OASIS: An interpretable, finite-sample valid alternative to Pearson's X2 for scientific discovery

Contingency tables, data represented as counts matrices, are ubiquitous across quantitative research and data-science applications. Existing statistical tests are insufficient however, as none are simultaneously computationally efficient and statistically valid for a finite number of observations. In this work, motivated by a recent application in reference-free genomic inference [K. Chaung et al., Cell 186, 5440-5456 (2023)], we develop Optimized Adaptive Statistic for Inferring Structure (OASIS), a family of statistical tests for contingency tables. OASIS constructs a test statistic which is linear in the normalized data matrix, providing closed-form P-value bounds through classical concentration inequalities. In the process, OASIS provides a decomposition of the table, lending interpretability to its rejection of the null. We derive the asymptotic distribution of the OASIS test statistic, showing that these finite-sample bounds correctly characterize the test statistic's P-value up to a variance term. Experiments on genomic sequencing data highlight the power and interpretability of OASIS. Using OASIS, we develop a method that can detect SARS-CoV-2 and Mycobacterium tuberculosis strains de novo, which existing approaches cannot achieve. We demonstrate in simulations that OASIS is robust to overdispersion, a common feature in genomic data like single-cell RNA sequencing, where under accepted noise models OASIS provides good control of the false discovery rate, while Pearson's [Formula: see text] consistently rejects the null. Additionally, we show in simulations that OASIS is more powerful than Pearson's [Formula: see text] in certain regimes, including for some important two group alternatives, which we corroborate with approximate power calculations.

Joint Representation Learning for Retrieval and Annotation of Genomic Interval Sets

As available genomic interval data increase in scale, we require fast systems to search them. A common approach is simple string matching to compare a search term to metadata, but this is limited by incomplete or inaccurate annotations. An alternative is to compare data directly through genomic region overlap analysis, but this approach leads to challenges like sparsity, high dimensionality, and computational expense. We require novel methods to quickly and flexibly query large, messy genomic interval databases. Here, we develop a genomic interval search system using representation learning. We train numerical embeddings for a collection of region sets simultaneously with their metadata labels, capturing similarity between region sets and their metadata in a low-dimensional space. Using these learned co-embeddings, we develop a system that solves three related information retrieval tasks using embedding distance computations: retrieving region sets related to a user query string, suggesting new labels for database region sets, and retrieving database region sets similar to a query region set. We evaluate these use cases and show that jointly learned representations of region sets and metadata are a promising approach for fast, flexible, and accurate genomic region information retrieval.

In silico characterisation of minor wave genes and LINE-1s transcriptional dynamics at murine zygotic genome activation

Introduction: In mouse, the zygotic genome activation (ZGA) is coordinated by MERVL elements, a class of LTR retrotransposons. In addition to MERVL, another class of retrotransposons, LINE-1 elements, recently came under the spotlight as key regulators of murine ZGA. In particular, LINE-1 transcripts seem to be required to switch-off the transcriptional program started by MERVL sequences, suggesting an antagonistic interplay between LINE-1 and MERVL pathways. Methods: To better investigate the activities of LINE-1 and MERVL elements at ZGA, we integrated publicly available transcriptomics (RNA-seq), chromatin accessibility (ATAC-seq) and Pol-II binding (Stacc-seq) datasets and characterised the transcriptional and epigenetic dynamics of such elements during murine ZGA. Results: We identified two likely distinct transcriptional activities characterising the murine zygotic genome at ZGA onset. On the one hand, our results confirmed that ZGA minor wave genes are preferentially transcribed from MERVL-rich and gene-dense genomic compartments, such as gene clusters. On the other hand, we identified a set of evolutionary young and likely transcriptionally autonomous LINE-1s located in intergenic and gene-poor regions showing, at the same stage, features such as open chromatin and RNA Pol II binding suggesting them to be, at least, poised for transcription. Discussion: These results suggest that, across evolution, transcription of two different classes of transposable elements, MERVLs and LINE-1s, have likely been confined in genic and intergenic regions respectively in order to maintain and regulate two successive transcriptional programs at ZGA.

Characterization of De Novo Promoter Variants in Autism Spectrum Disorder with Massively Parallel Reporter Assays

Autism spectrum disorder (ASD) is a common, complex, and highly heritable condition with contributions from both common and rare genetic variations. While disruptive, rare variants in protein-coding regions clearly contribute to symptoms, the role of rare non-coding remains unclear. Variants in these regions, including promoters, can alter downstream RNA and protein quantity; however, the functional impacts of specific variants observed in ASD cohorts remain largely uncharacterized. Here, we analyzed 3600 de novo mutations in promoter regions previously identified by whole-genome sequencing of autistic probands and neurotypical siblings to test the hypothesis that mutations in cases have a greater functional impact than those in controls. We leveraged massively parallel reporter assays (MPRAs) to detect transcriptional consequences of these variants in neural progenitor cells and identified 165 functionally high confidence de novo variants (HcDNVs). While these HcDNVs are enriched for markers of active transcription, disruption to transcription factor binding sites, and open chromatin, we did not identify differences in functional impact based on ASD diagnostic status.

