Software

  1. Developing solutions for missing variant-haplotype information (missing VHI)

Missing VHI is a fundamental bioinformatics problem caused by the fact that humans have a diploid genome, but most bioinformatics pipelines were developed based on a human reference containing only one set of chromosomes and without VHI retained. Issues resulted from missing VHI, especially ignoring proximal variants on the same chromosome, can cause many problems for variant annotation and interpretation (left).

a) identifying and fixing incorrect amino-acid-change predictions caused by missing VHI.

We developed MAC (Multi-Nucleotide Variant Annotation Corrector):

Download: github.com/leiwei-bioinfo/MAC

Reference: Wei L*, §, Liu LT*, Conroy JR, Hu Q, Conroy JM, Morrison CD, Johnson CS, Wang J, Liu S§. MAC: identifying and correcting annotation for multi-nucleotide variations. BMC Genomics. 2015 Aug 1;16:569.

MAC is the first software for solving a type of missing VHI problem. Since it was published, MAC has been cited in many high-impact papers:

  • Riaz N, et. al. Cell. 2017 Nov 2;171(4):934-949.e16.

  • Łuksza M, et. al. Nature. 2017 Nov 23;551(7681):517-520.

  • Wang Q, et. al. Nat Commun. 2020 May 27;11(1):2539.

b) Collaboration with Dr. Ailong Ke (Cornell) to develop on-demand computational tools for CRISPR-Cas based genome editing, including identifying therapeutic target and quantifying the risk of off-target effect based on the sequence differences. Stay tuned for updates as the project progresses.

c) Collaboration with Drs. Martin Morgan and Kunle Odunsi to develop bioinformatics algorithms for solving many types of systematic errors introduced by missing VHI in computational predictions. Stay tuned for updates as the project progresses.

2. Defining negative status for multi-sample comparison using next-generation sequencing

Cancer-related somatic mutations can be measured using next-generation sequencing (NGS). For a specific mutation in a given sample, an NGS test may yield three possible statuses: positive, negative, or unknown mostly due to low coverage. A common solution is to require the coverage at the site of a mutation to pass a universal minimum coverage (UMC). However, this method relies on an arbitrarily chosen threshold, and does not take into account the mutations' relative abundances.

We propose an adaptive mutation-specific negative (MSN) method to improve the classification of negative and unknown mutation statuses in the comparison of multiple "related" tumor samples of the same patient. We evaluated the performance of MSN using a real dual-platform single-cell sequencing dataset, the MSN method not only assigned negative statuses with better accuracy than the conventional UMC method, but more importantly rescued more data which would otherwise being classified as “unknown” using UMC.

Two negative-defining methods, UMC and MSN, were tested in a single-cell dual-platform sequencing data set using varying thresholds (indicated under each dot). The performance of each run was evaluated by 1) the concordance of mutation statuses between two paired runs (Nextera, NXT and Agilent, AGL) of the same single cell (X-axis); 2) the total number of informative data points after excluding unknown statuses (Y-axis).


To the best of our knowledge, MSN is the first method dedicated to defining negative mutaitonal status. In addition to tumor heterogeneity analyses, this method also works on other samples containing tumor cells or tumor DNA, such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA).

Download: github.com/leiwei-bioinfo/Negative_data

Paper: Hutson N, Zhan F, Graham J, Murakami M, Zhang H, Ganaparti S, Hu Q, Yan L, Ma C, Liu S, Xie J §, Wei L §. An adaptive method of defining negative mutation status for multi-sample comparison using next-generation sequencing. (In press, BMC Medical Genomics).