Mihai Pop

Summary

Affiliation: University of Maryland
Country: USA

Publications

  1. pmc De novo likelihood-based measures for comparing genome assemblies
    Mohammadreza Ghodsi
    Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
    BMC Res Notes 6:334. 2013
  2. pmc We are what we eat: how the diet of infants affects their gut microbiome
    Mihai Pop
    Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA
    Genome Biol 13:152. 2012
  3. pmc Genome assembly reborn: recent computational challenges
    Mihai Pop
    Department of Computer Science and the Center for Bioinformatics and Computational Biology at the University of Maryland, College Park, MD 20742, USA
    Brief Bioinform 10:354-66. 2009
  4. ncbi Differential abundance analysis for microbial marker-gene surveys
    Joseph N Paulson
    1 Graduate Program in Applied Mathematics and Statistics, and Scientific Computation, University of Maryland, College Park, Maryland, USA 2 Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
    Nat Methods 10:1200-2. 2013
  5. pmc DNACLUST: accurate and efficient clustering of phylogenetic marker genes
    Mohammadreza Ghodsi
    Department of Computer Science, University of Maryland, College Park, MD 20742, USA
    BMC Bioinformatics 12:271. 2011
  6. pmc Survey of culture, goldengate assay, universal biosensor assay, and 16S rRNA Gene sequencing as alternative methods of bacterial pathogen detection
    Brianna Lindsay
    University of Maryland, School of Medicine, Baltimore, Maryland, USA
    J Clin Microbiol 51:3263-9. 2013
  7. pmc Accurate and fast estimation of taxonomic profiles from metagenomic shotgun sequences
    Bo Liu
    Center for Bioinformatics and Computational Biology, University of Maryland, College Park, USA
    BMC Genomics 12:S4. 2011
  8. pmc Alignment and clustering of phylogenetic markers--implications for microbial diversity studies
    James R White
    Department of Computer Science, University of Maryland College Park, College Park, MD 20742, USA
    BMC Bioinformatics 11:152. 2010
  9. pmc Gene prediction with Glimmer for metagenomic sequences augmented by classification and clustering
    David R Kelley
    Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies, Department of Computer Science, 3115 Biomolecular Sciences Building 296, University of Maryland, College Park, MD 20742, USA
    Nucleic Acids Res 40:e9. 2012
  10. pmc Scaffolding and validation of bacterial genome assemblies using optical restriction maps
    Niranjan Nagarajan
    University of Maryland, College Park, MD 20742, USA
    Bioinformatics 24:1229-35. 2008

Collaborators

Detail Information

Publications30

  1. pmc De novo likelihood-based measures for comparing genome assemblies
    Mohammadreza Ghodsi
    Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
    BMC Res Notes 6:334. 2013
    ..A key property of our metric is that the true genome sequence maximizes the score, unlike other commonly used metrics...
  2. pmc We are what we eat: how the diet of infants affects their gut microbiome
    Mihai Pop
    Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA
    Genome Biol 13:152. 2012
    ..Simultaneous analysis of the gut microbiome and host gene expression in infants reveals the impact of diet (breastfeeding versus formula) on host-microbiome interactions...
  3. pmc Genome assembly reborn: recent computational challenges
    Mihai Pop
    Department of Computer Science and the Center for Bioinformatics and Computational Biology at the University of Maryland, College Park, MD 20742, USA
    Brief Bioinform 10:354-66. 2009
    ..In this article, we outline the major algorithmic approaches for genome assembly and describe recent developments in this domain...
  4. ncbi Differential abundance analysis for microbial marker-gene surveys
    Joseph N Paulson
    1 Graduate Program in Applied Mathematics and Statistics, and Scientific Computation, University of Maryland, College Park, Maryland, USA 2 Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
    Nat Methods 10:1200-2. 2013
    ..Using simulated data and several published microbiota data sets, we show that metagenomeSeq outperforms the tools currently used in this field. ..
  5. pmc DNACLUST: accurate and efficient clustering of phylogenetic marker genes
    Mohammadreza Ghodsi
    Department of Computer Science, University of Maryland, College Park, MD 20742, USA
    BMC Bioinformatics 12:271. 2011
    ..g., only one sequence per cluster needs to be provided to downstream analyses)...
  6. pmc Survey of culture, goldengate assay, universal biosensor assay, and 16S rRNA Gene sequencing as alternative methods of bacterial pathogen detection
    Brianna Lindsay
    University of Maryland, School of Medicine, Baltimore, Maryland, USA
    J Clin Microbiol 51:3263-9. 2013
    ..The agreement among methods was higher in cases than in controls. The new molecular technologies have a high potential for highly sensitive identification of bacterial diarrheal pathogens. ..
  7. pmc Accurate and fast estimation of taxonomic profiles from metagenomic shotgun sequences
    Bo Liu
    Center for Bioinformatics and Computational Biology, University of Maryland, College Park, USA
    BMC Genomics 12:S4. 2011
    ..One major limitation of prior computational methods used for this purpose is the use of a universal classification threshold for all genes at all taxonomic levels...
  8. pmc Alignment and clustering of phylogenetic markers--implications for microbial diversity studies
    James R White
    Department of Computer Science, University of Maryland College Park, College Park, MD 20742, USA
    BMC Bioinformatics 11:152. 2010
    ..To date, however, there exists significant variability in analysis methods employed in these studies...
  9. pmc Gene prediction with Glimmer for metagenomic sequences augmented by classification and clustering
    David R Kelley
    Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies, Department of Computer Science, 3115 Biomolecular Sciences Building 296, University of Maryland, College Park, MD 20742, USA
    Nucleic Acids Res 40:e9. 2012
    ..In a comparison among multiple gene finding methods, Glimmer-MG makes the most sensitive and precise predictions on simulated and real metagenomes for all read lengths and error rates tested...
  10. pmc Scaffolding and validation of bacterial genome assemblies using optical restriction maps
    Niranjan Nagarajan
    University of Maryland, College Park, MD 20742, USA
    Bioinformatics 24:1229-35. 2008
    ..In this article we propose methods to overcome such limitations by incorporating information from optical restriction maps...
  11. pmc AGORA: Assembly Guided by Optical Restriction Alignment
    Henry C Lin
    Center for Bioinformatics and Computational Biology, University of Maryland College Park, College Park, MD, USA
    BMC Bioinformatics 13:189. 2012
    ....
  12. pmc Genome assembly forensics: finding the elusive mis-assembly
    Adam M Phillippy
    Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA
    Genome Biol 9:R55. 2008
    ..The software described is compatible with common assembly formats and is released, open-source, at http://amos.sourceforge.net...
  13. pmc Assembly complexity of prokaryotic genomes using short reads
    Carl Kingsford
    Department of Computer Science, Institute for Advanced Computer Studies, University of Maryland, College Park, MD, USA
    BMC Bioinformatics 11:21. 2010
    ..We describe an application of de Bruijn graphs to analyze the global repeat structure of prokaryotic genomes...
  14. doi Finding biologically accurate clusterings in hierarchical tree decompositions using the variation of information
    Saket Navlakha
    Department of Computer Science, University of Maryland, College Park, Maryland 20742, USA
    J Comput Biol 17:503-16. 2010
    ..For these two applications, we test the quality of our clusters by using them to predict complex and species membership, respectively. We find that our approach generally outperforms the commonly used heuristic methods...
  15. doi Sequencing and genome assembly using next-generation technologies
    Niranjan Nagarajan
    Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies and Department of Computer Science, University of Maryland, College Park, MD, USA
    Methods Mol Biol 673:1-17. 2010
