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The Structural Classification of Proteins-extended (SCOPe, https://scop.berkeley.edu) knowledgebase aims to provide an accurate, detailed, and comprehensive description of the structural and evolutionary relationships amongst the majority of proteins of known structure, along with resources for analyzing the protein structures and their sequences. Structures from the PDB are divided into domains and classified using a combination of manual curation and highly precise automated methods. In the current release of SCOPe, 2.08, we have developed search and display tools for analysis of genetic variants we mapped to structures classified in SCOPe. In order to improve the utility of SCOPe to automated methods such as deep learning classifiers that rely on multiple alignment of sequences of homologous proteins, we have introduced new machine-parseable annotations that indicate aberrant structures as well as domains that are distinguished by a smaller repeat unit. We also classified structures from 74 of the largest Pfam families not previously classified in SCOPe, and we improved our algorithm to remove N- and C-terminal cloning, expression and purification sequences from SCOPe domains. SCOPe 2.08-stable classifies 106 976 PDB entries (about 60% of PDB entries).

Newborn screening for severe combined immunodeficiency (SCID) has not only accelerated diagnosis and improved treatment for affected infants, but also led to identification of novel genes required for human T cell development. A male proband had SCID newborn screening showing very low T cell receptor excision circles (TRECs), a biomarker for thymic output of nascent T cells. He had persistent profound T lymphopenia, but normal numbers of B and natural killer (NK) cells. Despite an allogeneic hematopoietic stem cell transplant from his brother, he failed to develop normal T cells. Targeted resequencing excluded known SCID genes; however, whole exome sequencing (WES) of the proband and parents revealed a maternally inherited X-linked missense mutation in MED14 (MED14V763A), a component of the mediator complex. Morpholino (MO)-mediated loss of MED14 function attenuated T cell development in zebrafish. Moreover, this arrest was rescued by ectopic expression of cDNA encoding the wild type human MED14 ortholog, but not by MED14V763A , suggesting that the variant impaired MED14 function. Modeling of the equivalent mutation in mouse (Med14V769A) did not disrupt T cell development at baseline. However, repopulation of peripheral T cells upon competitive bone marrow transplantation was compromised, consistent with the incomplete T cell reconstitution experienced by the proband upon transplantation with bone marrow from his healthy male sibling, who was found to have the same MED14V763A variant. Suspecting that the variable phenotypic expression between the siblings was influenced by further mutation(s), we sought to identify genetic variants present only in the affected proband. Indeed, WES revealed a mutation in the L1 cell adhesion molecule (L1CAMQ498H); however, introducing that mutation in vivo in mice did not disrupt T cell development. Consequently, immunodeficiency in the proband may depend upon additional, unidentified gene variants.

Newborn screening for severe combined immunodeficiency (SCID) has not only accelerated diagnosis and improved treatment for affected infants, but also led to identification of novel genes required for human T cell development. A male proband had SCID newborn screening showing very low T cell receptor excision circles (TRECs), a biomarker for thymic output of nascent T cells. He had persistent profound T lymphopenia, but normal numbers of B and natural killer (NK) cells. Despite an allogeneic hematopoietic stem cell transplant from his brother, he failed to develop normal T cells. Targeted resequencing excluded known SCID genes; however, whole exome sequencing (WES) of the proband and parents revealed a maternally inherited X-linked missense mutation in MED14 (MED14V763A), a component of the mediator complex. Morpholino (MO)-mediated loss of MED14 function attenuated T cell development in zebrafish. Moreover, this arrest was rescued by ectopic expression of cDNA encoding the wild type human MED14 ortholog, but not by MED14V763A , suggesting that the variant impaired MED14 function. Modeling of the equivalent mutation in mouse (Med14V769A) did not disrupt T cell development at baseline. However, repopulation of peripheral T cells upon competitive bone marrow transplantation was compromised, consistent with the incomplete T cell reconstitution experienced by the proband upon transplantation with bone marrow from his healthy male sibling, who was found to have the same MED14V763A variant. Suspecting that the variable phenotypic expression between the siblings was influenced by further mutation(s), we sought to identify genetic variants present only in the affected proband. Indeed, WES revealed a mutation in the L1 cell adhesion molecule (L1CAMQ498H); however, introducing that mutation in vivo in mice did not disrupt T cell development. Consequently, immunodeficiency in the proband may depend upon additional, unidentified gene variants.

The Structural Classification of Proteins-extended (SCOPe, https://scop.berkeley.edu) knowledgebase aims to provide an accurate, detailed, and comprehensive description of the structural and evolutionary relationships amongst the majority of proteins of known structure, along with resources for analyzing the protein structures and their sequences. Structures from the PDB are divided into domains and classified using a combination of manual curation and highly precise automated methods. In the current release of SCOPe, 2.08, we have developed search and display tools for analysis of genetic variants we mapped to structures classified in SCOPe. In order to improve the utility of SCOPe to automated methods such as deep learning classifiers that rely on multiple alignment of sequences of homologous proteins, we have introduced new machine-parseable annotations that indicate aberrant structures as well as domains that are distinguished by a smaller repeat unit. We also classified structures from 74 of the largest Pfam families not previously classified in SCOPe, and we improved our algorithm to remove N- and C-terminal cloning, expression and purification sequences from SCOPe domains. SCOPe 2.08-stable classifies 106 976 PDB entries (about 60% of PDB entries).

