Eugene Kolker

Publications of Eugene Kolker

• Opportunities and challenges for the life sciences community.

  Authors: Eugene Kolker, Elizabeth Stewart, Vural Ozdemir

  Omics : a journal of integrative biology. 03/2012; 16(3):138-47.

  Abstract Twenty-first century life sciences have transformed into data-enabled (also called data-intensive, data-driven, or big data) sciences. They principally depend on data-, computation-, andAbstract Twenty-first century life sciences have transformed into data-enabled (also called data-intensive, data-driven, or big data) sciences. They principally depend on data-, computation-, and instrumentation-intensive approaches to seek comprehensive understanding of complex biological processes and systems (e.g., ecosystems, complex diseases, environmental, and health challenges). Federal agencies including the National Science Foundation (NSF) have played and continue to play an exceptional leadership role by innovatively addressing the challenges of data-enabled life sciences. Yet even more is required not only to keep up with the current developments, but also to pro-actively enable future research needs. Straightforward access to data, computing, and analysis resources will enable true democratization of research competitions; thus investigators will compete based on the merits and broader impact of their ideas and approaches rather than on the scale of their institutional resources. This is the Final Report for Data-Intensive Science Workshops DISW1 and DISW2. The first NSF-funded Data Intensive Science Workshop (DISW1, Seattle, WA, September 19-20, 2010) overviewed the status of the data-enabled life sciences and identified their challenges and opportunities. This served as a baseline for the second NSF-funded DIS workshop (DISW2, Washington, DC, May 16-17, 2011). Based on the findings of DISW2 the following overarching recommendation to the NSF was proposed: establish a community alliance to be the voice and framework of the data-enabled life sciences. After this Final Report was finished, Data-Enabled Life Sciences Alliance (DELSA, www.delsall.org ) was formed to become a Digital Commons for the life sciences community.

• MOPED: Model Organism Protein Expression Database.

  Authors: Eugene Kolker, Roger Higdon, Winston Haynes, Dean Welch, William Broomall, Doron Lancet, Larissa Stanberry, Natali Kolker

  Nucleic acids research. 12/2011; 40(Database issue):D1093-9.

  Large numbers of mass spectrometry proteomics studies are being conducted to understand all types of biological processes. The size and complexity of proteomics data hinders efforts to easily share,Large numbers of mass spectrometry proteomics studies are being conducted to understand all types of biological processes. The size and complexity of proteomics data hinders efforts to easily share, integrate, query and compare the studies. The Model Organism Protein Expression Database (MOPED, htttp://moped.proteinspire.org) is a new and expanding proteomics resource that enables rapid browsing of protein expression information from publicly available studies on humans and model organisms. MOPED is designed to simplify the comparison and sharing of proteomics data for the greater research community. MOPED uniquely provides protein level expression data, meta-analysis capabilities and quantitative data from standardized analysis. Data can be queried for specific proteins, browsed based on organism, tissue, localization and condition and sorted by false discovery rate and expression. MOPED empowers users to visualize their own expression data and compare it with existing studies. Further, MOPED links to various protein and pathway databases, including GeneCards, Entrez, UniProt, KEGG and Reactome. The current version of MOPED contains over 43,000 proteins with at least one spectral match and more than 11 million high certainty spectra.

• In-silico human genomics with GeneCards.

  Authors: Gil Stelzer, Irina Dalah, Tsippi Iny Stein, Yigeal Satanower, Naomi Rosen, Noam Nativ, Danit Oz-Levi, Tsviya Olender, Frida Belinky, Iris Bahir, Hagit Krug, Paul Perco, Bernd Mayer, Eugene Kolker, Marilyn Safran, Doron Lancet

  Human genomics. 10/2011; 5(6):709-17.

  Since 1998, the bioinformatics, systems biology, genomics and medical communities have enjoyed a synergistic relationship with the GeneCards database of human genes (http://www.genecards.org). ThisSince 1998, the bioinformatics, systems biology, genomics and medical communities have enjoyed a synergistic relationship with the GeneCards database of human genes (http://www.genecards.org). This human gene compendium was created to help to introduce order into the increasing chaos of information flow. As a consequence of viewing details and deep links related to specific genes, users have often requested enhanced capabilities, such that, over time, GeneCards has blossomed into a suite of tools (including GeneDecks, GeneALaCart, GeneLoc, GeneNote and GeneAnnot) for a variety of analyses of both single human genes and sets thereof. In this paper, we focus on inhouse and external research activities which have been enabled, enhanced, complemented and, in some cases, motivated by GeneCards. In turn, such interactions have often inspired and propelled improvements in GeneCards. We describe here the evolution and architecture of this project, including examples of synergistic applications in diverse areas such as synthetic lethality in cancer, the annotation of genetic variations in disease, omics integration in a systems biology approach to kidney disease, and bioinformatics tools.

