Main Menu (Mobile)- Block

Main Menu - Block

custom | custom

Search Results

filters_region_cap | custom


facetapi-Q2b17qCsTdECvJIqZJgYMaGsr8vANl1n | block

Associated Lab

facetapi-PV5lg7xuz68EAY8eakJzrcmwtdGEnxR0 | block
general_search_page-panel_pane_1 | views_panes

3 Janelia Publications

Showing 1-3 of 3 results
Your Criteria:
    Menon Lab
    07/16/14 | A high-resolution spatiotemporal atlas of gene expression of the developing mouse brain.
    Thompson CL, Ng L, Menon V, Martinez S, Lee C, Glattfelder K, Sunkin SM, Henry A, Lau C, Dang C, Garcia-Lopez R, Martinez-Ferre A, Pombero A, Rubenstein JL, Wakeman WB, Hohmann J, Dee N, Sodt AJ, Young R, Smith K, Nguyen T, Kidney J, Kuan L, Jeromin A, Kaykas A, Miller J, Page D, Orta G, Bernard A, Riley Z, Smith S, Wohnoutka P, Hawrylycz MJ, Puelles L, Jones AR
    Neuron. 2014 Jul 16;83(2):309-23. doi: 10.1016/j.neuron.2014.05.033

    To provide a temporal framework for the genoarchitecture of brain development, we generated in situ hybridization data for embryonic and postnatal mouse brain at seven developmental stages for ∼2,100 genes, which were processed with an automated informatics pipeline and manually annotated. This resource comprises 434,946 images, seven reference atlases, an ontogenetic ontology, and tools to explore coexpression of genes across neurodevelopment. Gene sets coinciding with developmental phenomena were identified. A temporal shift in the principles governing the molecular organization of the brain was detected, with transient neuromeric, plate-based organization of the brain present at E11.5 and E13.5. Finally, these data provided a transcription factor code that discriminates brain structures and identifies the developmental age of a tissue, providing a foundation for eventual genetic manipulation or tracking of specific brain structures over development. The resource is available as the Allen Developing Mouse Brain Atlas (

    View Publication Page
    Menon Lab
    06/04/14 | Modeling proteins using a super-secondary structure library and NMR chemical shift information.
    Menon V, Vallat BK, Dybas JM, Fiser A
    Structure (London, England : 1993). 2013 Jun 4;21(6):891-9. doi: 10.1016/j.str.2013.04.012

    A remaining challenge in protein modeling is to predict structures for sequences with no sequence similarity to any experimentally solved structure. Based on earlier observations, the library of protein backbone supersecondary structure motifs (Smotifs) saturated about a decade ago. Therefore, it should be possible to build any structure from a combination of existing Smotifs with the help of limited experimental data that are sufficient to relate the backbone conformations of Smotifs between target proteins and known structures. Here, we present a hybrid modeling algorithm that relies on an exhaustive Smotif library and on nuclear magnetic resonance chemical shift patterns without any input of primary sequence information. In a test of 102 proteins, the algorithm delivered 90 homology-model-quality models, among them 24 high-quality ones, and a topologically correct solution for almost all cases. The current approach opens a venue to address the modeling of larger protein structures for which chemical shifts are available.

    View Publication Page
    Menon Lab
    02/24/14 | Improving reliability and absolute quantification of human brain microarray data by filtering and scaling probes using RNA-Seq.
    Miller JA, Menon V, Goldy J, Kaykas A, Lee C, Smith KA, Shen EH, Phillips JW, Lein ES, Hawrylycz MJ
    BMC genomics. 2014;15:154. doi: 10.1186/1471-2164-15-154

    BACKGROUND: High-throughput sequencing is gradually replacing microarrays as the preferred method for studying mRNA expression levels, providing nucleotide resolution and accurately measuring absolute expression levels of almost any transcript, known or novel. However, existing microarray data from clinical, pharmaceutical, and academic settings represent valuable and often underappreciated resources, and methods for assessing and improving the quality of these data are lacking.

    RESULTS: To quantitatively assess the quality of microarray probes, we directly compare RNA-Seq to Agilent microarrays by processing 231 unique samples from the Allen Human Brain Atlas using RNA-Seq. Both techniques provide highly consistent, highly reproducible gene expression measurements in adult human brain, with RNA-Seq slightly outperforming microarray results overall. We show that RNA-Seq can be used as ground truth to assess the reliability of most microarray probes, remove probes with off-target effects, and scale probe intensities to match the expression levels identified by RNA-Seq. These sequencing scaled microarray intensities (SSMIs) provide more reliable, quantitative estimates of absolute expression levels for many genes when compared with unscaled intensities. Finally, we validate this result in two human cell lines, showing that linear scaling factors can be applied across experiments using the same microarray platform.

    CONCLUSIONS: Microarrays provide consistent, reproducible gene expression measurements, which are improved using RNA-Seq as ground truth. We expect that our strategy could be used to improve probe quality for many data sets from major existing repositories.

    View Publication Page