In the US, newborn screening (NBS) is a unique health program that supports health equity and screens virtually every baby after birth, and has brought timely treatments to babies since the 1960's. With the decreasing cost of sequencing and the improving methods to interpret genetic data, there is an opportunity to add DNA sequencing as a screening method to facilitate the identification of babies with treatable conditions that cannot be identified in any other scalable way, including highly penetrant genetic neurodevelopmental disorders (NDD). However, the lack of effective dietary or drug-based treatments has made it nearly impossible to consider NDDs in the current NBS framework, yet it is anticipated that any treatment will be maximally effective if started early. Hence there is a critical need for large scale pilot studies to assess if and how NDDs can be effectively screened at birth, if parents desire that information, and what impact early diagnosis may have. Here we attempt to provide an overview of the recent advances in NDD treatments, explore the possible framework of setting up a pilot study to genetically screen for NDDs, highlight key technical, practical, and ethical considerations and challenges, and examine the policy and health system implications.

The major cap-binding protein eukaryotic translation initiation factor 4E (eIF4E), an ancient protein required for translation of all eukaryotic genomes, is a surprising yet potent oncogenic driver. The genetic interactions that maintain the oncogenic activity of this key translation factor remain unknown. In this study, we carry out a genome-wide CRISPRi screen wherein we identify more than 600 genetic interactions that sustain eIF4E oncogenic activity. Our data show that eIF4E controls the translation of Tfeb, a key executer of the autophagy response. This autophagy survival response is triggered by mitochondrial proteotoxic stress, which allows cancer cell survival. Our screen also reveals a functional interaction between eIF4E and a single anti-apoptotic factor, Bcl-xL, in tumor growth. Furthermore, we show that eIF4E and the exon-junction complex (EJC), which is involved in many steps of RNA metabolism, interact to control the migratory properties of cancer cells. Overall, we uncover several cancer-specific vulnerabilities that provide further resolution of the cancer translatome.

Current genetic testenhancer and narrows the diagnostic intervals for rare diseases provide a diagnosis in only a modest proportion of cases. The Full-Genome Analysis method, FGA, combines long-range assembly and whole-genome sequencing to detect small variants, structural variants with breakpoint resolution, and phasing. We built a variant prioritization pipeline and tested FGA's utility for diagnosis of rare diseases in a clinical setting. FGA identified structural variants and small variants with an overall diagnostic yield of 40% (20 of 50 cases) and 35% in exome-negative cases (8 of 23 cases), 4 of these were structural variants. FGA detected and mapped structural variants that are missed by short reads, including non-coding duplication, and phased variants across long distances of more than 180 kb. With the prioritization algorithm, longer DNA technologies could replace multiple tests for monogenic disorders and expand the range of variants detected. Our study suggests that genomes produced from technologies like FGA can improve variant detection and provide higher resolution genome maps for future application.

Genome sequencing is enabling precision medicine-tailoring treatment to the unique constellation of variants in an individual's genome. The impact of recurrent pathogenic variants is often understood, however there is a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance with functionally consequential variants and associated mechanisms. Variants of uncertain significance (VUSs) in these genes are discovered at a rate that outpaces current ability to classify them with databases of previous cases, experimental evaluation, and computational predictors. Clinicians are thus left without guidance about the significance of variants that may have actionable consequences. Computational prediction of the impact of rare genetic variation is increasingly becoming an important capability. In this paper, we review the technical and ethical challenges of interpreting the function of rare variants in two settings: inborn errors of metabolism in newborns and pharmacogenomics. We propose a framework for a genomic learning healthcare system with an initial focus on early-onset treatable disease in newborns and actionable pharmacogenomics. We argue that (1) a genomic learning healthcare system must allow for continuous collection and assessment of rare variants, (2) emerging machine learning methods will enable algorithms to predict the clinical impact of rare variants on protein function, and (3) ethical considerations must inform the construction and deployment of all rare-variation triage strategies, particularly with respect to health disparities arising from unbalanced ancestry representation.

Current genetic testenhancer and narrows the diagnostic intervals for rare diseases provide a diagnosis in only a modest proportion of cases. The Full-Genome Analysis method, FGA, combines long-range assembly and whole-genome sequencing to detect small variants, structural variants with breakpoint resolution, and phasing. We built a variant prioritization pipeline and tested FGA's utility for diagnosis of rare diseases in a clinical setting. FGA identified structural variants and small variants with an overall diagnostic yield of 40% (20 of 50 cases) and 35% in exome-negative cases (8 of 23 cases), 4 of these were structural variants. FGA detected and mapped structural variants that are missed by short reads, including non-coding duplication, and phased variants across long distances of more than 180 kb. With the prioritization algorithm, longer DNA technologies could replace multiple tests for monogenic disorders and expand the range of variants detected. Our study suggests that genomes produced from technologies like FGA can improve variant detection and provide higher resolution genome maps for future application.

Genome sequencing is enabling precision medicine-tailoring treatment to the unique constellation of variants in an individual's genome. The impact of recurrent pathogenic variants is often understood, however there is a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance with functionally consequential variants and associated mechanisms. Variants of uncertain significance (VUSs) in these genes are discovered at a rate that outpaces current ability to classify them with databases of previous cases, experimental evaluation, and computational predictors. Clinicians are thus left without guidance about the significance of variants that may have actionable consequences. Computational prediction of the impact of rare genetic variation is increasingly becoming an important capability. In this paper, we review the technical and ethical challenges of interpreting the function of rare variants in two settings: inborn errors of metabolism in newborns and pharmacogenomics. We propose a framework for a genomic learning healthcare system with an initial focus on early-onset treatable disease in newborns and actionable pharmacogenomics. We argue that (1) a genomic learning healthcare system must allow for continuous collection and assessment of rare variants, (2) emerging machine learning methods will enable algorithms to predict the clinical impact of rare variants on protein function, and (3) ethical considerations must inform the construction and deployment of all rare-variation triage strategies, particularly with respect to health disparities arising from unbalanced ancestry representation.

