BACKGROUND: Genetic and epigenetic biological studies often combine different types of experiments and multiple conditions. While the corresponding raw and processed data are made available through specialized public databases, the processed files are usually limited to a specific research question. Hence, they are unsuitable for an unbiased, systematic overview of a complex dataset. However, possible combinations of different sample types and conditions grow exponentially with the amount of sample types and conditions. Therefore the risk to miss a correlation or to overrate an identified correlation should be mitigated in a complex dataset. Since reanalysis of a full study is rarely a viable option, new methods are needed to address these issues systematically, reliably, reproducibly and efficiently. RESULTS: Cogito "COmpare annotated Genomic Intervals TOol" provides a workflow for an unbiased, structured overview and systematic analysis of complex genomic datasets consisting of different data types (e.g. RNA-seq, ChIP-seq) and conditions. Cogito is able to visualize valuable key information of genomic or epigenomic interval-based data, thereby providing a straightforward analysis approach for comparing different conditions. It supports getting an unbiased impression of a dataset and developing an appropriate analysis strategy for it. In addition to a text-based report, Cogito offers a fully customizable report as a starting point for further in-depth investigation. CONCLUSIONS: Cogito implements a novel approach to facilitate high-level overview analyses of complex datasets, and offers additional insights into the data without the need for a full, time-consuming reanalysis. The R/Bioconductor package is freely available at https://bioconductor.org/packages/release/bioc/html/Cogito.html , a comprehensive documentation with detailed descriptions and reproducible examples is included.

MOTIVATION: Each year, the number of published bulk and single-cell RNA-seq datasets is growing exponentially. Studies analyzing such data are commonly looking at gene-level differences, while the collected RNA-seq data inherently represents reads of transcript isoform sequences. Utilizing transcriptomic quantifiers, RNA-seq reads can be attributed to specific isoforms, allowing for analysis of transcript-level differences. A differential transcript usage (DTU) analysis is testing for proportional differences in a gene's transcript composition, and has been of rising interest for many research questions, such as analysis of differential splicing or cell-type identification. RESULTS: We present the R package DTUrtle, the first DTU analysis workflow for both bulk and single-cell RNA-seq datasets, and the first package to conduct a 'classical' DTU analysis in a single-cell context. DTUrtle extends established statistical frameworks, offers various result aggregation and visualization options and a novel detection probability score for tagged-end data. It has been successfully applied to bulk and single-cell RNA-seq data of human and mouse, confirming and extending key results. In addition, we present novel potential DTU applications like the identification of cell-type specific transcript isoforms as biomarkers. AVAILABILITY AND IMPLEMENTATION: The R package DTUrtle is available at https://github.com/TobiTekath/DTUrtle with extensive vignettes and documentation at https://tobitekath.github.io/DTUrtle/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: With its capacity for high-resolution data output in one region of interest, chromosome conformation capture combined with high-throughput sequencing (4C-seq) is a state-of-the-art next-generation sequencing technique that provides epigenetic insights, and regularly advances current medical research. However, 4C-seq data are complex and prone to biases, and while specialized programs exist, an unbiased, extensive benchmarking is still lacking. Furthermore, neither substantial datasets with fully characterized ground truth, nor simulation programs for realistic 4C-seq data have been published. RESULTS: We conducted a benchmarking study on 66 4C-seq samples from 20 datasets, and developed a novel 4C-seq simulation software, Basic4CSim, to allow for detailed comparisons of 4C-seq algorithms on 50 simulated datasets with 10-120 samples each. Simulations and benchmarking were adapted to address different characteristics of 4C-seq data. Simulated data were compared with published samples to validate simulation settings. We identified differences between 4C-seq algorithms in terms of precision, recall, interaction structure, and run time, and observed general trends. Novel differential pipeline versions of single-sample based 4C-seq algorithms were included in the benchmarking. While no single tool was optimally suited for both near-cis and far-cis, and both single-sample and differential analyses, choosing a high-performing algorithm variant did improve results considerably. For near-cis scenarios, r3Cseq, peakC and FourCSeq offered high precision, while fourSig demonstrated high overall F1 scores in far-cis analyses. Finally, 4C-seq simulations may aid in the development of improved analysis algorithms. AVAILABILITY AND IMPLEMENTATION: Basic4CSim is available at https://github.com/walter-ca/Basic4CSim. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: The application of next-generation sequencing in research and particularly in clinical routine requires valid variant calling results. However, evaluation of several commonly used tools has pointed out that not a single tool meets this requirement. False positive as well as false negative calls necessitate additional experiments and extensive manual work. Intelligent combination and output filtration of different tools could significantly improve the current situation. Results: We developed appreci8, an automatic variant calling pipeline for calling single nucleotide variants and short indels by combining and filtering the output of eight open-source variant calling tools, based on a novel artifact- and polymorphism score. Appreci8 was trained on two data sets from patients with myelodysplastic syndrome, covering 165 Illumina samples. Subsequently, appreci8's performance was tested on five independent data sets, covering 513 samples. Variation in sequencing platform, target region and disease entity was considered. All calls were validated by re-sequencing on the same platform, a different platform or expert-based review. Sensitivity of appreci8 ranged between 0.93 and 1.00, while positive predictive value ranged between 0.65 and 1.00. In all cases, appreci8 showed superior performance compared to any evaluated alternative approach. Availability and implementation: Appreci8 is freely available at https://hub.docker.com/r/wwuimi/appreci8/. Sequencing data (BAM files) of the 678 patients analyzed with appreci8 have been deposited into the NCBI Sequence Read Archive (BioProjectID: 388411; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA388411). Supplementary information: Supplementary data are available at Bioinformatics online.

