ArticleBioinformatics (Oxford, England)2019
lordFAST: sensitive and Fast Alignment Search Tool for LOng noisy Read sequencing Data.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Current perspectives and challenges of using artificial intelligence in immunodeficiencies.The Journal of allergy and clinical immunology · 2025Review
- Comprehensive and deep evaluation of structural variation detection pipelines with third-generation sequencing data.Genome biology · 2024Article
- pathMap: a path-based mapping tool for long noisy reads with high sensitivity.Briefings in bioinformatics · 2024Article
- invMap: a sensitive mapping tool for long noisy reads with inversion structural variants.Bioinformatics (Oxford, England) · 2023Article
- A survey of mapping algorithms in the long-reads era.Genome biology · 2023Review
- Article
- Long-read mapping to repetitive reference sequences using Winnowmap2.Nature methods · 2022Article
- kngMap: Sensitive and Fast Mapping Algorithm for Noisy Long Reads Based on theFrontiers in genetics · 2022Article
- Nanopore sequencing technology, bioinformatics and applications.Nature biotechnology · 2021Review
- Genomic and transcriptomic analyses reveal a tandem amplification unit of 11 genes and mutations in mismatch repair genes in methotrexate-resistant HT-29 cells.Experimental & molecular medicine · 2021Article
- Technology dictates algorithms: recent developments in read alignment.Genome biology · 2021Review
- HASLR: Fast Hybrid Assembly of Long Reads.iScience · 2020Article
- smsMap: mapping single molecule sequencing reads by locating the alignment starting positions.BMC bioinformatics · 2020Article
- Mobile genomics: tools and techniques for tackling transposons.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2020Review
- Context-aware seeds for read mapping.Algorithms for molecular biology : AMB · 2020Article
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Authors and funding
3 authors.
Funding
Abstract
Motivation: Recent advances in genomics and precision medicine have been made possible through the application of high throughput sequencing (HTS) to large collections of human genomes. Although HTS technologies have proven their use in cataloging human genome variation, computational analysis of the data they generate is still far from being perfect. The main limitation of Illumina and other popular sequencing technologies is their short read length relative to the lengths of (common) genomic repeats. Newer (single molecule sequencing - SMS) technologies such as Pacific Biosciences and Oxford Nanopore are producing longer reads, making it theoretically possible to overcome the difficulties imposed by repeat regions. Unfortunately, because of their high sequencing error rate, reads generated by these technologies are very difficult to work with and cannot be used in many of the standard downstream analysis pipelines. Note that it is not only difficult to find the correct mapping locations of such reads in a reference genome, but also to establish their correct alignment so as to differentiate sequencing errors from real genomic variants. Furthermore, especially since newer SMS instruments provide higher throughput, mapping and alignment need to be performed much faster than before, maintaining high sensitivity. Results: We introduce lordFAST, a novel long-read mapper that is specifically designed to align reads generated by PacBio and potentially other SMS technologies to a reference. lordFAST not only has higher sensitivity than the available alternatives, it is also among the fastest and has a very low memory footprint. Availability and implementation: lordFAST is implemented in C++ and supports multi-threading. The source code of lordFAST is available at https://github.com/vpc-ccg/lordfast. 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.