scholarly journals Opportunity to improve livestock traits using 3D genomics

2021 ◽  
Author(s):  
C. MacPhillamy ◽  
W. S. Pitchford ◽  
H. Alinejad‐Rokny ◽  
W. Y. Low
Keyword(s):  
3D Genomics

Storage, visualization, and navigation of 3D genomics data

Methods ◽  
2018 ◽  
Vol 142 ◽  
pp. 74-80 ◽  
Author(s):  
Jérôme Waldispühl ◽  
Eric Zhang ◽  
Alexander Butyaev ◽  
Elena Nazarova ◽  
Yan Cyr
Keyword(s):  
3D Genomics

scholarly journals The proteome: structure, function and evolution

2006 ◽  
Vol 361 (1467) ◽  
pp. 441-451 ◽  
Author(s):  
Keiran Fleming ◽  
Lawrence A Kelley ◽  
Suhail A Islam ◽  
Robert M MacCallum ◽  
Arne Muller ◽  
...  
Keyword(s):  
Protein Function ◽  
Sequence Similarity ◽  
Structural Annotation ◽  
New Approach ◽  
Link Type ◽  
University College London ◽  
Automated Pipeline ◽  
3D Genomics ◽  
And Function ◽  
Structural Homologue

This paper reports two studies to model the inter-relationships between protein sequence, structure and function. First, an automated pipeline to provide a structural annotation of proteomes in the major genomes is described. The results are stored in a database at Imperial College, London (3D-GENOMICS) that can be accessed at www.sbg.bio.ic.ac.uk . Analysis of the assignments to structural superfamilies provides evolutionary insights. 3D-GENOMICS is being integrated with related proteome annotation data at University College London and the European Bioinformatics Institute in a project known as e-protein ( http://www.e-protein.org/ ). The second topic is motivated by the developments in structural genomics projects in which the structure of a protein is determined prior to knowledge of its function. We have developed a new approach PHUNCTIONER that uses the gene ontology (GO) classification to supervise the extraction of the sequence signal responsible for protein function from a structure-based sequence alignment. Using GO we can obtain profiles for a range of specificities described in the ontology. In the region of low sequence similarity (around 15%), our method is more accurate than assignment from the closest structural homologue. The method is also able to identify the specific residues associated with the function of the protein family.

A Brief Review of Current 3D Genomics Research

2021 ◽  
pp. 75-84
Author(s):  
Le Zhang ◽  
Jingtian Zhao
Keyword(s):  
Genomics Research ◽  
3D Genomics

Advances in technologies for 3D genomics research

2020 ◽  
Vol 63 (6) ◽  
pp. 811-824 ◽  
Author(s):  
Yan Zhang ◽  
Guoliang Li
Keyword(s):  
Genomics Research ◽  
3D Genomics

scholarly journals Exonuclease combinations reduce noises in 3D genomics technologies

2020 ◽  
Vol 48 (8) ◽  
pp. e44-e44
Author(s):  
Siyuan Kong ◽  
Qing Li ◽  
Gaolin Zhang ◽  
Qiujia Li ◽  
Qitong Huang ◽  
...  
Keyword(s):  
Noise Source ◽  
Chromosome Conformation Capture ◽  
Dna Fragments ◽  
Chromosome Conformation ◽  
High Noise ◽  
Dna Preparation ◽  
Linear Dna ◽  
Proximity Ligation ◽  
Interaction Detection ◽  
3D Genomics

Abstract Chromosome conformation-capture technologies are widely used in 3D genomics; however, experimentally, such methods have high-noise limitations and, therefore, require significant bioinformatics efforts to extract reliable distal interactions. Miscellaneous undesired linear DNAs, present during proximity-ligation, represent a main noise source, which needs to be minimized or eliminated. In this study, different exonuclease combinations were tested to remove linear DNA fragments from a circularized DNA preparation. This method efficiently removed linear DNAs, raised the proportion of annulation and increased the valid-pairs ratio from ∼40% to ∼80% for enhanced interaction detection in standard Hi-C. This strategy is applicable for development of various 3D genomics technologies, or optimization of Hi-C sequencing efficiency.

scholarly journals 3D-GENOMICS: a database to compare structural and functional annotations of proteins between sequenced genomes

2004 ◽  
Vol 32 (90001) ◽  
pp. 245D-250 ◽  
Author(s):  
K. Fleming
Keyword(s):  
Functional Annotations ◽  
3D Genomics

scholarly journals Visualization of Biomedical Data

2018 ◽  
Author(s):  
Seán I O'Donoghue ◽  
Benedetta F Baldi ◽  
Susan J Clark ◽  
Aaron E Darling ◽  
James M Hogan ◽  
...  
Keyword(s):  
Interdisciplinary Collaboration ◽  
Automated Analysis ◽  
Biomedical Data ◽  
Clinical Practices ◽  
High Data ◽  
Research Areas ◽  
Computer Scientists ◽  
Difficult Challenge ◽  
3D Genomics ◽  
Complex Phenomena

The rapid increase in volume and complexity of biomedical data requires changes in research, communication, training, and clinical practices. This includes learning how to effectively integrate automated analysis with high-data-density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including: 3D genomics, single-cell RNA-seq, the protein structure universe, phosphoproteomics, augmented-reality surgery, and metagenomics. While specific areas need highly tailored visualization tools, there are common visualization challenges that can be addressed with general methods and strategies. Unfortunately, poor visualization practices are also common; however, there are good prospects for improvements and innovations that will revolutionize how we see and think about our data. We outline initiatives aimed at fostering these improvements via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers.

scholarly journals Identification of a RAI1-associated disease network through integration of exome sequencing, transcriptomics, and 3D genomics

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Maria Nicla Loviglio ◽  
Christine R. Beck ◽  
Janson J. White ◽  
Marion Leleu ◽  
Tamar Harel ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Disease Network ◽  
3D Genomics

scholarly journals Visualization of Biomedical Data

2018 ◽  
Author(s):  
Seán I O'Donoghue ◽  
Bernadetta F Baldi ◽  
Susan J Clark ◽  
Aaron E Darling ◽  
James M Hogan ◽  
...  
Keyword(s):  
Interdisciplinary Collaboration ◽  
Automated Analysis ◽  
Biomedical Data ◽  
Clinical Practices ◽  
High Data ◽  
Research Areas ◽  
Computer Scientists ◽  
Difficult Challenge ◽  
3D Genomics ◽  
Complex Phenomena

The rapid increase in volume and complexity of biomedical data requires changes in research, communication, training, and clinical practices. This includes learning how to effectively integrate automated analysis with high-data-density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including: 3D genomics, single-cell RNA-seq, the protein structure universe, phosphoproteomics, augmented-reality surgery, and metagenomics. While specific areas need highly tailored visualization tools, there are common visualization challenges that can be addressed with general methods and strategies. Unfortunately, poor visualization practices are also common; however, there are good prospects for improvements and innovations that will revolutionize how we see and think about our data. We outline initiatives aimed at fostering these improvements via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers.

Sign in / Sign up

Export Citation Format

Share Document