Animal metagenomic studies, in which host-associated microbiomes are profiled, are an increasingly important contribution to our understanding of the physiological functions, health and susceptibility to diseases of livestock. One of the major challenges in these studies is host DNA contamination, which limits the sequencing capacity for metagenomic content and reduces the accuracy of metagenomic profiling. This is the first study comparing the effectiveness of different sequencing methods for profiling bovine vaginal metagenomic samples. We compared the new method of Oxford Nanopore Technologies (ONT) adaptive sequencing, which can be used to target or eliminate defined genetic sequences, to standard ONT sequencing, Illumina 16S rDNA amplicon sequencing, and Illumina shotgun sequencing. The efficiency of each method in recovering the metagenomic data and recalling the metagenomic profiles was assessed. ONT adaptive sequencing yielded a higher amount of metagenomic data than the other methods per 1 Gb of sequence data. The increased sequencing efficiency of ONT adaptive sequencing consequently reduced the amount of raw data needed to provide sufficient coverage for the metagenomic samples with high host-to-microbe DNA ratio. Additionally, the long reads generated by ONT adaptive sequencing retained the continuity of read information, which benefited the in-depth annotations for both taxonomical and functional profiles of the metagenome. The different methods resulted in the identification of different taxa. Genera Clostridium, which was identified at low abundances and categorized under Order "Unclassified Clostridiales" when using the 16S rDNA amplicon sequencing method, was identified to be the dominant genera in the sample when sequenced with the three other methods. Additionally, higher numbers of annotated genes were identified with ONT adaptive sequencing, which also produced high coverage on most of the commonly annotated genes. This study illustrates the advantages of ONT adaptive sequencing in improving the amount of metagenomic data derived from microbiome samples with high host-to-microbe DNA ratio and the advantage of long reads in preserving intact information for accurate annotations.