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Summary: We describe a novel computational method for genotyping repeats using sequence graphs. This method addresses the long-standing need to accurately genotype medically important loci containing repeats adjacent to other variants or imperfect DNA repeats such as polyalanine repeats. Here we introduce a new version of our repeat genotyping software, ExpansionHunter, that uses this method to perform targeted genotyping of a broad class of such loci., Availability and Implementation: ExpansionHunter is implemented in C++ and is available under the Apache License Version 2.0. The source code, documentation, and Linux/macOS binaries are available at https://github.com/Illumina/ExpansionHunter/., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press.)

Motivation: Next-generation sequencing technology is transitioning quickly from research labs to clinical settings. The diagnosis and treatment selection for many acquired and autosomal conditions necessitate a method for accurately detecting somatic and germline variants., Results: We have developed Pisces, a rapid, versatile and accurate small-variant calling suite designed for somatic and germline amplicon sequencing applications. Accuracy is achieved by four distinct modules, each incorporating a number of novel algorithmic strategies., Availability and Implementation: Pisces is distributed under an open source license and can be downloaded from https://github.com/Illumina/Pisces. Pisces is available on the BaseSpace™ SequenceHub. It is distributed on Illumina sequencing platforms such as the MiSeq™ and is included in the Praxis™ Extended RAS Panel test which was recently approved by the FDA., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2018. Published by Oxford University Press.)

In jawed vertebrates, oligodendrocytes (OLs) are the myelin-producing glial cells responsible for ensheathment of axons within the central nervous system and are also crucial for remyelination following injury or disease. Olig2 is a crucial factor in the specification and differentiation of oligodendrocyte precursor cells (OPCs) that give rise to mature, myelin-producing OLs in the developing and postnatal CNS; however, its role in adulthood is less well understood. To investigate the role Olig2 plays in regulating gene expression in the adult OL lineage in a physiologically-relevant context, we performed chromatin immunoprecipitation followed by next generation sequencing analysis (ChIP-Seq) using whole spinal cord tissue harvested from adult mice. We found that many of the Olig2-bound sites were associated with genes with biological processes corresponding to OL differentiation (Nkx2.2, Nkx6.2, and Sip1), myelination and ensheathment (Mbp, Cldn11, and Mobp), as well as cell cycle and cytoskeletal regulation. This suggests Olig2 continues to play a critical role in processes related to OL differentiation and myelination well into adulthood.

Detailed analysis of disease-affected tissue provides insight into molecular mechanisms contributing to pathogenesis. Substantia nigra, striatum, and cortex are functionally connected with increasing degrees of alpha-synuclein pathology in Parkinson's disease. We undertook functional and causal pathway analysis of gene expression and proteomic alterations in these three regions, and the data revealed pathways that correlated with disease progression. In addition, microarray and RNAseq experiments revealed previously unidentified causal changes related to oligodendrocyte function and synaptic vesicle release, and these and other changes were reflected across all brain regions. Importantly, subsets of these changes were replicated in Parkinson's disease blood; suggesting peripheral tissue may provide important avenues for understanding and measuring disease status and progression. Proteomic assessment revealed alterations in mitochondria and vesicular transport proteins that preceded gene expression changes indicating defects in translation and/or protein turnover. Our combined approach of proteomics, RNAseq and microarray analyses provides a comprehensive view of the molecular changes that accompany functional loss and alpha-synuclein pathology in Parkinson's disease, and may be instrumental to understand, diagnose and follow Parkinson's disease progression.

Recent proteomic and genetic studies have aimed to identify a complete network of interactions between HIV and human proteins and genes. This HIV-human interaction network provides invaluable information as to how HIV exploits the host machinery and can be used as a starting point for further functional analyses. We integrated this network with complementary datasets of protein function and interaction to nominate human protein complexes with likely roles in viral infection. Based on our approach we identified a global map of 40 HIV-human protein complexes with putative roles in HIV infection, some of which are involved in DNA replication and repair, transcription, translation, and cytoskeletal regulation. Targeted RNAi screens were used to validate several proteins and complexes for functional impact on viral infection. Thus, our HIV-human protein complex map provides a significant resource of potential HIV-host interactions for further study.

