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Spilornis perplexus

ABSTRACT: Whole genome resequencing data from Crested serpent eagles

PROVIDER: PRJDB18639 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	DRR587185_1.fastq.gz	Fastqsanger.gz
	DRR587185_2.fastq.gz	Fastqsanger.gz
	DRR587186_1.fastq.gz	Fastqsanger.gz
	DRR587186_2.fastq.gz	Fastqsanger.gz
	DRR587187_1.fastq.gz	Fastqsanger.gz

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Project description:Sequence-based deep learning models have become the state of the art for the analysis of the genomic regulatory code. Particularly for transcriptional enhancers, deep learning models excel at deciphering sequence features and grammar that underlie their spatiotemporal activity. To enable end-to-end enhancer modeling and design, we developed a software and modeling package, called CREsted. It combines preprocessing starting from single-cell ATAC-seq data; modeling with a choice of several architectures for training classification and regression models on either topics or pseudobulk peak heights; sequence design using multiple strategies; and downstream analysis through a collection of tools to locate transcription factor (TF) binding sites, infer the effect of a TF (activating or repressing) on enhancer accessibility, decipher enhancer grammar, and score gene loci. We demonstrate CREsted using a mouse cortex model that we validate using the BICCN collection of in vivo validated mouse brain enhancers. Classical enhancers in immune cells, including the IFN-β enhanceosome are revisited using a PBMC model, and we assess the accuracy of TF binding site predictions with ChIP-seq. Additionally, we use CREsted to compare mesenchymal-like cancer cell states between tumor types; and we investigate different fine-tuning strategies of Borzoi within CREsted, comparing their performance and explainability with CREsted models trained from scratch. Finally, we train a CREsted model on a scATAC-seq atlas of zebrafish development, and use this to design and in vivo validate cell type-specific synthetic enhancers in 3 tissues. For varying datasets we demonstrate that CREsted facilitates efficient training and analyses, enabling scrutinization of the enhancer logic and design of synthetic enhancers across tissues and species. CREsted is available at https://crested.readthedocs.io.

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Laboratory evolution of Jen1p-independent lactate transport in Saccharomyces cerevisiae: identification of ADY2 alleles by whole genome resequencing and mRNA expression analysis

Project description:Background: Evolutionary engineering is a powerful approach to isolate suppressor mutants and industrially relevant genotypes. Until recently, DNA microarray analysis was the only affordable genome-wide approach to identify the responsible mutations. This situation has changed due to the rapidly decreasing costs of whole genome (re)sequencing. DNA microarray-based mRNA expression analysis and whole genome resequencing were combined in a study on lactate transport in Saccharomyces cerevisiae. Jen1p is the only S. cerevisiae lactate transporter reported in literature. To identify alternative lactate transporters, a jen1Δ strain was evolved for growth on lactate. Results: Two independent evolution experiments yielded Jen1p-independent growth on lactate (μmax 0.14 and 0.18 h-1 for single-cell lines IMW004 and IMW005, respectively). Whereas mRNA expression analysis did not provide leads, whole-genome resequencing showed different single nucleotide changes (C755G/Leu219Val and C655G/Ala252Gly) in the acetate transporter gene ADY2. Analysis of mRNA levels and depth of coverage of DNA sequencing combined with karyotyping, gene deletions and diagnostic PCR showed that in IMW004 an isochromosome III (~475 kb), which contains two additional copies of ADY2C755G, was formed via crossover between YCLWΔ15 and YCRCΔ6. Introduction of the ADY2 alleles in a jen1 ady2 strain resulted in growth on lactate (μmax 0.14 h-1 for Ady2pLeu219Val and 0.12 h-1 for Ady2pAla252Gly). Conclusions: Whole-genome resequencing of yeast strains obtained from independent evolution experiments enabled rapid identification of a key gene that was not identified by mRNA expression analysis of the same strains. Reverse metabolic engineering showed that mutated alleles of ADY2 (C655G and C755G) encode efficient lactate transporters.

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