Article in Nature methods, 2026. 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.
Robert WangCenter for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA. wangr5@chop.edu.ORCID http://orcid.org/0000-0003-2614-5956
Elizabeth M McCormickMitochondrial Medicine Frontier Program, Division of Human Genetics, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Joseph Jee-Hwan ParkCenter for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.ORCID http://orcid.org/0009-0004-4298-595X
Matthew T SullenbergerCenter for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Marni J FalkMitochondrial Medicine Frontier Program, Division of Human Genetics, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0002-1723-6728
Lan LinDepartment of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0002-9905-8928
Yi XingCenter for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA. xingyi@chop.edu.ORCID http://orcid.org/0000-0001-9257-7613
Funding
Pilot and Feasibility CoreU54NS115198 · NINDS · MAYO CLINIC ROCHESTER · PI MORAVA-KOZICZ, EVA · 2019 to 2023
$8.2M
Mitochondrial respiratory chain disease mechanistic and therapeutic modelingR35GM134863 · NIGMS · CHILDREN'S HOSP OF PHILADELPHIA · PI MARNI J FALK · 2020 to 2026
$4.3M
Targeting alternative isoform variation for TCR discovery in platinum-resistant ovarian cancerR01CA287673 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Sanaz Memarzadeh, Yi Xing · 2024 to 2026
$2.1M
Comprehensive identification and functional study of Esrp-regulated isoforms during epithelial-mesenchymal transition.R01HD114705 · NICHD · CHILDREN'S HOSP OF PHILADELPHIA · PI Eric Chien-Wei Liao, Yi Xing · 2025 to 2026
$1.5M
Long-read strategies for elucidating transcriptome complexity and advancing genomic medicineR35GM158057 · NIGMS · CHILDREN'S HOSP OF PHILADELPHIA · PI Lan Lin · 2025 to 2026
$890k
Computational tools and resources to study alternative splicing and mRNA isoform variationR56HG012310 · NHGRI · CHILDREN'S HOSP OF PHILADELPHIA · PI XING, YI · 2022 to 2022
$569k
NCI NIH HHS R01 CA287673NHGRI NIH HHS R56 HG012310NICHD NIH HHS R01 HD114705NIGMS NIH HHS R35 GM134863NIGMS NIH HHS R35 GM158057NINDS NIH HHS U54 NS115198U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01CA287673U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01HD114705U.S. Department of Health & Human Services | National Institutes of Health (NIH) R35GM134863U.S. Department of Health & Human Services | National Institutes of Health (NIH) R35GM158057U.S. Department of Health & Human Services | National Institutes of Health (NIH) R56HG012310U.S. Department of Health & Human Services | National Institutes of Health (NIH) U54NS115198
6 · The paper itself
Abstract
Accurate variant detection using nanopore long-read transcriptome data remains challenging. Here we present NanoTS-a deep learning-based tool for single nucleotide polymorphism detection from diverse types of nanopore transcriptome sequencing data. NanoTS outperforms existing methods, achieving F
Indexed as
Deep LearningGene Expression ProfilingNanoporesNanopore SequencingPolymorphism, Single NucleotideSoftwareTranscriptomeHumans
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 Socratic graph.
NanoTS: a deep learning tool for accurate SNP calling in nanopore long-read transcriptome data. · full record | Socratic