Help and documentation.

Genome is a macOS app for bioinformatic analysis of your own WGS data. It can align FASTQ→BAM, read BAM/CRAM/SAM and run separate analyses for HLA, KIR, LPA/KIV-2, pharmacogenetics, repeat expansions, microarray exports and PGS files. A complete report can bring genomic and pharmacogenetic results together in separate chapters. Genome Deep can create a local HTML overview with rsID search; SNP Search can look up rsIDs directly in an indexed BAM or CRAM file. Genomic files are processed on your Mac and are not sent to PJ Labs; Genome has no telemetry. An optional reference lookup may send the requested rsIDs to the public databases named for that lookup.

macOS 14 Sonoma or newer · Apple Silicon required · An SSD is recommended for large files and temporary working data. Storage needs and runtime depend on the selected workflow, file size, coverage, reference data and available resources. Check free space in Data Management before starting. An internet connection is needed for tool and reference downloads and optional public database lookups.

Prepare Genome in this order. You can find each named section in the app sidebar.

1. Configure directories in Data Management: Set the output, temporary, reference library and tools directories. Use a fast SSD for output and temporary files. All paths must remain available during analysis.
2. Install tools: Open Tools and install the managed tools required by the selected workflow. Wait until the status check reports them as ready. Downloads and updates need an internet connection; optional public reference lookups do as well.
3. Install references: Open References and install the reference genome matching the BAM, CRAM or FASTQ workflow, including index files and required panels. hs38d1 is recommended for GRCh38 WGS unless the source data uses another build. HLA*LA additionally requires the matching PRG_MHC_GRCh38_withIMGT graph.
4. Prepare raw files: Use a BAM file with its matching BAI index, a CRAM file with its CRAI index and matching reference genome, or a complete FASTQ pair, R1 and R2. Keep source files unchanged on a fast drive that remains connected.
5. Check storage: In Data Management under Available Storage, check free capacity on the source, output and temporary volumes. Allow space for raw files, results and intermediate files. Large WGS runs can need substantial temporary space; use the selected pipeline and Genome's storage display as your guide.

Load WGS BAM → choose the desired domain → check prerequisites → start analysis → export PDF report. HLA uses HLA*LA G-group output and optionally T1K as a second evidence layer. T1K-KIR stays separate from HLA. KILDA reports KIV-2/LPA context including quantile=NA as a raw value, Aldy4 belongs exclusively in pharmacogenetics, ExpansionHunter separates repeat and small-variant output. PGS scores are documented from PGS Catalog files, without diagnostic over-promising.

Genome 2.0 includes four built-in presets: FASTQ → BAM (hs38d1), FASTQ → Microarray, FASTQ + BAM → Complete Report, and FASTQ → BAM + Complete Report. Each profile shows its steps and the inputs, tools and references it needs.

Each card shows the preset name, readiness of required tools and references, and the pipeline steps. Selecting a card reveals its description, prerequisites and workflow. Optional databases are only needed by features that use them.

The play button on the card starts the pipeline directly. Genome first checks that input files (FASTQ R1/R2 or BAM/CRAM) and prerequisites are present. While a run is active the button turns into a red stop button; cancelling requires confirmation.

If a required FASTQ, BAM or CRAM file is missing, Genome shows which input is still needed. Required tools, reference genomes and panels are shown with their status. dbSNP and ClinVar are optional local knowledge bases, not general prerequisites for starting a workflow.

The expanded checklist shows the required tools, reference genomes, HLA*LA graphs and panels with their status. An arrow links to Tools or References. The pipeline view shows the steps in order; after an interrupted run, a resume notice may appear.

The plus button creates a custom preset: name, description, symbol and a freely assembled pipeline (add, reorder, toggle steps). The icon buttons above let you view (built-in), edit (custom), delete, and import or export as JSON. Built-in presets are read-only.

Custom presets can define pipeline steps, reference genome, aligner, split-read mode, duplicate-marking tool, Fastp trimming, Haplogrep version and tree, plus required panels and references. The bcftools variant-calling step can be added to a pipeline.

Use File > New Project, Open Project, Save Project or Save Project As to keep your workbench state in a .gpj file. It stores selected input and reference paths, workflow and report settings, run-history entries, and references to report files. BAM, FASTQ and reference files are not copied into the project file; they must remain reachable at their saved locations. Genome reports missing paths when you open a project, and you can use File > Relink Missing Project Paths to reconnect them. You can switch projects only when no run is active.