• Vaccines of the 21st century and vaccinomics: data-enabled science meets global health to spark collective action for vaccine innovation.

  Authors: Vural Ozdemir, Tikki Pang, Bartha M Knoppers, Denise Avard, Samer A Faraj, Ma'n H Zawati, Eugene Kolker

  Omics : a journal of integrative biology. 08/2011; 15(9):523-7.

• IPM: An integrated protein model for false discovery rate estimation and identification in high-throughput proteomics.

  Authors: Roger Higdon, Lukas Reiter, Gregory Hather, Winston Haynes, Natali Kolker, Elizabeth Stewart, Andrew T Bauman, Paola Picotti, Alexander Schmidt, Gerald van Belle, Ruedi Aebersold, Eugene Kolker

  Journal of proteomics. 06/2011; 75(1):116-21.

  In high-throughput mass spectrometry proteomics, peptides and proteins are not simply identified as present or not present in a sample, rather the identifications are associated with differing levelsIn high-throughput mass spectrometry proteomics, peptides and proteins are not simply identified as present or not present in a sample, rather the identifications are associated with differing levels of confidence. The false discovery rate (FDR) has emerged as an accepted means for measuring the confidence associated with identifications. We have developed the Systematic Protein Investigative Research Environment (SPIRE) for the purpose of integrating the best available proteomics methods. Two successful approaches to estimating the FDR for MS protein identifications are the MAYU and our current SPIRE methods. We present here a method to combine these two approaches to estimating the FDR for MS protein identifications into an integrated protein model (IPM). We illustrate the high quality performance of this IPM approach through testing on two large publicly available proteomics datasets. MAYU and SPIRE show remarkable consistency in identifying proteins in these datasets. Still, IPM results in a more robust FDR estimation approach and additional identifications, particularly among low abundance proteins. IPM is now implemented as a part of the SPIRE system.

• SPIRE: Systematic protein investigative research environment.

  Authors: Eugene Kolker, Roger Higdon, Dean Welch, Andrew Bauman, Elizabeth Stewart, Winston Haynes, William Broomall, Natali Kolker

  Journal of proteomics. 05/2011; 75(1):122-6.

  The SPIRE (Systematic Protein Investigative Research Environment) provides web-based experiment-specific mass spectrometry (MS) proteomics analysis (https://www.proteinspire.org). Its emphasis is onThe SPIRE (Systematic Protein Investigative Research Environment) provides web-based experiment-specific mass spectrometry (MS) proteomics analysis (https://www.proteinspire.org). Its emphasis is on usability and integration of the best analytic tools. SPIRE provides an easy to use web-interface and generates results in both interactive and simple data formats. In contrast to run-based approaches, SPIRE conducts the analysis based on the experimental design. It employs novel methods to generate false discovery rates and local false discovery rates (FDR, LFDR) and integrates the best and complementary open-source search and data analysis methods. The SPIRE approach of integrating X!Tandem, OMSSA and SpectraST can produce an increase in protein IDs (52-88%) over current combinations of scoring and single search engines while also providing accurate multi-faceted error estimation. One of SPIRE's primary assets is combining the results with data on protein function, pathways and protein expression from model organisms. We demonstrate some of SPIRE's capabilities by analyzing mitochondrial proteins from the wild type and 3 mutants of C. elegans. SPIRE also connects results to publically available proteomics data through its Model Organism Protein Expression Database (MOPED). SPIRE can also provide analysis and annotation for user supplied protein ID and expression data.

• Education and data-intensive science in the beginning of the 21st century.

  Authors: Fredric Wolf, Russ Hobby, Sonya Lowry, Andrew Bauman, B Robert Franza, Biaoyang Lin, Sean Rapson, Elizabeth Stewart, Eugene Kolker

  Omics : a journal of integrative biology. 04/2011; 15(4):217-9.

  Data-intensive science will open up new avenues to explore, new questions to ask, and new ways to answer. Yet, this potential cannot be unlocked without new emphasis on education of the researchersData-intensive science will open up new avenues to explore, new questions to ask, and new ways to answer. Yet, this potential cannot be unlocked without new emphasis on education of the researchers gathering data, the analysts analyzing data and the cross-disciplinary participants working together to make it happen. This article is a summary of the education issues and challenges of data-intensive sciences and cloud computing as discussed in the Data-Intensive Science (DIS) workshop in Seattle, September 19-20, 2010.

• Communication and data-intensive science in the beginning of the 21st century.

