Podcasts about cytoscape

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.11.29.518394v1?rss=1 Authors: CHAKRABORTY, S., MAITI, T., Bhowmick, S., Sarkar, S. Abstract: The molecular pathway associated with Multiple sclerosis (MS) is complex and symptomatic treatments are only available right now. Early diagnosis of MS creates a window for healthcare providers to manage the disease more efficiently. Blood-based biomarker study has been done in the past to identify the upregulated and downregulated genes but in this present study, a novel approach has been taken for identifying genes associated with the disease. In this present study, hub genes are identified and the top ten hub genes were used to identify drugs associated with them. Upregulated genes were identified using the dataset GSE21942 (which contains information related to genes identified in the blood of multiple sclerosis patients) and datasets GSE17846 and GSE61741(which contains information related to microRNAs taken from multiple sclerosis patients). Genes associated with microRNAs were identified using miRWalk. Common genes from both miRWalk and the dataset GSE21942 were identified and were subjected to STRINGdb for the creation of a protein-protein interaction network and this network was then imported to Cytoscape for identifying the top ten hub genes. The top ten hub genes were subjected to EnrichR for enrichment analysis of genes. In our study, it was found that CTNNB1 is the gene with the highest degree (116). Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

過去2年分のお便りを紹介しました。Show notes Researchat.fm お便りフォーム … こちらからお便り受け付けております。 原腸陥入はお済みですか？ ルイス・ウォルパート「人生において最も重要なのは、誕生でも結婚でも死でもなく、原腸陥入の瞬間だ。」 V(D)J組換えはお済みですか？ アレグラ飲んで実験してるよね？ THIS WEEK IN VIROLOGY … 石原さんご紹介ありがとうございます。 TWシリーズ NEB podcast eLife podcast The Lonely Pipette : helping scientists do better science … SHIOTAさんが教えてくれた海外のresearchat.fmっぽいポッドキャスト 葬送のフリーレン 湯神くんには友達がいない … Ayaneさん、ご紹介ありがとうございました。 Artiste 防衛漫玉日記 神戸在住 76. The Chimeric RNA (Researchat.fm) … dessanがCRISPRについて説明してくれた回 79. Connecting Dots (Researchat.fm) … pomeさんがバイオスタートアップについて説明してくれた回 Zhu et al., Science Advances (2019) … “A prokaryotic-eukaryotic hybrid viral vector for delivery of large cargos of genes and proteins into human cells” たうちさん紹介ありがとうございます。おもしろい論文です。 いいねの数だけ論文を今年紹介する 2020年の論文紹介実績 2019年の論文紹介実績 登さんのブログ 登さんの情熱大陸 論理的思考の放棄 相分離 (Phase separation) LLPSに関する言及 … LLPS研究の歴史から問題点まで chromatin/DNA loop extrusion … 興味がある方はググってください。 Cytoscape Bioconductor Fiji/ImageJ Chan Zuckerberg Initiative BioPerl Biopython BioRuby 74. Imaging-by-Sequencing (Researchat.fm) … DNA origamiについての軽い言及があります。 37. Biological Enigma (Researchat.fm) … dessanによる分子生物学入門 Editorial notes こう見えて懸命に答えをだそうとはしています (soh) 遅くなりましたが、お便りありがとうございます。(tadasu) ポッドキャストでの回答のペースが遅くて申し訳ないです。お便りは全て目を通させて頂います！とても励みになっております。(coela)

Добрый день уважаемые слушатели. Представляем новый выпуск подкаста RWpod. В этом выпуске: Ruby Rails 7 adds database-specific setup and reset tasks for multi DB configurations Async Ruby GitHub Issue-style File Uploader Using Stimulus and Active Storage Ruby Structs Rack Middlewares in Ruby on Rails Prettier Ruby 2.0.0 Caffeinate - a drip email engine for managing, creating, and sending scheduled email sequences from your Ruby on Rails application Gammo - A pure-Ruby HTML5 parser Web The New React Docs, In Progress and Now In Beta React Router v6 Photoshop's journey to the web Get started with Medusa Part 1: the open-source alternative to Shopify Record, replay and measure user flows Cytoscape.js - graph theory (network) library for visualisation and analysis Vizzu - Library for animated data visualizations and data stories Liqe - lightweight and performant Lucene-like parser and search engine RWpod Cafe 27 (04.12.2021) Сбор и голосование за темы новостей

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.21.305847v1?rss=1 Authors: Hasegawa, K., Yamaguchi, Y., Pengjam, Y. Abstract: Pyruvic acid therapy is used for various diseases, but the therapeutic effect decreases at high doses. The molecular mechanism of high-dose pyruvate is not well understood. The purpose of this study was to identify the effects of high dose pyruvate addition on skeletal muscle using C2C12. The gene expression profile for the GSE5497 dataset was taken from the Gene Expression Omnibus database. GEO2R was used to identify specifically expressed genes (DEGs). Functional analysis and pathway enrichment analysis of DEG were performed using the DAVID database. The protein-protein interaction (PPI) network was built in the STRING database and visualized using Cytoscape. GO analysis showed that up-regulated DEG was primarily involved in angiogenesis, cell adhesion, and inflammatory response. We also showed that down-regulated DEG is involved in the regulation of muscle contraction, skeletal muscle fiber development. In addition, the upregulated KEGG pathway of DEG included Rheumatoid arthritis, Chemokine signaling pathway, and Cytokine-cytokine receptor interaction. Downregulated DEG included Calcium signaling pathway, hypertrophic cardiomyopathy (HCM), Dilated cardiomyopathy, Neuroactive ligand-receptor interaction, and Cardiac muscle contraction. Further, analysis of two modules selected from the PPI network showed that high-dose pyruvate exposure to C2C12 was primarily associated with muscle contraction, muscle organ morphogenesis, leukocyte chemotaxis, and chemokine activity. In conclusion, High-dose pyruvate treatment of C2C12 was found to be associated with an increased inflammatory response and decreased skeletal muscle formation. However, further studies are still needed to verify the function of these molecules at high doses of pyruvate. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.30.273870v1?rss=1 Authors: Subramanian, U., Devarajan, B. Abstract: Retinal angiogenesis is a common neovascularization mechanism that causes severe irreversible vision loss in the number of retinal diseases worldwide. Patients often do not respond to the current antiangiogenic therapies and have a vision loss. Understanding the various angiogenic pathways and factors involved in the pathogenic mechanism is vital for disease management. In this study, to identify dysregulated angiogenic pathways and specific angiogenic factors involved in vision-threatening diseases namely proliferative diabetic retinopathy (PDR), retinopathy of prematurity (ROP) and neovascular age related macular degeneration (nAMD), we downloaded microarray metadata of samples and obtained the differentially expressed genes (DEGs) in all the disease and each disease samples compared to controls. Subsequently, we performed Gene Set Enrichment (GESA) analysis for pathways, a protein-protein interaction (PPI), and angiome network analysis using R and Cytoscape software. We identified highly enriched dysregulated pathways that were neuroactive ligand receptor interaction and cytokine cytokine receptor interaction. The angiogenic associated DEGs were predominately related to the cytokine cytokine receptor interaction pathway, which we further confirmed with RNA-seq data of PDR samples. Together, our analysis of these data elucidated the molecular mechanisms of retinal angiogenesis and provided potential angiogenic targets for therapeutics. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.03.232959v1?rss=1 Authors: Murgas, L., Contreras-Riquelme, S., Martinez, J. E., Villaman, C., Santibanez, R., Martin, A. J. Abstract: Motivation: The regulation of gene expression is a key factor in the development and maintenance of life in all organisms. This process is carried out mainly through the action of transcription factors (TFs), although other actors such as ncRNAs are involved. In this work, we propose a new method to construct Gene Regulatory Networks (GRNs) depicting regulatory events in a certain context for Drosophila melanogaster. Our approach is based on known relationships between epigenetics and the activity of transcription factors. Results: We developed method, Tool for Weighted Epigenomic Networks in D. melanogaster (Fly T-WEoN), which generates GRNs starting from a reference network that contains all known gene regulations in the fly. Regulations that are unlikely taking place are removed by applying a series of knowledge-based filters. Each of these filters is implemented as an independent module that considers a type of experimental evidence, including DNA methylation, chromatin accessibility, histone modifications, and gene expression. Fly T-WEoN is based on heuristic rules that reflect current knowledge on gene regulation in D. melanogaster obtained from literature. Experimental data files can be generated with several standard procedures and used solely when and if available.Fly T-WEoN is available as a Cytoscape application that permits integration with other tools, and facilitates downstream network analysis. In this work, we first demonstrate the reliability of our method to then provide a relevant application case of our tool: early development of D. melanogaster. Availability: Fly T-WEoN, together with its step-by-step guide is available at https://weon.readthedocs.io Copy rights belong to original authors. Visit the link for more info

Jane Ferguson:                 Hi everyone. Welcome to Getting Personal: Omics of the Heart. This is episode 11 from December 2017. I'm Jane Ferguson, and this podcast comes to you courtesy of Circulation: Cardiovascular Genetics and the AHA Functional Genomics and Translational Biology Council. I'm particularity excited about our interview this month. Doctor Kent Arell from the Mayo Clinic talked to Doctor Jennifer Hall from the University of Minnesota about her role as Chief of the AHA institute for Precision Cardiovascular Medicine. This is a really exciting initiative, which is bringing together researchers, patients and stakeholder to foster the growth and development of cardiovascular genomic and precision medicine. Through their Precision Medicine Platform you can access a virtual big data research hub and find data, tools, and collaborations. The institute also provides funding for projects related to precision cardiovascular medicine. You can find out more and register to access the platform at precision.heart.org. And listen on to hear Jen describe the platform and how this initiative first came about. Dr. Kent A.:                         Hello. My name is Doctor Kent Arell. I'm the Chair of the American Heart Association's FGTB Council early Career Committee, and a proteomic and network systems biologist in the Department of Cardiovascular Medicine and the Center for Regenerative Medicine at Mayo Clinic in Rochester, Minnesota. As part of a highly collaborative team, my research efforts focus on omic space systems approaches for heart failure prediction, diagnosis and therapy. Specifically regenerative approaches to cardiac repair.                                                 