Towards an Automated Population Genomics Pipeline for Microsatellite
Screening and Primer Design
Abstract
Analysis of intra- and inter-population diversity has become important
for defining the genetic status and distribution patterns of a species
and a powerful tool for conservation programs, since high levels of
inbreeding could lead into a whole population extinction in few
generations. Microsatellites (SSR) are commonly used in population
studies, but discovering highly variable regions across species’ genomes
requires demanding computation and laboratorial optimization. In this
work, we combine next generation sequencing (NGS) with automatic
computing to develop a genomic-oriented tool for characterizing SSRs at
the population level. Herein, we describe a new Python pipeline, named
Micro-Primers, designed to identify and design PCR primers for
amplification of SSR loci from a multi-individual enriched
microsatellite library. The pipeline takes as input a fastq file
containing sequences from NGS and returns a text file with information
regarding the microsatellite markers, including number of alleles in the
population, the melting temperature and the respective product of primer
sets to easily guide the selection of optimal markers for the species.
Experimental results show that Micro-Primers is able to reduce
significantly a manual analysis that takes about 24 hours to 2 minutes,
while keeping the same quality of the results.