gtars

Name: gtars
Author: LeonChaoX

698stars61forks

How to install gtars?

leonchaox/qinyan-academic-skills/gtars

$npx -y skills add LeonChaoX/qinyan-academic-skills --skill gtars

Installs into the current project.

Summary

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

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SKILL.md

1	# Gtars: Genomic Tools and Algorithms in Rust
2
3	## Overview
4
5	Gtars is a high-performance Rust toolkit for manipulating, analyzing, and processing genomic interval data. It provides specialized tools for overlap detection, coverage analysis, tokenization for machine learning, and reference sequence management.
6
7	Use this skill when working with:
8	- Genomic interval files (BED format)
9	- Overlap detection between genomic regions
10	- Coverage track generation (WIG, BigWig)
11	- Genomic ML preprocessing and tokenization
12	- Fragment analysis in single-cell genomics
13	- Reference sequence retrieval and validation
14
15	## Installation
16
17	### Python Installation
18
19	Install gtars Python bindings:
20
21	```bash
22	uv uv pip install gtars
23	```
24
25	### CLI Installation
26
27	Install command-line tools (requires Rust/Cargo):
28
29	```bash
30	# Install with all features
31	cargo install gtars-cli --features "uniwig overlaprs igd bbcache scoring fragsplit"
32
33	# Or install specific features only
34	cargo install gtars-cli --features "uniwig overlaprs"
35	```
36
37	### Rust Library
38
39	Add to Cargo.toml for Rust projects:
40
41	```toml
42	[dependencies]
43	gtars = { version = "0.1", features = ["tokenizers", "overlaprs"] }
44	```
45
46	## Core Capabilities
47
48	Gtars is organized into specialized modules, each focused on specific genomic analysis tasks:
49
50	### 1. Overlap Detection and IGD Indexing
51
52	Efficiently detect overlaps between genomic intervals using the Integrated Genome Database (IGD) data structure.
53
54	When to use:
55	- Finding overlapping regulatory elements
56	- Variant annotation
57	- Comparing ChIP-seq peaks
58	- Identifying shared genomic features
59
60	Quick example:
61	```python
62	import gtars
63
64	# Build IGD index and query overlaps
65	igd = gtars.igd.build_index("regions.bed")
66	overlaps = igd.query("chr1", 1000, 2000)
67	```
68
69	See `references/overlap.md` for comprehensive overlap detection documentation.
70
71	### 2. Coverage Track Generation
72
73	Generate coverage tracks from sequencing data with the uniwig module.
74
75	When to use:
76	- ATAC-seq accessibility profiles
77	- ChIP-seq coverage visualization
78	- RNA-seq read coverage
79	- Differential coverage analysis
80
81	Quick example:
82	```bash
83	# Generate BigWig coverage track
84	gtars uniwig generate --input fragments.bed --output coverage.bw --format bigwig
85	```
86
87	See `references/coverage.md` for detailed coverage analysis workflows.
88
89	### 3. Genomic Tokenization
90
91	Convert genomic regions into discrete tokens for machine learning applications, particularly for deep learning models on genomic data.
92
93	When to use:
94	- Preprocessing for genomic ML models
95	- Integration with geniml library
96	- Creating position encodings
97	- Training transformer models on genomic sequences
98
99	Quick example:
100	```python
101	from gtars.tokenizers import TreeTokenizer
102
103	tokenizer = TreeTokenizer.from_bed_file("training_regions.bed")
104	token = tokenizer.tokenize("chr1", 1000, 2000)
105	```
106
107	See `references/tokenizers.md` for tokenization documentation.
108
109	### 4. Reference Sequence Management
110
111	Handle reference genome sequences and compute digests following the GA4GH refget protocol.
112
113	When to use:
114	- Validating reference genome integrity
115	- Extracting specific genomic sequences
116	- Computing sequence digests
117	- Cross-reference comparisons
118
119	Quick example:
120	```python
121	# Load reference and extract sequences
122	store = gtars.RefgetStore.from_fasta("hg38.fa")
123	sequence = store.get_subsequence("chr1", 1000, 2000)
124	```
125
126	See `references/refget.md` for reference sequence operations.
127
128	### 5. Fragment Processing
129
130	Split and analyze fragment files, particularly useful for single-cell genomics data.
131
132	When to use:
133	- Processing single-cell ATAC-seq data
134	- Splitting fragments by cell barcodes
135	- Cluster-based fragment analysis
136	- Fragment quality control
137
138	Quick example:
139	```bash
140	# Split fragments by clusters
141	gtars fragsplit cluster-split --input fragments.tsv --clusters clusters.txt --output-dir ./by_cluster/
142	```
143
144	See `references/cli.md` for fragment processing commands.
145
146	### 6. Fragment Scoring
147
148	Score fragment overlaps against reference datasets.
149
150	When to use:
151	- Evaluating fragment enrichment
152	- Comparing experimental data to references
153	- Quality metrics computation
154	- Batch scoring across samples
155
156	Quick example:
157	```bash
158	# Score fragments against reference
159	gtars scoring score --fragments fragments.bed --reference reference.bed --output scores.txt
160	```
161
162	## Common Workflows
163
164	### Workflow 1: Peak Overlap Analysis
165
166	Identify overlapping genomic features:
167
168	```python
169	import gtars
170
171	# Load two region sets
172	peaks = gtars.RegionSet.from_bed("chip_peaks.bed")
173	promoters = gtars.RegionSet.from_bed("promoters.bed")
174
175	# Find overlaps
176	overlapping_peaks = peaks.filter_overlapping(promoters)
177
178	# Export

