In 2005, the first next-generation sequencing (NGS) technology was released by 454 Life Sciences (now Roche) [1]. Within the past ten years, different sequencing technologies and platforms, including Illumina, SOLiD, Ion Torrent, Complete Genomics, were released to the public. The much faster sequencing speed, high-throughput capacity and now up to several hundred bases read length, together with a greatly reduced cost, revolutionized the scope and efficiency of biomedical related field researches [2]. Paired with the increasingly diverse range of biological application of NGS technologies, numerous computational and informatics tools, frameworks and pipelines emerged to enable researchers to harness the power of NGS technologies. Statistical models suitable for count data modeling gained much attention in NGS data analysis due to the discrete count nature of the data generated by NGS sequencers. Such models were broadly applied in DNA sequencing (DNA-Seq) based variants identification such as samtools [3], VarScan2 [4], and SNVMix [5]. For DNA sequencing based single nucleotide variant (SNV) identification, emerging new applications bring challenges to refine the statistical modeling methods and pushing the limit of NGS technologies.
