Evidence map›Paper›PMID 34140043›Full record

ArticleGenome biology2021

LIQA: long-read isoform quantification and analysis.

Yu Hu, Li Fang, Xuelian Chen, Jiang F Zhong, Mingyao Li, Kai Wang

Abstract read
In one paragraph

Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.

0numbers the graph read from it
0cells of the map it votes in
55citing 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

55 citing papers in PubMed.

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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

6 authors.

Yu HuRaymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
Li FangRaymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
Xuelian ChenDepartment of Otolaryngology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
Jiang F ZhongDepartment of Otolaryngology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
Mingyao LiDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Kai WangRaymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA. wangk@email.chop.edu.ORCID 0000-0002-5585-982X

Funding

Detection and annotation of structural variants from long-read sequencingR01GM132713 · NIGMS · CHILDREN'S HOSP OF PHILADELPHIA · PI WANG, KAI · 2019 to 2022
$1.9M
NIGMS NIH HHS R01 GM132713
6 · The paper itself

Abstract

Long-read RNA sequencing (RNA-seq) technologies can sequence full-length transcripts, facilitating the exploration of isoform-specific gene expression over short-read RNA-seq. We present LIQA to quantify isoform expression and detect differential alternative splicing (DAS) events using long-read direct mRNA sequencing or cDNA sequencing data. LIQA incorporates base pair quality score and isoform-specific read length information in a survival model to assign different weights across reads, and uses an expectation-maximization algorithm for parameter estimation. We apply LIQA to long-read RNA-seq data from the Universal Human Reference, acute myeloid leukemia, and esophageal squamous epithelial cells and demonstrate its high accuracy in profiling alternative splicing events.

Indexed as

Alternative SplicingGenome, HumanSoftwareTranscriptomeAlgorithmsCell Line, TumorDatasets as TopicDNA, ComplementaryGene Expression ProfilingHigh-Throughput Nucleotide SequencingHumansNeoplasmsRNA, MessengerDNA, ComplementaryRNA, Messenger

Identifiers

PMID34140043
PMCPMC8212471

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.