  Authors: Jack Faris, Evelyne Kolker, Alex Szalay, Leon Bradlow, Ewa Deelman, Wu Feng, Judy Qiu, Donna Russell, Elizabeth Stewart, Eugene Kolker

  Omics : a journal of integrative biology. 04/2011; 15(4):213-5.

  The advent of data-intensive science has sharpened our need for better communication within and between the fields of science and technology, to name a few. No one mind can encompass all that isThe advent of data-intensive science has sharpened our need for better communication within and between the fields of science and technology, to name a few. No one mind can encompass all that is necessary to be successful in controlling and analyzing the data deluge we are experiencing. Therefore, we must bring together diverse fields, communicate clearly, and build crossdisciplinary methods and tools to realize its potential. This article is a summary of the communication issues and challenges as discussed in the Data-Intensive Science (DIS) workshop in Seattle, September 19-20, 2010.

• Biology and data-intensive scientific discovery in the beginning of the 21st century.

  Authors: Arnold Smith, Magdalena Balazinska, Chaitan Baru, Mark Gomelsky, Michael McLennan, Lynn Rose, Burton Smith, Elizabeth Stewart, Eugene Kolker

  Omics : a journal of integrative biology. 04/2011; 15(4):209-12.

  The life sciences are poised at the beginning of a paradigm-changing evolution in the way scientific questions are answered. Data-Intensive Science (DIS) promise to provide new ways of approachingThe life sciences are poised at the beginning of a paradigm-changing evolution in the way scientific questions are answered. Data-Intensive Science (DIS) promise to provide new ways of approaching scientific challenges and answering questions. This article is a summary of the life sciences issues and challenges as discussed in the DIS workshop in Seattle, September 19-20, 2010.

• Bioinformatics and data-intensive scientific discovery in the beginning of the 21st century.

  Authors: Roger Barga, Bill Howe, David Beck, Stuart Bowers, William Dobyns, Winston Haynes, Roger Higdon, Chris Howard, Christian Roth, Elizabeth Stewart, Dean Welch, Eugene Kolker

  Omics : a journal of integrative biology. 04/2011; 15(4):199-201.

  This article is a summary of the bioinformatics issues and challenges of data-intensive science as discussed in the NSF-funded Data-Intensive Science (DIS) workshop in Seattle, September 19-20, 2010.

• Technology and data-intensive science in the beginning of the 21st century.

  Authors: Philip A Bernstein, Dave Wecker, Ashok Krishnamurthy, Dinesh Manocha, Jeffrey Gardner, Natali Kolker, Chance Reschke, Jesse Stombaugh, Pamela Vagata, Elizabeth Stewart, Dean Welch, Eugene Kolker

  Omics : a journal of integrative biology. 04/2011; 15(4):203-7.

  This article is a summary of the technology issues and challenges of data-intensive science and cloud computing as discussed in the Data-Intensive Science (DIS) workshop in Seattle, September 19-20,This article is a summary of the technology issues and challenges of data-intensive science and cloud computing as discussed in the Data-Intensive Science (DIS) workshop in Seattle, September 19-20, 2010.

• Policy and data-intensive scientific discovery in the beginning of the 21st century.

  Authors: Vural Ozdemir, Charles Smith, Kathleen Bongiovanni, David Cullen, Bartha M Knoppers, Andrew Lowe, Mette Peters, Robert Robbins, Elizabeth Stewart, Gene Yee, Yi-Kuo Yu, Eugene Kolker

  Omics : a journal of integrative biology. 04/2011; 15(4):221-5.

  Recent developments in our ability to capture, curate, and analyze data, the field of data-intensive science (DIS), have indeed made these interesting and challenging times for scientific practice asRecent developments in our ability to capture, curate, and analyze data, the field of data-intensive science (DIS), have indeed made these interesting and challenging times for scientific practice as well as policy making in real time. We are confronted with immense datasets that challenge our ability to pool, transfer, analyze, or interpret scientific observations. We have more data available than ever before, yet more questions to be answered as well, and no clear path to answer them. We are excited by the potential for science-based solutions to humankind's problems, yet stymied by the limitations of our current cyberinfrastructure and existing public policies. Importantly, DIS signals a transformation of the hypothesis-driven tradition of science ("first hypothesize, then experiment") to one that is typified by "first experiment, then hypothesize" mode of discovery. Another hallmark of DIS is that it amasses data that are public goods (i.e., creates a "commons") that can further be creatively mined for various applications in different sectors. As such, this calls for a science policy vision that is long term. We herein reflect on how best to approach to policy making at this critical inflection point when DIS applications are being diversified in agriculture, ecology, marine biology, and environmental research internationally. This article outlines the key policy issues and gaps that emerged from the multidisciplinary discussions at the NSF-funded DIS workshop held at the Seattle Children's Research Institute in Seattle, on September 19-20, 2010.

