Introduction

Gendas is a “pandas” like library focus on genomic datasets. It’s a data query engine highly focus to work with genomic dataset that they can always be map to a genomic position and a sequence id.

The two main goals that differentiate Gendas from other generic data query engines are:

• Native parallelization: When we apply some data manipulations in a genomic dataset, most of the times it’s possible to split the genome in segments and compute each segment in parallel. Gendas tries to hide the parallelization complexity to the end users using a declarative syntax (very similar to Pandas and other dataframe libraries).
• Native data sources: In the genomics field there are several widely use data formats (tabix, bams, fasta, fastq, bed…) Gendas can directly work and mix this data formats without reading everything in memory or inserting all the data to a different data storage system.