Improvement of variant calling in next-generation sequence data requires a comprehensive, genome-wide catalog of highconfidence variants called in a set of genomes for use as a benchmark. We generated deep, whole-genome sequence data of 17 individuals in a three-generation pedigree and called variants in each genome using a range of currently available algorithms. We used haplotype transmission information to create a phased "Platinum" variant catalog of 4.7 million singlenucleotide variants (SNVs) plus 0.7 million small (1-50 bp) insertions and deletions (indels) that are consistent with the pattern of inheritance in the parents and 11 children of this pedigree. Platinum genotypes are highly concordant with the current catalog of the National Institute of Standards and Technology for both SNVs (>99.99%) and indels (99.92%) and add a validated truth catalog that has 26% more SNVs and 45% more indels. Analysis of 334,652 SNVs that were consistent between informatics pipelines yet inconsistent with haplotype transmission ("nonplatinum") revealed that the majority of these variants are de novo and cell-line mutations or reside within previously unidentified duplications and deletions. The reference materials from this study are a resource for objective assessment of the accuracy of variant calls throughout genomes.

Methylation of CpG islands (CGIs) plays an important role in gene silencing. For genome-wide methylation analysis of CGIs in female white blood cells and in sperm, we used four restriction enzymes and a size selection step to prepare DNA libraries enriched with CGIs. The DNA libraries were treated with sodium bisulfite and subjected to a modified 454/Roche Genome Sequencer protocol. We obtained 163 034 and 129 620 reads from blood and sperm, respectively, with an average read length of 133 bp. Bioinformatic analysis revealed that 12 358 (7.6%) blood library reads and 10 216 (7.9%) sperm library reads map to 6167 and 5796 different CGIs, respectively. In blood and sperm DNA, we identified 824 (13.7%) and 482 (8.5%) fully methylated autosomal CGIs, respectively. Differential methylation, which is characterized by the presence of methylated and unmethylated reads of the same CGI, was observed in 53 and 52 autosomal CGIs in blood and sperm DNA, respectively. Remarkably, methylation of X-chromosomal CGIs in female blood cells was most often incomplete (25-75%). Such incomplete methylation was mainly found on the X-chromosome, suggesting that it is linked to X-chromosome inactivation.

Improvement of variant calling in next-generation sequence data requires a comprehensive, genome-wide catalog of highconfidence variants called in a set of genomes for use as a benchmark. We generated deep, whole-genome sequence data of 17 individuals in a three-generation pedigree and called variants in each genome using a range of currently available algorithms. We used haplotype transmission information to create a phased "Platinum" variant catalog of 4.7 million singlenucleotide variants (SNVs) plus 0.7 million small (1-50 bp) insertions and deletions (indels) that are consistent with the pattern of inheritance in the parents and 11 children of this pedigree. Platinum genotypes are highly concordant with the current catalog of the National Institute of Standards and Technology for both SNVs (>99.99%) and indels (99.92%) and add a validated truth catalog that has 26% more SNVs and 45% more indels. Analysis of 334,652 SNVs that were consistent between informatics pipelines yet inconsistent with haplotype transmission ("nonplatinum") revealed that the majority of these variants are de novo and cell-line mutations or reside within previously unidentified duplications and deletions. The reference materials from this study are a resource for objective assessment of the accuracy of variant calls throughout genomes.

