Whole Genome Sequencing of Mycobacterium tuberculosis Clinical Isolates From India Reveals Genetic Heterogeneity and Region-Specific Variations That Might Affect Drug Susceptibility

Whole genome sequencing (WGS) of Mycobacterium tuberculosis has been constructive in understanding its evolution, genetic diversity and the mechanisms involved in drug resistance. A large number of sequencing efforts from across the globe have revealed genetic diversity among clinical isolates and the genetic determinants for their resistance to anti-tubercular drugs. Considering the high TB burden in India, the availability of WGS studies is limited. Here we present, WGS results of 200 clinical isolates of M. tuberculosis from North India which are categorized as sensitive to first-line drugs, mono-resistant, multi-drug resistant and pre-extensively drug resistant isolates. WGS revealed that 20% of the isolates were co-infected with M. tuberculosis and non-tuberculous mycobacteria species. We identified 12,802 novel genetic variations in M. tuberculosis isolates including 343 novel SNVs in 38 genes which are known to be associated with drug resistance and are not currently used in the diagnostic kits for detection of drug resistant TB. We also identified M. tuberculosis lineage 3 to be predominant in the northern region of India. Additionally, several novel SNVs, which may potentially confer drug resistance were found to be enriched in the drug resistant isolates sampled. This study highlights the significance of employing WGS in diagnosis and for monitoring further development of MDR-TB strains

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

© 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

Surgery, Octreotide, Temozolomide, Bevacizumab, Radiotherapy, and Pegvisomant Treatment of an AIP Mutation-Positive Child

UK India Education Research Initiative and the British Council (75-2014; to P.D.)Council of Scientific and Industrial Research, University Grants Commission, for financial support (2061330632; to A.R.).Medical Research Council (MR/M018539/1; to M.K

A new RGB based fusion for forged IMEI number detection in mobile images

As technology advances to make living comfortable for people, at the same time, different crimes also increase. One such sensitive crime is creating fake International Mobile Equipment Identity (IMEI) for smart mobile devices. In this paper, we present a new fusion based method using R, G and B color components for detecting forged IMEI numbers. To the best of our knowledge, this is the first work for forged IMEI number detection in mobile images. The proposed method first finds variances for R, G and B images of a forged input image to study local changes. The variances are used to derive weights for respective color components. The same weights are convolved with respective pixel values of R, G and B components, which results in the fused image. For the fused image, the proposed method extracts features based on sparsity, the number of connected components, and the average intensity values for edge components in respective R, G and B components, which gives six features. The proposed method finds absolute difference between fused and input images, which gives feature vector containing six difference values. The proposed method constructs templates based on samples chosen randomly. Feature vectors are compared with the templates for detecting forged IMEI numbers. Experiments are conducted on our own dataset and standard datasets to evaluate the proposed method. Furthermore, comparative studies with the related existing methods show that the proposed method outperforms the existing methods