Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
ImaGene: a convolutional neural network to quantify natural selection from genomic data
Download
s12859-019-2927-x.pdf
Date
2019-11-22
Author
Torada, Luis
Lorenzon, Lucrezia
Beddis, Alice
Isildak, Ulas
Pattini, Linda
Mathieson, Sara
Fumagalli, Matteo
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
227
views
103
downloads
Cite This
Background: The genetic bases of many complex phenotypes are still largely unknown, mostly due to the polygenic nature of the traits and the small effect of each associated mutation. An alternative approach to classic association studies to determining such genetic bases is an evolutionary framework. As sites targeted by natural selection are likely to harbor important functionalities for the carrier, the identification of selection signatures in the genome has the potential to unveil the genetic mechanisms underpinning human phenotypes. Popular methods of detecting such signals rely on compressing genomic information into summary statistics, resulting in the loss of information. Furthermore, few methods are able to quantify the strength of selection. Here we explored the use of deep learning in evolutionary biology and implemented a program, called ImaGene, to apply convolutional neural networks on population genomic data for the detection and quantification of natural selection.
Subject Keywords
Biochemistry
,
Applied Mathematics
,
Molecular Biology
,
Structural Biology
,
Computer Science Applications
URI
https://hdl.handle.net/11511/68431
Journal
BMC BIOINFORMATICS
DOI
https://doi.org/10.1186/s12859-019-2927-x
Collections
Department of Biology, Article
Suggestions
OpenMETU
Core
MicroarrayDesigner: an online search tool and repository for near-optimal microarray experimental designs
Sacan, Ahmet; Ferhatosmanoglu, Nilgun; Ferhatosmanoglu, Hakan (Springer Science and Business Media LLC, 2009-9-22)
Background: Dual-channel microarray experiments are commonly employed for inference of differential gene expressions across varying organisms and experimental conditions. The design of dual-channel microarray experiments that can help minimize the errors in the resulting inferences has recently received increasing attention. However, a general and scalable search tool and a corresponding database of optimal designs were still missing. Description: An efficient and scalable search method for finding nea...
Chromosome segregation in Escherichia coli division: A free energy-driven string model
Fan, J.; Tuncay, Kağan; Ortoleva, P. J. (Elsevier BV, 2007-08-01)
Although the mechanisms of eukaryotic chromosome segregation and cell division have been elucidated to a certain extent, those for bacteria remain largely unknown. Here we present a computational string model for simulating the dynamics of Escherichia coli chromosome segregation. A novel thermal-average force field accounting for stretching, bending, volume exclusion, friction and random fluctuation is introduced. A Langevin equation is used to simulate the chromosome structural changes. The mechanism of ch...
Discovering functional interaction patterns in protein-protein interaction networks
Turanalp, Mehmet E.; Can, Tolga (Springer Science and Business Media LLC, 2008-06-11)
Background: In recent years, a considerable amount of research effort has been directed to the analysis of biological networks with the availability of genome-scale networks of genes and/or proteins of an increasing number of organisms. A protein-protein interaction (PPI) network is a particular biological network which represents physical interactions between pairs of proteins of an organism. Major research on PPI networks has focused on understanding the topological organization of PPI networks, evolution...
Structural properties of an engineered outer membrane protein G mutant, OmpG-16SL, investigated with infrared spectroscopy
Yilmaz, Irem; Yildiz, Ozkan; KORKMAZ ÖZKAN, FİLİZ (Informa UK Limited, 2019-05-31)
The structural and functional differences between wild type (WT) outer membrane protein G and its two mutants are investigated with Fourier transform infrared spectroscopy. Both mutants have a long extension to the primary sequence to increase the number of beta-strands from 14 (wild type) to 16 in an attempt to enlarge the pore diameter. The comparison among proteins is made in terms of pH-dependent conformational changes and thermal stability. Results show that all proteins respond to pH change but at dif...
Autoinflammation in addition to combined immunodeficiency: SLC29A3 gene defect
Cagdas, Deniz; Surucu, Naz; TAN, ÇAĞMAN; ÖZGÜL, RIZA KÖKSAL; Akkaya-Ulum, Yeliz Z.; Aydinoglu, Ayse Tulay; Aytac, Selin; GÜMRÜK, FATMA; Balci-Hayta, Burcu; Balci-Peynircioglu, Banu; ÖZEN, SEZA; Gürsel, Mayda; Tezcan, Ilhan (Elsevier BV, 2020-05-01)
Introduction: H Syndrome is an autosomal recessive (AR) disease caused by defects in SLCA29A3 gene. This gene encodes the equilibrative nucleoside transporter, the protein which is highly expressed in spleen, lymph node and bone marrow. Autoinflammation and autoimmunity accompanies H Syndrome (HS).
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
L. Torada et al., “ImaGene: a convolutional neural network to quantify natural selection from genomic data,”
BMC BIOINFORMATICS
, pp. 0–0, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/68431.