Antimicrobial resistance in bacteria poses major challenges in selection of the therapeutic regime for managing the infectious disease. There is currently an upsurge in the appearance of multiple drug resistance in bacterial pathogens and a decline in the discovery of novel antibiotics. DNA gyrase is an attractive target used for antibiotic discovery due to its vital role in bacterial DNA replication and segregation in addition to its absence in mammalian organisms. Despite the presence of successful antibiotics targeting this enzyme, there is a need to bypass the resistance against this validated drug target. Hence, drug development in DNA gyrase is a highly active research area. In addition to the conventional binding sites for the novobiocin and fluoroquinolone antibiotics, several novel sites are being exploited for drug discovery. The binding sites for novel bacterial type II topoisomerase inhibitor (NBTI), simocyclinone, YacG, Thiophene and CcdB are structurally and biochemically validated active sites, which inhibit the supercoiling activity of topoisomerases. The novel chemical moieties with varied scaffolds have been identified to target DNA gyrase. Amongst them, the NBTI constitutes the most advanced DNA gyrase inhibitor which are in phase III trial of drug development. The present review aims to classify the novel binding sites other than the conventional novobiocin and quinolone binding pocket to bypass the resistance due to mutations in the DNA gyrase enzyme. These sites can be exploited for the identification of new scaffolds for the development of novel antibacterial compounds.

In the past decade, digital PCR (dPCR), as a new nucleic acid absolute quantification technology, has been widely used in clinical research. dPCR does not rely on the standard curve and has a higher tolerance to inhibitors. Therefore, it is more accurate than quantitative real-time PCR (qPCR) for the absolute quantification of target sequences. In this article, we aim to review the application of dPCR in noninvasive prenatal testing (NIPT). We focused on the progress of dPCR in screening and identifying fetal chromosome aneuploidies and monogenic mutations. We introduced some common strategies for dPCR in NIPT and analyzed the advantages and disadvantages of different methods. In addition, we compared dPCR with qPCR and next-generation sequencing, respectively, and described their superiority and shortcomings in clinical applications. Finally, we envisaged what the future of dPCR might be in NIPT. Although dPCR can provide reproducible results with improved accuracy due to the digital detection system, it is essential to combine the merits of dPCR and other molecular techniques to achieve more effective and accurate prenatal diagnostic strategies.

Classifying lower-grade gliomas (LGGs) is a crucial step for accurate therapeutic intervention. The histopathological classification of various subtypes of LGG, including astrocytoma, oligodendroglioma and oligoastrocytoma, suffers from intraobserver and interobserver variability leading to inaccurate classification and greater risk to patient health. We designed an efficient machine learning-based classification framework to diagnose LGG subtypes and grades using transcriptome data. First, we developed an integrated feature selection method based on correlation and support vector machine (SVM) recursive feature elimination. Then, implementation of the SVM classifier achieved superior accuracy compared with other machine learning frameworks. Most importantly, we found that the accuracy of subtype classification is always high (>90%) in a specific grade rather than in mixed grade (~80%) cancer. Differential co-expression analysis revealed higher heterogeneity in mixed grade cancer, resulting in reduced prediction accuracy. Our findings suggest that it is necessary to identify cancer grades and subtypes to attain a higher classification accuracy. Our six-class classification model efficiently predicts the grades and subtypes with an average accuracy of 91% (±0.02). Furthermore, we identify several predictive biomarkers using co-expression, gene set enrichment and survival analysis, indicating our framework is biologically interpretable and can potentially support the clinician.

Identification and classification of enhancers are highly significant because they play crucial roles in controlling gene transcription. Recently, several deep learning-based methods for identifying enhancers and their strengths have been developed. However, existing methods are usually limited because they use only local or only global features. The combination of local and global features is critical to further improve the prediction performance. In this work, we propose a novel deep learning-based method, called iEnhancer-DLRA, to identify enhancers and their strengths. iEnhancer-DLRA extracts local and multi-scale global features of sequences by using a residual convolutional network and two bidirectional long short-term memory networks. Then, a self-attention fusion strategy is proposed to deeply integrate these local and global features. The experimental results on the independent test dataset indicate that iEnhancer-DLRA performs better than nine existing state-of-the-art methods in both identification and classification of enhancers in almost all metrics. iEnhancer-DLRA achieves 13.8% (for identifying enhancers) and 12.6% (for classifying strengths) improvement in accuracy compared with the best existing state-of-the-art method. This is the first time that the accuracy of an enhancer identifier exceeds 0.9 and the accuracy of the enhancer classifier exceeds 0.8 on the independent test set. Moreover, iEnhancer-DLRA achieves superior predictive performance on the rice dataset compared with the state-of-the-art method RiceENN.

Next-Generation Sequencing has produced incredible amounts of short-reads sequence data for de novo genome assembly over the last decades. For efficient transmission of these huge datasets, high-performance compression algorithms have been intensively studied. As both the de novo assembly and error correction methods utilize the overlaps between reads data, a concern is that the will the sequencing errors bring up negative effects on genome assemblies also affect the compression of the NGS data. This work addresses two problems: how current error correction algorithms can enable the compression algorithms to make the sequence data much more compact, and whether the sequence-modified reads by the error-correction algorithms will lead to quality improvement for de novo contig assembly. As multiple sets of short reads are often produced by a single biomedical project in practice, we propose a graph-based method to reorder the files in the collection of multiple sets and then compress them simultaneously for a further compression improvement after error correction. We use examples to illustrate that accurate error correction algorithms can significantly reduce the number of mismatched nucleotides in the reference-free compression, hence can greatly improve the compression performance. Extensive test on practical collections of multiple short-read sets does confirm that the compression performance on the error-corrected data (with unchanged size) significantly outperforms that on the original data, and that the file reordering idea contributes furthermore. The error correction on the original reads has also resulted in quality improvements of the genome assemblies, sometimes remarkably. However, it is still an open question that how to combine appropriate error correction methods with an assembly algorithm so that the assembly performance can be always significantly improved.