Browsing by Author "Aghamirzaie, Delasa"

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    Changes in RNA Splicing in Developing Soybean (Glycine max) Embryos
    Aghamirzaie, Delasa; Nabiyouni, Mahdi; Fang, Yihui; Klumas, Curtis; Heath, Lenwood S.; Grene, Ruth; Collakova, Eva (MDPI, 2013-11-21)
    Developing soybean seeds accumulate oils, proteins, and carbohydrates that are used as oxidizable substrates providing metabolic precursors and energy during seed germination. The accumulation of these storage compounds in developing seeds is highly regulated at multiple levels, including at transcriptional and post-transcriptional regulation. RNA sequencing was used to provide comprehensive information about transcriptional and post-transcriptional events that take place in developing soybean embryos. Bioinformatics analyses lead to the identification of different classes of alternatively spliced isoforms and corresponding changes in their levels on a global scale during soybean embryo development. Alternative splicing was associated with transcripts involved in various metabolic and developmental processes, including central carbon and nitrogen metabolism, induction of maturation and dormancy, and splicing itself. Detailed examination of selected RNA isoforms revealed alterations in individual domains that could result in changes in subcellular localization of the resulting proteins, protein-protein and enzyme-substrate interactions, and regulation of protein activities. Different isoforms may play an important role in regulating developmental and metabolic processes occurring at different stages in developing oilseed embryos.
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    Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
    Menden, Michael P.; Wang, Dennis; Mason, Mike J.; Szalai, Bence; Bulusu, Krishna C.; Guan, Yuanfang; Yu, Thomas; Kang, Jaewoo; Jeon, Minji; Wolfinger, Russ; Nguyen, Tin; Zaslavskiy, Mikhail; Jang, In Sock; Ghazoui, Zara; Ahsen, Mehmet Eren; Vogel, Robert; Neto, Elias Chaibub; Norman, Thea; Tang, Eric K. Y.; Garnett, Mathew J.; Di Veroli, Giovanni Y.; Fawell, Stephen; Stolovitzky, Gustavo; Guinney, Justin; Dry, Jonathan R.; Saez-Rodriguez, Julio; Abante, Jordi; Abecassis, Barbara Schmitz; Aben, Nanne; Aghamirzaie, Delasa; Aittokallio, Tero; Akhtari, Farida S.; Al-lazikani, Bissan; Alam, Tanvir; Allam, Amin; Allen, Chad; de Almeida, Mariana Pelicano; Altarawy, Doaa; Alves, Vinicius; Amadoz, Alicia; Anchang, Benedict; Antolin, Albert A.; Ash, Jeremy R.; Romeo Aznar, Victoria; Ba-alawi, Wail; Bagheri, Moeen; Bajic, Vladimir; Ball, Gordon; Ballester, Pedro J.; Baptista, Delora; Bare, Christopher; Bateson, Mathilde; Bender, Andreas; Bertrand, Denis; Wijayawardena, Bhagya; Boroevich, Keith A.; Bosdriesz, Evert; Bougouffa, Salim; Bounova, Gergana; Brouwer, Thomas; Bryant, Barbara; Calaza, Manuel; Calderone, Alberto; Calza, Stefano; Capuzzi, Stephen; Carbonell-Caballero, Jose; Carlin, Daniel; Carter, Hannah; Castagnoli, Luisa; Celebi, Remzi; Cesareni, Gianni; Chang, Hyeokyoon; Chen, Guocai; Chen, Haoran; Chen, Huiyuan; Cheng, Lijun; Chernomoretz, Ariel; Chicco, Davide; Cho, Kwang-Hyun; Cho, Sunghwan; Choi, Daeseon; Choi, Jaejoon; Choi, Kwanghun; Choi, Minsoo; De