Evidence map›Paper›PMID 29795706›Full record

ArticleThe computer journal2018

Relative Suffix Trees.

Andrea Farruggia, Travis Gagie, Gonzalo Navarro, Simon J Puglisi, Jouni Sirén

Abstract read
In one paragraph

Article in The computer journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Andrea FarruggiaDepartment of Computer Science, University of Pisa, Largo Bruno Pontecorvo 3, 56127 Pisa PI, Italy.
Travis GagieCeBiB-Center for Biotechnology and Bioengineering, Santiago, Chile.
Gonzalo NavarroCeBiB-Center for Biotechnology and Bioengineering, Santiago, Chile.
Simon J PuglisiDepartment of Computer Science, University of Helsinki, Helsinki, Finland.
Jouni SirénWellcome Trust Sanger Institute, Hinxton CB10 1SA, UK.

Funding

Wellcome Trust 098051
6 · The paper itself

Abstract

Suffix trees are one of the most versatile data structures in stringology, with many applications in bioinformatics. Their main drawback is their size, which can be tens of times larger than the input sequence. Much effort has been put into reducing the space usage, leading ultimately to compressed suffix trees. These compressed data structures can efficiently simulate the suffix tree, while using space proportional to a compressed representation of the sequence. In this work, we take a new approach to compressed suffix trees for repetitive sequence collections, such as collections of individual genomes. We compress the suffix trees of individual sequences relative to the suffix tree of a reference sequence. These relative data structures provide competitive time/space trade-offs, being almost as small as the smallest compressed suffix trees for repetitive collections, and competitive in time with the largest and fastest compressed suffix trees.

Indexed as

compressed text indexingrepetitive collectionssuffix trees

Identifiers

PMID29795706
PMCPMC5956352

What OpenQuestion holds

Textmetadata
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.