The discovery of novel drug targets is a significant challenge in drug development. Although the human genome comprises approximately 30,000 genes, proteins encoded by fewer than 400 are used as drug targets in the treatment of diseases. Therefore, novel drug targets are extremely valuable as the source for first in class drugs. On the other hand, many of the currently known drug targets are functionally pleiotropic and involved in multiple pathologies. Several of them are exploited for treating multiple diseases, which highlights the need for methods to reliably reposition drug targets to new indications. Network-based methods have been successfully applied to prioritize novel disease-associated genes. In recent years, several such algorithms have been developed, some focusing on local network properties only, and others taking the complete network topology into account. Common to all approaches is the understanding that novel disease-associated candidates are in close overall proximity to known disease genes. However, the relevance of these methods to the prediction of novel drug targets has not yet been assessed. Here, we present a network-based approach for the prediction of drug targets for a given disease. The method allows both repositioning drug targets known for other diseases to the given disease and the prediction of unexploited drug targets which are not used for treatment of any disease. Our approach takes as input a disease gene expression signature and a high-quality interaction network and outputs a prioritized list of drug targets. We demonstrate the high performance of our method and highlight the usefulness of the predictions in three case studies. We present novel drug targets for scleroderma and different types of cancer with their underlying biological processes. Furthermore, we demonstrate the ability of our method to identify non-suspected repositioning candidates using diabetes type 1 as an example.

Mitogen-activated protein kinases (MAPKs) have a docking groove that interacts with linear "docking" motifs in binding partners. To determine the structural basis of binding specificity between MAPKs and docking motifs, we quantitatively analyzed the ability of 15 docking motifs from diverse MAPK partners to bind to c-Jun amino-terminal kinase 1 (JNK1), p38α, and extracellular signal-regulated kinase 2 (ERK2). Classical docking motifs mediated highly specific binding only to JNK1, and only those motifs with a sequence pattern distinct from the classical MAPK binding docking motif consensus differentiated between the topographically similar docking grooves of ERK and p38α. Crystal structures of four complexes of MAPKs with docking peptides, representing JNK-specific, ERK-specific, or ERK- and p38-selective binding modes, revealed that the regions located between consensus positions in the docking motifs showed conformational diversity. Although the consensus positions in the docking motifs served as anchor points that bound to common MAPK surface features and mostly contributed to docking in a nondiscriminatory fashion, the conformation of the intervening region between the anchor points mostly determined specificity. We designed peptides with tailored MAPK binding profiles by rationally changing the length and amino acid composition of intervening regions located between anchor points. These results suggest a coherent structural model for MAPK docking specificity that reveals how short linear motifs binding to a common kinase docking groove can mediate diverse interaction patterns and contribute to correct MAPK partner selection in signaling networks.

Complex biological systems comprise a large number of interacting molecules. The identification and detailed characterization of the functions of the involved genes and proteins are crucial for modeling and understanding such systems. To interrogate the various cellular processes, high-throughput techniques such as the Affymetrix Exon Array or RNA interference (RNAi) screens are powerful experimental approaches for functional genomics. However, they typically yield long gene lists that require computational methods to further analyze and functionally annotate the experimental results and to gain more insight into important molecular interactions. Here, we focus on bioinformatics software tools for the functional interpretation of exon expression data to discover alternative splicing events and their impact on gene and protein architecture, molecular networks, and pathways. We additionally demonstrate how to explore large lists of candidate genes as they also result from RNAi screens. In particular, our exemplary application studies show how to analyze the function of human genes that play a major role in human stem cells or viral infections.

Proteins and their interactions are essential for the survival of each human cell. Knowledge of their tissue occurrence is important for understanding biological processes. Therefore, we analyzed microarray and high-throughput RNA-sequencing data to identify tissue-specific and universally expressed genes. Gene expression data were used to investigate the presence of proteins, protein interactions and protein complexes in different tissues. Our comparison shows that the detection of tissue-specific genes and proteins strongly depends on the applied measurement technique. We found that microarrays are less sensitive for low expressed genes than high-throughput sequencing. Functional analyses based on microarray data are thus biased towards high expressed genes. This also means that previous biological findings based on microarrays might have to be re-examined using high-throughput sequencing results.

