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1. SBGNview: towards data analysis, integration and visualization on all pathways

   https://doi.org/10.1093/bioinformatics/btab793  

   Dong, Xiaoxi ; Vegesna, Kovidh ; Brouwer, Cory ; Luo, Weijun ; Mathelier, ed., Anthony ( November 2021 , Bioinformatics)

   Pathway analysis is widely used in genomics and omics research, but the data visualization has been highly limited in function, pathway coverage and data format. Here, we develop SBGNview a comprehensive R package to address these needs. By adopting the standard SBGN format, SBGNview greatly extend the coverage of pathway-based analysis and data visualization to essentially all major pathway databases beyond KEGG, including 5200 reference pathways and over 3000 species. In addition, SBGNview substantially extends or exceeds current tools (esp. Pathview) in both design and function, including standard input format (SBGN), high-quality output graphics (SVG format) convenient for both interpretation and further update, and flexible and open-end workflow for iterative editing and interactive visualization (Highlighter module). In addition to pathway analysis and data visualization, SBGNview provides essential infrastructure for SBGN data manipulation and processing.

   The data underlying this article are available as part of the SBGNview package is available on both GitHub and Bioconductor: https://github.com/datapplab/SBGNview, https://bioconductor.org/packages/SBGNview.

   Supplementary data are available at Bioinformatics online.

    

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2. diffloop: a computational framework for identifying and analyzing differential DNA loops from sequencing data

   https://doi.org/10.1093/bioinformatics/btx623  

   Lareau, Caleb A. ; Aryee, Martin J. ; Berger, ed., Bonnie ( September 2017 , Bioinformatics)

   The 3D architecture of DNA within the nucleus is a key determinant of interactions between genes, regulatory elements, and transcriptional machinery. As a result, differences in DNA looping structure are associated with variation in gene expression and cell state. To systematically assess changes in DNA looping architecture between samples, we introduce diffloop, an R/Bioconductor package that provides a suite of functions for the quality control, statistical testing, annotation, and visualization of DNA loops. We demonstrate this functionality by detecting differences between ENCODE ChIA-PET samples and relate looping to variability in epigenetic state.

   Diffloop is implemented as an R/Bioconductor package available at https://bioconductor.org/packages/release/bioc/html/diffloop.html

   Supplementary data are available at Bioinformatics online.

    

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3. Compression of quantification uncertainty for scRNA-seq counts

   https://doi.org/10.1093/bioinformatics/btab001  

   Van Buren, Scott ; Sarkar, Hirak ; Srivastava, Avi ; Rashid, Naim U ; Patro, Rob ; Love, Michael I ( January 2021 , Bioinformatics)

   Birol, Inanc (Ed.)

   Abstract Motivation Quantification estimates of gene expression from single-cell RNA-seq (scRNA-seq) data have inherent uncertainty due to reads that map to multiple genes. Many existing scRNA-seq quantification pipelines ignore multi-mapping reads and therefore underestimate expected read counts for many genes. alevin accounts for multi-mapping reads and allows for the generation of ‘inferential replicates’, which reflect quantification uncertainty. Previous methods have shown improved performance when incorporating these replicates into statistical analyses, but storage and use of these replicates increases computation time and memory requirements. Results We demonstrate that storing only the mean and variance from a set of inferential replicates (‘compression’) is sufficient to capture gene-level quantification uncertainty, while reducing disk storage to as low as 9% of original storage, and memory usage when loading data to as low as 6%. Using these values, we generate ‘pseudo-inferential’ replicates from a negative binomial distribution and propose a general procedure for incorporating these replicates into a proposed statistical testing framework. When applying this procedure to trajectory-based differential expression analyses, we show false positives are reduced by more than a third for genes with high levels of quantification uncertainty. We additionally extend the Swish method to incorporate pseudo-inferential replicates and demonstrate improvements in computation time and memory usage without any loss in performance. Lastly, we show that discarding multi-mapping reads can result in significant underestimation of counts for functionally important genes in a real dataset. Availability and implementation makeInfReps and splitSwish are implemented in the R/Bioconductor fishpond package available at https://bioconductor.org/packages/fishpond. Analyses and simulated datasets can be found in the paper’s GitHub repo at https://github.com/skvanburen/scUncertaintyPaperCode. Supplementary information Supplementary data are available at Bioinformatics online. 

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4. iDINGO—integrative differential network analysis in genomics with Shiny application

   https://doi.org/10.1093/bioinformatics/btx750  

   Class, Caleb A. ; Ha, Min Jin ; Baladandayuthapani, Veerabhadran ; Do, Kim-Anh ; Berger, ed., Bonnie ( November 2017 , Bioinformatics)

   Differential network analysis is an important way to understand network rewiring involved in disease progression and development. Building differential networks from multiple ‘omics data provides insight into the holistic differences of the interactive system under different patient-specific groups. DINGO was developed to infer group-specific dependencies and build differential networks. However, DINGO and other existing tools are limited to analyze data arising from a single platform, and modeling each of the multiple ‘omics data independently does not account for the hierarchical structure of the data.

   We developed the iDINGO R package to estimate group-specific dependencies and make inferences on the integrative differential networks, considering the biological hierarchy among the platforms. A Shiny application has also been developed to facilitate easier analysis and visualization of results, including integrative differential networks and hub gene identification across platforms.

   R package is available on CRAN (https://cran.r-project.org/web/packages/iDINGO) and Shiny application at https://github.com/MinJinHa/iDINGO.

   Supplementary data are available at Bioinformatics online.

    

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5. Heritability estimation and differential analysis of count data with generalized linear mixed models in genomic sequencing studies

   https://doi.org/10.1093/bioinformatics/bty644  

   Sun, Shiquan ; Zhu, Jiaqiang ; Mozaffari, Sahar ; Ober, Carole ; Chen, Mengjie ; Zhou, Xiang ; Birol, ed., Inanc ( July 2018 , Bioinformatics)

   Genomic sequencing studies, including RNA sequencing and bisulfite sequencing studies, are becoming increasingly common and increasingly large. Large genomic sequencing studies open doors for accurate molecular trait heritability estimation and powerful differential analysis. Heritability estimation and differential analysis in sequencing studies requires the development of statistical methods that can properly account for the count nature of the sequencing data and that are computationally efficient for large datasets.

   Here, we develop such a method, PQLseq (Penalized Quasi-Likelihood for sequencing count data), to enable effective and efficient heritability estimation and differential analysis using the generalized linear mixed model framework. With extensive simulations and comparisons to previous methods, we show that PQLseq is the only method currently available that can produce unbiased heritability estimates for sequencing count data. In addition, we show that PQLseq is well suited for differential analysis in large sequencing studies, providing calibrated type I error control and more power compared to the standard linear mixed model methods. Finally, we apply PQLseq to perform gene expression heritability estimation and differential expression analysis in a large RNA sequencing study in the Hutterites.

   PQLseq is implemented as an R package with source code freely available at www.xzlab.org/software.html and https://cran.r-project.org/web/packages/PQLseq/index.html.

   Supplementary data are available at Bioinformatics online.

    

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