This November during American Heart Association Scientific Sessions 2017, FGTB Early Career Committee programming complimented the FGTB Council Precision Medicine Summit with a hands-on bootcamp on network systems biology, followed by a session introducing online computational portals and tools designed to enhance and facilitate basic and clinical cardiovascular research. A highlight of the second Early Career session was an introduction on the use of the Precision Medicine Platform of the American Heart Association's Institute for Precision Cardiovascular Medicine presented by the Institute's lead bioinformaticist, Laura Stevens.                                                 With these recent presentations in the many ongoing developments in precision molecular medicine combined with my own research approach and interests, I'm especially delighted today to be speaking with Doctor Jennifer Hall, Chief of the American Heart Association's Institute for Precision cardiovascular Medicine. Doctor Hall received her PhD from U.C. Berkeley and completed post-docs at Stanford and Harvard prior to taking up her first faculty position. Welcome, Doctor Hall. Is there anything I've missed that you would like to add? Dr. Jennifer H.:                  No, you're doing a great job, Kent. Dr. Kent A.:                         All right. Doctor Hall, the Institute for Precision Cardiovascular Medicine is one of the newest additions to the American Heart Association. And as far as I'm aware, you've been involved with the Institute since the beginning. Could you first tell us a little bit about the history of how the Institute came to be, and perhaps how you first became involved? Dr. Jennifer H.:                  I would be very happy to do that. So the Institute actually started in 2013 in the Fall, when the American Heart Association Board of Directors made an initial investment. And the vision for the Institute for Precision Cardiovascular Medicine was Nancy Brown’s, our CEO. And in the early years it was really led by the Chief of Staff, Laura Sol. And our lead strategist, Prad Presoon. And the original grants came out from the institute but was then called the Cardiovascular Genome-Phenome Center, or Study. Dr. Kent A.:                         Right. I'm familiar with that. Okay. Dr. Jennifer H.:                  Yes. That was the very early history. And it was rebranded in 2015. So that's the history. I joined about a year and a half ago now. And I'm just thrilled to be part of the team. Dr. Kent A.:                         Well, there's exciting things on the horizon, definitely. So what is the mandate of the institute? And what are its principal or primary objectives then? Dr. Jennifer H.:                  So the mandate is to really provide a better understanding in the area of precision cardiovascular medicine to all individuals. And that means participants across the United States and across the globe, patients and those that are healthy individuals as well. And finally to scientists, researchers and clinicians. Dr. Kent A.:                         Okay. Dr. Jennifer H.:                  And the five things that we are really focused on in terms of, I would say our principal objectives are really to convene people all over the world, experts, students, trainees. Provide transformative grants and this is at least half of our budget every year, if not up to 80% of our budget. Enable data discoverability and access. And that's through some of the new tools that we are creating. Act as a translation agent. And we can talk more about that if you'd wish. And finally to offer research enabling services or tools to our young trainees as well as our established investigators. Dr. Kent A.:                         Okay. Perfect. Well, I think I had some questions to cover most of those topics. But the translation agent would be interesting, if you wouldn't mind sort of expanding on that a little bit right now maybe, perhaps before I get to the other questions I had. Dr. Jennifer H.:                  Yes, absolutely. I think this means a lot of things to a lot of different people. Like one of the ways it can help people are, scientist that have been very focused in academics for a long time has not thought about intellectual property, or ways to really begin to commercialize their ideas and take them forward. So we used to think about bench to bedside. And the institute is focusing on helping these individuals. Helping these grantees really begin to think through how to talk about their ideas and to take them to market. So I'd say that is one way of thinking of the translation agent.                                                 