Highly efficient clustering of long-read transcriptomic data with GeLuster.

MOTIVATION: The advancement of long-read RNA sequencing technologies leads to a bright future for transcriptome analysis, in which clustering long reads according to their gene family of origin is of great importance. However, existing de novo clustering algorithms require plenty of computing resources. RESULTS: We developed a new algorithm GeLuster for clustering long RNA-seq reads. Based on our tests on one simulated dataset and nine real datasets, GeLuster exhibited superior performance. On the tested Nanopore datasets it ran 2.9-17.5 times as fast as the second-fastest method with less than one-seventh of memory consumption, while achieving higher clustering accuracy. And on the PacBio data, GeLuster also had a similar performance. It sets the stage for large-scale transcriptome study in future. AVAILABILITY AND IMPLEMENTATION: GeLuster is freely available at https://github.com/yutingsdu/GeLuster.

Turning Polysaccharides into Injectable and Rapid Self-Healing Antibacterial Hydrogels for Antibacterial Treatment and Bacterial-Infected Wound Healing.

Great efforts have been devoted to the development of novel and multifunctional wound dressing materials to meet the different needs of wound healing. Herein, we covalently grafted quaternary ammonium groups (QAGs) containing 12-carbon straight-chain alkanes to the dextran polymer skeleton. We then oxidized the resulting product into oxidized quaternized dextran (OQD). The obtained OQD polymer is rich in antibacterial QAGs and aldehyde groups. It can react with glycol chitosan (GC) via the Schiff-base reaction to form a multifunctional GC@OQD hydrogel with good self-healing behavior, hemostasis, injectability, inherent superior antibacterial activity, biocompatibility, and excellent promotion of healing of methicillin-resistant Staphylococcus aureus (MRSA)-infected wounds. The biosafe and nontoxic GC@OQD hydrogel with a three-dimensional porous network structure possesses an excellent swelling rate and water retention capacity. It can be used for hemostasis and treating irregular wounds. The designed GC@OQD hydrogel with inherent antibacterial activity possesses good antibacterial efficacy on both S. aureus (Gram-positive bacteria) and Escherichia coli (Gram-negative bacteria), as well as MRSA bacteria, with antibacterial activity greater than 99%. It can be used for the treatment of wounds infected by MRSA and significantly promotes the healing of wounds. Thus, the multifunctional antibacterial GC@OQD hydrogel has the potential to be applied in clinical practice as a wound dressing.

DCGR: feature extractions from protein sequences based on CGR via remodeling multiple information.

BACKGROUND: Protein feature extraction plays an important role in the areas of similarity analysis of protein sequences and prediction of protein structures, functions and interactions. The feature extraction based on graphical representation is one of the most effective and efficient ways. However, most existing methods suffer limitations from their method design. RESULTS: We introduce DCGR, a novel method for extracting features from protein sequences based on the chaos game representation, which is developed by constructing CGR curves of protein sequences according to physicochemical properties of amino acids, followed by converting the CGR curves into multi-dimensional feature vectors by using the distributions of points in CGR images. Tested on five data sets, DCGR was significantly superior to the state-of-the-art feature extraction methods. CONCLUSION: The DCGR is practically powerful for extracting effective features from protein sequences, and therefore important in similarity analysis of protein sequences, study of protein-protein interactions and prediction of protein functions. It is freely available at https://sourceforge.net/projects/transcriptomeassembly/files/Feature%20Extraction .

Revealing favorable and unfavorable residues in cooperative positions in protease cleavage sites.

Proteases play critical roles in a wide variety of fundamental biological functions, and numerous protease inhibitors have been developed to treat various diseases including cancer. A wide range of experimental and computational methods have been developed to investigate the specificity and catalytic mechanisms of proteases. However, these methods only focused on the preferences of a single position around a cleavage site in a substrate, rarely on the compositionality of the subsites. We present new methods to quantify the specificity of proteases by considering the combinatorial patterns of amino acid residuals of cleavage sites in substrates. By incorporating the preference at positions, we modeled three types of favorable combinations of residues in cleavage sites. Moreover, by constructing a relationship weight matrix of residues between two positions, we can easily identify unfavorable combinations of residues at the positions. Applying these methods to a set of known cleavage sites of proteases, we revealed numerous favorable and unfavorable residues in cooperative positions in the protease cleavage sites. The results can help understand the specificity and catalytic mechanisms of proteases. To our knowledge, this is the first study that quantifies unfavorable combinations of amino acids between two sites. Furthermore, this method is not limited to the study of proteases and cleavage sites, and can be generalized to uncover the relationships of residues at meaningful sites in other proteins.

Block-based characterization of protease specificity from substrate sequence profile.

BACKGROUND: The mechanism of action of proteases has been widely studied based on substrate specificity. Prior research has been focused on the amino acids at a single amino acid site, but rarely on combinations of amino acids around the cleavage bond. RESULTS: We propose a novel block-based approach to reveal the potential combinations of amino acids which may regulate the action of proteases. Using the entropies of eight blocks centered at a cleavage bond, we created a distance matrix for 61 proteases to compare their specificities. After quantitative analysis, we discovered a number of prominent blocks, each of which consists of successive amino acids near a cleavage bond, intuitively characterizing the site cooperation of the substrate sequences. CONCLUSION: This approach will help in the discovery of specific substrate sequences which may bridge between proteases and cleavage substrate as more substrate information becomes available.

Insights into protease sequence similarities by comparing substrate sequences and phylogenetic dynamics.

Based on substrate sequences, we proposed a novel method for comparing sequence similarities among 68 proteases compiled from the MEROPS online database. The rank vector was defined based on the frequencies of amino acids at each site of the substrate, aiming to eliminate the different order variances of magnitude between proteases. Without any assumption on homology, a protease specificity tree is constructed with a striking clustering of proteases from different evolutionary origins and catalytic types. Compared with other methods, almost all the homologous proteases are clustered in small branches in our phylogenetic tree, and the proteases belonging to the same catalytic type are also clustered together, which may reflect the genetic relationship among the proteases. Meanwhile, certain proteases clustered together may play a similar role in key pathways categorized using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Consequently, this method can provide new insights into the shared similarities among proteases. This may inspire the design and development of targeted drugs that can specifically regulate protease activity.