Background: In bioinformatics, estimation of k-mer abundance histograms or just enumerat-ing the number of unique k-mers and the number of singletons are desirable in many genome sequence analysis applications. The applications include predicting genome sizes, data pre-processing for de Bruijn graph assembly methods (tune runtime parameters for analysis tools), repeat detection, sequenc-ing coverage estimation, measuring sequencing error rates, etc. Different methods for cardinality estima-tion in sequencing data have been developed in recent years. Objective: In this article, we present a comparative assessment of the different k-mer frequency estima-tion programs (ntCard, KmerGenie, KmerStream and Khmer (abundance-dist-single.py and unique-kmers.py) to assess their relative merits and demerits. Methods: Principally, the miscounts/error-rates of these tools are analyzed by rigorous experimental analysis for a varied range of k. We also present experimental results on runtime, scalability for larger datasets, memory, CPU utilization as well as parallelism of k-mer frequency estimation methods. Results: The results indicate that ntCard is more accurate in estimating F0, f1 and full k-mer abundance histograms compared with other methods. ntCard is the fastest but it has more memory requirements compared to KmerGenie. Conclusion: The results of this evaluation may serve as a roadmap to potential users and practitioners of streaming algorithms for estimating k-mer coverage frequencies, to assist them in identifying an appro-priate method. Such results analysis also help researchers to discover remaining open research ques-tions, effective combinations of existing techniques and possible avenues for future research

中文翻译：

---

估计基因组数据集中的 k-mer 覆盖频率：对最新技术的比较评估

背景：在生物信息学中，k-mer 丰度直方图的估计或仅枚举唯一 k-mer 的数量和单例的数量在许多基因组序列分析应用中是可取的。应用包括预测基因组大小、de Bruijn 图组装方法的数据预处理（调整分析工具的运行时参数）、重复检测、测序覆盖率估计、测量测序错误率等。近年来已经开发了用于测序数据中基数估计的不同方法。目标：在本文中，我们对不同的 k-mer 频率估计程序（ntCard、KmerGenie、KmerStream 和 Khmer（abundance-dist-single.py 和 unique-kmers.py）进行比较评估，以评估它们的相对优点和缺点。方法：主要是，这些工具的错误计数/错误率通过严格的实验分析对不同范围的 k 进行分析。我们还展示了关于运行时、更大数据集的可扩展性、内存、CPU 利用率以及 k-mer 频率估计方法的并行性的实验结果。结果：结果表明，与其他方法相比，ntCard 在估计 F0、f1 和全 k-mer 丰度直方图方面更加准确。ntCard 是最快的，但与 KmerGenie 相比，它需要更多的内存。结论：该评估的结果可以作为估计 k-mer 覆盖频率的流算法的潜在用户和从业者的路线图，以帮助他们确定适当的方法。这种结果分析还有助于研究人员发现剩余的开放研究问题，