The other is to really begin to take participant data and to bring new people into the field. So not only will we think about volunteers as being Heart Walkers anymore or being Go Red for Women volunteers, or one of the many 30 million volunteers we have throughout the United States within the American Heart Association and around the globe. But we want to ask them to contribute to precision medicine, and ask them to be a part of this new exciting movement as well and contribute data, interact, provide answers to surveys, and be educated a little bit more in the area of precision medicine. So those are the two big ways that we think about acting as a translation agent. Dr. Kent A.:                         Okay. Excellent. With respect to intellectual property, is there an intermediary that you connect researchers to, to facilitate that? Or is it more of an awareness of how to approach your own institutional technology transfer offices, or what have you? Dr. Jennifer H.:                  Well, you're going right where I would've led the conversation. So we're trying to do both I should say. Dr. Kent A.:                         Okay. Dr. Jennifer H.:                  It's just originally getting off the ground and talking to our volunteers at those academic institutions and figuring out the best way to do that. And then talking to some people in the industry as well to figure out how does it work best coming from that side. Dr. Kent A.:                         Okay. Excellent. Well, I think the next thing that I might transition to is the grants, because points one, three and five that you made about convening people, enabling data access and enabling services and platforms may all sort of relate to the Precision Medicine Platform. And we'll definitely be speaking about that quite a bit. But one thing I wanted to mention is the variety and the number of grants that have been awarded by the institute since it inaugurated grants I believe in 2015, is that correct? Dr. Jennifer H.:                  Yes. That's exactly right. And- Dr. Kent A.:                         Yeah. Dr. Jennifer H.:                  ... I think- Dr. Kent A.:                         Go ahead. Dr. Jennifer H.:                  ... we're quite happy. We've given over 72 grants and that totals, I think, just over 15 million. But more importantly the grantees have just done a remarkable job I think. The running total I have, which I know is a little bit out of date is 98 publications, and many of those in high impact journals. And certainly in new areas that AHA has not been in the past, which is artificial intelligence, machine learning, and creating new tools. Dr. Kent A.:                         So you're also trying to bridge multiple disciplines then I guess as well with some of these grants, correct? Dr. Jennifer H.:                  Yes. And many of the fields like you're working in as well, thinking about systems biology and proteomics, and bringing the field to beta science along with that as well. So exactly. And in many cases we have private partnerships or strategic partnerships with others like Amazon Web Services. So many of the grants at least, one of the portfolios we have has many grants in it in which the tech credits, or the cloud computing credits if you will, are given to us by our strategic partner Amazon Web Services. So AHA provides the salary support for the PI and others on the grant, as well as any supplies that are needed. And then those tech credits for cloud compute and storage come from Amazon. Dr. Kent A.:                         Excellent. Okay. And then there are other collaborative efforts with other institutions as well, is that correct? Like the Broad Institute? Dr. Jennifer H.:                  Yes, exactly. We have a great relationship with the Broad Institute as well. They've been extremely helpful in helping us get our direct to participant recruitment program off the ground. And we are co-investigators with them on an upcoming NIH data platform grant. So we're extremely excited to be working with them and their team. We've worked with several people, and Doctor Anthony Philippakis' team, and with Noel Burtt and David [inaudible 00:11:24] and others there. So we just couldn't be happier with that relationship. Dr. Kent A.:                         Perfect. Perfect. I know the current round of funding is defined as Uncovering New Patterns, and there are fellowships in grants available for that round of funding. What is the scope of the Uncovering New Patterns effort here? Dr. Jennifer H.:                  There's a lot of both science and tech built into that. And so, one of the ideas from the institute executive committee ... So one question people might ask is, "How do these ... How do you come up with these grants?" And it's really talking people, finding a need, listening to a lot of people no matter where we're traveling. And then bringing that up to our Institute Executive Committee who makes the final call on these grants.                                                 