Species-agnostic local ancestry inference with convolutions

Local Ancestry Inference (LAI) is the high resolution prediction of ancestry (African, European, ...) across a DNA sequence. LAI is becoming increasingly important in DNA sequence analysis for the study of human ancestry and migrations. It is also necessary for polygenic risk scores research (predic...

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Detalles Bibliográficos
Autor: Oriol Sàbat, Benet
Tipo de recurso: tesis de maestría
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/360308
Acceso en línea:https://hdl.handle.net/2117/360308
Access Level:acceso abierto
Palabra clave:Genomics
Neural networks (Computer science)
genomics
convolution
ancestry
dna
local ancestry inference
neural
Genòmica
Xarxes neuronals (Informàtica)
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
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oai_identifier_str oai:upcommons.upc.edu:2117/360308
network_acronym_str ES
network_name_str España
repository_id_str
spelling Species-agnostic local ancestry inference with convolutionsOriol Sàbat, BenetGenomicsNeural networks (Computer science)genomicsconvolutionancestrydnalocal ancestry inferenceneuralGenòmicaXarxes neuronals (Informàtica)Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadorsLocal Ancestry Inference (LAI) is the high resolution prediction of ancestry (African, European, ...) across a DNA sequence. LAI is becoming increasingly important in DNA sequence analysis for the study of human ancestry and migrations. It is also necessary for polygenic risk scores research (prediction of traits and disease risk). Most current LAI models are built for specific species, set of ancestries and chromosomes, hence a new model needs to be trained from scratch for every slightly different setting. This creates a big barrier for research and industry to shift across different LAI scenarios. In this thesis we present SALAI-Net, the first statistical method for LAI with reference panel that can be used on any set of species and ancestries (species-agnostic). Loter is the state of the art in species-agnostic models with reference panel, and is based on a dynamic programming algorithm. However, it is slow and does not perform very well in small reference panel settings. Our model is based on a novel hand-engineered template matching block followed by a convolutional smoothing filter optimized to minimize cross-entropy loss on a training dataset. The right choice of DNA sequence encoding, similarity features and architecture is what makes our model able to generalize well to unseen ancestries, species, and different chromosomes. We benchmark our models on whole genome data of humans and we test the ability to generalize to dog species when trained on human data. Our models outperform the state-of-the-art method by a big margin in terms of accuracy, testing in different settings and datasets. Moreover, it is up to two orders of magnitude faster. Our model also shows close to no generalization gap when switching between species.Universitat Politècnica de CatalunyaMas Montserrat, DanielGiró Nieto, XavierIoannidis, Alexander20212021-10-2920222022-01-20master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2117/360308reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3603082026-05-27T15:37:01Z
dc.title.none.fl_str_mv Species-agnostic local ancestry inference with convolutions
title Species-agnostic local ancestry inference with convolutions
spellingShingle Species-agnostic local ancestry inference with convolutions
Oriol Sàbat, Benet
Genomics
Neural networks (Computer science)
genomics
convolution
ancestry
dna
local ancestry inference
neural
Genòmica
Xarxes neuronals (Informàtica)
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
title_short Species-agnostic local ancestry inference with convolutions
title_full Species-agnostic local ancestry inference with convolutions
title_fullStr Species-agnostic local ancestry inference with convolutions
title_full_unstemmed Species-agnostic local ancestry inference with convolutions
title_sort Species-agnostic local ancestry inference with convolutions
dc.creator.none.fl_str_mv Oriol Sàbat, Benet
author Oriol Sàbat, Benet
author_facet Oriol Sàbat, Benet
author_role author
dc.contributor.none.fl_str_mv Mas Montserrat, Daniel
Giró Nieto, Xavier
Ioannidis, Alexander
dc.subject.none.fl_str_mv Genomics
Neural networks (Computer science)
genomics
convolution
ancestry
dna
local ancestry inference
neural
Genòmica
Xarxes neuronals (Informàtica)
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
topic Genomics
Neural networks (Computer science)
genomics
convolution
ancestry
dna
local ancestry inference
neural
Genòmica
Xarxes neuronals (Informàtica)
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
description Local Ancestry Inference (LAI) is the high resolution prediction of ancestry (African, European, ...) across a DNA sequence. LAI is becoming increasingly important in DNA sequence analysis for the study of human ancestry and migrations. It is also necessary for polygenic risk scores research (prediction of traits and disease risk). Most current LAI models are built for specific species, set of ancestries and chromosomes, hence a new model needs to be trained from scratch for every slightly different setting. This creates a big barrier for research and industry to shift across different LAI scenarios. In this thesis we present SALAI-Net, the first statistical method for LAI with reference panel that can be used on any set of species and ancestries (species-agnostic). Loter is the state of the art in species-agnostic models with reference panel, and is based on a dynamic programming algorithm. However, it is slow and does not perform very well in small reference panel settings. Our model is based on a novel hand-engineered template matching block followed by a convolutional smoothing filter optimized to minimize cross-entropy loss on a training dataset. The right choice of DNA sequence encoding, similarity features and architecture is what makes our model able to generalize well to unseen ancestries, species, and different chromosomes. We benchmark our models on whole genome data of humans and we test the ability to generalize to dog species when trained on human data. Our models outperform the state-of-the-art method by a big margin in terms of accuracy, testing in different settings and datasets. Moreover, it is up to two orders of magnitude faster. Our model also shows close to no generalization gap when switching between species.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-10-29
2022
2022-01-20
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/360308
url https://hdl.handle.net/2117/360308
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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