Cock, Martine; Coker, Elizabeth; Cortes-Ciriano, Isidro; Cserzo, Miklos; Cubuk, Cankut; Curtis, Christina; Van Daele, Dries; Dang, Cuong C.; Dijkstra, Tjeerd; Dopazo, Joaquin; Draghici, Sorin; Drosou, Anastasios; Dumontier, Michel; Ehrhart, Friederike; Eid, Fatma-Elzahraa; ElHefnawi, Mahmoud; Elmarakeby, Haitham A.; van Engelen, Bo; Engin, Hatice Billur; de Esch, Iwan; Evelo, Chris; Falcao, Andre O.; Farag, Sherif; Fernandez-Lozano, Carlos; Fisch, Kathleen; Flobak, Asmund; Fornari, Chiara; Foroushani, Amir B. K.; Fotso, Donatien Chedom; Fourches, Denis; Friend, Stephen; Frigessi, Arnoldo; Gao, Feng; Gao, Xiaoting; Gerold, Jeffrey M.; Gestraud, Pierre; Ghosh, Samik; Gillberg, Jussi; Godoy-Lorite, Antonia; Godynyuk, Lizzy; Godzik, Adam; Goldenberg, Anna; Gomez-Cabrero, David; Gonen, Mehmet; de Graaf, Chris; Gray, Harry; Grechkin, Maxim; Guimera, Roger; Guney, Emre; Haibe-Kains, Benjamin; Han, Younghyun; Hase, Takeshi; He, Di; He, Liye; Heath, Lenwood S.; Hellton, Kristoffer H.; Helmer-Citterich, Manuela; Hidalgo, Marta R.; Hidru, Daniel; Hill, Steven M.; Hochreiter, Sepp; Hong, Seungpyo; Hovig, Eivind; Hsueh, Ya-Chih; Hu, Zhiyuan; Huang, Justin K.; Huang, R. Stephanie; Hunyady, Laszlo; Hwang, Jinseub; Hwang, Tae Hyun; Hwang, Woochang; Hwang, Yongdeuk; Isayev, Olexandr; Walk, Oliver Bear Don't; Jack, John; Jahandideh, Samad; Ji, Jiadong; Jo, Yousang; Kamola, Piotr J.; Kanev, Georgi K.; Karacosta, Loukia; Karimi, Mostafa; Kaski, Samuel; Kazanov, Marat; Khamis, Abdullah M.; Khan, Suleiman Ali; Kiani, Narsis A.; Kim, Allen; Kim, Jinhan; Kim, Juntae; Kim, Kiseong; Kim, Kyung; Kim, Sunkyu; Kim, Yongsoo; Kim, Yunseong; Kirk, Paul D. W.; Kitano, Hiroaki; Klambauer, Gunter; Knowles, David; Ko, Melissa; Kohn-Luque, Alvaro; Kooistra, Albert J.; Kuenemann, Melaine A.; Kuiper, Martin; Kurz, Christoph; Kwon, Mijin; van Laarhoven, Twan; Laegreid, Astrid; Lederer, Simone; Lee, Heewon; Lee, Jeon; Lee, Yun Woo; Leppaho, Eemeli; Lewis, Richard; Li, Jing; Li, Lang; Liley, James; Lim, Weng Khong; Lin, Chieh; Liu, Yiyi; Lopez, Yosvany; Low, Joshua; Lysenko, Artem; Machado, Daniel; Madhukar, Neel; De Maeyer, Dries; Malpartida, Ana Belen; Mamitsuka, Hiroshi; Marabita, Francesco; Marchal, Kathleen; Marttinen, Pekka; Mason, Daniel; Mazaheri, Alireza; Mehmood, Arfa; Mehreen, Ali; Michaut, Magali; Miller, Ryan A.; Mitsopoulos, Costas; Modos, Dezso; Van Moerbeke, Marijke; Moo, Keagan; Motsinger-Reif, Alison; Movva, Rajiv; Muraru, Sebastian; Muratov, Eugene; Mushthofa, Mushthofa; Nagarajan, Niranjan; Nakken, Sigve; Nath, Aritro; Neuvial, Pierre; Newton, Richard; Ning, Zheng; De Niz, Carlos; Oliva, Baldo; Olsen, Catharina; Palmeri, Antonio; Panesar, Bhawan; Papadopoulos, Stavros; Park, Jaesub; Park, Seonyeong; Park, Sungjoon; Pawitan, Yudi; Peluso, Daniele; Pendyala, Sriram; Peng, Jian; Perfetto, Livia; Pirro, Stefano; Plevritis, Sylvia; Politi, Regina; Poon, Hoifung; Porta, Eduard; Prellner, Isak; Preuer, Kristina; Angel Pujana, Miguel; Ramnarine, Ricardo; Reid, John E.; Reyal, Fabien; Richardson, Sylvia; Ricketts, Camir; Rieswijk, Linda; Rocha, Miguel; Rodriguez-Gonzalvez, Carmen; Roell, Kyle; Rotroff, Daniel; de Ruiter, Julian R.; Rukawa, Ploy; Sadacca, Benjamin; Safikhani, Zhaleh; Safitri, Fita; Sales-Pardo, Marta; Sauer, Sebastian; Schlichting, Moritz; Seoane, Jose