in vivo fly RNAi (TRiP)

Quick link to GeneLookup (search DRSC/TRiP reagents)
Quick link to UP-TORR (batch search fly RNAi reagents)
Quick link to RSVP (search in vivo fly RNAi data)
Quick link to TRiP fly stock batch query

Since 2008, the Transgenic RNAi Project (TRiP) has generated transgenic RNAi fly stocks based on our optimized approach that are made immediately and openly available to the community through the Bloomington Drosophila Stock Center (BDSC).

See links below to relevant online tools, reagents, protocols, publications, and more.

Contact Us

Please contact us for any questions.


Lizabeth A Perkins, Laura Holderbaum, Rong Tao, Yanhui Hu, Richelle Sopko, Kim McCall, Donghui Yang-Zhou, Ian Flockhart, Richard Binari, Hye-Seok Shim, Audrey Miller, Amy Housden, Marianna Foos, Sakara Randkelv, Colleen Kelley, Pema Namgyal, Christians Villalta, Lu-Ping Liu, Xia Jiang, Qiao Huan-Huan, Xia Wang, Asao Fujiyama, Atsushi Toyoda, Kathleen Ayers, Allison Blum, Benjamin Czech, Ralph Neumuller, Dong Yan, Amanda Cavallaro, Karen Hibbard, Don Hall, Lynn Cooley, Gregory J Hannon, Ruth Lehmann, Annette Parks, Stephanie E Mohr, Ryu Ueda, Shu Kondo, Jian-Quan Ni, and Norbert Perrimon. 2015. “The Transgenic RNAi Project at Harvard Medical School: Resources and Validation..” Genetics, 3, 201: 843-52, 2015 Nov.Abstract

To facilitate large-scale functional studies in Drosophila, the Drosophila Transgenic RNAi Project (TRiP) at Harvard Medical School (HMS) was established along with several goals: developing efficient vectors for RNAi that work in all tissues, generating a genome-scale collection of RNAi stocks with input from the community, distributing the lines as they are generated through existing stock centers, validating as many lines as possible using RT-qPCR and phenotypic analyses, and developing tools and web resources for identifying RNAi lines and retrieving existing information on their quality. With these goals in mind, here we describe in detail the various tools we developed and the status of the collection, which is currently composed of 11,491 lines and covering 71% of Drosophila genes. Data on the characterization of the lines either by RT-qPCR or phenotype is available on a dedicated website, the RNAi Stock Validation and Phenotypes Project (RSVP,, and stocks are available from three stock centers, the Bloomington Drosophila Stock Center (United States), National Institute of Genetics (Japan), and TsingHua Fly Center (China).

Richelle Sopko, Marianna Foos, Arunachalam Vinayagam, Bo Zhai, Richard Binari, Yanhui Hu, Sakara Randklev, Lizabeth A Perkins, Steven P Gygi, and Norbert Perrimon. 2014. “Combining genetic perturbations and proteomics to examine kinase-phosphatase networks in Drosophila embryos..” Dev Cell, 1, 31: 114-27, 2014 Oct 13.Abstract

Connecting phosphorylation events to kinases and phosphatases is key to understanding the molecular organization and signaling dynamics of networks. We have generated a validated set of transgenic RNA-interference reagents for knockdown and characterization of all protein kinases and phosphatases present during early Drosophila melanogaster development. These genetic tools enable collection of sufficient quantities of embryos depleted of single gene products for proteomics. As a demonstration of an application of the collection, we have used multiplexed isobaric labeling for quantitative proteomics to derive global phosphorylation signatures associated with kinase-depleted embryos to systematically link phosphosites with relevant kinases. We demonstrate how this strategy uncovers kinase consensus motifs and prioritizes phosphoproteins for kinase target validation. We validate this approach by providing auxiliary evidence for Wee kinase-directed regulation of the chromatin regulator Stonewall. Further, we show how correlative phosphorylation at the site level can indicate function, as exemplified by Sterile20-like kinase-dependent regulation of Stat92E.

Stephanie E Mohr. 2014. “RNAi screening in Drosophila cells and in vivo..” Methods, 1, 68: 82-8, 2014 Jun 15.Abstract

Here, I discuss how RNAi screening can be used effectively to uncover gene function. Specifically, I discuss the types of high-throughput assays that can be done in Drosophila cells and in vivo, RNAi reagent design and available reagent collections, automated screen pipelines, analysis of screen results, and approaches to RNAi results verification.

Dong Yan, Ralph A Neumüller, Michael Buckner, Kathleen Ayers, Hua Li, Yanhui Hu, Donghui Yang-Zhou, Lei Pan, Xiaoxi Wang, Colleen Kelley, Arunachalam Vinayagam, Richard Binari, Sakara Randklev, Lizabeth A Perkins, Ting Xie, Lynn Cooley, and Norbert Perrimon. 2014. “A regulatory network of Drosophila germline stem cell self-renewal..” Dev Cell, 4, 28: 459-73, 2014 Feb 24.Abstract

Stem cells possess the capacity to generate two cells of distinct fate upon division: one cell retaining stem cell identity and the other cell destined to differentiate. These cell fates are established by cell-type-specific genetic networks. To comprehensively identify components of these networks, we performed a large-scale RNAi screen in Drosophila female germline stem cells (GSCs) covering ∼25% of the genome. The screen identified 366 genes that affect GSC maintenance, differentiation, or other processes involved in oogenesis. Comparison of GSC regulators with neural stem cell self-renewal factors identifies common and cell-type-specific self-renewal genes. Importantly, we identify the histone methyltransferase Set1 as a GSC-specific self-renewal factor. Loss of Set1 in neural stem cells does not affect cell fate decisions, suggesting a differential requirement of H3K4me3 in different stem cell lineages. Altogether, our study provides a resource that will help to further dissect the networks underlying stem cell self-renewal.

Joshua M Shulman, Selina Imboywa, Nikolaos Giagtzoglou, Martin P Powers, Yanhui Hu, Danelle Devenport, Portia Chipendo, Lori B Chibnik, Allison Diamond, Norbert Perrimon, Nicholas H Brown, Philip L De Jager, and Mel B Feany. 2014. “Functional screening in Drosophila identifies Alzheimer's disease susceptibility genes and implicates Tau-mediated mechanisms..” Hum Mol Genet, 4, 23: 870-7, 2014 Feb 15.Abstract

Using a Drosophila model of Alzheimer's disease (AD), we systematically evaluated 67 candidate genes based on AD-associated genomic loci (P < 10(-4)) from published human genome-wide association studies (GWAS). Genetic manipulation of 87 homologous fly genes was tested for modulation of neurotoxicity caused by human Tau, which forms neurofibrillary tangle pathology in AD. RNA interference (RNAi) targeting 9 genes enhanced Tau neurotoxicity, and in most cases reciprocal activation of gene expression suppressed Tau toxicity. Our screen implicates cindr, the fly ortholog of the human CD2AP AD susceptibility gene, as a modulator of Tau-mediated disease mechanisms. Importantly, we also identify the fly orthologs of FERMT2 and CELF1 as Tau modifiers, and these loci have been independently validated as AD susceptibility loci in the latest GWAS meta-analysis. Both CD2AP and FERMT2 have been previously implicated with roles in cell adhesion, and our screen additionally identifies a fly homolog of the human integrin adhesion receptors, ITGAM and ITGA9, as a modifier of Tau neurotoxicity. Our results highlight cell adhesion pathways as important in Tau toxicity and AD susceptibility and demonstrate the power of model organism genetic screens for the functional follow-up of human GWAS.

Yanhui Hu, Charles Roesel, Ian Flockhart, Lizabeth Perkins, Norbert Perrimon, and Stephanie E Mohr. 2013. “UP-TORR: online tool for accurate and Up-to-Date annotation of RNAi Reagents..” Genetics, 1, 195: 37-45, 2013 Sep.Abstract

RNA interference (RNAi) is a widely adopted tool for loss-of-function studies but RNAi results only have biological relevance if the reagents are appropriately mapped to genes. Several groups have designed and generated RNAi reagent libraries for studies in cells or in vivo for Drosophila and other species. At first glance, matching RNAi reagents to genes appears to be a simple problem, as each reagent is typically designed to target a single gene. In practice, however, the reagent-gene relationship is complex. Although the sequences of oligonucleotides used to generate most types of RNAi reagents are static, the reference genome and gene annotations are regularly updated. Thus, at the time a researcher chooses an RNAi reagent or analyzes RNAi data, the most current interpretation of the RNAi reagent-gene relationship, as well as related information regarding specificity (e.g., predicted off-target effects), can be different from the original interpretation. Here, we describe a set of strategies and an accompanying online tool, UP-TORR (for Updated Targets of RNAi Reagents;, useful for accurate and up-to-date annotation of cell-based and in vivo RNAi reagents. Importantly, UP-TORR automatically synchronizes with gene annotations daily, retrieving the most current information available, and for Drosophila, also synchronizes with the major reagent collections. Thus, UP-TORR allows users to choose the most appropriate RNAi reagents at the onset of a study, as well as to perform the most appropriate analyses of results of RNAi-based studies.