DNA-MP: a generalized DNA modifications predictor for multiple species based on powerful sequence encoding method

Accurate prediction of deoxyribonucleic acid (DNA) modifications is essential to explore and discern the process of cell differentiation, gene expression and epigenetic regulation. Several computational approaches have been proposed for particular type-specific DNA modification prediction. Two recent generalized computational predictors are capable of detecting three different types of DNA modifications; however, type-specific and generalized modifications predictors produce limited performance across multiple species mainly due to the use of ineffective sequence encoding methods. The paper in hand presents a generalized computational approach "DNA-MP" that is competent to more precisely predict three different DNA modifications across multiple species. Proposed DNA-MP approach makes use of a powerful encoding method "position specific nucleotides occurrence based 117 on modification and non-modification class densities normalized difference" (POCD-ND) to generate the statistical representations of DNA sequences and a deep forest classifier for modifications prediction. POCD-ND encoder generates statistical representations by extracting position specific distributional information of nucleotides in the DNA sequences. We perform a comprehensive intrinsic and extrinsic evaluation of the proposed encoder and compare its performance with 32 most widely used encoding methods on $17$ benchmark DNA modifications prediction datasets of $12$ different species using $10$ different machine learning classifiers. Overall, with all classifiers, the proposed POCD-ND encoder outperforms existing $32$ different encoders. Furthermore, combinedly over 5-fold cross validation benchmark datasets and independent test sets, proposed DNA-MP predictor outperforms state-of-the-art type-specific and generalized modifications predictors by an average accuracy of 7% across 4mc datasets, 1.35% across 5hmc datasets and 10% for 6ma datasets. To facilitate the scientific community, the DNA-MP web application is available at https://sds_genetic_analysis.opendfki.de/DNA_Modifications/.

L-RAPiT: A Cloud-Based Computing Pipeline for the Analysis of Long-Read RNA Sequencing Data

Long-read sequencing (LRS) has been adopted to meet a wide variety of research needs, ranging from the construction of novel transcriptome annotations to the rapid identification of emerging virus variants. Amongst other advantages, LRS preserves more information about RNA at the transcript level than conventional high-throughput sequencing, including far more accurate and quantitative records of splicing patterns. New studies with LRS datasets are being published at an exponential rate, generating a vast reservoir of information that can be leveraged to address a host of different research questions. However, mining such publicly available data in a tailored fashion is currently not easy, as the available software tools typically require familiarity with the command-line interface, which constitutes a significant obstacle to many researchers. Additionally, different research groups utilize different software packages to perform LRS analysis, which often prevents a direct comparison of published results across different studies. To address these challenges, we have developed the Long-Read Analysis Pipeline for Transcriptomics (L-RAPiT), a user-friendly, free pipeline requiring no dedicated computational resources or bioinformatics expertise. L-RAPiT can be implemented directly through Google Colaboratory, a system based on the open-source Jupyter notebook environment, and allows for the direct analysis of transcriptomic reads from Oxford Nanopore and PacBio LRS machines. This new pipeline enables the rapid, convenient, and standardized analysis of publicly available or newly generated LRS datasets.

Intrahost SARS-CoV-2 k-mer Identification Method (iSKIM) for Rapid Detection of Mutations of Concern Reveals Emergence of Global Mutation Patterns

Despite unprecedented global sequencing and surveillance of SARS-CoV-2, timely identification of the emergence and spread of novel variants of concern (VoCs) remains a challenge. Several million raw genome sequencing runs are now publicly available. We sought to survey these datasets for intrahost variation to study emerging mutations of concern. We developed iSKIM ("intrahost SARS-CoV-2 k-mer identification method") to relatively quickly and efficiently screen the many SARS-CoV-2 datasets to identify intrahost mutations belonging to lineages of concern. Certain mutations surged in frequency as intrahost minor variants just prior to, or while lineages of concern arose. The Spike N501Y change common to several VoCs was found as a minor variant in 834 samples as early as October 2020. This coincides with the timing of the first detected samples with this mutation in the Alpha/B.1.1.7 and Beta/B.1.351 lineages. Using iSKIM, we also found that Spike L452R was detected as an intrahost minor variant as early as September 2020, prior to the observed rise of the Epsilon/B.1.429/B.1.427 lineages in late 2020. iSKIM rapidly screens for mutations of interest in raw data, prior to genome assembly, and can be used to detect increases in intrahost variants, potentially providing an early indication of novel variant spread.

