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2. A method for comparing multiple imputation techniques: a case study on the U.S. National COVID Cohort Collaborative

Author

Casiraghi, Elena, Wong, Rachel, Hall, Margaret, Coleman, Ben, Notaro, Marco, Evans, Michael D., Tronieri, Jena S., Blau, Hannah, Laraway, Bryan, Callahan, Tiffany J., Chan, Lauren E., Bramante, Carolyn T., Buse, John B., Moffitt, Richard A., Sturmer, Til, Johnson, Steven G., Shao, Yu Raymond, Reese, Justin, Robinson, Peter N., Paccanaro, Alberto, Valentini, Giorgio, Huling, Jared D., Wilkins, Kenneth, Bennet, Tell, Chute, Christopher, DeWitt, Peter, Gersing, Kenneth, Girvin, Andrew, Haendel, Melissa, Harper, Jeremy, Hajagos, Janos, Hong, Stephanie, Pfaff, Emily, Reusch, Jane, Antoniescu, Corneliu, and Robaski, Kimberly

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Statistics - Applications
Abstract

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful to assess associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases and the simple removal of these cases may introduce severe bias. For these reasons, several multiple imputation algorithms have been proposed to attempt to recover the missing information. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithms works best in a given scenario. Furthermore, the selection of each algorithm parameters and data-related modelling choices are also both crucial and challenging. In this paper, we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. The experiments presented here show that our approach could effectively highlight the most valid and performant missing-data handling strategy for our case study. Moreover, our methodology allowed us to gain an understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

Published
2022

3. Learning Interpretable Disease Self-Representations for Drug Repositioning

Author

Frasca, Fabrizio, Galeano, Diego, Gonzalez, Guadalupe, Laponogov, Ivan, Veselkov, Kirill, Paccanaro, Alberto, and Bronstein, Michael M.

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Drug repositioning is an attractive cost-efficient strategy for the development of treatments for human diseases. Here, we propose an interpretable model that learns disease self-representations for drug repositioning. Our self-representation model represents each disease as a linear combination of a few other diseases. We enforce proximity in the learnt representations in a way to preserve the geometric structure of the human phenome network - a domain-specific knowledge that naturally adds relational inductive bias to the disease self-representations. We prove that our method is globally optimal and show results outperforming state-of-the-art drug repositioning approaches. We further show that the disease self-representations are biologically interpretable., Comment: 10 pages, 2 figures, v2 corresponds to the camera ready version accepted at the Graph Representation Learning Workshop, NeurIPS 2019

Published
2019

4. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Author

Zhou, Naihui, Jiang, Yuxiang, Bergquist, Timothy R, Lee, Alexandra J, Kacsoh, Balint Z, Crocker, Alex W, Lewis, Kimberley A, Georghiou, George, Nguyen, Huy N, Hamid, Md Nafiz, Davis, Larry, Dogan, Tunca, Atalay, Volkan, Rifaioglu, Ahmet S, Dalkıran, Alperen, Cetin Atalay, Rengul, Zhang, Chengxin, Hurto, Rebecca L, Freddolino, Peter L, Zhang, Yang, Bhat, Prajwal, Supek, Fran, Fernández, José M, Gemovic, Branislava, Perovic, Vladimir R, Davidović, Radoslav S, Sumonja, Neven, Veljkovic, Nevena, Asgari, Ehsaneddin, Mofrad, Mohammad RK, Profiti, Giuseppe, Savojardo, Castrense, Martelli, Pier Luigi, Casadio, Rita, Boecker, Florian, Schoof, Heiko, Kahanda, Indika, Thurlby, Natalie, McHardy, Alice C, Renaux, Alexandre, Saidi, Rabie, Gough, Julian, Freitas, Alex A, Antczak, Magdalena, Fabris, Fabio, Wass, Mark N, Hou, Jie, Cheng, Jianlin, Wang, Zheng, Romero, Alfonso E, Paccanaro, Alberto, Yang, Haixuan, Goldberg, Tatyana, Zhao, Chenguang, Holm, Liisa, Törönen, Petri, Medlar, Alan J, Zosa, Elaine, Borukhov, Itamar, Novikov, Ilya, Wilkins, Angela, Lichtarge, Olivier, Chi, Po-Han, Tseng, Wei-Cheng, Linial, Michal, Rose, Peter W, Dessimoz, Christophe, Vidulin, Vedrana, Dzeroski, Saso, Sillitoe, Ian, Das, Sayoni, Lees, Jonathan Gill, Jones, David T, Wan, Cen, Cozzetto, Domenico, Fa, Rui, Torres, Mateo, Warwick Vesztrocy, Alex, Rodriguez, Jose Manuel, Tress, Michael L, Frasca, Marco, Notaro, Marco, Grossi, Giuliano, Petrini, Alessandro, Re, Matteo, Valentini, Giorgio, Mesiti, Marco, Roche, Daniel B, Reeb, Jonas, Ritchie, David W, Aridhi, Sabeur, Alborzi, Seyed Ziaeddin, Devignes, Marie-Dominique, Koo, Da Chen Emily, Bonneau, Richard, Gligorijević, Vladimir, Barot, Meet, Fang, Hai, Toppo, Stefano, and Lavezzo, Enrico

