Qayyum, H. | bioRxiv Science

bioRxiv2026

Haplotypes variations of yellow stripe like (TaYSL) genes are associated with grain iron and zinc contents in wheat (Triticum aestivum L.)

The availability of pangenome and resequencing of wheat collections have facilitated the discovery of gene-trait associations in wheat. Yellow stripe-like (YSL) proteins play a key role in the uptake and translocation of metals and yet have not been fully identified and analyzed at the genome-wide level in wheat. In this study, 26 TaYSL genes were identified and divided into four distinct clades, each clade sharing similar domains and motif compositions. Most genes were upregulated under iron deficiency, whereas homoeologs of TaYSL1 were downregulated. Both SNP-based and haplotype-based association studies were used to dissect the role of TaYSLs underpinning grain iron contents (GFeC) and zinc contents (GZnC) in wheat. TaYSL6-2B and TaYSL16-1A haplotypes showed strong association with GFeC, and TaYSL14-6A showed strong association with GZnC in multiple field trials. The distribution of favorable haplotypes in global wheat collection of [~]3000 accessions showed that majority of haplotypes were more prevalent in landraces and winter wheat compared to modern cultivars and spring types, indicating their potential for use in breeding. The combination of favorable haplotypes of three YSL genes associated with GFeC and GZnC were very rare, and most of the wheat accessions has single or double favorable haplotypes. These findings provide the first comprehensive characterization of the TaYSL gene family in wheat and identify significant SNPs and elite haplotypes that can be utilized for genetic improvement and biofortification.

plant biology↗

bioRxiv2024

Efficient De Novo Assembly and Recovery of Microbial Genomes from Complex Metagenomes Using a Reduced Set of k-mers

In recent years, the analysis of metagenomic data to recover unculturable microbes has revolutionized microbial genomics by rapidly expanding the reference genome catalog. Central to this, are the computational approaches of de novo assembly and genome binning that enable large-scale reference-independent recovery of microbial genomes from the metagenomic sequencing data. Despite the advancements in bioinformatics approaches to address the computational challenges inherent to these tasks, the limitation of computational resources continues to be a significant barrier to harvesting the full potential of these techniques. Consequently, there is a stressed need to devise strategies involving the fine-tuning of the employed parameters for the effective utilization of the available metagenomic tools. As most of the available metagenome assembly tools are based on the de Bruijn graph framework that relies on a parameter k, selecting an appropriate subset of k-mers has become a common approach in bioinformatics for efficient computations. In this study, we propose a reduced set of k-mers, optimized to strike a balance between computational efficiency and the quality of the high- and low-complexity metagenome assemblies. Utilizing this set of k-mers with MEGAHIT reduces the metagenome assembly time by half compared to the default set, thus greatly reducing the associated computational cost. In addition, it also brings the promise to improve large-scale genome binning studies that adopt this set in the future as we observed an increase in the total number of the recovered genomes as well as obtained higher proportions of high- and medium-quality genomes recovered from the reduced k-mers-based metagenome assemblies.

bioinformatics↗