In this case, we were looking for a way to bring science and technology together. So if there was a way to combine datasets that hadn't been combined in the past, which creates some new data harmonization standards, perhaps there's some new methodologies around that. And that's one way to uncover new patterns that we were thinking about. That was the- Dr. Kent A.:                         Right. Dr. Jennifer H.:                  ... original example that came to mind. Dr. Kent A.:                         Okay. Or some new algorithms for- Dr. Jennifer H.:                  Yeah. Dr. Kent A.:                         ... interpreting data or what have you. And it sounds like there's goo dialogue back and forth and between the institute, and those who are sort of interested in the topic as well from the researchers' standpoint both clinically and for basic researchers. Dr. Jennifer H.:                  Yeah. We try. Dr. Kent A.:                         Yeah. Yep. Oh, I think you do more than try. I think it's a wonderful operation. So I see on the website that there's a new round of funding opportunities are soon to be announced. Can you give us any hint as to what the focus will be for the upcoming round or other things that may be in the pipeline in that regard for the upcoming months? Or do you want to keep it a secret? Dr. Jennifer H.:                  I can't disclose that. But there will be some things that are a little bit similar to what we've done in the past. Our focus is to really be on the cutting edge, and we are democratizing data on this Precision Medicine Platform making it open in a controlled access way. So you have to fill out a form. But we're really trying to get to those forms and turn over access to people, qualified researchers and scientists as quickly as possible in a very responsible way. And so we're piloting some new objectives around that. We'd love to bring all cardiovascular and brain health data together. And so people can identify and find new discoveries in this area. Dr. Kent A.:                         Right. Dr. Jennifer H.:                  And use new technique to do it. So the grants will be focused in that particular area. Dr. Kent A.:                         Okay. Well that give me an easy segue then into my next question, which is focusing on some of the resources that are currently available to basic and clinical investigators. And after maybe perhaps describing those, where or how can individuals access these particular resources? So I know definitely the Precision Medicine Platform is one that we'll hopefully hear quite a bit about. Dr. Jennifer H.:                  Yes. That's something that's been underway for about a year now. And we started with two beta testers that you can find the site at precision.heart.org. And we had two beta testers who Gabe [Lucenero 00:15:07] and Laura Stevens, and they were absolutely fantastic and gave us a lot of feedback. We've since hired Laura. Gabe runs his own company up in Canada in the area of bioinformatics. And they've just been fantastic. And since those two, in the last year we've grown to over a 1000 registered users. And once you register on the platform, you can register with your Google account, or however you ... It's a very simple process.                                                 Then you can search across all the datasets. Once you do that, you can access a workspace. And once you request access to the data, the data's put into a workspace for you that is really in the cloud. And allows you to use the power of cloud compute, meaning something that might've taken you weeks on your supercomputer back home in your institution, once you got in the queue, takes a day of a couple of hours using the power of the cloud and the compute power behind it.                                                 Each workspace also has over 85 analytical tools from visualization to statistics, to simple things. And we understand everybody doesn't know how to code. And so we're trying to make that as easy as possible as well. So there's some new things there. We're coming out with new things every week, and looking for ... If people are looking for help, we're taking emails and questions by phone and trying to reach as many people as possible. So we're looking for people to both contribute data and utilize that data to accelerate new discoveries. Dr. Kent A.:                         Perfect. Well, I know, I'm working with Laura actually on trying to implement Cytoscape within the cloud space as well. So it's exciting opportunities. And I'm not a coder myself, but for those that are I know most of the applications that Laura is looking at are based I believe in terms of the language that they're using, is that correct? Dr. Jennifer H.:                  That's right. Dr. Kent A.:                         I think so. Dr. Jennifer H.:                  So most are in R, or Python. Dr. Kent A.:                         Python. Okay. Dr. Jennifer H.:                  And they're on Jupiter Notebooks today. So the nice thing about that is that you can take that Jupiter Notebook, and those are being published in many journals today. So it's really a quick way to analyze the data, get feedback on the data from the community if you're looking for that, that's another piece that we're building in today. And you can have your collaborators work on that same workspace with you. And then just turn that Jupiter Notebook and publish it with all your methodology and code on it. Dr. Kent A.:                         Oh, okay. Excellent. Dr. Jennifer H.:                  And we're also establishing drag and drop code, so if you are just learning, you can take kind of from the recipe book and drag and drop, and practice and learn. And soon we should be hopefully working on some educational tools with our strategic primary, Amazon. So we're really excited about that coming out in the near future. Dr. Kent A.:                         Excellent. And we're