Evidence map›Paper›PMID 30911095›Full record

ArticleScientific reports2019

Efficient Genomic Interval Queries Using Augmented Range Trees.

Chengsheng Mao, Alal Eran, Yuan Luo

Open access · goldAbstract 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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 24% of its field
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

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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

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Temporal Cohort Logic.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022
    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 at 2 institutions in 2 countries.

Chengsheng MaoDepartment of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Alal EranDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Yuan LuoDepartment of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA. yuan.luo@northwestern.edu.
Northwestern University · USHarvard University · US

Funding

Bayesian Generative Methods for Extracting and Modeling Relations in EHR NarrativesR21LM012618 · NLM · NORTHWESTERN UNIVERSITY AT CHICAGO · PI LUO, YUAN · 2017 to 2018
$390k
NLM NIH HHS R21 LM012618
6 · The paper itself

Abstract

Efficient large-scale annotation of genomic intervals is essential for personal genome interpretation in the realm of precision medicine. There are 13 possible relations between two intervals according to Allen's interval algebra. Conventional interval trees are routinely used to identify the genomic intervals satisfying a coarse relation with a query interval, but cannot support efficient query for more refined relations such as all Allen's relations. We design and implement a novel approach to address this unmet need. Through rewriting Allen's interval relations, we transform an interval query to a range query, then adapt and utilize the range trees for querying. We implement two types of range trees: a basic 2-dimensional range tree (2D-RT) and an augmented range tree with fractional cascading (RTFC) and compare them with the conventional interval tree (IT). Theoretical analysis shows that RTFC can achieve the best time complexity for interval queries regarding all Allen's relations among the three trees. We also perform comparative experiments on the efficiency of RTFC, 2D-RT and IT in querying noncoding element annotations in a large collection of personal genomes. Our experimental results show that 2D-RT is more efficient than IT for interval queries regarding most of Allen's relations, RTFC is even more efficient than 2D-RT. The results demonstrate that RTFC is an efficient data structure for querying large-scale datasets regarding Allen's relations between genomic intervals, such as those required by interpreting genome-wide variation in large populations.

Indexed as

AlgorithmsSoftwareComputational BiologyGenomicsHumans

Identifiers

PMID30911095
PMCPMC6434014
OpenAlexW2963885058

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.