Dataset or data portal

2023
Hong-Wen Tang, Kerstin Spirohn, Yanhui Hu, Tong Hao, István A Kovács, Yue Gao, Richard Binari, Donghui Yang-Zhou, Kenneth H Wan, Joel S Bader, Dawit Balcha, Wenting Bian, Benjamin W Booth, Atina G Coté, Steffi de Rouck, Alice Desbuleux, Kah Yong Goh, Dae-Kyum Kim, Jennifer J Knapp, Wen Xing Lee, Irma Lemmens, Cathleen Li, Mian Li, Roujia Li, Hyobin Julianne Lim, Yifang Liu, Katja Luck, Dylan Markey, Carl Pollis, Sudharshan Rangarajan, Jonathan Rodiger, Sadie Schlabach, Yun Shen, Dayag Sheykhkarimli, Bridget TeeKing, Frederick P. Roth, Jan Tavernier, Michael A Calderwood, David E Hill, Susan E Celniker, Marc Vidal, Norbert Perrimon, and Stephanie E. Mohr. 2023. “Next-generation large-scale binary protein interaction network for Drosophila melanogaster,” 14, 1, Pp. 2162. Publisher's VersionAbstract
Generating reference maps of interactome networks illuminates genetic studies by providing a protein-centric approach to finding new components of existing pathways, complexes, and processes. We apply state-of-the-art methods to identify binary protein-protein interactions (PPIs) for Drosophila melanogaster. Four all-by-all yeast two-hybrid (Y2H) screens of > 10,000 Drosophila proteins result in the ‘FlyBi’ dataset of 8723 PPIs among 2939 proteins. Testing subsets of data from FlyBi and previous PPI studies using an orthogonal assay allows for normalization of data quality; subsequent integration of FlyBi and previous data results in an expanded binary Drosophila reference interaction network, DroRI, comprising 17,232 interactions among 6511 proteins. We use FlyBi data to generate an autophagy network, then validate in vivo using autophagy-related assays. The deformed wings (dwg) gene encodes a protein that is both a regulator and a target of autophagy. Altogether, these resources provide a foundation for building new hypotheses regarding protein networks and function.
s41467-023-37876-0.pdf
2021
Raghuvir Viswanatha, Enzo Mameli, Jonathan Rodiger, Pierre Merckaert, Fabiana Feitosa-Suntheimer, Tonya M. Colpitts, Stephanie E. Mohr, Yanhui Hu, and Norbert Perrimon. 3/30/2021. “Bioinformatic and cell-based tools for pooled CRISPR knockout screening in mosquitos [NOTE: A modified final version was published in Nat Comm and is now available.].” bioRxiv. Publisher's VersionAbstract
Mosquito-borne diseases present a worldwide public health burden. Genome-scale screening tools that could inform our understanding of mosquitos and their control are lacking. Here, we adapt a recombination-mediated cassette exchange system for delivery of CRISPR sgRNA libraries into cell lines from several mosquito species and perform pooled CRISPR screens in an Anopheles cell line. To implement this method, we engineered modified mosquito cell lines, validated promoters and developed bioinformatics tools for multiple mosquito species.Competing Interest StatementThe authors have declared no competing interest.
2021.03.29.437496v2.full_.pdf
2018
R. Hung, Y. Hu, R. Kirchner, Fengge Li, C. Xu, A. Comjean, S.G. Tattikota, W.R. Song, S. Ho Sui, and N. Perrimon. 9/8/2018. “Data portal for "A cell atlas of the adult Drosophila midgut" (BioRxiv)”. Click here to access data portal.
Raghuvir Viswanatha, Aram Comjean, and Norbert Perrimon. 8/10/2018. “Raw data download access for: Viswanatha et al. 2018 eLife "Pooled genome-wide CRISPR screening .."”. Click here to access links to raw data files
2016
Arunachalam Vinayagam, Travis E Gibson, Ho-Joon Lee, Bahar Yilmazel, Charles Roesel, Yanhui Hu, Young Kwon, Amitabh Sharma, Yang-Yu Liu, Norbert Perrimon, and Albert-László Barabási. 5/3/2016. “Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets.” Proc Natl Acad Sci U S A, 113, 18, Pp. 4976-81.Abstract