All generated files (BAMs, VCFs, microarray text files, reports) end up here. Default is the directory of the loaded BAM file. Recommended: choose a dedicated output directory on an SSD. The directory is restored automatically on launch.

Intermediate files during running processes: unpacked reference genomes, alignment intermediates, sort files. Default: ~/Library/Caches/Genome. These files are deleted automatically after successful processing. On abort, leftovers may remain and can be deleted manually.

Central directory for reference genomes (.fa / .fa.gz with index), microarray panels, HLA graphs and other reference data. Storage needs vary considerably by selection; check the displayed download size and available space before installing. Missing resources are shown in References and Tools.

Loads a BAM, CRAM or SAM file as input. When loading, the app automatically reads: reference-genome name from the BAM header, genome build (hg38/hg19/hs37d5), average read depth (coverage), biological sex (Y/X chromosome reads), file content (WGS/WES/panel) and index status (.bai / .crai). CRAM requires a matching reference genome in the Reference Library.

A list of detected volumes with name, interface (PCIe, Thunderbolt, USB), suitability and free/total. If free internal storage falls below the recommended temp requirement, a warning appears.

Sample ID & file name sets the base name for output files (status Automatic or Set, resettable). Under Inputs you can also set the SNP file (TXT) for SNP search, FASTQ R1/R2 and the tools install path.

For an indexed BAM, Compute statistics uses samtools flagstat/idxstats to report total, mapped and properly-paired reads, duplicates and the chromosome distribution. Without .bai/.crai a Not indexed note appears.

The built-in FASTQ→BAM preset aligns paired-end FASTQ files (R1 + R2) to hs38d1 with bwa mem by default and enables Fastp trimming. The standard path then runs samtools fixmate, sort and markdup; Fastp checks read quality afterwards. Custom presets can select minimap2, Picard for duplicate marking and different trimming settings. The result is a sorted, duplicate-marked and indexed BAM file.

Depending on the preset and settings, the run needs samtools, the selected aligner (bwa or minimap2), the configured duplicate-marking tool and the matching reference genome. Fastp is required when Fastp trimming is enabled. If a bwa index is missing, Genome can create it for the selected reference; minimap2 does not need a bwa index in this workflow.

Threads: set automatically to the number of logical CPU cores. The read group is generated from the file name (RGID, RGSM, RGPL=ILLUMINA, RGLB=lib1). Markdup removes PCR duplicates. Sorting is coordinate-based (needed for indexing).

Controls how bwa labels shorter parts of a read. Supplementary is the default; Secondary corresponds to bwa's -M option and can be selected for compatibility with particular downstream tools. The setting is in the conversion area with the alignment parameters.

If a running FASTQ→BAM alignment is aborted or fails, the app automatically detects existing intermediate files on the next launch. When you click 'Start alignment' a dialog appears with three options:

• Resume: continues the pipeline from the last successful step (e.g. from sort-merge, index or flagstat). • Restart: deletes all intermediate files and starts completely over. • Cancel: no action.

The pipeline can be resumed from any step: sort chunks → merge → markdup → index → flagstat/load.

Converts a BAM file back into two FASTQ files (R1, R2) via samtools collate + fastq. Useful when the original files are missing or a re-alignment against a different reference genome is needed. Unmapped reads are optionally included.

Fastp checks FASTQ files and can filter adapters and low-quality reads. A run produces a quality report and can write cleaned FASTQ files. Results and runtime depend on the input and selected settings.

FastQC produces a detailed HTML quality report per FASTQ file. Includes: per-base sequence quality, per-sequence quality scores, sequence duplication levels, overrepresented reads, adapter contamination, k-mer analysis. Requires a Java runtime. Result: an HTML file in the output directory.

Converts a loaded BAM to CRAM (reference genome required) or a CRAM back to BAM.

Turns a loaded BAM/CRAM back into FASTQ (_R1/_R2), e.g. for re-alignment.

Lifts a VCF between builds hg38 and hg19 (both directions selectable).

The reference panel defines which SNP positions are evaluated for a microarray export. Genome detects installed panels in the Reference Library and shows their status. Choose a panel that matches the intended output and reference genome.

Choose among the available local formats in the expandable menu: Genome, Genome Deep and MTHFR Genetics. Exports contain calls for selected panel positions; Genome Deep adds technical read-evidence fields. External services have their own import requirements, so acceptance of a file is not guaranteed.

Output files are prefixed with a Genome header line; its timestamp is set to the current date and time when the files are created. The build references in the header adapt automatically to the reference genome (build 37/38). The plain genotype records follow below.