The major cap-binding protein eukaryotic translation initiation factor 4E (eIF4E), an ancient protein required for translation of all eukaryotic genomes, is a surprising yet potent oncogenic driver. The genetic interactions that maintain the oncogenic activity of this key translation factor remain unknown. In this study, we carry out a genome-wide CRISPRi screen wherein we identify more than 600 genetic interactions that sustain eIF4E oncogenic activity. Our data show that eIF4E controls the translation of Tfeb, a key executer of the autophagy response. This autophagy survival response is triggered by mitochondrial proteotoxic stress, which allows cancer cell survival. Our screen also reveals a functional interaction between eIF4E and a single anti-apoptotic factor, Bcl-xL, in tumor growth. Furthermore, we show that eIF4E and the exon-junction complex (EJC), which is involved in many steps of RNA metabolism, interact to control the migratory properties of cancer cells. Overall, we uncover several cancer-specific vulnerabilities that provide further resolution of the cancer translatome.

Current genetic testenhancer and narrows the diagnostic intervals for rare diseases provide a diagnosis in only a modest proportion of cases. The Full-Genome Analysis method, FGA, combines long-range assembly and whole-genome sequencing to detect small variants, structural variants with breakpoint resolution, and phasing. We built a variant prioritization pipeline and tested FGA's utility for diagnosis of rare diseases in a clinical setting. FGA identified structural variants and small variants with an overall diagnostic yield of 40% (20 of 50 cases) and 35% in exome-negative cases (8 of 23 cases), 4 of these were structural variants. FGA detected and mapped structural variants that are missed by short reads, including non-coding duplication, and phased variants across long distances of more than 180 kb. With the prioritization algorithm, longer DNA technologies could replace multiple tests for monogenic disorders and expand the range of variants detected. Our study suggests that genomes produced from technologies like FGA can improve variant detection and provide higher resolution genome maps for future application.

Public health newborn screening (NBS) programs provide population-scale ascertainment of rare, treatable conditions that require urgent intervention. Tandem mass spectrometry (MS/MS) is currently used to screen newborns for a panel of rare inborn errors of metabolism (IEMs)1-4. The NBSeq project evaluated whole-exome sequencing (WES) as an innovative methodology for NBS. We obtained archived residual dried blood spots and data for nearly all IEM cases from the 4.5 million infants born in California between mid-2005 and 2013 and from some infants who screened positive by MS/MS, but were unaffected upon follow-up testing. WES had an overall sensitivity of 88% and specificity of 98.4%, compared to 99.0% and 99.8%, respectively for MS/MS, although effectiveness varied among individual IEMs. Thus, WES alone was insufficiently sensitive or specific to be a primary screen for most NBS IEMs. However, as a secondary test for infants with abnormal MS/MS screens, WES could reduce false-positive results, facilitate timely case resolution and in some instances even suggest more appropriate or specific diagnosis than that initially obtained. This study represents the largest, to date, sequencing effort of an entire population of IEM-affected cases, allowing unbiased assessment of current capabilities of WES as a tool for population screening.

The CAGI-5 pericentriolar material 1 (PCM1) challenge aimed to predict the effect of 38 transgenic human missense mutations in the PCM1 protein implicated in schizophrenia. Participants were provided with 16 benign variants (negative controls), 10 hypomorphic, and 12 loss of function variants. Six groups participated and were asked to predict the probability of effect and standard deviation associated to each mutation. Here, we present the challenge assessment. Prediction performance was evaluated using different measures to conclude in a final ranking which highlights the strengths and weaknesses of each group. The results show a great variety of predictions where some methods performed significantly better than others. Benign variants played an important role as negative controls, highlighting predictors biased to identify disease phenotypes. The best predictor, Bromberg lab, used a neural-network-based method able to discriminate between neutral and non-neutral single nucleotide polymorphisms. The CAGI-5 PCM1 challenge allowed us to evaluate the state of the art techniques for interpreting the effect of novel variants for a difficult target protein.

The SCOPe (Structural Classification of Proteins-extended, https://scop.berkeley.edu) database hierarchically classifies domains from the majority of proteins of known structure according to their structural and evolutionary relationships. SCOPe also incorporates and updates the ASTRAL compendium, which provides multiple databases and tools to aid in the analysis of the sequences and structures of proteins classified in SCOPe. Protein structures are classified using a combination of manual curation and highly precise automated methods. In the current release of SCOPe, 2.07, we have focused our manual curation efforts on larger protein structures, including the spliceosome, proteasome and RNA polymerase I, as well as many other Pfam families that had not previously been classified. Domains from these large protein complexes are distinctive in several ways: novel non-globular folds are more common, and domains from previously observed protein families often have N- or C-terminal extensions that were disordered or not present in previous structures. The current monthly release update, SCOPe 2.07-2018-10-18, classifies 90 992 PDB entries (about two thirds of PDB entries).

Background: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results: Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-Term memory. Conclusion: We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens. © 2019 The Author(s).

The SCOPe (Structural Classification of Proteins-extended, https://scop.berkeley.edu) database hierarchically classifies domains from the majority of proteins of known structure according to their structural and evolutionary relationships. SCOPe also incorporates and updates the ASTRAL compendium, which provides multiple databases and tools to aid in the analysis of the sequences and structures of proteins classified in SCOPe. Protein structures are classified using a combination of manual curation and highly precise automated methods. In the current release of SCOPe, 2.07, we have focused our manual curation efforts on larger protein structures, including the spliceosome, proteasome and RNA polymerase I, as well as many other Pfam families that had not previously been classified. Domains from these large protein complexes are distinctive in several ways: novel non-globular folds are more common, and domains from previously observed protein families often have N- or C-terminal extensions that were disordered or not present in previous structures. The current monthly release update, SCOPe 2.07-2018-10-18, classifies 90 992 PDB entries (about two thirds of PDB entries).

The CAGI-5 pericentriolar material 1 (PCM1) challenge aimed to predict the effect of 38 transgenic human missense mutations in the PCM1 protein implicated in schizophrenia. Participants were provided with 16 benign variants (negative controls), 10 hypomorphic, and 12 loss of function variants. Six groups participated and were asked to predict the probability of effect and standard deviation associated to each mutation. Here, we present the challenge assessment. Prediction performance was evaluated using different measures to conclude in a final ranking which highlights the strengths and weaknesses of each group. The results show a great variety of predictions where some methods performed significantly better than others. Benign variants played an important role as negative controls, highlighting predictors biased to identify disease phenotypes. The best predictor, Bromberg lab, used a neural-network-based method able to discriminate between neutral and non-neutral single nucleotide polymorphisms. The CAGI-5 PCM1 challenge allowed us to evaluate the state of the art techniques for interpreting the effect of novel variants for a difficult target protein.

Accurate prediction of the impact of genomic variation on phenotype is a major goal of computational biology and an important contributor to personalized medicine. Computational predictions can lead to a better understanding of the mechanisms underlying genetic diseases, including cancer, but their adoption requires thorough and unbiased assessment. Cystathionine-beta-synthase (CBS) is an enzyme that catalyzes the first step of the transsulfuration pathway, from homocysteine to cystathionine, and in which variations are associated with human hyperhomocysteinemia and homocystinuria. We have created a computational challenge under the CAGI framework to evaluate how well different methods can predict the phenotypic effect(s) of CBS single amino acid substitutions using a blinded experimental data set. CAGI participants were asked to predict yeast growth based on the identity of the mutations. The performance of the methods was evaluated using several metrics. The CBS challenge highlighted the difficulty of predicting the phenotype of an ex vivo system in a model organism when classification models were trained on human disease data. We also discuss the variations in difficulty of prediction for known benign and deleterious variants, as well as identify methodological and experimental constraints with lessons to be learned for future challenges.

Background: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results: Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-Term memory. Conclusion: We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens. © 2019 The Author(s).

Paroxysmal kinesigenic dyskinesia (PKD) is a rare neurological disorder characterized by recurrent attacks of dyskinetic movements without alteration of consciousness that are often triggered by the initiation of voluntary movements. Whole-exome sequencing has revealed a cluster of pathogenic variants in PRRT2 (proline-rich transmembrane protein), a gene with a function in synaptic regulation that remains poorly understood. Here, we report the discovery of a novel PRRT2 pathogenic variant inherited in an autosomal dominant pattern in a family with PKD and benign familial infantile seizures (BFIS). After targeted Sanger sequencing did not identify the presence of previously described PRRT2 pathogenic variants, we carried out whole-exome sequencing in the proband and her affected paternal grandfather. This led to the discovery of a novel PRRT2 variant, NM_001256442:exon3:c.C959T/NP_660282.2:p.A320V, altering an evolutionarily conserved alanine at the amino acid position 320 located in the M2 transmembrane region. Sanger sequencing further confirmed the presence of this variant in four affected family members (paternal grandfather, father, brother, and proband) and its absence in two unaffected ones (paternal grandmother and mother). This newly found variant further reinforces the importance of PRRT2 in PKD, BFIS, and possibly other movement disorders. Future functional studies using animal models and human pluripotent stem cell models will provide new insights into the role of PRRT2 and the significance of this variant in regulating neural development and/or function.

Paroxysmal kinesigenic dyskinesia (PKD) is a rare neurological disorder characterized by recurrent attacks of dyskinetic movements without alteration of consciousness that are often triggered by the initiation of voluntary movements. Whole-exome sequencing has revealed a cluster of pathogenic variants in PRRT2 (proline-rich transmembrane protein), a gene with a function in synaptic regulation that remains poorly understood. Here, we report the discovery of a novel PRRT2 pathogenic variant inherited in an autosomal dominant pattern in a family with PKD and benign familial infantile seizures (BFIS). After targeted Sanger sequencing did not identify the presence of previously described PRRT2 pathogenic variants, we carried out whole-exome sequencing in the proband and her affected paternal grandfather. This led to the discovery of a novel PRRT2 variant, NM_001256442:exon3:c.C959T/NP_660282.2:p.A320V, altering an evolutionarily conserved alanine at the amino acid position 320 located in the M2 transmembrane region. Sanger sequencing further confirmed the presence of this variant in four affected family members (paternal grandfather, father, brother, and proband) and its absence in two unaffected ones (paternal grandmother and mother). This newly found variant further reinforces the importance of PRRT2 in PKD, BFIS, and possibly other movement disorders. Future functional studies using animal models and human pluripotent stem cell models will provide new insights into the role of PRRT2 and the significance of this variant in regulating neural development and/or function.

The Global Alliance for Genomics and Health (GA4GH) proposes a data access policy model-"registered access"-to increase and improve access to data requiring an agreement to basic terms and conditions, such as the use of DNA sequence and health data in research. A registered access policy would enable a range of categories of users to gain access, starting with researchers and clinical care professionals. It would also facilitate general use and reuse of data but within the bounds of consent restrictions and other ethical obligations. In piloting registered access with the Scientific Demonstration data sharing projects of GA4GH, we provide additional ethics, policy and technical guidance to facilitate the implementation of this access model in an international setting.

The rapid development of genomic sequencing technologies has decreased the cost of genetic analysis to the extent that it seems plausible that genome-scale sequencing could have widespread availability in pediatric care. Genomic sequencing provides a powerful diagnostic modality for patients who manifest symptoms of monogenic disease and an opportunity to detect health conditions before their development. However, many technical, clinical, ethical, and societal challenges should be addressed before such technology is widely deployed in pediatric practice. This article provides an overview of the Newborn Sequencing in Genomic Medicine and Public Health Consortium, which is investigating the application of genome-scale sequencing in newborns for both diagnosis and screening.