• The necessity of adjusting tests of protein category enrichment in discovery proteomics.

  Authors: Brenton Louie, Roger Higdon, Eugene Kolker

  Bioinformatics (Oxford, England). 11/2010; 26(24):3007-11.

  MOTIVATION: Enrichment tests are used in high-throughput experimentation to measure the association between gene or protein expression and membership in groups or pathways. The Fisher's exact test isMOTIVATION: Enrichment tests are used in high-throughput experimentation to measure the association between gene or protein expression and membership in groups or pathways. The Fisher's exact test is commonly used. We specifically examined the associations produced by the Fisher test between protein identification by mass spectrometry discovery proteomics, and their Gene Ontology (GO) term assignments in a large yeast dataset. We found that direct application of the Fisher test is misleading in proteomics due to the bias in mass spectrometry to preferentially identify proteins based on their biochemical properties. False inference about associations can be made if this bias is not corrected. Our method adjusts Fisher tests for these biases and produces associations more directly attributable to protein expression rather than experimental bias. RESULTS: Using logistic regression, we modeled the association between protein identification and GO term assignments while adjusting for identification bias in mass spectrometry. The model accounts for five biochemical properties of peptides: (i) hydrophobicity, (ii) molecular weight, (iii) transfer energy, (iv) beta turn frequency and (v) isoelectric point. The model was fit on 181 060 peptides from 2678 proteins identified in 24 yeast proteomics datasets with a 1% false discovery rate. In analyzing the association between protein identification and their GO term assignments, we found that 25% (134 out of 544) of Fisher tests that showed significant association (q-value ≤0.05) were non-significant after adjustment using our model. Simulations generating yeast protein sets enriched for identification propensity show that unadjusted enrichment tests were biased while our approach worked well.

• A vision for 21st century U.S. Policy to support sustainable advancement of scientific discovery and technological innovation.

  Authors: Eugene Kolker

  Omics : a journal of integrative biology. 08/2010; 14(4):333-5.

• Meta-analysis for protein identification: a case study on yeast data.

  Authors: Roger Higdon, Winston Haynes, Eugene Kolker

  Omics : a journal of integrative biology. 06/2010; 14(3):309-14.

  Large amounts of mass spectrometry (MS) proteomics data are now publicly available; however, little attention has been given to how to best combine these data and assess the error rates for proteinLarge amounts of mass spectrometry (MS) proteomics data are now publicly available; however, little attention has been given to how to best combine these data and assess the error rates for protein identification. The objective of this article is to show how variation in the type and amount of data included with each study impacts coverage of the yeast proteome and estimation of the false discovery rate (FDR). Our analysis of a subset of the publicly available yeast data showed that failure to reevaluate the FDR when combining protein IDs from different experiments resulted in an underestimation of the FDR by approximately threefold. A worst-case approximation of the FDR was only slightly larger than estimating the FDR by randomized database matches. The use of a weighted model to emphasize the most informative experimental data provided an increase in the number of IDs at a 1% FDR when compared to other meta-analysis approaches. Also, using an FDR higher than 1% results in a very high rate of false discoveries for IDs above the 1% threshold. Ideally, raw MS data will be made publicly available for complete and consistent reanalysis. In the circumstance that raw data is not available, determining a combined FDR on the basis of the worst-case estimation provides a reasonable approximation of the FDR. When combining experimental results, adding additional experiments results in diminishing and in some cases negative returns on protein identifications. It may be beneficial to include only those experiments generating the most unique identifications due to solid experimental design and sensitive instrumentation.

• Estimating false discovery rates for peptide and protein identification using randomized databases.

  Authors: Gregory Hather, Roger Higdon, Andrew Bauman, Priska D von Haller, Eugene Kolker

  Proteomics. 04/2010; 10(12):2369-76.

  MS-based proteomics characterizes protein contents of biological samples. The most common approach is to first match observed MS/MS peptide spectra against theoretical spectra from a protein sequenceMS-based proteomics characterizes protein contents of biological samples. The most common approach is to first match observed MS/MS peptide spectra against theoretical spectra from a protein sequence database and then to score these matches. The false discovery rate (FDR) can be estimated as a function of the score by searching together the protein sequence database and its randomized version and comparing the score distributions of the randomized versus nonrandomized matches. This work introduces a straightforward isotonic regression-based method to estimate the cumulative FDRs and local FDRs (LFDRs) of peptide identification. Our isotonic method not only performed as well as other methods used for comparison, but also has the advantages of being: (i) monotonic in the score, (ii) computationally simple, and (iii) not dependent on assumptions about score distributions. We demonstrate the flexibility of our approach by using it to estimate FDRs and LFDRs for protein identification using summaries of the peptide spectra scores. We reconfirmed that several of these methods were superior to a two-peptide rule. Finally, by estimating both the FDRs and LFDRs, we showed for both peptide and protein identification, moderate FDR values (5%) corresponded to large LFDR values (53 and 60%).

