Vitiligo is a chronic asymptomatic disorder affecting melanocytes from the basal layer of the epidermis which leads to a patchy loss of skin color. Even though it is one of the neglected disease conditions, people suffering from vitiligo are more prone to psychological disorders. As of now, various studies have been done in order to project auto-immune implications as the root cause. To understand the complexity of vitiligo, we propose the Vitiligo Information Resource (VIRdb) that integrates both the drug-target and systems approach to produce a comprehensive repository entirely devoted to vitiligo, along with curated information at both protein level and gene level along with potential therapeutics leads. These 25,041 natural compounds are curated from Natural Product Activity and Species Source Database. VIRdb is an attempt to accelerate the drug discovery process and laboratory trials for vitiligo through the computationally derived potential drugs. It is an exhaustive resource consisting of 129 differentially expressed genes, which are validated through gene ontology and pathway enrichment analysis. We also report 22 genes through enrichment analysis which are involved in the regulation of epithelial cell differentiation. At the protein level, 40 curated protein target molecules along with their natural hits that are derived through virtual screening. We also demonstrate the utility of the VIRdb by exploring the Protein–Protein Interaction Network and Gene–Gene Interaction Network of the target proteins and differentially expressed genes. For maintaining the quality and standard of the data in the VIRdb, the gold standard in bioinformatics toolkits like Cytoscape, Schrödinger’s GLIDE, along with the server installation of MATLAB, are used for generating results. VIRdb can be accessed through “http://www.vitiligoinfores.com/”.

The outbreak of COVID-19 had spread at a deadly rate since its onset at Wuhan, China and is now spread across 216 countries and has affected more than 6 million people all over the world. The global response throughout the world has been primarily the implementation of lockdown measures, testing and contact tracing to minimise the spread of the disease. The aim of the present study was to predict the COVID-19 prevalence and disease progression rate in Indian scenario in order to provide an analysis that can shed light on comprehending the trends of the outbreak and outline an impression of the epidemiological stage for each state of a diverse country like India. In addition, the forecast of COVID-19 incidence trends of these states can help take safety measures and policy design for this epidemic in the days to come. In order to achieve the same, we have utilized an approach where we test modelling choices of the spatially unambiguous kind, proposed by the wave of infections spreading from the initial slow progression to a higher curve. We have estimated the parameters of an individual state using factors like population density and mobility. The findings can also be used to strategize the testing and quarantine processes to manipulate the spread of the disease in the future. This is especially important for a country like India that has several limitations about healthcare infrastructure, diversity in socioeconomic status, high population density, housing conditions, health care coverage that can be important determinants for the overall impact of the pandemic. The results of our 5-phase model depict a projection of the state wise infections/disease over time. The model can generate live graphs as per the change in the data values as the values are automatically being fetched from the crowd-sourced database.

: Stem cell based toxicity prediction plays very important role in the development of drug. Unexpected adverse effects of the drugs during clinical trials are a major reason for termination or withdrawal of drugs. Methods for predicting toxicity employ in vitro as well as in vivo models, however, the major drawback seen in the data derived from these animal models is lack of extrapolation, owing to interspecies variations. Due to these limitations, researchers have been striving to develop more robust drug screening platforms based on stem cells. The application of stem cells based toxicity testing has opened up robust methods to study the impact of new chemical entities on not only specific cell types, but also organs. Pluripotent stem cells, as well as cells derived from them, can be evaluated for modulation of cell function in response to drugs. Moreover, the combination of state-of-the -art techniques such as tissue engineering and microfluidics to fabricate organ-on-a-chip, has led to assays which are amenable to high throughput screening to understand the adverse and toxic effects of chemicals and drugs. This review summarizes the important aspects of the establishment of the embryonic stem cell test (EST), use of stem cells, pluripotent, induced pluripotent stem cells and organoids for toxicity prediction and drug development.

Transcription factors are trans-acting proteins that interact with specific nucleotide sequences known as transcription factor binding site (TFBS), and these interactions are implicated in regulation of the gene expression. Regulation of transcriptional activation of a gene often involves multiple interactions of transcription factors with various sequence elements. Identification of these sequence elements is the first step in understanding the underlying molecular mechanism(s) that regulate the gene expression. For in silico identification of these sequence elements, we have developed an online computational tool named transcription factor information system (TFIS) for detecting TFBS for the first time using a collection of JAVA programs and is mainly based on TFBS detection using position weight matrix (PWM). The database used for obtaining position frequency matrices (PFM) is JASPAR and HOCOMOCO, which is an open-access database of transcription factor binding profiles. Pseudo-counts are used while converting PFM to PWM, and TFBS detection is carried out on the basis of percent score taken as threshold value. TFIS is equipped with advanced features such as direct sequence retrieving from NCBI database using gene identification number and accession number, detecting binding site for common TF in a batch of gene sequences, and TFBS detection after generating PWM from known raw binding sequences in addition to general detection methods. TFIS can detect the presence of potential TFBSs in both the directions at the same time. This feature increases its efficiency. And the results for this dual detection are presented in different colors specific to the orientation of the binding site. Results obtained by the TFIS are more detailed and specific to the detected TFs as integration of more informative links from various related web servers are added in the result pages like Gene Ontology, PAZAR database and Transcription Factor Encyclopedia in addition to NCBI and UniProt. Common TFs like SP1, AP1 and NF-KB of the Amyloid beta precursor gene is easily detected using TFIS along with multiple binding sites. In another scenario of embryonic developmental process, TFs of the FOX family (FOXL1 and FOXC1) were also identified. TFIS is platform-independent which is publicly available along with its support and documentation at http://tfistool.appspot.com and http://www.bioinfoplus.com/tfis/ . TFIS is licensed under the GNU General Public License, version 3 (GPL-3.0).

The amount of data on molecular interactions is growing at an enormous pace, whereas the progress of methods for analysing this data is still lacking behind. Particularly, in the area of comparative analysis of biological networks, where one wishes to explore the similarity between two biological networks, this holds a potential problem. In consideration that the functionality primarily runs at the network level, it advocates the need for robust comparison methods. In this paper, we describe Net2Align, an algorithm for pairwise global alignment that can perform node-to-node correspondences as well as edge-to-edge correspondences into consideration. The uniqueness of our algorithm is in the fact that it is also able to detect the type of interaction, which is essential in case of directed graphs. The existing algorithm is only able to identify the common nodes but not the common edges. Another striking feature of the algorithm is that it is able to remove duplicate entries in case of variable datasets being aligned. This is achieved through creation of a local database which helps exclude duplicate links. In a pervasive computational study on gene regulatory network, we establish that our algorithm surpasses its counterparts in its results. Net2Align has been implemented in Java 7 and the source code is available as supplementary files.