Cultivated groundnut (Arachis hypogaea L.) is an agronomically and economically important oilseed crop grown extensively throughout the semi-arid tropics of Asia, Africa and Latin America. The genetic base of the cultivated groundnut is very narrow as a result of the genetic bottleneck associated with recent polyploidization which makes it critical to determine the levels of genetic diversity within available germplasm collections prior to breeding. In groundnut, the use of SSRs for diversity assessment may offer the potential to reveal genetic variation within the genome of the cultivated species. An alternative bioinformatics, or in silico approach, to identifying SSRs suitable for application in cultivated groundnut is presented, as a low-cost alternative to wet lab SSR identification. All available nucleotide sequences from species within the aeschynomenoid/ dalbergoid and genistoid clades of the Leguminosae family were searched for SSR motifs and primers designed from 109 unique SSRs. Representative accessions from six genera within the aeschynomenoid/dalbergoid and genistoid clades were selected for assessing SSRtransferability rates. In total, 60% of the total cross-genera transfer testing reactions gave prominent and reproducible amplicons, with 51 of the 109 SSRs amplifying in A. hypogaea. These 51 SSRs were further tested against 27 diverse Arachis accessions and 18 revealed polymorphism, demonstrating that the in silico approach to SSR identification and development is a valid strategy in lesser-studied crops.

Background: With the advances in DNA sequencer-based technologies, it has become possible to automate several steps of the genotyping process leading to increased throughput. To efficiently handle the large amounts of genotypic data generated and help with quality control, there is a strong need for a software system that can help with the tracking of samples and capture and management of data at different steps of the process. Such systems, while serving to manage the workflow precisely, also encourage good laboratory practice by standardizing protocols, recording and annotating data from every step of the workflow.