A.; Serra, Jordi; Shang, Ming-Mei; Sharma, Alok; Sharma, Hari; Shen, Yang; Shiga, Motoki; Shin, Moonshik; Shkedy, Ziv; Shopsowitz, Kevin; Sinai, Sam; Skola, Dylan; Smirnov, Petr; Soerensen, Izel Fourie; Soerensen, Peter; Song, Je-Hoon; Song, Sang Ok; Soufan, Othman; Spitzmueller, Andreas; Steipe, Boris; Suphavilai, Chayaporn; Tamayo, Sergio Pulido; Tamborero, David; Tang, Jing; Tanoli, Zia-ur-Rehman; Tarres-Deulofeu, Marc; Tegner, Jesper; Thommesen, Liv; Tonekaboni, Seyed Ali Madani; Tran, Hong T.; De Troyer, Ewoud; Truong, Amy; Tsunoda, Tatsuhiko; Turu, Gabor; Tzeng, Guang-Yo; Verbeke, Lieven; Videla, Santiago; Vis, Daniel; Voronkov, Andrey; Votis, Konstantinos; Wang, Ashley; Wang, Hong-Qiang Horace; Wang, Po-Wei; Wang, Sheng; Wang, Wei; Wang, Xiaochen; Wang, Xin; Wennerberg, Krister; Wernisch, Lorenz; Wessels, Lodewyk; van Westen, Gerard J. P.; Westerman, Bart A.; White, Simon Richard; Willighagen, Egon; Wurdinger, Tom; Xie, Lei; Xie, Shuilian; Xu, Hua; Yadav, Bhagwan; Yau, Christopher; Yeerna, Huwate; Yin, Jia Wei; Yu, Michael; Yu, MinHwan; Yun, So Jeong; Zakharov, Alexey; Zamichos, Alexandros; Zanin, Massimiliano; Zeng, Li; Zenil, Hector; Zhang, Frederick; Zhang, Pengyue; Zhang, Wei; Zhao, Hongyu; Zhao, Lan; Zheng, Wenjin; Zoufir, Azedine; Zucknick, Manuela (Springer Nature, 2019-06-17)
    The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
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    CoSpliceNet: a framework for co-splicing network inference from transcriptomics data
    Aghamirzaie, Delasa; Collakova, Eva; Li, Song; Grene, Ruth (BMC, 2016)
    Background: Alternative splicing has been proposed to increase transcript diversity and protein plasticity in eukaryotic organisms, but the extent to which this is the case is currently unclear, especially with regard to the diversification of molecular function. Eukaryotic splicing involves complex interactions of splicing factors and their targets. Inference of co-splicing networks capturing these types of interactions is important for understanding this crucial, highly regulated post-transcriptional process at the systems level. Results: First, several transcript and protein attributes, including coding potential of transcripts and differences in functional domains of proteins, were compared between splice variants and protein isoforms to assess transcript and protein diversity in a biological system. Alternative splicing was shown to increase transcript and functionrelated protein diversity in developing Arabidopsis embryos. Second, CoSpliceNet, which integrates co-expression and motif discovery at splicing regulatory regions to infer co-splicing networks, was developed. CoSpliceNet was applied to temporal RNA sequencing data to identify candidate regulators of splicing events and predict RNAbinding motifs, some of which are supported by prior experimental evidence. Analysis of inferred splicing factor targets revealed an unexpected role for the unfolded protein response in embryo development. Conclusions: The methods presented here can be used in any biological system to assess transcript diversity and protein plasticity and to predict candidate regulators, their targets, and RNA-binding motifs for splicing factors. CoSpliceNet is freely available at http://delasa.github.io/co-spliceNet/.