DNA microarrays are a popular technique for the detection of microorganisms. Several approaches using specific oligomers targeting one or a few marker genes for each species have been proposed. Data analysis is usually limited to call a species present when its oligomer exceeds a certain intensity threshold. While this strategy works reasonably well for distantly related species, it does not work well for very closely related species: Cross-hybridization of nontarget DNA prevents a simple identification based on signal intensity. The majority of species of the same genus has a sequence similarity of over 90%. For biodiversity studies down to the species level, it is therefore important to increase the detection power of closely related species. We propose a simple, cost-effective and robust approach for biodiversity studies using DNA microarray technology and demonstrate it on scenedesmacean green algae. The internal transcribed spacer 2 (ITS2) rDNA sequence was chosen as marker because it is suitable to distinguish all eukaryotic species even though parts of it are virtually identical in closely related species. We show that by modelling hybridization behaviour with a matrix algebra approach, we are able to identify closely related species that cannot be distinguished with a threshold on signal intensity. Thus this proof-of-concept study shows that by adding a simple and robust data analysis step to the evaluation of DNA microarrays, species detection can be significantly improved for closely related species with a high sequence similarity.

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Background The scale and quality of the global scientific response to the COVID-19 pandemic have unquestionably saved lives. However, the COVID-19 pandemic has also triggered an unprecedented “infodemic”; the velocity and volume of data production have overwhelmed many key stakeholders such as clinicians and policy makers, as they have been unable to process structured and unstructured data for evidence-based decision making. Solutions that aim to alleviate this data synthesis–related challenge are unable to capture heterogeneous web data in real time for the production of concomitant answers and are not based on the high-quality information in responses to a free-text query. Objective The main objective of this project is to build a generic, real-time, continuously updating curation platform that can support the data synthesis and analysis of a scientific literature framework. Our secondary objective is to validate this platform and the curation methodology for COVID-19–related medical literature by expanding the COVID-19 Open Research Dataset via the addition of new, unstructured data. Methods To create an infrastructure that addresses our objectives, the PanSurg Collaborative at Imperial College London has developed a unique data pipeline based on a web crawler extraction methodology. This data pipeline uses a novel curation methodology that adopts a human-in-the-loop approach for the characterization of quality, relevance, and key evidence across a range of scientific literature sources. Results REDASA (Realtime Data Synthesis and Analysis) is now one of the world’s largest and most up-to-date sources of COVID-19–related evidence; it consists of 104,000 documents. By capturing curators’ critical appraisal methodologies through the discrete labeling and rating of information, REDASA rapidly developed a foundational, pooled, data science data set of over 1400 articles in under 2 weeks. These articles provide COVID-19–related information and represent around 10% of all papers about COVID-19. Conclusions This data set can act as ground truth for the future implementation of a live, automated systematic review. The three benefits of REDASA’s design are as follows: (1) it adopts a user-friendly, human-in-the-loop methodology by embedding an efficient, user-friendly curation platform into a natural language processing search engine; (2) it provides a curated data set in the JavaScript Object Notation format for experienced academic reviewers’ critical appraisal choices and decision-making methodologies; and (3) due to the wide scope and depth of its web crawling method, REDASA has already captured one of the world’s largest COVID-19–related data corpora for searches and curation.

In many signal processing and data mining applications, we need to approximate a given matrix Y with a low-rank product Y ≈ AX. Both matrices A and X are to be determined, but we assume that from the specifics of the application we have an important piece of a-priori knowledge: A must have zeros at certain positions.In general, different AX factorizations approximate a given Y equally well, so a fundamental question is whether the known zero pattern of A contributes to the uniqueness of the factorization. Using the notion of structural rank, we present a combinatorial characterization of uniqueness up to diagonal scaling (subject to a mild non-degeneracy condition on the factors), called structural identifiability of the model.Next, we define an optimization problem that arises in the need for efficient experimental design. In this context, Y contains sensor measurements over several time samples, X contains source signals over time samples and A contains the sourcesensor mixing coefficients. Our task is to monitor the signal sources with the cheapest subset of sensors, while maintaining structural identifiability. Firstly, we show that E. Fritzilas ( ) Faculty 314 Algorithmica (2010) 56: 313-332 this problem is NP-hard. Secondly, we present a mixed integer linear program for its exact solution together with two practical incremental approaches. We also propose a greedy approximation algorithm. Finally, we perform computational experiments on simulated problem instances of various sizes.