Dr.Nod: computational framework for discovery of regulatory non-coding drivers in tissue-matched distal regulatory elements.

The discovery of cancer driver mutations is a fundamental goal in cancer research. While many cancer driver mutations have been discovered in the protein-coding genome, research into potential cancer drivers in the non-coding regions showed limited success so far. Here, we present a novel comprehensive framework Dr.Nod for detection of non-coding cis-regulatory candidate driver mutations that are associated with dysregulated gene expression using tissue-matched enhancer-gene annotations. Applying the framework to data from over 1500 tumours across eight tissues revealed a 4.4-fold enrichment of candidate driver mutations in regulatory regions of known cancer driver genes. An overarching conclusion that emerges is that the non-coding driver mutations contribute to cancer by significantly altering transcription factor binding sites, leading to upregulation of tissue-matched oncogenes and down-regulation of tumour-suppressor genes. Interestingly, more than half of the detected cancer-promoting non-coding regulatory driver mutations are over 20 kb distant from the cancer-associated genes they regulate. Our results show the importance of tissue-matched enhancer-gene maps, functional impact of mutations, and complex background mutagenesis model for the prediction of non-coding regulatory drivers. In conclusion, our study demonstrates that non-coding mutations in enhancers play a previously underappreciated role in cancer and dysregulation of clinically relevant target genes.

SVDSS: structural variation discovery in hard-to-call genomic regions using sample-specific strings from accurate long reads.

Structural variants (SVs) account for a large amount of sequence variability across genomes and play an important role in human genomics and precision medicine. Despite intense efforts over the years, the discovery of SVs in individuals remains challenging due to the diploid and highly repetitive structure of the human genome, and by the presence of SVs that vastly exceed sequencing read lengths. However, the recent introduction of low-error long-read sequencing technologies such as PacBio HiFi may finally enable these barriers to be overcome. Here we present SV discovery with sample-specific strings (SVDSS)-a method for discovery of SVs from long-read sequencing technologies (for example, PacBio HiFi) that combines and effectively leverages mapping-free, mapping-based and assembly-based methodologies for overall superior SV discovery performance. Our experiments on several human samples show that SVDSS outperforms state-of-the-art mapping-based methods for discovery of insertion and deletion SVs in PacBio HiFi reads and achieves notable improvements in calling SVs in repetitive regions of the genome.

Prediction of Neurodevelopmental Disorders Based on De Novo Coding Variation.

The early detection of neurodevelopmental disorders (NDDs) can significantly improve patient outcomes. The differential burden of non-synonymous de novo mutation among NDD cases and controls indicates that de novo coding variation can be used to identify a subset of samples that will likely display an NDD phenotype. Thus, we have developed an approach for the accurate prediction of NDDs with very low false positive rate (FPR) using de novo coding variation for a small subset of cases. We use a shallow neural network that integrates de novo likely gene-disruptive and missense variants, measures of gene constraint, and conservation information to predict a small subset of NDD cases at very low FPR and prioritizes NDD risk genes for future clinical study.

MAGI-MS: multiple seed-centric module discovery.

Summary: Complex disorders manifest by the interaction of multiple genetic and environmental factors. Through the construction of genetic modules that consist of highly coexpressed genes, it is possible to identify genes that participate in common biological pathways relevant to specific phenotypes. We have previously developed tools MAGI and MAGI-S for genetic module discovery by incorporating coexpression and protein interaction networks. Here, we introduce an extension to MAGI-S, denoted as Merging Affected Genes into Integrated Networks-Multiple Seeds (MAGI-MS), which permits the user to further specify a disease pathway of interest by selecting multiple seed genes likely to function in the same molecular mechanism. By providing MAGI-MS with seed genes involved in processes underlying certain classes of neurodevelopmental disorders, such as epilepsy, we demonstrate that MAGI-MS can reveal modules enriched in genes relevant to chemical synaptic transmission, glutamatergic synapse and other functions associated with the provided seed genes. Availability and implementation: MAGI-MS is free and available at https://github.com/jchow32/MAGI-MS. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Nebula: ultra-efficient mapping-free structural variant genotyper.

