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With the advent of single-cell DNA sequencing, it is now possible to infer the evolutionary history of thousands of tumor cells obtained from a single patient. This evolutionary history, which takes the shape of a tree, reveals the mode of evolution of the specific cancer under study and, in turn, helps with clinical diagnosis, prognosis, and therapeutic treatment. In this study we focus on the question of determining the mode of evolution of tumor cells from their inferred evolutionary history. In particular, we employ recursive neural networks that capture tree structures to classify the evolutionary history of tumor cells into one of four modes—linear, branching, neutral, and punctuated. We trained our model, MoTERNN, using simulated data in a supervised fashion and applied it to a real phylogenetic tree obtained from single-cell DNA sequencing data. MoTERNN is implemented in Python and is publicly available at https://github.com/NakhlehLab/MoTERNN.

Cancers develop and progress as mutations accumulate, and with the advent of single-cell DNA and RNA sequencing, researchers can observe these mutations, their transcriptomic effects, and predict proteomic changes with remarkable temporal and spatial precision. However, to connect genomic mutations with their transcriptomic and proteomic consequences, cells with either only DNA data or only RNA data must be mapped to a common domain. For this purpose, we present MaCroDNA, a novel method which uses maximum weighted bipartite matching of per-gene read counts from single-cell DNA and RNA-seq data. Using ground truth information from colorectal cancer data, we demonstrate the overwhelming advantage of MaCroDNA over existing methods in accuracy and speed. Exemplifying the utility of single-cell data integration in cancer research, we propose, based on results derived using MaCroDNA, that genomic mutations of large effect size increasingly contribute to differential expression between cells as Barrett’s esophagus progresses to esophageal cancer.

Copy number aberrations (CNAs) are ubiquitous in many types of cancer. Inferring CNAs from cancer genomic data could help shed light on the initiation, progression, and potential treatment of cancer. While such data have traditionally been available via “bulk sequencing”, the more recently introduced techniques for single-cell DNA sequencing (scDNAseq) provide the type of data that makes CNA inference possible at the single-cell resolution.In this paper, we introduce a new birth-death evolutionary model of CNAs as well as a Bayesian method, NestedBD, for the inference of evolutionary trees (topologies and branch lengths with relative mutation rates) from single-cell data under this model. We assessed the accuracy of our method on both simulated and biological data and compared it to the accuracy of two standard phylogenetic tools, namely neighbor-joining and maximum parsimony (MP). We show through simulations that our method infers more accurate topologies and branch lengths. We also studied the ancestral state reconstruction accuracy with the birth-death evolutionary model and found it outperformed MP. Finally, running all three methods on a colorectal cancer data set, we observed that among all three methods, only the phylogeny inferred by NestedBD clearly separated the primary tumor cells from the metastatic ones, providing a more plausible history of the tumor cells.

Single-cell DNA sequencing technologies are enabling the study of mutations and their evolutionary trajectories in cancer. Somatic copy number aberrations (CNAs) have been implicated in the development and progression of various types of cancer. A wide array of methods for CNA detection has been either developed specifically for or adapted to single-cell DNA sequencing data. Understanding the strengths and limitations that are unique to each of these methods is very important for obtaining accurate copy number profiles from single-cell DNA sequencing data. Here we review the major steps that are followed by these methods when analyzing such data, and then review the strengths and limitations of the methods individually. In terms of segmenting the genome into regions of different copy numbers, we categorize the methods into three groups, select a representative method from each group that has been commonly used in this context, and benchmark them on simulated as well as real datasets. While single-cell DNA sequencing is very promising for elucidating and understanding CNAs, even the best existing method does not exceed 80% accuracy. New methods that significantly improve upon the accuracy of these three methods are needed. Furthermore, with the large datasets being generated, the methods must be computationally efficient.

Single-cell sequencing provides a powerful approach for elucidating intratumor heterogeneity by resolving cell-to-cell variability. However, it also poses additional challenges including elevated error rates, allelic dropout and non-uniform coverage. A recently introduced single-cell-specific mutation detection algorithm leverages the evolutionary relationship between cells for denoising the data. However, due to its probabilistic nature, this method does not scale well with the number of cells. Here, we develop a novel combinatorial approach for utilizing the genealogical relationship of cells in detecting mutations from noisy single-cell sequencing data. Our method, called scVILP, jointly detects mutations in individual cells and reconstructs a perfect phylogeny among these cells. We employ a novel Integer Linear Program algorithm for deterministically and efficiently solving the joint inference problem. We show that scVILP achieves similar or better accuracy but significantly better runtime over existing methods on simulated data. We also applied scVILP to an empirical human cancer dataset from a high grade serous ovarian cancer patient.

Background Current development of sequencing technologies is towards generating longer and noisier reads. Evidently, accurate alignment of these reads play an important role in any downstream analysis. Similarly, reducing the overall cost of sequencing is related to the time consumption of the aligner. The tradeoff between accuracy and speed is the main challenge in designing long read aligners. Results We propose Meta-aligner which aligns long and very long reads to the reference genome very efficiently and accurately. Meta-aligner incorporates available short/long aligners as subcomponents and uses statistics from the reference genome to increase the performance. Meta-aligner estimates statistics from reads and the reference genome automatically. Meta-aligner is implemented in C++ and runs in popular POSIX-like operating systems such as Linux. Conclusions Meta-aligner achieves high recall rates and precisions especially for long reads and high error rates. Also, it improves performance of alignment in the case of PacBio long-reads in comparison with traditional schemes. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1518-y) contains supplementary material, which is available to authorized users.

Additional file 1 Review History.

Additional file 1 Review History.