Subjects
Human Genome, Networking and Information Technology R&D (NITRD), Genetics, Generic health relevance, Animals, Biofilms, Candida albicans, Drosophila melanogaster, Genome, Bacterial, Genome, Fungal, Humans, Locomotion, Memory, Long-Term, Molecular Sequence Annotation, Pseudomonas aeruginosa, Protein function prediction, Long-term memory, Biofilm, Critical assessment, Community challenge, Environmental Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics
Abstract

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.ConclusionWe conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

Published
2019

5. A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative

Author

Casiraghi, Elena, Wong, Rachel, Hall, Margaret, Coleman, Ben, Notaro, Marco, Evans, Michael D., Tronieri, Jena S., Blau, Hannah, Laraway, Bryan, Callahan, Tiffany J., Chan, Lauren E., Bramante, Carolyn T., Buse, John B., Moffitt, Richard A., Stürmer, Til, Johnson, Steven G., Raymond Shao, Yu, Reese, Justin, Robinson, Peter N., Paccanaro, Alberto, Valentini, Giorgio, Huling, Jared D., and Wilkins, Kenneth J.

Published
2023
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9. An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Author

Jiang, Yuxiang, Oron, Tal Ronnen, Clark, Wyatt T, Bankapur, Asma R, D'Andrea, Daniel, Lepore, Rosalba, Funk, Christopher S, Kahanda, Indika, Verspoor, Karin M, Ben-Hur, Asa, Koo, Emily, Penfold-Brown, Duncan, Shasha, Dennis, Youngs, Noah, Bonneau, Richard, Lin, Alexandra, Sahraeian, Sayed ME, Martelli, Pier Luigi, Profiti, Giuseppe, Casadio, Rita, Cao, Renzhi, Zhong, Zhaolong, Cheng, Jianlin, Altenhoff, Adrian, Skunca, Nives, Dessimoz, Christophe, Dogan, Tunca, Hakala, Kai, Kaewphan, Suwisa, Mehryary, Farrokh, Salakoski, Tapio, Ginter, Filip, Fang, Hai, Smithers, Ben, Oates, Matt, Gough, Julian, Törönen, Petri, Koskinen, Patrik, Holm, Liisa, Chen, Ching-Tai, Hsu, Wen-Lian, Bryson, Kevin, Cozzetto, Domenico, Minneci, Federico, Jones, David T, Chapman, Samuel, C., Dukka B K., Khan, Ishita K, Kihara, Daisuke, Ofer, Dan, Rappoport, Nadav, Stern, Amos, Cibrian-Uhalte, Elena, Denny, Paul, Foulger, Rebecca E, Hieta, Reija, Legge, Duncan, Lovering, Ruth C, Magrane, Michele, Melidoni, Anna N, Mutowo-Meullenet, Prudence, Pichler, Klemens, Shypitsyna, Aleksandra, Li, Biao, Zakeri, Pooya, ElShal, Sarah, Tranchevent, Léon-Charles, Das, Sayoni, Dawson, Natalie L, Lee, David, Lees, Jonathan G, Sillitoe, Ian, Bhat, Prajwal, Nepusz, Tamás, Romero, Alfonso E, Sasidharan, Rajkumar, Yang, Haixuan, Paccanaro, Alberto, Gillis, Jesse, Sedeño-Cortés, Adriana E, Pavlidis, Paul, Feng, Shou, Cejuela, Juan M, Goldberg, Tatyana, Hamp, Tobias, Richter, Lothar, Salamov, Asaf, Gabaldon, Toni, Marcet-Houben, Marina, Supek, Fran, Gong, Qingtian, Ning, Wei, Zhou, Yuanpeng, Tian, Weidong, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Toppo, Stefano, Ferrari, Carlo, Giollo, Manuel, Piovesan, Damiano, Tosatto, Silvio, del Pozo, Angela, Fernández, José M, Maietta, Paolo, Valencia, Alfonso, Tress, Michael L, Benso, Alfredo, Di Carlo, Stefano, Politano, Gianfranco, Savino, Alessandro, Rehman, Hafeez Ur, Re, Matteo, Mesiti, Marco, Valentini, Giorgio, Bargsten, Joachim W, van Dijk, Aalt DJ, Gemovic, Branislava, Glisic, Sanja, Perovic, Vladmir, Veljkovic, Veljko, Veljkovic, Nevena, Almeida-e-Silva, Danillo C, Vencio, Ricardo ZN, Sharan, Malvika, Vogel, Jörg, Kansakar, Lakesh, Zhang, Shanshan, Vucetic, Slobodan, Wang, Zheng, Sternberg, Michael JE, Wass, Mark N, Huntley, Rachael P, Martin, Maria J, O'Donovan, Claire, Robinson, Peter N, Moreau, Yves, Tramontano, Anna, Babbitt, Patricia C, Brenner, Steven E, Linial, Michal, Orengo, Christine A, Rost, Burkhard, Greene, Casey S, Mooney, Sean D, Friedberg, Iddo, and Radivojac, Predrag

