dnanexus-integration

Name: dnanexus-integration
Author: K-Dense-AI

31kstars3.1kforks

How to install dnanexus-integration?

k-dense-ai/scientific-agent-skills/dnanexus-integration

$npx -y skills add K-Dense-AI/scientific-agent-skills --skill dnanexus-integration

Installs into the current project.

Summary

DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.

Files · 1

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SKILL.md

1	# DNAnexus Integration
2
3	## Overview
4
5	DNAnexus is a cloud platform for biomedical data analysis and genomics. Build and deploy apps/applets, manage data objects, run workflows, and use the dxpy Python SDK for genomics pipeline development and execution.
6
7	## When to Use This Skill
8
9	This skill should be used when:
10	- Creating, building, or modifying DNAnexus apps/applets
11	- Uploading, downloading, searching, or organizing files and records
12	- Running analyses, monitoring jobs, creating workflows
13	- Writing scripts using dxpy to interact with the platform
14	- Setting up dxapp.json, managing dependencies, using Docker
15	- Processing FASTQ, BAM, VCF, or other bioinformatics files
16	- Managing projects, permissions, or platform resources
17
18	## Core Capabilities
19
20	The skill is organized into five main areas, each with detailed reference documentation:
21
22	### 1. App Development
23
24	Purpose: Create executable programs (apps/applets) that run on the DNAnexus platform.
25
26	Key Operations:
27	- Generate app skeleton with `dx-app-wizard`
28	- Write Python or Bash apps with proper entry points
29	- Handle input/output data objects
30	- Deploy with `dx build` or `dx build --app`
31	- Test apps on the platform
32
33	Common Use Cases:
34	- Bioinformatics pipelines (alignment, variant calling)
35	- Data processing workflows
36	- Quality control and filtering
37	- Format conversion tools
38
39	Reference: See `references/app-development.md` for:
40	- Complete app structure and patterns
41	- Python entry point decorators
42	- Input/output handling with dxpy
43	- Development best practices
44	- Common issues and solutions
45
46	### 2. Data Operations
47
48	Purpose: Manage files, records, and other data objects on the platform.
49
50	Key Operations:
51	- Upload/download files with `dxpy.upload_local_file()` and `dxpy.download_dxfile()`
52	- Create and manage records with metadata
53	- Search for data objects by name, properties, or type
54	- Clone data between projects
55	- Manage project folders and permissions
56
57	Common Use Cases:
58	- Uploading sequencing data (FASTQ files)
59	- Organizing analysis results
60	- Searching for specific samples or experiments
61	- Backing up data across projects
62	- Managing reference genomes and annotations
63
64	Reference: See `references/data-operations.md` for:
65	- Complete file and record operations
66	- Data object lifecycle (open/closed states)
67	- Search and discovery patterns
68	- Project management
69	- Batch operations
70
71	### 3. Job Execution
72
73	Purpose: Run analyses, monitor execution, and orchestrate workflows.
74
75	Key Operations:
76	- Launch jobs with `applet.run()` or `app.run()`
77	- Monitor job status and logs
78	- Create subjobs for parallel processing
79	- Build and run multi-step workflows
80	- Chain jobs with output references
81
82	Common Use Cases:
83	- Running genomics analyses on sequencing data
84	- Parallel processing of multiple samples
85	- Multi-step analysis pipelines
86	- Monitoring long-running computations
87	- Debugging failed jobs
88
89	Reference: See `references/job-execution.md` for:
90	- Complete job lifecycle and states
91	- Workflow creation and orchestration
92	- Parallel execution patterns
93	- Job monitoring and debugging
94	- Resource management
95
96	### 4. Python SDK (dxpy)
97
98	Purpose: Programmatic access to DNAnexus platform through Python.
99
100	Key Operations:
101	- Work with data object handlers (DXFile, DXRecord, DXApplet, etc.)
102	- Use high-level functions for common tasks
103	- Make direct API calls for advanced operations
104	- Create links and references between objects
105	- Search and discover platform resources
106
107	Common Use Cases:
108	- Automation scripts for data management
109	- Custom analysis pipelines
110	- Batch processing workflows
111	- Integration with external tools
112	- Data migration and organization
113
114	Reference: See `references/python-sdk.md` for:
115	- Complete dxpy class reference
116	- High-level utility functions
117	- API method documentation
118	- Error handling patterns
119	- Common code patterns
120
121	### 5. Configuration and Dependencies
122
123	Purpose: Configure app metadata and manage dependencies.
124
125	Key Operations:
126	- Write dxapp.json with inputs, outputs, and run specs
127	- Install system packages (execDepends)
128	- Bundle custom tools and resourc

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dnanexus-integration

31kstars3.1kforks

View on GitHub

How to install dnanexus-integration?

k-dense-ai/scientific-agent-skills/dnanexus-integration

$npx -y skills add K-Dense-AI/scientific-agent-skills --skill dnanexus-integration

Installs into the current project.

