Retroelements are considered as one of the important sources of genomic variability in modern humans. It is known that transposition activity of retroelements in germline cells generates new insertions in various genomic loci and sometimes results in genetic diseases. Retroelements activity in somatic cells is restricted by different cellular mechanisms; however, there is an evidence for it in some tissue types. Somatic insertions can trigger tumorigenesis or participate in normal functioning such as generation of neurons' plasticity. In spite of the rapid development of high-throughput sequencing methods a confident detection of somatic insertions is still quite a challenging task. That, in part, is due to the absence of adequate bioinformatic tools for the analysis of sequencing data. Here, we propose an advanced computational pipeline for the identification of somatic insertions in datasets generated by selective amplification and high-throughput sequencing of genomic regions flanking insertions of AluYa5. Particular attention is paid for the identification of various artifacts arising in course of library preparation and the parameters for their filtration. Pipeline sensitivity is confirmed by in silico experiments with artificial datasets. Using the proposed pipeline we remove at least 80% of artifacts and preserve 75% of potentially somatic insertions. The approaches used in this work can be applied for the study of other mobile elements insertion variability.