Guida Rapida a Genetica Viewer — Funzionalità, Importazione e Esportazione

Genetica Viewer — Step‑by‑Step Tutorial for BeginnersGenetica Viewer is a user‑friendly genomic data visualization tool designed to help researchers, clinicians, educators, and curious individuals explore DNA sequencing results, variant annotations, and genomic regions without steep technical barriers. This step‑by‑step tutorial will guide beginners through installation, importing data, navigating the interface, performing common analyses, interpreting results, and exporting findings. Practical tips and troubleshooting steps are included to help you get productive quickly.

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What Genetica Viewer does (brief overview)

Genetica Viewer lets you:

• Load sequencing and variant files (e.g., FASTA, FASTQ, VCF, BED).
• Visualize reads, alignments, and genomic features across chromosomes.
• Annotate variants with gene names, predicted effects, and population frequencies.
• Compare multiple samples and track differences across cohorts.
• Export images, reports, and filtered variant lists.

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System requirements and installation

Minimum recommended:

• OS: Windows ⁄₁₁, macOS 10.15+, or a recent Linux (Ubuntu 20.04+).
• RAM: 8 GB (16 GB+ for large datasets).
• Disk: 5 GB free (more for local projects).
• CPU: modern multi‑core processor; GPU optional for accelerated rendering.

Installation steps:

1. Download the installer or package from the official Genetica Viewer site (choose Windows .exe, macOS .dmg, or Linux .AppImage/.deb).
2. Run the installer and follow prompts. macOS users may need to allow apps from identified developers.
3. Launch Genetica Viewer. On first run, configure default folders for projects and temporary files.

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Getting started: interface tour

Main interface components:

• Top toolbar: project actions (New, Open, Save), import, and export tools.
• Left panel: file/project browser and sample list.
• Center canvas/viewer: interactive genome browser with tracks.
• Right panel: detailed metadata and variant/feature inspector.
• Bottom console: logs, job status, and search bar.

Navigating the genome:

• Click and drag on the center canvas to pan horizontally.
• Scroll or use zoom controls to zoom in/out; double‑click to zoom to a feature.
• Use the coordinate box to jump to a specific chromosome and position (e.g., chr7:140453136‑140453236).

Tracks and layers:

• Read alignments: show individual sequencing reads aligned to the reference.
• Variant track: displays SNVs, indels, and structural variants.
• Annotation tracks: genes, exons, regulatory regions (from GTF/GFF/BED).
• Coverage plot: read depth across the region.
• Custom tracks: import BED/BigWig files for additional data.

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Importing data: formats and best practices

Common supported formats:

• Reference sequences: FASTA (.fa, .fasta)
• Raw reads: FASTQ (.fastq, .fq) — often compressed (.gz)
• Alignments: BAM/CRAM (requires index .bai/.crai)
• Variants: VCF (bgzip + tabix indexing recommended)
• Annotations: GTF, GFF, BED, BigWig

Step‑by‑step import:

1. Create a new project (File → New Project) and name it.
2. Click Import → Add Files and select your reference FASTA first (index if available).
3. Add alignment files (BAM/CRAM). If indexes are missing, Genetica Viewer will prompt to create them.
4. Add VCFs and annotation files.
5. Confirm coordinate systems and reference versions (e.g., GRCh37 vs GRCh38). Mismatched references produce incorrect visualizations.

Best practices:

• Always include an indexed reference FASTA and matching chromosome naming conventions.
• Use bgzip + tabix for VCFs to improve performance.
• For large cohorts, use CRAM + remote indices or cloud storage links.

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First walkthrough: load a sample and inspect a variant

Example task: inspect a missense variant in BRCA1.

1. Open your project and select the sample from the left panel.
2. Use the search/coordinate box to jump to BRCA1 locus (e.g., chr17:43044295‑43125482 for GRCh37).
3. Enable the variant track and filter to show only variants in BRCA1 (use gene filter or coordinates).
4. Click the variant marker to open the inspector in the right panel. It shows:
   • Variant ID, position, reference/alternate alleles.
   • Annotation: gene, transcript, predicted consequence (missense_variant), amino‑acid change.
   • Population frequencies (if annotated) and ClinVar assertions (if available).
5. Toggle the reads track to see supporting reads; check strand balance and allele fraction in the coverage plot.

Interpreting basic indicators:

• High read support and balanced strands increase confidence.
• Low allele fraction may indicate mosaicism or low tumor purity.
• Known ClinVar pathogenic annotation warrants further clinical validation.

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Filtering and prioritizing variants

Use the Variant Filter panel to narrow candidates:

• Frequency filter: exclude variants with population frequency > 1% (or set threshold).
• Effect filter: keep high‑impact consequences (stop_gain, frameshift, splice_site).
• Quality filter: require minimum depth, genotype quality (GQ), and variant quality (QUAL).
• Inheritance filters: if family trios are loaded, filter by de novo, compound het, homozygous recessive patterns.
• ClinVar/ClinGen tags: prioritize pathogenic/likely pathogenic.

Example filter preset for rare disease:

• MAF < 0.01
• Effect: missense, nonsense, splice_acceptor/donor, frameshift
• GQ ≥ 20, DP ≥ 10

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Comparative views and cohort analyses

Comparing samples:

• Add multiple samples to a project; the viewer aligns them on the same genomic coordinates.
• Use sample grouping to color‑code cohorts (e.g., cases vs controls).
• Use the difference track to highlight variants present in one group but absent in another.

Basic cohort metrics:

• Allele frequency across the cohort.
• Variant burden per gene.
• Coverage uniformity heatmaps.

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Annotation sources and customization

Built‑in annotation options:

• Gene models (RefSeq, Ensembl)
• Known variant databases (dbSNP, gnomAD, ClinVar)
• Predictive scores (SIFT, PolyPhen, CADD) — may require separate downloads.

Custom annotations:

• Import local GTF/GFF/BED files to display experimental features (e.g., ChIP peaks).
• Upload TSV/CSV with variant annotations to merge with VCF records.

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Exporting results and creating reports

Export options:

• Snapshot images: PNG/SVG of the current view.
• Variant lists: filtered variants exported as VCF, TSV, or Excel.
• Project archive: bundle reference, alignments, and settings for sharing.
• Automated reports: PDF summaries with key variants, coverage stats, and screenshots.

Example: export a filtered variant list

1. Apply your filters.
2. Click Export → Variants → Select format (TSV recommended for spreadsheets).
3. Choose columns (CHROM, POS, REF, ALT, GENE, CONSEQUENCE, AF, QUAL).
4. Save and review in Excel or R.

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Useful tips and keyboard shortcuts

Tips:

• Keep a local index for large files to avoid repeated re‑indexing.
• Use remote references (HTTP/S3) for shared large genomes.
• Regularly update annotation databases for the latest ClinVar/gnomAD data.

Common shortcuts (examples):

• Ctrl/Cmd+F: search region/gene
• Ctrl/Cmd+S: save project
• Space: toggle play/auto‑scroll in long regions
• Z/X: zoom in/out

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Troubleshooting common problems

Problem: Chromosomes or coordinates don’t match

• Cause: Reference mismatch (GRCh37 vs GRCh38) or different chromosome naming (chr1 vs 1).
• Fix: Ensure reference FASTA matches annotation and VCF; rename chromosomes or remap coordinates if necessary.

Problem: Slow rendering with large BAMs

• Cause: No index or network latency.
• Fix: Create BAM index (.bai), use CRAM or downsample reads, or view smaller regions.

Problem: Missing annotations

• Cause: Annotation files not loaded or outdated.
• Fix: Load appropriate GTF/GFF/BED files; update built‑in annotation databases.

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Example workflows

Germline rare disease:

1. Load trio BAMs and VCF.
2. Filter for rare, high‑impact variants.
3. Use inheritance filter to find de novo or compound heterozygous variants.
4. Export candidate variants for validation.

Somatic tumor profiling:

1. Load tumor/normal BAMs and somatic VCF.
2. Inspect variant allele fractions and read support.
3. Cross‑reference with COSMIC/ClinVar and report actionable mutations.

Population scan:

1. Load cohort VCFs.
2. Compute allele frequencies and visualize per‑gene burden.
3. Export summary statistics for downstream analysis.

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Security, privacy, and data sharing (brief)

When sharing projects, strip sensitive sample identifiers and use anonymized IDs. Use encrypted transfers (SFTP, HTTPS) for remote references. Confirm institutional policies for clinical data handling before sharing.

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Where to learn more

• Official Genetica Viewer documentation and FAQs.
• Community forums and user‑contributed tutorials.
• Genomics courses for background on variant interpretation.

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If you want, I can convert this into a printed quick‑start PDF, create step‑by‑step screenshots for a specific example dataset, or draft filter presets tailored to your use case.