enoLOGOS: a versatile web tool for energy normalized sequence logos

enoLOGOS is a web-based tool that generates sequence logos from various input sources. Sequence logos have become a popular way to graphically represent DNA and amino acid sequence patterns from a set of aligned sequences. Each position of the alignment is represented by a column of stacked symbols with its total height reflecting the information content in this position. Currently, the available web servers are able to create logo images from a set of aligned sequences, but none of them generates weighted sequence logos directly from energy measurements or other sources. With the advent of high-throughput technologies for estimating the contact energy of different DNA sequences, tools that can create logos directly from binding affinity data are useful to researchers. enoLOGOS generates sequence logos from a variety of input data, including energy measurements, probability matrices, alignment matrices, count matrices and aligned sequences. Furthermore, enoLOGOS can represent the mutual information of different positions of the consensus sequence, a unique feature of this tool. Another web interface for our software, C2H2-enoLOGOS, generates logos for the DNA-binding preferences of the C2H2 zinc-finger transcription factor family members. enoLOGOS and C2H2-enoLOGOS are accessible over the web at

A Unified Analytic Framework for Prioritization of Non-Coding Variants of Uncertain Significance in Heritable Breast and Ovarian Cancer

Background Sequencing 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) and prioritizing patients with large intragenic deletions. Methods We 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, anonymized patients without identified mutations in BRCA1/2. Aside from protein coding and copy number 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. Results 15,311 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. An intragenic 32.1 kb interval in BRCA2 that was likely hemizygous was detected in one patient. We also identified 4 stop-gain variants and 3 reading-frame altering exonic insertions/deletions (indels). Conclusions We 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

Sequence features responsible for intron retention in human

Abstract\ud \ud \ud \ud Background\ud \ud One of the least common types of alternative splicing is the complete retention of an intron in a mature transcript. Intron retention (IR) is believed to be the result of intron, rather than exon, definition associated with failure of the recognition of weak splice sites flanking short introns. Although studies on individual retained introns have been published, few systematic surveys of large amounts of data have been conducted on the mechanisms that lead to IR.\ud \ud \ud \ud Results\ud \ud TTo understand how sequence features are associated with or control IR, and to produce a generalized model that could reveal previously unknown signals that regulate this type of alternative splicing, we partitioned intron retention events observed in human cDNAs into two groups based on the relative abundance of both isoforms and compared relevant features. We found that a higher frequency of IR in human is associated with individual introns that have weaker splice sites, genes with shorter intron lengths, higher expression levels and lower density of both a set of exon splicing silencers (ESSs) and the intronic splicing enhancer GGG. Both groups of retained introns presented events conserved in mouse, in which the retained introns were also short and presented weaker splice sites.\ud \ud \ud \ud Conclusion\ud \ud Although our results confirmed that weaker splice sites are associated with IR, they showed that this feature alone cannot explain a non-negligible fraction of events. Our analysis suggests that cis-regulatory elements are likely to play a crucial role in regulating IR and also reveals previously unknown features that seem to influence its occurrence. These results highlight the importance of considering the interplay among these features in the regulation of the relative frequency of IR.We thank Maria Vibranovski, Pedro AF Galante and Robson de Souza for discussions along the work and comments on the manuscript. We also thank PAFG for providing the cDNA mapping and clustering, the SAGE database and technical help and CB Burge for kindly providing the sequences of the FAS-hex3 ESSs. NJS was supported by a PhD fellowship from FAPESP.We thank Maria Vibranovski, Pedro AF Galante and Robson de Souza for discussions along the work and comments on the manuscript. We also thank PAFG for providing the cDNA mapping and clustering, the SAGE database and technical help and CB Burge for kindly providing the sequences of the FAShex3 ESSs. NJS was supported by a PhD fellowship from FAPESP

A Unified Framework for the Prioritization of Variants of Uncertain Significance in Hereditary Breast and Ovarian Cancer Patients

