University of Sussex
Browse
Zhan_et_al-2016-Molecular_Ecology_Resources.pdf (460.85 kB)

MEGASAT: automated inference of microsatellite genotypes from sequence data

Download (460.85 kB)
journal contribution
posted on 2023-06-09, 02:54 authored by Luyao Zhan, Ian G Paterson, Bonnie A Fraser, Beth Watson, Ian R Bradbury, Praveen Nadukkalam Ravindran, David Reznick, Robert G Beiko, Paul Bentzen
MEGASAT is software that enables genotyping of microsatellite loci using next-generation sequencing data. Microsatellites are amplified in large multiplexes, and then sequenced in pooled amplicons. MEGASAT reads sequence files and automatically scores microsatellite genotypes. It uses fuzzy matches to allow for sequencing errors and applies decision rules to account for amplification artefacts, including nontarget amplification products, replication slippage during PCR (amplification stutter) and differential amplification of alleles. An important fea- ture of MEGASAT is the generation of histograms of the length–frequency distributions of amplification products for each locus and each individual. These histograms, analogous to electropherograms traditionally used to score microsatellite genotypes, enable rapid evaluation and editing of automatically scored genotypes. MEGASAT is written in Perl, runs on Windows, Mac OS X and Linux systems, and includes a simple graphical user interface. We demon- strate MEGASAT using data from guppy, Poecilia reticulata. We genotype 1024 guppies at 43 microsatellites per run on an Illumina MiSeq sequencer. We evaluated the accuracy of automatically called genotypes using two methods, based on pedigree and repeat genotyping data, and obtained estimates of mean genotyping error rates of 0.021 and 0.012. In both estimates, three loci accounted for a disproportionate fraction of genotyping errors; conversely, 26 loci were scored with 0–1 detected error (error rate =0.007). Our results show that with appropriate selection of loci, automated genotyping of microsatellite loci can be achieved with very high throughput, low genotyping error and very low genotyping costs.

History

Publication status

  • Published

File Version

  • Accepted version

Journal

Molecular Ecology Resources

ISSN

1755-098X

Publisher

Wiley

Issue

2

Volume

17

Page range

247-256

Department affiliated with

  • Evolution, Behaviour and Environment Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2016-09-14

First Open Access (FOA) Date

2017-07-20

First Compliant Deposit (FCD) Date

2016-09-14

Usage metrics

    University of Sussex (Publications)

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC