Evidence map›Paper›PMID 30867495›Full record

ArticleScientific reports2019

Featherweight long read alignment using partitioned reference indexes.

Hasindu Gamaarachchi, Sri Parameswaran, Martin A Smith

Abstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. LazyB: fast and cheap genome assembly.Algorithms for molecular biology : AMB · 2021
    Article
  9. A Round Trip to the Desert:Frontiers in microbiology · 2021
    Article
  10. Portable nanopore analytics: are we there yet?Bioinformatics (Oxford, England) · 2020
    Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Hasindu GamaarachchiKinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, 370 Victoria St, Darlinghurst, NSW, Australia.ORCID 0000-0002-9034-9905
Sri ParameswaranSchool of Computer Science and Engineering, UNSW Sydney, Kensington, NSW, Australia.
Martin A SmithKinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, 370 Victoria St, Darlinghurst, NSW, Australia. martinalexandersmith@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The advent of Nanopore sequencing has realised portable genomic research and applications. However, state of the art long read aligners and large reference genomes are not compatible with most mobile computing devices due to their high memory requirements. We show how memory requirements can be reduced through parameter optimisation and reference genome partitioning, but highlight the associated limitations and caveats of these approaches. We then demonstrate how these issues can be overcome through an appropriate merging technique. We incorporated multi-index merging into the Minimap2 aligner and demonstrate that long read alignment to the human genome can be performed on a system with 2 GB RAM with negligible impact on accuracy.

Indexed as

AlgorithmsComputer Storage DevicesGenome, HumanGenomicsHigh-Throughput Nucleotide SequencingHumansNanopore SequencingSequence Analysis, DNASoftware

Identifiers

PMID30867495
PMCPMC6416333

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.