A Python script to merge Sanger sequences

Cen Chen; Bingguo Lu; Xiaofang Huang; Chuyun Bi; Lili Zhao; Yunzhuo Hu; Xuanyang Chen; Shiqiang Lin; Kai Huang

doi:10.7717/peerj.11354

A Python script to merge Sanger sequences

PeerJ. 2021 Apr 27:9:e11354. doi: 10.7717/peerj.11354. eCollection 2021.

Authors

Cen Chen^#^{1

2

3}, Bingguo Lu^#⁴, Xiaofang Huang⁵, Chuyun Bi⁵, Lili Zhao⁶, Yunzhuo Hu⁵, Xuanyang Chen⁵, Shiqiang Lin⁵, Kai Huang¹

Affiliations

¹ Clinical Center for Human Genomic Research, Union Hospital, Huazhong University of Science and Technology, Wuhan, China.
² Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
³ Department of Cardiovascular Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
⁴ Provincial University Key Laboratory of Cellular Stress Response and Metabolic Regulation, College of Life Science, Fujian Normal University, Fuzhou, China.
⁵ Key Laboratory of Crop Biotechnology, Fujian Agriculture and Forestry University, Fujian Province Universities, Fuzhou, China.
⁶ State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control, Beijing, China.

^# Contributed equally.

Abstract

Merging Sanger sequences is frequently needed during the gene cloning process. In this study, we provide a Python script that is able to assemble multiple overlapping Sanger sequences. The script utilizes the overlapping regions within the tandem Sanger sequences to merge the Sanger sequences. The results demonstrate that the script can produce the merged sequence from the input Sanger sequences in a single run. The script offers a simple and free method for merging Sanger sequences and is useful for gene cloning.

Keywords: Merge; Python script; Sanger sequencing.

Grants and funding

This study is supported by the National Natural Science Foundation of China, Grant/Award Number: 91949201 (Kai Huang). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.