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    Isoform-Specific Expression During Embryo Development in Arabidopsis and Soybean
    Aghamirzaie, Delasa (Virginia Tech, 2016-06-19)
    Almost every precursor mRNA (pre-mRNA) in a eukaryotic organism undergoes splicing, in some cases resulting in the formation of more than one splice variant, a process called alternative splicing. RNA-Seq provides a major opportunity to capture the state of the transcriptome, which includes the detection of alternative spicing events. Alternative splicing is a highly regulated process occurring in a complex machinery called the spliceosome. In this dissertation, I focus on identification of different splice variants and splicing factors that are produced during Arabidopsis and soybean embryo development. I developed several data analysis pipelines for the detection and the functional characterization of active splice variants and splicing factors that arise during embryo development. The main goal of this dissertation was to identify transcriptional changes associated with specific stages of embryo development and infer possible associations between known regulatory genes and their targets. We identified several instances of exon skipping and intron retention as products of alternative splicing. The coding potential of the splice variants were evaluated using CodeWise. I developed CodeWise, a weighted support vector machine classifier to assess the coding potential of novel transcripts with respect to RNA secondary structure free energy, conserved domains, and sequence properties. We also examined the effect of alternative splicing on the domain composition of resulting protein isoforms. The majority of splice variants pairs encode proteins with identical domains or similar domains with truncation and in less than 10% of the cases alternative splicing results in gain or loss of a conserved domain. I constructed several possible regulatory networks that occur at specific stages of embryo development. In addition, in order to gain a better understanding of splicing regulation, we developed the concept of co-splicing networks, as a group of transcripts containing common RNA-binding motifs, which are co-expressed with a specific splicing factor. For this purpose, I developed a multi-stage analysis pipeline to integrate the co-expression networks with de novo RNA binding motif discovery at inferred splice sites, resulting in the identification of specific splicing factors and the corresponding cis-regulatory sequences that cause the production of splice variants. This approach resulted in the development of several novel hypotheses about the regulation of minor and major splicing in developing Arabidopsis embryos. In summary, this dissertation provides a comprehensive view of splicing regulation in Arabidopsis and soybean embryo development using computational analysis.
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    A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis
    Grene, Ruth; Heath, Lenwood S.; Li, Song; Collakova, Eva; Elmarakeby, Haitham A.; Ni, Ying; Aghamirzaie, Delasa (Frontiers, 2016-12-23)
    Gene regulatory networks (GRNs) provide a representation of relationships between regulators and their target genes. Several methods for GRN inference, both unsupervised and supervised, have been developed to date Because regulatory relationships consistently reprogram in diverse tissues or under different conditions, GRNs inferred without specific biological contexts are of limited applicability. In this report, a machine learning approach is presented to predict GRNs specific to developing Arabidopsis thaliana embryos. We developed the Beacon GRN inference tool to predict GRNs occurring during seed development in Arabidopsis based on a support vector machine (SVM) model. We developed both global and local inference models and compared their performance, demonstrating that local models are generally superior for our application. Using both the expression levels of the genes expressed in developing embryos and prior known regulatory relationships, GRNs were predicted for specific embryonic developmental stages. The targets that are strongly positively correlated with their regulators are mostly expressed at the beginning of seed development. Potential direct targets were identified based on a match between the promoter regions of these inferred targets and the cis elements recognized by specific regulators. Our analysis also provides evidence for previously unknown inhibitory effects of three positive regulators of gene expression. The Beacon GRN inference tool provides a valuable model system for context-specific GRN inference and is freely available at https://github.com/BeaconProjectAtVirginiaTech/beacon_network_inference.git.
