Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-CRISPR associated protein (CRISPR-Cas) has emerged and evolved as a revolutionary genome editing technology, transforming research across diverse biological disciplines. Over the past decade, this technology has unveiled numerous opportunities for precise genome manipulation. However, the processes of discovering Cas proteins, repurposing them as editing tools, selecting appropriate candidate tool from the CRISPR-toolbox, designing experiments, and analyzing data are often complex and require careful consideration. To support researchers at every stage of CRISPR experimentation, a wide array of web resources has been developed. In this article, we provide a comprehensive overview of standalone and web-based tools that assist in the identification of CRISPR-Cas systems and the design of guide RNAs (gRNAs). We also highlight tools for evaluating gRNA efficiency, predicting CRISPR-Cas9 mutation profiles, as well as tools for base editing and prime editing, and the analysis and visualization of experimental results. Additionally, we introduce CRISPR-Gateway for Accessing Tools and Resources (CRISPR-GATE), an all-inclusive web repository that consolidates publicly available tools for genome editing research. This repository offers a categorized and user-friendly interface, allowing researchers to quickly access relevant tools based on their specific needs. CRISPR-GATE aims to streamline the search for CRISPR resources, facilitating both education and accelerating innovation. The web repository can be accessed from https://crispr-gate.daasbioinfromaticsteam.in/.

MOTIVATION: Tea is a cross-pollinated woody perennial plant, which is why, application of conventional breeding is limited for its genetic improvement. However, lack of the genome-wide high-density SNP markers and genome-wide haplotype information has greatly hampered the utilization of tea genetic resources toward fast-track tea breeding programs. To address this challenge, we have generated a first-generation haplotype map of tea (Tea HapMap-1). Out-crossing and highly heterozygous nature of tea plants, make them more complicated for DNA-level variant discovery. RESULTS: In this study, whole genome re-sequencing data of 369 tea genotypes were used to generate 2,334,564 biallelic SNPs and 1,447,985 InDels. Around 2928.04 million paired-end reads were used with an average mapping depth of ∼0.31× per accession. Identified polymorphic sites in this study will be useful in mapping the genomic regions responsible for important traits of tea. These resources lay the foundation for future research to understand the genetic diversity within tea germplasm and utilize genes that determine tea quality. This will further facilitate the understanding of tea genome evolution and tea metabolite pathways thus, offers an effective germplasm utilization for breeding the tea varieties. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RNA-seq data analysis with rapidly advancing high-throughput sequencing technology, nowadays provides large number of transcripts or genes to perform downstream analysis including functional annotation and pathway analysis. However for the data from multiple tissues, downstream analysis with tissue-specific or tissue-enriched transcripts is highly preferable. However, there is still a need of tool for quickly performing tissue-enrichment and gene expression analysis irrespective of number of input genes or tissues at various fragments per kilobase of transcript per million fragments mapped (FPKM) thresholds. To fulfill this need, we presented a freely available R package and web-interface tool, TEnGExA, which allows tissue-enrichment analysis (TEA) for any number of genes or transcripts for any species provided only a read-count or FPKM-value matrix as input. Based on the different FPKM value and fold thresholds, TEnGExA classifies the user provided gene lists into tissue-enriched or tissue-specific transcripts along with other standard classes. By analyzing the published sample data from human, plant and microorganism, we signifies that TEnGExA can easily handle complex or large data from any species to provided tissue-enriched gene list for downstream analysis in quick time. In summary, TEnGExA is quick, easy to use and an efficient tool for TEA. The R package is freely available at https://github.com/ubagithub/TEnGExA/ and the GUI web interface is accessible at http://webtom.cabgrid.res.in/tissue_enrich/.

Alignment and comparison of protein 3D structures is an important and fundamental task in structural biology to study evolutionary, functional and structural relatedness among proteins. Since two decades, the research on protein structure alignment has been taken up on priority and numbers of research articles are being published. There are incremental advances over previous efforts, and still these methods continue to improve over the time and still this is an open problem in structural biology. A novel methodology has been developed for comparing protein 3D structure by employing conversion of pair of protein 3D structures into 2D graphs (undirected weighted graph), partitioning of 2D graphs into sub-graphs, matching sub-graphs with main graphs and finally these sub-graphs matches calculates similarity between the pair of proteins. The proposed method has been implemented in MATLAB and R Package. The performance of the developed methodology is tested with four existing best methods such as CE, jFATCAT, TM_Align and Dali on 100 proteins benchmark dataset with SCOP database. The proposed method is efficient in terms of time complexity, accuracy, grouping of proteins in relevant structural groups and provides additional information towards non-bonded interactions and sub-graphs indicates the dominance of secondary structure.

One of the major research directions in bioinformatics is that of assigning superfamily classification to a given set of proteins. The classification reflects the structural, evolutionary, and functional relatedness. These relationships are embodied in a hierarchical classification, such as the Structural Classification of Protein (SCOP), which is mostly manually curated. Such a classification is essential for the structural and functional analyses of proteins. Yet a large number of proteins remain unclassified. In this study, we have proposed an unsupervised machine learning approach to classify and assign a given set of proteins to SCOP superfamilies. In the method, we have constructed a database and similarity matrix using P-values obtained from an all-against-all BLAST run and trained the network with the ART2 unsupervised learning algorithm using the rows of the similarity matrix as input vectors, enabling the trained network to classify the proteins from 0.82 to 0.97 f-measure accuracy. The performance of ART2 has been compared with that of spectral clustering, Random forest, SVM, and HHpred. ART2 performs better than the others except HHpred. HHpred performs better than ART2 and the sum of errors is smaller than that of the other methods evaluated.