> For the complete documentation index, see [llms.txt](https://svai.gitbook.io/research-to-the-people/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://svai.gitbook.io/research-to-the-people/about-the-data/what-data-do-we-work-with.md).

# What Data Do We Work With?

Created by Lily Vittayarukskul for SVAI research community. Open to collaborators!

## Introduction

Here, we dive into the data that allows our research community to yield robust, promising insights into understanding a patient's clinical case. Depending on the type of disease the patient has, we gather a unique portfolio of data. That data may be genomic/genetics, transcriptomic, proteomic,  metabolic, and/or clinical. 

**Minimally, we try to gather clinical and genomic data. A great next plus would be transcriptome data.** 

Let's dive in.

## Common Genomic Data Files

In this section, we'll dive into how the most commonly used genomic data is organized in the real world. 

### Whole Genome Sequencing (WGS)

### Whole Exome Sequencing (WES)

### Variant Calling File (VCF) 

## Gene Expression data

There are three main ways to analyze gene expression: qPCR, microarrays, or RNA-seq. The industry choice at this point is RNAseq data because relative to the other techniques, **RNA-seq enables us to look at differential expressions at a much broader dynamic range, to examine DNA variations (SNPs, insertions, deletions) and even discover new genes or alternative splice variations using just one dataset**.

If we're providing RNA-seq data, it'll in a data format called **FASTA/FASTQ.** This article provides a really nice [comprehensive introduction](https://galaxyproject.org/tutorials/rb_rnaseq/) into the entire RNA-seq curation, processing, and mapping process.

**However, relevant to you is probably just mapping the RNA-seq data to a reference,** or performing alignment free methods if you have a really high coverage rate. **STAR** is a great option for reference-mapping and **salmon** for reference-free alignment. Note, there's a lot of really great, easy-to-use alignment software packages! 

[Here's an example folder to practice alignment using TopHat2.](ftp://ftp.ebi.ac.uk/pub/training/Train_online/RNA-seq_exercise/) 

## Clinical Data

(soon to be added!)

## Metabolomic Data

(soon to be added!)

##