Starts microarray extraction. bcftools evaluates the positions in the selected panel; available calls are then written in the chosen formats and annotated with rsIDs where possible. Scope and runtime depend on the panel, coverage and input file.

Each enabled format can produce a tab-separated .txt file in the output directory, for example [name]_Genome.txt, [name]_GenomeDeep.txt or [name]_MTHFR_Genetics.txt. Genome Deep also creates PDF and SVG overview files and an .overview.json file. If “Create HTML overview” is enabled, it adds a standalone .overview.html file. The export contains calls supported by the panel and input data; it is not a complete list of every genomic variant.

Genome Deep is an extended export format for WGS-derived genotypes at known SNP positions. Unlike a standard microarray export, each genotype includes read-depth and alignment-confidence fields.

Columns: rsid · chromosome · position · genotype (observed bases, e.g. GG/GA/AA) · refDepth (reads with the reference allele) · altDepth (reads with the alternate allele) · depth (total depth) · vaf (alternate-allele fraction) · confidence (hom_ref / hom_alt / het / uncertain).

Tip: review rows marked „uncertain“ separately when interpreting the export.

When “Create HTML overview” is enabled, Genome saves a standalone HTML overview alongside the TXT file. It works offline and includes a genome map and a search across exported rows by rsID. A match shows the exported position, genotype, read depth and alternate-allele fraction. The TXT file remains the complete table; the HTML view summarizes exported SNP rows and does not represent whole-genome coverage or medical risk.

Extracts the mitochondrial chromosome as a FASTA consensus sequence. Uses: samtools view (MT reads) → samtools mpileup → consensus computation. Suitable for yFull (female), Mitoverse, EMPOP. Output: [name]_MT.fasta.

Extracts all MT reads as a separate BAM file. The chromosome name adapts automatically (chrM for hg38, MT for hs37d5). Suitable for manual analysis and further processing. Output: [name]_MT.bam + .bai index.

bcftools calls variants in the mitochondrial sequence and writes a compressed VCF containing the calls found in that run. Output: [name]_MT.vcf.gz. Check the target tool's current format and reference requirements before using the file elsewhere.

Extracts reads from the Y chromosome and mitochondrial DNA together into an indexed BAM. Output: [name]_YMT.bam and its index. For external analysis, check the service's current reference, sample and file requirements.

Extracts Y-chromosome reads into an indexed BAM. The chromosome name follows the selected reference genome. Output: [name]_Y.bam and its index.

bcftools calls variants on the Y chromosome and creates a compressed VCF. It contains variants called in that run, not a complete list of all SNPs or STRs. Output: [name]_Y.vcf.gz.

Genome evaluates Y variants locally against its integrated haplogroup reference. The result describes the paternal lineage and the markers used; it does not represent a person's full ancestry. The available reads and reference limit the level of detail.

Haplogrep 3 assigns variants from an MT VCF to a maternal haplogroup. Genome can use an automatically generated or selected VCF. The assignment describes the mitochondrial lineage and depends on the variants detected and the selected phylotree.

HLA*LA evaluates a GRCh38-aligned BAM using the PRG_MHC_GRCh38_withIMGT reference graph. It requires HLA*LA, the matching graph and a BAM index. Genome creates technical HLA results plus HTML and PDF reports; these document available alleles and limitations. The output is not a clinical confirmation and needs expert interpretation for medical decisions.

T1K complements HLA*LA as an independent second evidence layer. Genome does not use T1K-HLA as a replacement for HLA*LA, but for concordance checking: HLA*LA G-groups remain primary, T1K results are compared orderless and normalized. Discrepancies are shown as technical evidence, not automatically as a clinical statement.

T1K can additionally type KIR genes. Genome handles KIR separately from HLA: KIR gene status, KIR-HLA context and technical evidence appear as their own section in the medical genomics report. Missing or low evidence stays visible and is not hidden.

KILDA analyses LPA/KIV-2 context from local inputs. Genome shows raw values, technical evidence and interpretation separately: values like quantile=NA are kept as raw values instead of being replaced by placeholders or clinical simplifications. Result rows stay compact and verifiable.

Aldy4 analyses complex pharmacogenetic genes and produces diplotype/allele information for PGx reports. Genome uses Aldy4 exclusively in the pharmacogenetics context; HLA, KIR or general medical genomics are not mixed in. The PDF report separates variant basis, diplotype, gene-drug context and cautious interpretation.

ExpansionHunter evaluates defined repeat loci. Genome separates repeat records from small-variant output and documents allele sizes, read evidence and confidence intervals. Locus-specific limits stay visible; a repeat finding does not replace diagnostics.

PGS Catalog files can be used for score determination. Genome documents the score file, variant basis, considered markers and limits. PGS results are population- and file-dependent risk contexts, not a diagnosis.

ACMG-SF screening evaluates the genes in the ACMG Secondary Findings v3.3 list as a separate technical search. Genome keeps variants, evidence, coverage and limitations traceable and separate. A hit is not a clinical finding and requires specialist review plus confirmation by an accredited laboratory.

Telomere-length analysis uses TelomereHunter2 on local sequence data. Genome presents the technical estimate with quality context; it is neither age determination nor a diagnosis and requires a validated clinical method for medical use.

Extracts reads that were not aligned against the reference genome. Possible causes: non-human DNA (bacteria, viruses), sequencing errors, very short reads, sequences in reference gaps. Output: a FASTQ file with unmapped reads. Useful for metagenomic analysis (Kaiju, CosmosID).

Sets variant IDs in the scheme CHROM:POS:REF:ALT via bcftools annotate. If no VCF is present in the output directory, variant calling from the loaded BAM is performed first automatically (bcftools mpileup | call). Output: _annotated.vcf.gz.

Filters VCF variants by quality criteria: QUAL≥20 and read depth DP≥10 via bcftools view. If no VCF is present, variant calling is run first. Output: _filtered.vcf.gz.

Computes VCF metrics with bcftools stats, including Ts/Tv ratio, SNP/indel ratio, heterozygosity and variants per chromosome. These values depend on the data and analysis and need to be interpreted in context. If no VCF is present, Genome may call variants first. Output: _stats.txt.

Derives an EBV VCF and a coverage summary (samtools, mosdepth) from the BAM/CRAM. Requires a reference with an EBV contig. Presents viral hits as technical evidence, not a diagnosis.

Exports unmapped reads as FASTQ for Kaiju and CosmosID; requires samtools.

Actions on the loaded VCF: annotate, filter, soft-filter, normalize and VarQC.

In TXT mode, select a Genome or MTHFR Genetics file, or another tab-separated table with rsID, position and genotype columns, under Data Management. Genome shows how many SNP entries were loaded.

Choose BAM as the search mode and enter one or more rsIDs. Genome resolves each rsID to a position and evaluates reads at that position in the loaded, indexed BAM or CRAM file. You can also enter a position as chr:pos, for example chr6:26000000. This requires a matching reference dataset with a FASTA file, the BAM/CRAM index, samtools and bcftools. Genome may contact NCBI to resolve an rsID and can use a local dbSNP reference as a fallback when available. The requested rsIDs may be sent; the genomic file is not. Available sequence evidence depends on the reference, coverage and read quality. If coverage is insufficient, the position cannot be assessed reliably; that does not confirm that the variant is absent.

In FASTQ mode, you can check rsIDs or positions against your local sequence files. R1 is required; R2 can also be selected. Genome needs minimap2, samtools, bcftools, and a selected, indexed reference genome. Choose the reference in the search area.

Enter one or more rsIDs into the text field, one per line. Format: rs123456 or simply 123456. You can also copy and paste from other programs; the filter cleans up extra characters automatically. The counter shows how many rsID(s) you have entered.

The filter button appears automatically when you enter text with special characters or whitespace. One click extracts all rsID patterns (rs + digits) from the text and removes everything else, perfect for cleaning up copied lists with extra spaces or commas.

Click Search. In TXT mode, Genome compares the entered rsIDs with entries in the loaded file. In BAM or FASTQ mode, it evaluates the matching sequence evidence in the selected files.

In TXT mode, the results table includes fields such as rsID, chromosome, position and genotype; entries not found in the file are listed separately. BAM and FASTQ searches also show available read evidence and search status. Results can be copied to the clipboard as tab-separated values.

Save frequently searched rsID lists as templates with a name (e.g. 'My Ancestry SNPs', 'Health panel'). Each template can have an individual SF Symbol icon and a note. Templates appear in the dropdown picker at the top. Built-in templates are predefined and cannot be deleted.

Each template has options to load, edit and delete. 'Load' fills the search field with the saved rsIDs. 'Edit' opens a form to change name, icon and rsID list. Notes are saved automatically (debounce). Import/Export allows sharing templates as a TXT file.

