Bioinformatics Challenges Research Articles

A modified harmony search method for biclustering microarray gene expression data

Microarray experiments generate vast amounts of data, leading to new requirements and challenges for bioinformatics. The crucial step in considering gene expression data is to discover a group of genes that have similar patterns. Biclustering is a two-dimensional clustering problem where the genes and samples are grouped simultaneously. Latest research showed that biclustering has a great potential in finding marker genes that are associated with certain tissues or diseases. Here, we present an efficient biclustering algorithm called Modified Harmony Search (MHS) method. The MHS algorithm includes a step for generating the new solution by using levy flight. Levy flight increases the diversity of the solutions of harmony search (HS) algorithm and the Pitch Adjusting Rate (PAR) is changed dynamically to escape from local optima. To know the performance of the MHS, it is applied on various benchmark optimisation functions and the benchmark gene expression data sets. The experimental results show that the MHS outperforms HS in optimisation functions and biclustering problems. Also, this work determines the biological relevance of the biclusters with Gene Ontology in terms of function, process and component.

A Comparative Analysis of Motif Discovery Algorithms

One of the major challenges in bioinformatics is the development of efficient computational algorithms for biological sequence motif discovery. In the post-genomic era, the ability to predict the behavior, the function, or the structure of biological entities or motifs such as genes and proteins, as well as interactions among them, play a fundamental role in the discovery of information to help explain biological mechanisms. This necessitated the development of computational methods for identifying these entities. Consequently, a large number of motif finding algorithms have been implemented and applied to various organisms over the past decade. This paper presents a comparative analysis of the latest developments in motif finding algorithms and proposed an algorithm for motif discovery based on a combinatorial approach of pattern driven and statistical based approach. The proposed algorithm, Suffix Tree Gene Enrichment Motif Searching (STGEMS) as reported in [30], proved effective in identifying motifs from organisms with peculiarity in their genomic structure such as the AT-rich sequence of the malaria parasite, P. falciparum. The empirical time analysis of seven motif discovery algorithms was evaluated using four sets of genes from the intraerythrocytic development cycle of P. falciparum. The result shows that algorithms based on a combinatorial approach are more desirable.

Freshwater Metaviromics and Bacteriophages: A Current Assessment of the State of the Art in Relation to Bioinformatic Challenges.

Advances in bioinformatics and sequencing technologies have allowed for the analysis of complex microbial communities at an unprecedented rate. While much focus is often placed on the cellular members of these communities, viruses play a pivotal role, particularly bacteria-infecting viruses (bacteriophages); phages mediate global biogeochemical processes and drive microbial evolution through bacterial grazing and horizontal gene transfer. Despite their importance and ubiquity in nature, very little is known about the diversity and structure of viral communities. Though the need for culture-based methods for viral identification has been somewhat circumvented through metagenomic techniques, the analysis of metaviromic data is marred with many unique issues. In this review, we examine the current bioinformatic approaches for metavirome analyses and the inherent challenges facing the field as illustrated by the ongoing efforts in the exploration of freshwater phage populations.

9th Workshop on Biomedical and Bioinformatics Challenges for Computer Science – BBC2016

9th Workshop on Biomedical and Bioinformatics Challenges for Computer Science – BBC2016

Detecting Spatial Chromatin Organization by Chromosome Conformation Capture II: Genome-Wide Profiling by Hi-C.

The chromosome conformation capture (3C) method has been invaluable in studying chromatin interactions in a population of cells at a resolution surpassing that of light microscopy, for example in the detection of functional contacts between enhancers and promoters. Recent developments in sequencing-based chromosomal contact mapping (Hi-C, 5C and 4C-Seq) have allowed researchers to interrogate pairwise chromatin interactions on a wider scale, shedding light on the three-dimensional organization of chromosomes. These methods present significant technical and bioinformatic challenges to consider at the start of the project. Here, we describe two alternative methods for Hi-C, depending on the size of the genome, and discuss the major computational approaches to convert the raw sequencing data into meaningful models of how genomes are organized.

TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data.

The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.