doing our best at the council level to try to help spread the word as well with respect to the platform, and that was part of our offerings as I said, at Scientific Sessions back in November. Laura did a wonderful job presenting the platform and introducing it to a wide variety of backgrounds. I think in a way that is really ... It's really helpful bringing these people together with different backgrounds, with different abilities. People are ... Regardless of your background, people are coming up with wonderful ideas and ways to implement these applications. Are you taking say, feedback in terms of how people are using these tools and how they can utilize different applications in terms of making adjustments and modifications as time proceeds? Dr. Jennifer H.:                  Yes. We're really excited about that, because we'd like to keep all of that data like any scientist, and publish that. So not only ours, it's better, but others doing similar things also will improve. And this platform is meant to work with other groups and to interact with other platforms as well and be part of the NIH data commons as well. And so we're really working to interact a use standard language and standard systems to interact globally as well as within and U.S. So we're trying to make it simple for all scientists. But gather as much data as we can. And a big shout out and thanks to you, and Functional Genomics Council for allowing us to be part of that workshop at Scientific Sessions. Dr. Kent A.:                         Oh, we really appreciate the opportunity to be involved with it as well. I think one of the ... Another strength that as you mentioned is that collaborators can access the same workspace from different institutions so that they can be working together and collaborating without having to download an entire dataset in one space, and have someone working on it and modifying things, and then finding out that your collaborator at the other institute has modified something else. And then trying to- Dr. Jennifer H.:                  Right. Dr. Kent A.:                         ... sync those version together can be quite troublesome at times. Dr. Jennifer H.:                  And you weren't sure you had the right version, and datasets are so large now that- Dr. Kent A.:                         Yeah. Dr. Jennifer H.:                  ... really people think about it in terms of the researchers going to the data, instead of the data going to the researchers. Dr. Kent A.:                         Yeah. Excellent. Dr. Jennifer H.:                  [inaudible 00:20:55] the new ways. Dr. Kent A.:                         That's perfect. Well, I'll maybe just reiterate that website. It's precision.heart.org. For the Precision Medicine Platform. And while I'm mentioning websites, maybe I'll mention institute.heart.org, which is the homepage for the institute for Precision Cardiovascular Medicine, I believe. Is that correct? Dr. Jennifer H.:                  Thank you for mentioning that. Dr. Kent A.:                         Yeah, 'cause if I'm wrong, please correct it. Dr. Jennifer H.:                  Well, you see, I'm a scientist, not a marketer because I never mentioned that at the beginning. But thank you for doing that. And we'd love any feedback that anybody has of other things they'd really like to see there, or how we can be more helpful to the community. Dr. Kent A.:                         Definitely. And how would people go about contacting you in that regard or in terms of setting up a workspace, or accessing a workspace? Would they go through the institute homepage? Dr. Jennifer H.:                  In terms of setting up a workspace, you can do that directly from precision.heart.org. Dr. Kent A.:                         Okay. Perfect. Dr. Jennifer H.:                  And once you request access to data, if you check on a box and hit, "Request access," it starts you down that road. It doesn't mean you have to commit. But it will start you down that road. Dr. Kent A.:                         Yeah, it will set it up. Okay. Perfect. Dr. Jennifer H.:                  It will begin to set up the process for you. It takes ... You can put in there your grant, it takes tokens, it takes all sorts of things, because there's a small cost that is a pass through really from ... It's like if you were just to go directly to Amazon Web Services to create that workspace. Dr. Kent A.:                         All right. So it's a small cost incurred for ... Okay. Excellent. Dr. Jennifer H.:                  Yeah. For what you're doing. Dr. Kent A.:                         Okay. Dr. Jennifer H.:                  And so everybody should be aware of that. Dr. Kent A.:                         Sure. Dr. Jennifer H.:                  But the institute is giving the grants for that. NIH gives grants and tokens for researchers to allow to use that as well. And like I said, the cost is a couple dollars an hour really. So it is not something that's gonna break the bank. And the AHA has built some stopgaps to try to keep those costs as low as we can. Dr. Kent A.:                         Perfect. Well, that's much more reasonable than some of the bioinformatic applications I work with these days. So that's good to know. Dr. Jennifer H.:                  Well, just the license