Cell line identity finding by fingerprinting, an optimized resource for short tandem repeat profile authentication.

The generation of biological data on wide panels of tumor cell lines is recognized as a valid contribution to the cancer research community. However, research laboratories can benefit from this knowledge only after the identity of each individual cell line used in the experiments is verified and matched to external sources. Among the methods employed to assess cell line identity, DNA fingerprinting by profiling Short Tandem Repeat (STR) at variable loci has become the method of choice. However, the analysis of cancer cell lines is sometimes complicated by their intrinsic genetic instability, resulting in multiple allele calls per locus. In addition, comparison of data across different sources must deal with the heterogeneity of published profiles both in terms of number and type of loci used. The aim of this work is to provide the scientific community a homogeneous reference dataset for 300 widely used tumor cell lines, profiled in parallel on 16 loci. This large dataset is interfaced with an in-house developed software tool for Cell Line Identity Finding by Fingerprinting (CLIFF), featuring an original identity score calculation, which facilitates the comparison of STR profiles from different sources and enables accurate calls when multiple loci are present. CLIFF additionally allows import and query of proprietary STR profile datasets.