17 research outputs found

    Integrating transcriptomic and proteomic data for accurate assembly and annotation of genomes

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    © 2017 Wong et al.; Published by Cold Spring Harbor Laboratory Press. Complementing genome sequence with deep transcriptome and proteome data could enable more accurate assembly and annotation of newly sequenced genomes. Here, we provide a proof-of-concept of an integrated approach for analysis of the genome and proteome of Anopheles stephensi, which is one of the most important vectors of the malaria parasite. To achieve broad coverage of genes, we carried out transcriptome sequencing and deep proteome profiling of multiple anatomically distinct sites. Based on transcriptomic data alone, we identified and corrected 535 events of incomplete genome assembly involving 1196 scaffolds and 868 protein-coding gene models. This proteogenomic approach enabled us to add 365 genes that were missed during genome annotation and identify 917 gene correction events through discovery of 151 novel exons, 297 protein extensions, 231 exon extensions, 192 novel protein start sites, 19 novel translational frames, 28 events of joining of exons, and 76 events of joining of adjacent genes as a single gene. Incorporation of proteomic evidence allowed us to change the designation of more than 87 predicted noncoding RNAs to conventional mRNAs coded by protein-coding genes. Importantly, extension of the newly corrected genome assemblies and gene models to 15 other newly assembled Anopheline genomes led to the discovery of a large number of apparent discrepancies in assembly and annotation of these genomes. Our data provide a framework for how future genome sequencing efforts should incorporate transcriptomic and proteomic analysis in combination with simultaneous manual curation to achieve near complete assembly and accurate annotation of genomes

    PTC CREO SIMULATE ENGINE UPDATES

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    Dysregulation of splicing proteins in head and neck squamous cell carcinoma

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    Signaling plays an important role in regulating all cellular pathways. Altered signaling is one of the hallmarks of cancers. Phosphoproteomics enables interrogation of kinase mediated signaling pathways in biological systems. In cancers, this approach can be utilized to identify aberrantly activated pathways that potentially drive proliferation and tumorigenesis. To identify signaling alterations in head and neck squamous cell carcinoma (HNSCC), we carried out proteomic and phosphoproteomic analysis of HNSCC cell lines using a combination of tandem mass tag (TMT) labeling approach and titanium dioxide-based enrichment. We identified 4,920 phosphosites corresponding to 2,212 proteins in six HNSCC cell lines compared to a normal oral cell line. Our data indicated significant enrichment of proteins associated with splicing. We observed hyperphosphorylation of SRSF protein kinase 2 (SRPK2) and its downstream substrates in HNSCC cell lines. SRPK2 is a splicing kinase, known to phosphorylate serine/arginine (SR) rich domain proteins and regulate splicing process in eukaryotes. Although genome-wide studies have reported the contribution of alternative splicing events of several genes in the progression of cancer, the involvement of splicing kinases in HNSCC is not known. In this study, we studied the role of SRPK2 in HNSCC. Inhibition of SRPK2 resulted in significant decrease in colony forming and invasive ability in a panel of HNSCC cell lines. Our results indicate that phosphorylation of SRPK2 plays a crucial role in the regulation of splicing process in HNSCC and that splicing kinases can be developed as a new class of therapeutic target in HNSCC

    National Mental Health Survey of India, 2016 - Rationale, design and methods.

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    Understanding the burden and pattern of mental disorders as well as mapping the existing resources for delivery of mental health services in India, has been a felt need over decades. Recognizing this necessity, the Ministry of Health and Family Welfare, Government of India, commissioned the National Mental Health Survey (NMHS) in the year 2014-15. The NMHS aimed to estimate the prevalence and burden of mental health disorders in India and identify current treatment gaps, existing patterns of health-care seeking, service utilization patterns, along with an understanding of the impact and disability due to these disorders. This paper describes the design, steps and the methodology adopted for phase 1 of the NMHS conducted in India. The NMHS phase 1 covered a representative population of 39,532 from 12 states across 6 regions of India, namely, the states of Punjab and Uttar Pradesh (North); Tamil Nadu and Kerala (South); Jharkhand and West Bengal (East); Rajasthan and Gujarat (West); Madhya Pradesh and Chhattisgarh (Central) and Assam and Manipur (North East). The NMHS of India (2015-16) is a unique representative survey which adopted a uniform and standardized methodology which sought to overcome limitations of previous surveys. It employed a multi-stage, stratified, random cluster sampling technique, with random selection of clusters based on Probability Proportionate to Size. It was expected that the findings from the NMHS 2015-16 would reveal the burden of mental disorders, the magnitude of the treatment gap, existing challenges and prevailing barriers in the mental-health delivery systems in the country at a single point in time. It is hoped that the results of NMHS will provide the evidence to strengthen and implement mental health policies and programs in the near future and provide the rationale to enhance investment in mental health care in India. It is also hoped that the NMHS will provide a framework for conducting similar population based surveys on mental health and other public health problems in low and middle-income countries
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