Introduction

Breast cancer is one of the most important morbidity and mortality cases around the world. In 2022, 2.3 million women were diagnosed with breast cancer and about 670,000 died from the disease, according to the World Health Organization.

Traditional breast cancer treatment with chemotherapy may be complicated …

Introduction

Sometimes, some computations can be carried out in parallel. Certain large tasks can be divided into independent ones, allowing them to be solved at the same time, rather than waiting for each task to be solved sequentially.

I find the native R parallel functions such as mclapply(), or those …

Introduction

Dealing with big files is a routine for everyone working in genomics. FASTQ, VCF, BAM, and GTF/GFF3 files, to name a few, can range from some hundreds of megabytes to several gigabytes in size. Usually, we can use cloud services to configure computing instances with a lot of …

Introduction

Hello, long time no see! Since I lasted posted, many things happened. Since March I have been working as Post-Doc Researcher, hired by the Hospital Israelita Albert Einstein (HIAE, São Paulo, Brazil) to work for the Projeto Genomas Raros (“Rare Genomes Project”, GRAR from here on), a public-private partnership …

Introduction

Recently, me and my colleagues wrote a manuscript involving meta-analysis of RNA-Seq studies. One of my tasks of this project was to perform a Gene Ontology (GO) enrichment analysis: “[G]iven a set of genes that are up-regulated under certain conditions, an enrichment analysis will find which GO …

Introduction

In a previous post, I showed how to retrieve The Cancer Genome Atlas (TCGA) data from the Cancer Genomics Cloud (CGC) platform. I downloaded gene expression quantification data, created a relational database with PostgreSQL, and created a dataset uniting the raw quantification data for 675 differentially expressed genes identified …

Introduction

In a previous post, I showed how to retrieve The Cancer Genome Atlas (TCGA) data from the Cancer Genomics Cloud (CGC) platform. I downloaded gene expression quantification data, created a relational database with PostgreSQL, and created a dataset uniting the raw quantification data for 675 differentially expressed genes identified …

Introduction

In my previous post I demonstrated how to organize the CGC prostate cancer data to a format suited to differential expression analysis (DEA).

Nowadays, DEA usually arises from high-throughput sequencing of a collection (library) of RNA molecules expressed by single cells or tissue given their conditions upon collection and …

Introduction

In my previous post I demonstrated how to obtain a prostate cancer dataset with genomic information in the form of gene expression quantification and created a local PostgreSQL database to hold the data.

Here, I will use R to connect to the PostgreSQL database, retrieve and then prepare the …

Introduction

Recently I have been looking for publicly-available genomics datasets to test machine learning models in Python. During my searches for such a “toy dataset”, I came upon the Cancer Genomics Cloud (CGC) initiative.

Anyone can register in CGC and have access to open access massive public datasets, like The …

Introduction

Recently I have been looking for publicly-available genomics datasets to test machine learning models in Python. During my searches for such a “toy dataset”, I came upon the Cancer Genomics Cloud (CGC) initiative.

Anyone can register in CGC and have access to open access massive public datasets, like The …

Introduction

In a previous post, I showed how to configure an Ubuntu system to install Bioinformatics programs.

Now, using the environment I created, I will demonstrate a bash script, FastQ_to_Annotation.sh that takes next generation sequencing (NGS) raw reads from human whole genome sequencing as input and produces …

Introduction

In a previous post, I showed how to configure an Ubuntu system to install Bioinformatics programs.

Now, using the environment I created, I will demonstrate a bash script, FastQ_to_Annotation.sh that takes next generation sequencing (NGS) raw reads from human whole genome sequencing as input and produces …

Introduction

In a previous post, I showed how to configure an Ubuntu system to install Bioinformatics programs.

Now, using the environment I created, I will demonstrate a bash script, FastQ_to_Annotation.sh that takes next generation sequencing (NGS) raw reads from human whole genome sequencing as input and produces …

Introduction

In my previous post, I showed how to configure an Ubuntu system to install Bioinformatics programs.

Now, using the environment I created, I will demonstrate a bash script, FastQ_to_Annotation.sh that takes next generation sequencing (NGS) raw reads from human whole genome sequencing as input and produces …

Introduction

In this post I will show how I set up my Unix machine to use Bioinformatics programs and tools. I am currently using Ubuntu 20.04 LTS (Focal Fossa) 64-bit on a Windows Subsystem for Linux (WSL2) on Windows 10, so no GUI today!

The code and files used …