A two-step procedure for detecting change points in genomic sequences

The field of whole genomic studies and investigations is currently focused on change-point detection. Over time, various segmentation techniques have been proposed to identify these change points. To effectively locate segments within a genome, it is helpful to pinpoint the intervals or boundaries between them, which are known as change points. By treating these change points as outliers, they can be identified. The anomalies or outliers in a dataset are the observations which are significantly different from the rest of the observations. They can be attributed to some measurement errors or properties of the data themselves. Studying the fluctuations over different segments also revealed the heterogeneity bet-ween consecutive segments. In this paper, anomaly iden-tification approach or influential point detection has been discussed and studied in cow genome data of chromo-some 25. Furthermore, the observed anomalies have been confirmed to determine whether or not they are true change points. The two-step technique resulted in the identification of change sites based on observed abnormalities and is efficient in terms of calculation time and cost. This study aims to detect any anomalies in genomic data and determine the exact points at which the data segment significantly differed from the rest of the segments. We have developed relevant R codes for data processing and applied methodologies.