ArticleBMC genomics2013
Accelerating read mapping with FastHASH.
Article in BMC genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed.
- Taming large-scale genomic analyses via sparsified genomics.Nature communications · 2025Article
- invMap: a sensitive mapping tool for long noisy reads with inversion structural variants.Bioinformatics (Oxford, England) · 2023Article
- A framework for high-throughput sequence alignment using real processing-in-memory systems.Bioinformatics (Oxford, England) · 2023Article
- Scrooge: a fast and memory-frugal genomic sequence aligner for CPUs, GPUs, and ASICs.Bioinformatics (Oxford, England) · 2023Article
- BLEND: a fast, memory-efficient and accurate mechanism to find fuzzy seed matches in genome analysis.NAR genomics and bioinformatics · 2023Article
- Pattern matching for high precision detection of LINE-1s in human genomes.BMC bioinformatics · 2022Article
- From molecules to genomic variations: Accelerating genome analysis via intelligent algorithms and architectures.Computational and structural biotechnology journal · 2022Review
- Technology dictates algorithms: recent developments in read alignment.Genome biology · 2021Review
- Accel-Align: a fast sequence mapper and aligner based on the seed-embed-extend method.BMC bioinformatics · 2021Article
- smsMap: mapping single molecule sequencing reads by locating the alignment starting positions.BMC bioinformatics · 2020Article
- Article
- Context-aware seeds for read mapping.Algorithms for molecular biology : AMB · 2020Article
- Shouji: a fast and efficient pre-alignment filter for sequence alignment.Bioinformatics (Oxford, England) · 2019Article
- Nanopore sequencing technology and tools for genome assembly: computational analysis of the current state, bottlenecks and future directions.Briefings in bioinformatics · 2019Review
- Elimination of Reference Mapping Bias Reveals Robust Immune Related Allele-Specific Expression in Crossbred Sheep.Frontiers in genetics · 2019Article
- lordFAST: sensitive and Fast Alignment Search Tool for LOng noisy Read sequencing Data.Bioinformatics (Oxford, England) · 2019Article
- GRIM-Filter: Fast seed location filtering in DNA read mapping using processing-in-memory technologies.BMC genomics · 2018Article
- Fast and efficient short read mapping based on a succinct hash index.BMC bioinformatics · 2018Article
- GateKeeper: a new hardware architecture for accelerating pre-alignment in DNA short read mapping.Bioinformatics (Oxford, England) · 2017Article
- Short Read Mapping: An Algorithmic Tour.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
With the introduction of next-generation sequencing (NGS) technologies, we are facing an exponential increase in the amount of genomic sequence data. The success of all medical and genetic applications of next-generation sequencing critically depends on the existence of computational techniques that can process and analyze the enormous amount of sequence data quickly and accurately. Unfortunately, the current read mapping algorithms have difficulties in coping with the massive amounts of data generated by NGS.We propose a new algorithm, FastHASH, which drastically improves the performance of the seed-and-extend type hash table based read mapping algorithms, while maintaining the high sensitivity and comprehensiveness of such methods. FastHASH is a generic algorithm compatible with all seed-and-extend class read mapping algorithms. It introduces two main techniques, namely Adjacency Filtering, and Cheap K-mer Selection.We implemented FastHASH and merged it into the codebase of the popular read mapping program, mrFAST. Depending on the edit distance cutoffs, we observed up to 19-fold speedup while still maintaining 100% sensitivity and high comprehensiveness.
Indexed as
Identifiers
What OpenQuestion holds
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.