emanuelmfonseca
Computational biologist specializing in scalable bioinformatics pipelines and applying machine learning to biological challenges.
University of ArizonaRemote
Pinned Repositories
Model_selection_using_CNN
Phylogeography model selection using Convolutional Neural Networks
P2C2M.GMYC
Demographic_LandscapeGenetics_Frogs
genome-assembly-sulfidic-fish
Global_genetic_diversity
landscape-effects-neotropical-lizard
low-coverage-sfs
sequencing-migration
This repository contains a Snakemake pipeline for automating genomic data analysis, with instructions and example datasets. It includes a scalable AWS cloud architecture using services like S3 and EC2, and provides cost estimates for running workflows and data storage. The solution optimizes cloud-based genomic workflows.
single-cell-sequencing-migration
This repository contains a Snakemake pipeline for automating single-cell genomic data analysis. It provides instructions, example datasets, and a scalable AWS architecture using services like S3 and EC2. Cost estimates for workflows and storage are included, optimizing cloud-based single-cell genomic workflows for efficiency and scalability.
emanuelmfonseca's Repositories
emanuelmfonseca/Model_selection_using_CNN
Phylogeography model selection using Convolutional Neural Networks
emanuelmfonseca/P2C2M.GMYC
emanuelmfonseca/Demographic_LandscapeGenetics_Frogs
emanuelmfonseca/Global_genetic_diversity
emanuelmfonseca/landscape-effects-neotropical-lizard
emanuelmfonseca/low-coverage-sfs
emanuelmfonseca/sequencing-migration
This repository contains a Snakemake pipeline for automating genomic data analysis, with instructions and example datasets. It includes a scalable AWS cloud architecture using services like S3 and EC2, and provides cost estimates for running workflows and data storage. The solution optimizes cloud-based genomic workflows.
emanuelmfonseca/single-cell-sequencing-migration
This repository contains a Snakemake pipeline for automating single-cell genomic data analysis. It provides instructions, example datasets, and a scalable AWS architecture using services like S3 and EC2. Cost estimates for workflows and storage are included, optimizing cloud-based single-cell genomic workflows for efficiency and scalability.