • Only quality trim from 3' end When checked, only the 3' ends of reads are trimmed for low quality nucleotides. This option is recommended for analyses where deduplication is included, because deduplication relies on the 5' positions of R1 and R2.

Figure 3.7: Options for trimming.

Next, options are available for UMI and duplicate reads (figure 3.8):

• UMI
  • UMI preset Specify where the UMI is placed in the reads. If a preset for a specific panel type is selected, the UMI and common sequence position and length are automatically adjusted to match the panel design.
    • No UMI Select for reads that do not have UMIs.
    • Custom Select to specify a protocol-specific read and UMI structure.
    • QIAseq Targeted DNA Select if you are analyzing data from a QIAseq Targeted DNA panel.
    • QIAseq Targeted DNA Pro Select if you are analyzing data from a QIAseq Targeted DNA Pro panel.
    • TSO500 Select if you are analyzing data from a TSO500 protocol utilizing duplex UMIs.
    • Twist UMI Select if you are analyzing data from a Twist protocol utilizing duplex UMIs.
    • Roche KAPA UMI Select if you are analyzing data from a KAPA protocol utilizing duplex UMIs.
    • Agilent MBC Select if you are analyzing data from an Agilent protocol utilizing duplex UMIs.
  • Retrieve UMI from fastq header Check this option if the UMI sequence is present in the fastq file read headers instead of being part of the sequencing reads. UMI and common sequence lengths must be specified below when this option is used.
  • UMI length (Read 1) The number of nucleotides at the start of read 1 that are part of the UMI.
  • Common sequence length (Read 1) The number of nucleotides between the UMI and the biological sequence in read 1. These nucleotides are discarded.
  • UMI length (Read 2) The number of nucleotides at the start of read 2 that are part of the UMI.
  • Common sequence length (Read 2) The number of nucleotides between the UMI and the biological sequence in read 2. These nucleotides are discarded.
  • Minimum UMI group size Only UMI groups consisting of at least this number of input read pairs will be merged to consensus UMI reads. UMI groups with fewer input read pairs than this number will be discarded. A UMI group is a group of input read pairs with the same UMI sequence that maps to the same genomic position.
  • Minimum size of a big UMI group Define the number of read pairs required for a UMI group to be considered a big UMI group. When variants are called, they are annotated with the number of big UMI groups supporting them.
  • Maximum UMI differences Add input read pairs to the same UMI group when their UMI sequence have at most this number of mismatches. If UMIs are present on both read 1 and read 2 (duplex), this threshold operates on the sum of mismatches from the two UMIs.
  • UMI window size Add input read pairs to the same UMI group when they map to genomic positions at most this number of bases apart.
  • Keep duplex consensus reads only For duplex UMI protocols, check this option if only duplex reads should be retained. Simplex reads will be discarded.

If UMIs are defined on both read 1 and read 2, the data is considered duplex. Consequently, simplex UMI reads sharing a UMI and mapping position that originate from different strands will be collapsed to a duplex UMI read.

• Mapped read handling
  • Discard duplicate mapped reads Reads likely representing PCR duplicates are collapsed. This option is disabled when UMIs are used to group reads.

Figure 3.8: Options for UMI and duplicate reads.

Next, options are available for primer trimming (figure 3.9):

• Trim primers
  • No primer trim Disable the trim primers step.
  • Start of read 1 Primers are at the start of read 1.
  • Start of read 2 Primers are at the start of read 2.
  • Primers track Annotation track with location and strand of primers. Unalign parts of mapped reads that overlap a primer.
  • Discard reads without primer Discard reads that do not overlap with a primer in the primers track.
  • Minimum read length after primer trim Reads that are shorter than this number of nucleotides after primer trim are discarded.
  • Additional bases to trim Unalign this number of additional mapped bases in reads matching a primer. Bases are unaligned at the beginning of the mapped read downstream from the primer.
  • Minimum primer overlap (%) Reads overlap a primer when the expected part of the read (start of read 1 or read 2) maps to a genomic location that overlaps at least this percentage of a primer. Reads that do not meet the threshold are discarded.

