Single-cell mRNA sequencing, which permits whole transcriptional profiling of individual cells, has been widely applied to study growth and development of tissues and tumors. Resolving cell cycle for such groups of cells is significant, but may not be adequately achieved by commonly used approaches. Here we develop a traveling salesman problem and hidden Markov model-based computational method named reCAT, to recover cell cycle along time for unsynchronized single-cell transcriptome data. We independently test reCAT for accuracy and reliability using several data sets. We find that cell cycle genes cluster into two major waves of expression, which correspond to the two well-known checkpoints, G1 and G2. Moreover, we leverage reCAT to exhibit methylation variation along the recovered cell cycle. Thus, reCAT shows the potential to elucidate diverse profiles of cell cycle, as well as other cyclic or circadian processes (e.g., in liver), on single-cell resolution.

Human gut microbiomes consist of a large number of microbial genomes, which vary by diet and health conditions and from individual to individual. In the present work, we asked whether such variation or similarity could be measured and, if so, whether the results could be used for personal microbiome identification (PMI). To address this question, we herein propose a method to estimate the significance of similarity among human gut metagenomic samples based on reference-free, long k-mer features. Using these features, we find that pairwise similarities between the metagenomes of any two individuals obey a beta distribution and that a p value derived accordingly well characterizes whether two samples are from the same individual or not. We develop a computational framework called GePMI (Generating inter-individual similarity distribution for Personal Microbiome Identification) and apply it to several human gut metagenomic datasets (>300 individuals and >600 samples in total). From the results of GePMI, most of the human gut microbiomes can be identified (auROC = 0.9470, auPRC = 0.8702). Even after antibiotic treatment or fecal microbiota transplantation, the individual k-mer signature still maintains a certain specificity.

In the original version of this Article some funding information was missing from the Acknowledgements. This has now been corrected in the PDF and HTML versions of the Article.

We introduce isQ, a new software stack for quantum programming in an imperative programming language, also named isQ. The aim of isQ is to make the programmers write quantum programs as conveniently as possible. In particular:1) The isQ language and its compiler contain many features, including some not well supported by (most) other quantum programming platforms, e.g. classical control flow such as recursion; decomposition of selfdefined unitary gates; and oracle programming and its circuit realization. 2) To make it flexible, an isQ program can be compiled into several kinds of intermediate representation, including OpenQASM 3.0, QIR and QCIS (specially tailored for the superconducting quantum hardware at USTC). 3) Besides interfacing isQ with true superconducting hardware, a QIR simulator is also developed for demonstration and testing of isQ programs. Index Terms-quantum programming, compiler, quantum circuit.• We propose isQ, an imperative quantum program-

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