Matsubara, K. | bioRxiv Science

bioRxiv2021

Diversity of reptile sex chromosome evolution revealed by cytogenetic and linked-read sequencing

Reptile sex determination is attracting much attention because the great diversity of sex-determination and dosage compensation mechanisms permits us to approach fundamental questions about sex chromosome turnover and evolution. However, reptile sex chromosome variation remains largely uncharacterized and no reptile master sex determination genes have yet been identified. Here we describe a powerful and cost-effective "chromosomics" approach, combining probes generated from the microdissected sex chromosomes with transcriptome sequencing to explore this diversity in non-model Australian reptiles with heteromorphic or cryptic sex chromosomes. We tested the pipeline on a turtle, a gecko, and a worm-lizard, and we also identified sequences located on sex chromosomes in a monitor lizard using linked-read sequencing. Genes identified on sex chromosomes were compared to the chicken genome to identify homologous regions among the four species. We identified candidate sex determining genes within these regions, including conserved vertebrate sex-determining genes pdgfa, pdgfra amh and wt1, and demonstrated their testis or ovary-specific expression. All four species showed gene-by-gene rather than chromosome-wide dosage compensation. Our results imply that reptile sex chromosomes originated by independent acquisition of sex-determining genes on different autosomes, as well as translocations between different ancestral macro- and micro-chromosomes. We discuss the evolutionary drivers of the slow differentiation, but rapid turnover, of reptile sex chromosomes.

genomics↗

bioRxiv2020

Collection of 2429 constrained headshots of 277 volunteers for deep learning

Deep learning has rapidly been filtrating many aspects of human lives. In particular, image recognition by convolutional neural networks has inspired numerous studies in this area. Hardware and software technologies as well as large quantities of data have contributed to the drastic development of the field. However, the application of deep learning is often hindered by the need for big data and the laborious manual annotation thereof. To experience deep learning using the data compiled by us, we collected 2429 constrained headshot images of 277 volunteers. The collection of face photographs is challenging in terms of protecting personal information; we established an online procedure in which both the informed consent and image data could be obtained. We did not collect personal information, but issued agreement numbers to deal with withdrawal requests. Gender and smile labels were manually and subjectively annotated only from the appearances, and final labels were determined by majority among our team members. Rotated, trimmed, resolution-reduced, decolorized, and matrix-formed data were allowed to be publicly released. Moreover, simplified feature vectors for data sciences were released. We performed gender recognition by building convolutional neural networks based on the Inception V3 model with pre-trained ImageNet data to demonstrate the usefulness of our dataset.

animal behavior and cognition↗