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Pacific Symposium on Biocomputing

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https://www.readbyqxmd.com/read/27897016/open-data-for-discovery-science
#1
Philip R O Payne, Kun Huang, Nigam H Shah, Jessica Tenenbaum
The modern healthcare and life sciences ecosystem is moving towards an increasingly open and data-centric approach to discovery science. This evolving paradigm is predicated on a complex set of information needs related to our collective ability to share, discover, reuse, integrate, and analyze open biological, clinical, and population level data resources of varying composition, granularity, and syntactic or semantic consistency. Such an evolution is further impacted by a concomitant growth in the size of data sets that can and should be employed for both hypothesis discovery and testing...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27897015/no-boundary-thinking-in-bioinformatics
#2
Jason H Moore, Steven F Jennings, Casey S Greene, Lawrence E Hunter, Andy D Perkins, Clarlynda Williams-Devane, Donald C Wunsch, Zhongming Zhao, Xiuzhen Huang
The following sections are included:Bioinformatics is a Mature DisciplineThe Golden Era of Bioinformatics Has BegunNo-Boundary Thinking in BioinformaticsReferences.
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27897014/the-training-of-next-generation-data-scientists-in-biomedicine
#3
Lana X Garmire, Stephen Gliske, Quynh C Nguyen, Jonathan H Chen, Shamim Nemati, John D VAN Horn, Jason H Moore, Carol Shreffler, Michelle Dunn
With the booming of new technologies, biomedical science has transformed into digitalized, data intensive science. Massive amount of data need to be analyzed and interpreted, demand a complete pipeline to train next generation data scientists. To meet this need, the transinstitutional Big Data to Knowledge (BD2K) Initiative has been implemented since 2014, complementing other NIH institutional efforts. In this report, we give an overview the BD2K K01 mentored scientist career awards, which have demonstrated early success...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27897013/harnessing-big-data-for-precision-medicine-infrastructures-and-applications
#4
Kun-Hsing Yu, Steven N Hart, Rachel Goldfeder, Qiangfeng Cliff Zhang, Stephen C J Parker, Michael Snyder
Precision medicine is a health management approach that accounts for individual differences in genetic backgrounds and environmental exposures. With the recent advancements in high-throughput omics profiling technologies, collections of large study cohorts, and the developments of data mining algorithms, big data in biomedicine is expected to provide novel insights into health and disease states, which can be translated into personalized disease prevention and treatment plans. However, petabytes of biomedical data generated by multiple measurement modalities poses a significant challenge for data analysis, integration, storage, and result interpretation...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27897012/scalable-visualization-for-high-dimensional-single-cell-data
#5
Juho Kim, Nate Russell, Jian Peng
Single-cell analysis can uncover the mysteries in the state of individual cells and enable us to construct new models about the analysis of heterogeneous tissues. State-of-the-art technologies for single-cell analysis have been developed to measure the properties of single-cells and detect hidden information. They are able to provide the measurements of dozens of features simultaneously in each cell. However, due to the high-dimensionality, heterogeneous complexity and sheer enormity of single-cell data, its interpretation is challenging...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27897011/a-spatiotemporal-model-to-simulate-chemotherapy-regimens-for-heterogeneous-bladder-cancer-metastases-to-the-lung
#6
Kimberly R Kanigel Winner, James C Costello
Tumors are composed of heterogeneous populations of cells. Somatic genetic aberrations are one form of heterogeneity that allows clonal cells to adapt to chemotherapeutic stress, thus providing a path for resistance to arise. In silico modeling of tumors provides a platform for rapid, quantitative experiments to inexpensively study how compositional heterogeneity contributes to drug resistance. Accordingly, we have built a spatiotemporal model of a lung metastasis originating from a primary bladder tumor, incorporating in vivo drug concentrations of first-line chemotherapy, resistance data from bladder cancer cell lines, vascular density of lung metastases, and gains in resistance in cells that survive chemotherapy...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27897010/mapping-neuronal-cell-types-using-integrative-multi-species-modeling-of-human-and-mouse-single-cell-rna-sequencing
#7
Travis Johnson, Zachary Abrams, Yan Zhang, Kun Huang
Mouse brain transcriptomic studies are important in the understanding of the structural heterogeneity in the brain. However, it is not well understood how cell types in the mouse brain relate to human brain cell types on a cellular level. We propose that it is possible with single cell granularity to find concordant genes between mouse and human and that these genes can be used to separate cell types across species. We show that a set of concordant genes can be algorithmically derived from a combination of human and mouse single cell sequencing data...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27897009/an-updated-debarcoding-tool-for-mass-cytometry-with-cell-type-specific-and-cell-sample-specific-stringency-adjustment
#8
Kristen I Fread, William D Strickland, Garry P Nolan, Eli R Zunder
Pooled sample analysis by mass cytometry barcoding carries many advantages: reduced antibody consumption, increased sample throughput, removal of cell doublets, reduction of cross-contamination by sample carryover, and the elimination of tube-to-tube-variability in antibody staining. A single-cell debarcoding algorithm was previously developed to improve the accuracy and yield of sample deconvolution, but this method was limited to using fixed parameters for debarcoding stringency filtering, which could introduce cell-specific or sample-specific bias to cell yield in scenarios where barcode staining intensity and variance are not uniform across the pooled samples...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27897008/tracing-co-regulatory-network-dynamics-in-noisy-single-cell-transcriptome-trajectories
#9
Pablo Cordero, Joshua M Stuart
The availability of gene expression data at the single cell level makes it possible to probe the molecular underpinnings of complex biological processes such as differentiation and oncogenesis. Promising new methods have emerged for reconstructing a progression 'trajectory' from static single-cell transcriptome measurements. However, it remains unclear how to adequately model the appreciable level of noise in these data to elucidate gene regulatory network rewiring. Here, we present a framework called Single Cell Inference of MorphIng Trajectories and their Associated Regulation (SCIMITAR) that infers progressions from static single-cell transcriptomes by employing a continuous parametrization of Gaussian mixtures in high-dimensional curves...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27897007/production-of-a-preliminary-quality-control-pipeline-for-single-nuclei-rna-seq-and-its-application-in-the-analysis-of-cell-type-diversity-of-post-mortem-human-brain-neocortex
#10
Brian Aevermann, Jamison McCorrison, Pratap Venepally, Rebecca Hodge, Trygve Bakken, Jeremy Miller, Mark Novotny, Danny N Tran, Francisco Diezfuertes, Lena Christiansen, Fan Zhang, Frank Steemers, Roger S Lasken, E D Lein, Nicholas Schork, Richard H Scheuermann
Next generation sequencing of the RNA content of single cells or single nuclei (sc/nRNA-seq) has become a powerful approach to understand the cellular complexity and diversity of multicellular organisms and environmental ecosystems. However, the fact that the procedure begins with a relatively small amount of starting material, thereby pushing the limits of the laboratory procedures required, dictates that careful approaches for sample quality control (QC) are essential to reduce the impact of technical noise and sample bias in downstream analysis applications...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27897006/session-introduction
#11
Nikolay Samusik, Nima Aghaeepour, Sean Bendall
Recent technological developments allow gathering single-cell measurements across different domains (genomic, transcriptomics, proteomics, imaging etc). Sophisticated computational algorithms are required in order to harness the power of single-cell data. This session is dedicated to computational methods for single-cell analysis in various biological domains, modelling of population heterogeneity, as well as translational applications of single cell data.
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27897005/strategies-for-equitable-pharmacogenomic-guided-warfarin-dosing-among-european-and-african-american-individuals-in-a-clinical-population
#12
Laura K Wiley, Jacob P Vanhouten, David C Samuels, Melinda C Aldrich, Dan M Roden, Josh F Peterson, Joshua C Denny
The blood thinner warfarin has a narrow therapeutic range and high inter- and intra-patient variability in therapeutic doses. Several studies have shown that pharmacogenomic variants help predict stable warfarin dosing. However, retrospective and randomized controlled trials that employ dosing algorithms incorporating pharmacogenomic variants under perform in African Americans. This study sought to determine if: 1) including additional variants associated with warfarin dose in African Americans, 2) predicting within single ancestry groups rather than a combined population, or 3) using percentage African ancestry rather than observed race, would improve warfarin dosing algorithms in African Americans...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27897004/identifying-genetic-associations-with-variability-in-metabolic-health-and-blood-count-laboratory-values-diving-into-the-quantitative-traits-by-leveraging-longitudinal-data-from-an-ehr
#13
Shefali S Verma, Anastasia M Lucas, Daniel R Lavage, Joseph B Leader, Raghu Metpally, Sarathbabu Krishnamurthy, Frederick Dewey, Ingrid Borecki, Alexander Lopez, John Overton, John Penn, Jeffrey Reid, Sarah A Pendergrass, Gerda Breitwieser, Marylyn D Ritchie
A wide range of patient health data is recorded in Electronic Health Records (EHR). This data includes diagnosis, surgical procedures, clinical laboratory measurements, and medication information. Together this information reflects the patient's medical history. Many studies have efficiently used this data from the EHR to find associations that are clinically relevant, either by utilizing International Classification of Diseases, version 9 (ICD-9) codes or laboratory measurements, or by designing phenotype algorithms to extract case and control status with accuracy from the EHR...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27897003/de-novo-mutations-in-autism-implicate-the-synaptic-elimination-network
#14
Guhan Ram Venkataraman, Chloe O'Connell, Fumiko Egawa, Dorna Kashef-Haghighi, Dennis P Wall
