Single-cell assembly software tools | Single-cell DNA sequencing data analysis
Whole genome amplification by the multiple displacement amplification (MDA) method allows sequencing of genomes from single cells of bacteria that cannot be cultured. However, genome assembly is challenging because of highly non-uniform read coverage generated by MDA.
A single-cell assembler for capturing and sequencing “microbial dark matter” that forms small pools of randomly selected single cells (called a mini-metagenome) and further sequences all genomes from the mini-metagenome at once. SPAdes is intended for both standard isolates and single-cell MDA bacteria assemblies. It works with Illumina or IonTorrent reads and is capable of providing hybrid assemblies using PacBio, Oxford Nanopore and Sanger reads. You can also provide Additional contigs can also be provided to be used as long reads. SPAdes supports paired-end reads, mate-pairs and unpaired reads and can take as input several paired-end and mate-pair libraries simultaneously.
An approach tailored package for single cell Illumina sequences that incorporates a progressively increasing coverage cutoff. Velvet-SC allows variable coverage datasets to be utilized effectively with assembly of E. coli and S. aureus single cell reads capturing >91% of genes within contigs, approaching the 95% captured from a multi-cell E. coli assembly. It assembles a single cell genome of the uncultivated SAR324 clade of Deltaproteobacteria, a cosmopolitan bacterial lineage in the global ocean. Velvet-SC enable acquisition of genome assemblies for individual uncultivated bacteria, providing cellspecific genetic information absent from metagenomic studies.
Allows automated and interactive analysis of single-cell copy-number variations. Ginkgo enables quality assessment, GC bias correction, segmentation, copy-number calling, visualization and exploration of results. It can determine absolute copy-number state from a segmented raw read depth. This tool integrates ploidy information from fluorescence-activated cell sorting (FACS) to accurately call copy number.
Allows users to identify the evolutionary history of a tumor from noisy and incomplete mutation profiles of single cells. SCITE comprises a flexible Markov chain Monte Carlo (MCMC) sampling scheme that permits researchers to: (1) compute the maximum-likelihood mutation history; (2) sample from the posterior probability distribution; and (3) estimate the error rates of the underlying sequencing experiments.
A probabilistic method for inferring intra-tumor evolutionary lineage trees from somatic single nucleotide variants of single cells. The OncoNEM algorithm consists of two main parts: (1) a probabilistic score that models the accumulation of mutations by noisy subset relations and (2) a sequence of inference algorithms to search for high-scoring models in the space of possible tree structures. OncoNEM identifies homogeneous cellular subpopulations and infers their genotypes as well as a tree describing their evolutionary relationships.
Automates the main steps in microbial genome analysis-assembly, gene prediction, functional annotation-in a way that allows parameter and algorithm exploration at each step in the process. It also manages the data created by these analyses and provides visualization methods for rapid analysis of the results.