The pernicious nature of low quality sequencing data warrants improvement in the bioinformatics workflow for profiling microbial diversity. The conventional merging approach, which drops a copious amount of sequencing reads when processing low quality amplicon data, requires alternative methods. In this study, a computational workflow, a combination of merging and direct-joining where the paired-end reads lacking overlaps are concatenated and pooled with the merged sequences, is proposed to handle the low quality amplicon data in microbial ecology research. The proposed computational strategy was compared with two workflows; the merging approach where the paired-end reads were merged, and the direct-joining approach where the reads were concatenated. The results showed that the merging approach generates a significantly low number of amplicon sequences, limits the microbiome inference and obscures some microbial associations. In comparison to other workflows, the combination of merging and direct-joining strategy reduces the loss of amplicon data, improves the taxonomy classification and importantly, abates the misleading results associated with the merging approach when analysing the low quality amplicon data. The mock community analysis also supports the findings. Thus, the researchers are suggested to analyse merged sequences along with directly-joined unmerged reads to avoid problems associated with low quality data while profiling the microbial community structure.