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gtars

آمن ⚙️ الأوامر الخارجية📁 الوصول إلى نظام الملفات

Analyze genomic intervals and coverage tracks

متاح أيضًا من: davila7

Process and analyze genomic interval data for bioinformatics research. Gtars provides high-performance tools for overlap detection, coverage analysis, and ML tokenization of genomic sequences.

يدعم: Claude Codex Code(CC)
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اختبرها

استخدام "gtars". Find overlaps between my ChIP-seq peaks and gene promoters

النتيجة المتوقعة:

  • Overlapping peaks: 1,234 regions found
  • Peak distribution: 45% in promoter regions, 30% in enhancers, 25% intergenic
  • Results saved to peaks_in_promoters.bed

استخدام "gtars". Generate coverage track from ATAC-seq data

النتيجة المتوقعة:

  • Coverage track generated: atac_coverage.bw
  • Resolution: 10bp
  • Total bases covered: 2.1 billion
  • Peak accessibility regions identified: 150,000

التدقيق الأمني

آمن
v4 • 1/17/2026

All 209 static findings are false positives. The static analyzer incorrectly flagged markdown documentation syntax as security risks. No malicious code present. This is a legitimate genomic analysis toolkit.

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الملفات التي تم فحصها
3,532
الأسطر التي تم تحليلها
2
النتائج
4
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عوامل الخطر

⚙️ الأوامر الخارجية (3)
📁 الوصول إلى نظام الملفات (1)
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درجة الجودة

45
الهندسة المعمارية
100
قابلية الصيانة
85
المحتوى
21
المجتمع
100
الأمان
91
الامتثال للمواصفات

ماذا يمكنك بناءه

Peak overlap analysis

Identify overlapping regulatory elements and annotate variants by comparing ChIP-seq peaks.

Coverage visualization

Generate coverage tracks from ATAC-seq or ChIP-seq data for genome browser visualization.

Genomic ML preprocessing

Convert genomic regions into discrete tokens for training transformer models on DNA sequences.

جرّب هذه الموجهات

Basic overlap query
Use gtars to build an IGD index from my peaks.bed file and query overlaps with promoters.bed.
Coverage generation
Generate a BigWig coverage track from my ATAC-seq fragments using gtars uniwig with 10bp resolution.
ML tokenization
Create a TreeTokenizer from my training_regions.bed and tokenize the genomic coordinates for ML model input.
Fragment processing
Split my fragments.tsv by cell barcodes using gtars fragsplit and output to cluster-specific directories.

أفضل الممارسات

  • Use memory-mapped mode (mmap=True) when processing large genomic files for better performance.
  • Build IGD indexes once and reuse them for multiple overlap queries.
  • Specify thread count explicitly for predictable resource usage in batch processing.

تجنب

  • Do not process GB-scale files without enabling memory-mapped mode.
  • Avoid running multiple gtars commands sequentially when parallel processing is available.
  • Do not skip format validation when integrating with external pipelines.

الأسئلة المتكررة

What file formats does gtars support?
Gtars supports BED for intervals, WIG/BigWig for coverage tracks, FASTA for reference genomes, and TSV for fragment files.
Do I need Rust installed to use gtars?
Python bindings work without Rust. The CLI requires Rust/Cargo only if installing from source.
How does gtars compare to bedtools?
Gtars offers similar interval operations with Rust performance, plus specialized ML tokenization and BigWig support.
Can gtars handle chromosome-scale files?
Yes, memory-mapped mode enables processing of chromosome-scale files with minimal RAM usage.
What is IGD indexing?
Integrated Genome Database is a data structure for fast overlap queries on large genomic interval collections.
Is gtars compatible with geniml?
Yes, gtars is the foundation for geniml and provides core preprocessing operations for genomic ML workflows.

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