Study of the Subclonal Mutations in Primary Diffuse Large B-Cell Lymphoma

Tumors arise and evolve by iterative steps of mutation, subclonal selection, and clonal expansion due to growth advantage of the fittest subclones and external mutation induction and selection pressure from radiation and drug therapies. Various studies have shown that greater clonal complexities in primary tumors are correlated with poor clinical outcome of tumor progression and/or drug resistance. Recently, we have reported that very few of the driver mutations, all of which were clonal, detected in primary diffuse large B-cell lymphoma (DLBCL) were associated with DLBCL outcome as measured by 24-month event-free survival (EFS24). In the current project, we identified and studied subclonal mutations in primary DLBCL tumors and assessed their associations with EFS24, as well as the role of activation-induced cytidine deaminase (AID) in genomic mutations occurred in DLBCL tumors.The detection of subclonal mutations is still a significant bioinformatics challenge. In addition, identification of clonal mutations in samples with lower tumor purities faces similar challenges due to the low concentration of reads supporting the mutant alleles. The current methods of somatic mutation calling are not sufficiently sensitive to identify the low concentration mutations in tumor DNA sequencing data. We implemented a bioinformatics workflow for low-concentration and subclonal mutation detection which is based on the positional read pile-up data and a back-fill approach (PUB). The pile-up files were first generated using the re-aligned and re-calibrated BAM files after read alignment using Burrows-Wheeler Aligner (BWA). The variant positions with alternative bases were annotated by attributes defined in the Variant Quality Score Recalibration (VQSR). PUB then used a boosting method and a generalized linear model (GLM) to train a model of 'good quality' variants using common variants from HapMap, and prioritized and called clonal and subclonal variants based on the trained model. The VQSR attributes related to alternative allele depth were less-weighed in order to call subclonal mutations. The somatic mutations were then identified by Fisher's Exact Test using sequencing depths of the reference and alternative alleles from paired tumor and germline sequencing data.The exome sequencing data of paired tumor and peripheral blood from 48 newly diagnosed DLBCL patients were analyzed using PUB. Thirty-six of the 48 patients achieved EFS24 with the other 12 patients experienced primary treatment failure. Most of the tumors studied had tumor purities between 40-70%. PUB identified substantially higher number of somatic mutations, both clonal and subclonal, compared to those detected using existing somatic callers. We observed that the prevalence of mutations in previously reported driver genes were higher using thresholds of mutation concentration ≥ 5% and Fisher's Exact Test p ≤ 0.05, including EZH2 (mutated in 26% of DLBCL tumors analyzed by PUB, compared to 12.7% as previously reported), MLL2 (72% vs. 31%), CD79B (22% vs. 14%), TNFRSF14 (36% vs. 20%), MEF2B (22% vs. 16.4%), CARD11 (46% vs. 21.8%), and MYD88 (18% vs. 11%). In addition, other genes involved tumorigenesis that have not been previously linked to DLBCL also harbored both clonal and subclonal mutations with substantial prevalence, including FGFR3 (12%), KIT (24%), and ATM (28%). Furthermore, association of genes displaying clonal and subclonal mutations with EFS24 identified potential biomarkers for DLBCL outcome. Among these, two genes EPGN and ASTE are involved in epithelial growth factor receptor signaling and had association p values of 0.0001 and 0.0016, respectively. The oncogene MAFB was also associated with EFS24 (P = 0.0016).We searched for AID induced mutations among all identified variant positions and concluded that there was no evidence of AID site enrichments compared to a simulated data set.In summary, we developed and applied a sensitive bioinformatics pipeline for the identification of both clonal and low concentration somatic mutations in primary DLBCL exome sequencing data which further revealed the clonal complexity of the primary DLBCL tumors. Several of the genes were identified as potential biomarkers for DLBCL outcome. DisclosuresMaurer:Kite Pharma: Research Funding. Ansell:Bristol-Myers Squibb: Research Funding; Celldex: Research Funding. Link:Genentech: Consultancy, Research Funding; Kite Pharma: Research Funding. Cerhan:Kite Pharma: Research Funding.

In the loop: promoter-enhancer interactions and bioinformatics.

Enhancer–promoter regulation is a fundamental mechanism underlying differential transcriptional regulation. Spatial chromatin organization brings remote enhancers in contact with target promoters in cis to regulate gene expression. There is considerable evidence for promoter–enhancer interactions (PEIs). In the recent years, genome-wide analyses have identified signatures and mapped novel enhancers; however, being able to precisely identify their target gene(s) requires massive biological and bioinformatics efforts. In this review, we give a short overview of the chromatin landscape and transcriptional regulation. We discuss some key concepts and problems related to chromatin interaction detection technologies, and emerging knowledge from genome-wide chromatin interaction data sets. Then, we critically review different types of bioinformatics analysis methods and tools related to representation and visualization of PEI data, raw data processing and PEI prediction. Lastly, we provide specific examples of how PEIs have been used to elucidate a functional role of non-coding single-nucleotide polymorphisms. The topic is at the forefront of epigenetic research, and by highlighting some future bioinformatics challenges in the field, this review provides a comprehensive background for future PEI studies.