Large scale catalogs of common genetic variants (including indels and structural variants) are being created using data from second and third generation whole-genome sequencing technologies. However, the genotyping of these variants in newly sequenced samples is a nontrivial task that requires extensive computational resources. Furthermore, current approaches are mostly limited to only specific types of variants and are generally prone to various errors and ambiguities when genotyping complex events. We are proposing an ultra-efficient approach for genotyping any type of structural variation that is not limited by the shortcomings and complexities of current mapping-based approaches. Our method Nebula utilizes the changes in the count of k-mers to predict the genotype of structural variants. We have shown that not only Nebula is an order of magnitude faster than mapping based approaches for genotyping structural variants, but also has comparable accuracy to state-of-the-art approaches. Furthermore, Nebula is a generic framework not limited to any specific type of event. Nebula is publicly available at https://github.com/Parsoa/Nebula.

Comparative genome analysis using sample-specific string detection in accurate long reads.

Motivation: Comparative genome analysis of two or more whole-genome sequenced (WGS) samples is at the core of most applications in genomics. These include the discovery of genomic differences segregating in populations, case-control analysis in common diseases and diagnosing rare disorders. With the current progress of accurate long-read sequencing technologies (e.g. circular consensus sequencing from PacBio sequencers), we can dive into studying repeat regions of the genome (e.g. segmental duplications) and hard-to-detect variants (e.g. complex structural variants). Results: We propose a novel framework for comparative genome analysis through the discovery of strings that are specific to one genome ('samples-specific' strings). We have developed a novel, accurate and efficient computational method for the discovery of sample-specific strings between two groups of WGS samples. The proposed approach will give us the ability to perform comparative genome analysis without the need to map the reads and is not hindered by shortcomings of the reference genome and mapping algorithms. We show that the proposed approach is capable of accurately finding sample-specific strings representing nearly all variation (>98%) reported across pairs or trios of WGS samples using accurate long reads (e.g. PacBio HiFi data). Availability and implementation: Data, code and instructions for reproducing the results presented in this manuscript are publicly available at https://github.com/Parsoa/PingPong. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Meltos: multi-sample tumor phylogeny reconstruction for structural variants.

MOTIVATION: We propose Meltos, a novel computational framework to address the challenging problem of building tumor phylogeny trees using somatic structural variants (SVs) among multiple samples. Meltos leverages the tumor phylogeny tree built on somatic single nucleotide variants (SNVs) to identify high confidence SVs and produce a comprehensive tumor lineage tree, using a novel optimization formulation. While we do not assume the evolutionary progression of SVs is necessarily the same as SNVs, we show that a tumor phylogeny tree using high-quality somatic SNVs can act as a guide for calling and assigning somatic SVs on a tree. Meltos utilizes multiple genomic read signals for potential SV breakpoints in whole genome sequencing data and proposes a probabilistic formulation for estimating variant allele fractions (VAFs) of SV events. RESULTS: In order to assess the ability of Meltos to correctly refine SNV trees with SV information, we tested Meltos on two simulated datasets with five genomes in both. We also assessed Meltos on two real cancer datasets. We tested Meltos on multiple samples from a liposarcoma tumor and on a multi-sample breast cancer data (Yates et al., 2015), where the authors provide validated structural variation events together with deep, targeted sequencing for a collection of somatic SNVs. We show Meltos has the ability to place high confidence validated SV calls on a refined tumor phylogeny tree. We also showed the flexibility of Meltos to either estimate VAFs directly from genomic data or to use copy number corrected estimates. AVAILABILITY AND IMPLEMENTATION: Meltos is available at https://github.com/ih-lab/Meltos. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dissecting the genetic basis of comorbid epilepsy phenotypes in neurodevelopmental disorders.

BACKGROUND: Neurodevelopmental disorders (NDDs) such as autism spectrum disorder, intellectual disability, developmental disability, and epilepsy are characterized by abnormal brain development that may affect cognition, learning, behavior, and motor skills. High co-occurrence (comorbidity) of NDDs indicates a shared, underlying biological mechanism. The genetic heterogeneity and overlap observed in NDDs make it difficult to identify the genetic causes of specific clinical symptoms, such as seizures. METHODS: We present a computational method, MAGI-S, to discover modules or groups of highly connected genes that together potentially perform a similar biological function. MAGI-S integrates protein-protein interaction and co-expression networks to form modules centered around the selection of a single "seed" gene, yielding modules consisting of genes that are highly co-expressed with the seed gene. We aim to dissect the epilepsy phenotype from a general NDD phenotype by providing MAGI-S with high confidence NDD seed genes with varying degrees of association with epilepsy, and we assess the enrichment of de novo mutation, NDD-associated genes, and relevant biological function of constructed modules. RESULTS: The newly identified modules account for the increased rate of de novo non-synonymous mutations in autism, intellectual disability, developmental disability, and epilepsy, and enrichment of copy number variations (CNVs) in developmental disability. We also observed that modules seeded with genes strongly associated with epilepsy tend to have a higher association with epilepsy phenotypes than modules seeded at other neurodevelopmental disorder genes. Modules seeded with genes strongly associated with epilepsy (e.g., SCN1A, GABRA1, and KCNB1) are significantly associated with synaptic transmission, long-term potentiation, and calcium signaling pathways. On the other hand, modules found with seed genes that are not associated or weakly associated with epilepsy are mostly involved with RNA regulation and chromatin remodeling. CONCLUSIONS: In summary, our method identifies modules enriched with de novo non-synonymous mutations and can capture specific networks that underlie the epilepsy phenotype and display distinct enrichment in relevant biological processes. MAGI-S is available at https://github.com/jchow32/magi-s .

