4CAC: 4-class classifier of metagenome contigs using machine learning and assembly graphs.

Microbial communities usually harbor a mix of bacteria, archaea, plasmids, viruses and microeukaryotes. Within these communities, viruses, plasmids, and microeukaryotes coexist in relatively low abundance, yet they engage in intricate interactions with bacteria. Moreover, viruses and plasmids, as mobile genetic elements, play important roles in horizontal gene transfer and the development of antibiotic resistance within microbial populations.

Efficient minimizer orders for large values of using minimum decycling sets.

Minimizers are ubiquitously used in data structures and algorithms for efficient searching, mapping, and indexing of high-throughput DNA sequencing data. Minimizer schemes select a minimum -mer in every -long subsequence of the target sequence, where minimality is with respect to a predefined -mer order. Commonly used minimizer orders select more -mers than necessary and therefore provide limited improvement in runtime and memory usage of downstream analysis tasks.

3CAC: improving the classification of phages and plasmids in metagenomic assemblies using assembly graphs.

Motivation: Bacteriophages and plasmids usually coexist with their host bacteria in microbial communities and play important roles in microbial evolution. Accurately identifying sequence contigs as phages, plasmids and bacterial chromosomes in mixed metagenomic assemblies is critical for further unraveling their functions. Many classification tools have been developed for identifying either phages or plasmids in metagenomic assemblies.

AIEpred: An Ensemble Predictive Model of Classifier Chain to Identify Anti-Inflammatory Peptides.

Anti-inflammatory peptides (AIEs) have recently emerged as promising therapeutic agent for treatment of various inflammatory diseases, such as rheumatoid arthritis and Alzheimer's disease. Therefore, detecting the correlation between amino acid sequence and its anti-inflammatory property is of great importance for the discovery of new AIEs. To address this issue, we propose a novel prediction tool for accurate identification of peptides as anti-inflammatory epitopes or non anti-inflammatory epitopes.

Detection and analysis of ancient segmental duplications in mammalian genomes.

Although segmental duplications (SDs) represent hotbeds for genomic rearrangements and emergence of new genes, there are still no easy-to-use tools for identifying SDs. Moreover, while most previous studies focused on recently emerged SDs, detection of ancient SDs remains an open problem. We developed an SDquest algorithm for SD finding and applied it to analyzing SDs in human, gorilla, and mouse genomes.