When the database match is enabled, Genome enriches the hits after the search via bcftools with gene, reference allele and clinical significance from ClinVar and dbSNP (colored traffic-light dot in the Clinic column).

The medical genomics report bundles HLA*LA, T1K-HLA concordance, T1K-KIR, KILDA/LPA, ExpansionHunter, SNP/ClinVar/PGS context, mtDNA/Haplogrep and optional EBV/microbiome evidence. Sections appear only when matching inputs and artifacts are present. The complete-report preset can add a separate pharmacogenetics chapter to the same PDF.

A standalone pharmacogenetics report covers Aldy4 diplotypes, PharmCAT/SNP rules, CPIC/DPWG/PharmGKB context and cautious drug notes. The complete report can include this material as a separate pharmacogenetics chapter in the combined PDF; HLA, KIR, LPA and repeat findings remain separate report sections.

Every report documents the data basis, files used, references, tool versions, warnings and limits. Technical raw values stay visible so a result remains traceable and verifiable.

Optional field that sets the sample/case name for the report, overriding the derived name.

Homebrew is a package manager for macOS and may be used to install dependencies for some components. Genome installs its tools through several managed paths; they are not all supplied by Homebrew.

Tool for BAM/SAM processing, including sorting and indexing, extracting reads, creating a pileup, converting BAM to FASTQ and inspecting coverage. Check the installed version with 'samtools --version'.

Variant calling and VCF processing. Used for: mpileup (pileup creation), call (variant calling), view (filter/convert VCF), annotate (annotation), stats (quality statistics). Often installed together with htslib.

Genome offers bwa mem for short reads and minimap2 for long reads. Choose the aligner for the workflow; the reference and any required index files must match it.

Fastp checks FASTQ files and can filter adapters and low-quality reads. A run produces a quality report and can write cleaned FASTQ files. Results and runtime depend on the input and settings.

FastQC creates a report about the quality of FASTQ reads. Genome manages the required tools and runtime components through the installation paths shown in the app.

sambamba is an optional tool for selected BAM-processing tasks. It is not a general replacement for samtools in every workflow; check whether the selected pipeline uses it.

Haplogrep 3 evaluates variants from mitochondrial data against a phylotree and returns a haplogroup assignment with technical evidence. Genome manages the tool and its required data through the app.

HLA*LA uses a reference graph to evaluate HLA from a matching GRCh38 BAM file. It requires the tool, the PRG_MHC_GRCh38_withIMGT graph and a BAM index. Genome creates technical results and HTML/PDF reports. Runtime depends on the input data and available hardware.

T1K is used for two separate Genome analyses: HLA analysis as a second evidence layer to HLA*LA, and KIR genotyping. HLA results are compared with HLA*LA; KIR stays its own result block. Genome shows technical evidence and concordance without mixing HLA and KIR into a diagnosis.

KILDA provides the LPA analysis for KIV-2/LPA context. Genome takes raw values and tool limits visibly into the report. Values like quantile=NA are not smoothed over but documented as technical information.

Aldy4 is used for pharmacogenetics, especially for complex PGx genes and diplotypes. Genome uses Aldy4 output only in the PGx report and keeps it separate from HLA, KIR, LPA and repeat analyses.

ExpansionHunter analyses repeat expansions at defined loci. Genome takes repeat records, allele sizes, read evidence and confidence intervals and keeps small-variant output separate.

TelomereHunter2 estimates telomere length from local sequence data. Genome manages the tool through the Conda-based toolchain and keeps the result, technical quality and method limitations separate.

PharmCAT is part of the optional pharmacogenetics extensions. Genome provides the required position reference and includes the result in the PGx report. The report shows the variants used and its interpretive limits; it does not replace medical advice.

Installs the tools managed in the current release, where they are listed in the Tools section. Different tools may use different package managers and managed runtimes. An internet connection is required; progress and errors appear in the log.

Rechecks the installation status of the listed tools. Use this after changing managed tool paths or installations.

The overview shows the current installation status and readiness of the tools. Optional tools that are not installed are marked as available. The header contains actions to check, detect, install and remove tools; available updates are shown separately.

Genome uses different installation paths depending on the tool. Local components include samtools, bcftools and bwa for sequencing data; Fastp and FastQC for quality control; and HLA*LA, T1K, KILDA, Aldy and ExpansionHunter for specialized analyses. The Tools section shows status, version and available actions for the current installation.