Functional disease architectures reveal unique biological role of transposable elements.

Transposable elements (TE) comprise roughly half of the human genome. Though initially derided as junk DNA, they have been widely hypothesized to contribute to the evolution of gene regulation. However, the contribution of TE to the genetic architecture of diseases remains unknown. Here, we analyze data from 41 independent diseases and complex traits to draw three conclusions. First, TE are uniquely informative for disease heritability. Despite overall depletion for heritability (54% of SNPs, 39 ± 2% of heritability), TE explain substantially more heritability than expected based on their depletion for known functional annotations. This implies that TE acquire function in ways that differ from known functional annotations. Second, older TE contribute more to disease heritability, consistent with acquiring biological function. Third, Short Interspersed Nuclear Elements (SINE) are far more enriched for blood traits than for other traits. Our results can help elucidate the biological roles that TE play in the genetic architecture of diseases.

Kevlar: A Mapping-Free Framework for Accurate Discovery of De Novo Variants.

De novo genetic variants are an important source of causative variation in complex genetic disorders. Many methods for variant discovery rely on mapping reads to a reference genome, detecting numerous inherited variants irrelevant to the phenotype of interest. To distinguish between inherited and de novo variation, sequencing of families (parents and siblings) is commonly pursued. However, standard mapping-based approaches tend to have a high false-discovery rate for de novo variant prediction. Kevlar is a mapping-free method for de novo variant discovery, based on direct comparison of sequences between related individuals. Kevlar identifies high-abundance k-mers unique to the individual of interest. Reads containing these k-mers are partitioned into disjoint sets by shared k-mer content for variant calling, and preliminary variant predictions are sorted using a probabilistic score. We evaluated Kevlar on simulated and real datasets, demonstrating its ability to detect both de novo single-nucleotide variants and indels with high accuracy.

Discovery of tandem and interspersed segmental duplications using high-throughput sequencing.

MOTIVATION: Several algorithms have been developed that use high-throughput sequencing technology to characterize structural variations (SVs). Most of the existing approaches focus on detecting relatively simple types of SVs such as insertions, deletions and short inversions. In fact, complex SVs are of crucial importance and several have been associated with genomic disorders. To better understand the contribution of complex SVs to human disease, we need new algorithms to accurately discover and genotype such variants. Additionally, due to similar sequencing signatures, inverted duplications or gene conversion events that include inverted segmental duplications are often characterized as simple inversions, likewise, duplications and gene conversions in direct orientation may be called as simple deletions. Therefore, there is still a need for accurate algorithms to fully characterize complex SVs and thus improve calling accuracy of more simple variants. RESULTS: We developed novel algorithms to accurately characterize tandem, direct and inverted interspersed segmental duplications using short read whole genome sequencing datasets. We integrated these methods to our TARDIS tool, which is now capable of detecting various types of SVs using multiple sequence signatures such as read pair, read depth and split read. We evaluated the prediction performance of our algorithms through several experiments using both simulated and real datasets. In the simulation experiments, using a 30× coverage TARDIS achieved 96% sensitivity with only 4% false discovery rate. For experiments that involve real data, we used two haploid genomes (CHM1 and CHM13) and one human genome (NA12878) from the Illumina Platinum Genomes set. Comparison of our results with orthogonal PacBio call sets from the same genomes revealed higher accuracy for TARDIS than state-of-the-art methods. Furthermore, we showed a surprisingly low false discovery rate of our approach for discovery of tandem, direct and inverted interspersed segmental duplications prediction on CHM1 (<5% for the top 50 predictions). AVAILABILITY AND IMPLEMENTATION: TARDIS source code is available at https://github.com/BilkentCompGen/tardis, and a corresponding Docker image is available at https://hub.docker.com/r/alkanlab/tardis/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multi-platform discovery of haplotype-resolved structural variation in human genomes.