SCOPe (Structural Classification of Proteins-extended, http://scop.berkeley.edu) is a database of relationships between protein structures that extends the Structural Classification of Proteins (SCOP) database. SCOP is an expert-curated ordering of domains from the majority of proteins of known structure in a hierarchy according to structural and evolutionary relationships. SCOPe classifies the majority of protein structures released since SCOP development concluded in 2009, using a combination of manual curation and highly precise automated tools, aiming to have the same accuracy as fully hand-curated SCOP releases. SCOPe also incorporates and updates the ASTRAL compendium, which provides several databases and tools to aid in the analysis of the sequences and structures of proteins classified in SCOPe. SCOPe continues high-quality manual classification of new superfamilies, a key feature of SCOP. Artifacts such as expression tags are now separated into their own class, in order to distinguish them from the homology-based annotations in the remainder of the SCOPe hierarchy. SCOPe 2.06 contains 77,439 Protein Data Bank entries, double the 38,221 structures classified in SCOP.

The rapid development of genomic sequencing technologies has decreased the cost of genetic analysis to the extent that it seems plausible that genome-scale sequencing could have widespread availability in pediatric care. Genomic sequencing provides a powerful diagnostic modality for patients who manifest symptoms of monogenic disease and an opportunity to detect health conditions before their development. However, many technical, clinical, ethical, and societal challenges should be addressed before such technology is widely deployed in pediatric practice. This article provides an overview of the Newborn Sequencing in Genomic Medicine and Public Health Consortium, which is investigating the application of genome-scale sequencing in newborns for both diagnosis and screening.

SCOPe (Structural Classification of Proteins-extended, http://scop.berkeley.edu) is a database of relationships between protein structures that extends the Structural Classification of Proteins (SCOP) database. SCOP is an expert-curated ordering of domains from the majority of proteins of known structure in a hierarchy according to structural and evolutionary relationships. SCOPe classifies the majority of protein structures released since SCOP development concluded in 2009, using a combination of manual curation and highly precise automated tools, aiming to have the same accuracy as fully hand-curated SCOP releases. SCOPe also incorporates and updates the ASTRAL compendium, which provides several databases and tools to aid in the analysis of the sequences and structures of proteins classified in SCOPe. SCOPe continues high-quality manual classification of new superfamilies, a key feature of SCOP. Artifacts such as expression tags are now separated into their own class, in order to distinguish them from the homology-based annotations in the remainder of the SCOPe hierarchy. SCOPe 2.06 contains 77,439 Protein Data Bank entries, double the 38,221 structures classified in SCOP.

The advent of next-generation sequencing has dramatically decreased the cost for whole-genome sequencing and increased the viability for its application in research and clinical care. The Personal Genome Project (PGP) provides unrestricted access to genomes of individuals and their associated phenotypes. This resource enabled the Critical Assessment of Genome Interpretation (CAGI) to create a community challenge to assess the bioinformatics community's ability to predict traits from whole genomes. In the CAGI PGP challenge, researchers were asked to predict whether an individual had a particular trait or profile based on their whole genome. Several approaches were used to assess submissions, including ROC AUC (area under receiver operating characteristic curve), probability rankings, the number of correct predictions, and statistical significance simulations. Overall, we found that prediction of individual traits is difficult, relying on a strong knowledge of trait frequency within the general population, whereas matching genomes to trait profiles relies heavily upon a small number of common traits including ancestry, blood type, and eye color. When a rare genetic disorder is present, profiles can be matched when one or more pathogenic variants are identified. Prediction accuracy has improved substantially over the last 6 years due to improved methodology and a better understanding of features.

Correct phenotypic interpretation of variants of unknown significance for cancer-associated genes is a diagnostic challenge as genetic screenings gain in popularity in the next-generation sequencing era. The Critical Assessment of Genome Interpretation (CAGI) experiment aims to test and define the state of the art of genotype-phenotype interpretation. Here, we present the assessment of the CAGI p16INK4a challenge. Participants were asked to predict the effect on cellular proliferation of 10 variants for the p16INK4a tumor suppressor, a cyclin-dependent kinase inhibitor encoded by the CDKN2A gene. Twenty-two pathogenicity predictors were assessed with a variety of accuracy measures for reliability in a medical context. Different assessment measures were combined in an overall ranking to provide more robust results. The R scripts used for assessment are publicly available from a GitHub repository for future use in similar assessment exercises. Despite a limited test-set size, our findings show a variety of results, with some methods performing significantly better. Methods combining different strategies frequently outperform simpler approaches. The best predictor, Yang&Zhou lab, uses a machine learning method combining an empirical energy function measuring protein stability with an evolutionary conservation term. The p16INK4a challenge highlights how subtle structural effects can neutralize otherwise deleterious variants.

Numerous affinity purification-mass spectrometry (AP-MS) and yeast two-hybrid screens have each defined thousands of pairwise protein-protein interactions (PPIs), most of which are between functionally unrelated proteins. The accuracy of these networks, however, is under debate. Here, we present an AP-MS survey of the bacterium Desulfovibrio vulgaris together with a critical reanalysis of nine published bacterial yeast two-hybrid and AP-MS screens. We have identified 459 high confidence PPIs from D. vulgaris and 391 from Escherichia coli Compared with the nine published interactomes, our two networks are smaller, are much less highly connected, and have significantly lower false discovery rates. In addition, our interactomes are much more enriched in protein pairs that are encoded in the same operon, have similar functions, and are reproducibly detected in other physical interaction assays than the pairs reported in prior studies. Our work establishes more stringent benchmarks for the properties of protein interactomes and suggests that bona fide PPIs much more frequently involve protein partners that are annotated with similar functions or that can be validated in independent assays than earlier studies suggested.

Identifying protein-protein interactions (PPIs) at an acceptable false discovery rate (FDR) is challenging. Previously we identified several hundred PPIs from affinity purification - mass spectrometry (AP-MS) data for the bacteria Escherichia coli and Desulfovibrio vulgaris These two interactomes have lower FDRs than any of the nine interactomes proposed previously for bacteria and are more enriched in PPIs validated by other data than the nine earlier interactomes. To more thoroughly determine the accuracy of ours or other interactomes and to discover further PPIs de novo, here we present a quantitative tagless method that employs iTRAQ MS to measure the copurification of endogenous proteins through orthogonal chromatography steps. 5273 fractions from a four-step fractionation of a D. vulgaris protein extract were assayed, resulting in the detection of 1242 proteins. Protein partners from our D. vulgaris and E. coli AP-MS interactomes copurify as frequently as pairs belonging to three benchmark data sets of well-characterized PPIs. In contrast, the protein pairs from the nine other bacterial interactomes copurify two- to 20-fold less often. We also identify 200 high confidence D. vulgaris PPIs based on tagless copurification and colocalization in the genome. These PPIs are as strongly validated by other data as our AP-MS interactomes and overlap with our AP-MS interactome for D.vulgaris within 3% of expectation, once FDRs and false negative rates are taken into account. Finally, we reanalyzed data from two quantitative tagless screens of human cell extracts. We estimate that the novel PPIs reported in these studies have an FDR of at least 85% and find that less than 7% of the novel PPIs identified in each screen overlap. Our results establish that a quantitative tagless method can be used to validate and identify PPIs, but that such data must be analyzed carefully to minimize the FDR.

The nearly 50,000 known Nudix proteins have a diverse array of functions, of which the most extensively studied is the catalyzed hydrolysis of aberrant nucleotide triphosphates. The functions of 171 Nudix proteins have been characterized to some degree, although physiological relevance of the assayed activities has not always been conclusively demonstrated. We investigated substrate specificity for eight structurally characterized Nudix proteins, whose functions were unknown. These proteins were screened for hydrolase activity against a 74-compound library of known Nudix enzyme substrates. We found substrates for four enzymes with kcat /Km values >10,000 M-1 s-1 : Q92EH0_LISIN of Listeria innocua serovar 6a against ADP-ribose, Q5LBB1_BACFN of Bacillus fragilis against 5-Me-CTP, and Q0TTC5_CLOP1 and Q0TS82_CLOP1 of Clostridium perfringens against 8-oxo-dATP and 3'-dGTP, respectively. To ascertain whether these identified substrates were physiologically relevant, we surveyed all reported Nudix hydrolytic activities against NTPs. Twenty-two Nudix enzymes are reported to have activity against canonical NTPs. With a single exception, we find that the reported kcat /Km values exhibited against these canonical substrates are well under 105 M-1 s-1 . By contrast, several Nudix enzymes show much larger kcat /Km values (in the range of 105 to >107 M-1 s-1 ) against noncanonical NTPs. We therefore conclude that hydrolytic activities exhibited by these enzymes against canonical NTPs are not likely their physiological function, but rather the result of unavoidable collateral damage occasioned by the enzymes' inability to distinguish completely between similar substrate structures. Proteins 2016; 84:1810-1822. © 2016 Wiley Periodicals, Inc.

The nearly 50,000 known Nudix proteins have a diverse array of functions, of which the most extensively studied is the catalyzed hydrolysis of aberrant nucleotide triphosphates. The functions of 171 Nudix proteins have been characterized to some degree, although physiological relevance of the assayed activities has not always been conclusively demonstrated. We investigated substrate specificity for eight structurally characterized Nudix proteins, whose functions were unknown. These proteins were screened for hydrolase activity against a 74-compound library of known Nudix enzyme substrates. We found substrates for four enzymes with kcat /Km values >10,000 M-1  s-1 : Q92EH0_LISIN of Listeria innocua serovar 6a against ADP-ribose, Q5LBB1_BACFN of Bacillus fragilis against 5-Me-CTP, and Q0TTC5_CLOP1 and Q0TS82_CLOP1 of Clostridium perfringens against 8-oxo-dATP and 3'-dGTP, respectively. To ascertain whether these identified substrates were physiologically relevant, we surveyed all reported Nudix hydrolytic activities against NTPs. Twenty-two Nudix enzymes are reported to have activity against canonical NTPs. With a single exception, we find that the reported kcat /Km values exhibited against these canonical substrates are well under 105 M-1  s-1 . By contrast, several Nudix enzymes show much larger kcat /Km values (in the range of 105 to >107 M-1  s-1 ) against noncanonical NTPs. We therefore conclude that hydrolytic activities exhibited by these enzymes against canonical NTPs are not likely their physiological function, but rather the result of unavoidable collateral damage occasioned by the enzymes' inability to distinguish completely between similar substrate structures. Proteins 2016; 84:1810-1822. © 2016 Wiley Periodicals, Inc.

Numerous affinity purification-mass spectrometry (AP-MS) and yeast two-hybrid screens have each defined thousands of pairwise protein-protein interactions (PPIs), most of which are between functionally unrelated proteins. The accuracy of these networks, however, is under debate. Here, we present an AP-MS survey of the bacterium Desulfovibrio vulgaris together with a critical reanalysis of nine published bacterial yeast two-hybrid and AP-MS screens. We have identified 459 high confidence PPIs from D. vulgaris and 391 from Escherichia coli Compared with the nine published interactomes, our two networks are smaller, are much less highly connected, and have significantly lower false discovery rates. In addition, our interactomes are much more enriched in protein pairs that are encoded in the same operon, have similar functions, and are reproducibly detected in other physical interaction assays than the pairs reported in prior studies. Our work establishes more stringent benchmarks for the properties of protein interactomes and suggests that bona fide PPIs much more frequently involve protein partners that are annotated with similar functions or that can be validated in independent assays than earlier studies suggested.

