The Muller F element (4.2 Mb, ~80 protein-coding genes) is an unusual autosome of Drosophila melanogaster; it is mostly heterochromatic with a low recombination rate. To investigate how these properties impact the evolution of repeats and genes, we manually improved the sequence and annotated the genes on the D. erecta, D. mojavensis, and D. grimshawi F elements and euchromatic domains from the Muller D element. We find that F elements have greater transposon density (25–50%) than euchromatic reference regions (3–11%). Among the F elements, D. grimshawi has the lowest transposon density (particularly DINE-1: 2% vs. 11–27%). F element genes have larger coding spans, more coding exons, larger introns, and lower codon bias. Comparison of the Effective Number of Codons with the Codon Adaptation Index shows that, in contrast to the other species, codon bias in D. grimshawi F element genes can be attributed primarily to selection instead of mutational biases, suggesting that density and types of transposons affect the degree of local heterochromatin formation. F element genes have lower estimated DNA melting temperatures than D element genes, potentially facilitating transcription through heterochromatin. Most F element genes (~90%) have remained on that element, but the F element has smaller syntenic blocks than genome averages (3.4–3.6 vs. 8.4–8.8 genes per block), indicating greater rates of inversion despite lower rates of recombination. Overall, the F element has maintained characteristics that are distinct from other autosomes in the Drosophila lineage, illuminating the constraints imposed by a heterochromatic milieu.

No abstract

No abstract

A n algorithm deployed across the United States is now known to underestimate the health needs of black patients 1. The algorithm uses health-care costs as a proxy for health needs. But black patients' health-care costs have historically been lower because systemic racism has impeded their access to treatment-not because they are healthier. This example illustrates how machine learning and artificial intelligence can maintain and amplify inequity. Most algorithms exploit crude correlations in data. Yet these correlations are often by-products of more salient social relationships (in the health-care example, treatment that is inaccessible is, by definition, cheaper), or chance occurrences that will not replicate. To identify and mitigate discriminatory relationships in data, we need models that capture or account for the causal pathways that give rise to them. Here we outline what is required to build models that would allow us to explore ethical issues underlying seemingly objective analyses. Only by unearthing the true causes of discrimination can we build algorithms that correct for these.