G-Links: a gene-centric link acquisition service.

With the availability of numerous curated databases, researchers are now able to efficiently use the multitude of biological data by integrating these resources via hyperlinks and cross-references. A large proportion of bioinformatics research tasks, however, may include labor-intensive tasks such as fetching, parsing, and merging datasets and functional annotations from distributed multi-domain databases. This data integration issue is one of the key challenges in bioinformatics. We aim to provide an identifier conversion and data aggregation system as a part of solution to solve this problem with a service named G-Links, 1) by gathering resource URI information from 130 databases and 30 web services in a gene-centric manner so that users can retrieve all available links about a given gene, 2) by providing RESTful API for easy retrieval of links including facet searching based on keywords and/or predicate types, and 3) by producing a variety of outputs as visual HTML page, tab-delimited text, and in Semantic Web formats such as Notation3 and RDF. G-Links as well as other relevant documentation are available at http://link.g-language.org/.

Pladipus Enables Universal Distributed Computing in Proteomics Bioinformatics.

The use of proteomics bioinformatics substantially contributes to an improved understanding of proteomes, but this novel and in-depth knowledge comes at the cost of increased computational complexity. Parallelization across multiple computers, a strategy termed distributed computing, can be used to handle this increased complexity; however, setting up and maintaining a distributed computing infrastructure requires resources and skills that are not readily available to most research groups. Here we propose a free and open-source framework named Pladipus that greatly facilitates the establishment of distributed computing networks for proteomics bioinformatics tools. Pladipus is straightforward to install and operate thanks to its user-friendly graphical interface, allowing complex bioinformatics tasks to be run easily on a network instead of a single computer. As a result, any researcher can benefit from the increased computational efficiency provided by distributed computing, hence empowering them to tackle more complex bioinformatics challenges. Notably, it enables any research group to perform large-scale reprocessing of publicly available proteomics data, thus supporting the scientific community in mining these data for novel discoveries.

G-Links: a gene-centric link acquisition service

With the availability of numerous curated databases, researchers are now able to efficiently use the multitude of biological data by integrating these resources via hyperlinks and cross-references. A large proportion of bioinformatics research tasks, however, may include labor-intensive tasks such as fetching, parsing, and merging datasets and functional annotations from distributed multi-domain databases. This data integration issue is one of the key challenges in bioinformatics. We aim to provide an identifier conversion and data aggregation system as a part of solution to solve this problem with a service named G-Links, 1) by gathering resource URI information from 130 databases and 30 web services in a gene-centric manner so that users can retrieve all available links about a given gene, 2) by providing RESTful API for easy retrieval of links including facet searching based on keywords and/or predicate types, and 3) by producing a variety of outputs as visual HTML page, tab-delimited text, and in Semantic Web formats such as Notation3 and RDF. G-Links as well as other relevant documentation are available at http://link.g-language.org/

Graph-based unsupervised feature selection and multiview clustering for microarray data.

A challenge in bioinformatics is to analyse volumes of gene expression data generated through microarray experiments and obtain useful information. Consequently, most microarray studies demand complex data analysis to infer biologically meaningful information from such high-throughput data. Selection of informative genes is an important data analysis step to identify a set of genes which can further help in finding the biological information embedded in microarray data, and thus assists in diagnosis, prognosis and treatment of the disease. In this article we present an unsupervised feature selection technique which attempts to address the goal of explorative data analysis, unfolding the multi-faceted nature of data. It focuses on extracting multiple clustering views considering the diversity of each view from high-dimensional data. We evaluated our technique on benchmark data sets and the experimental results indicates the potential and effectiveness of the proposed model in comparison to the traditional single view clustering models, as well as other existing methods used in the literature for the studied datasets.

Posttranslational Protein Modifications in Plant Metabolism.

Posttranslational modifications (PTMs) of proteins greatly expand proteome diversity, increase functionality, and allow for rapid responses, all at relatively low costs for the cell. PTMs play key roles in plants through their impact on signaling, gene expression, protein stability and interactions, and enzyme kinetics. Following a brief discussion of the experimental and bioinformatics challenges of PTM identification, localization, and quantification (occupancy), a concise overview is provided of the major PTMs and their (potential) functional consequences in plants, with emphasis on plant metabolism. Classic examples that illustrate the regulation of plant metabolic enzymes and pathways by PTMs and their cross talk are summarized. Recent large-scale proteomics studies mapped many PTMs to a wide range of metabolic functions. Unraveling of the PTM code, i.e. a predictive understanding of the (combinatorial) consequences of PTMs, is needed to convert this growing wealth of data into an understanding of plant metabolic regulation.