fees alone can be challenging [crosstalk 00:23:06]. Dr. Kent A.:                         Exactly. Well, even institutional licenses, you still- Dr. Jennifer H.:                  Yeah. Dr. Kent A.:                         ... you're paying a portion of, and it can add up quickly when you're on there frequently. So- Dr. Jennifer H.:                  Yeah. Dr. Kent A.:                         ... a couple dollars an hour is quite reasonable. Dr. Jennifer H.:                  Yeah. Dr. Kent A.:                         Well, I don't wanna take up too much of your time. I know you probably have another meeting scheduled right after this. So I wanna thank you for taking the time today to talk with me and discuss the AHA's Institute for Precision Cardiovascular Medicine. I seem to have trouble pronouncing, "Precision," today for some reason. And for sort of introducing the Precision Medicine Platform for our listeners. So thanks again for bringing these important advances to the American Heart Association membership, and for sort of introducing them to us in today's podcast. Dr. Jennifer H.:                  Oh, thank you, Kent. And thank you for all you do for functional genomics and the American Heart Association. Dr. Kent A.:                         Thanks Doctor Hall. Much appreciated. Dr. Jennifer H.:                  Take care. Dr. Kent A.:                         Take care. Jane Ferguson:                 Thanks again to Doctor Hall for joining us, and to Doctor Arell and the FGTB Early Career Committee for supporting this podcast. And as we bring 2017 to a close, I want to thank all of you out there for subscribing and listening. We're excited to come back in 2018 with even more content and would love any feedback or suggestions of topics you'd like us to cover. You can leave a comment or review through iTunes or other podcast aggregator, or contact me directly at jane.f.furgeson@vanderbilt.edu. Whishing safe and happy holidays for anyone who celebrates. And we'll talk to you again in 2018.

Recent technological advances have made it possible to measure various parameters of biological processes in a genome-wide manner. While traditional molecular biology focusses on individual processes using targeted experiments (reductionistic approach), the field of systems biology utilizes high-throughput experiments to determine the state of a complete system such as a cell at once (holistic approach). Systems biology is not only carried out in wet-lab, but for the most part also requires tailored computational methods. High-throughput experiments are able to produce massive amounts of data, that are often too complex for a human to comprehend directly, that are affected by substantial noise, i.e. random measurement variation, and that are often subject to considerable bias, i.e. systematic deviations of the measurement from the truth. Thus, computer science and statistical methods are necessary for a proper analysis of raw data from such large-scale experiments. The goal of systems biology is to understand a whole system such as a cell in a quantitative manner. Thus, the computational part does not end with analyzing raw data but also involves visualization, statistical analyses, integration and interpretation. One example for these four computational tasks is as follows: Processes in biological systems are often modeled as networks, for instance, gene regulatory networks (GRNs) that represent the interactions of transcription factors (TFs) and their target genes. Experiments can provide both, the identity and wiring of all constituent parts of the network as well as parameters that allow to describe the processes in the system in a quantative manner. A network provides a straight-forward way to visualize the state and processes of a whole system, its statistical analysis can reveal interesting properties of biological systems, it is able to integrate several datasets from various experiments and simulations of the network can aid to interpret the data. In recent years, microRNAs emerged as important contributors to gene regulation in eukaryotes, breaking the traditional dogma of molecular biology, where DNA is transcribed to RNA which is subsequently translated into proteins. MicroRNAs are small RNAs that are not translated but functional as RNAs: They are able to target specific messenger RNAs (mRNA) and typically lead to their downregulation. Thus, in addition to TFs, microRNAs also play important roles in GRNs. Interestingly, not only animal genomes including the human genome encode microRNAs, but microRNAs are also encoded by several pathogens such as viruses. In this work I developed several computational systems biology methods and applied them to high-throughout experimental data in the context of a project about herpes viral microRNAs. Three methods, ALPS, PARma and REA, are designed for the analysis of certain types of raw data, namely short RNA-seq, PAR-CLIP and RIP-Chip data, respectively. All of theses experiments are widely used and my methods are publicly available on the internet and can be utilized by the research community to analyze new datasets. For these methods I developed non-trivial statistical methods (e.g. the EM algorithm kmerExplain in PARma) and implemented and adapted algorithms from traditional computer science and bioinformatics (e.g. alignment of pattern matrices in ALPS). I applied these novel methods to data measured by our cooperation partners in the herpes virus project. I.a., I discovered and investigated an important aspect of microRNA-mediated regulation: MicroRNAs recognize their targets in a context-dependent manner. The widespread impact of context on regulation is widely accepted for transcriptional regulation, and only few examples are known for microRNA-mediated regulation. By integrating various herpes-related datasets, I could show that context-dependency is not restricted to few examples but is a widespread feature in post-transcriptional regulation mediated by microRNAs. Importantly, this is true for both, for human host microRNAs as well as for viral microRNAs. Furthermore, I considered additional aspects in the data measured in the context of the herpes virus project: Alternative splicing has been shown to be a major contributor to protein diversity. Splicing is tightly regulated and possibly important in virus infection. Mass spectrometry is able to measure peptides quantitatively genome-wide in high-throughput. However, no method was available to detect splicing patterns in mass spectrometry data, which was one of the datasets that has been meausred in the project. Thus, I investigated whether mass spectrometry offers the opportunity to identify cases of differential splicing in large-scale. Finally, I also focussed on networks in systems biology, especially on their simulation. To be able to simulate networks for the prediction of the behavior of systems is one of the central goals in computational systems biology. In my diploma thesis, I developed a comprehensive modeling platform (PNMA, the Petri net modeling application), that is able to simulate biological systems in various ways. For highly detailed simulations, I further developed FERN, a framework for stochastic simulation that is not only integrated in PNMA, but also available stand-alone or as plugins for the widely used software tools Cytoscape or CellDesigner. In systems biology, the major bottleneck is computational analysis, not the generation of data. Experiments become cheaper every year and the throughput and diversity of data increases accordingly. Thus, developing new methods and usable software tools is essential for further progress. The methods I have developed in this work are a step into this direction but it is apparent, that more effort must be devoted to keep up with the massive amounts of data that is being produced and will be produced in the future.

Trey Ideker (http://www.idekerlab.ucsd.edu) discusses methods for visualizing high-throughput protein-protein and protein-DNA interaction data, which often produce the infamous 'hairballs'. He presents several compelling examples, using the widely-used Cytoscape tool he founded, that demonstrate how network comparison and other methods focusing on biological or bio-medically relevant questions can identify sub-networks that are easily manageable. This talk was presented at VIZBI 2011, an international conference series on visualizing biological data (http://www.vizbi.org) funded by NIH & EMBO.

Background: Stochastic simulation can be used to illustrate the development of biological systems over time and the stochastic nature of these processes. Currently available programs for stochastic simulation, however, are limited in that they either a) do not provide the most efficient simulation algorithms and are difficult to extend, b) cannot be easily integrated into other applications or c) do not allow to monitor and intervene during the simulation process in an easy and intuitive way. Thus, in order to use stochastic simulation in innovative high-level modeling and analysis approaches more flexible tools are necessary. Results: In this article, we present FERN (Framework for Evaluation of Reaction Networks), a Java framework for the efficient simulation of chemical reaction networks. FERN is subdivided into three layers for network representation, simulation and visualization of the simulation results each of which can be easily extended. It provides efficient and accurate state-of-the-art stochastic simulation algorithms for well-mixed chemical systems and a powerful observer system, which makes it possible to track and control the simulation progress on every level. To illustrate how FERN can be easily integrated into other systems biology applications, plugins to Cytoscape and CellDesigner are included. These plugins make it possible to run simulations and to observe the simulation progress in a reaction network in real-time from within the Cytoscape or CellDesigner environment. Conclusion: FERN addresses shortcomings of currently available stochastic simulation programs in several ways. First, it provides a broad range of efficient and accurate algorithms both for exact and approximate stochastic simulation and a simple interface for extending to new algorithms. FERN's implementations are considerably faster than the C implementations of gillespie2 or the Java implementations of ISBJava. Second, it can be used in a straightforward way both as a stand-alone program and within new systems biology applications. Finally, complex scenarios requiring intervention during the simulation progress can be modelled easily with FERN.