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Daniel A. Morales_

Vector Biologist, Computional Evolutionary Virologist

Age:
27
Phone:
(305) 348-2201
Email:
dmora127{at}fiu.edu
Address:
11200 SW 8th Street, AHC1-229, Miami, FL 33199

Hi_

I am PhD Candidate at Florida International University's Laboratory of Tropical Genetics under the mentorship of Dr. Matthew DeGennaro, PhD. My research aims to further understand the rules of engagement and co-evolutionary trajectories of arboviruses and their mosquito hosts, with a particular focus on arboviruses of clinical relevance. Currently my research focus surrounds the leverging of genome editing and third-generation sequencing technologies to best interrogate mosquito immunology.

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Resume_

I am a dynamic biology researcher at the intersection of bioinformatics, computational biology, and molecular evolution, with a bold focus on synthetic biology and genomics. My research at Florida International University dives into the fascinating world of host-virus interactions and transposable elements in mosquitoes, using cutting-edge high-throughput sequencing and machine learning to fuel groundbreaking discoveries. With a knack for building powerful computational models and genomic pipelines, I thrive on pushing the boundaries of what's possible in genomic analysis. Driven by a passion for innovation, I’m eager to leverage my expertise to accelerate game-changing breakthroughs in gene synthesis and genome engineering, shaping the future of synthetic biology.

education

Bachelor of Arts in Natural & Applied Sciences

2020 - 2021

Florida International University

Masters of Science in Biology

2021 - 2024

Florida International University

Doctor of Philosophy (PhD) in Biology

2021 - Current (Est. 2026)

Florida International University

employment

Florida International University

2020 - Current

Transdisciplinary Biomolecular and Biomedical Sciences Training Program Fellow

  • Led genetic manipulation studies in Aedes aegypti, exploring viral immune pathways and contributing to research on heritable adaptive immunity.
  • Conducted bioinformatic analyses on endogenous viral elements (EVEs) and viral integration, revealing host-virus co-evolution and applications for gene editing.
  • Developed computational models of host-pathogen interactions and profiled viral genomic changes using 2nd and 3rd generation sequencing data.
  • Spearheaded in-silico studies on post-translational modifications in coronavirus proteomes, collaborating on evolutionary dynamics of SLiMs.
  • Designed machine learning pipelines and custom Python scripts to automate bioinformatics tasks, efficiently managing large datasets and detecting contamination in genome assemblies.
  • Participated in team-based research, regularly presenting findings to principal investigators and collaborating with peers to analyze and interpret complex bioinformatic data.

National Institute of Allergy and Infectious Diseases (NIAID-NIH)

2022 - Current

Graduate Summer Opportunity for Advancing Research (G-SOAR) Trainee

Research Advisor – Dr. Patrick T. Dolan, Ph.D.

Division/Unit: Laboratory of Viral Diseases/Quantitative Virology and Evolution Unit

  • Developed bioinformatic pipelines for identifying transposable elements and viral elements, contributing to projects on viral integration mechanisms and genomic defense pathways.
  • Applied computational biology to model viral integration, focusing on potential applications in gene editing and genetic manipulation for viral suppression.
  • Designed and implemented custom DNA and RNA extraction protocols for non-model organisms

National Oceanic and Atmospheric Admininstration (NOAA)

2013 - 2016

Senior Research Support Intern – Environmental Microbiology Lab

Research Advisor – Dr. Christopher Sinigalliano, Ph.D.

Division/Unit: Ocean Chemistry and Ecosystems Division/Environmental Microbiology Lab

  • Developed and managed a small single-lab experimental cluster for the deployment of QIIME and other bioinformatic pipelines
  • Participated in field sample collections of sand, vegetation, and water in throughout South Florida’s coastline
  • Conducted DNA, RNA, and protein purification and amplification for use in point-source microbial qPCR assays and next-generation sequencing

general skills

Python 65%

High Performance/Throughput Computing 70%

Computational Biology 90%

Genomics 80%

Molecular Biology 80%

git 60%

Machine Learning 30%

Research Computing Cyberinfrastructure 60%

My projects_

project-img

Genetic Gain- and Loss-of-Function Mutants of Aedes mosquitoes

In my research with Aedes mosquitoes, I employed loss- and gain-of-function mutagenesis to explore the role of Piwi proteins in the suppression of transposable elements (TEs) and viral silencing. By knocking out or overexpressing specific Piwi genes, I observed how these mutations influenced heritable immune responses, providing insights into the molecular domestication of Piwi proteins and their role in adaptive immunity within mosquito populations.

Skills:

  • Genetics
  • Mutagenesis
  • CRISPR
  • Cas9
  • Molecular Biology
project-img

Annotation and Analysis of Transposable Elements

In my work on Aedes mosquitoes, I focused on the annotation and analysis of transposable elements (TEs) to investigate their evolutionary dynamics within the genome. By identifying and categorizing various classes of TEs, including LTR retrotransposons and non-LTR elements, I explored how these elements impact genome structure, contribute to genome size variation, and interact with Piwi proteins in the context of viral suppression and adaptive immunity. This analysis provides key insights into the role of TEs in mosquito genome evolution.

Skills:

  • Transposable Elements
  • Bioinformatics
  • Genomics
  • Sequencing
  • Annotation
  • Data Science
project-img

Deep Learning Assisted Genomic Sequence Classification and Contamination Detection

I developed an autoencoder neural network pipeline to identify contamination and misassemblies in assembled genomes. By analyzing high-dimensional genomic data, the pipeline detects anomalies in sequence features such as GC content, read depth, and contig length. The unsupervised learning approach allows for accurate classification of potential contaminants, improving the quality and reliability of genome assemblies. This method is particularly effective in handling large-scale, complex datasets from genome assembly projects.

Skills:

  • Machine Learning
  • Neural Networks
  • Autoencoder
  • Deep Learning
  • TensorFlow
  • PyTorch

Get in touch_

Phone:
+01 (786) 346-7168
LinkedIn:
daniel-a-morales
Email:
dmora {at} fiu {dot} com

Or just write me a letter here_

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