LOHGIC infers mutational status using AIC weights (W). Khiabanian et al. 2018.
LOH-Germline Inference Calculator (LOHGIC) determines mutational status of variants identified in deep-sequencing assays. It also predicts loss of heterozygosity (LOH) and provides additional information on the number of mutated alleles in tumor cells based on specimen’s purity and a variant’s allele frequency, sequencing depth, and ploidy. Statistical uncertainties inherent to   these parametrs are also considred.


MERIT precisely quantifies ultra-deep sequencing error. Hadigol and Khiabanian 2017.
Mutation Error Rare Identification Toolkit (MERIT) is a comprehensive   pipeline designed for in-depth quantification of erroneous substitutions and small indels in high-throughput sequencing data, specifically, for ultra-deep applications.

MERIT considers the genomic context of the errors, including the nucleotides immediately at their 5’ and 3’, and establishes error rates at 96 possible substitutions as well as four single-base and 16 double-base indels.


Error depth distribution in ultra-deep sequencing of a TP53 locus at 100,000x for transitions (left) and transversions (right). There is a strong concordance between estimates from the beta-binomial model, its NB approximation, and ultra-deep sequencing data. Rabadan et al. 2017.
Backtrack is a robust computational method to discern low-abundance mutations from background error in ultra-deep sequencing data. We have shown that a beta-binomial distribution or aggregate negative binomial (NB) distributions describe PCR amplification error depths.

Backtrack utilizes a statistical multi-sample approach that goes beyond estimating fixed detection thresholds allowed the discovery of variants with high confidence after false discovery correction.


Subtype enrichment (ER/HER2 status) of TuBA's biclusters in the METABRIC cohort of 1,970 breast tumors. Biclusters of proximally located genes with copy number gains, color-coded according to their chromosomes (left), and the rest arranged according to their serial numbers (right). Singh et al. 2018.
Tunable Biclustering Algorithm (TuBA) is a novel graph-based unsupervised biclustering algorithm, customized to identify alterations in tumors based on the hypothesis that gene pairs relevant to a clinical process share a statistically significant   number of samples with extreme expression.

TuBA identifies samples in pre-determined upper or lower percentile sets whose pairwise comparison identifies gene pairs that share a statistically significant number of samples. Each significant gene pair is illustrated graphically as pairs of nodes connected by an edge that represents the shared samples in their percentile sets.

© Khiabanian Lab 2015

Rutgers University
Rutgers Cancer Institute
Center for Systems and Computational Biology
Department of Pathology and Laboratory Medicine

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