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Schaumont, D.

Publications and source records attributed to Schaumont, D..

2 recordsLinked to original sources

BREEDIT: A novel multiplex genome editing strategy to improve complex quantitative traits in maize (Zea mays L.)

Ensuring food security for an ever-growing global population while adapting to climate change is the main challenge for agriculture in the 21st century. Though new technologies are being applied to tackle the problem, we are approaching a plateau in crop improvement using conventional breeding. Recent advances in gene engineering via the CRISPR/Cas technology pave the way to accelerate plant breeding and meet this increasing demand. Here, we present a gene discovery pipeline named BREEDIT that combines multiplex genome editing of whole gene families with crossing schemes to improve complex traits such as yield and drought resistance. We induced gene knockouts in 48 growth-related genes using CRISPR/Cas9 and generated a collection of over 1000 gene-edited maize plants. Edited populations displayed, on average, significant increases of 5 to 10% for leaf length and up to 20% for leaf width compared with controls. For each gene family, edits in subsets of genes could be associated with increased traits, allowing us to reduce the gene space needed to focus on for trait improvement. We propose BREEDIT as a gene discovery pipeline which can be rapidly applied to generate a diverse collection of mutants to identify subsets of promising candidates that could be later incorporated in breeding programs.

systems biology↗

Stack Mapping Anchor Points (SMAP): a versatile suite of tools for read-backed haplotyping

Here we present SMAP, a software package that implements a suite of computational tools to extract multi-allelic haplotypes using read-backed haplotyping. SMAP tools first perform accurate read processing and analyze read mapping distributions across sample sets. Then, two complementary modules can be invoked for haplotype calling: SMAP haplotype-sites combines known Single Nucleotide Polymorphisms (SNPs) and/or read mapping position polymorphisms (SMAPs) to reconstruct compressed, read-reference-encoded haplotype strings. In contrast, SMAP haplotype-window works independent of prior knowledge of polymorphisms, groups reads by locus, defines a window enclosed between two custom border sequences, and retains the entire corresponding DNA sequence as haplotype. Haplotype-window is, among many applications, especially useful for high-throughput CRISPR/Cas mutation screens. Either way, SMAP creates a single integrated haplotype call table across all loci and samples. SMAP haplotyping is extremely versatile and can be applied to highly multiplex amplicon sequencing (HiPlex), Shotgun (e.g. whole genome shotgun (WGS) sequencing, probe capture and RNA-Seq), or Genotyping-by-Sequencing (GBS) data; and to Illumina short reads, PacBio and MinION long reads. SMAP creates discrete genotype calls for individuals of any ploidy or quantitative haplotype frequency spectra for Pool-Seq data, and can scale from tens to thousands of loci and/or samples. SMAP, including the source code written in Python is available at https://gitlab.com/truttink/smap, and a detailed user manual and guidelines for accurate read processing is available at https://ngs-smap.readthedocs.io/, under the GNU Affero General Public License v3.0.

genomics↗