Autism has been shown to have a major genetic risk component; the architecture of documented autism in families has been over and again shown to be passed down for generations. While inherited risk plays an important role in the autistic nature of children, de novo (germline) mutations have also been implicated in autism risk. Here we find that autism de novo variants verified and published in the literature are Bonferroni-significantly enriched in a gene set implicated in synaptic elimination. Additionally, several of the genes in this synaptic elimination set that were enriched in protein-protein interactions (CACNA1C, SHANK2, SYNGAP1, NLGN3, NRXN1, and PTEN) have been previously confirmed as genes that confer risk for the disorder...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27897002/a-methylation-to-expression-feature-model-for-generating-accurate-prognostic-risk-scores-and-identifying-disease-targets-in-clear-cell-kidney-cancer
#15
Jeffrey A Thompson, Carmen J Marsit
Many researchers now have available multiple high-dimensional molecular and clinical datasets when studying a disease. As we enter this multi-omic era of data analysis, new approaches that combine different levels of data (e.g. at the genomic and epigenomic levels) are required to fully capitalize on this opportunity. In this work, we outline a new approach to multi-omic data integration, which combines molecular and clinical predictors as part of a single analysis to create a prognostic risk score for clear cell renal cell carcinoma...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27897001/differential-pathway-dependency-discovery-associated-with-drug-response-across-cancer-cell-lines
#16
Gil Speyer, Divya Mahendra, Hai J Tran, Jeff Kiefer, Stuart L Schreiber, Paul A Clemons, Harshil Dhruv, Michael Berens, Seungchan Kim
The effort to personalize treatment plans for cancer patients involves the identification of drug treatments that can effectively target the disease while minimizing the likelihood of adverse reactions. In this study, the gene-expression profile of 810 cancer cell lines and their response data to 368 small molecules from the Cancer Therapeutics Research Portal (CTRP) are analyzed to identify pathways with significant rewiring between genes, or differential gene dependency, between sensitive and non-sensitive cell lines...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27897000/identifying-cancer-specific-metabolic-signatures-using-constraint-based-models
#17
A Schultz, S Mehta, C W Hu, F W Hoff, T M Horton, S M Kornblau, A A Qutub
Cancer metabolism differs remarkably from the metabolism of healthy surrounding tissues, and it is extremely heterogeneous across cancer types. While these metabolic differences provide promising avenues for cancer treatments, much work remains to be done in understanding how metabolism is rewired in malignant tissues. To that end, constraint-based models provide a powerful computational tool for the study of metabolism at the genome scale. To generate meaningful predictions, however, these generalized human models must first be tailored for specific cell or tissue sub-types...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27896999/identify-cancer-driver-genes-through-shared-mendelian-disease-pathogenic-variants-and-cancer-somatic-mutations
#18
Meng Ma, Changchang Wang, Benjamin S Glicksberg, Eric E Schadt, Shuyu D Li, Rong Chen
Genomic sequencing studies in the past several years have yielded a large number of cancer somatic mutations. There remains a major challenge in delineating a small fraction of somatic mutations that are oncogenic drivers from a background of predominantly passenger mutations. Although computational tools have been developed to predict the functional impact of mutations, their utility is limited. In this study, we applied an alternative approach to identify potentially novel cancer drivers as those somatic mutations that overlap with known pathogenic mutations in Mendelian diseases...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27896998/methyldmv-simultaneous-detection-of-differential-dna-methylation-and-variability-with-confounder-adjustment
#19
Pei Fen Kuan, Junyan Song, Shuyao He
DNA methylation has emerged as promising epigenetic markers for disease diagnosis. Both the differential mean (DM) and differential variability (DV) in methylation have been shown to contribute to transcriptional aberration and disease pathogenesis. The presence of confounding factors in large scale EWAS may affect the methylation values and hamper accurate marker discovery. In this paper, we propose a exible framework called methylDMV which allows for confounding factors adjustment and enables simultaneous characterization and identification of CpGs exhibiting DM only, DV only and both DM and DV...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27896997/improved-performance-of-gene-set-analysis-on-genome-wide-transcriptomics-data-when-using-gene-activity-state-estimates
#20
Thomas Kamp, Micah Adams, Craig Disselkoen, Nathan Tintle
Gene set analysis methods continue to be a popular and powerful method of evaluating genome-wide transcriptomics data. These approach require a priori grouping of genes into biologically meaningful sets, and then conducting downstream analyses at the set (instead of gene) level of analysis. Gene set analysis methods have been shown to yield more powerful statistical conclusions than single-gene analyses due to both reduced multiple testing penalties and potentially larger observed effects due to the aggregation of effects across multiple genes in the set...
2016: Pacific Symposium on Biocomputing
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