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Background: The increasing volume and variety of genotypic and phenotypic data is a major defining characteristic of modern biomedical sciences. At the same time, the limitations in technology for generating data and the inherently stochastic nature of biomolecular events have led to the discrepancy between the volume of data and the amount of knowledge gleaned from it. A major bottleneck in our ability to understand the molecular underpinnings of life is the assignment of function to biological macromolecules, especially proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, accurately assessing methods for protein function prediction and tracking progress in the field remain challenging. Methodology: We have conducted the second Critical Assessment of Functional Annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. One hundred twenty-six methods from 56 research groups were evaluated for their ability to predict biological functions using the Gene Ontology and gene-disease associations using the Human Phenotype Ontology on a set of 3,681 proteins from 18 species. CAFA2 featured significantly expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis also compared the best methods participating in CAFA1 to those of CAFA2. Conclusions: The top performing methods in CAFA2 outperformed the best methods from CAFA1, demonstrating that computational function prediction is improving. This increased accuracy can be attributed to the combined effect of the growing number of experimental annotations and improved methods for function prediction., Comment: Submitted to Genome Biology

Published
2016
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11. A large-scale evaluation of computational protein function prediction

Author

Radivojac, Predrag, Clark, Wyatt T, Oron, Tal Ronnen, Schnoes, Alexandra M, Wittkop, Tobias, Sokolov, Artem, Graim, Kiley, Funk, Christopher, Verspoor, Karin, Ben-Hur, Asa, Pandey, Gaurav, Yunes, Jeffrey M, Talwalkar, Ameet S, Repo, Susanna, Souza, Michael L, Piovesan, Damiano, Casadio, Rita, Wang, Zheng, Cheng, Jianlin, Fang, Hai, Gough, Julian, Koskinen, Patrik, Törönen, Petri, Nokso-Koivisto, Jussi, Holm, Liisa, Cozzetto, Domenico, Buchan, Daniel WA, Bryson, Kevin, Jones, David T, Limaye, Bhakti, Inamdar, Harshal, Datta, Avik, Manjari, Sunitha K, Joshi, Rajendra, Chitale, Meghana, Kihara, Daisuke, Lisewski, Andreas M, Erdin, Serkan, Venner, Eric, Lichtarge, Olivier, Rentzsch, Robert, Yang, Haixuan, Romero, Alfonso E, Bhat, Prajwal, Paccanaro, Alberto, Hamp, Tobias, Kaßner, Rebecca, Seemayer, Stefan, Vicedo, Esmeralda, Schaefer, Christian, Achten, Dominik, Auer, Florian, Boehm, Ariane, Braun, Tatjana, Hecht, Maximilian, Heron, Mark, Hönigschmid, Peter, Hopf, Thomas A, Kaufmann, Stefanie, Kiening, Michael, Krompass, Denis, Landerer, Cedric, Mahlich, Yannick, Roos, Manfred, Björne, Jari, Salakoski, Tapio, Wong, Andrew, Shatkay, Hagit, Gatzmann, Fanny, Sommer, Ingolf, Wass, Mark N, Sternberg, Michael JE, Škunca, Nives, Supek, Fran, Bošnjak, Matko, Panov, Panče, Džeroski, Sašo, Šmuc, Tomislav, Kourmpetis, Yiannis AI, van Dijk, Aalt DJ, Braak, Cajo JF ter, Zhou, Yuanpeng, Gong, Qingtian, Dong, Xinran, Tian, Weidong, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Di Camillo, Barbara, Toppo, Stefano, Lan, Liang, Djuric, Nemanja, Guo, Yuhong, Vucetic, Slobodan, Bairoch, Amos, Linial, Michal, Babbitt, Patricia C, Brenner, Steven E, Orengo, Christine, and Rost, Burkhard

