Nature Methods (NAT METHODS)

Publications in this journal

• Article: Nucleotide-resolution DNA double-strand break mapping by next-generation sequencing.

  Crosetto N, Mitra A, Silva MJ, Bienko M, Dojer N, Wang Q, Karaca E, Chiarle R, Skrzypczak M, Ginalski K, Pasero P, Rowicka M, Dikic I
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  ABSTRACT: We present a genome-wide approach to map DNA double-strand breaks (DSBs) at nucleotide resolution by a method we termed BLESS (direct in situ breaks labeling, enrichment on streptavidin and next-generation sequencing). We validated and tested BLESS using human and mouse cells and different DSBs-inducing agents and sequencing platforms. BLESS was able to detect telomere ends, Sce endonuclease–induced DSBs and complex genome-wide DSB landscapes. As a proof of principle, we characterized the genomic landscape of sensitivity to replication stress in human cells, and we identified >2,000 nonuniformly distributed aphidicolin-sensitive regions (ASRs) overrepresented in genes and enriched in satellite repeats. ASRs were also enriched in regions rearranged in human cancers, with many cancer-associated genes exhibiting high sensitivity to replication stress. Our method is suitable for genome-wide mapping of DSBs in various cells and experimental conditions, with a specificity and resolution unachievable by current techniques.
  Nature Methods 03/2013;
• Article: Critical assessment of automated flow cytometry data analysis techniques.

  Nima Aghaeepour, Greg Finak, The FlowCAP Consortium, The DREAM Consortium, Holger Hoos, Tim R Mosmann, Ryan Brinkman, Raphael Gottardo & Richard H Scheuermann
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  ABSTRACT: Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.
  Nature Methods 02/2013;
• Article: Conversion of human fibroblasts to angioblast-like progenitor cells.

  Kurian L, Sancho-Martinez I, Nivet E, Aguirre A, Moon K, Pendaries C, Volle-Challier C, Bono F, Herbert JM, Pulecio J, [......], Ruiz S, Dubova I, Rodriguez C, Denli AM, Boscolo FS, Thiagarajan RD, Gage FH, Loring JF, Laurent LC, Izpisua Belmonte JC
  Nature Methods 12/2012;
• Article: Staining and embedding the whole mouse brain for electron microscopy

  Shawn Mikula, Jonas Binding, Winfried Denk
  Nature Methods 11/2012;
• Article: Wisdom of crowds for robust gene network inference

  Marbach D, Costello JC, Küffner R, Vega NM, Prill RJ, Camacho DM, Allison KR, The DREAM5 Consortium, Kellis M, Collins JJ, Stolovitzky G
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  ABSTRACT: Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ∼1,700 transcriptional interactions at a precision of ∼50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.
  Nature Methods 08/2012;
• Article: BioImageXD: an open, general-purpose and high-throughput image-processing platform

  Pasi Kankaanpä&auml, Lassi Paavolainen, Silja Tiitta, Mikko Karjalainen, Joacim Päivärinne, Jonna Nieminen, Varpu Marjomäki, Jyrki Heino, Daniel J White
  Nature Methods 06/2012; 9(7):683-689.
• Article: Expresso[reg] CMV system: effortless mammalian expression cloning

  Saurabh Sen, Heather Sternard, Eric Steinmetz, Ronald Godiska
  Nature Methods 02/2012; 9(3).
• Article: RNA structures

  Petya V Krasteva
  Nature Methods 12/2011; 9(1):38-38.
• Article: Imaging life with thin sheets of light

  Erika Pastrana
  Nature Methods 12/2011; 9(1):37-37.
• Article: Functional genomic resources

  Nicole Rusk
  Nature Methods 12/2011; 9(1):35-35.
• Article: Erratum: Reply to [ldquo]More on color blindness[rdquo]

  Bang Wong
  Nature Methods 12/2011; 9(1):110-110.
• Article: Causal mutations in a haploid landscape

  Nicole Rusk
  Nature Methods 12/2011; 9(1):36-36.
• Article: Single-cell methods

  Natalie de Souza
  Nature Methods 12/2011; 9(1):35-35.