Project description:The naked mole-rat (NMR; Heterocephalus glaber) has recently gained considerable attention in the scientific community for its unique potential to unveil novel insights in the fields of medicine, biochemistry, and evolution. NMRs exhibit unique adaptations that include protracted fertility, cancer resistance, eusociality, and anoxia. This suite of adaptations is not found in other rodent species, suggesting that interrogating conserved and accelerated regions in the NMR genome will find regions of the NMR genome fundamental to their unique adaptations. However, the current NMR genome assembly has limits that make studying structural variations, heterozygosity, and non-coding adaptations challenging. We present a complete diploid naked-mole rat genome assembly by integrating long-read and 10X-linked read genome sequencing of a male NMR and its parents, and Hi-C sequencing in the NMR hypothalamus (N=2). Reads were identified as maternal, paternal or ambiguous (TrioCanu). We then polished genomes with Flye, Racon and Medaka. Assemblies were then scaffolded using the following tools in order: Scaff10X, Salsa2, 3d-DNA, Minimap2-alignment between assemblies, and the Juicebox Assembly Tools. We then subjected the assemblies to another round of polishing, including short-read polishing with Freebayes. We assembled the NMR mitochondrial genome with mitoVGP. Y chromosome contigs were identified by aligning male and female 10X linked reads to the paternal genome and finding male-biased contigs not present in the maternal genome. Contigs were assembled with publicly available male NMR Fibroblast Hi-C-seq data (SRR820318). Both assemblies have their sex chromosome haplotypes merged so that both assemblies have a high-quality X and Y chromosome. Finally, assemblies were evaluated with Quast, BUSCO, and Merqury, which all reported the base-pair quality and contiguity of both assemblies as high-quality. The assembly will next be annotated by Ensembl using public RNA-seq data from multiple tissues (SRP061363). Together, this assembly will provide a high-quality resource to the NMR and comparative genomics communities.

Project description:Background: Gene expression profiling has shown its ability to identify with high accuracy low cytogenetic risk acute myeloid leukemia such as acute promyelocytic leukemia and leukemias with t(8;21) or inv(16). The aim of this gene expression profiling study was to evaluate to what extent suboptimal samples with low leukemic blast load (range, 2-59%) and/or poor quality control criteria could also be correctly identified. Methods: Specific signatures were first defined so that all 71 acute promyelocytic leukemia, leukemia with t(8;21) or inv(16)-AML as well as cytogenetically normal acute myeloid leukemia samples with at least 60% blasts and good quality control criteria were correctly classified (training set). The classifiers were then evaluated for their ability to assign to the expected class 111 samples considered as suboptimal because of a low leukemic blast load (n=101) and/or poor quality control criteria (n=10) (test set). Results: With 10-marker classifiers, all training set samples as well as 97 of the 101 test samples with a low blast load, and all 10 samples with poor quality control criteria were correctly classified. Regarding test set samples, the overall error rate of the class prediction was below 4 percent, even though the leukemic blast load was as low as 2%. Sensitivity, specificity, negative and positive predictive values of the class assignments ranged from 91% to 100%. Of note, for acute promyelocytic leukemia and leukemias with t(8;21) or inv(16), the confidence level of the class assignment was influenced by the leukemic blast load. Conclusion: Gene expression profiling and a supervised method requiring 10-marker classifiers enable the identification of favorable cytogenetic risk acute myeloid leukemia even when samples contain low leukemic blast loads or display poor quality control criterion. Specific signatures were first defined so that all 71 acute promyelocytic leukemia, leukemia with t(8;21) or inv(16)-AML as well as cytogenetically normal acute myeloid leukemia samples with at least 60% blasts and good quality control criteria were correctly classified (training set). The classifiers were then evaluated for their ability to assign to the expected class 111 samples considered as suboptimal because of a low leukemic blast load (n=101) and/or poor quality control criteria (n=10) (test set).

			Action	DRS
	101.zip	Other
	102.zip	Other
	4.zip	Other
	80.zip	Other
	a_MTBLS781_NMR_spectroscopy.txt	Txt

Dataset Information

RANCM: a new ranking scheme for assigning confidence levels to metabolite assignments in NMR-based metabolomics studies

Dataset's files

Publications

RANCM: a new ranking scheme for assigning confidence levels to metabolite assignments in NMR-based metabolomics studies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets