In one paragraphArticle in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from itWhat 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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
44 authors.
Yu PanDepartment of Human Genetics, University of California, Los Angeles, USA.
Mohammed Faizal Eeman MootorDepartment of Human Genetics, University of California, Los Angeles, USA.ORCID 0000-0003-4154-455X Andrew ParkDepartment of Human Genetics, University of California, Los Angeles, USA.
Aakarsh AnandDepartment of Human Genetics, University of California, Los Angeles, USA.
Edward HwangDepartment of Human Genetics, University of California, Los Angeles, USA.
Anna Neiman-GoldenDepartment of Human Genetics, University of California, Los Angeles, USA.
Philippa SteinbergDepartment of Human Genetics, University of California, Los Angeles, USA.
Prateek AnandDepartment of Human Genetics, University of California, Los Angeles, USA.
Funding
Women's CancersP30CA016042 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Robert Damoiseaux · 1985 to 2026
$134.5MUCLA-Caltech Medical Scientist Training ProgramT32GM008042 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI AJIJOLA, OLUJIMI A, DAWSON, DAVID WAYNE · 1985 to 2023
$29.9MTool Core- BoutrosU54HG012517 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BUI, ALEX, PING, PEIPEI · 2022 to 2025
$10.6MTUMOR CELL SURFACES AND CHROMOSOMEST32CA009056 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Gay M Crooks, David A. Nathanson · 1985 to 2026
$10.5MTraining Grant in Genomic Analysis and InterpretationT32HG002536 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Valerie A Arboleda, Harold Pimentel · 2002 to 2026
$8.6MUCLA-Caltech Medical Scientist Training ProgramT32GM152342 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Olujimi A Ajijola, David Wayne Dawson · 2024 to 2026
$5.3MAdvancing method benchmarking and data sharing through crowd-sourced competitions in cancer researchU24CA248265 · NCI · SAGE BIONETWORKS · PI BOUTROS, PAUL CHRISTOPHER, VARMA, SUSHEEL · 2020 to 2024
$4.1MRandomized Trial of Exercise Therapy on Markers of Progression in Localized Prostate Cancer:R01CA272678 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI Paul Christopher Boutros, LEE W JONES · 2022 to 2026
$3.6MThe Evolution of Sarcoma Drug Sensitivity through Time and SpaceR01CA244729 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BOUTROS, PAUL CHRISTOPHER, SORAGNI, ALICE · 2020 to 2024
$3.4MVirginia-UCLA-Toronto Biomarker Characterization CenterU2CCA271894 · NCI · OLD DOMINION UNIVERSITY · PI Paul Christopher Boutros, OLIVER John SEMMES · 2022 to 2026
$3.2MGermline Determinants of Prostate Cancer EvolutionR01CA270108 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PAUL T. SPELLMAN · 2023 to 2026
$2.9MStatistical methods for genomic analysis of heterogeneous tumorsR01CA268380 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Wenyi Wang · 2022 to 2026
$2.4MNCI NIH HHS F31 CA281168NCI NIH HHS P30 CA016042NCI NIH HHS R01 CA244729NCI NIH HHS R01 CA268380NCI NIH HHS R01 CA270108NCI NIH HHS R01 CA272678NCI NIH HHS T32 CA009056NCI NIH HHS U24 CA248265NCI NIH HHS U2C CA271894NHGRI NIH HHS T32 HG002536NHGRI NIH HHS U54 HG012517NIGMS NIH HHS T32 GM008042NIGMS NIH HHS T32 GM152342NLM NIH HHS T15 LM013976
6 · The paper itselfAbstract
The price, quality and throughout of DNA sequencing continue to improve. Algorithmic innovations have allowed inference of a growing range of features from DNA sequencing data, quantifying nuclear, mitochondrial and evolutionary aspects of both germline and somatic genomes. To automate analyses of the full range of genomic characteristics, we created an extensible Nextflow meta-pipeline called metapipeline-DNA. Metapipeline-DNA analyzes targeted and whole-genome sequencing data from raw reads through pre-processing, feature detection by multiple algorithms, quality-control and data-visualization. Each step can be run independently and is supported robust software engineering including automated failure-recovery, robust testing and consistent verifications of inputs, outputs and parameters. Metapipeline-DNA is cloud-compatible and highly configurable, with options to subset and optimize each analysis. Metapipeline-DNA facilitates high-scale, comprehensive analysis of DNA sequencing data.
Identifiers
PMID39282325
PMCPMC11398472
What OpenQuestion holds
Textmetadata
LicenceCC BY-NC
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