Repetitive sequences are prevalent across all domains of life and play intricate roles in genetic regulation, stability, and evolution. To better understand the functions of these repetitive sequences, various methods have been developed for assembling short tandem repeats such as microsatellites, minisatellites, and satellites. However, existing assembly methods overlook a special case of ultra-long tandem repeat, where the length of a single repeat unit exceeds tens of thousands of base pairs. It is challenging for existing assembly methods to accurately reconstruct these repeatsHere we present ULTR-asm, an assembler specifically designed to tackle ultra-long tandem repeats. ULTR-asm builds on the overlap-layout-consensus (OLC) paradigm, with an additional polishing optimization step. Evaluations on simulated data demonstrated that ULTR-asm outperforms state-of-the-art assemblers, enhancing genome integrity and accuracy, and contributing to the assembly of more complete genomes.

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. However, only two classifiers, PPR-Meta and viralVerify, were proposed to simultaneously identify phages and plasmids in mixed metagenomic assemblies. Due to the very high fraction of chromosome contigs in the assemblies, both tools achieve high precision in the classification of chromosomes but perform poorly in classifying phages and plasmids. Short contigs in these assemblies are often wrongly classified or classified as uncertain. RESULTS: Here we present 3CAC, a new three-class classifier that improves the precision of phage and plasmid classification. 3CAC starts with an initial three-class classification generated by existing classifiers and improves the classification of short contigs and contigs with low confidence classification by using proximity in the assembly graph. Evaluation on simulated metagenomes and on real human gut microbiome samples showed that 3CAC outperformed PPR-Meta and viralVerify in both precision and recall, and increased F1-score by 10-60 percentage points. AVAILABILITY AND IMPLEMENTATION: The 3CAC software is available on https://github.com/Shamir-Lab/3CAC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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. Most of all, we encode the original peptide sequence for better mining and exploring the information and patterns, based on the three feature representations as amino acid contact, position specific scoring matrix, physicochemical property. At the same time, we exploit several feature extraction models and utilize one feature selection model, in order to construct many base classifiers from various feature representations. More specifically, we develop an effective classification model, with which we can extract and learn a set of informative features from the ensemble classifier chain model with different group of base classifiers. Furthermore, in order to test the predictive power of our model, we conduct the comparative experiments on the leave-one-out cross-validation and the independent test. It shows that our novel predictor performs great accurate for identification of AIEs as well as existing outstanding prediction tools. Source codes are available at https://github.com/guofei-tju/Ensemble-classifier-chain-model.