Probabilistic Alignment Leads to Improved Accuracy and Read Coverage for Bisulfite Sequencing Data

Access full-text files




Hong, Changjin
Clement, Nathan L.
Clement, Spencer
Hammoud, Saher Sue
Carrell, Douglas T.
Cairns, Bradley R.
Snell, Quinn
Clement, Mark J.
Johnson, William Evan

Journal Title

Journal ISSN

Volume Title



DNA methylation has been linked to many important biological phenomena. Researchers have recently begun to sequence bisulfite treated DNA to determine its pattern of methylation. However, sequencing reads from bisulfite-converted DNA can vary significantly from the reference genome because of incomplete bisulfite conversion, genome variation, sequencing errors, and poor quality bases. Therefore, it is often difficult to align reads to the correct locations in the reference genome. Furthermore, bisulfite sequencing experiments have the additional complexity of having to estimate the DNA methylation levels within the sample. Results: Here, we present a highly accurate probabilistic algorithm, which is an extension of the Genomic Next-generation Universal MAPper to accommodate bisulfite sequencing data (GNUMAP-bs), that addresses the computational problems associated with aligning bisulfite sequencing data to a reference genome. GNUMAP-bs integrates uncertainty from read and mapping qualities to help resolve the difference between poor quality bases and the ambiguity inherent in bisulfite conversion. We tested GNUMAP-bs and other commonly-used bisulfite alignment methods using both simulated and real bisulfite reads and found that GNUMAP-bs and other dynamic programming methods were more accurate than the more heuristic methods. Conclusions: The GNUMAP-bs aligner is a highly accurate alignment approach for processing the data from bisulfite sequencing experiments. The GNUMAP-bs algorithm is freely available for download at: The software runs on multiple threads and multiple processors to increase the alignment speed.


LCSH Subject Headings


Hong, Changjin, Nathan L. Clement, Spencer Clement, Saher Sue Hammoud, Douglas T. Carrell, Bradley R. Cairns, Quinn Snell, Mark J. Clement, and William Evan Johnson. "Probabilistic alignment leads to improved accuracy and read coverage for bisulfite sequencing data." BMC bioinformatics, Vol. 14, No. 1 (Nov., 2013): 1.