Biology subjects

Peter K Rogan

Publications and source records attributed to Peter K Rogan.

4 recordsLinked to original sources

bioRxiv2016

Discovery of Primary, Cofactor, and Novel Transcription Factor Binding Site Motifs by Recursive, Thresholded Entropy Minimization

Data from ChIP-seq experiments can determine the genome-wide binding specificities of transcription factors (TFs) and other regulatory proteins. In the present study, we analyzed 745 ENCODE ChIP-seq peak datasets of 189 human TFs with a novel motif discovery method that is based on recursive, thresholded entropy minimization. This method is able to distinguish correct information models from noisy motifs, quantify the strengths of individual sites based on affinity, and detect adjacent cofactor binding sites that coordinate with primary TFs. We derived homogeneous and bipartite information models for 89 sequence-specific TFs, which enabled discovery of 24 cofactor motifs for 118 TFs, and revealed 6 high-confidence novel motifs. The reliability and accuracy of these models were determined via three independent quality control criteria, including the detection of experimentally proven binding sites, comparison with previously published motifs and statistical analyses. We also predict previously unreported TF cobinding interactions, and new components of known TF complexes. Because they are based on information theory, the derived models constitute a powerful tool for detecting and predicting the effects of variants in known binding sites, and predicting previously unrecognized binding sites and target genes.

Bioinformatics↗

bioRxiv2016

Prioritizing variants in complete Hereditary Breast and Ovarian Cancer (HBOC) genes in patients lacking known BRCA mutations

BRCA1 and BRCA2 testing for HBOC does not identify all pathogenic variants. Sequencing of 20 complete genes in HBOC patients with uninformative test results (N=287), including non-coding and flanking sequences of ATM, BARD1, BRCA1, BRCA2, CDH1, CHEK2, EPCAM, MLH1, MRE11A, MSH2, MSH6, MUTYH, NBN, PALB2, PMS2, PTEN, RAD51B, STK11, TP53, and XRCC2, identified 38,372 unique variants. We apply information theory (IT) to predict novel functions for and prioritize non-coding variants of uncertain significance (VUS) in throughout regulatory, coding, and intronic regions based on changes in binding sites in these genesof these genes. Besides mRNA splicing, IT provides a common framework to evaluate potential affinity changes inin transcription factor (TFBSs), splicing regulatory (SRBSs), and RNA-binding protein (RBBSs) protein binding sites following mutationat mutated binding sites. We prioritized variants affecting the strengths of 10 variants affecting splice sites (4 natural, 6 cryptic), 148 SRBS, 36 TFBS, and 31 RBBS binding strength-affecting variantss. Three variants were also prioritized based on their predicted effects on mRNA secondary (2{degrees}) structure, and 17 for pseudoexon activation. Additionally, 4 frameshift, 2 in-frame deletions, and 5 stop-gain mutations were identified. When combined with pedigree information, complete gene sequence analysis can focus attention on a limited set of variants in a wide spectrum of functional mutation types for downstream functional and co-segregation analysis.

Genetics↗

bioRxiv2016

Automated Discrimination of Dicentric and Monocentric Chromosomes by Machine Learning-based Image Processing

Dose from radiation exposure can be estimated from dicentric chromosome (DC) frequencies in metaphase cells of peripheral blood lymphocytes. We automated DC detection by extracting features in Giemsa-stained metaphase chromosome images and classifying objects by machine learning (ML). DC detection involves i) intensity thresholded segmentation of metaphase objects, ii) chromosome separation by watershed transformation and elimination of inseparable chromosome clusters, fragments and staining debris using a morphological decision tree filter, iii) determination of chromosome width and centreline, iv) derivation of centromere candidates and v) distinction of DCs from monocentric chromosomes (MC) by ML. Centromere candidates are inferred from 14 image features input to a Support Vector Machine (SVM). 16 features derived from these candidates are then supplied to a Boosting classifier and a second SVM which determines whether a chromosome is either a DC or MC. The SVM was trained with 292 DCs and 3135 MCs, and then tested with cells exposed to either low (1 Gy) or high (2-4 Gy) radiation dose. Results were then compared with those of 3 experts. True positive rates (TPR) and positive predictive values (PPV) were determined for the tuning parameter, {sigma}. At larger {sigma}, PPV decreases and TPR increases. At high dose, for {sigma}= 1.3, TPR = 0.52 and PPV = 0.83, while at {sigma}= 1.6, the TPR = 0.65 and PPV = 0.72. At low dose and {sigma} = 1.3, TPR = 0.67 and PPV = 0.26. The algorithm differentiates DCs from MCs, overlapped chromosomes and other objects with acceptable accuracy over a wide range of radiation exposures.

Genetics↗

bioRxiv2015

A unified analytic framework for prioritization of non-coding variants of uncertain significance in heritable breast and ovarian cancer

BackgroundSequencing of both healthy and disease singletons yields many novel and low frequency variants of uncertain significance (VUS). Complete gene and genome sequencing by next generation sequencing (NGS) significantly increases the number of VUS detected. While prior studies have emphasized protein coding variants, non-coding sequence variants have also been proven to significantly contribute to high penetrance disorders, such as hereditary breast and ovarian cancer (HBOC). We present a strategy for analyzing different functional classes of non-coding variants based on information theory (IT).\n\nMethodsWe captured and enriched for coding and non-coding variants in genes known to harbor mutations that increase HBOC risk. Custom oligonucleotide baits spanning the complete coding, non-coding, and intergenic regions 10 kb up- and downstream of ATM, BRCA1, BRCA2, CDH1, CHEK2, PALB2, and TP53 were synthesized for solution hybridization enrichment. Unique and divergent repetitive sequences were sequenced in 102 high-risk patients without identified mutations in BRCA1/2. Aside from protein coding changes, IT-based sequence analysis was used to identify and prioritize pathogenic non-coding variants that occurred within sequence elements predicted to be recognized by proteins or protein complexes involved in mRNA splicing, transcription, and untranslated region (UTR) binding and structure. This approach was supplemented by in silico and laboratory analysis of UTR structure.\n\nResults15,311 unique variants were identified, of which 245 occurred in coding regions. With the unified IT-framework, 132 variants were identified and 87 functionally significant VUS were further prioritized. We also identified 4 stop-gain variants and 3 reading-frame altering exonic insertions/deletions (indels).\n\nConclusionsWe have presented a strategy for complete gene sequence analysis followed by a unified framework for interpreting non-coding variants that may affect gene expression. This approach distills large numbers of variants detected by NGS to a limited set of variants prioritized as potential deleterious changes.

Genetics↗