Panels contain SNP positions from common chip platforms and are tied to a reference build. Choose a panel that matches the input build and intended export. Available panels and their scope vary. Genome stores panels as .tab.gz or .vcf.gz files in the Reference Library.

GRCh38 without alternative contigs. Choose this reference when it matches the build of your input file and the selected workflow. Storage needs and content depend on the installed reference version.

GRCh38 with additional decoy sequences. These sequences can receive reads that might otherwise map ambiguously to assembly regions. Use hs38d1 when the input data or selected workflow expects this build.

GRCh38 reference from UCSC with the main assembly and alternative sequences. Chromosome names commonly use the chr prefix. Use it when the BAM or CRAM was aligned to this build.

GRCh37 reference, also known as hg19. Chromosome names commonly appear without the chr prefix. Use it with input aligned to GRCh37/hg19 and a matching panel.

GRCh37-based reference with additional hs37d5 decoy sequences. Check the build of the BAM or CRAM and choose a compatible reference; the provider name alone is not enough to determine it.

The References overview counts the core reference library of 8 assets: hs38d1.fa + .fai, hs38.fa + .fai, dbSNP, ClinVar, HLA-LA graph and T1K (KIR/HLA reference). The card shows 'X/8 reference assets ready' with a ring. Top right is the same action cluster as in Tools: Check, Detect (re-read status), Install all (sequential, skips what is present), Update all (re-download only flagged updates), Delete all (trash, destructive, with confirmation). Microarray panels are a separate category and not part of the 'all' actions.

Reference graph for HLA*LA. It contains the GRCh38/IMGT-HLA graph structure used by the HLA workflow. HLA*LA cannot run without the matching graph; check its download status and available storage in References.

PGS Catalog files describe score definitions and marker lists for PGS score determination. Genome stores them in the Reference Library and documents in the PDF report the score file, the variant basis used, missing markers and limits. PGS scores are population- and file-dependent risk contexts, not a diagnosis.

Every executed shell command is shown with its full output text. Color coding: normal text = stdout, red entries = stderr/errors. Progress-bar output is updated as a running line.

Copies the entire visible log content to the clipboard. Useful for error reports or debugging. The content includes all timestamps and commands of the current session.

Clears the log display in the app (the visible area). The physical log file under ~/Library/Application Support/Genome/logs/ remains fully intact.

Each app session is saved automatically as a log file: ~/Library/Application Support/Genome/logs/genome_YYYY-MM-DD_HHmmss.log. The last 20 sessions are kept, older ones deleted automatically. Reachable in Finder via: Go → Library → Application Support → Genome → logs.

Can be enabled in Settings → Debug logging. Additionally shows internal states, parsing results and decision logic. Recommended only for error analysis, slows down the display with heavy output.

The run history logs all completed analyses with type, date, duration, success/failure and full log. The last 100 runs are stored in ~/Library/Application Support/Genome/run_history.json.

Recorded types: Alignment, Extraction, Microarray, Haplogroup, LPA, Other.

The log records each run by type (Alignment, Analysis/Export, Microarray, Clinical Pipeline and more) and shows heartbeats, progress, warnings and technical output. Older log files are pruned automatically.

Color scheme: System (follows macOS), Light or Dark. Language: System (follows macOS), German or English. Both settings are applied and saved immediately.

When enabled (default: on), the app plays an alert sound when a running process unexpectedly slows down. Helps to spot problems such as I/O timeouts or SSD sleep without constantly watching the screen.

The developer menu can be enabled in Settings. It shows advanced options: pipeline tool selection, test-data generator, dock-icon settings and debug logging. The accent color switches to blue as a visual cue.

Depending on the preset, pipeline settings include the aligner (bwa or minimap2), split-read mode, duplicate-marking tool, Fastp trimming and Haplogrep tree. Variant calling uses the bcftools step. Results depend on the reference, tool version, settings and input data.

Generates a synthetic mini dataset (100 kb reference + 5,000 read pairs) for a short function check. It helps test supported workflows with sample data, but does not replace an analysis of real WGS data. Data is stored under ~/GenomeTest/.

The app icon in the Dock and the app overview can be set to Light, Dark or Auto independently of the system appearance.

Separate selection for app language and report language (each System, German or English). The app language drives the interface, the report language the generated PDF reports.

Enable beta updates, check for updates and set the cache limit (maximum storage for cached data).

Problem: 'No index file found', extraction does not start. Solution: run samtools index <file.bam> in the Terminal. For CRAM: samtools index <file.cram>. The index (.bai/.crai) must be in the same directory as the BAM/CRAM file.