The protein-protein interaction (PPI) network is crucial for cellular information processing and decision-making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferation or cell death. Here, we characterize the structural controllability of a large directed human PPI network comprising 6,339 proteins and 34,813 interactions. This network allows us to classify proteins as "indispensable," "neutral," or "dispensable," which correlates to increasing, no effect, or decreasing the number of driver nodes in the network upon removal of that protein. We find that 21% of the proteins in the PPI network are indispensable. Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network's control property is critical for the transition between healthy and disease states. Furthermore, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are indispensable. Among the 56 genes, 46 of them have not been previously associated with cancer. This suggests that controllability analysis is very useful in identifying novel disease genes and potential drug targets.

2016_PNAS_Vinayagam.pdf
2012
Ian T Flockhart, Matthew Booker, Yanhui Hu, Benjamin McElvany, Quentin Gilly, Bernard Mathey-Prevot, Norbert Perrimon, and Stephanie E Mohr. 2012. “FlyRNAi.org--the database of the Drosophila RNAi screening center: 2012 update.” Nucleic Acids Res, 40, Database issue, Pp. D715-9.Abstract

FlyRNAi (http://www.flyrnai.org), the database and website of the Drosophila RNAi Screening Center (DRSC) at Harvard Medical School, serves a dual role, tracking both production of reagents for RNA interference (RNAi) screening in Drosophila cells and RNAi screen results. The database and website is used as a platform for community availability of protocols, tools, and other resources useful to researchers planning, conducting, analyzing or interpreting the results of Drosophila RNAi screens. Based on our own experience and user feedback, we have made several changes. Specifically, we have restructured the database to accommodate new types of reagents; added information about new RNAi libraries and other reagents; updated the user interface and website; and added new tools of use to the Drosophila community and others. Overall, the result is a more useful, flexible and comprehensive website and database.

2012_Nuc Acids Res_Flockhart.pdf
2010
Michael Schnall-Levin, Yong Zhao, Norbert Perrimon, and Bonnie Berger. 2010. “Conserved microRNA targeting in Drosophila is as widespread in coding regions as in 3'UTRs.” Proc Natl Acad Sci U S A, 107, 36, Pp. 15751-6.Abstract

MicroRNAs (miRNAs) are a class of short noncoding RNAs that regulate protein-coding genes posttranscriptionally. In animals, most known miRNA targeting occurs within the 3'UTR of mRNAs, but the extent of biologically relevant targeting in the ORF or 5'UTR of mRNAs remains unknown. Here, we develop an algorithm (MinoTar-miRNA ORF Targets) to identify conserved regulatory motifs within protein-coding regions and use it to estimate the number of preferentially conserved miRNA-target sites in ORFs. We show that, in Drosophila, preferentially conserved miRNA targeting in ORFs is as widespread as it is in 3'UTRs and that, while far less abundant, conserved targets in Drosophila 5'UTRs number in the hundreds. Using our algorithm, we predicted a set of high-confidence ORF targets and selected seven miRNA-target pairs from among these for experimental validation. We observed down-regulation by the miRNA in five out of seven cases, indicating our approach can recover functional sites with high confidence. Additionally, we observed additive targeting by multiple sites within a single ORF. Altogether, our results demonstrate that the scale of biologically important miRNA targeting in ORFs is extensive and that computational tools such as ours can aid in the identification of such targets. Further evidence suggests that our results extend to mammals, but that the extent of ORF and 5'UTR targeting relative to 3'UTR targeting may be greater in Drosophila.

2010_PNAS_Schnall-Levin.pdf Supplement.pdf