Microsatellites, also known as Simple Sequence Repeats (SSRs), are short tandem repeats of 1–6 nt motifs present in all genomes, particularly eukaryotes. Besides their usefulness as genome markers, SSRs have been shown to perform important regulatory functions, and variations in their length at coding regions are linked to several disorders in humans. Microsatellites show a taxon-specific enrichment in eukaryotic genomes, and some may be functional. MSDB (Microsatellite Database) is a collection of >650 million SSRs from 6,893 species including Bacteria, Archaea, Fungi, Plants, and Animals. This database is by far the most exhaustive resource to access and analyze SSR data of multiple species. In addition to exploring data in a customizable tabular format, users can view and compare the data of multiple species simultaneously using our interactive plotting system. MSDB is developed using the Django framework and MySQL. It is freely available at http://tdb.ccmb.res.in/msdb.

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Poriferal Vision by Saketh Saxena Sponges provide nourishment as well as a habitat for various aquatic organisms. Anatomically, sponges are made up of soft tissue with a silica based exoskeleton which serves both as support and protection for the underlying tissue. The exoskeleton persists after the tissue decomposes, and microscopic parts of the exoskeleton break away to form spicules. Oceanographic studies have shown that the density of the sponge spicules is a good indicator of the sponge population in an area. This measure can be used to study sponge population dynamics over time. The spicule density is measured by imaging spicules from samples of water extracted from the oceans using an instrument called FlowCAM, which separates and photographs individual small items in a sample. It has a high processing rate, but is inefficient at computationally analyzing large numbers of photographs. Computer vision technologies, particularly deep learning using Artificial Neural Networks, and Support Vector Machines have shown to be effective in handling large scale image classification problems and are the de-facto standard in image recognition problems. Typically, these models require a large amount of data to learn the underlying distribution in datasets effectively and avoid model overfitting, which is currently a challenge to procure a vast dataset of images. To mitigate this challenge and achieve the overarching purpose of developing a highperformance classifier, we demonstrate various geometrical image transformation techniques to enhance the size of the dataset. We also show initial experimental results for training Generative Adversarial Networks for artificial synthesis of spicule images. Finally, we develop a Convolutional Neural Network and compare its performance against a Support Vector Machine for classifying images of sponge spicules training both the models on the original set of images and the newly generated set of images and achieve a test accuracy of 95% with a CNN trained on the newly generated images. Index terms-Artificial neural networks (ANN), sponge spicules, bioinformatics, computer vision, deep convoluted neural networks (CNN), FlowCAM, generative adversarial networks (GAN), global silica biogeochemical cycle, image transformations