Genomics Report
This report has been generated by the Zymo-Research/aladdin-genomics analysis pipeline. For information about how to interpret these results, please see the report documentation.
If you have any question regarding this report, please contact us via the Aladdin platform.
General Statistics
Showing 3/3 rows and 7/26 columns.| Sample Name | % Dups | GC content | % PF | % Dups | Error rate | % Mapped | ≥ 30X |
|---|---|---|---|---|---|---|---|
| SRR22476789 | 10.6% | 41.4% | 98.6% | 11.6% | 0.70% | 99.9% | 80.0% |
| SRR22476790 | 15.6% | 41.2% | 98.5% | 12.0% | 0.71% | 99.9% | 82.0% |
| SRR22476791 | 16.4% | 41.2% | 98.6% | 13.6% | 0.64% | 100.0% | 83.0% |
FastQC (raw)
FastQC (raw) is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.
FASTQC aims to provide quality control checks on raw sequencing data coming from high throughput sequencing pipelines. It offers a range of modular analyses to quickly assess data quality and identify potential issues before proceeding with further analysis.Sequence Counts
Sequence counts for each sample. Duplicate read counts are an estimate only.
This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).
You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:
Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.
The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.
Sequence Quality Histograms
The mean quality value across each base position in the read.
To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).
Taken from the FastQC help:
The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.
Per Sequence Quality Scores
The number of reads with average quality scores. Shows if a subset of reads has poor quality.
From the FastQC help:
The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.
Per Base Sequence Content
The proportion of each base position for which each of the four normal DNA bases has been called.
To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.
To see the data as a line plot, as in the original FastQC graph, click on a sample track.
From the FastQC help:
Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.
In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.
It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.
Rollover for sample name
Per Sequence GC Content
The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.
From the FastQC help:
This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.
In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.
An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.
Per Base N Content
The percentage of base calls at each position for which an N was called.
From the FastQC help:
If a sequencer is unable to make a base call with sufficient confidence then it will
normally substitute an N rather than a conventional base call. This graph shows the
percentage of base calls at each position for which an N was called.
It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.
Sequence Length Distribution
Sequence Duplication Levels
The relative level of duplication found for every sequence.
From the FastQC Help:
In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.
Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.
The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.
In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.
Overrepresented sequences
The total amount of overrepresented sequences found in each library.
FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as over represented.
Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.
From the FastQC Help:
A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.
FastQC lists all of the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.
Adapter Content
The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.
Note that only samples with ≥ 0.1% adapter contamination are shown.
There may be several lines per sample, as one is shown for each adapter detected in the file.
From the FastQC Help:
The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.
Status Checks
Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).
FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).
It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.
Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.
In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.
FastP (Read preprocessing)
FastP (Read preprocessing) An ultra-fast all-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).DOI: 10.1093/bioinformatics/bty560.
Filtered Reads
Filtering statistics of sampled reads.
Insert Sizes
Insert size estimation of sampled reads.
Sequence Quality
Average sequencing quality over each base of all reads.
GC Content
Average GC content over each base of all reads.
N content
Average N content over each base of all reads.
GATK4 MarkDuplicates
GATK4 MarkDuplicates Metrics generated by GATK4 MarkDuplicates.
GATK4 MarkDuplicates is used to identify and flag duplicate reads. It helps to improve the accuracy of downstream analyses such as variant calling by reducing the impact of PCR duplicates and sequencing artifacts.Mark Duplicates
Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.
The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.
To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:
READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATESREADS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATESREADS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATESREADS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICALREADS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATESREADS_UNMAPPED = UNMAPPED_READS
Samtools Flagstat
Samtools is a suite of programs for interacting with high-throughput sequencing data.DOI: 10.1093/bioinformatics/btp352.
It generates a statistical summary of various attributes of sequence alignment files, including the total number of reads, number of properly paired reads, number of singletons, and mapping quality.Percent Mapped
Alignment metrics from samtools stats; mapped vs. unmapped reads.
For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.
Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).
Alignment metrics
This module parses the output from samtools stats. All numbers in millions.
Mosdepth
Mosdepth performs fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing.DOI: 10.1093/bioinformatics/btx699.