Subjects
Generic health relevance, Algorithms, Animals, Computational Biology, Databases, Protein, Exoribonucleases, Forecasting, Humans, Molecular Biology, Molecular Sequence Annotation, Proteins, Species Specificity, Biological Sciences, Technology, Medical and Health Sciences, Developmental Biology
Abstract

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

Published
2013

16. Author Correction: Subclonal mutation selection in mouse lymphomagenesis identifies known cancer loci and suggests novel candidates

Author

Webster, Philip, Dawes, Joanna C., Dewchand, Hamlata, Takacs, Katalin, Iadarola, Barbara, Bolt, Bruce J., Caceres, Juan J., Kaczor, Jakub, Dharmalingam, Gopuraja, Dore, Marian, Game, Laurence, Adejumo, Thomas, Elliott, James, Naresh, Kikkeri, Karimi, Mohammad, Rekopoulou, Katerina, Tan, Ge, Paccanaro, Alberto, and Uren, Anthony G.

Published
2019
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19. Subclonal mutation selection in mouse lymphomagenesis identifies known cancer loci and suggests novel candidates

Author

Webster, Philip, Dawes, Joanna C., Dewchand, Hamlata, Takacs, Katalin, Iadarola, Barbara, Bolt, Bruce J., Caceres, Juan J., Kaczor, Jakub, Dharmalingam, Gopuraja, Dore, Marian, Game, Laurence, Adejumo, Thomas, Elliott, James, Naresh, Kikkeri, Karimi, Mohammad, Rekopoulou, Katerina, Tan, Ge, Paccanaro, Alberto, and Uren, Anthony G.

Published
2018
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24. Unlike Chloroquine, Mefloquine Inhibits SARS-CoV-2 Infection in Physiologically Relevant Cells

Author

Sacramento, Carolina Q., primary, Fintelman-Rodrigues, Natalia, additional, Dias, Suelen S. G., additional, Temerozo, Jairo R., additional, Da Silva, Aline de Paula D., additional, da Silva, Carine S., additional, Blanco, Camilla, additional, Ferreira, André C., additional, Mattos, Mayara, additional, Soares, Vinicius C., additional, Pereira-Dutra, Filipe, additional, Miranda, Milene Dias, additional, Barreto-Vieira, Debora F., additional, da Silva, Marcos Alexandre N., additional, Santos, Suzana S., additional, Torres, Mateo, additional, Chaves, Otávio Augusto, additional, Rajoli, Rajith K. R., additional, Paccanaro, Alberto, additional, Owen, Andrew, additional, Bou-Habib, Dumith Chequer, additional, Bozza, Patrícia T., additional, and Souza, Thiago Moreno L., additional

Published
2022
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29. Role of miR-2392 in driving SARS-CoV-2 infection

