Data visualization

A.M. Conard, N. Goodman, Hu, Y, N. Perrimon, R. Singh, C. Lawrence, and E. Larschan. Submitted. “TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data.” BioRxiv. Publisher's VersionAbstract
Uncovering how transcription factors (TFs) regulate their targets at the DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) in normal and diseased states. RNA-seq has become a standard method to measure gene regulation using an established set of analysis steps. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. Furthermore, methods which integrate ordered RNA-seq data with transcription factor binding data are urgently needed. Here, we present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time series multi-omics pipeline method which infers the relationship between gene regulatory events across time. TIMEOR addresses the critical need for methods to predict causal regulatory mechanism networks between TFs from time series multi-omics data. We used TIMEOR to identify a new link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at https://github.com/ashleymaeconard/TIMEOR.git.
Yanhui Hu, Verena Chung, Aram Comjean, Jonathan Rodiger, Fnu Nipun, Norbert Perrimon, and Stephanie E Mohr. 2020. “BioLitMine: Advanced Mining of Biomedical and Biological Literature About Human Genes and Genes from Major Model Organisms.” G3 (Bethesda).Abstract
The accumulation of biological and biomedical literature outpaces the ability of most researchers and clinicians to stay abreast of their own immediate fields, let alone a broader range of topics. Although available search tools support identification of relevant literature, finding relevant and key publications is not always straightforward. For example, important publications might be missed in searches with an official gene name due to gene synonyms. Moreover, ambiguity of gene names can result in retrieval of a large number of irrelevant publications. To address these issues and help researchers and physicians quickly identify relevant publications, we developed BioLitMine, an advanced literature mining tool that takes advantage of the medical subject heading (MeSH) index and gene-to-publication annotations already available for PubMed literature. Using BioLitMine, a user can identify what MeSH terms are represented in the set of publications associated with a given gene of the interest, or start with a term and identify relevant publications. Users can also use the tool to find co-cited genes and a build a literature co-citation network. In addition, BioLitMine can help users build a gene list relevant to a MeSH terms, such as a list of genes relevant to "stem cells" or "breast neoplasms." Users can also start with a gene or pathway of interest and identify authors associated with that gene or pathway, a feature that makes it easier to identify experts who might serve as collaborators or reviewers. Altogether, BioLitMine extends the value of PubMed-indexed literature and its existing expert curation by providing a robust and gene-centric approach to retrieval of relevant information.
Image of an anesthetized male Drosophila fruit fly

DRSC/TRiP presentations from June 2020 Boston Area Drosophila Meeting

June 12, 2020
Did you miss the presentations from Claire Hu and Jonathan Zirin at the June 2020 Boston Area Drosophila Meeting? No problem! The slides can be accessed from this post. Click the title above to view the whole post, then scroll down to access the PDFs. These presentations describe what's new and next in bioinformatics and in vivo technologies at the DRSC/TRiP. Feel free to reach out with questions. Interested in the BAD meeting? Info about the meeting can be found here. Read more about DRSC/TRiP presentations from June 2020 Boston Area Drosophila Meeting
Screenshot of the FlyScape tool

Wilinski and colleagues release "FlyScape" for metabolic network visualization

November 7, 2019

The DRSC congratulates Wilinski et al. at the University of Michigan for their release and publication of FlyScape, a tool for metabolic network visualization.

Rapid metabolic shifts occur during the transition between hunger and satiety in Drosophila melanogaster

Daniel Wilinski, Jasmine Winzeler, William Duren, Jenna L. Persons, Kristina J. Holme, Johan Mosquera, Morteza Khabiri, Jason M. Kinchen, Peter L. Freddolino, Alla Karnovsky & Monica Dus 

...

Read more about Wilinski and colleagues release "FlyScape" for metabolic network visualization
Screenshot of online tools

Navigating our online tools -- orthologs, literature mining, qPCR primers, and so much more!

February 14, 2019

We have been taking a critical look at how we organize our online tools on the Online Tools Overview page. And more generally, we have been thinking about new ways to spread the word about the many resources in our suite of online tools. One way that we at the DRSC like to think about these tools is how they fit into the start-to-finish order of events in a screen or other experimental project. Various tools help define lists of genes to be studied, help identify reagents for the study,...

Read more about Navigating our online tools -- orthologs, literature mining, qPCR primers, and so much more!
flySAM

Missed us at ADRC 2018? View our workshop slides!

April 19, 2018
Thank you to all those who attended our workshop at last week's Annual Drosophila Research Conference in Philadelphia, PA, USA. It was great to talk fly stocks, cell screens, and bioinformatics with the community. We are here to help and look forward to continued feedback on the resources we are building to empower your research. PDFs of our workshop presentations are attached to this news item. The slides will help you learn more about our in vivo resources for CRISPR, new pooled cell-based CRISPR screen technology, and bioinformatics resources at our facility.  Feel free to contact... Read more about Missed us at ADRC 2018? View our workshop slides!
Yanhui Hu, Arunachalam Vinayagam, Ankita Nand, Aram Comjean, Verena Chung, Tong Hao, Stephanie E Mohr, and Norbert Perrimon. 11/16/2017. “Molecular Interaction Search Tool (MIST): an integrated resource for mining gene and protein interaction data.” Nucleic Acids Res, 46, D1, Pp. D567-D574.Abstract
Model organism and human databases are rich with information about genetic and physical interactions. These data can be used to interpret and guide the analysis of results from new studies and develop new hypotheses. Here, we report the development of the Molecular Interaction Search Tool (MIST; http://fgrtools.hms.harvard.edu/MIST/). The MIST database integrates biological interaction data from yeast, nematode, fly, zebrafish, frog, rat and mouse model systems, as well as human. For individual or short gene lists, the MIST user interface can be used to identify interacting partners based on protein-protein and genetic interaction (GI) data from the species of interest as well as inferred interactions, known as interologs, and to view a corresponding network. The data, interologs and search tools at MIST are also useful for analyzing 'omics datasets. In addition to describing the integrated database, we also demonstrate how MIST can be used to identify an appropriate cut-off value that balances false positive and negative discovery, and present use-cases for additional types of analysis. Altogether, the MIST database and search tools support visualization and navigation of existing protein and GI data, as well as comparison of new and existing data.
2018 Apr 13

DRSC & TRiP Workshop at ADRC

1:45pm to 3:45pm

Location: 

Philadelphia, PA, USA
The DRSC & TRiP will be hosting a workshop at the Annual Drosophila Research Conference in Philadelphia, PA. The workshop is scheduled for Friday, April 13th from 1:45 to 3:45 PM. Come hear from DRSC & TRiP leaders Norbert Perrimon, Jonathan Zirin (organizer), Claire Yanhui Hu, and Stephanie Mohr. At the workshop, you will learn about new opportunities for community nomination and experiments using CRISPR knockout and activation, as well as learn what's new and popular among our online software and database tools. There will be something for everyone -- we will provide information... Read more about DRSC & TRiP Workshop at ADRC
Yanhui Hu, Aram Comjean, Stephanie E Mohr, The FlyBase Consortium, and Norbert Perrimon. 8/7/2017. “Gene2Function: An Integrated Online Resource for Gene Function Discovery.” G3 (Bethesda).Abstract
One of the most powerful ways to develop hypotheses regarding biological functions of conserved genes in a given species, such as in humans, is to first look at what is known about function in another species. Model organism databases (MODs) and other resources are rich with functional information but difficult to mine. Gene2Function (G2F) addresses a broad need by integrating information about conserved genes in a single online resource.
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.

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