    ..This paper surveys the recent software packages aimed specifically at analyzing new generation sequencing data...
  16. pmc Next generation sequence assembly with AMOS
    Todd J Treangen
    Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA
    Curr Protoc Bioinformatics . 2011
    ..Additionally, we provide three tutorial examples that include bacterial, viral, and metagenomic datasets with specific tips for improving assembly quality...
  17. pmc Deep sequencing of the oral microbiome reveals signatures of periodontal disease
    Bo Liu
    Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America
    PLoS ONE 7:e37919. 2012
    ....
  18. pmc Bambus 2: scaffolding metagenomes
    Sergey Koren
    Department of Computer Science, University of Maryland, College Park, MD 20742, USA
    Bioinformatics 27:2964-71. 2011
    ..The software tools developed for assembling and analyzing sequencing data for clonal organisms are, however, unable to adequately process data derived from non-clonal sources...
  19. pmc Bioinformatics challenges of new sequencing technology
    Mihai Pop
    Center for Bioinformatics and Computational Biology, University of Maryland, MD 20742, USA
    Trends Genet 24:142-9. 2008
    ....
  20. pmc Minimus: a fast, lightweight genome assembler
    Daniel D Sommer
    Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA
    BMC Bioinformatics 8:64. 2007
    ..Many of the most common uses of assemblers, however, are best served by a simpler type of assembler that requires fewer software components, uses less memory, and is far easier to install and run...
  21. pmc ARDB--Antibiotic Resistance Genes Database
    Bo Liu
    Center for Bioinformatics and Computational Biology and Department of Computer Science, University of Maryland, College Park, MD 20742, USA
    Nucleic Acids Res 37:D443-7. 2009
    ..Currently, ARDB contains resistance information for 13,293 genes, 377 types, 257 antibiotics, 632 genomes, 933 species and 124 genera. ARDB is available at http://ardb.cbcb.umd.edu/...
  22. pmc Figaro: a novel statistical method for vector sequence removal
    James Robert White
    Center for Bioinformatics and Computational Biology, University of Maryland College Park, MD 20742, USA
    Bioinformatics 24:462-7. 2008
    ..Correct clipping information is essential to scientists attempting to validate, improve and even finish the increasingly large number of genomes released at a 'draft' quality level...
  23. doi Parametric complexity of sequence assembly: theory and applications to next generation sequencing
    Niranjan Nagarajan
    Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies, University of Maryland, College Park, Maryland 20742, USA
    J Comput Biol 16:897-908. 2009
    ..Our work suggests at least two ways in which existing assemblers can be extended in a rigorous fashion, in addition to delineating directions for future theoretical investigations...
  24. pmc Statistical methods for detecting differentially abundant features in clinical metagenomic samples
    James Robert White
    Applied Mathematics and Scientific Computation Program, Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America
    PLoS Comput Biol 5:e1000352. 2009
    ..g. SAGE). A web server implementation of our methods and freely available source code can be found at http://metastats.cbcb.umd.edu/...
  25. pmc Ultrafast and memory-efficient alignment of short DNA sequences to the human genome
    Ben Langmead
    Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742, USA
    Genome Biol 10:R25. 2009
    ..Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source (http://bowtie.cbcb.umd.edu)...
  26. ncbi Exploring variation-aware contig graphs for (comparative) metagenomics using MaryGold
    Jurgen F Nijkamp
    Department of Intelligent Systems, The Delft Bioinformatics Lab, Delft University of Technology, 2628 CD Delft, The Netherlands, Kluyver Centre for Genomics of Industrial Fermentation, 2600 GA Delft, The Netherlands and Department of Computer Science, Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA
    Bioinformatics 29:2826-34. 2013
    ..In this article, we develop a method to perform reference-free detection and visual exploration of genomic variation, both within a single metagenome and between metagenomes...
  27. pmc Assessing the benefits of using mate-pairs to resolve repeats in de novo short-read prokaryotic assemblies
    Joshua Wetzel
    Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
    BMC Bioinformatics 12:95. 2011
    ....
  28. ncbi Comparative genome sequencing for discovery of novel polymorphisms in Bacillus anthracis
    Timothy D Read
    The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, MD 20850, USA, Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ 86011, USA
    Science 296:2028-33. 2002
    ..These results demonstrate that genome-based analysis of microbial pathogens will provide a powerful new tool for investigation of infectious disease outbreaks...
  29. pmc Quantitative PCR for detection of Shigella improves ascertainment of Shigella burden in children with moderate-to-severe diarrhea in low-income countries
    Brianna Lindsay
    University of Maryland, School of Medicine, Baltimore, Maryland, USA
    J Clin Microbiol 51:1740-6. 2013
    ..The acceptance of this new standard would substantially increase the fraction of MSD cases that are attributable to Shigella...
  30. pmc MetaPath: identifying differentially abundant metabolic pathways in metagenomic datasets
    Bo Liu
    Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742, USA
    BMC Proc 5:S9. 2011
    ..Here we describe a powerful analytical method (MetaPath) that can identify differentially abundant pathways in metagenomic datasets, relying on a combination of metagenomic sequence data and prior metabolic pathway knowledge...