Research Assistant Professor at the University of Utah.
Investigator in genomics, proteomics, phenomics, and their clinical application in cancer and autoimmune diseases.
Permanent URL: BJFengLab.org
We develop novel bioinformatic and biostatistic methods and software for genome sequence analysis.
Classification of genetic variants into either neutral or pathogenic category is an important step in clinical genetic testing. We develop algorithm and software for this purpose. We participate in the development of national standards and guidelines.
We conduct multi-omic (genomic, proteomic, interactomic) studies to find biomarkers for prognostic prediction in head and neck cancer patients.
Patients with cutaneous psoriasis frequently suffer from unrecognized psoriatic arthritis (PsA). Delays in PsA diagnosis and treatment frequently contribute to functional limitations and irreversible joint damage. We develop screening tool and diagnostic test for the early detection of PsA among psoriasis patients.
PERCH (Polymorphism Evaluation, Ranking, and Classification for Heritable traits) is a framework for the interpretation of genetic variants identified from next-generation sequencing. This software implements a novel deleteriousness score named BayesDel, an improved guilt-by-association algorithm, rare-variant association tests, and a modified linkage analysis. These components are integrated in a quantitative fashion for gene and variant prioritization [Bing-Jian Feng. Human Mutation 2017 Mar;38(3):243-251. PMID: 27995669]. BayesDel has been selected to be a component of the gene-specific variant classification guidelines for TP53, BRCA1, and BRCA2 compliant to the American College of Medical Genetics and Genomics (ACMG) and the Association of Molecular Pathology (AMP) standards [Cristina Fortuno, et al. Human Mutation 2021 Mar;42(3):223-236. PMID:33300245] and the quantitative multifactorial variant classification strategy [Cristina Fortuno, et al. Human Mutation 2021 Oct;42(10):1351-1361. PMID: 34273903]. For ACMG/AMP guidelines for other genes, please be referred to Pejaver et al. The American Journal of Human Genetics. 2022 (PMID: 36413997). If you are interested in performing secondary and tertiary analyses of large-scale whole-exome / whole-genome sequencing data to search for novel disease genes, please check out the VICTOR package.
VICTOR (Variant Interpretation for Clinical Testing Or Research) is a software package for the secondary and tertiary analysis of next-generation sequencing data starting from a raw Variant Call Format (VCF) file. It has a pipeline for quality control, cryptic relatedness and population structure inference, database querying, functional interpretation, rare-variant association test accounting for ancestry and population substructure, gene-set analysis, cosegregation analysis, variant classification, secondary finding reporting, polygenic risk score calculation, gene network analysis, and causal inference. This package includes programs, data files for the GRCh37 and GRCh38 genomes, and several slurm scripts for high-performance computing. The package has been successfully used on a consortium data (Dumont et al. Cancers. 2022;14(14):3363. PMID:35884425). It has been tested on WEX/WGS data from ORIEN AVATAR (M2GEN), UK Biobank, Regeneron Genomic Center, and many other research teams.
Classification of germline variants into pathogenic or neutral category is essential for interpreting genetic test results. Cosegregation analysis (testing whether a genetic variant and an associated disease segregate together within a pedigree) is a useful tool for assessing germline variant pathogenicity. COOL (COsegregation OnLine) version 2 is a web server to perform cosegregation analysis by the Full-Likelihood Bayes factor method (Thompson et al.) and outputs a Bayes factor that can be integrated into a multifactorial variant classification scheme. It can also be transformed into a strength category to be used in the application of the variant classification guidelines developed by the American College of Medical Genetics and Genomics (ACMG) and the Association of Molecular Pathology (AMP). This server provides penetrance for 16 cancer genes (BRCA1, BRCA2, CDH1, MLH1, MSH2, MSH6, NF1, PALB2, PMS2, PTEN, RAD51C, RAD51D, TP53, ATM, CHEK2, MEN1). It also support other cancer genes if you provide a relative risk file, or non-cancer genes if you provide a penetrance file. The website makes pedigree drawings from the input pedigree file. Unique COOL features, including the survival penetrance model, are described in Sophie Belman, Michael T. Parsons, Amanda B. Spurdle, David E. Goldgar, Bing-Jian Feng. Genetics in Medicine 2020 Dec; 22(12):2052-2059. PMID:32773770.
The following link is the tentative release of the COOL version 3 beta.
PedPro: PedPro is a program to handle pedigrees. It can check for errors, detect and break loops, remove uninformative individuals for linkage analysis, find obligatory carriers, identify clusters of individuals based on connections and affection status, identify and remove isolated individuals, merge connected families, and calculate individual weights to be used in a case-control association test. This program can also flexibly convert different pedigree files into Comprehensive Pedigree Format (CPF), a format designed to facilitate file sharing among laboratories and improve usability (i.e., directly analyzable) and re-usability (i.e., using an old pedigree file without modification even when we change the disease risk model or the gene of interest). This format contains necessary information for cosegregation analysis, risk prediction, and penetrance estimation, the main usages of a pedigree file in clinical genetic testing.
TrendTDT (Trend Transmission Disequilibrium Test): TrendTDT is a program to perform a novel family-based trend association test for copy-number variations (CNVs) or variable number tandem repeats (VNTRs) (Bing-Jian Feng, David E Goldgar, Marilys Corbex. BMC Genet. 2007;8:75. PMID:17976242).
PAPRIKA (Psoriatic Arthritis Prediction and Identification Question Bank for Various Ancestries) version 1 is an assembly of questions about clinical features for the prediction and identification of psoriatic arthritis (PsA). PsA is an inflammatory joint disease that can lead to irreversible joint destruction, functional limitations, and increased mortality. Early treatment of PsA will favorably impact function, quality of life, and work ability. This question bank was created to facilitate the detection of PsA early in the disease course. It contains novel clinical predictors we previously discovered from a prospective cohort study [Sophie Belman, Jessica A Walsh, Courtney Carroll, Michael Milliken, Benjamin Haaland, Kristina Callis Duffin, Gerald G Krueger, Bing-Jian Feng. The Journal of Rheumatology 2021 Apr 15; jrheum.201123. PMID: 33858978].
This Bank uses a "Psoriasis Thickness Reference Card." We provide a YouTube video about how to make this Card at home. The questions can be completed entirely by patient without assistance from a provider. PAPRIKA contains example images obtained from various databases; we are not allowed to put the images in the public domain due to licensing issues. If you need the images, please contact us. We will point you to the databases.
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