Cumulative coverage distribution
Proportion of bases in the reference genome with, at least, a given depth of coverage. Note that for 3 samples, a BED file was provided, so the data was calculated across those regions. For 3 samples, it's calculated across the entire genome length. 3 samples have both global and region reports, and we are showing the data for regions
For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).
Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).
For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.
Coverage distribution
Proportion of bases in the reference genome with a given depth of coverage. Note that for 3 samples, a BED file was provided, so the data was calculated across those regions. For 3 samples, it's calculated across the entire genome length. 3 samples have both global and region reports, and we are showing the data for regions
For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position (Sims et al. 2014).
Bases of a reference sequence (y-axis) are groupped by their depth of coverage (0×, 1×, …, N×) (x-axis). This plot shows the frequency of coverage depths relative to the reference sequence for each read dataset, which provides an indirect measure of the level and variation of coverage depth in the corresponding sequenced sample.
If reads are randomly distributed across the reference sequence, this plot should resemble a Poisson distribution (Lander & Waterman 1988), with a peak indicating approximate depth of coverage, and more uniform coverage depth being reflected in a narrower spread. The optimal level of coverage depth depends on the aims of the experiment, though it should at minimum be sufficiently high to adequately address the biological question; greater uniformity of coverage is generally desirable, because it increases breadth of coverage for a given depth of coverage, allowing equivalent results to be achieved at a lower sequencing depth (Sampson et al. 2011; Sims et al. 2014). However, it is difficult to achieve uniform coverage depth in practice, due to biases introduced during sample preparation (van Dijk et al. 2014), sequencing (Ross et al. 2013) and read mapping (Sims et al. 2014).
This plot may include a small peak for regions of the reference sequence with zero depth of coverage. Such regions may be absent from the given sample (due to a deletion or structural rearrangement), present in the sample but not successfully sequenced (due to bias in sequencing or preparation), or sequenced but not successfully mapped to the reference (due to the choice of mapping algorithm, the presence of repeat sequences, or mismatches caused by variants or sequencing errors). Related factors cause most datasets to contain some unmapped reads (Sims et al. 2014).
Average coverage per contig
Average coverage per contig or chromosome
XY coverage
GATK4 BQSR
GATK is a toolkit offering a wide variety of tools with a primary focus on variant discovery and genotyping.DOI: 10.1101/201178; 10.1002/0471250953.bi1110s43; 10.1038/ng.806; 10.1101/gr.107524.110.
GATK BQSR (Base Quality Score Recalibration) involves recalibrating the base quality scores to improve the accuracy of variant calls produced by tools like GATK.Observed Quality Scores
This plot shows the distribution of base quality scores in each sample before and after base quality score recalibration (BQSR). Applying BQSR should broaden the distribution of base quality scores.
For more information see the Broad's description of BQSR.
Reported Quality vs. Empirical Quality
Plot shows the reported quality score vs the empirical quality score.
BCFtools
Bcftools contains utilities for variant calling and manipulating VCFs and BCFs.DOI: 10.1093/gigascience/giab008.
BCFtools offers functions to detect genetic variants, including Single Nucleotide Polymorphisms (SNP), Insertions - Deletions (INDEL), and Transition/Transversion (Ts/Tv) from aligned sequencing data (in BAM format). By allowing the comparison and integration of variant call sets across multiple samples, it facilitates the identification of shared and unique variants among different datasets.Bcftools Stats
| Sample Name | Vars | SNP | Indel | Ts/Tv |
|---|---|---|---|---|
| SRR22476789.deepvariant | 6548675 | 5359449 | 1194798 | 1.19 |
| SRR22476789.manta.diploid_sv | 10697 | 0 | 5603 | 0.00 |
| SRR22476790.deepvariant | 6661185 | 5466188 | 1200464 | 1.14 |
| SRR22476790.manta.diploid_sv | 10558 | 0 | 5466 | 0.00 |
| SRR22476791.deepvariant | 6496555 | 5294208 | 1207881 | 1.26 |
| SRR22476791.manta.diploid_sv | 10836 | 0 | 5751 | 0.00 |
Variant Substitution Types
Variant Quality
Indel Distribution
VCFtools
VCFtools is a program for working with and reporting on VCF files.DOI: 10.1093/bioinformatics/btr330.