Intrahost SARS-CoV-2 k-mer identification method (iSKIM) for rapid detection of mutations of concern reveals emergence of global mutation patterns

Despite unprecedented global sequencing and surveillance of SARS-CoV-2, timely identification of the emergence and spread of novel variants of concern (VoCs) remains a challenge. Several million raw genome sequencing runs are now publicly available. We sought to survey these datasets for intrahost variation to study emerging mutations of concern. We developed iSKIM ("intrahost SARS-CoV-2 k-mer identification method") to relatively quickly and efficiently screen the many SARS-CoV-2 datasets to identify intrahost mutations belonging to lineages of concern. Certain mutations surged in frequency as intrahost minor variants just prior to, or while lineages of concern arose. The Spike N501Y change common to several VoCs was found as a minor variant in 834 samples as early as October 2020. This coincides with the timing of the first detected samples with this mutation in the Alpha/B.1.1.7 and Beta/B.1.351 lineages. Using iSKIM, we also found that Spike L452R was detected as an intrahost minor variant as early as September 2020, prior to the observed rise of the Epsilon/B.1.429/B.1.427 lineages in late 2020. iSKIM rapidly screens for mutations of interest in raw data, prior to genome assembly, and can be used to detect increases in intrahost variants, potentially providing an early indication of novel variant spread.

eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale

Even though automated functional annotation of genes represents a fundamental step in most genomic and metagenomic workflows, it remains challenging at large scales. Here, we describe a major upgrade to eggNOG-mapper, a tool for functional annotation based on precomputed orthology assignments, now optimized for vast (meta)genomic data sets. Improvements in version 2 include a full update of both the genomes and functional databases to those from eggNOG v5, as well as several efficiency enhancements and new features. Most notably, eggNOG-mapper v2 now allows for: 1) de novo gene prediction from raw contigs, 2) built-in pairwise orthology prediction, 3) fast protein domain discovery, and 4) automated GFF decoration. eggNOG-mapper v2 is available as a standalone tool or as an online service at http://eggnog-mapper.embl.de.

eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale

Even though automated functional annotation of genes represents a fundamental step in most genomic and metagenomic workflows, it remains challenging at large scales. Here, we describe a major upgrade to eggNOG-mapper, a tool for functional annotation based on precomputed orthology assignments, now optimized for vast (meta)genomic data sets. Improvements in version 2 include a full update of both the genomes and functional databases to those from eggNOG v5, as well as several efficiency enhancements and new features. Most notably, eggNOG-mapper v2 now allows for: 1) de novo gene prediction from raw contigs, 2) built-in pairwise orthology prediction, 3) fast protein domain discovery, and 4) automated GFF decoration. eggNOG-mapper v2 is available as a standalone tool or as an online service at http://eggnog-mapper.embl.de.

Quantification, Dynamic Visualization, and Validation of Bias in ATAC-Seq Data with ataqv

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) has become the preferred method for mapping chromatin accessibility due to its time and input material efficiency. However, it can be difficult to evaluate data quality and identify sources of technical bias across samples. Here, we present ataqv, a computational toolkit for efficiently measuring, visualizing, and comparing quality control (QC) results across samples and experiments. We use ataqv to analyze 2,009 public ATAC-seq datasets; their QC metrics display a 10-fold range. Tn5 dosage experiments and statistical modeling show that technical variation in the ratio of Tn5 transposase to nuclei and sequencing flowcell density induces systematic bias in ATAC-seq data by changing the enrichment of reads across functional genomic annotations including promoters, enhancers, and transcription-factor-bound regions, with the notable exception of CTCF. ataqv can be integrated into existing computational pipelines and is freely available at https://github.com/ParkerLab/ataqv/.

Toward a fine-scale population health monitoring system

Understanding population health disparities is an essential component of equitable precision health efforts. Epidemiology research often relies on definitions of race and ethnicity, but these population labels may not adequately capture disease burdens and environmental factors impacting specific sub-populations. Here, we propose a framework for repurposing data from electronic health records (EHRs) in concert with genomic data to explore the demographic ties that can impact disease burdens. Using data from a diverse biobank in New York City, we identified 17 communities sharing recent genetic ancestry. We observed 1,177 health outcomes that were statistically associated with a specific group and demonstrated significant differences in the segregation of genetic variants contributing to Mendelian diseases. We also demonstrated that fine-scale population structure can impact the prediction of complex disease risk within groups. This work reinforces the utility of linking genomic data to EHRs and provides a framework toward fine-scale monitoring of population health.