Problem: 'Reference genome not found' or CRAM cannot be opened. Solution: References → download the matching reference genome. Make sure the Reference Library points to the correct directory (Directories → Reference Library). CRAM needs exactly the same genome it was aligned against.

Problem: a required tool is missing. Open Tools and use the available install or repair action, then recheck the status. Review the log for the failing package or path before trying another installation method.

Problem: the bwa index is missing or does not match the selected genome. Genome can create the required index for the selected reference. Check the log and confirm that the input, reference build and index all match.

Low or uneven coverage can leave some positions without enough evidence for a reliable call. There is no single depth threshold that applies to every region and variant type. Review the per-position evidence, read quality, reference and assay design; a missing call does not mean a variant is absent.

Problem: a CRAM file does not open or returns empty output. A common cause is a reference genome that does not exactly match the original alignment. Choose the matching build in the Reference Library and, if needed, compare the reference checksum with the alignment metadata or file documentation.

Problem: MT haplogroup cannot be computed. Solution: Tools → install Haplogrep 3. Make sure the Reference Library is set. Java must be installed. Check manually: haplogrep3 --help.

If installation fails, read the Tools log and check the reported build or dependency error before retrying. For a missing graph, open References and install PRG_MHC_GRCh38_withIMGT. HLA*LA requires a matching GRCh38-aligned BAM file with an index; Y-only and MT-only files do not provide the required HLA-region evidence.

Problem: extraction runs through but no files in the output directory. Possible causes: (1) output directory set incorrectly, check Directories. (2) no write permissions in the output directory. (3) the BAM contains no reads for the chosen region (e.g. no Y chromosome in a female sample). Check the log for error messages.

Problem: the pipeline aborts with 'Operation timed out' or a 'bgzf_read' error, especially on external USB SSDs. Cause: the SSD goes to sleep or the USB connection is briefly interrupted. Solution: on I/O timeouts a retry dialog appears. 'Retry' re-attempts the step. Prevent SSD sleep: System Settings → Energy → turn off 'Put hard disks to sleep'. For long pipelines: use an internal SSD or connect the external SSD directly (no hub).

Alignment, extraction and download times vary with read count, coverage, reference data, selected tools, hardware and storage. Genome shows progress while a workflow runs. For large datasets, use a fast drive with enough free space and keep it connected for the full run.

SAM is a text format for aligned sequencing reads; BAM and CRAM are compressed binary formats. CRAM decoding requires the matching reference genome. BAM and CRAM need an index (.bai or .crai) for fast access to regions.

Raw format for sequencing reads with quality values. Each read consists of four lines: name, sequence, '+', and quality values encoded as ASCII. Paired-end data stores R1 and R2 in separate files. File size depends on the sequencing design and number of reads.

Variant Call Format stores variants called by a particular pipeline against a reference genome. Fields include CHROM, POS, ID, REF, ALT, QUAL, FILTER, INFO, FORMAT and sample genotype. A compressed .vcf.gz file can use a Tabix index (.tbi) for fast access.

SNP (Single Nucleotide Polymorphism): a single base variation (e.g. A→G). rsID: a unique identifier from the NCBI dbSNP database (e.g. rs1805007 = MC1R red-hair variant). Indel: insertion or deletion of one or more bases. Microarray chips mainly measure known SNPs.

A haplogroup is a branch on a genetic lineage tree. Y haplogroups describe a paternal line; mitochondrial haplogroups describe a maternal line. They do not summarize a person's full ancestry, and the detail of an assignment depends on the observed variants and reference tree.

Coverage or read depth describes how many sequencing reads support a genomic position. Coverage varies across a sample and by assay. Whether evidence is sufficient depends on read quality, variant type, reference and analysis goal; there is no universal threshold for all applications. Use 'samtools coverage' or 'samtools depth' to inspect it.

The reference build defines the coordinates and sequences used when reads were aligned. GRCh38 is also called hg38, and GRCh37 hg19; hs37d5 extends GRCh37 with decoy and EBV sequences. Genome reads the build from known features in the BAM header, but may not identify it reliably when the header is incomplete or ambiguous. Use the same build as the alignment for panels and later processing.

The transition/transversion ratio compares single-base changes within the same base class (A↔G or C↔T) with changes between classes. Its expected value depends on the assay, region selection, variant set and calling pipeline; interpret it with those details rather than as a standalone pass/fail threshold.