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    Metabolic and Transcriptional Reprogramming in Developing Soybean (Glycine max) Embryos
    Collakova, Eva; Aghamirzaie, Delasa; Fang, Yihui; Klumas, Curtis; Tabataba, Farzaneh; Kakumanu, Akshay; Myers, Elijah; Heath, Lenwood S.; Grene, Ruth (MDPI, 2013-05-14)
    Soybean (Glycine max) seeds are an important source of seed storage compounds, including protein, oil, and sugar used for food, feed, chemical, and biofuel production. We assessed detailed temporal transcriptional and metabolic changes in developing soybean embryos to gain a systems biology view of developmental and metabolic changes and to identify potential targets for metabolic engineering. Two major developmental and metabolic transitions were captured enabling identification of potential metabolic engineering targets specific to seed filling and to desiccation. The first transition involved a switch between different types of metabolism in dividing and elongating cells. The second transition involved the onset of maturation and desiccation tolerance during seed filling and a switch from photoheterotrophic to heterotrophic metabolism. Clustering analyses of metabolite and transcript data revealed clusters of functionally related metabolites and transcripts active in these different developmental and metabolic programs. The gene clusters provide a resource to generate predictions about the associations and interactions of unknown regulators with their targets based on guilt-by-association relationships. The inferred regulators also represent potential targets for future metabolic engineering of relevant pathways and steps in central carbon and nitrogen metabolism in soybean embryos and drought and desiccation tolerance in plants.
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    Transcriptome-wide functional characterization reveals novel relationships among differentially expressed transcripts in developing soybean embryos
    Aghamirzaie, Delasa; Batra, Dhruv; Heath, Lenwood S.; Schneider, Andrew; Grene, Ruth; Collakova, Eva (Biomed Central, 2015-11-14)
    Background Transcriptomics reveals the existence of transcripts of different coding potential and strand orientation. Alternative splicing (AS) can yield proteins with altered number and types of functional domains, suggesting the global occurrence of transcriptional and post-transcriptional events. Many biological processes, including seed maturation and desiccation, are regulated post-transcriptionally (e.g., by AS), leading to the production of more than one coding or noncoding sense transcript from a single locus. Results We present an integrated computational framework to predict isoform-specific functions of plant transcripts. This framework includes a novel plant-specific weighted support vector machine classifier called CodeWise, which predicts the coding potential of transcripts with over 96 % accuracy, and several other tools enabling global sequence similarity, functional domain, and co-expression network analyses. First, this framework was applied to all detected transcripts (103,106), out of which 13 % was predicted by CodeWise to be noncoding RNAs in developing soybean embryos. Second, to investigate the role of AS during soybean embryo development, a population of 2,938 alternatively spliced and differentially expressed splice variants was analyzed and mined with respect to timing of expression. Conserved domain analyses revealed that AS resulted in global changes in the number, types, and extent of truncation of functional domains in protein variants. Isoform-specific co-expression network analysis using ArrayMining and clustering analyses revealed specific sub-networks and potential interactions among the components of selected signaling pathways related to seed maturation and the acquisition of desiccation tolerance. These signaling pathways involved abscisic acid- and FUSCA3-related transcripts, several of which were classified as noncoding and/or antisense transcripts and were co-expressed with corresponding coding transcripts. Noncoding and antisense transcripts likely play important regulatory roles in seed maturation- and desiccation-related signaling in soybean. Conclusions This work demonstrates how our integrated framework can be implemented to make experimentally testable predictions regarding the coding potential, co-expression, co-regulation, and function of transcripts and proteins related to a biological process of interest.