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SPAdes
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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.
Velvet-SC / Velvet Single Cell
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.
SCG / Single Cell Genotyper
An interpretable probabilistic model, simultaneously addressing technical sources of noise in single-cell sequencing data and inferring a discrete set of genotypes present in a cell population and the genotype ‘membership’ of each cell. Using a cell-target matrix as input, SCG infers genotypes defined by point-like events (possibly from multiple data types such as SNVs and rearrangement breakpoints) with a discrete number of observable states. SCG output allows for accurate phylogenetic tree inference using routine methods, thereby advancing a major goal of single-cell sequencing of cancer populations: the accurate reconstruction of population evolutionary histories.
Monovar
A statistical method for detecting and genotyping single-nucleotide variants in single-cell data. Monovar exhibited superior performance over standard algorithms on benchmarks and in identifying driver mutations and delineating clonal substructure in three different human tumor data sets. Monovar is capable of analyzing large-scale data sets and handling different whole-genome amplification (WGA) protocols, and thus it is well suited for addressing the growing need for accurate single-cell DNA variant detection.
OncoNEM / Oncogenetic Nested Effects Model
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.
MeCorS
Corrects chimeric reads and sequencing errors in Illumina data generated from single amplified genomes (SAGs). MeCorS uses sequence information derived from accompanying metagenome sequencing to accurately correct errors in SAG reads, even from ultra-low coverage regions. It takes advantage of largely unbiased metagenomic coverage, enabling it to correct positions with too low a coverage for SAG-only error correction, and to correct chimeric SAG reads through non-chimeric metagenome reads. In evaluations on real data, we show that MeCorS outperforms BayesHammer, the most widely used state-of-the-art approach. MeCorS performs particularly well in correcting chimeric reads, which greatly improves both accuracy and contiguity of de novo SAG assemblies.
HyDA / Hybrid De novo Assembler
Demonstrates the power of coassembly of multiple single-cell genomic data sets through significant improvement of the assembly quality in terms of predicted functional elements and length statistics. Coassemblies contain significantly more base pairs and protein coding genes, cover more subsystems, and consist of longer contigs compared to individual assemblies by the same algorithm as well as state-of-the-art single-cell assemblers SPAdes and IDBA-UD. HyDA is also able to avoid chimeric assemblies by detecting and separating shared and exclusive pieces of sequence for input data sets. By replacing one deep single-cell sequencing experiment with a few single-cell sequencing experiments of lower depth, the coassembly method can hedge against the risk of failure and loss of the sample, without significantly increasing sequencing cost.
SASC / Simulated Annealing Single Cell inference
Allows violations of the infinite sites assumption (ISA). SASC consists of a maximum likelihood tree search framework based on the Dollo parsimony model. It can deduce intra-tumor progression and subclonal composition from single cell sequencing (SCS) data. This tool is robust to various degrees of noise in the dataset. It constructs neighborhood of each feasible candidate since it regulates the way in which the feasible space is explored.
SCICAST / Single Cell Iterative Clustering and Significance Testing
Automates many of the repetitive steps of analyzing single cell sequencing data. SCICAST could be used for (i) clustering and subclustering of data to identify ‘stable’ sets of cells, (ii) statistical testing to identify top genes that indentify stable cluster, (iii) correlation search and analysis to identify gene networks driving cluster identity or (iv) outputs both plots for visualization (PCA and heatmap) cell and gene lists that can be used to refine analysis.
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