Author

McDonald, J. Tyson, primary, Enguita, Francisco J., additional, Taylor, Deanne, additional, Griffin, Robert J., additional, Priebe, Waldemar, additional, Emmett, Mark R., additional, Sajadi, Mohammad M., additional, Harris, Anthony D., additional, Clement, Jean, additional, Dybas, Joseph M., additional, Aykin-Burns, Nukhet, additional, Guarnieri, Joseph W., additional, Singh, Larry N., additional, Grabham, Peter, additional, Baylin, Stephen B., additional, Yousey, Aliza, additional, Pearson, Andrea N., additional, Corry, Peter M., additional, Saravia-Butler, Amanda, additional, Aunins, Thomas R., additional, Sharma, Sadhana, additional, Nagpal, Prashant, additional, Meydan, Cem, additional, Foox, Jonathan, additional, Mozsary, Christopher, additional, Cerqueira, Bianca, additional, Zaksas, Viktorija, additional, Singh, Urminder, additional, Wurtele, Eve Syrkin, additional, Costes, Sylvain V., additional, Davanzo, Gustavo Gastão, additional, Galeano, Diego, additional, Paccanaro, Alberto, additional, Meinig, Suzanne L., additional, Hagan, Robert S., additional, Bowman, Natalie M., additional, Wolfgang, Matthew C., additional, Altinok, Selin, additional, Sapoval, Nicolae, additional, Treangen, Todd J., additional, Moraes-Vieira, Pedro M., additional, Vanderburg, Charles, additional, Wallace, Douglas C., additional, Schisler, Jonathan C., additional, Mason, Christopher E., additional, Chatterjee, Anushree, additional, Meller, Robert, additional, Beheshti, Afshin, additional, Wallet, Shannon M., additional, Maile, Robert, additional, Mock, Jason R., additional, Torres-Castillo, Jose L., additional, Love, Miriya K., additional, Lovell, Will, additional, Rice, Colleen, additional, Mitchem, Olivia, additional, Burgess, Dominique, additional, Suggs, Jessica, additional, and Jacobs, Jordan, additional

Published
2021
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32. Unlike Chloroquine, mefloquine inhibits SARS-CoV-2 infection in physiologically relevant cells and does not induce viral variants

Author

Sacramento, Carolina Q., primary, Fintelman-Rodrigues, Natalia, additional, Dias, Suelen S. G., additional, Temerozo, Jairo R., additional, Da Silva, Aline de Paula D., additional, da Silva, Carine S., additional, Ferreira, André C., additional, Mattos, Mayara, additional, Soares, Vinicius C., additional, Pereira-Dutra, Filipe, additional, Miranda, Milene D., additional, Barreto-Vieira, Debora F., additional, da Silva, Marcos Alexandre N., additional, Santos, Suzana S., additional, Torres, Mateo, additional, Rajoli, Rajith K R, additional, Paccanaro, Alberto, additional, Owen, Andrew, additional, Bou-Habib, Dumith Chequer, additional, Bozza, Patrícia T., additional, and Souza, Thiago Moreno L., additional

Published
2021
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36. Additional file 1 of LUMI-PCR: an Illumina platform ligation-mediated PCR protocol for integration site cloning, provides molecular quantitation of integration sites

Author

Dawes, Joanna, Webster, Philip, Iadarola, Barbara, Garcia-Diaz, Claudia, Dore, Marian, Bolt, Bruce, Hamlata Dewchand, Gopuraja Dharmalingam, McLatchie, Alex, Kaczor, Jakub, Caceres, Juan, Paccanaro, Alberto, Game, Laurence, Parrinello, Simona, and Uren, Anthony