VCFtools enables the filtering of Ts/Tv based on quality and counts, which helps in selecting high-confidence variants and removing potential false positives or artifacts.TsTv by Count
Plot of TSTV-BY-COUNT - the transition to transversion ratio as a function of alternative allele count from the output of vcftools TsTv-by-count.
Transition is a purine-to-purine or pyrimidine-to-pyrimidine point mutations.
Transversion is a purine-to-pyrimidine or pyrimidine-to-purine point mutation.
Alternative allele count is the number of alternative alleles at the site.
Note: only bi-allelic SNPs are used (multi-allelic sites and INDELs are skipped.)
Refer to Vcftools's manual (https://vcftools.github.io/man_latest.html) on --TsTv-by-count
TsTv by Qual
Plot of TSTV-BY-QUAL - the transition to transversion ratio as a function of SNP quality from the output of vcftools TsTv-by-qual.
Transition is a purine-to-purine or pyrimidine-to-pyrimidine point mutations.
Transversion is a purine-to-pyrimidine or pyrimidine-to-purine point mutation.
Quality here is the Phred-scaled quality score as given in the QUAL column of VCF.
Note: only bi-allelic SNPs are used (multi-allelic sites and INDELs are skipped.)
Refer to Vcftools's manual (https://vcftools.github.io/man_latest.html) on --TsTv-by-qual
VEP
VEP Ensembl VEP determines the effect of your variants on genes, transcripts and protein sequences, as well as regulatory regions.DOI: 10.1186/s13059-016-0974-4.
VEP is utilized to annotate variants in VCF files and predict their functional consequences. It provides variant information, including chromosome position, reference and alternate alleles. VEP also predicts the potential impact of genetic variants on genes and proteins by classifying them into different functional categories.General Statistics
Table showing general statistics of VEP annotaion run
| Sample Name | Overlapped regulatory features | Overlapped transcripts | Overlapped genes | Existing variants | Novel variants | Variants filtered out | Variants processed |
|---|---|---|---|---|---|---|---|
| SRR22476789.deepvariant | 303603 | 71796 | 61150 | 6548675 | 0 | 0 | 6548675 |
| SRR22476789.manta.diploid_sv | 13033 | 8076 | 7894 | 10672 | 0 | 0 | 10672 |
| SRR22476790.deepvariant | 305467 | 71898 | 61213 | 6661185 | 0 | 0 | 6661185 |
| SRR22476790.manta.diploid_sv | 19909 | 8519 | 8337 | 10539 | 0 | 0 | 10539 |
| SRR22476791.deepvariant | 304297 | 71387 | 60750 | 6496555 | 0 | 0 | 6496555 |
| SRR22476791.manta.diploid_sv | 14138 | 8213 | 8019 | 10815 | 0 | 0 | 10815 |
Variant classes
Classes of variants found in the data.
Consequences
Predicted consequences of variations.
SIFT summary
SIFT variant effect prediction.
PolyPhen summary
PolyPhen variant effect prediction.
Variants by chromosome
Number of variants found on each chromosome.
Position in protein
Relative position of affected amino acids in protein.
nf-core/sarek Methods Description
Suggested text and references to use when describing pipeline usage within the methods section of a publication.
Methods
Data was processed using nf-core/sarek v3.2.3 (doi: 10.12688/f1000research.16665.2, 10.5281/zenodo.4063683) of the nf-core collection of workflows (Ewels et al., 2020).
The pipeline was executed with Nextflow v23.04.2 (Di Tommaso et al., 2017) with the following command:
nextflow run main.nf -profile awsbatch,dev --split_fastq 50000000 --igenomes_base 's3://pacinfo-fs/tmp/hhoang/ref' --design 's3://pacinfo-fs/tmp/hhoang/data/wgs/PRJNA907182/input/sample.csv' --metadata 's3://pacinfo-fs/tmp/hhoang/data/wgs/PRJNA907182/input/sample_meta.csv' --main_caller germline --structural_caller --pcgr --outdir 's3://pacinfo-fs/tmp/hhoang/aladdin-genomics/sample-report' -work-dir 's3://pacinfo-nextflow-workdir' --awsqueue 'arn:aws:batch:us-west-2:097055364163:job-queue/rnaseq' --awsregion us-west-2 --copy_number_caller -resumeReferences
- Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. https://doi.org/10.1038/nbt.3820
- Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. https://doi.org/10.1038/s41587-020-0439-x
Notes:
- The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
- You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.
nf-core/sarek Software Versions
are collected at run time from the software output.
| Process Name | Software | Version |
|---|---|---|
| BCFTOOLS_CONCAT | bcftools | 1.17 |
| BCFTOOLS_STATS | bcftools | 1.17 |
| BGZIPTABIX_CPSR | tabix | 1.12 |
| BWAMEM2_MEM | bwamem2 | 2.2.1 |
| samtools | 1.16.1 | |
| CNVKIT_ANTITARGET | cnvkit | 0.9.9 |
| CNVKIT_BATCH | cnvkit | 0.9.9 |
| samtools | 1.16.1 | |
| CNVKIT_REFERENCE | cnvkit | 0.9.9 |
| CPSR_VALIDATE_INPUT | pcgr | 1.2.0 |
| CREATE_INTERVALS_BED | gawk | 5.1.0 |
| CUSTOM_DUMPSOFTWAREVERSIONS | python | 3.11.0 |
| yaml | 6.0 | |
| DEEPVARIANT | deepvariant | 1.5.0 |
| ENSEMBLVEP_VEP | ensemblvep | 108.2 |
| FASTP | fastp | 0.23.4 |
| FASTQC | fastqc | 0.11.9 |
| FILTER_VARIANTS | bcftools | 1.16 |
| GATK4_APPLYBQSR | gatk4 | 4.4.0.0 |
| GATK4_BASERECALIBRATOR | gatk4 | 4.4.0.0 |
| GATK4_GATHERBQSRREPORTS | gatk4 | 4.4.0.0 |
| GATK4_MARKDUPLICATES | gatk4 | 4.4.0.0 |
| samtools | 1.17 | |
| INDEX_CRAM | samtools | 1.17 |
| MANTA_GERMLINE | manta | 1.6.0 |
| MERGE_CRAM | samtools | 1.17 |
| MERGE_DEEPVARIANT_GVCF | gatk4 | 4.4.0.0 |
| MERGE_DEEPVARIANT_VCF | gatk4 | 4.4.0.0 |
| MOSDEPTH | mosdepth | 0.3.3 |
| NORMALISE_VARIANTS | bcftools | 1.16 |
| RUN_CPSR | pcgr | 1.0.1 |
| SAMTOOLS_STATS | samtools | 1.17 |
| TABIX_BGZIPTABIX_INTERVAL_COMBINED | tabix | 1.12 |
| TABIX_BGZIPTABIX_INTERVAL_SPLIT | tabix | 1.12 |
| TABIX_CONCAT | tabix | 1.12 |
| TABIX_FILTERED | tabix | 1.12 |
| TABIX_TABIX | tabix | 1.12 |
| VCFTOOLS_TSTV_COUNT | vcftools | 0.1.16 |
| Workflow | Nextflow | 23.04.2 |
| nf-core/sarek | 3.2.3 |
nf-core/sarek Workflow Summary
- this information is collected when the pipeline is started.
Core Nextflow options
- runName
- berserk_meninsky
- launchDir
- /home/hanhhoang/ag-sample
- workDir
- /pacinfo-nextflow-workdir/
- projectDir
- /home/hanhhoang/ag-sample
- userName
- hanhhoang
- profile
- awsbatch,dev
- configFiles
- /home/hanhhoang/ag-sample/nextflow.config
Input/output options
- design
- s3://pacinfo-fs/tmp/hhoang/data/wgs/PRJNA907182/input/sample.csv
- metadata
- s3://pacinfo-fs/tmp/hhoang/data/wgs/PRJNA907182/input/sample_meta.csv
- outdir
- s3://pacinfo-fs/tmp/hhoang/aladdin-genomics/sample-report
Main options
- intervals
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Annotation/intervals/wgs_calling_regions_noseconds.hg38.bed
- tools
- deepvariant,vep,manta,cnvkit
- main_caller
- germline
- structural_caller
- true
- copy_number_caller
- true
- enable_remote_read
- true
Preprocessing
- save_mapped
- N/A
Variant Calling
- cf_chrom_len
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Sequence/Length/Homo_sapiens_assembly38.len
- pon
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Annotation/GATKBundle/1000g_pon.hg38.vcf.gz
- pon_tbi
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Annotation/GATKBundle/1000g_pon.hg38.vcf.gz.tbi
Reference genome options
- ascat_genome
- hg38
- ascat_alleles
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Annotation/ASCAT/G1000_alleles_hg38.zip
- ascat_loci
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Annotation/ASCAT/G1000_loci_hg38.zip
- ascat_loci_gc
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Annotation/ASCAT/GC_G1000_hg38.zip
- ascat_loci_rt
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Annotation/ASCAT/RT_G1000_hg38.zip