Identifying protein-protein interactions (PPIs) at an acceptable false discovery rate (FDR) is challenging. Previously we identified several hundred PPIs from affinity purification - mass spectrometry (AP-MS) data for the bacteria Escherichia coli and Desulfovibrio vulgaris These two interactomes have lower FDRs than any of the nine interactomes proposed previously for bacteria and are more enriched in PPIs validated by other data than the nine earlier interactomes. To more thoroughly determine the accuracy of ours or other interactomes and to discover further PPIs de novo, here we present a quantitative tagless method that employs iTRAQ MS to measure the copurification of endogenous proteins through orthogonal chromatography steps. 5273 fractions from a four-step fractionation of a D. vulgaris protein extract were assayed, resulting in the detection of 1242 proteins. Protein partners from our D. vulgaris and E. coli AP-MS interactomes copurify as frequently as pairs belonging to three benchmark data sets of well-characterized PPIs. In contrast, the protein pairs from the nine other bacterial interactomes copurify two- to 20-fold less often. We also identify 200 high confidence D. vulgaris PPIs based on tagless copurification and colocalization in the genome. These PPIs are as strongly validated by other data as our AP-MS interactomes and overlap with our AP-MS interactome for D.vulgaris within 3% of expectation, once FDRs and false negative rates are taken into account. Finally, we reanalyzed data from two quantitative tagless screens of human cell extracts. We estimate that the novel PPIs reported in these studies have an FDR of at least 85% and find that less than 7% of the novel PPIs identified in each screen overlap. Our results establish that a quantitative tagless method can be used to validate and identify PPIs, but that such data must be analyzed carefully to minimize the FDR.

BackgroundSevere combined immunodeficiency (SCID) is characterized by arrested T-lymphocyte production and by B-lymphocyte dysfunction, which result in life-threatening infections. Early diagnosis of SCID through population-based screening of newborns can aid clinical management and help improve outcomes; it also permits the identification of previously unknown factors that are essential for lymphocyte development in humans.MethodsSCID was detected in a newborn before the onset of infections by means of screening of T-cell-receptor excision circles, a biomarker for thymic output. On confirmation of the condition, the affected infant was treated with allogeneic hematopoietic stem-cell transplantation. Exome sequencing in the patient and parents was followed by functional analysis of a prioritized candidate gene with the use of human hematopoietic stem cells and zebrafish embryos.ResultsThe infant had "leaky" SCID (i.e., a form of SCID in which a minimal degree of immune function is preserved), as well as craniofacial and dermal abnormalities and the absence of a corpus callosum; his immune deficit was fully corrected by hematopoietic stem-cell transplantation. Exome sequencing revealed a heterozygous de novo missense mutation, p.N441K, in BCL11B. The resulting BCL11B protein had dominant negative activity, which abrogated the ability of wild-type BCL11B to bind DNA, thereby arresting development of the T-cell lineage and disrupting hematopoietic stem-cell migration; this revealed a previously unknown function of BCL11B. The patient's abnormalities, when recapitulated in bcl11ba-deficient zebrafish, were reversed by ectopic expression of functionally intact human BCL11B but not mutant human BCL11B.ConclusionsNewborn screening facilitated the identification and treatment of a previously unknown cause of human SCID. Coupling exome sequencing with an evaluation of candidate genes in human hematopoietic stem cells and in zebrafish revealed that a constitutional BCL11B mut

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.

PurposeSevere combined immunodeficiency (SCID) encompasses a group of disorders characterized by reduced or absent T-cell number and function and identified by newborn screening utilizing T-cell receptor excision circles (TRECs). This screening has also identified infants with T lymphopenia who lack mutations in typical SCID genes. We report an infant with low TRECs and non-SCID T lymphopenia, who proved upon whole exome sequencing to have Nijmegen breakage syndrome (NBS).MethodsExome sequencing of DNA from the infant and his parents was performed. Genomic analysis revealed deleterious variants in the NBN gene. Confirmatory testing included Sanger sequencing and immunoblotting and radiosensitivity testing of patient lymphocytes.ResultsTwo novel nonsense mutations in NBN were identified in genomic DNA from the family. Immunoblotting showed absence of nibrin protein. A colony survival assay demonstrated radiosensitivity comparable to patients with ataxia telangiectasia.ConclusionsAlthough TREC screening was developed to identify newborns with SCID, it has also identified T lymphopenic disorders that may not otherwise be diagnosed until later in life. Timely identification of an infant with T lymphopenia allowed for prompt pursuit of underlying etiology, making possible a diagnosis of NBS, genetic counseling, and early intervention to minimize complications.

The Structural Classification of Proteins (SCOP) and Class, Architecture, Topology, Homology (CATH) databases have been valuable resources for protein structure classification for over 20 years. Development of SCOP (version 1) concluded in June 2009 with SCOP 1.75. The SCOPe (SCOP-extended) database offers continued development of the classic SCOP hierarchy, adding over 33,000 structures. We have attempted to assess the impact of these two decade old resources and guide future development. To this end, we surveyed recent articles to learn how structure classification data are used. Of 571 articles published in 2012-2013 that cite SCOP, 439 actually use data from the resource. We found that the type of use was fairly evenly distributed among four top categories: A) study protein structure or evolution (27% of articles), B) train and/or benchmark algorithms (28% of articles), C) augment non-SCOP datasets with SCOP classification (21% of articles), and D) examine the classification of one protein/a small set of proteins (22% of articles). Most articles described computational research, although 11% described purely experimental research, and a further 9% included both. We examined how CATH and SCOP were used in 158 articles that cited both databases: while some studies used only one dataset, the majority used data from both resources. Protein structure classification remains highly relevant for a diverse range of problems and settings.

