ArticleBioinformatics (Oxford, England)2016
RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 1 of them a synthesis that pooled it.
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Who cites it
59 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Single-cell RNA sequencing offers novel perspectives in viral infection research.Frontiers in cellular and infection microbiology · 2026Pooled it
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- Key Concepts in Transcriptomics Data Analysis in the Era of Next-Gen Sequencing.Advances in experimental medicine and biology · 2026Review
- Unveiling Novel Viral Diversity, Biogeography, and Host Networks in Wildlife Through High-Throughput Sequencing Data Mining.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
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- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- DNA-protein quasi-mapping for rapid differential gene expression analysis in non-model organisms.BMC bioinformatics · 2024Article
- Claustrum and dorsal endopiriform cortex complex cell-identity is determined by Nurr1 and regulates hallucinogenic-like states in mice.Nature communications · 2024Article
- De novo assembly of transcriptomes and differential gene expression analysis using short-read data from emerging model organisms - a brief guide.Frontiers in zoology · 2024Review
- A pairwise cytokine code explains the organism-wide response to sepsis.Nature immunology · 2024Article
- Cellular Heterogeneity of Activated Primary Human Macrophages and Associated Drug-Gene Networks: From Biology to Precision Therapeutics.Circulation · 2023Article
- An integrative pipeline for circular RNA quantitative trait locus discovery with application in human T cells.Bioinformatics (Oxford, England) · 2023Article
- Counting pseudoalignments to novel splicing events.Bioinformatics (Oxford, England) · 2023Article
- Computational approaches for circRNAs prediction and in silico characterization.Briefings in bioinformatics · 2023Review
- Computational Approaches and Challenges in Spatial Transcriptomics.Genomics, proteomics & bioinformatics · 2023Review
- Toblerone: detecting exon deletion events in cancer using RNA-seq.F1000Research · 2023Article
- Kidney transcriptome and cystic kidney disease genes in zebrafish.Frontiers in physiology · 2023Article
- KMCP: accurate metagenomic profiling of both prokaryotic and viral populations by pseudo-mapping.Bioinformatics (Oxford, England) · 2023Article
- RNA-seq data science: From raw data to effective interpretation.Frontiers in genetics · 2023Review
- Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods.Computational and structural biotechnology journal · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationThe alignment of sequencing reads to a transcriptome is a common and important step in many RNA-seq analysis tasks. When aligning RNA-seq reads directly to a transcriptome (as is common in the de novo setting or when a trusted reference annotation is available), care must be taken to report the potentially large number of multi-mapping locations per read. This can pose a substantial computational burden for existing aligners, and can considerably slow downstream analysis.
resultsWe introduce a novel concept, quasi-mapping, and an efficient algorithm implementing this approach for mapping sequencing reads to a transcriptome. By attempting only to report the potential loci of origin of a sequencing read, and not the base-to-base alignment by which it derives from the reference, RapMap-our tool implementing quasi-mapping-is capable of mapping sequencing reads to a target transcriptome substantially faster than existing alignment tools. The algorithm we use to implement quasi-mapping uses several efficient data structures and takes advantage of the special structure of shared sequence prevalent in transcriptomes to rapidly provide highly-accurate mapping information. We demonstrate how quasi-mapping can be successfully applied to the problems of transcript-level quantification from RNA-seq reads and the clustering of contigs from de novo assembled transcriptomes into biologically meaningful groups. AVAILABILITY AND IMPLEMENTATION: RapMap is implemented in C ++11 and is available as open-source software, under GPL v3, at https://github.com/COMBINE-lab/RapMap CONTACT: rob.patro@cs.stonybrook.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.