Perspectives of using Cloud computing in integrative analysis of multi-omics data

Integrative analysis of multi-omics data is usually computationally demanding. It frequently requires building complex, multi-step analysis pipelines, applying dedicated techniques for data processing and combining several data sources. These efforts lead to a better understanding of life processes, current health state or the effects of therapeutic activities. However, many omics data analysis solutions focus only on a selected problem, disease, types of data or organisms. Moreover, they are implemented for general-purpose scientific computational platforms that most often do not easily scale the calculations natively. These features are not conducive to advances in understanding genotype-phenotypic relationships. Fortunately, with new technological paradigms, including Cloud computing, virtualization and containerization, these functionalities could be orchestrated for easy scaling and building independent analysis pipelines for omics data. Therefore, solutions can be re-used for purposes that they were not primarily designed. This paper shows perspectives of using Cloud computing advances and containerization approach for such a purpose. We first review how the Cloud computing model is utilized in multi-omics data analysis and show weak points of the adopted solutions. Then, we introduce containerization concepts, which allow both scaling and linking of functional services designed for various purposes. Finally, on the Bioconductor software package example, we disclose a verified concept model of a universal solution that exhibits the potentials for performing integrative analysis of multiple omics data sources.

Identifying Cancer-Relevant Mutations in the DLC START Domain Using Evolutionary and Structure-Function Analyses

Rho GTPase signaling promotes proliferation, invasion, and metastasis in a broad spectrum of cancers. Rho GTPase activity is regulated by the deleted in liver cancer (DLC) family of bona fide tumor suppressors which directly inactivate Rho GTPases by stimulating GTP hydrolysis. In addition to a RhoGAP domain, DLC proteins contain a StAR-related lipid transfer (START) domain. START domains in other organisms bind hydrophobic small molecules and can regulate interacting partners or co-occurring domains through a variety of mechanisms. In the case of DLC proteins, their START domain appears to contribute to tumor suppressive activity. However, the nature of this START-directed mechanism, as well as the identities of relevant functional residues, remain virtually unknown. Using the Catalogue of Somatic Mutations in Cancer (COSMIC) dataset and evolutionary and structure-function analyses, we identify several conserved residues likely to be required for START-directed regulation of DLC-1 and DLC-2 tumor-suppressive capabilities. This pan-cancer analysis shows that conserved residues of both START domains are highly overrepresented in cancer cells from a wide range tissues. Interestingly, in DLC-1 and DLC-2, three of these residues form multiple interactions at the tertiary structural level. Furthermore, mutation of any of these residues is predicted to disrupt interactions and thus destabilize the START domain. As such, these mutations would not have emerged from traditional hotspot scans of COSMIC. We propose that evolutionary and structure-function analyses are an underutilized strategy which could be used to unmask cancer-relevant mutations within COSMIC. Our data also suggest DLC-1 and DLC-2 as high-priority candidates for development of novel therapeutics that target their START domain.

Dissecting the Effect of a 3D Microscaffold on the Transcriptome of Neural Stem Cells with Computational Approaches: A Focus on Mechanotransduction

3D cell cultures are becoming more and more important in the field of regenerative medicine due to their ability to mimic the cellular physiological microenvironment. Among the different types of 3D scaffolds, we focus on the Nichoid, a miniaturized scaffold with a structure inspired by the natural staminal niche. The Nichoid can activate cellular responses simply by subjecting the cells to mechanical stimuli. This kind of influence results in different cellular morphology and organization, but the molecular bases of these changes remain largely unknown. Through RNA-Seq approach on murine neural precursors stem cells expanded inside the Nichoid, we investigated the deregulated genes and pathways showing that the Nichoid causes alteration in genes strongly connected to mechanobiological functions. Moreover, we fully dissected this mechanism highlighting how the changes start at a membrane level, with subsequent alterations in the cytoskeleton, signaling pathways, and metabolism, all leading to a final alteration in gene expression. The results shown here demonstrate that the Nichoid influences the biological and genetic response of stem cells thorough specific alterations of cellular signaling. The characterization of these pathways elucidates the role of mechanical manipulation on stem cells, with possible implications in regenerative medicine applications.

Mobile genomics: tools and techniques for tackling transposons

Next-generation sequencing approaches have fundamentally changed the types of questions that can be asked about gene function and regulation. With the goal of approaching truly genome-wide quantifications of all the interaction partners and downstream effects of particular genes, these quantitative assays have allowed for an unprecedented level of detail in exploring biological interactions. However, many challenges remain in our ability to accurately describe and quantify the interactions that take place in those hard to reach and extremely repetitive regions of our genome comprised mostly of transposable elements (TEs). Tools dedicated to TE-derived sequences have lagged behind, making the inclusion of these sequences in genome-wide analyses difficult. Recent improvements, both computational and experimental, allow for the better inclusion of TE sequences in genomic assays and a renewed appreciation for the importance of TE biology. This review will discuss the recent improvements that have been made in the computational analysis of TE-derived sequences as well as the areas where such analysis still proves difficult. This article is part of a discussion meeting issue 'Crossroads between transposons and gene regulation'.

