Computational Comparison of Human Genomic Sequence Assemblies for a Region of Chromosome 4

2002 ◽  
Vol 12 (3) ◽  
pp. 424-429 ◽  
Author(s):  
C. A.M. Semple ◽  
S. W. Morris ◽  
D. J. Porteous ◽  
K. L. Evans
Keyword(s):  
Genomic Sequence ◽  
Chromosome 4 ◽  
Human Genomic ◽  
Computational Comparison ◽  
Human Genomic Sequence

scholarly journals A heterodimer of evolved designer-recombinases precisely excises a human genomic DNA locus

2019 ◽  
Vol 48 (1) ◽  
pp. 472-485 ◽  
Author(s):  
Felix Lansing ◽  
Maciej Paszkowski-Rogacz ◽  
Lukas Theo Schmitt ◽  
Paul Martin Schneider ◽  
Teresa Rojo Romanos ◽  
...  
Keyword(s):  
Human Genome ◽  
Genome Editing ◽  
Large Scale ◽  
Genome Engineering ◽  
Genomic Sequence ◽  
Safe Alternative ◽  
Protein Coding ◽  
Dna Binding Specificity ◽  
Human Genomic ◽  
Human Genomic Sequence

Abstract Site-specific recombinases (SSRs) such as the Cre/loxP system are useful genome engineering tools that can be repurposed by altering their DNA-binding specificity. However, SSRs that delete a natural sequence from the human genome have not been reported thus far. Here, we describe the generation of an SSR system that precisely excises a 1.4 kb fragment from the human genome. Through a streamlined process of substrate-linked directed evolution we generated two separate recombinases that, when expressed together, act as a heterodimer to delete a human genomic sequence from chromosome 7. Our data indicates that designer-recombinases can be generated in a manageable timeframe for precision genome editing. A large-scale bioinformatics analysis suggests that around 13% of all human protein-coding genes could be targetable by dual designer-recombinase induced genomic deletion (dDRiGD). We propose that heterospecific designer-recombinases, which work independently of the host DNA repair machinery, represent an efficient and safe alternative to nuclease-based genome editing technologies.

scholarly journals Analysis of EST-Driven Gene Annotation in Human Genomic Sequence

1998 ◽  
Vol 8 (4) ◽  
pp. 362-376 ◽  
Author(s):  
L. Charles Bailey ◽  
David B. Searls ◽  
G. Christian Overton
Keyword(s):  
Genomic Sequence ◽  
Gene Annotation ◽  
Human Genomic ◽  
Human Genomic Sequence

scholarly journals Cloning and Characterization of a Human Genomic Sequence that Alleviates Repeat-Induced Gene Silencing

PLoS ONE ◽  
2016 ◽  
Vol 11 (4) ◽  
pp. e0153338 ◽  
Author(s):  
Miki Fukuma ◽  
Yuto Ganmyo ◽  
Osamu Miura ◽  
Takashi Ohyama ◽  
Noriaki Shimizu
Keyword(s):  
Gene Silencing ◽  
Genomic Sequence ◽  
Human Genomic ◽  
Human Genomic Sequence

Making effective use of human genomic sequence data

1999 ◽  
Vol 15 (7) ◽  
pp. 284-286 ◽  
Author(s):  
Wonhee Jang ◽  
Hsiu-Chuan Chen ◽  
Hugues Sicotte ◽  
Gregory D. Schuler
Keyword(s):  
Genomic Sequence ◽  
Sequence Data ◽  
Human Genomic ◽  
Effective Use ◽  
Human Genomic Sequence

scholarly journals Detecting adaptive introgression in human evolution using convolutional neural networks

2020 ◽  
Author(s):  
Graham Gower ◽  
Pablo Iáñez Picazo ◽  
Matteo Fumagalli ◽  
Fernando Racimo
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Simulated Data ◽  
Alternative Methods ◽  
Adaptive Introgression ◽  
Donor Population ◽  
Human Genomic ◽  
Related Population

AbstractStudies in a variety of species have shown evidence for positively selected variants introduced into one population via introgression from another, distantly related population—a process known as adaptive introgression. However, there are few explicit frameworks for jointly modelling introgression and positive selection, in order to detect these variants using genomic sequence data. Here, we develop an approach based on convolutional neural networks (CNNs). CNNs do not require the specification of an analytical model of allele frequency dynamics, and have outperformed alternative methods for classification and parameter estimation tasks in various areas of population genetics. Thus, they are potentially well suited to the identification of adaptive introgression. Using simulations, we trained CNNs on genotype matrices derived from genomes sampled from the donor population, the recipient population and a related non-introgressed population, in order to distinguish regions of the genome evolving under adaptive introgression from those evolving neutrally or experiencing selective sweeps. Our CNN architecture exhibits 95% accuracy on simulated data, even when the genomes are unphased, and accuracy decreases only moderately in the presence of heterosis. As a proof of concept, we applied our trained CNNs to human genomic datasets—both phased and unphased—to detect candidates for adaptive introgression that shaped our evolutionary history.

Sign in / Sign up

Export Citation Format

Share Document