Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM).

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:21775302", "kind": "science-record", "revision": 2, "content_hash": "sha256:286085cd05e23874f19c66a03402b144ed3bfd212392a5aa2aa9393965304cc7", "created_at": "2026-11-01T17:30:55Z", "updated_at": "2026-11-01T18:46:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM).", "doi": "10.1093/bioinformatics/btr427", "pmid": "21775302", "authors": ["Gregory R Grant", "Michael H Farkas", "Angel D Pizarro", "Nicholas F Lahens", "Jonathan Schug", "Brian P Brunk", "Christian J Stoeckert", "John B Hogenesch", "Eric A Pierce"], "journal": "Bioinformatics (Oxford, England)", "year": 2011, "abstract": "MOTIVATION: A critical task in high-throughput sequencing is aligning millions of short reads to a reference genome. Alignment is especially complicated for RNA sequencing (RNA-Seq) because of RNA splicing. A number of RNA-Seq algorithms are available, and claim to align reads with high accuracy and efficiency while detecting splice junctions. RNA-Seq data are discrete in nature; therefore, with reasonable gene models and comparative metrics RNA-Seq data can be simulated to sufficient accuracy to enable meaningful benchmarking of alignment algorithms. The exercise to rigorously compare all viable published RNA-Seq algorithms has not been performed previously.\nRESULTS: We developed an RNA-Seq simulator that models the main impediments to RNA alignment, including alternative splicing, insertions, deletions, substitutions, sequencing errors and intron signal. We used this simulator to measure the accuracy and robustness of available algorithms at the base and junction levels. Additionally, we used reverse transcription-polymerase chain reaction (RT-PCR) and Sanger sequencing to validate the ability of the algorithms to detect novel transcript features such as novel exons and alternative splicing in RNA-Seq data from mouse retina. A pipeline based on BLAT was developed to explore the performance of established tools for this problem, and to compare it to the recently developed methods. This pipeline, the RNA-Seq Unified Mapper (RUM), performs comparably to the best current aligners and provides an advantageous combination of accuracy, speed and usability.\nAVAILABILITY: The RUM pipeline is distributed via the Amazon Cloud and for computing clusters using the Sun Grid Engine (http://cbil.upenn.edu/RUM).\nCONTACT: ggrant@pcbi.upenn.edu; epierce@mail.med.upenn.edu\nSUPPLEMENTARY INFORMATION: The RNA-Seq sequence reads described in the article are deposited at GEO, accession GSE26248.", "claims": [{"id": "claim-data-access", "statement": "The RNA-Seq sequence reads described in the article are deposited at GEO, accession GSE26248.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:21775302", "locator": "sections[0] \"Abstract\" paragraph 5", "quote": "The RNA-Seq sequence reads described in the article are deposited at GEO, accession GSE26248."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:21775302"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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