Integrated Analysis of Germline and Tumor DNA Identifies New Candidate Genes Involved in Familial Colorectal Cancer

Colorectal cancer (CRC) shows aggregation in some families but no alterations in the known hereditary CRC genes. We aimed to identify new candidate genes which are potentially involved in germline predisposition to familial CRC. An integrated analysis of germline and tumor whole-exome sequencing data was performed in 18 unrelated CRC families. Deleterious single nucleotide variants (SNV), short insertions and deletions (indels), copy number variants (CNVs) and loss of heterozygosity (LOH) were assessed as candidates for first germline or second somatic hits. Candidate tumor suppressor genes were selected when alterations were detected in both germline and somatic DNA, fulfilling Knudson’s two-hit hypothesis. Somatic mutational profiling and signature analysis were also performed. A series of germline-somatic variant pairs were detected. In all cases, the first hit was presented as a rare SNV/indel, whereas the second hit was either a different SNV (3 genes) or LOH affecting the same gene (141 genes). BRCA2, BLM, ERCC2, RECQL, REV3L and RIF1 were among the most promising candidate genes for germline CRC predisposition. The identification of new candidate genes involved in familial CRC could be achieved by our integrated analysis. Further functional studies and replication in additional cohorts are required to confirm the selected candidates.

Nervous System Regionalization Entails Axial Allocation before Neural Differentiation

Neural induction in vertebrates generates a CNS that extends the rostral-caudal length of the body. The prevailing view is that neural cells are initially induced with anterior (forebrain) identity; caudalizing signals then convert a proportion to posterior fates (spinal cord). To test this model, we used chromatin accessibility to define how cells adopt region-specific neural fates. Together with genetic and biochemical perturbations, this identified a developmental time window in which genome-wide chromatin-remodeling events preconfigure epiblast cells for neural induction. Contrary to the established model, this revealed that cells commit to a regional identity before acquiring neural identity. This “primary regionalization” allocates cells to anterior or posterior regions of the nervous system, explaining how cranial and spinal neurons are generated at appropriate axial positions. These findings prompt a revision to models of neural induction and support the proposed dual evolutionary origin of the vertebrate CNS.

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Chromatin accessibility defines neural progenitor identity

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A limited developmental window exists to establish spinal cord competency

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Cells acquire axial identity prior to neural identity

Genotype Fingerprints Enable Fast and Private Comparison of Genetic Testing Results for Research and Direct-to-Consumer Applications

Genetic testing has expanded out of the research laboratory into medical practice and the direct-to-consumer market. Rapid analysis of the resulting genotype data now has a significant impact. We present a method for summarizing personal genotypes as ‘genotype fingerprints’ that meets these needs. Genotype fingerprints can be derived from any single nucleotide polymorphism-based assay, and remain comparable as chip designs evolve to higher marker densities. We demonstrate that these fingerprints support distinguishing types of relationships among closely related individuals and closely related individuals from individuals from the same background population, as well as high-throughput identification of identical genotypes, individuals in known background populations, and de novo separation of subpopulations within a large cohort through extremely rapid comparisons. Although fingerprints do not preserve anonymity, they provide a useful degree of privacy by summarizing a genotype while preventing reconstruction of individual marker states. Genotype fingerprints are therefore well-suited as a format for public aggregation of genetic information to support ancestry and relatedness determination without revealing personal health risk status.

Copy number variation is associated with gene expression change in archaea

Genomic instability, although frequently deleterious, is also an important mechanism for microbial adaptation to environmental change. Although widely studied in bacteria, in archaea the effect of genomic instability on organism phenotypes and fitness remains unclear. Here we use DNA segmentation methods to detect and quantify genome-wide copy number variation (CNV) in large compendia of high-throughput datasets in a model archaeal species, Halobacterium salinarum. CNV hotspots were identified throughout the genome. Some hotspots were strongly associated with changes in gene expression, suggesting a mechanism for phenotypic innovation. In contrast, CNV hotspots in other genomic loci left expression unchanged, suggesting buffering of certain phenotypes. The correspondence of CNVs with gene expression was validated with strain- and condition-matched transcriptomics and DNA quantification experiments at specific loci. Significant correlation of CNV hotspot locations with the positions of known insertion sequence (IS) elements suggested a mechanism for generating genomic instability. Given the efficient recombination capabilities in H. salinarum despite stability at the single nucleotide level, these results suggest that genomic plasticity mediated by IS element activity can provide a source of phenotypic innovation in extreme environments.