The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (<50 bp) and 27,622 SVs (≥50 bp) per genome. We also discover 156 inversions per genome and 58 of the inversions intersect with the critical regions of recurrent microdeletion and microduplication syndromes. Taken together, our SV callsets represent a three to sevenfold increase in SV detection compared to most standard high-throughput sequencing studies, including those from the 1000 Genomes Project. The methods and the dataset presented serve as a gold standard for the scientific community allowing us to make recommendations for maximizing structural variation sensitivity for future genome sequencing studies.

TAD fusion score: discovery and ranking the contribution of deletions to genome structure.

Deletions that fuse two adjacent topologically associating domains (TADs) can cause severe developmental disorders. We provide a formal method to quantify deletions based on their potential disruption of the three-dimensional genome structure, denoted as the TAD fusion score. Furthermore, we show that deletions that cause TAD fusion are rare and under negative selection in the general population. Finally, we show that our method correctly gives higher scores to deletions reported to cause various disorders, including developmental disorders and cancer, in comparison to the deletions reported in the 1000 Genomes Project. The TAD fusion score tool is publicly available at https://github.com/HormozdiariLab/TAD-fusion-score .

Neurodevelopmental disease genes implicated by de novo mutation and copy number variation morbidity.

We combined de novo mutation (DNM) data from 10,927 individuals with developmental delay and autism to identify 253 candidate neurodevelopmental disease genes with an excess of missense and/or likely gene-disruptive (LGD) mutations. Of these genes, 124 reach exome-wide significance (P < 5 × 10-7) for DNM. Intersecting these results with copy number variation (CNV) morbidity data shows an enrichment for genomic disorder regions (30/253, likelihood ratio (LR) +1.85, P = 0.0017). We identify genes with an excess of missense DNMs overlapping deletion syndromes (for example, KIF1A and the 2q37 deletion) as well as duplication syndromes, such as recurrent MAPK3 missense mutations within the chromosome 16p11.2 duplication, recurrent CHD4 missense DNMs in the 12p13 duplication region, and recurrent WDFY4 missense DNMs in the 10q11.23 duplication region. Network analyses of genes showing an excess of DNMs highlights functional networks, including cell-specific enrichments in the D1+ and D2+ spiny neurons of the striatum.

Combinatorial Approach for Complex Disorder Prediction: Case Study of Neurodevelopmental Disorders.

Early prediction of complex disorders (e.g., autism and other neurodevelopmental disorders) is one of the fundamental goals of precision medicine and personalized genomics. An early prediction of complex disorders can improve the prognosis, increase the effectiveness of interventions and treatments, and enhance the life quality of affected patients. Considering the genetic heritability of neurodevelopmental disorders, we are proposing a novel framework for utilizing rare coding variation for early prediction of these disorders in subset of affected samples. We provide a combinatorial framework for addressing this problem, denoted as Odin (Oracle for DIsorder predictioN), to make a prediction for a small, yet significant, subset of affected cases while having very low false positive rate (FPR) prediction for unaffected samples. Odin also takes advantage of the available functional information (e.g., pairwise coexpression of genes during brain development) to increase the prediction power beyond genes with recurrent variants. Application of our method accurately recovers an additional 8% of autism cases without any severe variant in known recurrent mutated genes with a <1% FPR. Furthermore, Odin predicted a set of 391 genes that severe variants in these genes can cause autism or other developmental delay disorders. Approaches such as the one presented in this paper are needed to translate the biomedical discoveries into actionable items by clinicians. Odin is publicly available at https://github.com/HormozdiariLab/Odin.

High-resolution comparative analysis of great ape genomes.

Genetic studies of human evolution require high-quality contiguous ape genome assemblies that are not guided by the human reference. We coupled long-read sequence assembly and full-length complementary DNA sequencing with a multiplatform scaffolding approach to produce ab initio chimpanzee and orangutan genome assemblies. By comparing these with two long-read de novo human genome assemblies and a gorilla genome assembly, we characterized lineage-specific and shared great ape genetic variation ranging from single- to mega-base pair-sized variants. We identified ~17,000 fixed human-specific structural variants identifying genic and putative regulatory changes that have emerged in humans since divergence from nonhuman apes. Interestingly, these variants are enriched near genes that are down-regulated in human compared to chimpanzee cerebral organoids, particularly in cells analogous to radial glial neural progenitors.