Figure 3.9: Options for primer trimming.

A number of options are available for variant detection in the next two wizard steps (figure 3.10 and figure 3.11).

• Variant detection general filters, see figure 3.10
  • Restrict calling to target regions Optional. A track defining where variants are called.
  • Ignore non-specific matches Reads that map equally well to more than one genomic position, are not used for variant calling.
  • SNV minimum allele count Minimum allele count required for SNVs and MNVs to be called.
  • SNV minimum per-strand allele count Minimum allele count required on each strand for SNVs and MNVs to be called.
  • Indel minimum allele count Minimum allele count required for indels to be called.
  • Indel minimum per-strand allele count Minimum allele count required on each strand for indels to be called.
  • SNV significance threshold using global error rate p-value threshold for SNVs and MNVs. The p-value is calculated from a binomial test given count, coverage and an error rate of 0.005. Allowed range: 0 - 0.1.
  • Indel significance threshold using global error rate p-value threshold for indels. The p-value is calculated from a binomial test given count, coverage and an error rate of 0.005. Allowed range: 0 - 0.1.

Figure 3.10: The first wizard step containing options for variant detection.

• Variant detection noise filters, see figure 3.11
  • Significance threshold using local error rate p-value threshold for all variants. The p-value is the minimum p-value from two individual tests: 1. A binomial test given forward count, forward coverage and a local error rate for forward reads estimated from the data. 2. A binomial test given reverse count, reverse coverage and a local error rate for reverse reads estimated from the data. Allowed range: 0 - 0.1.
  • Significance threshold for low complexity regions p-value threshold for variants that are located in positions where two upstream and two downstream reference symbols are identical to the variant. The p-value is calculated from a binomial test given count and coverage. This filter only removes alleles that have a frequency greater than 10%. Allowed range: 0 - 1.
  • SNV strand balance threshold Threshold for a strand balance score for SNVs and MNVs. The score is calculated as 1 - (p-value from binomial test given forward count, count, and forward count/coverage). Allowed range: 0.9 - 1.
  • Indel strand balance threshold Threshold for a strand balance score for indels. The score is calculated as 1 - (p-value from binomial test given forward count, count, and forward count/coverage). Allowed range: 0.9 - 1.

For the options under Variant detection general filters and Variant detection noise filters

• All of the options, except "SNV minimum allele count" and "Indel minimum allele count" only removes alleles with a frequency of less than 30%.
• For all of the options that include threshold in the name, lowering the value will reduce the number of called variants.

• Homopolymer/STR annotations and filter, see figure 3.11
  • Minimum repeat count The minimum number of repeats excluding the variant, that an allele must have to be annotated as a Homopolymer/STR variant. For example if a reference allele "AAAA" is called, and a low frequent stutter insertion allele is called "AAAAA", the repeat count is 4.
  • Minimum repeat region length The minimum length of all repeats combined excluding the variant, that an allele must have to be annotated as a Homopolymer/STR variant. For example if a reference allele "AAAA" is called, and a low frequent stutter insertion allele "AAAAA" is called, the repeat region length is 4.
  • Maximum element length The maximum length of a repeat unit, that an allele can have to be annotated as a Homopolymer/STR variant. For example the repeat element length of a homopolymer is 1.
  • Homopolymer/STR filter Enable to filter variants annotated as Homopolymer/STR Yes.
  • Remove homopolymer/STR variants with frequency below Specify the frequency threshold below which homopolymer/STR variants will be filtered.

Figure 3.11: The second wizard step containing options for variant detection.

• Structural variant detection
  • Lenient inversion detection Enable lenient inversion detection to allow detection of inversions which only has read support in one direction on each of the breakpoints. This is recommended for targeted data. Enabling this option can increase processing time and can result in detection of more false positive inversions.

In the final wizard step, choose which outputs should be generated and whether results should be saved or opened. If a reads track is selected as output, runtime will increase.

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Subsections

• LightSpeed Fastq to Somatic Variants outputs

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