Summary: With the rapid development in next-generation sequencing, cost and time requirements for genomic sequencing are decreasing, enabling applications in many areas such as cancer research. Many tools have been developed to analyze genomic variation ranging from single nucleotide variants to whole chromosomal aberrations. As sequencing throughput increases, the number of variants called by such tools also grows. Often employed manual inspection of such calls is thus becoming a time-consuming procedure. We developed the Variant InsPector and Expert Rating tool (VIPER) to speed up this process by integrating the Integrative Genomics Viewer into a web application. Analysts can then quickly iterate through variants, apply filters and make decisions based on the generated images and variant metadata. VIPER was successfully employed in analyses with manual inspection of more than 10 000 calls. Availability and implementation: VIPER is implemented in Java and Javascript and is freely available at https://github.com/MarWoes/viper. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Objectives: To systematically classify the clinical impact of computerized clinical decision support systems (CDSSs) in inpatient care. Materials and Methods: Medline, Cochrane Trials, and Cochrane Reviews were searched for CDSS studies that assessed patient outcomes in inpatient settings. For each study, 2 physicians independently mapped patient outcome effects to a predefined medical effect score to assess the clinical impact of reported outcome effects. Disagreements were measured by using weighted kappa and solved by consensus. An example set of promising disease entities was generated based on medical effect scores and risk of bias assessment. To summarize technical characteristics of the systems, reported input variables and algorithm types were extracted as well. Results: Seventy studies were included. Five (7%) reported reduced mortality, 16 (23%) reduced life-threatening events, and 28 (40%) reduced non-life-threatening events, 20 (29%) had no significant impact on patient outcomes, and 1 showed a negative effect (weighted κ: 0.72, P < .001). Six of 24 disease entity settings showed high effect scores with medium or low risk of bias: blood glucose management, blood transfusion management, physiologic deterioration prevention, pressure ulcer prevention, acute kidney injury prevention, and venous thromboembolism prophylaxis. Most of the implemented algorithms (72%) were rule-based. Reported input variables are shared as standardized models on a metadata repository. Discussion and Conclusion: Most of the included CDSS studies were associated with positive patient outcomes effects but with substantial differences regarding the clinical impact. A subset of 6 disease entities could be filtered in which CDSS should be given special consideration at sites where computer-assisted decision-making is deemed to be underutilized. Registration number on PROSPERO: CRD42016049946.

BACKGROUND: Deriving valid variant calling results from raw next-generation sequencing data is a particularly challenging task, especially with respect to clinical diagnostics and personalized medicine. However, when using classic variant calling software, the user usually obtains nothing more than a list of variants that pass the corresponding caller's internal filters. Any expected mutations (e.g. hotspot mutations), that have not been called by the software, need to be investigated manually. RESULTS: BBCAnalyzer (Bases By CIGAR Analyzer) provides a novel visual approach to facilitate this step of time-consuming, manual inspection of common mutation sites. BBCAnalyzer is able to visualize base counts at predefined positions or regions in any sequence alignment data that are available as BAM files. Thereby, the tool provides a straightforward solution for evaluating any list of expected mutations like hotspot mutations, or even whole regions of interest. In addition to an ordinary textual report, BBCAnalyzer reports highly customizable plots. Information on the counted number of bases, the reference bases, known mutations or polymorphisms, called mutations and base qualities is summarized in a single plot. By uniting this information in a graphical way, the user may easily decide on a variant being present or not - completely independent of any internal filters or frequency thresholds. CONCLUSIONS: BBCAnalyzer provides a unique, novel approach to facilitate variant calling where classical tools frequently fail to call. The R package is freely available at http://bioconductor.org . The local web application is available at Additional file 2. A documentation of the R package (Additional file 1) as well as the web application (Additional file 2) with detailed descriptions, examples of all input- and output elements, exemplary code as well as exemplary data are included. A video demonstrates the exemplary usage of the local web application (Additional file 3). Additional file 3: Supplement_3. Video demonstrating the exemplary usage of the web application "BBCAnalyzer". (MP4 11571 kb).