Infer Metagenomic Abundance and Reveal Homologous Genomes Based on the Structure of Taxonomy Tree.

Metagenomic research uses sequencing technologies to investigate the genetic biodiversity of microbiomes presented in various ecosystems or animal tissues. The composition of a microbial community is highly associated with the environment in which the organisms exist. As large amount of sequencing short reads of microorganism genomes obtained, accurately estimating the abundance of microorganisms within a metagenomic sample is becoming an increasing challenge in bioinformatics. In this paper, we describe a hierarchical taxonomy tree-based mixture model (HTTMM) for estimating the abundance of taxon within a microbial community by incorporating the structure of the taxonomy tree. In this model, genome-specific short reads and homologous short reads among genomes can be distinguished and represented by leaf and intermediate nodes in the taxonomy tree, respectively. We adopt an expectation-maximization algorithm to solve this model. Using simulated and real-world data, we demonstrate that the proposed method is superior to both flat mixture model and lowest common ancestry-based methods. Moreover, this model can reveal previously unaddressed homologous genomes.

YamiPred: A Novel Evolutionary Method for Predicting Pre-miRNAs and Selecting Relevant Features.

MicroRNAs (miRNAs) are small non-coding RNAs, which play a significant role in gene regulation. Predicting miRNA genes is a challenging bioinformatics problem and existing experimental and computational methods fail to deal with it effectively. We developed YamiPred, an embedded classification method that combines the efficiency and robustness of support vector machines (SVM) with genetic algorithms (GA) for feature selection and parameters optimization. YamiPred was tested in a new and realistic human dataset and was compared with state-of-the-art computational intelligence approaches and the prevalent SVM-based tools for miRNA prediction. Experimental results indicate that YamiPred outperforms existing approaches in terms of accuracy and of geometric mean of sensitivity and specificity. The embedded feature selection component selects a compact feature subset that contributes to the performance optimization. Further experimentation with this minimal feature subset has achieved very high classification performance and revealed the minimum number of samples required for developing a robust predictor. YamiPred also confirmed the important role of commonly used features such as entropy and enthalpy, and uncovered the significance of newly introduced features, such as %A-U aggregate nucleotide frequency and positional entropy. The best model trained on human data has successfully predicted pre-miRNAs to other organisms including the category of viruses.

Taking Bioinformatics to Systems Medicine.

Systems medicine promotes a range of approaches and strategies to study human health and disease at a systems level with the aim of improving the overall well-being of (healthy) individuals, and preventing, diagnosing, or curing disease. In this chapter we discuss how bioinformatics critically contributes to systems medicine. First, we explain the role of bioinformatics in the management and analysis of data. In particular we show the importance of publicly available biological and clinical repositories to support systems medicine studies. Second, we discuss how the integration and analysis of multiple types of omics data through integrative bioinformatics may facilitate the determination of more predictive and robust disease signatures, lead to a better understanding of (patho)physiological molecular mechanisms, and facilitate personalized medicine. Third, we focus on network analysis and discuss how gene networks can be constructed from omics data and how these networks can be decomposed into smaller modules. We discuss how the resulting modules can be used to generate experimentally testable hypotheses, provide insight into disease mechanisms, and lead to predictive models. Throughout, we provide several examples demonstrating how bioinformatics contributes to systems medicine and discuss future challenges in bioinformatics that need to be addressed to enable the advancement of systems medicine.

Better tools, new problems: New technologies help to advance research in the life sciences, but the quantities of data generated are proving hard to manage and interpret.