Abstract

Additional file 1: Figure S1. Step by step summary of LUMI-PCR library construction including all adapter and primer sequences. a) The figure depicts an LTR (blue bases) containing DNA fragment with a variable number of bases of genomic DNA (black X(X)nX bases) and a 5′ LTR sequence (blue). DNA is sheared by sonication. Overhanging ends are blunted, cleaned and then A-tailed (yellow highlighted bases) and cleaned. b) Adaptors containing unique indices (light green XXXXXXXXXX bases) and UMIs (dark green NNNNNNNN bases) are ligated to A-tailed DNA using T overhangs. c) Ligated fragments containing MuLV LTR sequence are bound by the LTR primary PCR primer. The first strand is synthesized from the LTR bound primer creating a lower strand that is compatible to the adapter primer. d) After the first strand of synthesis, the adapter primer and LTR primer can then amplify with exponential kinetics. Non-LTR containing fragments in the ligation are not amplified. e) A nested secondary PCR primer is used to add a second index sequence (light green XXXXXXXXXX bases) to the primary PCR product and further amplify the sequences. d) The secondary PCR products are quantitated, libraries are pooled and then loaded onto a MiSeq or HiSeq flow cell and clusters are generated. e) After cluster generation the first strand acts as the template for read 1 (the adapter end sequencing primer), index 1 (sequencing the LTR index added during the secondary PCR) and index 2 (the adapter index including the UMI sequenced from the flow cell primer). The index 2 read is a non-standard 18–20 bp, instead of the usual 10 bp, to include the UMI sequence. f) After strand regeneration, read 2 sequences the LTR-genome junction (using an LTR primer that can be placed at the junction or offset back from the junction). Figure S2. Graphical summary of informatics pipeline used to process reads into integration sites. Detailed step by step instructions for executing all scripts are available at https://github.com/anthonyuren/LUMI-PCR-pipeline/. Figure S3. Plate layouts for Beckman Biomek liquid handling workstation (below) Plate layouts for each program are listed on the next 4 pages. All programs begin with a box of tips loaded in the tip loader. Some programs require replacement of the tip box 30 min into the protocol. Detailed step by step protocols can be obtained by loading the .xpl files for each protocol into the Beckman Biomek software. Table S1. Diverse studies employ ligation-mediated PCR protocols for positioning of mobile genetic elements, viruses, transposons and transgene vectors. Table S2. Summary statistics for each library. Replicate libraries were prepared from a single spleen DNA sample from an MuLV-infected mouse. The number of reads, unique DNA fragments and integration sites are summarized.

Published
2020
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41. MOESM2 of The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Author

Naihui Zhou, Yuxiang Jiang, Bergquist, Timothy, Lee, Alexandra, Balint Kacsoh, Crocker, Alex, Lewis, Kimberley, Georghiou, George, Nguyen, Huy, Md Nafiz Hamid, Davis, Larry, Tunca Dogan, Atalay, Volkan, Rifaioglu, Ahmet, Dalkıran, Alperen, Rengul Cetin Atalay, Chengxin Zhang, Hurto, Rebecca, Freddolino, Peter, Zhang, Yang, Prajwal Bhat, Supek, Fran, Fernández, José, Gemovic, Branislava, Perovic, Vladimir, Davidović, Radoslav, Sumonja, Neven, Veljkovic, Nevena, Ehsaneddin Asgari, Mofrad, Mohammad, Profiti, Giuseppe, Castrense Savojardo, Martelli, Pier Luigi, Casadio, Rita, Boecker, Florian, Schoof, Heiko, Indika Kahanda, Thurlby, Natalie, McHardy, Alice, Renaux, Alexandre, Saidi, Rabie, Gough, Julian, Freitas, Alex, Antczak, Magdalena, Fabris, Fabio, Wass, Mark, Hou, Jie, Jianlin Cheng, Wang, Zheng, Romero, Alfonso, Paccanaro, Alberto, Haixuan Yang, Goldberg, Tatyana, Chenguang Zhao, Holm, Liisa, Törönen, Petri, Medlar, Alan, Zosa, Elaine, Borukhov, Itamar, Novikov, Ilya, Wilkins, Angela, Lichtarge, Olivier, Po-Han Chi, Tseng, Wei-Cheng, Linial, Michal, Rose, Peter, Dessimoz, Christophe, Vidulin, Vedrana, Saso Dzeroski, Sillitoe, Ian, Sayoni Das, Lees, Jonathan Gill, Jones, David, Wan, Cen, Cozzetto, Domenico, Fa, Rui, Torres, Mateo, Vesztrocy, Alex Warwick, Rodriguez, Jose Manuel, Tress, Michael, Frasca, Marco, Notaro, Marco, Grossi, Giuliano, Petrini, Alessandro, Re, Matteo, Valentini, Giorgio, Mesiti, Marco, Roche, Daniel, Reeb, Jonas, Ritchie, David, Sabeur Aridhi, Alborzi, Seyed Ziaeddin, Marie-Dominique Devignes, Koo, Da Chen Emily, Bonneau, Richard, Gligorijević, Vladimir, Meet Barot, Fang, Hai, Toppo, Stefano, Lavezzo, Enrico, Falda, Marco, Berselli, Michele, Tosatto, Silvio, Carraro, Marco, Piovesan, Damiano, Hafeez Ur Rehman, Qizhong Mao, Shanshan Zhang, Vucetic, Slobodan, Black, Gage, Jo, Dane, Suh, Erica, Dayton, Jonathan, Larsen, Dallas, Omdahl, Ashton, McGuffin, Liam, Brackenridge, Danielle, Babbitt, Patricia, Yunes, Jeffrey, Fontana, Paolo, Zhang, Feng, Shanfeng Zhu, Ronghui You, Zihan Zhang, Suyang Dai, Shuwei Yao, Weidong Tian, Renzhi Cao, Chandler, Caleb, Amezola, Miguel, Johnson, Devon, Chang, Jia-Ming, Wen-Hung Liao, Liu, Yi-Wei, Pascarelli, Stefano, Yotam Frank, Hoehndorf, Robert, Kulmanov, Maxat, Boudellioua, Imane, Politano, Gianfranco, Carlo, Stefano Di, Benso, Alfredo, Hakala, Kai, Ginter, Filip, Mehryary, Farrokh, Suwisa Kaewphan, Björne, Jari, Moen, Hans, Tolvanen, Martti, Salakoski, Tapio, Kihara, Daisuke, Aashish Jain, Šmuc, Tomislav, Altenhoff, Adrian, Ben-Hur, Asa, Rost, Burkhard, Brenner, Steven, Orengo, Christine, Jeffery, Constance, Bosco, Giovanni, Hogan, Deborah, Martin, Maria, O’Donovan, Claire, Mooney, Sean, Greene, Casey, Radivojac, Predrag, and Friedberg, Iddo

