Daniel Matías-Sánchez

ORCID: 0000-0003-4760-6512
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About
Contact & Profiles
Research Areas
  • Genetic Associations and Epidemiology
  • Genomics and Rare Diseases
  • Molecular Biology Techniques and Applications
  • Genetic Mapping and Diversity in Plants and Animals
  • Genomic variations and chromosomal abnormalities
  • Cancer Genomics and Diagnostics

Universitat Politècnica de Catalunya
2021-2022

Barcelona Supercomputing Center
2021-2022

Abstract The combined analysis of haplotype panels with phenotype clinical cohorts is a common approach to explore the genetic architecture human diseases. However, studies are mainly based on single nucleotide variants (SNVs) and small insertions deletions (indels). Here, we contribute fill this gap by generating dense map focused identification, characterization, phasing structural (SVs). By integrating multiple variant identification methods Logistic Regression Models (LRMs), present...

10.1093/nar/gkac076 article EN cc-by-nc Nucleic Acids Research 2022-02-09

ABSTRACT Long-read sequencing (LRS) technologies, namely Oxford Nanopore Technologies (ONT) and Pacific Biosciences, have emerged as promising solutions to overcome the limitations of short-read (SRS). Nevertheless, still higher error rates compared SRS, need for customized pipelines, rapidly updating software incipient scalability are proving adoption ONT standard clinical practice be challenging. Here we assess performance (R9 R10 chemistries) in comparison Illumina MGI across 17...

10.1101/2024.10.30.24316253 preprint EN medRxiv (Cold Spring Harbor Laboratory) 2024-11-04

ABSTRACT The combined analysis of haplotype panels with phenotype clinical cohorts is a common approach to explore the genetic architecture human diseases. However, studies are mainly based on single nucleotide variants (SNVs) and small insertions deletions (indels). Here, we contribute fill this gap by generating dense map focused identification, characterization phasing structural (SVs). By integrating multiple variant identification methods Logistic Regression models, present catalogue...

10.1101/2021.07.20.453041 preprint EN cc-by-nc-nd bioRxiv (Cold Spring Harbor Laboratory) 2021-07-21
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