BACKGROUND: Single-cell RNA-sequencing (scRNA-seq) technologies have advanced rapidly in recent years and enabled the quantitative characterization at a microscopic resolution. With the exponential growth of the number of cells profiled in individual scRNA-seq experiments, the demand for identifying putative cell types from the data has become a great challenge that appeals for novel computational methods. Although a variety of algorithms have recently been proposed for single-cell clustering, such limitations as low accuracy, inferior robustness, and inadequate stability greatly impede the scope of applications of these methods. RESULTS: We propose a novel model-based algorithm, named VPAC, for accurate clustering of single-cell transcriptomic data through variational projection, which assumes that single-cell samples follow a Gaussian mixture distribution in a latent space. Through comprehensive validation experiments, we demonstrate that VPAC can not only be applied to datasets of discrete counts and normalized continuous data, but also scale up well to various data dimensionality, different dataset size and different data sparsity. We further illustrate the ability of VPAC to detect genes with strong unique signatures of a specific cell type, which may shed light on the studies in system biology. We have released a user-friendly python package of VPAC in Github ( https://github.com/ShengquanChen/VPAC ). Users can directly import our VPAC class and conduct clustering without tedious installation of dependency packages. CONCLUSIONS: VPAC enables highly accurate clustering of single-cell transcriptomic data via a statistical model. We expect to see wide applications of our method to not only transcriptome studies for fully understanding the cell identity and functionality, but also the clustering of more general data.

BACKGROUND: Ribosome profiling brings insight to the process of translation. A basic step in profile construction at transcript level is to map Ribo-seq data to transcripts, and then assign a huge number of multiple-mapped reads to similar isoforms. Existing methods either discard the multiple mapped-reads, or allocate them randomly, or assign them proportionally according to transcript abundance estimated from RNA-seq data. RESULTS: Here we present DeepShape, an RNA-seq free computational method to estimate ribosome abundance of isoforms, and simultaneously compute their ribosome profiles using a deep learning model. Our simulation results demonstrate that DeepShape can provide more accurate estimations on both ribosome abundance and profiles when compared to state-of-the-art methods. We applied DeepShape to a set of Ribo-seq data from PC3 human prostate cancer cells with and without PP242 treatment. In the four cell invasion/metastasis genes that are translationally regulated by PP242 treatment, different isoforms show very different characteristics of translational efficiency and regulation patterns. Transcript level ribosome distributions were analyzed by "Codon Residence Index (CRI)" proposed in this study to investigate the relative speed that a ribosome moves on a codon compared to its synonymous codons. We observe consistent CRI patterns in PC3 cells. We found that the translation of several codons could be regulated by PP242 treatment. CONCLUSION: In summary, we demonstrate that DeepShape can serve as a powerful tool for Ribo-seq data analysis.

Microorganisms are everywhere. Recent studies showed that the mixture of microbes or the microbiome on the human body plays important roles in human physiology and diseases. Metagenomic sequencing is a key technology for studying microbiomes. It produces massive amounts of data in the form of short sequencing reads. A single metagenomic sample can contain 107to 108reads of about 100-nucleotide (nt) length each in a typical shotgun metagenomic sequencing study. They contain rich information about microbiomes and their functions, but reading out those information from the huge highly fragmented data has multiple challenges for mathematical models, bioinformatics methods, and computer algorithms. In this paper, we review the basic bioinformatics tasks and existing methods in processing and analyzing metagenomic data, and discuss remaining open challenges and practical observations. The aim of the paper is to provide readers a whole picture of metagenomic data processing and analysis, and a reference and perspective to start with for computational scientists who are interested in this exciting field.