1 - 11 of 11 results

SinCHet / SINgle Cell HETerogeneity

Can quantify cellular heterogeneity and identify novel candidate biomarkers. SinCHet is a MATLAB package with a graphical user interface (GUI) for visualization and user interaction, originally for cancer research but with the potential to be used for any single cell research. It provides unique insights into emerging or disappearing clones at different clonal resolutions between cell populations in different contexts. This method could be easily applied to compare heterogeneity between groups.

SCcaller / Single Cell Caller

Offers a validated protocol to accurately identify single-nucleotide variants (SNVs) across the genome from a single cell after whole-genome amplification (WGA). SCcaller is a single-cell-variant caller that was designed to adjust allelic amplification bias when estimating the likelihoods of three possibilities—artifact, heterozygous SNV and homozygous SNV for every candidate SNV locus. It also corrects for local allelic amplification bias in SNV calling.

smrt pipeline

Enables recovery of high identity allele structures for the sequence regions whose length was confirmed by Polymerase Chain Reaction (PCR) from cell line genomic DNA. smrt_pipeline is a method for the collection, processing and local assembly of single-molecule sequence data to form accurate contiguous local reconstructions. The pipeline incorporates Perl scripts for data download and downstream analysis. Also included in the pipeline are third-party assembly programs, managed by Perl wrapper scripts.


Identifies and excludes non-target sequences independent of database. SAG-QC calculates the probability that a sequence was derived from contaminants by comparing k-mer compositions with the no template control sequences. It can determine bins of target sequences without any existing information. The tool is designed to exclude contaminant sequences from contigs. It can predict the distribution of target sequences accurately unless the single-amplified genome (SAG) sequences are extremely contaminated.

ACDC / Automated Contamination Detection and Confidence estimation

Detects both known and de novo contaminants. ACDC was specifically developed to aid the quality control process of genomic sequence data. First, 16S rRNA gene prediction and the inclusion of ultrafast exact alignment techniques allow sequence classification using existing knowledge from databases. Second, reference-free inspection is enabled by the use of state-of-the-art machine learning techniques that include fast, non-linear dimensionality reduction of oligonucleotide signatures and subsequent clustering algorithms that automatically estimate the number of clusters. The latter also enables the removal of any contaminant, yielding a clean sample.