Biology subjects

Korani, W.

Publications and source records attributed to Korani, W..

5 recordsLinked to original sources

bioRxiv2025

KhufuEnv, an auxiliary toolkit for building computational pipelines for plant and animal breeding

In the era of short and long read sequencing, vast amounts of DNA sequencing data are being generated. While a variety of tools exist for analyzing and manipulating genomics data, many have a finite number of tools, and thus, require users to depend on multiple sources for conducting analyses and processing data. An integrative environment of tools which is accessible to users of different computational backgrounds would facilitate more efficient data processing and level the playing field for researchers whose research depends on analyzing genomic data. We developed the KhufuEnv, an open-source, flexible, auxiliary environment for manipulating and analyzing genomic data, among other datasets, in the Unix environment. The KhufuEnv provides a buildable platform for constructing custom pipelines for several genomic analyses across different species. As a proof of concept, we demonstrate rapid de novo identification of previously characterized quantitative trait loci (QTL) and calculate the proportion of the genome containing runs of homozygosity (ROH) with in-house tools on published datasets. Additionally, we introduce our custom HapMap and PanMap file formats. The KhufuEnv can be exploited for a variety of applications and implemented for quick analysis, supporting users with minimal computational experience.

genomics↗

bioRxiv2025

Long-Read Low-Pass Sequencing for High-Resolution Trait Mapping

Accelerating crop improvement is critical to meeting food security demands in a changing climate. Long-read sequencing offers advantages over short-reads in resolving structural variations (SVs) and aligning to complex genomes, but its high cost has limited adoption in breeding programs. Here we develop a high-throughput, scalable approach for long-read low-pass (LRLP) sequencing and variant analysis with PacBio HiFi reads, and apply it to trait mapping in a complex tetraploid peanut (Arachis hypogaea) genome multi-parent advanced generation intercross. We analyze LRLP using both a single reference genome and a pangraph, using both proprietary and open-source tools to analyze SVs and coverage. An increased number of variants are consistently called for LRLP data compared to short-read data. At 1.63x average depth, LRLP sequencing covered 55% of the genome and 58% of gene space, outperforming 1.68x depth short-read low-pass sequencing, which achieved only 17% and 11%, respectively. Enhanced data retention after filtering for probabilistic misalignment and an [~]8.5x decrease in cost per value further demonstrated LRLPs efficacy. Our results highlight LRLP sequencing as a scalable, cost-effective tool for high-resolution trait mapping, with transformative potential for plant breeding and broader genomic applications.

genetics↗

bioRxiv2021

Accurate analysis of short read sequencing in complex genomes: A case study using QTL-seq to target blanchability in peanut (Arachis hypogaea)

Next Generation sequencing was a step change for molecular genetics and genomics. Illumina sequencing in particular still provides substantial value to animal and plant genomics. A simple yet powerful technique, referred to as QTL sequencing (QTL-seq) is susceptible to high levels of noise due to ambiguity of alignment of short reads in complex regions of the genome. This noise is particularly high when working with polyploid and/or outcrossing crop species, which impairs the efficacy of QTL-seq in identifying functional variation. By filtering loci based on the optimal alignment of short reads, we have developed a pipeline, named Khufu, that substantially improves the accuracy of QTL-seq analysis in complex genomes, allowing de novo variant discovery directly from bulk sequence. We first demonstrate the pipeline by identifying and validating loci contributing to blanching percentage in peanut using lines from multiple related populations. Using other published datasets in peanut, Brassica rapa, Hordeum volgare, Lactua satvia, and Felis catus, we demonstrate that Khufu produces more accurate results straight from bulk sequence. Khufu works across species, genome ploidy level, and data types. In cases where identified QTL were fine mapped, the fine mapped region corresponds to the top of the peak identified by Khufu. The accuracy of Khufu allows the analysis of population sequencing at very low coverage (<3x), greatly decreasing the amount of sequence needed to genotype even the most complex genomes.

genomics↗

bioRxiv2020

Gene disruption by structural mutations drives selection in US rice breeding over the last century

The genetic basis of general plant vigor is of major interest to food producers, yet the trait is recalcitrant to genetic mapping because of the number of loci involved, their small effects, and linkage. Observations of heterosis in many crops suggests that recessive, malfunctioning versions of genes are a major cause of poor performance, yet we have little information on the mutational spectrum underlying these disruptions. To address this question, we generated a long-read assembly of a tropical japonica rice (Oryza sativa) variety, Carolina Gold, which allowed us to identify structural mutations (>50 bp) and orient them with respect to their ancestral state using the outgroup, Oryza glaberrima. Supporting prior work, we find substantial genome expansion is the sativa branch. While transposable elements (TEs) account for the largest share of size variation, the majority of events are not directly TE-mediated. Tandem duplications are the most common source of insertions and are highly enriched among 50-200bp mutations. To explore the relative impact of various mutational classes on crop fitness, we then track these structural events over the last century of US rice improvement using 101 resequenced varieties. Within this material, a pattern of temporary hybridization between medium and long-grain varieties was followed by recent divergence. During this long-term selection, structural mutations that impact gene exons have been removed at a greater rate than intronic indels and single-nucleotide mutations. These results support the use of ab initio estimates of mutational burden, based on structural data, as an orthogonal predictor in genomic selection. Significance StatementSome crop varieties have superior performance across years and environments. In hybrids, harmful mutations in one parent are masked by the ancestral alleles in the other parent, resulting in increased vigor. Unfortunately, these mutations are very difficult to identify precisely because, individually, they only have a small effect. In this study, we use long-read sequencing to characterize the entire mutational spectrum between two rice varieties. We then track these mutations through the last century of rice breeding. We show that large structural mutations in exons are selected against at a greater rate than any other mutational class. These findings illuminate the nature of deleterious alleles and will guide attempts to predict variety vigor based solely on genomic information.

genetics↗

bioRxiv2018

Machine learning as an effective method for identifying true SNPs in polyploid plants

Single Nucleotide Polymorphisms (SNPs) have many advantages as molecular markers since they are ubiquitous and co-dominant. However, the discovery of true SNPs especially in polyploid species is difficult. Peanut is an allopolyploid, which has a very low rate of true SNP calling. A large set of true and false SNPs identified from the Arachis 58k Affymetrix array was leveraged to train machine learning models to select true SNPs straight from sequence data. These models achieved accuracy rates of above 80% using real peanut RNA-seq and whole genome shotgun (WGS) re-sequencing data, which is higher than previously reported for polyploids. A 48K SNP array, Axiom Arachis2, was designed using the approach which revealed 75% accuracy of calling SNPs from different tetraploid peanut genotypes. Using the method to simulate SNP variation in peanut, cotton, wheat, and strawberry, we show that models built with our parameter sets achieve above 98% accuracy in selecting true SNPs. Additionally, models built with simulated genotypes were able to select true SNPs at above 80% accuracy using real peanut data, demonstrating that our model can be used even if real data are not available to train the models. This work demonstrates an effective approach for calling highly reliable SNPs from polyploids using machine learning. A novel tool was developed for predicting true SNPs from sequence data, designated as SNP-ML (SNP-Machine Learning, pronounced \"snip mill\"), using the described models. SNP-ML additionally provides functionality to train new models not included in this study for customized use, designated SNP-MLer (SNP-Machine Learner, pronounced \"snip miller\"). SNP-ML is freely available for public use.

bioinformatics↗