Genome assembly of Scorias spongiosa and comparative genomics provide insights into ecological adaptation of honeydew-dependent sooty mould fungi.

Sooty moulds are fungi of economic importance and with unique lifestyle mainly growing on insect honeydew. However, the lack of genomic data hinders investigation of genetic mechanisms underlying their ecological adaptation. With long-read sequencing technology, we generated the genome of Scorias spongiosa, an extraordinary sooty mould fungus associated with honeydew of colony aphids and producing large fruiting bodies. A 24.21 Mb high-quality genome assembly with a N50 length of 3.37 Mb was obtained. The genome contained 7758 protein coding genes, 97.13% of which were homologous to known genes, and approximately 0.29 Mb repeat sequences. Comparative genomics showed S. spongiosa lost relatively more gene families and contained fewer species-specific genes and gene families, with many CAZyme families and sugar transporters reduced or absent. This study not only promotes understanding of the ecological adaptation of sooty moulds, but also provides valuable genomic data resource for future comparative genomic and genetic studies.

A dataset on type specimens of hemipteran insects in China.

BACKGROUND: Type specimens are valuable resources for investigating and exploring biodiversity on Earth, which has high academic and conservation value. Hemipteran insects are one of the most important and diverse groups in Insecta and their type specimens have important reference value for many research fields. So far, the data on the type specimens of the Hemiptera in China have not been fully collated. NEW INFORMATION: Through extensive literature review, we have constructed a dataset of type specimens for the new species of hemipteran insects in China published from 1950 to 2017, which includes the data such as collection date, specimen gender, preservation institution and geographical distribution. A total of 6,583 type specimen records were collected, covering 3,783 new species belonging to 1,299 genera and 88 families. This dataset can support the international community in conducting research on taxonomy, biodiversity, evolution and pest management.