- bwa
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Sequence/BWAIndex/
- bwamem2
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Sequence/BWAmem2Index/
- chr_dir
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Sequence/Chromosomes
- dbsnp
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Annotation/GATKBundle/dbsnp_146.hg38.vcf.gz
- dbsnp_tbi
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Annotation/GATKBundle/dbsnp_146.hg38.vcf.gz.tbi
- dbsnp_vqsr
- --resource:dbsnp,known=false,training=true,truth=false,prior=2.0 dbsnp_146.hg38.vcf.gz
- dict
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Sequence/WholeGenomeFasta/Homo_sapiens_assembly38.dict
- dragmap
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Sequence/dragmap/
- fasta
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Sequence/WholeGenomeFasta/Homo_sapiens_assembly38.fasta
- fasta_fai
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Sequence/WholeGenomeFasta/Homo_sapiens_assembly38.fasta.fai
- germline_resource
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Annotation/GATKBundle/af-only-gnomad.hg38.vcf.gz
- germline_resource_tbi
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Annotation/GATKBundle/af-only-gnomad.hg38.vcf.gz.tbi
- known_indels
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Annotation/GATKBundle/{Mills_and_1000G_gold_standard.indels.hg38,beta/Homo_sapiens_assembly38.known_indels}.vcf.gz
- known_indels_tbi
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Annotation/GATKBundle/{Mills_and_1000G_gold_standard.indels.hg38,beta/Homo_sapiens_assembly38.known_indels}.vcf.gz.tbi
- known_indels_vqsr
- --resource:gatk,known=false,training=true,truth=true,prior=10.0 Homo_sapiens_assembly38.known_indels.vcf.gz --resource:mills,known=false,training=true,truth=true,prior=10.0 Mills_and_1000G_gold_standard.indels.hg38.vcf.gz
- known_snps
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Annotation/GATKBundle/1000G_omni2.5.hg38.vcf.gz
- known_snps_tbi
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Annotation/GATKBundle/1000G_omni2.5.hg38.vcf.gz.tbi
- known_snps_vqsr
- --resource:1000G,known=false,training=true,truth=true,prior=10.0 1000G_omni2.5.hg38.vcf.gz
- mappability
- s3://pacinfo-fs/tmp/hhoang/ref/Homo_sapiens/GATK/GRCh38/Annotation/Control-FREEC/out100m2_hg38.gem
- snpeff_db
- 105
- snpeff_genome
- GRCh38
- snpeff_version
- 5.1
- vep_genome
- GRCh38
- vep_species
- homo_sapiens
- vep_cache_version
- 108
- vep_version
- 108.2
- igenomes_base
- s3://pacinfo-fs/tmp/hhoang/ref
Institutional config options
- config_profile_name
- AWSBATCH
- config_profile_description
- AWSBATCH Cloud Profile
- config_profile_contact
- Alexander Peltzer (@apeltzer)
- config_profile_url
- https://aws.amazon.com/batch/
Max job request options
- max_memory
- 120.GB
PCGR subworkflow options
- pcgr
- true
- pcgr_database
- s3://pacinfo-fs/tmp/nphan/PCGR
- assay
- WGS
- target_size_mb
- 34
- filter_deepvariant
- -i'FORMAT/DP>10'
- filter_freebayes_germline
- -i'FORMAT/DP>10'
- filter_freebayes_somatic
- -i'FORMAT/DP>10'
- filter_haplotypecaller
- -i'FORMAT/DP>10'
- filter_mutect2
- -i'FORMAT/DP>10'
- filter_strelka_indels
- -i'FORMAT/DP>10'
- filter_strelka_snvs
- -i'FORMAT/DP>10'
- filter_strelka_variants
- -i'FORMAT/DP>10'
Report generated on 2024-06-03, 01:57 UTC.