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gtars

698stars61forks

View on GitHub

How to install gtars?

leonchaox/qinyan-academic-skills/gtars

$npx -y skills add LeonChaoX/qinyan-academic-skills --skill gtars

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Gtars: Genomic Tools and Algorithms in Rust
2
3	## Overview
4
5	Gtars is a high-performance Rust toolkit for manipulating, analyzing, and processing genomic interval data. It provides specialized tools for overlap detection, coverage analysis, tokenization for machine learning, and reference sequence management.
6
7	Use this skill when working with:
8	- Genomic interval files (BED format)
9	- Overlap detection between genomic regions
10	- Coverage track generation (WIG, BigWig)
11	- Genomic ML preprocessing and tokenization
12	- Fragment analysis in single-cell genomics
13	- Reference sequence retrieval and validation
14
15	## Installation
16
17	### Python Installation
18
19	Install gtars Python bindings:
20
21	```bash
22	uv uv pip install gtars
23	```
24
25	### CLI Installation
26
27	Install command-line tools (requires Rust/Cargo):
28
29	```bash
30	# Install with all features
31	cargo install gtars-cli --features "uniwig overlaprs igd bbcache scoring fragsplit"
32
33	# Or install specific features only
34	cargo install gtars-cli --features "uniwig overlaprs"
35	```
36
37	### Rust Library
38
39	Add to Cargo.toml for Rust projects:
40
41	```toml
42	[dependencies]
43	gtars = { version = "0.1", features = ["tokenizers", "overlaprs"] }
44	```
45
46	## Core Capabilities
47
48	Gtars is organized into specialized modules, each focused on specific genomic analysis tasks:
49
50	### 1. Overlap Detection and IGD Indexing
51
52	Efficiently detect overlaps between genomic intervals using the Integrated Genome Database (IGD) data structure.
53
54	When to use:
55	- Finding overlapping regulatory elements
56	- Variant annotation
57	- Comparing ChIP-seq peaks
58	- Identifying shared genomic features
59
60	Quick example:
61	```python
62	import gtars
63
64	# Build IGD index and query overlaps
65	igd = gtars.igd.build_index("regions.bed")
66	overlaps = igd.query("chr1", 1000, 2000)
67	```
68
69	See `references/overlap.md` for comprehensive overlap detection documentation.
70
71	### 2. Coverage Track Generation
72
73	Generate coverage tracks from sequencing data with the uniwig module.
74
75	When to use:
76	- ATAC-seq accessibility profiles
77	- ChIP-seq coverage visualization
78	- RNA-seq read coverage
79	- Differential coverage analysis
80
81	Quick example:
82	```bash
83	# Generate BigWig coverage track
84	gtars uniwig generate --input fragments.bed --output coverage.bw --format bigwig
85	```
86
87	See `references/coverage.md` for detailed coverage analysis workflows.
88
89	### 3. Genomic Tokenization
90
91	Convert genomic regions into discrete tokens for machine learning applications, particularly for deep learning models on genomic data.
92
93	When to use:
94	- Preprocessing for genomic ML models
95	- Integration with geniml library
96	- Creating position encodings
97	- Training transformer models on genomic sequences
98
99	Quick example:
100	```python
101	from gtars.tokenizers import TreeTokenizer
102
103	tokenizer = TreeTokenizer.from_bed_file("training_regions.bed")
104	token = tokenizer.tokenize("chr1", 1000, 2000)
105	```
106
107	See `references/tokenizers.md` for tokenization documentation.
108
109	### 4. Reference Sequence Management
110
111	Handle reference genome sequences and compute digests following the GA4GH refget protocol.
112
113	When to use:
114	- Validating reference genome integrity
115	- Extracting specific genomic sequences
116	- Computing sequence digests
117	- Cross-reference comparisons
118
119	Quick example:
120	```python
121	# Load reference and extract sequences
122	store = gtars.RefgetStore.from_fasta("hg38.fa")
123	sequence = store.get_subsequence("chr1", 1000, 2000)
124	```
125
126	See `references/refget.md` for reference sequence operations.
127
128	### 5. Fragment Processing
129
130	Split and analyze fragment files, particularly useful for single-cell genomics data.
131
132	When to use:
133	- Processing single-cell ATAC-seq data
134	- Splitting fragments by cell barcodes
135	- Cluster-based fragment analysis
136	- Fragment quality control
137
138	Quick example:
139	```bash
140	# Split fragments by clusters
141	gtars fragsplit cluster-split --input fragments.tsv --clusters clusters.txt --output-dir ./by_cluster/
142	```
143
144	See `references/cli.md` for fragment processing commands.
145
146	### 6. Fragment Scoring
147
148	Score fragment overlaps against reference datasets.
149
150	When to use:
151	- Evaluating fragment enrichment
152	- Comparing experimental data to references
153	- Quality metrics computation
154	- Batch scoring across samples
155
156	Quick example:
157	```bash
158	# Score fragments against reference
159	gtars scoring score --fragments fragments.bed --reference reference.bed --output scores.txt
160	```
161
162	## Common Workflows
163
164	### Workflow 1: Peak Overlap Analysis
165
166	Identify overlapping genomic features:
167
168	```python
169	import gtars
170
171	# Load two region sets
172	peaks = gtars.RegionSet.from_bed("chip_peaks.bed")
173	promoters = gtars.RegionSet.from_bed("promoters.bed")
174
175	# Find overlaps
176	overlapping_peaks = peaks.filter_overlapping(promoters)
177
178	# Export

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