Suresh Babu Mudunuri

Degenerative cervical myelopathy must be diagnosed with magnetic resonance imaging (MRI) which predicts spinal cord injury (SCI). The growing volume of medical imaging data can be managed by deep learning models, which provide a preliminary interpretation of images taken in basic care settings. Our main goal was to create a deep-learning approach that could identify SCI using MRI data. This work concentrates on modeling a novel 2D-convolutional neural networks (2D-CNN) approach for predicting SCI. For holdouts, training, and validation, various datasets of patients were created. Two experts assigned labels to the images. The holdout dataset was used to evaluate the performance of our deep convolutional neural network (DCNN) over the image data from the available dataset. The dataset is acquired from the online resource for training and validation purpose. With the available dataset, the anticipated model attains 94% AUC, 0.1 p-value, and 92.2% accuracy. The anticipated model might make cervical spine MRI scan interpretation more accurate and reliable.

Organelle genomes evolve rapidly as compared with nuclear genomes and have been widely used for developing microsatellites or simple sequence repeats (SSRs) markers for delineating phylogenomics. In our previous reports, we have established the largest repository of organelle SSRs, ChloroMitoSSRDB, which provides access to 2161 organelle genomes (1982 mitochondrial and 179 chloroplast genomes) with a total of 5838 perfect chloroplast SSRs, 37 297 imperfect chloroplast SSRs, 5898 perfect mitochondrial SSRs and 50 355 imperfect mitochondrial SSRs across organelle genomes. In the present research, we have updated ChloroMitoSSRDB by systematically analyzing and adding additional 191 chloroplast and 2102 mitochondrial genomes. With the recent update, ChloroMitoSSRDB 2.00 provides access to a total of 4454 organelle genomes displaying a total of 40 653 IMEx Perfect SSRs (11 802 Chloroplast Perfect SSRs and 28 851 Mitochondria Perfect SSRs), 275 981 IMEx Imperfect SSRs (78 972 Chloroplast Imperfect SSRs and 197 009 Mitochondria Imperfect SSRs), 35 250 MISA (MIcroSAtellite identification tool) Perfect SSRs and 3211 MISA Compound SSRs and associated information such as location of the repeats (coding and non-coding), size of repeat, motif and length polymorphism, and primer pairs. Additionally, we have integrated and made available several in silico SSRs mining tools through a unified web-portal for in silico repeat mining for assembled organelle genomes and from next generation sequencing reads. ChloroMitoSSRDB 2.00 allows the end user to perform multiple SSRs searches and easy browsing through the SSRs using two repeat algorithms and provide primer pair information for identified SSRs for evolutionary genomics. Database URL:http://www.mcr.org.in/chloromitossrdb

We have created tRFdb, the first database of transfer RNA fragments (tRFs), available at http://genome.bioch.virginia.edu/trfdb/. With over 100 small RNA libraries analyzed, the database currently contains the sequences and read counts of the three classes of tRFs for eight species: R. sphaeroides, S. pombe, D. melanogaster, C. elegans, Xenopus, zebra fish, mouse and human, for a total of 12 877 tRFs. The database can be searched by tRF ID or tRF sequence, and the results can be limited by organism. The search results show the genome coordinates and names of the tRNAs the sequence may derive from, and there are links for the sequence of the tRF and parental tRNA, and links for the read counts in all the corresponding small RNA libraries. As a case study for how this database may be used, we have shown that a certain class of tRFs, tRF-1s, is highly upregulated in B-cell malignancies.

