Evaluating metagenomics tools for genome binning with real metagenomic datasets and CAMI datasets.

BACKGROUND: Shotgun metagenomics based on untargeted sequencing can explore the taxonomic profile and the function of unknown microorganisms in samples, and complement the shortage of amplicon sequencing. Binning assembled sequences into individual groups, which represent microbial genomes, is the key step and a major challenge in metagenomic research. Both supervised and unsupervised machine learning methods have been employed in binning. Genome binning belonging to unsupervised method clusters contigs into individual genome bins by machine learning methods without the assistance of any reference databases. So far a lot of genome binning tools have emerged. Evaluating these genome tools is of great significance to microbiological research. In this study, we evaluate 15 genome binning tools containing 12 original binning tools and 3 refining binning tools by comparing the performance of these tools on chicken gut metagenomic datasets and the first CAMI challenge datasets. RESULTS: For chicken gut metagenomic datasets, original genome binner MetaBat, Groopm2 and Autometa performed better than other original binner, and MetaWrap combined the binning results of them generated the most high-quality genome bins. For CAMI datasets, Groopm2 achieved the highest purity (> 0.9) with good completeness (> 0.8), and reconstructed the most high-quality genome bins among original genome binners. Compared with Groopm2, MetaBat2 had similar performance with higher completeness and lower purity. Genome refining binners DASTool predicated the most high-quality genome bins among all genomes binners. Most genome binner performed well for unique strains. Nonetheless, reconstructing common strains still is a substantial challenge for all genome binner. CONCLUSIONS: In conclusion, we tested a set of currently available, state-of-the-art metagenomics hybrid binning tools and provided a guide for selecting tools for metagenomic binning by comparing range of purity, completeness, adjusted rand index, and the number of high-quality reconstructed bins. Furthermore, available information for future binning strategy were concluded.

Bio-based, biodegradable and amorphous polyurethanes with shape memory behavior at body temperature.

In this work, a series of bio-based, biodegradable and amorphous shape memory polyurethanes were synthesized by a two-step pre-polymerization process from polylactide (PLA) diol, polycaprolactone (PCL) diol and diphenylmethane diisocyanate-50 (MDI-50). The ratio of PLA diol to PCL diol was adjusted to investigate their thermal and mechanical properties. These bio-based shape memory polyurethanes (bio-PUs) showed a glass transition temperature (T g) value in the range of -10.7-32.5 °C, which can be adjusted to be close to body temperature. The tensile strength and elongation of the bio-PUs could be tuned in the range from 1.7 MPa to 12.9 MPa and from 767.5% to 1345.7%, respectively. Through a series of shape memory tests, these bio-PUs exhibited good shape memory behavior at body temperature. Among them, PU with 2 : 1 as the PLA/PCL ratio showed the best shape recovery behavior with a shape recovery rate higher than 98% and could fully reach the original shape state in 15 s at 37 °C. Therefore, these shape memory bio-PUs are promising for applications in smart biomedical devices.