Biology subjects

Kretzschmar, T.

Publications and source records attributed to Kretzschmar, T..

5 recordsLinked to original sources

bioRxiv2024

Building a community-driven bioinformatics platform to facilitate Cannabis sativa multi-omics research

Global changes in Cannabis legislation after decades of stringent regulation, and heightened demand for its industrial and medicinal applications have spurred recent genetic and genomics research. An international research community emerged and identified the need for a web portal to host Cannabis-specific datasets that seamlessly integrates multiple data sources and serves omics-type analyses, fostering information sharing. The Tripal platform was used to host public genome assemblies, gene annotations, QTL and genetic maps, gene and protein expression, metabolic profile and their sample attributes. SNPs were called using public resequencing datasets on three genomes. Additional applications, such as SNP-Seek and MapManJS, were embedded into Tripal. A multi-omics data integration web-service API, developed on top of existing Tripal modules, returns generic tables of sample, property, and values. Use-cases demonstrate the APIs utility for various -omics analyses, enabling researchers to perform multi- omics analyses efficiently.

bioinformatics↗

bioRxiv2020

Characterization of the Cannabis sativa glandular trichome proteome

Cannabis sativa has been cultivated since antiquity as a source of fibre, food and medicine. The recent resurgence of Cannabis as a cash crop is mainly driven by the medicinal and therapeutic properties of its resin, which contains compounds that interact with the human endocannabinoid system. Compared to other medicinal crops of similar value, however, little is known about the biology of C. sativa. Glandular trichomes are small hair-like projections made up of stalk and head tissue and are responsible for the production of the resin in C. sativa. Trichome productivity, as determined by Cannabis sativa resin yield and composition, is only beginning to be understood at the molecular level. In this study the proteomes of glandular trichome stalks and heads, were investigated and compared to the proteome of the whole flower tissue, to help elucidate Cannabis sativa glandular trichome biochemistry. The data suggested that the floral tissue acts as a major source of carbon and energy to the glandular trichome head sink tissue, supplying sugars which drive secondary metabolite biosynthesis in the glandular trichome head; the location of the secretory cells. The trichome stalk seems to play only a limited role in secondary metabolism and acts as both source and sink.

plant biology↗

bioRxiv2019

Predicting agronomic traits and associated genomic regions in diverse rice landraces using marker stability

To secure the worlds food supply it is essential that we improve our knowledge of the genetic underpinnings of complex agronomic traits. In this paper, we report our findings from performing trait prediction and association mapping using marker stability in diverse rice landraces. We used the least absolute shrinkage and selection operator as our marker selection algorithm, and considered twelve real agronomic traits and a hundred simulated traits using a population with approximately a hundred thousand markers. For trait prediction, we considered several statistical/machine learning methods. We found that some of the methods considered performed best when preselected markers using marker stability were used. However, our results also show that one might need to make a trade-off between model size and performance for some learning methods. For association mapping, we compared marker stability to the genome-wide efficient mixed-model analysis (GEMMA), and for the simulated traits, we found that marker stability significantly outperforms GEMMA. For the real traits, marker stability successfully identifies multiple associated markers, which often entail those selected by GEMMA. Further analysis of the markers selected for the real traits using marker stability showed that they are located in known quantitative trait loci (QTL) using the QTL Annotation Rice Online database. Furthermore, co-functional network prediction of the selected markers using RiceNet v2 also showed association to known controlling genes. We argue that a wide adoption of the marker stability approach for the prediction of agronomic traits and association mapping could improve global rice breeding efforts.

bioinformatics↗

bioRxiv2018

Rice Galaxy: an open resource for plant science

Background\n\nRice molecular genetics, breeding, genetic diversity, and allied research (such as rice-pathogen interaction) have adopted sequencing technologies and high density genotyping platforms for genome variation analysis and gene discovery. Germplasm collections representing rice diversity, improved varieties and elite breeding materials are accessible through rice gene banks for use in research and breeding, with many having genome sequences and high density genotype data available. Combining phenotypic and genotypic information on these accessions enables genome-wide association analysis, which is driving quantitative trait loci (QTL) discovery and molecular marker development. Comparative sequence analyses across QTL regions facilitate the discovery of novel alleles. Analyses involving DNA sequences and large genotyping matrices for thousands of samples, however, pose a challenge to non-computer savvy rice researchers.\n\nFindings\n\nWe adopted the Galaxy framework to build the federated Rice Galaxy resource, with shared datasets, tools, and analysis workflows relevant to rice research. The shared datasets include high density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from nine published rice genomes. Rice Galaxy includes tools for designing single nucleotide polymorphism (SNP) assays, analyzing genome-wide association studies, population diversity, rice-bacterial pathogen diagnostics, and a suite of published genomic prediction methods. A prototype Rice Galaxy compliant to Open Access, Open Data, and Findable, Accessible, Interoperable, and Reproducible principles is also presented.\n\nConclusions\n\nRice Galaxy is a freely available resource that empowers the plant research community to perform state-of-the-art analyses and utilize publicly available big datasets for both fundamental and applied science.

bioinformatics↗

bioRxiv2016

rAmpSeq: Using repetitive sequences for robust genotyping

Repetitive sequences have been used for DNA fingerprinting and genotyping for more than a quarter century. Now, with our knowledge of whole genome sequences, repetitive sequences can be used to identify polymorphisms that can be mapped and scored in a systematic manner. We have developed a simple, robust platform for designing primers, PCR amplification, and high throughput cloning that allows hundreds to thousands of markers to be scored for less than $5 per sample. Conserved regions were used to design PCR primers for amplifying thousands of middle repetitive regions of the maize (Zea mays ssp. mays) genome. Bioinformatic scans were then used to identify DNA sequence polymorphisms in the low copy intervening sequences. When used in conjunction with simple DNA preps, optimized PCR conditions, high multiplex Illumina indexing and a bioinformatic marker calling platform tailored for repetitive sequences, this methodology provides a cost effective genotyping strategy for large-scale genomic selection projects. We show detailed results from four maize primer sets that produced between 1,335-3,225 good coverage loci with 1056 that segregated appropriately in a bi-parental family. This approach could have wide applicability to breeding and conservation biology, where hundreds of thousands of samples need to be genotyped for very minimal cost.

genomics↗