Tissue-specific gene expression can result in the presence or absence of certain protein interactions and complexes, leading to profound functional differences of biological processes between the tissues. In this study, we integrate human gene expression data based on RNA-sequencing with protein interactions, domains and complexes to analyze the functional implications of their tissue specificity. This reveals that tissue specificity is characterized by much fewer proteins, domains, interactions, and complexes than previously thought. In contrast to previous microarray studies, our analysis based on RNA-sequencing suggests that tissue-specific protein interactions are less common and mainly involved with transmembrane transport and receptor activation. Additionally, tissue-specific protein domains show enrichments in DNA-related functions. This confirms that receptor-activated signaling processes and transcriptional regulation are two key factors for tissue specificity. Furthermore, many protein complexes are widely expressed regardless of their size, and their formation is frequently controlled by very few tissue-specific proteins. Interestingly, the number of alternative transcripts is increased for widely expressed genes. This suggests that alternative splicing plays a prominent role in generating specific functional characteristics of tissues.

Transitivity Clustering is a method for the partitioning of biological data into groups of similar objects, such as genes, for instance. It provides integrated access to various functions addressing each step of a typical cluster analysis. To facilitate this, Transitivity Clustering is accessible online and offers three user-friendly interfaces: a powerful stand-alone version, a web interface, and a collection of Cytoscape plug-ins. In this paper, we describe three major workflows: (i) protein (super)family detection with Cytoscape, (ii) protein homology detection with incomplete gold standards and (iii) clustering of gene expression data. This protocol guides the user through the most important features of Transitivity Clustering and takes ∼1 h to complete.

Protein-protein interactions take place at defined binding interfaces. One protein may bind two or more proteins at different interfaces at the same time. So far it has been commonly accepted that non-overlapping interfaces allow a given protein to bind other proteins simultaneously while no collisions occur between the binding protein structures. To test this assumption, we performed a comprehensive analysis of structural protein interactions to detect potential collisions. Our results did not indicate cases of biologically relevant collisions in the Protein Data Bank of protein structures. However, we discovered a number of collisions that originate from alternative protein conformations or quaternary structures due to different experimental conditions.

Alternative splicing is an important mechanism for increasing protein diversity. However, its functional effects are largely unknown. Here, we present our new software workflow composed of the open-source application AltAnalyze and the Cytoscape plugin DomainGraph. Both programs provide an intuitive and comprehensive end-to-end solution for the analysis and visualization of alternative splicing data from Affymetrix Exon and Gene Arrays at the level of proteins, domains, microRNA binding sites, molecular interactions and pathways. Our software tools include easy-to-use graphical user interfaces, rigorous statistical methods (FIRMA, MiDAS and DABG filtering) and do not require prior knowledge of exon array analysis or programming. They provide new methods for automatic interpretation and visualization of the effects of alternative exon inclusion on protein domain composition and microRNA binding sites. These data can be visualized together with affected pathways and gene or protein interaction networks, allowing a straightforward identification of potential biological effects due to alternative splicing at different levels of granularity. Our programs are available at http://www.altanalyze.org and http://www.domaingraph.de. These websites also include extensive documentation, tutorials and sample data.

Motivation: Ever increasing amounts of biological interaction data are being accumulated worldwide, but they are currently not readily accessible to the biologist at a single site. New techniques are required for retrieving, sharing and presenting data spread over the Internet., Results: We introduce the DASMI system for the dynamic exchange, annotation and assessment of molecular interaction data. DASMI is based on the widely used Distributed Annotation System (DAS) and consists of a data exchange specification, web servers for providing the interaction data and clients for data integration and visualization. The decentralized architecture of DASMI affords the online retrieval of the most recent data from distributed sources and databases. DASMI can also be extended easily by adding new data sources and clients. We describe all DASMI components and demonstrate their use for protein and domain interactions., Availability: The DASMI tools are available at http://www.dasmi.de/ and http://ipfam.sanger.ac.uk/graph. The DAS registry and the DAS 1.53E specification is found at http://www.dasregistry.org/.

Unlabelled: Recent studies have revealed that alternative splicing plays an important role in the observed protein and interaction diversity. Special microarrays allow for measuring gene expression at the exon level and thus for studying alternative transcripts and their corresponding protein domain architecture. We have developed the Cytoscape plugin DomainGraph that enables the visualization and detailed study of domain-domain interactions forming protein interaction networks. In addition, the integration of exon expression data supports the analysis of alternative splicing events and the characterization of their effects on the protein and domain interaction network. Different expression patterns between human tissues or cells can be identified by comparing the generated domain graphs., Availability: The plugin DomainGraph and the online documentation are available at http://domaingraph.bioinf.mpi-inf.mpg.de.

