Project description:T cell receptor (TCR) signaling in Jurkat cells was investigated using the CROP-seq method for CRISPR single-cell sequencing. In CROP-seq, genetic perturbations are introduced into single cells in a pooled fashion, and single-cell RNA-seq is used to determine the transcriptional response to the CRISPR-induced perturbation in a large number of single cells in parallel. Importantly, the CROP-seq vector makes individual guide-RNAs detectable using standard single-cell RNA-seq technology. The dataset presented here is based on CROP-seq in combination with single-cell RNA-seq using the 10x Genomics v2 chemistry. It recapitulates a previously published CROP-seq dataset (Datlinger et al. 2017 Nature Methods; GEO: GSE92872) that used the Drop-seq protocol as the single-cell RNA-seq readout. Additional information on CROP-seq are available from the following website: http://crop-seq.computational-epigenetics.org/ .
Project description:The use of single cell sequencing technologies has exploded in non-model organisms, including fish and invertebrates in an aquaculture setting. One of the most expanding areas is the use sequencing nucleus from frozen tissues allowing being able to sample and sequence at different sites. Here we present a tested nucleus isolation protocol that outperforms the most reliable methods to extract nuclei (chopping in salt tween buffer) and improves its performance in non-model organisms.
Project description:Association Genetics can quickly and efficiently delineate regions of the genome that control traits and provide markers to accelerate breeding by marker-assisted selection. The requirements for many markers and a genome sequence to order those markers have limited its exploitation in crops. To harness this approach for use in a broad range of crops, even those with complex genomes, we developed an approach based on transcriptome sequencing to exploit markers representing variation in both gene sequences and gene expression. We term this approach Associative Transcriptomics. Applying it successfully in Brassica napus, as an example, we identified that genomic deletions including orthologues of the transcription factor controlling aliphatic glucosinolate biosynthesis in Arabidopsis thaliana, HAG1 (At5g61420), underlie two QTL for glucosinolate content of seeds.
Project description:Molecular biology aims to understand the molecular basis of cellular responses, unravel dynamic regulatory networks, and model complex biological systems. However, these studies remain challenging in non-model species as a result of poor functional annotation of regulatory proteins, like kinases or phosphatases. To overcome this limitation, we developed a multi-layer neural network that annotates proteins by determining functionality directly from the protein sequence. We annotated the kinases and phosphatases in the non-model species, Glycine max (soybean), achieving a prediction sensitivity of up to 97%. To demonstrate the applicability, we used our functional annotations in combination with Bayesian network principles to predict signaling cascades using time series phosphoproteomics. We shed light on phosphorylation cascades in soybean seedlings upon cold treatment and identified Glyma.10G173000 (TOI5) and Glyma.19G007300 (TOT3) as key temperature response regulators in soybean. Importantly, the signaling cascade predictions do not rely upon known upstream kinases, kinase motifs, or protein interaction data, enabling de novo identification of kinase-substrate interactions. In addition to high accuracy and strong generalization, we showed that our functional prediction neural network is scalable to other model and non-model species, including Oryza sativa (rice), Zea mays (maize), Sorghum bicolor (sorghum), and Triticum aestivum (wheat). Overall, we demonstrated a data-driven systems biology approach for non-model species leveraging our predicted upstream kinases and phosphatases.
Project description:Genomes and transcriptomes of non-model organisms can be analyzed using next-generation sequencing technologies, but de-novo sequencing and annotating a full eukaryotic genome is still challenging. So, -omics experimentation with non-model organisms requires a suite of technologies to obtain reliable results in a cost-effective manner. Here, a novel method for microarray-based genome analysis is presented which is especially suitable for non-model organisms. We show that it is useful for complementing regular aCGH analyses and for evaluating transcriptome next-generation sequencing reads. The principle of the method is straightforward: feature intensities obtained after hybridizing the test genome are compared with the feature intensities of a control hybridization. The control hybridization is performed with negative control probes (no targets in the control sample), and with positive control probes (with targets in the control sample). The method has in principle a resolution of a single probe and it does not depend on the structural information of a reference genome: the genomic ordering of probe targets is irrelevant. In a test, analyzing the genome content of a sequenced bacterial strain: Staphylococcus aureus MRSA252, this approach proved to be successful demonstrated by receiver operating characteristic area under the curve values larger than 0.9995.
Project description:Genomes and transcriptomes of non-model organisms can be analyzed using next-generation sequencing technologies, but de-novo sequencing and annotating a full eukaryotic genome is still challenging. So, -omics experimentation with non-model organisms requires a suite of technologies to obtain reliable results in a cost-effective manner. Here, a novel method for microarray-based genome analysis is presented which is especially suitable for non-model organisms. We show that it is useful for complementing regular aCGH analyses and for evaluating transcriptome next-generation sequencing reads. The principle of the method is straightforward: feature intensities obtained after hybridizing the test genome are compared with the feature intensities of a control hybridization. The control hybridization is performed with negative control probes (no targets in the control sample), and with positive control probes (with targets in the control sample). The method has in principle a resolution of a single probe and it does not depend on the structural information of a reference genome: the genomic ordering of probe targets is irrelevant. In a test, analyzing the genome content of a sequenced bacterial strain: Staphylococcus aureus MRSA252, this approach proved to be successful demonstrated by receiver operating characteristic area under the curve values larger than 0.9995.
Project description:Primary objectives: The primary objective is to investigate circulating tumor DNA (ctDNA) via deep sequencing for mutation detection and by whole genome sequencing for copy number analyses before start (baseline) with regorafenib and at defined time points during administration of regorafenib for treatment efficacy in colorectal cancer patients in terms of overall survival (OS).
Primary endpoints: circulating tumor DNA (ctDNA) via deep sequencing for mutation detection and by whole genome sequencing for copy number analyses before start (baseline) with regorafenib and at defined time points during administration of regorafenib for treatment efficacy in colorectal cancer patients in terms of overall survival (OS).