I've used this programming language as the basis of most of my coding routines. I'm using it for statistical analysis and building python applications, for ETL processes and visualization. Use object-oriented programming (OOP) as much as possible to adhere to DRY principles.

Main libraries used:

-- numpy

-- pandas

-- requests

-- matplotlib & seaborn

I've used this programming language for statistical analysis. Mainly for microbiome paper studies and to extract and transform microbiome NGS data for GUTHEALTH (a diet-microbiome related report).

Main libraries used:

-- Phyloseq

-- Caret

-- ggplot2

-- dplyr

I'm using this markup language for the basis of any readability/usability of any website/business application. Adept in using the Bootstrap toolkit for easy and quick cross-platform integration.

Main libraries used:

-- Bootstrap

-- FontAwesome

No website/web application is ever whole without some form of cascading style sheets supporting it. Adept in adapting bootstrap classes or creating new if needed.

No interactive website/application is ever complete without some use of javascript or jquery. It enables client-side real-time interactiveness and has helped improve the usability of any application i've built so far.

Main libraries used:

-- Vue.js

-- AG Grid

Love using this framework as it serves all purposes. From website to applications and considering the vast amount of packages it contains it hasn't failed me yet in building clean and fast projects. I've built an web application that serves as Laboratory Information Management System (LIMS) and as a genetics variant annotation and reporting tool for a genetics laboratory that is currently in use by Centro Laboratorial Germano de Sousa.

Experience in using Java as a class/object based language by applying it to standalone applications.

No self sufficient programmer is ever complete without knowing how to interact with relational databases. Adept in using PostgreSQL and MySQL management systems.

Love the terminal and how easy it is to use and interact with Linux. Have some shell scripts under my utility belt for quick use and no Bioinformatician is ever complete without understanding the inner workings of using bash as most bioinformatics tools are to be used with this.

Since i've discovered Docker as a way of containerize applications things have never been quite the same. Considering continuous integration (CI) and continuous delivery (CD), docker technology makes my job as a programmer much easier to manage. I've used this locally but also as a way of deploying websites to the cloud (using DigitalOcean).

As a bioinformatician i'm used to access multiple databases via API services for variant annotation. Integrating API's into scripting pipelines makes my work faster and easier. I also use this concept within Django (REST framework) as a way of accessing serialized information for real-time data analysis.

Currenty integrating this standard for clinical and administrative data between healtcare applications to receive and transmit relevant patient information between the main application used by Centro Laboratorial Germano de Sousa (Appolo) and the application currently in development and in use for the genetics team.

As a bioinformatician I assist the Genetics team in Centro Laboratorial Germano de Sousa with the task of understanding how the differences in one's genome can affect one's health by creating and using commercial tools to gather relevant data to annotation mutations. This will serve as basis for a possible diagnosis and clinical recommendations provided by the team of geneticists to clinicians and patients.

I also work in the metagenomics field and i'm tasked to gather bacterial identification 16S data, and creating end-to-end pipelines to produce a report, that identifies gut microbiome taxonomy and relates it with dietary information.

Working closely with NGS panel based data and experience also in shotgun data. Currently working with Thermofisher sequencing equipment and software but also have some experience with Illumina datasets.

Adept in using various bioinformatics algorithms and databases for genomics and metagenomics data. Also responsible for creating and maintaining ETL pipelines for NGS data annotation.

Main databases used:

-- Clinvar, Ensembl, dbSNP, Varsome, OMIM, COSMIC, gnomAD

Main algorithms used:

-- TVC VariantCaller, BLAST, KRAKEN2 + BRACKEN, MOTHUR, BOWTIE, FastQC, CUTAdapt, BEDTools

Main data types used:

-- BAM, uBAM, FASTQ, FASTA, BIOM

Experience in using classification algorithms such as Random Forests, SVM and Ensemble models as well as deploying these into production. Currently in production is an e-mail spam analysis model and an obesity-microbiome classification model. Looking for in-depth experience in traditional machine learning models and well as deep learning based ones.

Experience in using scikit-learn, Tensorflow, Keras as well as using Google Cloud to test commercial grade deep learning models.

Experience in using Pyspark and Spark Streaming in a learning environment. Currently looking for in-depth experience.

Where would informatics be without version control? Experience in git usage and github pushing, commiting, cloning, branching, pulling and merging.

A bioinformatician or developer is essentially a tool, a provider of means and ways to a goal or understanding. Collaborating and communication effectively are an integral part of being a programmer as programming is never an end but a vehicle.