The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseases-Reference-Cited by-同舟云学术

The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseases

Published:2020-12 Issue:12 Volume:16 Page:
ISSN:1744-4292
Container-title:Molecular Systems Biology
language:en
Short-container-title:Mol Syst Biol

Author:

Auslander Noam¹²^ORCID,Ramos Daniel M³,Zelaya Ivette⁴,Karathia Hiren⁵^ORCID,Crawford Thomas O.⁶⁷,Schäffer Alejandro A¹^ORCID,Sumner Charlotte J³⁷^ORCID,Ruppin Eytan¹^ORCID

Affiliation:

1. Cancer Data Science Laboratory (CDSL) National Cancer InstituteNational Institutes of Health Bethesda MD USA

2. National Center for Biotechnology Information National Library of MedicineNational Institutes of Health Bethesda MD USA

3. Department of Neuroscience Johns Hopkins University School of Medicine Baltimore MD USA

4. Interdepartmental Program in Bioinformatics University of California Los Angeles Los Angeles CA USA

5. Laboratory of Receptor Biology and Gene Expression National Cancer InstituteNational Institutes of Health MD USA

6. Department of Pediatrics Johns Hopkins University School of Medicine Baltimore MD USA

7. Department of Neurology Johns Hopkins University School of Medicine Baltimore MD USA

Funder

National Institute of Neurological Disorders and Stroke

Publisher

EMBO

Subject

Applied Mathematics,Computational Theory and Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Information Systems

Link

https://onlinelibrary.wiley.com/doi/pdf/10.15252/msb.20209701

Reference86 articles.

1. Genetic effects on gene expression across human tissues

2. HTSeq--a Python framework to work with high-throughput sequencing data

3. Mendelian disorders deserve more attention

4. Interaction between a novel TGFB1 haplotype and CFTR genotype is associated with improved lung function in cystic fibrosis

5. Genetic mapping of chronic childhood-onset spinal muscular atrophy to chromosome 5q1 1.2–13.3

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Single-cell RNA-Sequencing Co-Expression Analysis with CFTR in Lung Tissue;2024-05-17

2. Spinal muscular atrophy;Nature Reviews Disease Primers;2022-08-04

3. A high-throughput genome-wide RNAi screen identifies modifiers of survival motor neuron protein;Cell Reports;2021-05