Structural variation (SV) plays an important role in genetic diversity among the population in general and specifically in diseases such as cancer. Modern next-generation sequencing (NGS) technologies provide paired-end sequencing data at high depth with increasing read lengths. This development enabled the analysis of split-reads to detect SV breakpoints with single-nucleotide resolution. But ambiguous mappings and breakpoint sequences with further co-occurring mutations hamper split-read alignments against a reference sequence. The trade-off between high sensitivity and low false-positive rate is problematic and often requires a lot of fine-tuning of the analysis method based on knowledge about its algorithm and the characteristics of the data set. We present SoftSV, a method for exact breakpoint detection for small and large deletions, inversions, tandem duplications and inter-chromosomal translocations, which relies solely on the mutual alignment of soft-clipped reads within the neighborhood of discordantly mapped paired-end reads. Unlike other SV detection algorithms, our approach does not require thresholds regarding sequencing coverage or mapping quality. We evaluate SoftSV together with eight approaches (Breakdancer, Clever, CREST, Delly, GASVPro, Pindel, Socrates and SoftSearch) on simulated and real data sets. Our results show that sensitive and reliable SV detection is subject to many different factors like read length, sequence coverage and SV type. While most programs have their individual drawbacks, our greedy approach turns out to be the most robust and sensitive on many experimental setups. Sensitivities above 85% and positive predictive values between 80 and 100% could be achieved consistently for all SV types on simulated data sets starting at relatively short 75 bp reads and low 10-15× sequence coverage.

OBJECTIVE: Electronic medical record (EMR) systems have the potential of supporting clinical work by providing the right information at the right time to the right people and thus make efficient use of resources. This is especially important in low-resource settings where reliable data are also needed to support public health and local supporting organizations. In this systematic literature review, our objectives are to identify and collect literature about success criteria of EMR implementations in low-resource settings and to summarize them into recommendations. MATERIALS AND METHODS: Our search strategy relied on PubMed queries and manual bibliography reviews. Studies were included if EMR implementations in low-resource settings were described. The extracted success criteria and measurements were summarized into 7 categories: ethical, financial, functionality, organizational, political, technical, and training. RESULTS: We collected 381 success criteria with 229 measurements from 47 articles out of 223 articles. Most papers were evaluations or lessons learned from African countries, published from 1999 to 2013. Almost half of the EMR systems served a specific disease area like human immunodeficiency virus (HIV). The majority of criteria that were reported dealt with the functionality, followed by organizational issues, and technical infrastructures. Sufficient training and skilled personnel were mentioned in roughly 10%. Political, ethical, and financial considerations did not play a predominant role. More evaluations based on reliable frameworks are needed. CONCLUSIONS: Highly reliable data handling methods, human resources and effective project management, as well as technical architecture and infrastructure are all key factors for successful EMR implementation.

MOTIVATION: Histone modifications are a key epigenetic mechanism to activate or repress the transcription of genes. Datasets of matched transcription data and histone modification data obtained by ChIP-seq exist, but methods for integrative analysis of both data types are still rare. Here, we present a novel bioinformatics approach to detect genes that show different transcript abundances between two conditions putatively caused by alterations in histone modification. RESULTS: We introduce a correlation measure for integrative analysis of ChIP-seq and gene transcription data measured by RNA sequencing or microarrays and demonstrate that a proper normalization of ChIP-seq data is crucial. We suggest applying Bayesian mixture models of different types of distributions to further study the distribution of the correlation measure. The implicit classification of the mixture models is used to detect genes with differences between two conditions in both gene transcription and histone modification. The method is applied to different datasets, and its superiority to a naive separate analysis of both data types is demonstrated. AVAILABILITY AND IMPLEMENTATION: R/Bioconductor package epigenomix. CONTACT: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