Proteins and their interactions are essential for the functioning of all organisms and for understanding biological processes. Alternative splicing is an important molecular mechanism for increasing the protein diversity in eukaryotic cells. Splicing events that alter the protein structure and the domain composition can be responsible for the regulation of protein interactions and the functional diversity of different tissues. Discovering the occurrence of splicing events and studying protein isoforms have become feasible using Affymetrix Exon Arrays. Therefore, we have developed the versatile Cytoscape plugin DomainGraph that allows for the visual analysis of protein domain interaction networks and their integration with exon expression data. Protein domains affected by alternative splicing are highlighted and splicing patterns can be compared.

Objective: To investigate the differences in costs and outcomes of Down syndrome screening using data from the First and Second Trimester Evaluation of Risk (FASTER) Trial., Methods: Seven possible screening options for Down syndrome were compared: 1) Triple Screen-maternal serum alpha fetoprotein, estriol, and hCG; 2) Quad-maternal serum alpha fetoprotein, estriol, hCG, and Inhibin A; 3) Combined First-nuchal translucency, pregnancy-associated plasma protein A (PAPP-A), free beta-hCG; 4) Integrated-nuchal translucency, PAPP-A, plus Quad; 5) Serum Integrated-PAPP-A, plus Quad; 6) Stepwise Sequential-Combined First plus Quad with results given after each test; and 7) Contingent Sequential-Combined First and only those with risk between 1:30 and 1:1,500 have Quad screen. The detection rates for each option were used given a 5% false-positive rate except for Contingent Sequential with a 4.3% false-positive rate. Outcomes included societal costs of each screening regimen (screening tests, amniocentesis, management of complications, and cost of care of Down syndrome live births), Down syndrome fetuses identified and born, the associated quality-adjusted life years, and the incremental cost-utility ratio., Results: Based on the screening results derived from the 38,033 women evaluated in the FASTER trial, the Contingent Sequential screen dominated (lower costs with better outcomes) all other screens. For example, the Contingent Sequential cost 32.3 million dollars whereas the other screens ranged from 32.8 to 37.5 million dollars. The Sequential strategy led to the identification of the most Down syndrome fetuses of all of the screens, but at a higher cost per Down syndrome case diagnosed ($719,675 compared with $690,427) as compared with the Contingent Sequential. Because of the lower overall false-positive rate leading to fewer procedure-related miscarriages, the Contingent Sequential resulted in the highest quality-adjusted life years as well. The Contingent Sequential remained the most cost-effective option throughout sensitivity analysis of inputs, including amniocentesis rate after positive screen, rate of therapeutic abortion after Down syndrome diagnosis, and rate of procedure-related miscarriages., Conclusion: Analysis of this actual data from the FASTER Trial demonstrates that the Contingent Sequential test is the most cost-effective. This information can help shape future policy regarding Down syndrome screening.

Objective: This study was undertaken to determine whether obesity is associated with obstetric complications and cesarean delivery., Methods: A large prospective multicenter database was studied. Subjects were divided into 3 groups: body mass index (BMI) less than 30 (control), 30 to 34.9 (obese), and 35 or greater (morbidly obese). Groups were compared by using univariate and multivariable logistic regression analyses., Results: The study included 16,102 patients: 3,752 control, 1,473 obese, and 877 morbidly obese patients. Obesity and morbid obesity had a statistically significant association with gestational hypertension (odds ratios [ORs] 2.5 and 3.2), preeclampsia (ORs 1.6 and 3.3), gestational diabetes (ORs 2.6 and 4.0), and fetal birth weight greater than 4000 g (ORs 1.7 and 1.9) and greater than 4500 g (ORs 2.0 and 2.4). For nulliparous patients, the cesarean delivery rate was 20.7% for the control group, 33.8% for obese, and 47.4% for morbidly obese patients., Conclusion: Obesity is an independent risk factor for adverse obstetric outcome and is significantly associated with an increased cesarean delivery rate.