Identification of integrin drug targets for 17 solid tumor types

Integrins are contributors to remodeling of the extracellular matrix and cell migration. Integrins participate in the assembly of the actin cytoskeleton, regulate growth factor signaling pathways, cell proliferation, and control cell motility. In solid tumors, integrins are involved in promoting metastasis to distant sites, and angiogenesis. Integrins are a key target in cancer therapy and imaging. Integrin antagonists have proven successful in halting invasion and migration of tumors. Overexpressed integrins are prime anti-cancer drug targets. To streamline the development of specific integrin cancer therapeutics, we curated data to predict which integrin heterodimers are pausible therapeutic targets against 17 different solid tumors. Computational analysis of The Cancer Genome Atlas (TCGA) gene expression data revealed a set of integrin targets that are differentially expressed in tumors. Filtered by FPKM (Fragments Per Kilobase of transcript per Million mapped reads) expression level, overexpressed subunits were paired into heterodimeric protein targets. By comparing the RNA-seq differential expression results with immunohistochemistry (IHC) data, overexpressed integrin subunits were validated. Biologics and small molecule drug compounds against these identified overexpressed subunits and heterodimeric receptors are potential therapeutics against these cancers. In addition, high-affinity and high-specificity ligands against these integrins can serve as efficient vehicles for delivery of cancer drugs, nanotherapeutics, or imaging probes against cancer.

Machine Learning and Radiogenomics: Lessons Learned and Future Directions

Due to the rapid increase in the availability of patient data, there is significant interest in precision medicine that could facilitate the development of a personalized treatment plan for each patient on an individual basis. Radiation oncology is particularly suited for predictive machine learning (ML) models due to the enormous amount of diagnostic data used as input and therapeutic data generated as output. An emerging field in precision radiation oncology that can take advantage of ML approaches is radiogenomics, which is the study of the impact of genomic variations on the sensitivity of normal and tumor tissue to radiation. Currently, patients undergoing radiotherapy are treated using uniform dose constraints specific to the tumor and surrounding normal tissues. This is suboptimal in many ways. First, the dose that can be delivered to the target volume may be insufficient for control but is constrained by the surrounding normal tissue, as dose escalation can lead to significant morbidity and rare. Second, two patients with nearly identical dose distributions can have substantially different acute and late toxicities, resulting in lengthy treatment breaks and suboptimal control, or chronic morbidities leading to poor quality of life. Despite significant advances in radiogenomics, the magnitude of the genetic contribution to radiation response far exceeds our current understanding of individual risk variants. In the field of genomics, ML methods are being used to extract harder-to-detect knowledge, but these methods have yet to fully penetrate radiogenomics. Hence, the goal of this publication is to provide an overview of ML as it applies to radiogenomics. We begin with a brief history of radiogenomics and its relationship to precision medicine. We then introduce ML and compare it to statistical hypothesis testing to reflect on shared lessons and to avoid common pitfalls. Current ML approaches to genome-wide association studies are examined. The application of ML specifically to radiogenomics is next presented. We end with important lessons for the proper integration of ML into radiogenomics.

Systematic prediction of genes functionally linked to CRISPR-Cas systems by gene neighborhood analysis

The CRISPR-Cas systems of bacterial and archaeal adaptive immunity consist of direct repeat arrays separated by unique spacers and multiple CRISPR-associated (cas) genes encoding proteins that mediate all stages of the CRISPR response. In addition to the relatively small set of core cas genes that are typically present in all CRISPR-Cas systems of a given (sub)type and are essential for the defense function, numerous genes occur in CRISPR-cas loci only sporadically. Some of these have been shown to perform various ancillary roles in CRISPR response, but the functional relevance of most remains unknown. We developed a computational strategy for systematically detecting genes that are likely to be functionally linked to CRISPR-Cas. The approach is based on a "CRISPRicity" metric that measures the strength of CRISPR association for all protein-coding genes from sequenced bacterial and archaeal genomes. Uncharacterized genes with CRISPRicity values comparable to those of cas genes are considered candidate CRISPR-linked genes. We describe additional criteria to predict functionally relevance for genes in the candidate set and identify 79 genes as strong candidates for functional association with CRISPR-Cas systems. A substantial majority of these CRISPR-linked genes reside in type III CRISPR-cas loci, which implies exceptional functional versatility of type III systems. Numerous candidate CRISPR-linked genes encode integral membrane proteins suggestive of tight membrane association of CRISPR-Cas systems, whereas many others encode proteins implicated in various signal transduction pathways. These predictions provide ample material for improving annotation of CRISPR-cas loci and experimental characterization of previously unsuspected aspects of CRISPR-Cas system functionality.