Summary

DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.

Files · 1

View on GitHub

SKILL.md

1	# DNAnexus Integration
2
3	## Overview
4
5	DNAnexus is a cloud platform for biomedical data analysis and genomics. Build and deploy apps/applets, manage data objects, run workflows, and use the dxpy Python SDK for genomics pipeline development and execution.
6
7	## When to Use This Skill
8
9	This skill should be used when:
10	- Creating, building, or modifying DNAnexus apps/applets
11	- Uploading, downloading, searching, or organizing files and records
12	- Running analyses, monitoring jobs, creating workflows
13	- Writing scripts using dxpy to interact with the platform
14	- Setting up dxapp.json, managing dependencies, using Docker
15	- Processing FASTQ, BAM, VCF, or other bioinformatics files
16	- Managing projects, permissions, or platform resources
17
18	## Core Capabilities
19
20	The skill is organized into five main areas, each with detailed reference documentation:
21
22	### 1. App Development
23
24	Purpose: Create executable programs (apps/applets) that run on the DNAnexus platform.
25
26	Key Operations:
27	- Generate app skeleton with `dx-app-wizard`
28	- Write Python or Bash apps with proper entry points
29	- Handle input/output data objects
30	- Deploy with `dx build` or `dx build --app`
31	- Test apps on the platform
32
33	Common Use Cases:
34	- Bioinformatics pipelines (alignment, variant calling)
35	- Data processing workflows
36	- Quality control and filtering
37	- Format conversion tools
38
39	Reference: See `references/app-development.md` for:
40	- Complete app structure and patterns
41	- Python entry point decorators
42	- Input/output handling with dxpy
43	- Development best practices
44	- Common issues and solutions
45
46	### 2. Data Operations
47
48	Purpose: Manage files, records, and other data objects on the platform.
49
50	Key Operations:
51	- Upload/download files with `dxpy.upload_local_file()` and `dxpy.download_dxfile()`
52	- Create and manage records with metadata
53	- Search for data objects by name, properties, or type
54	- Clone data between projects
55	- Manage project folders and permissions
56
57	Common Use Cases:
58	- Uploading sequencing data (FASTQ files)
59	- Organizing analysis results
60	- Searching for specific samples or experiments
61	- Backing up data across projects
62	- Managing reference genomes and annotations
63
64	Reference: See `references/data-operations.md` for:
65	- Complete file and record operations
66	- Data object lifecycle (open/closed states)
67	- Search and discovery patterns
68	- Project management
69	- Batch operations
70
71	### 3. Job Execution
72
73	Purpose: Run analyses, monitor execution, and orchestrate workflows.
74
75	Key Operations:
76	- Launch jobs with `applet.run()` or `app.run()`
77	- Monitor job status and logs
78	- Create subjobs for parallel processing
79	- Build and run multi-step workflows
80	- Chain jobs with output references
81
82	Common Use Cases:
83	- Running genomics analyses on sequencing data
84	- Parallel processing of multiple samples
85	- Multi-step analysis pipelines
86	- Monitoring long-running computations
87	- Debugging failed jobs
88
89	Reference: See `references/job-execution.md` for:
90	- Complete job lifecycle and states
91	- Workflow creation and orchestration
92	- Parallel execution patterns
93	- Job monitoring and debugging
94	- Resource management
95
96	### 4. Python SDK (dxpy)
97
98	Purpose: Programmatic access to DNAnexus platform through Python.
99
100	Key Operations:
101	- Work with data object handlers (DXFile, DXRecord, DXApplet, etc.)
102	- Use high-level functions for common tasks
103	- Make direct API calls for advanced operations
104	- Create links and references between objects
105	- Search and discover platform resources
106
107	Common Use Cases:
108	- Automation scripts for data management
109	- Custom analysis pipelines
110	- Batch processing workflows
111	- Integration with external tools
112	- Data migration and organization
113
114	Reference: See `references/python-sdk.md` for:
115	- Complete dxpy class reference
116	- High-level utility functions
117	- API method documentation
118	- Error handling patterns
119	- Common code patterns
120
121	### 5. Configuration and Dependencies
122
123	Purpose: Configure app metadata and manage dependencies.
124
125	Key Operations:
126	- Write dxapp.json with inputs, outputs, and run specs
127	- Install system packages (execDepends)
128	- Bundle custom tools and resourc

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