Alternative splicing is regulated by RNA binding proteins (RBPs) that recognize pre-mRNA sequence elements and activate or repress adjacent exons. Here, we used RNA interference and RNA-seq to identify splicing events regulated by 56 Drosophila proteins, some previously unknown to regulate splicing. Nearly all proteins affected alternative first exons, suggesting that RBPs play important roles in first exon choice. Half of the splicing events were regulated by multiple proteins, demonstrating extensive combinatorial regulation. We observed that SR and hnRNP proteins tend to act coordinately with each other, not antagonistically. We also identified a cross-regulatory network where splicing regulators affected the splicing of pre-mRNAs encoding other splicing regulators. This large-scale study substantially enhances our understanding of recent models of splicing regulation and provides a resource of thousands of exons that are regulated by 56 diverse RBPs.

Genome-wide association studies have identified over 70 single-nucleotide polymorphisms (SNPs) associated with breast cancer. A subset of these SNPs are associated with quantitative expression of nearby genes, but the functional effects of the majority remain unknown. We hypothesized that some risk SNPs may regulate alternative splicing. Using RNA-sequencing data from breast tumors and germline genotypes from The Cancer Genome Atlas, we tested the association between each risk SNP genotype and exon-, exon-exon junction- or transcript-specific expression of nearby genes. Six SNPs were associated with differential transcript expression of seven nearby genes at FDR < 0.05 (BABAM1, DCLRE1B/PHTF1, PEX14, RAD51L1, SRGAP2D and STXBP4). We next developed a Bayesian approach to evaluate, for each SNP, the overlap between the signal of association with breast cancer and the signal of association with alternative splicing. At one locus (SRGAP2D), this method eliminated the possibility that the breast cancer risk and the alternate splicing event were due to the same causal SNP. Lastly, at two loci, we identified the likely causal SNP for the alternative splicing event, and at one, functionally validated the effect of that SNP on alternative splicing using a minigene reporter assay. Our results suggest that the regulation of differential transcript isoform expression is the functional mechanism of some breast cancer risk SNPs and that we can use these associations to identify causal SNPs, target genes and the specific transcripts that may mediate breast cancer risk.

We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access to precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. The SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.

PurposeSevere combined immunodeficiency (SCID) encompasses a group of disorders characterized by reduced or absent T-cell number and function and identified by newborn screening utilizing T-cell receptor excision circles (TRECs). This screening has also identified infants with T lymphopenia who lack mutations in typical SCID genes. We report an infant with low TRECs and non-SCID T lymphopenia, who proved upon whole exome sequencing to have Nijmegen breakage syndrome (NBS).MethodsExome sequencing of DNA from the infant and his parents was performed. Genomic analysis revealed deleterious variants in the NBN gene. Confirmatory testing included Sanger sequencing and immunoblotting and radiosensitivity testing of patient lymphocytes.ResultsTwo novel nonsense mutations in NBN were identified in genomic DNA from the family. Immunoblotting showed absence of nibrin protein. A colony survival assay demonstrated radiosensitivity comparable to patients with ataxia telangiectasia.ConclusionsAlthough TREC screening was developed to identify newborns with SCID, it has also identified T lymphopenic disorders that may not otherwise be diagnosed until later in life. Timely identification of an infant with T lymphopenia allowed for prompt pursuit of underlying etiology, making possible a diagnosis of NBS, genetic counseling, and early intervention to minimize complications.

The Structural Classification of Proteins (SCOP) and Class, Architecture, Topology, Homology (CATH) databases have been valuable resources for protein structure classification for over 20 years. Development of SCOP (version 1) concluded in June 2009 with SCOP 1.75. The SCOPe (SCOP-extended) database offers continued development of the classic SCOP hierarchy, adding over 33,000 structures. We have attempted to assess the impact of these two decade old resources and guide future development. To this end, we surveyed recent articles to learn how structure classification data are used. Of 571 articles published in 2012-2013 that cite SCOP, 439 actually use data from the resource. We found that the type of use was fairly evenly distributed among four top categories: A) study protein structure or evolution (27% of articles), B) train and/or benchmark algorithms (28% of articles), C) augment non-SCOP datasets with SCOP classification (21% of articles), and D) examine the classification of one protein/a small set of proteins (22% of articles). Most articles described computational research, although 11% described purely experimental research, and a further 9% included both. We examined how CATH and SCOP were used in 158 articles that cited both databases: while some studies used only one dataset, the majority used data from both resources. Protein structure classification remains highly relevant for a diverse range of problems and settings.

Ultraconserved elements, unusually long regions of perfect sequence identity, are found in genes encoding numerous RNA-binding proteins including arginine-serine rich (SR) splicing factors. Expression of these genes is regulated via alternative splicing of the ultraconserved regions to yield mRNAs that are degraded by nonsense-mediated mRNA decay (NMD), a process termed unproductive splicing (Lareau et al. 2007; Ni et al. 2007). As all human SR genes are affected by alternative splicing and NMD, one might expect this regulation to have originated in an early SR gene and persisted as duplications expanded the SR family. But in fact, unproductive splicing of most human SR genes arose independently (Lareau et al. 2007). This paradox led us to investigate the origin and proliferation of unproductive splicing in SR genes. We demonstrate that unproductive splicing of the splicing factor SRSF5 (SRp40) is conserved among all animals and even observed in fungi; this is a rare example of alternative splicing conserved between kingdoms, yet its effect is to trigger mRNA degradation. As the gene duplicated, the ancient unproductive splicing was lost in paralogs, and distinct unproductive splicing evolved rapidly and repeatedly to take its place. SR genes have consistently employed unproductive splicing, and while it is exceptionally conserved in some of these genes, turnover in specific events among paralogs shows flexible means to the same regulatory end.