Subjects
Pharmacology, FOS: Biological sciences, Genetics, Molecular Biology, 69999 Biological Sciences not elsewhere classified, Developmental Biology
Abstract

Additional file 2 Review History.

Published
2019
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45. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae

Author

Krogan, Nevan J., Cagney, Gerard, Yu, Haiyuan, Zhong, Gouqing, Guo, Xinghua, Ignatchenko, Alexandr, Li, Joyce, Pu, Shuye, Datta, Nira, Tikuisis, Aaron P., Punna, Thanuja, Peregrín-Alvarez, José M., Shales, Michael, Zhang, Xin, Davey, Michael, Robinson, Mark D., Paccanaro, Alberto, Bray, James E., Sheung, Anthony, Beattie, Bryan, Richards, Dawn P., Canadien, Veronica, Lalev, Atanas, Mena, Frank, Wong, Peter, Starostine, Andrei, Canete, Myra M., Vlasblom, James, Wu, Samuel, Orsi, Chris, Collins, Sean R., Chandran, Shamanta, Haw, Robin, Rilstone, Jennifer J., Gandi, Kiran, Thompson, Natalie J., Musso, Gabe, St Onge, Peter, Ghanny, Shaun, Lam, Mandy H. Y., Butland, Gareth, Altaf-Ul, Amin M., Kanaya, Shigehiko, Shilatifard, Ali, O'Shea, Erin, Weissman, Jonathan S., Ingles, C. James, Hughes, Timothy R., Parkinson, John, Gerstein, Mark, Wodak, Shoshana J., Emili, Andrew, and Greenblatt, Jack F.