The MICdb is a comprehensive relational database of perfect microsatellites extracted from completely sequenced and annotated genomes of bacteria and archaea. The current version MICdb3.0 is an updated and revised version of MICdb2.0. As compared with the previous version MICdb2.0, the current release is significantly improved in terms of much larger coverage of genomes, improved presentation of queried results, user-friendly administration module to manage Simple Sequence Repeat (SSR) data such as addition of new genomes, deletion of obsolete data, etc., and also removal of certain features deemed to be redundant. The new web-interface to the database called Microsatellite Analysis Server (MICAS) version 3.0 has been improved by the addition of powerful high-quality visualization tools to view the query results in the form of pie charts and bar graphs. All the query results and graphs can be exported in different formats so that the users can use them for further analysis. MICAS3.0 is also equipped with a unique genome comparison module using which users can do pair-wise comparison of genomes with regard to their microsatellite distribution. The advanced search module can be used to filter the repeats based on certain criteria such as filtering repeats of a particular motif/repeat size, extracting repeats of coding/non-coding regions, sort repeats, etc. The MICdb database has, therefore, been made portable to be administered by a person with the necessary administrative privileges. The MICdb3.0 database and analysis server can be accessed for free from www.cdfd.org.in/micas . Database URL:http://www.cdfd.org.in/micas

Microsatellites or simple sequence repeats (SSRs) are repetitive stretches of nucleotides (A, T, G, C) that are distributed either as single base pair stretches or as a combination of two- to six-nucleotides units that are non-randomly distributed within coding and in non-coding regions of the genome. ChloroMitoSSRDB is a complete curated web-oriented relational database of perfect and imperfect repeats in organelle genomes. The present version of the database contains perfect and imperfect SSRs of 2161 organelle genomes (1982 mitochondrial and 179 chloroplast genomes). We detected a total of 5838 chloroplast perfect SSRs, 37 297 chloroplast imperfect SSRs, 5898 mitochondrial perfect SSRs and 50 355 mitochondrial imperfect SSRs across these genomes. The repeats have been further hyperlinked to the annotated gene regions (coding or non-coding) and a link to the corresponding gene record in National Center for Biotechnology Information(www.ncbi.nlm.nih.gov/) to identify and understand the positional relationship of the repetitive tracts. ChloroMitoSSRDB is connected to a user-friendly web interface that provides useful information associated with the location of the repeats (coding and non-coding), size of repeat, motif and length polymorphism, etc. ChloroMitoSSRDB will serve as a repository for developing functional markers for molecular phylogenetics, estimating molecular variation across species. Database URL: ChloroMitoSSRDB can be accessed as an open source repository at www.mcr.org.in/chloromitossrdb.

Microsatellites are short, dynamic tandem repeats in DNA sequences that are unique in nature and are widely used in several fields including DNA Fingerprinting, Paternity Studies, Forensics, Population Studies and Evolutionary studies. This paper presents the first comprehensive computational study of microsatellite repeats in viruses. Microsatellites from more than 3,500 viral sequences have been analyzed to underpin the genomic differences in them, their variation in terms of size and genes, distribution of microsatellite repeats based on their motif-size, location and also the motif preferences of such microsatellite repeats in all these sequences. The study revealed that the viral genomes differ a lot in terms of their genome composition and also in terms of the microsatellite distribution. We found that the majority of microsatellites are in the coding regions suggesting selection towards pathogen adaptation and faster evolution. It is also observed that viruses are selective in nature to form microsatellites with certain motifs only.

Viruses are very small organisms that incorporate their DNA into host cells and cause several diseases. They evolve extremely faster when compared to other organisms. Viruses can develop into new strains easily due to their shorter generation times and due to rapid changes in their genome sequences (in the form of mutations) during the course of evolution. In view of the recent pandemics of viruses such as HIV, SARS, Swine flu, Huntavirus, etc., studying the evolution of viruses at genomic level would be very informative. A special type of DNA repeats named compound microsatellites are often studied to understand the genomic evolution of organisms. This paper presents the details of the study performed to analyze the distribution of compound microsatellites and their motif preferences in all sequenced viral genomes using the classical data mining algorithm called Apriori Algorithm. It is interesting to note that the dynamics of compound microsatellites in viruses differ from that of higher organisms. We observed that viral compound microsatellites are not composed of similar or identical motifs unlike humans and other higher organisms where more than 90% of them are composed of similar motifs.