Technological advances have almost always led to accelerated progress in the life sciences. The invention of the light microscope paved the way for the discovery of cells as the building blocks of life, and bacteria as the causes of many infectious diseases. The electron microscope allowed biologists to peer into cells themselves and directly observe viruses. The unravelling of the structure of DNA or proteins was directly enabled by X‐ray crystallo‐graphy. DNA sequencing and PCR became the technological cornerstones of molecular biology, whereas NMR and mass spectro‐metry further improved and accelerated the analysis of proteins and their structures. > As the sequencing of whole genomes becomes faster and cheaper, data production is running ahead of the ability to make sense of it… Many of the inventions themselves have also seen rapid development and improvement. The combination of novel cryological techniques and image analysis algorithms has revived interest in the electron microscope as a tool to visualize the atomic structural details of proteins and cellular structures without the need to create crystals. DNA sequencing has made enormous advances since Fred Sanger's original method based on DNA polymerase followed by gel electrophoresis; today's next‐generation sequencing (NGS) machines can perform millions of reactions in parallel. Light microscopy, one of the oldest tools of biologists, has broken the diffraction limit to let scientists observe the position and movement of individual protein molecules in living cells in real time in three dimensions. As much as all of these technologies help to understand how life works at the molecular level, they create a growing challenge for biologists to make sense of gigabytes or terabytes of data. This is particularly true for next‐generation sequencing, which has become a major challenge for bioinformatics. As the sequencing of whole genomes becomes faster and cheaper, data production is running ahead of the …

Prediction of problematic complexes from PPI networks: sparse, embedded, and small complexes.

BackgroundThe prediction of protein complexes from high-throughput protein-protein interaction (PPI) data remains an important challenge in bioinformatics. Three groups of complexes have been identified as problematic to discover. First, many complexes are sparsely connected in the PPI network, and do not form dense clusters that can be derived by clustering algorithms. Second, many complexes are embedded within highly-connected regions of the PPI network, which makes it difficult to accurately delimit their boundaries. Third, many complexes are small (composed of two or three distinct proteins), so that traditional topological markers such as density are ineffective.ResultsWe have previously proposed three approaches to address these challenges. First, Supervised Weighting of Composite Networks (SWC) integrates diverse data sources with supervised weighting, and successfully fills in missing co-complex edges in sparse complexes to allow them to be predicted. Second, network decomposition (DECOMP) splits the PPI network into spatially- and temporally-coherent subnetworks, allowing complexes embedded within highly-connected regions to be more clearly demarcated. Finally, Size-Specific Supervised Weighting (SSS) integrates diverse data sources with supervised learning to weight edges in a size-specific manner—of being in a small complex versus a large complex—and improves the prediction of small complexes. Here we integrate these three approaches into a single system. We test the integrated approach on the prediction of yeast and human complexes, and show that it outperforms SWC, DECOMP, or SSS when run individually, achieving the highest precision and recall levels.ConclusionThree groups of protein complexes remain challenging to predict from PPI data: sparse complexes, embedded complexes, and small complexes. Our previous approaches have addressed each of these challenges individually, through data integration, PPI-network decomposition, and supervised learning. Here we integrate these approaches into a single complex-discovery system, which improves the prediction of all three types of challenging complexes. With our approach, protein complexes can be more accurately and comprehensively predicted, allowing a clearer elucidation of the modular machinery of the cell.ReviewersThis article was reviewed by Prof. Masanori Arita and Dr. Yang Liu (nominated by Prof. Charles DeLisi).

Abstract 2979: A web portal of ‘next-generation’ clustered heat maps for user-friendly, interactive exploration of patterns in TCGA data

Abstract The Cancer Genome Atlas (TCGA) program is generating comprehensive molecular profiles of more than 30 clinical tumor types, the first 12 of which have been incorporated into a “Pan-Cancer12” project. One bioinformatic challenge is statistical analysis of the resulting profiles; a second is the visual detective work necessary to explore individual genes, pathways and patterns in the data. For that type of detective work, we introduced CHMs in the early 1990s for pharmacogenomic analysis (1) and later for integrated visualization of genomic, transcriptomic, proteomic, pharmacological, and functional data (2). As the ubiquitous first-order way of visualizing omic data, CHMs have appeared in many thousands of publications (3-9), including all of the major publications by the TCGA Research Network. However, a major limitation is that they have been static or only modestly interactive graphics. We have now developed “next-generation” clustered heat maps (NG-CHMs), which use a Google-maps-like tiling technology for extreme zooming and navigation without loss of resolution. NG-CHMs provide pathway and gene ontology information, re-coloring on the fly, tools for reproducibility, high-resolution graphics output, a statistical toolbox, and link-outs to public sources of information on genes, proteins, pathways and drugs. The result is a visually rich, dynamic environment for exploration of the masses of data produced by TCGA. The compendium of TCGA Pan-Cancer NG-CHMs currently includes 667 maps as an initial set, but the numbers will soon rise into the thousands as more data types, tumour types and algorithms are incorporated (at web portal http://bioinformatics.mdanderson.org/TCGA/NGCHMPortal/). As an illustrative example, NG-CHMs proved pivotal as a tool for discovering and analyzing molecular target themes common to multiple types of gynecological cancers and themes that distinguish them from each other. 1. Weinstein JN … Paull KD. Stem Cells 12; 13, 1994. 2. Weinstein JN … Paull KD. Science 275;343, 1997. 3. Myers TG … Weinstein JN. Electrophoresis 18; 467, 1997. 4. Eisen MB … Botstein D. Proc. Natl. Acad. Sci. U.S.A. 14863, 1998. 5. Golub TR … Lander ES. Science 286; 531, 1999. 6. Ross DT … Brown PA. Nature Genetics 24; 227, 2000 7. Scherf U … Weinstein JN. Nature Genetics 24; 236, 2000. 8. Zeeberg BR … Weinstein JN. BMC Bioinformatics 6; 168, 2005. 9. Weinstein JN. Science 319; 1772, 2008. Supported in part by NCI Grant No. U24CA143883, by a gift from the Mary K. Chapman Foundation, and by a grant from the Michael and Susan Dell Foundation honoring Lorraine Dell. Note: This abstract was not presented at the meeting. Citation Format: John N. Weinstein, Rehan Akbani, David W. Kane, James M. Melott, Tod D. Casasent, Rong Yao, Paul L. Roebuck, Gordon B. Mills, Michael C. Ryan, Christopher Wakefield, Bradley M. Broom. A web portal of ‘next-generation’ clustered heat maps for user-friendly, interactive exploration of patterns in TCGA data. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2979. doi:10.1158/1538-7445.AM2015-2979