Ultrafast Comparison of Personal Genomes via Precomputed Genome Fingerprints

We present an ultrafast method for comparing personal genomes. We transform the standard genome representation (lists of variants relative to a reference) into “genome fingerprints” via locality sensitive hashing. The resulting genome fingerprints can be meaningfully compared even when the input data were obtained using different sequencing technologies, processed using different pipelines, represented in different data formats and relative to different reference versions. Furthermore, genome fingerprints are robust to up to 30% missing data. Because of their reduced size, computation on the genome fingerprints is fast and requires little memory. For example, we could compute all-against-all pairwise comparisons among the 2504 genomes in the 1000 Genomes data set in 67 s at high quality (21 μs per comparison, on a single processor), and achieved a lower quality approximation in just 11 s. Efficient computation enables scaling up a variety of important genome analyses, including quantifying relatedness, recognizing duplicative sequenced genomes in a set, population reconstruction, and many others. The original genome representation cannot be reconstructed from its fingerprint, effectively decoupling genome comparison from genome interpretation; the method thus has significant implications for privacy-preserving genome analytics.

Sorting by Cuts, Joins, and Whole Chromosome Duplications

Genome rearrangement problems have been extensively studied due to their importance in biology. Most studied models assumed a single copy per gene. However, in reality, duplicated genes are common, most notably in cancer. In this study, we make a step toward handling duplicated genes by considering a model that allows the atomic operations of cut, join, and whole chromosome duplication. Given two linear genomes, [Formula: see text] with one copy per gene and [Formula: see text] with two copies per gene, we give a linear time algorithm for computing a shortest sequence of operations transforming [Formula: see text] into [Formula: see text] such that all intermediate genomes are linear. We also show that computing an optimal sequence with fewest duplications is NP-hard.

Pathway Tools version 19.0 update: software for pathway/genome informatics and systems biology

Pathway Tools is a bioinformatics software environment with a broad set of capabilities. The software provides genome-informatics tools such as a genome browser, sequence alignments, a genome-variant analyzer and comparative-genomics operations. It offers metabolic-informatics tools, such as metabolic reconstruction, quantitative metabolic modeling, prediction of reaction atom mappings and metabolic route search. Pathway Tools also provides regulatory-informatics tools, such as the ability to represent and visualize a wide range of regulatory interactions. This article outlines the advances in Pathway Tools in the past 5 years. Major additions include components for metabolic modeling, metabolic route search, computation of atom mappings and estimation of compound Gibbs free energies of formation; addition of editors for signaling pathways, for genome sequences and for cellular architecture; storage of gene essentiality data and phenotype data; display of multiple alignments, and of signaling and electron-transport pathways; and development of Python and web-services application programming interfaces. Scientists around the world have created more than 9800 Pathway/Genome Databases by using Pathway Tools, many of which are curated databases for important model organisms.

Bioinformatics as a first-line approach for understanding bacteriophage transcription

Current approach to understanding bacteriophage transcription strategies during infection includes a combination of experimental and bioinformatics approaches, which is often time and resource consuming. Given the exponentially growing number of sequenced bacteriophage genomes, it becomes sensible asking to what extent one can understand bacteriophage transcription by using bioinformatics methods alone. We here argue that a suitable choice of computational methods may provide a highly efficient first-line approach for underst-anding bacteriophage transcription.

The NIH BD2K center for big data in translational genomics

The world's genomics data will never be stored in a single repository - rather, it will be distributed among many sites in many countries. No one site will have enough data to explain genotype to phenotype relationships in rare diseases; therefore, sites must share data. To accomplish this, the genetics community must forge common standards and protocols to make sharing and computing data among many sites a seamless activity. Through the Global Alliance for Genomics and Health, we are pioneering the development of shared application programming interfaces (APIs) to connect the world's genome repositories. In parallel, we are developing an open source software stack (ADAM) that uses these APIs. This combination will create a cohesive genome informatics ecosystem. Using containers, we are facilitating the deployment of this software in a diverse array of environments. Through benchmarking efforts and big data driver projects, we are ensuring ADAM's performance and utility.

Untangling statistical and biological models to understand network inference: the need for a genomics network ontology

In this paper, we shed light on approaches that are currently used to infer networks from gene expression data with respect to their biological meaning. As we will show, the biological interpretation of these networks depends on the chosen theoretical perspective. For this reason, we distinguish a statistical perspective from a mathematical modeling perspective and elaborate their differences and implications. Our results indicate the imperative need for a genomic network ontology in order to avoid increasing confusion about the biological interpretation of inferred networks, which can be even enhanced by approaches that integrate multiple data sets, respectively, data types.

Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks

In recent years gene regulatory networks (GRNs) have attracted a lot of interest and many methods have been introduced for their statistical inference from gene expression data. However, despite their popularity, GRNs are widely misunderstood. For this reason, we provide in this paper a general discussion and perspective of gene regulatory networks. Specifically, we discuss their meaning, the consistency among different network inference methods, ensemble methods, the assessment of GRNs, the estimated number of existing GRNs and their usage in different application domains. Furthermore, we discuss open questions and necessary steps in order to utilize gene regulatory networks in a clinical context and for personalized medicine.

Using VAAST to Identify Disease-Associated Variants in Next-Generation Sequencing Data

The VAAST pipeline is specifically designed to identify disease-associated alleles in next-generation sequencing data. In the protocols presented in this paper, we outline the best practices for variant prioritization using VAAST. Examples and test data are provided for case-control, small pedigree, and large pedigree analyses. These protocols will teach users the fundamentals of VAAST, VAAST 2.0, and pVAAST analyses.

The gene regulatory network for breast cancer: integrated regulatory landscape of cancer hallmarks

In this study, we infer the breast cancer gene regulatory network from gene expression data. This network is obtained from the application of the BC3Net inference algorithm to a large-scale gene expression data set consisting of 351 patient samples. In order to elucidate the functional relevance of the inferred network, we are performing a Gene Ontology (GO) analysis for its structural components. Our analysis reveals that most significant GO-terms we find for the breast cancer network represent functional modules of biological processes that are described by known cancer hallmarks, including translation, immune response, cell cycle, organelle fission, mitosis, cell adhesion, RNA processing, RNA splicing and response to wounding. Furthermore, by using a curated list of census cancer genes, we find an enrichment in these functional modules. Finally, we study cooperative effects of chromosomes based on information of interacting genes in the beast cancer network. We find that chromosome 21 is most coactive with other chromosomes. To our knowledge this is the first study investigating the genome-scale breast cancer network.

The 3.2 Å resolution structure of a receptor: CheA:CheW signaling complex defines overlapping binding sites and key residue interactions within bacterial chemosensory arrays

Bacterial chemosensory arrays are composed of extended networks of chemoreceptors (also known as methyl-accepting chemotaxis proteins, MCPs), the histidine kinase CheA, and the adaptor protein CheW. Models of these arrays have been developed from cryoelectron microscopy, crystal structures of binary and ternary complexes, NMR spectroscopy, mutational, data and biochemical studies. A new 3.2 Å resolution crystal structure of a Thermotoga maritima MCP protein interaction region in complex with the CheA kinase-regulatory module (P4-P5) and adaptor protein CheW provides sufficient detail to define residue contacts at the interfaces formed among the three proteins. As in a previous 4.5 Å resolution structure, CheA-P5 and CheW interact through conserved hydrophobic surfaces at the ends of their β-barrels to form pseudo 6-fold symmetric rings in which the two proteins alternate around the circumference. The interface between P5 subdomain 1 and CheW subdomain 2 was anticipated from previous studies, whereas the related interface between CheW subdomain 1 and P5 subdomain 2 has only been observed in these ring assemblies. The receptor forms an unexpected structure in that the helical hairpin tip of each subunit has "unzipped" into a continuous α-helix; four such helices associate into a bundle, and the tetramers bridge adjacent P5-CheW rings in the lattice through interactions with both P5 and CheW. P5 and CheW each bind a receptor helix with a groove of conserved hydrophobic residues between subdomains 1 and 2. P5 binds the receptor helix N-terminal to the tip region (lower site), whereas CheW binds the same helix with inverted polarity near the bundle end (upper site). Sequence comparisons among different evolutionary classes of chemotaxis proteins show that the binding partners undergo correlated changes at key residue positions that involve the lower site. Such evolutionary analyses argue that both CheW and P5 bind to the receptor tip at overlapping positions. Computational genomics further reveal that two distinct CheW proteins in Thermotogae utilize the analogous recognition motifs to couple different receptor classes to the same CheA kinase. Important residues for function previously identified by mutagenesis, chemical modification and biophysical approaches also map to these same interfaces. Thus, although the native CheW-receptor interaction is not observed in the present crystal structure, the bioinformatics and previous data predict key features of this interface. The companion study of the P5-receptor interface in native arrays (accompanying paper Piasta et al. (2013) Biochemistry, DOI: 10.1021/bi400385c) shows that, despite the non-native receptor fold in the present crystal structure, the local helix-in-groove contacts of the crystallographic P5-receptor interaction are present in native arrays and are essential for receptor regulation of kinase activity.

Cloud computing for comparative genomics with windows azure platform

Cloud computing services have emerged as a cost-effective alternative for cluster systems as the number of genomes and required computation power to analyze them increased in recent years. Here we introduce the Microsoft Azure platform with detailed execution steps and a cost comparison with Amazon Web Services.