Highly contiguous assemblies of 101 drosophilid genomes
Abstract
Over 100 years of studies in Drosophila melanogaster and related species in the genus Drosophila have facilitated key discoveries in genetics, genomics, and evolution. While high-quality genome assemblies exist for several species in this group, they only encompass a small fraction of the genus. Recent advances in long-read sequencing allow high-quality genome assemblies for tens or even hundreds of species to be efficiently generated. Here, we utilize Oxford Nanopore sequencing to build an open community resource of genome assemblies for 101 lines of 93 drosophilid species encompassing 14 species groups and 35 sub-groups. The genomes are highly contiguous and complete, with an average contig N50 of 10.5 Mb and greater than 97% BUSCO completeness in 97/101 assemblies. We show that Nanopore-based assemblies are highly accurate in coding regions, particularly with respect to coding insertions and deletions. These assemblies, along with a detailed laboratory protocol and assembly pipelines, are released as a public resource and will serve as a starting point for addressing broad questions of genetics, ecology, and evolution at the scale of hundreds of species.
Data availability
All sequencing data and assemblies generated by this study are deposited at NCBI SRA and GenBank under NCBI BioProject PRJNA675888. Accession numbers for all data used but not generated by this study are provided in the supporting files. Dockerfiles and scripts for reproducing pipelines and analyses are provided on GitHub (https://github.com/flyseq/drosophila_assembly_pipelines). A detailed wet lab protocol is provided at Protocols.io (https://dx.doi.org/10.17504/protocols.io.bdfqi3mw).
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Nanopore-based assembly of many drosophilid genomesNCBI BioProject, PRJNA675888.
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Sequencing and assembly of 14 Drosophila speciesNCBI BioProject, ID: 427774.
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modENCODE Drosophila reference genome sequencing (fruit flies)NCBI BioProject, ID: 62477.
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Drosophila montium Species Group Genomes ProjectNCBI BioProject, ID: 554346.
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Invertebrate sample from Drosophila repletaNCBI BioProject, ID: 476692.
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Genome sequences of 10 Drosophila speciesNCBI BioProject, ID: 322011.
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Raw genomic sequencing data from 16 Drosophila speciesNCBI BioProject, ID: 550077.
Article and author information
Author details
Funding
National Institute of General Medical Sciences (F32GM135998)
- Bernard Y Kim
National Institute of General Medical Sciences (R35GM119816)
- Noah K Whiteman
Uehara Memorial Foundation (201931028)
- Teruyuki Matsunaga
Ministry of Education, Science and Technological Development of the Republic of Serbia (451-03-68/2020-14/200178)
- Marina Stamenković-Radak
- Mihailo Jelić
- Marija Savić Veselinović
Ministry of Education, Science and Technological Development of the Republic of Serbia (451-03-68/2020-14/200007)
- Marija Tanasković
- Pavle Erić
National Natural Science Foundation of China (32060112)
- Jian-Jun Gao
Japan Society for the Promotion of Science (JP18K06383)
- Masayoshi Watada
European Union Horizon 2020 Research and Innovation Program (765937-CINCHRON)
- Giulia Manoli
- Enrico Bertolini
Czech Science Foundation (19-13381S)
- Vladimír Košťál
Japan Society for the Promotion of Science (JP19H03276)
- Aya Takahashi
National Science Foundation (1345247)
- Donald K Price
National Institute of General Medical Sciences (R35GM118165)
- Dmitri A Petrov
National Institute of Diabetes and Digestive and Kidney Diseases (K01DK119582)
- Jeremy Wang
National Science Foundation (DEB-1457707)
- Corbin D Jones
National Institute of General Medical Sciences (R01GM121750)
- Daniel R Matute
National Institute of General Medical Sciences (R01GM125715)
- Daniel R Matute
Google Cloud Platform Research Credits
- Bernard Y Kim
Google Cloud Platform Research Credits
- Jeremy Wang
National Institute of General Medical Sciences (R35GM122592)
- Artyom Kopp
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Graham Coop, University of California, Davis, United States
Version history
- Preprint posted: December 15, 2020 (view preprint)
- Received: January 11, 2021
- Accepted: July 16, 2021
- Accepted Manuscript published: July 19, 2021 (version 1)
- Version of Record published: August 4, 2021 (version 2)
- Version of Record updated: March 18, 2022 (version 3)
Copyright
© 2021, Kim et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Further reading
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- Evolutionary Biology
- Genetics and Genomics
A protein’s genetic architecture – the set of causal rules by which its sequence produces its functions – also determines its possible evolutionary trajectories. Prior research has proposed that the genetic architecture of proteins is very complex, with pervasive epistatic interactions that constrain evolution and make function difficult to predict from sequence. Most of this work has analyzed only the direct paths between two proteins of interest – excluding the vast majority of possible genotypes and evolutionary trajectories – and has considered only a single protein function, leaving unaddressed the genetic architecture of functional specificity and its impact on the evolution of new functions. Here, we develop a new method based on ordinal logistic regression to directly characterize the global genetic determinants of multiple protein functions from 20-state combinatorial deep mutational scanning (DMS) experiments. We use it to dissect the genetic architecture and evolution of a transcription factor’s specificity for DNA, using data from a combinatorial DMS of an ancient steroid hormone receptor’s capacity to activate transcription from two biologically relevant DNA elements. We show that the genetic architecture of DNA recognition consists of a dense set of main and pairwise effects that involve virtually every possible amino acid state in the protein-DNA interface, but higher-order epistasis plays only a tiny role. Pairwise interactions enlarge the set of functional sequences and are the primary determinants of specificity for different DNA elements. They also massively expand the number of opportunities for single-residue mutations to switch specificity from one DNA target to another. By bringing variants with different functions close together in sequence space, pairwise epistasis therefore facilitates rather than constrains the evolution of new functions.