SUMMARY: Basic4Cseq is an R/Bioconductor package for basic filtering, analysis and subsequent near-cis visualization of 4C-seq data. The package processes aligned 4C-seq raw data stored in binary alignment/map (BAM) format and maps the short reads to a corresponding virtual fragment library. Functions are included to create virtual fragment libraries providing chromosome position and further information on 4C-seq fragments (length and uniqueness of the fragment ends, and blindness of a fragment) for any BSGenome package. An optional filter is included for BAM files to remove invalid 4C-seq reads, and further filter functions are offered for 4C-seq fragments. Additionally, basic quality controls based on the read distribution are included. Fragment data in the vicinity of the experiment's viewpoint are visualized as coverage plot based on a running median approach and a multi-scale contact profile. Wig files or csv files of the fragment data can be exported for further analyses and visualizations of interactions with other programs. AVAILABILITY AND IMPLEMENTATION: Basic4Cseq is implemented in R and available at http://www.bioconductor.org/. A vignette with detailed descriptions of the functions is included in the package. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A variety of genome-wide profiling techniques are available to investigate complementary aspects of genome structure and function. Integrative analysis of heterogeneous data sources can reveal higher level interactions that cannot be detected based on individual observations. A standard integration task in cancer studies is to identify altered genomic regions that induce changes in the expression of the associated genes based on joint analysis of genome-wide gene expression and copy number profiling measurements. In this review, we highlight common approaches to genomic data integration and provide a transparent benchmarking procedure to quantitatively compare method performances in cancer gene prioritization. Algorithms, data sets and benchmarking results are available at http://intcomp.r-forge.r-project.org.

UNLABELLED: RSVSim is a tool for the simulation of deletions, insertions, inversions, tandem duplications and translocations of various sizes in any genome available as FASTA-file or data package in R. The structural variations can be generated randomly, based on user-supplied genomic coordinates or associated to various kinds of repeats. The package further comprises functions to estimate the distribution of structural variation sizes from real datasets. AVAILABILITY: RSVSim is implemented in R and available at http://www.bioconductor.org. A vignette with detailed descriptions of the functions and examples is included.

MOTIVATION: Bisulfite sequencing is currently the gold standard to obtain genome-wide DNA methylation profiles in eukaryotes. In contrast to the rapid development of appropriate pre-processing and alignment software, methods for analyzing the resulting methylation profiles are relatively limited so far. For instance, an appropriate pipeline to detect DNA methylation differences between cancer and control samples is still required. RESULTS: We propose an algorithm that detects significantly differentially methylated regions in data obtained by targeted bisulfite sequencing approaches, such as reduced representation bisulfite sequencing. In a first step, this approach tests all target regions for methylation differences by taking spatial dependence into account. A false discovery rate procedure controls the expected proportion of incorrectly rejected regions. In a second step, the significant target regions are trimmed to the actually differentially methylated regions. This hierarchical procedure detects differentially methylated regions with increased power compared with existing methods. AVAILABILITY: R/Bioconductor package BiSeq. SUPPLEMENTARY INFORMATION: Supplementary Data are available at Bioinformatics online.

UNLABELLED: The R453Plus1Toolbox is an R/Bioconductor package for the analysis of 454 Sequencing data. Projects generated with Roche's data analysis software can be imported into R allowing advanced and customized analyses within the R/Bioconductor environment for sequencing data. Several methods were implemented extending the current functionality of Roche's software. These extensions include methods for quality assurance and annotation of detected variants. Further, a pipeline for the detection of structural variants, e.g. balanced chromosomal translocations, is provided. AVAILABILITY: The R453Plus1Toolbox is implemented in R and available at http://www.bioconductor.org/. A vignette outlining typical workflows is included in the package. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Visualization of DNA microarray data in two or three dimensional spaces is an important exploratory analysis step in order to detect quality issues or to generate new hypotheses. Principal Component Analysis (PCA) is a widely used linear method to define the mapping between the high-dimensional data and its low-dimensional representation. During the last decade, many new nonlinear methods for dimension reduction have been proposed, but it is still unclear how well these methods capture the underlying structure of microarray gene expression data. In this study, we assessed the performance of the PCA approach and of six nonlinear dimension reduction methods, namely Kernel PCA, Locally Linear Embedding, Isomap, Diffusion Maps, Laplacian Eigenmaps and Maximum Variance Unfolding, in terms of visualization of microarray data. RESULTS: A systematic benchmark, consisting of Support Vector Machine classification, cluster validation and noise evaluations was applied to ten microarray and several simulated datasets. Significant differences between PCA and most of the nonlinear methods were observed in two and three dimensional target spaces. With an increasing number of dimensions and an increasing number of differentially expressed genes, all methods showed similar performance. PCA and Diffusion Maps responded less sensitive to noise than the other nonlinear methods. CONCLUSIONS: Locally Linear Embedding and Isomap showed a superior performance on all datasets. In very low-dimensional representations and with few differentially expressed genes, these two methods preserve more of the underlying structure of the data than PCA, and thus are favorable alternatives for the visualization of microarray data.