Published
2006

50. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Author

Zhou, Naihui, primary, Jiang, Yuxiang, additional, Bergquist, Timothy R, additional, Lee, Alexandra J, additional, Kacsoh, Balint Z, additional, Crocker, Alex W, additional, Lewis, Kimberley A, additional, Georghiou, George, additional, Nguyen, Huy N, additional, Hamid, Md Nafiz, additional, Davis, Larry, additional, Dogan, Tunca, additional, Atalay, Volkan, additional, Rifaioglu, Ahmet S, additional, Dalkiran, Alperen, additional, Cetin-Atalay, Rengul, additional, Zhang, Chengxin, additional, Hurto, Rebecca L, additional, Freddolino, Peter L, additional, Zhang, Yang, additional, Bhat, Prajwal, additional, Supek, Fran, additional, Fernández, José M, additional, Gemovic, Branislava, additional, Perovic, Vladimir R, additional, Davidović, Radoslav S, additional, Sumonja, Neven, additional, Veljkovic, Nevena, additional, Asgari, Ehsaneddin, additional, Mofrad, Mohammad RK, additional, Profiti, Giuseppe, additional, Savojardo, Castrense, additional, Martelli, Pier Luigi, additional, Casadio, Rita, additional, Boecker, Florian, additional, Kahanda, Indika, additional, Thurlby, Natalie, additional, McHardy, Alice C, additional, Renaux, Alexandre, additional, Saidi, Rabie, additional, Gough, Julian, additional, Freitas, Alex A, additional, Antczak, Magdalena, additional, Fabris, Fabio, additional, Wass, Mark N, additional, Hou, Jie, additional, Cheng, Jianlin, additional, Wang, Zheng, additional, Romero, Alfonso E, additional, Paccanaro, Alberto, additional, Yang, Haixuan, additional, Goldberg, Tatyana, additional, Zhao, Chenguang, additional, Holm, Liisa, additional, Törönen, Petri, additional, Medlar, Alan J, additional, Zosa, Elaine, additional, Borukhov, Itamar, additional, Novikov, Ilya, additional, Wilkins, Angela, additional, Lichtarge, Olivier, additional, Chi, Po-Han, additional, Tseng, Wei-Cheng, additional, Linial, Michal, additional, Rose, Peter W, additional, Dessimoz, Christophe, additional, Vidulin, Vedrana, additional, Dzeroski, Saso, additional, Sillitoe, Ian, additional, Das, Sayoni, additional, Lees, Jonathan Gill, additional, Jones, David T, additional, Wan, Cen, additional, Cozzetto, Domenico, additional, Fa, Rui, additional, Torres, Mateo, additional, Vesztrocy, Alex Wiarwick, additional, Rodriguez, Jose Manuel, additional, Tress, Michael L, additional, Frasca, Marco, additional, Notaro, Marco, additional, Grossi, Giuliano, additional, Petrini, Alessandro, additional, Re, Matteo, additional, Valentini, Giorgio, additional, Mesiti, Marco, additional, Roche, Daniel B, additional, Reeb, Jonas, additional, Ritchie, David W, additional, Aridhi, Sabeur, additional, Alborzi, Seyed Ziaeddin, additional, Devignes, Marie-Dominique, additional, Emily Koo, Da Chen, additional, Bonneau, Richard, additional, Gligorijević, Vladimir, additional, Barot, Meet, additional, Fang, Hai, additional, Toppo, Stefano, additional, Lavezzo, Enrico, additional, Falda, Marco, additional, Berselli, Michele, additional, Tosatto, Silvio CE, additional, Carraro, Marco, additional, Piovesan, Damiano, additional, Rehman, Hafeez Ur, additional, Mao, Qizhong, additional, Zhang, Shanshan, additional, Vucetic, Slobodan, additional, Black, Gage S, additional, Jo, Dane, additional, Larsen, Dallas J, additional, Omdahl, Ashton R, additional, Sagers, Luke W, additional, Suh, Erica, additional, Dayton, Jonathan B, additional, McGuffin, Liam J, additional, Brackenridge, Danielle A, additional, Babbitt, Patricia C, additional, Yunes, Jeffrey M, additional, Fontana, Paolo, additional, Zhang, Feng, additional, Zhu, Shanfeng, additional, You, Ronghui, additional, Zhang, Zihan, additional, Dai, Suyang, additional, Yao, Shuwei, additional, Tian, Weidong, additional, Cao, Renzhi, additional, Chandler, Caleb, additional, Amezola, Miguel, additional, Johnson, Devon, additional, Chang, Jia-Ming, additional, Liao, Wen-Hung, additional, Liu, Yi-Wei, additional, Pascarelli, Stefano, additional, Frank, Yotam, additional, Hoehndorf, Robert, additional, Kulmanov, Maxat, additional, Boudellioua, Imane, additional, Politano, Gianfranco, additional, Di Carlo, Stefano, additional, Benso, Alfredo, additional, Hakala, Kai, additional, Ginter, Filip, additional, Mehryary, Farrokh, additional, Kaewphan, Suwisa, additional, Björne, Jari, additional, Moen, Hans, additional, Tolvanen, Martti E E, additional, Salakoski, Tapio, additional, Kihara, Daisuke, additional, Jain, Aashish, additional, Šmuc, Tomislav, additional, Altenhoff, Adrian, additional, Ben-Hur, Asa, additional, Rost, Burkhard, additional, Brenner, Steven E, additional, Orengo, Christine A, additional, Jeffery, Constance J, additional, Bosco, Giovanni, additional, Hogan, Deborah A, additional, Martin, Maria J, additional, O’Donovan, Claire, additional, Mooney, Sean D, additional, Greene, Casey S, additional, Radivojac, Predrag, additional, and Friedberg, Iddo, additional

Published
2019
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