{"id":38676433,"url":"https://github.com/wglab/liqa2","last_synced_at":"2026-01-17T10:01:10.216Z","repository":{"id":286903552,"uuid":"783803990","full_name":"WGLab/LIQA2","owner":"WGLab","description":null,"archived":false,"fork":false,"pushed_at":"2025-04-08T22:54:53.000Z","size":105,"stargazers_count":0,"open_issues_count":3,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-08T23:28:35.294Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/WGLab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-04-08T15:49:18.000Z","updated_at":"2025-04-08T22:54:56.000Z","dependencies_parsed_at":"2025-04-08T23:39:05.526Z","dependency_job_id":null,"html_url":"https://github.com/WGLab/LIQA2","commit_stats":null,"previous_names":["wglab/liqa2"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/WGLab/LIQA2","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WGLab%2FLIQA2","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WGLab%2FLIQA2/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WGLab%2FLIQA2/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WGLab%2FLIQA2/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/WGLab","download_url":"https://codeload.github.com/WGLab/LIQA2/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WGLab%2FLIQA2/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28505570,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-17T06:57:29.758Z","status":"ssl_error","status_checked_at":"2026-01-17T06:56:03.931Z","response_time":85,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2026-01-17T10:00:54.237Z","updated_at":"2026-01-17T10:01:10.174Z","avatar_url":"https://github.com/WGLab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# LIQA2\nLIQA2 is a computational tool for detecting and quantifying isoform expression (including novel isoforms) from long read sequencing. The tool uses a meta-regression model to quantify differential alternative splicing and incorporates a targeted approach to correct for 3' and 5' biases.\n\n## Installation\n```\ngit clone https://github.com/WGLab/LIQA2.git\ncd LIQA2\n```\n\n## Pipeline\nThe main function for running the LIQA2 pipeline is `main_preprocessing.py`. There are 4 steps, marked by the --task [step] command.\n\n  \n### 1. Prepare annotation file\nThe first step of the LIQA2 pipeline involves generaing annotation files from the reference genome and indexed/tagged BAM files. This is done with `--task annotation`. \n\nArguments include:\n- `--target`: path to output location(s)\n- `--bam`: path to bam location(s)\n- annotation mode (see below)\n- `--reference`: path to GTF annotation file\n- `reference_pkl`: path to picke gene annotation file generated by LIQA2 (geneStructureInformation.pkl)\n- `--workers`: threads for parallel processing\n- `--coverage_threshod_exon`: coverage threshold for exon discovery as a percent of maximum coverage. Default is 0.02, larger values are more conservative\n\nBelow is a sample command that can be run.\n\n```\npython3 src/main_preprocessing.py \\\n--task annotation \\\n--target path/to/output/folder/of/sample1 path/to/output/folder/of/sample2 \\\n--bam path/to/bam/file/or/bamfolder/sample1 path/to/bam/file/or/bamfolder/sample2 \\\n--update_gtf \\\n--reference path/to/reference/genes.gtf \\\n--reference_pkl data/geneStructureInformation.pkl \\\n--workers 30\n```\nThere are 3 different modes of annotation.\n1. Annotation-Only: \n2. Enhanced-Annotation:\n3. Annotation-Free: \n\n### 2. Generate compatible matrix\nThis step creates a compatible matrix that aligns reads to existing gene isoforms and identifies novel isoforms based on previously generated annotation files.\nArguments include:\n- `--target`: path to output location(s)\n- `--bam`: path to bam location(s)\n- `--reference`: path to GTF annotation file\n\nBelow is a sample command that can be run.\n```\npython3 src/main_preprocessing.py \\\n--task 'compatible matrix' \\\n--target path/to/output/folder/of/sample1 path/to/output/folder/of/sample2 \\\n--bam path/to/bam/file/or/bamfolder/sample1 path/to/bam/file/or/bamfolder/sample2 \\\n--reference path/to/reference/genes.gtf\n```\n### 3. Summarize novel gene annotations\nAfter annotation and compatible matrix generation, the LIQA2 pipeline generates a new summary annotation file that also includes novel isoforms:\n```\npython3 src/main_preprocessing.py \\\n--task 'summary' \\\n--target path/to/output/folder/\n```\n### 4. Generate count matrix\nThe final step generates a count matrix.\nArguments include:\n- `--target`: path to output location(s)\n- `--bam`: path to bam location(s)\n- `--reference`: path to GTF annotation file\n- `--workers`: threads for parallel processing\n-- `save_csv/--save_mtx`: set up output format\n--`group_novel`: whether group some novel isoforms that are potentially generated by read truncations together as one novel isoform.\n--`platform`: 10x-ont or 10x-pacbio\n\nBelow is an example:\n```\npython3 src/main_preprocessing.py \\\n--task 'count matrix' \\\n--target path/to/output/folder/ \\\n--platform 10x-ont\n--workers 8 --group_novel\n```\nWith these steps, you should be able to generate counts that you can use for expression analysis or further study.\n\n## Citation\nGouru, A., Xu, Z., Wang, K. “LIQA2: Efficient Isoform Detection and Quantification from Long Read Sequencing,” October 2024. International Conference on Intelligent Biology and Medicine, Houston, TX.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwglab%2Fliqa2","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwglab%2Fliqa2","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwglab%2Fliqa2/lists"}