MOTIVATION: The analysis of a number of different genetic features like copy number (CN) variation, gene expression (GE) or loss of heterocygosity has considerably increased in recent years, as well as the number of available datasets. This is particularly due to the success of microarray technology. Thus, to understand mechanisms of disease pathogenesis on a molecular basis, e.g. in cancer research, the challenge of analyzing such different data types in an integrated way has become increasingly important. In order to tackle this problem, we propose a new procedure for an integrated analysis of two different data types that searches for genes and genetic regions which for both inputs display strong equally directed deviations from the reference median. We employ this approach, based on a modified correlation coefficient and an explorative Wilcoxon test, to find DNA regions of such abnormalities in GE and CN (e.g. underexpressed genes accompanied by a loss of DNA material). RESULTS: In an application to acute myeloid leukemia, our procedure is able to identify various regions on different chromosomes with characteristic abnormalities in GE and CN data and shows a higher sensitivity to differences in abnormalities than standard approaches. While the results support various findings of previous studies, some new interesting DNA regions can be identified. In a simulation study, our procedure also shows more reliable results than standard approaches. AVAILABILITY: Code and data available as R packages edira and ediraAMLdata from http://www.statistik.tu-dortmund.de/~schaefer/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Multiple gene expression signatures derived from microarray experiments have been published in the field of leukemia research. A comparison of these signatures with results from new experiments is useful for verification as well as for interpretation of the results obtained. Currently, the percentage of overlapping genes is frequently used to compare published gene signatures against a signature derived from a new experiment. However, it has been shown that the percentage of overlapping genes is of limited use for comparing two experiments due to the variability of gene signatures caused by different array platforms or assay-specific influencing parameters. Here, we present a robust approach for a systematic and quantitative comparison of published gene expression signatures with an exemplary query dataset. RESULTS: A database storing 138 leukemia-related published gene signatures was designed. Each gene signature was manually annotated with terms according to a leukemia-specific taxonomy. Two analysis steps are implemented to compare a new microarray dataset with the results from previous experiments stored and curated in the database. First, the global test method is applied to assess gene signatures and to constitute a ranking among them. In a subsequent analysis step, the focus is shifted from single gene signatures to chromosomal aberrations or molecular mutations as modeled in the taxonomy. Potentially interesting disease characteristics are detected based on the ranking of gene signatures associated with these aberrations stored in the database. Two example analyses are presented. An implementation of the approach is freely available as web-based application. CONCLUSIONS: The presented approach helps researchers to systematically integrate the knowledge derived from numerous microarray experiments into the analysis of a new dataset. By means of example leukemia datasets we demonstrate that this approach detects related experiments as well as related molecular mutations and may help to interpret new microarray data.

Hospital Information Systems (HIS) handle a large number of different types of documents. Exchange and analysis of data from different HIS is facilitated by the use of standardized codes to identify document types. HL7's Clinical Document Architecture (CDA) uses LOINC (logical observation identifiers names and Codes) codes for clinical documents. The authors assessed the coverage of LOINC codes for document types in a German HIS. The authors analyzed document types that occurred more than 10 times in approximately 1.3 million documents in a commercial HIS at a major German University Hospital. Document types were mapped manually to LOINC using the Regenstrief LOINC Mapping Assistant (RELMA). Each document type was coded by two physicians. In case of discrepancies a third expert was consulted to reach consensus. For 76 of 86 document categories a LOINC code was identified, but for 38 of these categories, the LOINC code was not specific as deemed necessary. More than 93% of our local HIS documents had local document types that could be assigned a LOINC code.

Abstract Motivation: Unsupervised clustering of microarray data may detect potentially important, but not obvious characteristics of samples, for instance subgroups of diagnoses with distinct gene profiles or systematic errors in experimentation. Results: Multidimensional clustering (mdclust) is a method, which identifies sets of sample clusters and associated genes. It applies iteratively two-means clustering and score-based gene selection. For any phenotype variable best matching sets of clusters can be selected. This provides a method to identify gene–phenotype associations, suited even for settings with a large number of phenotype variables. An optional model based discriminant step may reduce further the number of selected genes. Availability: R-code and supplemental information available from http://martin-dugas.de/mdclust/ Supplementary information: http://martin-dugas.de/mdclust/