Exploring the cobia (Rachycentron canadum) genome: unveiling putative male heterogametic regions and identification of sex-specific markers.

BACKGROUND: Cobia (Rachycentron canadum) is the only member of the Rachycentridae family and exhibits considerable sexual dimorphism in growth rate. Sex determination in teleosts has been a long-standing basic biological question, and the molecular mechanisms of sex determination/differentiation in cobia are completely unknown. RESULTS: Here, we reported 2 high-quality, chromosome-level annotated male and female cobia genomes with assembly sizes of 586.51 Mb (contig/scaffold N50: 86.0 kb/24.3 Mb) and 583.88 Mb (79.9 kb/22.5 Mb), respectively. Synteny inference among perciform genomes revealed that cobia and the remora Echeneis naucrates were sister groups. Further, whole-genome resequencing of 31 males and 60 females, genome-wide association study, and sequencing depth analysis identified 3 short male-specific regions within a 10.7-kb continuous genomic region on male chromosome 18, which hinted at an undifferentiated sex chromosome system with a putative XX/XY mode of sex determination in cobia. Importantly, the only 2 genes within/between the male-specific regions, epoxide hydrolase 1 (ephx1, renamed cephx1y) and transcription factor 24 (tcf24, renamed ctcf24y), showed testis-specific/biased gene expression, whereas their counterparts cephx1x and ctf24x, located in female chromosome 18, were similarly expressed in both sexes. In addition, male-specific PCR targeting the cephx1y gene revealed that this genomic feature is conserved in cobia populations from Panama, Brazil, Australia, and Japan. CONCLUSION: The first comprehensive genomic survey presented here is a valuable resource for future studies on cobia population structure and dynamics, conservation, and evolutionary history. Furthermore, it establishes evidence of putative male heterogametic regions with 2 genes playing a potential role in the sex determination of the species, and it provides further support for the rapid evolution of sex-determining mechanisms in teleost fish.

A deep learning method for drug-target affinity prediction based on sequence interaction information mining.

Background: A critical aspect of in silico drug discovery involves the prediction of drug-target affinity (DTA). Conducting wet lab experiments to determine affinity is both expensive and time-consuming, making it necessary to find alternative approaches. In recent years, deep learning has emerged as a promising technique for DTA prediction, leveraging the substantial computational power of modern computers. Methods: We proposed a novel sequence-based approach, named KC-DTA, for predicting drug-target affinity (DTA). In this approach, we converted the target sequence into two distinct matrices, while representing the molecule compound as a graph. The proposed method utilized k-mers analysis and Cartesian product calculation to capture the interactions and evolutionary information among various residues, enabling the creation of the two matrices for target sequence. For molecule, it was represented by constructing a molecular graph where atoms serve as nodes and chemical bonds serve as edges. Subsequently, the obtained target matrices and molecule graph were utilized as inputs for convolutional neural networks (CNNs) and graph neural networks (GNNs) to extract hidden features, which were further used for the prediction of binding affinity. Results: In order to evaluate the effectiveness of the proposed method, we conducted several experiments and made a comprehensive comparison with the state-of-the-art approaches using multiple evaluation metrics. The results of our experiments demonstrated that the KC-DTA method achieves high performance in predicting drug-target affinity (DTA). The findings of this research underscore the significance of the KC-DTA method as a valuable tool in the field of in silico drug discovery, offering promising opportunities for accelerating the drug development process. All the data and code are available for access on https://github.com/syc2017/KCDTA.