title: PathoScope 2.0: a complete computational framework for strain identification in environmental or clinical sequencing samples.
abstract: BACKGROUND
  Recent innovations in sequencing technologies have provided researchers with the ability to rapidly characterize the microbial content of an environmental or clinical sample with unprecedented resolution. These approaches are producing a wealth of information that is providing novel insights into the microbial ecology of the environment and human health. However, these sequencing-based approaches produce large and complex datasets that require efficient and sensitive computational analysis workflows. Many recent tools for analyzing metagenomic-sequencing data have emerged, however, these approaches often suffer from issues of specificity, efficiency, and typically do not include a complete metagenomic analysis framework.
  
  
  RESULTS
  We present PathoScope 2.0, a complete bioinformatics framework for rapidly and accurately quantifying the proportions of reads from individual microbial strains present in metagenomic sequencing data from environmental or clinical samples. The pipeline performs all necessary computational analysis steps; including reference genome library extraction and indexing, read quality control and alignment, strain identification, and summarization and annotation of results. We rigorously evaluated PathoScope 2.0 using simulated data and data from the 2011 outbreak of Shiga-toxigenic Escherichia coli O104:H4.
  
  
  CONCLUSIONS
  The results show that PathoScope 2.0 is a complete, highly sensitive, and efficient approach for metagenomic analysis that outperforms alternative approaches in scope, speed, and accuracy. The PathoScope 2.0 pipeline software is freely available for download at: http://sourceforge.net/projects/pathoscope/.
authors.affiliation: Computational Biomedicine, Boston University School of Medicine, 72 E Concord St. E645, Boston, MA 02118, USA.
authors.forename: Changjin
authors.initials: C
authors.lastname: Hong
authors.affiliation: Computational Biomedicine, Boston University School of Medicine, 72 E Concord St. E645, Boston, MA 02118, USA.
authors.forename: Solaiappan
authors.initials: S
authors.lastname: Manimaran
authors.affiliation: Computational Biomedicine, Boston University School of Medicine, 72 E Concord St. E645, Boston, MA 02118, USA.
authors.forename: Ying
authors.initials: Y
authors.lastname: Shen
authors.affiliation: Computational Biomedicine, Boston University School of Medicine, 72 E Concord St. E645, Boston, MA 02118, USA ; Bioinformatics Program, Boston University, Boston, MA 02125, USA.
authors.forename: Joseph F
authors.initials: JF
authors.lastname: Perez-Rogers
authors.affiliation: Bioinformatics Program, Boston University, Boston, MA 02125, USA.
authors.forename: Allyson L
authors.initials: AL
authors.lastname: Byrd
authors.affiliation: Computational Biology Institute, George Washington University, Ashburn, VA 20147, USA.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Castro-Nallar
authors.affiliation: Computational Biology Institute, George Washington University, Ashburn, VA 20147, USA.
authors.forename: Keith A
authors.initials: KA
authors.lastname: Crandall
authors.affiliation: Computational Biomedicine, Boston University School of Medicine, 72 E Concord St. E645, Boston, MA 02118, USA ; Bioinformatics Program, Boston University, Boston, MA 02125, USA.
authors.forename: William Evan
authors.initials: WE
authors.lastname: Johnson