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.

Direct and sensitive detection of multiple illegal additives in complex food samples is still a challenge in on-site detection. In this study, an ultrasensitive immunochromatographic assay (ICA) using magnetic Fe₃O₄@Au nanotags as a capture/detection difunctional tool was developed for the direct detection of β2-adrenoceptor agonists in real samples. The Fe₃O₄@Au tag is composed of a large magnetic core (~160 nm), a rough Au nanoshell, dense surface-modified Raman molecules, and antibodies, which cannot only effectively enrich targets from complex solutions to reduce the matrix effects of food samples and improve detection sensitivity, but also provide strong colorimetric/surface-enhanced Raman scattering (SERS) dual signals for ICA testing. The dual readout signals of the proposed ICA can meet the detection requirements in different environments. Specifically, the colorimetric signal allows for rapid visual detection of the analyte, and the SERS signal is used for the sensitive and quantitative detection modes. The proposed dual-signal ICA can achieve the simultaneous determination of two illegal additives, namely, clenbuterol hydrochloride and ractopamine. The detection limits for the two targets via colorimetric and SERS signals were down to ng mL⁻¹ and pg mL⁻¹ levels, respectively. Moreover, the proposed assay has demonstrated high accuracy and stability in real food samples.