On the use of sequence-quality information in OTU clustering.

ABSTRACT:

Background

High-throughput sequencing has become an essential technology in life science research. Despite continuous improvements in technology, the produced sequences are still not entirely accurate. Consequently, the sequences are usually equipped with error probabilities. The quality information is already employed to find better solutions to a number of bioinformatics problems (e.g. read mapping). Data processing pipelines benefit in particular (especially when incorporating the quality information early), since enhanced outcomes of one step can improve all subsequent ones. Preprocessing steps, thus, quite regularly consider the sequence quality to fix errors or discard low-quality data. Other steps, however, like clustering sequences into operational taxonomic units (O

SUBMITTER: Muller R 

PROVIDER: S-EPMC8375510 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature