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  1. Article

    Open Access

    Global identification of functional microRNA-mRNA interactions in Drosophila

    MicroRNAs (miRNAs) are key mediators of post-transcriptional gene expression silencing. So far, no comprehensive experimental annotation of functional miRNA target sites exists in Drosophila. Here, we generated a...

    Hans-Hermann Wessels, Svetlana Lebedeva, Antje Hirsekorn in Nature Communications (2019)

  2. No Access

    Article

    Integrative classification of human coding and noncoding genes through RNA metabolism profiles

    Quantitative assessment of transcription, splicing, degradation, localization and translation of coding and noncoding genes allows classification of RNAs on the basis of their metabolism and may aid in inferen...

    Neelanjan Mukherjee, Lorenzo Calviello in Nature Structural & Molecular Biology (2017)

  3. No Access

    Article

    Detecting actively translated open reading frames in ribosome profiling data

    RiboTaper quantifies the three-nucleotide periodicity in Ribo-seq data to find translated open reading frames (ORFs). The de novo inferred set of ORFs comprehensively defines the cellular proteome across a wide e...

    Lorenzo Calviello, Neelanjan Mukherjee, Emanuel Wyler, Henrik Zauber in Nature Methods (2016)

  4. No Access

    Protocol

    Identifying RBP Targets with RIP-seq

    Throughout their lifetime RNA molecules interact with a variety of RNA-binding proteins (RBPs). RBPs control gene expression by regulating splicing, polyadenylation, editing, transport, stability, and translat...

    Hans-Herman Wessels, Antje Hirsekorn, Uwe Ohler in Post-Transcriptional Gene Regulation (2016)

  5. Article

    Open Access

    Global target mRNA specification and regulation by the RNA-binding protein ZFP36

    ZFP36, also known as tristetraprolin or TTP, and ELAVL1, also known as HuR, are two disease-relevant RNA-binding proteins (RBPs) that both interact with AU-rich sequences but have antagonistic roles. While ELA...

    Neelanjan Mukherjee, Nicholas C Jacobs, Markus Hafner in Genome Biology (2014)

  6. No Access

    Article

    MicroRNA target site identification by integrating sequence and binding information

    The integration of microRNA target sequence features and data from cross-linking and immunoprecipitation of Argonaute proteins, implemented in the hidden Markov model–based framework MUMMIE, provides accurate ...

    William H Majoros, Parawee Lekprasert, Neelanjan Mukherjee in Nature Methods (2013)

  7. No Access

    Article

    FMRP targets distinct mRNA sequence elements to regulate protein expression

    Fragile X syndrome (FXS) is a multi-organ disease that leads to mental retardation, macro-orchidism in males and premature ovarian insufficiency in female carriers. FXS is also a prominent monogenic disease as...

    Manuel Ascano, Neelanjan Mukherjee, Pradeep Bandaru, Jason B. Miller in Nature (2012)

  8. Article

    Open Access

    PARalyzer: definition of RNA binding sites from PAR-CLIP short-read sequence data

    Crosslinking and immunoprecipitation (CLIP) protocols have made it possible to identify transcriptome-wide RNA-protein interaction sites. In particular, PAR-CLIP utilizes a photoactivatable nucleoside for more...

    David L Corcoran, Stoyan Georgiev, Neelanjan Mukherjee, Eva Gottwein in Genome Biology (2011)

  9. No Access

    Article

    A viral microRNA functions as an orthologue of cellular miR-155

    Some viral microRNAs are known to bind host cell mRNAs to block translation or induce their degradation. Now a microRNA from the herpes virus associated with Kaposi's sarcoma has been found to mimic the ubiqui...

    Eva Gottwein, Neelanjan Mukherjee, Christoph Sachse, Corina Frenzel in Nature (2007)

  10. No Access

    Article

    Radiation Enhances the Invasive Potential of Primary Glioblastoma Cells via Activation of the Rho Signaling Pathway

    Glioblastoma multiforme (GBM) is among the most treatment-refractory of all human tumors. Radiation is effective at prolonging survival of GBM patients; however, the vast majority of GBM patients demonstrate p...

    Gary G. Zhai, Rajeev Malhotra, Meaghan Delaney, Douglas Latham in Journal of Neuro-Oncology (2006)

  11. No Access

    Chapter and Conference Paper

    Predicting Signal Peptides with Support Vector Machines

    We examine using a Support Vector Machine to predict secretory signal peptides. We predict signal peptides for both prokaryotic and eukaryotic signal organisms. Signalling peptides versus non-signaling peptide...

    Neelanjan Mukherjee, Sayan Mukherjee in Pattern Recognition with Support Vector Machines (2002)