A Phred score expresses the estimated base-call error probability on a logarithmic scale: Q20 corresponds to 1%, Q30 to 0.1%, and Q40 to 0.01%. FASTQ stores scores as ASCII characters (commonly offset 33). Fastp and FastQC show quality distributions; acceptable thresholds depend on the assay and analysis.

PCR duplicates are reads with matching alignment coordinates that can arise when library molecules are amplified before sequencing. Tools such as samtools markdup can flag them for downstream analyses. Their proportion depends on the library and assay and is not, by itself, a universal quality verdict.

Decoy contigs are additional reference sequences that can help represent reads not uniquely explained by the primary assembly. Their effect depends on the data, aligner and reference; they do not guarantee that ambiguous mappings or false calls are eliminated.

A read can be split into segments that align to different parts of the genome. The primary alignment represents the main segment; additional segments can be marked as supplementary or secondary. bwa uses the -M option to mark split alignments as secondary. The appropriate setting depends on the downstream tools. In Genome, find it in the conversion area with the alignment parameters.

Copy Number Variation, a deviation from the normal diploid copy number (2) of a genome region. Deletions (0-1 copies) and duplications (3+ copies) sometimes affect whole genes. Examples: LPA/KIV-2 CNV for lipoprotein(a) context and CYP2D6 CNV for pharmacogenetics.

DNA is double-stranded, each base has a complement (A↔T, C↔G). Genotyping platforms can use the plus strand (forward) or the minus strand (reverse) as reference. This means the same SNP can be reported as 'A' (plus strand) or 'T' (minus strand). When comparing data from different sources (e.g. SNP array vs. WGS extraction), strand conventions must be taken into account. A/T and C/G SNPs are especially ambiguous because plus and minus strands cannot be distinguished.

Conversion of genomic coordinates between different reference-genome versions (e.g. hg19 → hg38). Needed when data from different builds is to be compared. The same variant has different position values in hg19 and hg38 because the reference sequence changed between versions (gaps closed, contigs moved). Tools: UCSC LiftOver, CrossMap, Picard LiftoverVcf.

WGS sequences reads across the genome, while WES enriches selected coding regions. Neither guarantees uniform coverage of every position. Microarray extraction needs evidence at the selected panel positions; WES may not target many non-coding sites. Genome's result depends on the actual file and coverage.

For an existing BAM file, use the same reference build it was aligned against. Where possible, check the build in the file metadata or the workflow that created it and match it to the Reference Library. hs38d1 includes additional decoy sequences; hs38 refers to a GRCh38 reference without alternate contigs. For a new FASTQ analysis, choose the reference that matches the preset and keep it consistent through later steps.

Genome provides a general tab-separated SNP export and a dedicated MTHFR panel format. Whether an external service currently accepts these files depends on its import requirements. Before uploading, check the required columns, reference genome and the provider's privacy terms; a similar column layout does not guarantee compatibility.

The Genome format contains SNPs recognized in the input and matched to the available panel. Genome Deep adds the read-depth and call-confidence fields available for export. Before importing into GEDmatch or another service, check its current requirements for file format, reference genome and coverage.

Genome processes supported genomic files on your Mac and does not send them to PJ Labs. There is no account or telemetry. Network access is used for downloads you start and for optional public reference queries; those queries send the requested rsIDs to the databases shown. The source file is not uploaded for a lookup.

A single time estimate would be misleading. Searching a prepared BAM file is a different workload from aligning FASTQ to BAM. Runtime and storage depend on coverage, file size, reference data, tools and drive speed; Genome shows progress while a workflow runs.

Some BAM files have an incomplete header. Genome may then be unable to identify the build and sequence names. Check the header with 'samtools view -H <file.bam>' and obtain the correct header or reference details from the workflow that created the alignment. 'samtools reheader' can install a matching header; addreplacerg adds read-group fields and does not replace missing @SQ sequences.

Comparisons between genotyping platforms can differ for several reasons:

1. Reference builds such as hg19 and hg38 use different coordinates; a liftover can translate coordinates where supported.

2. Heterozygous alleles may be written in either order, such as AG or GA.

3. Strand conventions can represent the same bases as complements (A↔T, C↔G).

4. Different assays and variant-calling pipelines may report different calls, especially when coverage or quality is limited.

Compare build, strand convention, panel and call quality before treating a difference as biological.

Genome can export Y+MT BAM, Y BAM and Y VCF artifacts. Check yFull's current upload requirements, accepted reference builds and sample requirements before using an export there; Genome does not guarantee third-party import compatibility.