Genetic variation associated with increased insecticide resistance in the malaria mosquito, Anopheles coluzzii.

BACKGROUND: Malaria mortality rates in sub-Saharan Africa have declined significantly in recent years as a result of increased insecticide-treated bed net (ITN) usage. A major challenge to further progress is the emergence and spread of insecticide resistance alleles in the Anopheles mosquito vectors, like An. coluzzii. A non-synonymous mutation in the para voltage-gated sodium channel gene reduces pyrethroid-binding affinity, resulting in knockdown resistance (kdr). Metabolic mechanisms of insecticide resistance involving detoxification genes like cytochrome P450 genes, carboxylesterases, and glutathione S-transferases are also important. As some gene activity is tissue-specific and/or environmentally induced, gene regulatory variation may be overlooked when comparing expression from whole mosquito bodies under standard rearing conditions. RESULTS: We detected complex insecticide resistance in a 2014 An. coluzzii colony from southern Mali using bottle bioassays. Additional bioassays involving recombinant genotypes from a cross with a relatively susceptible 1995 An. coluzzii colony from Mali confirmed the importance of kdr and associated increased permethrin resistance to the CYP9K1 locus on the X chromosome. Significant differential expression of CYP9K1 was not observed among these colonies in Malpighian tubules. However, the P450 gene CYP6Z1 was overexpressed in resistant individuals following sublethal permethrin exposure and the carboxylesterase gene COEAE5G was constitutively overexpressed. CONCLUSIONS: The significant P450-related insecticide resistance observed in the 2014 An. coluzzii colony indicates that ITNs treated with the P450 inhibitor piperonyl butoxide (PBO) would be more effective in this region. The known insecticide resistance gene CYP6Z1 was differentially expressed exclusively in the context of sublethal permethrin exposure, highlighting the importance of tissue-specificity and environmental conditions in gene expression studies. The increased activity of the carboxylesterase COEAE5G in the resistant An. coluzzii colony suggests resistance to other insecticides like organophosphates. Additional gene expression studies involving other tissues (e.g. fat body) would provide a more comprehensive view of genes underlying metabolic insecticide resistance in An. coluzzii from Mali. Identifying genetic markers linked to these regulatory alleles is an important next step that would substantially improve insecticide resistance surveillance and population genetic studies in this important vector species.

Genomic Patterns of De Novo Mutation in Simplex Autism.

To further our understanding of the genetic etiology of autism, we generated and analyzed genome sequence data from 516 idiopathic autism families (2,064 individuals). This resource includes >59 million single-nucleotide variants (SNVs) and 9,212 private copy number variants (CNVs), of which 133,992 and 88 are de novo mutations (DNMs), respectively. We estimate a mutation rate of ∼1.5 × 10-8 SNVs per site per generation with a significantly higher mutation rate in repetitive DNA. Comparing probands and unaffected siblings, we observe several DNM trends. Probands carry more gene-disruptive CNVs and SNVs, resulting in severe missense mutations and mapping to predicted fetal brain promoters and embryonic stem cell enhancers. These differences become more pronounced for autism genes (p = 1.8 × 10-3, OR = 2.2). Patients are more likely to carry multiple coding and noncoding DNMs in different genes, which are enriched for expression in striatal neurons (p = 3 × 10-3), suggesting a path forward for genetically characterizing more complex cases of autism.

Toolkit for automated and rapid discovery of structural variants.

Structural variations (SV) are broadly defined as genomic alterations that affect >50bp of DNA, which are shown to have significant effect on evolution and disease. The advent of high throughput sequencing (HTS) technologies and the ability to perform whole genome sequencing (WGS), makes it feasible to study these variants in depth. However, discovery of all forms of SV using WGS has proven to be challenging as the short reads produced by the predominant HTS platforms (<200bp for current technologies) and the fact that most genomes include large amounts of repeats make it very difficult to unambiguously map and accurately characterize such variants. Furthermore, existing tools for SV discovery are primarily developed for only a few of the SV types, which may have conflicting sequence signatures (i.e. read pairs, read depth, split reads) with other, untargeted SV classes. Here we are introduce a new framework, Tardis, which combines multiple read signatures into a single package to characterize most SV types simultaneously, while preventing such conflicts. Tardis also has a modular structure that makes it easy to extend for the discovery of additional forms of SV.