Výsledky vyhledávání - "Regulatory and Functional Genomics"

ChromDL: A Next-Generation Regulatory DNA Classifier

Autor: Christopher Hill, Sanjarbek Hudaiberdiev, Ivan Ovcharenko

Publikováno v: bioRxiv
Bioinformatics

MotivationPredicting the regulatory function of non-coding DNA using only the DNA sequence continues to be a major challenge in genomics. With the advent of improved optimization algorithms, faster GPU speeds, and more intricate machine learning libr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8fbb34963b84be0f291fbfb018ced33c
https://europepmc.org/articles/PMC9928050/

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stPlus: a reference-based method for the accurate enhancement of spatial transcriptomics

Autor: Xiaoyang Chen, Xuegong Zhang, Boheng Zhang, Rui Jiang, Shengquan Chen

Publikováno v: Bioinformatics

MotivationSingle-cell RNA sequencing (scRNA-seq) techniques have revolutionized the investigation of tran-scriptomic landscape in individual cells. Recent advancements in spatial transcriptomic technologies further enable gene expression profiling an

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::372f4bbbaff5cf753089bd39dabc4ecc
http://europepmc.org/articles/PMC8336594

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DECODE: a Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays

Autor: Dong-Hoon Lee, Jing Zhang, Min Xu, Yi Dai, Martin Renqiang Min, Jason Liu, Mark Gerstein, Zhanlin Chen

Publikováno v: Bioinformatics

Motivation Mapping distal regulatory elements, such as enhancers, is a cornerstone for elucidating how genetic variations may influence diseases. Previous enhancer-prediction methods have used either unsupervised approaches or supervised methods with

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b416287b5a158253d5675d224d5bd2da
https://doi.org/10.1093/bioinformatics/btab283

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Resolving diverse protein-DNA footprints from exonuclease-based ChIP experiments

Autor: Leelavati Narlikar, Anushua Biswas

Publikováno v: Bioinformatics

Motivation High-throughput chromatin immunoprecipitation (ChIP) sequencing-based assays capture genomic regions associated with the profiled transcription factor (TF). ChIP-exo is a modified protocol, which uses lambda exonuclease to digest DNA close

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5664fcbff5afbe43510cb09368faeff5
https://pubmed.ncbi.nlm.nih.gov/34252930

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scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling

Autor: Zachary Hemminger, Jingyi Jessica Li, Dongyuan Song, Kexin Li, Roy Wollman

Publikováno v: Bioinformatics (Oxford, England), vol 37, iss Suppl_1
Bioinformatics

Motivation Single-cell RNA sequencing (scRNA-seq) captures whole transcriptome information of individual cells. While scRNA-seq measures thousands of genes, researchers are often interested in only dozens to hundreds of genes for a closer study. Then

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a90d0a4ca3cabce75c967c633a2acd2f
https://escholarship.org/uc/item/30b354nt

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Practical selection of representative sets of RNA-seq samples using a hierarchical approach

Autor: Carl Kingsford, Laura H. Tung

Publikováno v: Bioinformatics

Motivation Despite numerous RNA-seq samples available at large databases, most RNA-seq analysis tools are evaluated on a limited number of RNA-seq samples. This drives a need for methods to select a representative subset from all available RNA-seq sa

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::405e20a5cee14e8dde74c1ed408f3fcd
https://pubmed.ncbi.nlm.nih.gov/34252927

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Predicting mechanism of action of novel compounds using compound structure and transcriptomic signature coembedding

Autor: Sejeong Park, Jaewoo Kang, Sang Hoon Lee, Sungjoon Park, Sunkyu Kim, Gwanghoon Jang

Publikováno v: Bioinformatics

Motivation Identifying mechanism of actions (MoA) of novel compounds is crucial in drug discovery. Careful understanding of MoA can avoid potential side effects of drug candidates. Efforts have been made to identify MoA using the transcriptomic signa

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d5921fdeb25ea829672daf48e3373bc
https://pubmed.ncbi.nlm.nih.gov/34252937

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CROTON: an automated and variant-aware deep learning framework for predicting CRISPR/Cas9 editing outcomes

Autor: Victoria R Li, Zijun Zhang, Olga G. Troyanskaya

Publikováno v: Bioinformatics

Motivation CRISPR/Cas9 is a revolutionary gene-editing technology that has been widely utilized in biology, biotechnology and medicine. CRISPR/Cas9 editing outcomes depend on local DNA sequences at the target site and are thus predictable. However, e

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cbad66cd5bdcf9a4084f17a1bf87ff3a
https://pubmed.ncbi.nlm.nih.gov/34252931

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Thermodynamic modeling reveals widespread multivalent binding by RNA-binding proteins

Autor: Salma Sohrabi-Jahromi, Johannes Söding

Publikováno v: Bioinformatics

Motivation Understanding how proteins recognize their RNA targets is essential to elucidate regulatory processes in the cell. Many RNA-binding proteins (RBPs) form complexes or have multiple domains that allow them to bind to RNA in a multivalent, co

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::109707a15cfd99c642a6fd65bacbb95d
https://doi.org/10.1101/2021.01.30.428941

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