A significant proportion of hereditary breast and ovarian cancer (HBOC) patients receive uninformative genetic testing results, an issue exacerbated by the overwhelming quantity of variants of uncertain significance identified. This thesis describes a framework where, aside from protein coding changes, information theory (IT)-based sequence analysis identifies and prioritizes pathogenic variants occurring within sequence elements predicted to be recognized by proteins involved in mRNA splicing, transcription, and untranslated region binding and structure. To support the utilization of IT analysis, we established IT-based variant interpretation accuracy by performing a comprehensive review of mutations altering mRNA splicing in rare and common diseases. Custom probes targeting 20 complete HBOC genes for sequencing in 379 BRCA-uninformative patients identified 47,501 unique variants and we prioritized 429 variants in both BRCA and non-BRCA genes. Our approach focuses attention on a limited set of variants from a spectrum of functional mutation types for downstream functional and co-segregation analysis

Interpretation of mRNA splicing mutations in genetic disease: review of the literature and guidelines for information-theoretical analysis.

The interpretation of genomic variants has become one of the paramount challenges in the post-genome sequencing era. In this review we summarize nearly 20 years of research on the applications of information theory (IT) to interpret coding and non-coding mutations that alter mRNA splicing in rare and common diseases. We compile and summarize the spectrum of published variants analyzed by IT, to provide a broad perspective of the distribution of deleterious natural and cryptic splice site variants detected, as well as those affecting splicing regulatory sequences. Results for natural splice site mutations can be interrogated dynamically with Splicing Mutation Calculator, a companion software program that computes changes in information content for any splice site substitution, linked to corresponding publications containing these mutations. The accuracy of IT-based analysis was assessed in the context of experimentally validated mutations. Because splice site information quantifies binding affinity, IT-based analyses can discern the differences between variants that account for the observed reduced (leaky) versus abolished mRNA splicing. We extend this principle by comparing predicted mutations in natural, cryptic, and regulatory splice sites with observed deleterious phenotypic and benign effects. Our analysis of 1727 variants revealed a number of general principles useful for ensuring portability of these analyses and accurate input and interpretation of mutations. We offer guidelines for optimal use of IT software for interpretation of mRNA splicing mutations

Computational Modelling of Human Transcriptional Regulation by an Information Theory-based Approach

ChIP-seq experiments can identify the genome-wide binding site motifs of a transcription factor (TF) and determine its sequence specificity. Multiple algorithms were developed to derive TF binding site (TFBS) motifs from ChIP-seq data, including the entropy minimization-based Bipad that can derive both contiguous and bipartite motifs. Prior studies applying these algorithms to ChIP-seq data only analyzed a small number of top peaks with the highest signal strengths, biasing their resultant position weight matrices (PWMs) towards consensus-like, strong binding sites; nor did they derive bipartite motifs, disabling the accurate modelling of binding behavior of dimeric TFs. This thesis presents a novel motif discovery pipeline by adding the recursive masking and thresholding functionalities to Bipad to improve detection of primary binding motifs. Analyzing 765 ENCODE ChIP-seq datasets with this pipeline generated contiguous and bipartite information theory-based PWMs (iPWMs) for 93 sequence-specific TFs, discovered 23 cofactor motifs for 127 TFs and revealed six high-confidence novel motifs. The accuracy of these iPWMs were determined via four independent validation methods, including detection of experimentally proven TFBSs, explanation of effects of characterized SNPs, comparison with previously published motifs and statistical analyses. Novel cofactor motifs supported previously unreported TF coregulatory interactions. This thesis further presents a unified framework to identify variants in hereditary breast and ovarian cancer (HBOC), successfully applying these iPWMs to prioritize TFBS variants in 20 complete genes of HBOC patients. The spatial distribution and information composition of cis-regulatory modules (e.g. TFBS clusters) in promoters substantially determine gene expression patterns and TF target genes. Multiple algorithms were developed to detect TFBS clusters, including the information density-based clustering (IDBC) algorithm that simultaneously considers the spatial and information densities of TFBSs. Prior studies predicting tissue-specific gene expression levels and differentially expressed (DE) TF targets used log likelihood ratios to quantify TFBS strengths and merged adjacent TFBSs into clusters. This thesis presents a machine learning framework that uses the Bray-Curtis function to quantify the similarity between tissue-wide expression profiles of genes, and IDBC-identified clusters from iPWM-detected TFBSs to predict gene expression profiles and DE direct TF targets. Multiple clusters enable gene expression to be robust against TFBS mutations