• Interplay of heritage and habitat in the distribution of bacterial signal transduction systems.

  Authors: Michael Y Galperin, Roger Higdon, Eugene Kolker

  Molecular bioSystems. 04/2010; 6(4):721-8.

  Comparative analysis of the complete genome sequences from a variety of poorly studied organisms aims at predicting ecological and behavioral properties of these organisms and helping inComparative analysis of the complete genome sequences from a variety of poorly studied organisms aims at predicting ecological and behavioral properties of these organisms and helping in characterizing their habitats. This task requires finding appropriate descriptors that could be correlated with the core traits of each system and would allow meaningful comparisons. Using the relatively simple bacterial models, first attempts have been made to introduce suitable metrics to describe the complexity of organism's signaling machinery, which included introducing the "bacterial IQ" score. Here, we use an updated census of prokaryotic signal transduction systems to improve this parameter and evaluate its consistency within selected bacterial phyla. We also introduce a more elaborate descriptor, a set of profiles of relative abundance of members of each family of signal transduction proteins encoded in each genome. We show that these family profiles are well conserved within each genus and are often consistent within families of bacteria. Thus, they reflect evolutionary relationships between organisms as well as individual adaptations of each organism to its specific ecological niche.

• Modeling sequence and function similarity between proteins for protein functional annotation.

  Authors: Roger Higdon, Brenton Louie, Eugene Kolker

  Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, HPDC 2010, Chicago, Illinois, USA, June 21-25, 2010; 01/2010

• Quantifying protein function specificity in the gene ontology.

  Authors: Brenton Louie, Silas Bergen, Roger Higdon, Eugene Kolker

  Standards in genomic sciences. 01/2010; 2(2):238-44.

  Quantitative or numerical metrics of protein function specificity made possible by the Gene Ontology are useful in that they enable development of distance or similarity measures between proteinQuantitative or numerical metrics of protein function specificity made possible by the Gene Ontology are useful in that they enable development of distance or similarity measures between protein functions. Here we describe how to calculate four measures of function specificity for GO terms: 1) number of ancestor terms; 2) number of offspring terms; 3) proportion of terms; and 4) Information Content (IC). We discuss the relationship between the metrics and the strengths and weaknesses of each.

• The United States of America and scientific research.

  Authors: Gregory J Hather, Winston Haynes, Roger Higdon, Natali Kolker, Elizabeth A Stewart, Peter Arzberger, Patrick Chain, Dawn Field, B Robert Franza, Biaoyang Lin, Folker Meyer, Vural Ozdemir, Charles V Smith, Gerald van Belle, John Wooley, Eugene Kolker

  PloS one. 01/2010; 5(8):e12203.

  To gauge the current commitment to scientific research in the United States of America (US), we compared federal research funding (FRF) with the US gross domestic product (GDP) and industry researchTo gauge the current commitment to scientific research in the United States of America (US), we compared federal research funding (FRF) with the US gross domestic product (GDP) and industry research spending during the past six decades. In order to address the recent globalization of scientific research, we also focused on four key indicators of research activities: research and development (R&D) funding, total science and engineering doctoral degrees, patents, and scientific publications. We compared these indicators across three major population and economic regions: the US, the European Union (EU) and the People's Republic of China (China) over the past decade. We discovered a number of interesting trends with direct relevance for science policy. The level of US FRF has varied between 0.2% and 0.6% of the GDP during the last six decades. Since the 1960s, the US FRF contribution has fallen from twice that of industrial research funding to roughly equal. Also, in the last two decades, the portion of the US government R&D spending devoted to research has increased. Although well below the US and the EU in overall funding, the current growth rate for R&D funding in China greatly exceeds that of both. Finally, the EU currently produces more science and engineering doctoral graduates and scientific publications than the US in absolute terms, but not per capita. This study's aim is to facilitate a serious discussion of key questions by the research community and federal policy makers. In particular, our results raise two questions with respect to: a) the increasing globalization of science: "What role is the US playing now, and what role will it play in the future of international science?"; and b) the ability to produce beneficial innovations for society: "How will the US continue to foster its strengths?"