Abstract 3752: Integrating proteomics and metabolomics characterizes active pathways and potential drug targets in small cell lung cancer

Abstract Small cell lung cancer (SCLC) accounts for 13-15% of all new lung cancer cases and represents the sixth most commonly diagnosed cancer in the US. However, it is an understudied cancer for which no molecularly targeted approaches have shown benefit. Using high-throughput techniques such as proteomics and metabolomics we can provide a more global, unbiased characterization to the inner workings of this disease. Integrating disparate data is a challenge in data analysis and bioinformatics, but this challenge is worth confronting due to the potential for combined proteomics / metabolomics analyses to better interrogate and capture the global landscape of active pathways and networks in SCLC than either technology alone. We accomplished this by comparing SCLC cell lines with non-small cell lung cancer (NSCLC) cell lines, differentiating characteristics of these two lung cancer cell types. We first used activity-based protein profiling (ABPP) combined with LC-MS/MS to profile the ATP-binding proteome of SCLC cell lines (n = 18) and NSCLC cell lines (n = 18). ABPP uses chemical probes that are directed against the active sites of enzymes to interrogate the functional state of ATP-binding enzymes, particularly kinases, in biological samples. These experiments identified 6937 peptides (2319 proteins), of which 3891 peptides (1543 proteins) were differentially expressed. Several pathways related to metabolism, such as purine biosynthesis and glycolysis / gluconeogenesis, were identified as over-represented in this list. These results led us to perform broad spectrum UPLC-TOF-MS metabolomics on ten SCLC and ten NSCLC cell lines. Multivariate analysis demonstrated distinct metabolite profiles for SCLC and NSCLC. Over 100 metabolites with variable importance to projection greater than 1 contributed to the differentiation of the two groups. These included metabolites related to purine metabolism such as inosinic acid and adenosine monophosphate and suggest a connection between our proteomics and metabolomics results. Statistical modeling approaches such as linear modeling, nonparametric nonlinear correlation, and Bayesian network analysis were used to integrate proteomics and metabolomics data to jointly characterize the key pathways and constituent components in SCLC. Multiple regression revealed a statistically significant interaction between endoglin (ENG) and phosphatidylethanolamine (PE), both of which are involved in angiogenesis. These findings and other statistical modeling results have the potential to facilitate the identification of new subtypes in SCLC and allow for the identification of novel targeted therapies. Future work will use these results to help characterize SCLC patient-derived xenograft models and surgically resected patient tissue samples. Funded, in part, by NIH Common Fund, 1U24DK097193. Citation Format: Paul A. Stewart, Jiannong Li, Kate J. Fisher, Suraj Dhungana, Delisha Stewart, Susan Sumner, Eric Gardner, John Poirier, Charles M. Rudin, Eric A. Welsh, Steven Eschrich, Ann Chen, Eric B. Haura. Integrating proteomics and metabolomics characterizes active pathways and potential drug targets in small cell lung cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3752. doi:10.1158/1538-7445.AM2015-3752