Aberrant alterations in genome copy number, chromatin accessibility, and transcriptional programs all play pivotal roles in cancer. The Numbat algorithm has been widely adopted to perform copy number variation (CNV) inference from single-cell RNA sequencing (scRNA-seq) data. Here, we introduce Numbat-multiome that extends the capabilities of Numbat to perform CNV inference from both scRNA-seq and accessible chromatin profiles (through single-cell Assay of Transposase [Tn5]-Accessible Chromatin sequencing [scATAC-seq] data), either separately or in an integrated manner. Our approach unifies data originating from different modalities through a binning strategy that relies on a common genomic coordinate system across modalities. We demonstrate the tool's robust performance in four running modes (RNA gene, RNA bin, ATAC bin, and Combined bin) using benchmark cohorts of tumors with dynamic changes in expression patterns and copy number heterogeneity, including early-stage multiple myeloma and Richter's syndrome arising from chronic lymphocytic leukemia, validated against whole-genome sequencing. Numbat-multiome achieves high precision and recall (median F1>0.9) across different CNV event types, with consistent performance across sample types and event lengths. The tool's ability to track clonal evolution in serial samples and identify rare subclones allows for integration of epigenomic profiles at the subclonal level, providing new insights into the stepwise genetic and epigenetic changes underlying cancer phenotypic shifts.

MOTIVATION: Somatic copy-number alterations (SCNAs) play an important role in cancer development. Systematic noise in sequencing and array data present a significant challenge to the inference of SCNAs for cancer genome analyses. As part of The Cancer Genome Atlas, the Broad Institute Genome Characterization Center developed the Tangent normalization method to generate copy-number profiles using data from single-nucleotide polymorphism (SNP) arrays and whole-exome sequencing (WES) technologies for over 10 000 pairs of tumors and matched normal samples. Here, we describe the Tangent method, which uses a unique linear combination of normal samples as a reference for each tumor sample, to subtract systematic errors that vary across samples. We also describe a modification of Tangent, called Pseudo-Tangent, which enables denoising through comparisons between tumor profiles when few normal samples are available. RESULTS: Tangent normalization substantially increases signal-to-noise ratios (SNRs) compared to conventional normalization methods in both SNP array and WES analyses. Tangent and Pseudo-Tangent normalizations improve the SNR by reducing noise with minimal effect on signal and exceed the contribution of other steps in the analysis such as choice of segmentation algorithm. Tangent and Pseudo-Tangent are broadly applicable and enable more accurate inference of SCNAs from DNA sequencing and array data. AVAILABILITY AND IMPLEMENTATION: Tangent is available at https://github.com/broadinstitute/tangent and as a Docker image (https://hub.docker.com/r/broadinstitute/tangent). Tangent is also the normalization method for the copy-number pipeline in Genome Analysis Toolkit 4 (GATK4). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Post-sequencing quality control is a crucial component of RNA sequencing (RNA-seq) data generation and analysis, as sample quality can be affected by sample storage, extraction and sequencing protocols. RNA-seq is increasingly applied to cohorts ranging from hundreds to tens of thousands of samples in size, but existing tools do not readily scale to these sizes, and were not designed for a wide range of sample types and qualities. Here, we describe RNA-SeQC 2, an efficient reimplementation of RNA-SeQC (DeLuca et al., 2012) that adds multiple metrics designed to characterize sample quality across a wide range of RNA-seq protocols. AVAILABILITY AND IMPLEMENTATION: The command-line tool, documentation and C++ source code are available at the GitHub repository https://github.com/getzlab/rnaseqc. Code and data for reproducing the figures in this paper are available at https://github.com/getzlab/rnaseqc2-paper. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Bisulfite sequencing allows base-pair resolution profiling of DNA methylation and has recently been adapted for use in single-cells. Analyzing these data, including making comparisons with existing data, remains challenging due to the scale of the data and differences in preprocessing methods between published datasets. RESULTS: We present a set of preprocessing pipelines for bisulfite sequencing DNA methylation data that include a new R/Bioconductor package, scmeth, for a series of efficient QC analyses of large datasets. The pipelines go from raw data to CpG-level methylation estimates and can be run, with identical results, either on a single computer, in an HPC cluster or on Google Cloud Compute resources. These pipelines are designed to allow users to 1) ensure reproducibility of analyses, 2) achieve scalability to large whole genome datasets with 100 GB+ of raw data per sample and to single-cell datasets with thousands of cells, 3) enable integration and comparison between user-provided data and publicly available data, as all samples can be processed through the same pipeline, and 4) access to best-practice analysis pipelines. Pipelines are provided for whole genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS) and hybrid selection (capture) bisulfite sequencing (HSBS). CONCLUSIONS: The workflows produce data quality metrics, visualization tracks, and aggregated output for further downstream analysis. Optional use of cloud computing resources facilitates analysis of large datasets, and integration with existing methylome profiles. The workflow design principles are applicable to other genomic data types.

SUMMARY: Technological advances in high-throughput sequencing necessitate improved computational tools for processing and analyzing large-scale datasets in a systematic automated manner. For that purpose, we have developed PRADA (Pipeline for RNA-Sequencing Data Analysis), a flexible, modular and highly scalable software platform that provides many different types of information available by multifaceted analysis starting from raw paired-end RNA-seq data: gene expression levels, quality metrics, detection of unsupervised and supervised fusion transcripts, detection of intragenic fusion variants, homology scores and fusion frame classification. PRADA uses a dual-mapping strategy that increases sensitivity and refines the analytical endpoints. PRADA has been used extensively and successfully in the glioblastoma and renal clear cell projects of The Cancer Genome Atlas program. AVAILABILITY AND IMPLEMENTATION: http://sourceforge.net/projects/prada/ CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: We have developed Nozzle, an R package that provides an Application Programming Interface to generate HTML reports with dynamic user interface elements. Nozzle was designed to facilitate summarization and rapid browsing of complex results in data analysis pipelines where multiple analyses are performed frequently on big datasets. The package can be applied to any project where user-friendly reports need to be created. AVAILABILITY: The R package is available on CRAN at http://cran.r-project.org/package=Nozzle.R1. Examples and additional materials are available at http://gdac.broadinstitute.org/nozzle. The source code is also available at http://www.github.com/parklab/Nozzle. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: RNA-seq, the application of next-generation sequencing to RNA, provides transcriptome-wide characterization of cellular activity. Assessment of sequencing performance and library quality is critical to the interpretation of RNA-seq data, yet few tools exist to address this issue. We introduce RNA-SeQC, a program which provides key measures of data quality. These metrics include yield, alignment and duplication rates; GC bias, rRNA content, regions of alignment (exon, intron and intragenic), continuity of coverage, 3'/5' bias and count of detectable transcripts, among others. The software provides multi-sample evaluation of library construction protocols, input materials and other experimental parameters. The modularity of the software enables pipeline integration and the routine monitoring of key measures of data quality such as the number of alignable reads, duplication rates and rRNA contamination. RNA-SeQC allows investigators to make informed decisions about sample inclusion in downstream analysis. In summary, RNA-SeQC provides quality control measures critical to experiment design, process optimization and downstream computational analysis. AVAILABILITY AND IMPLEMENTATION: See www.genepattern.org to run online, or www.broadinstitute.org/rna-seqc/ for a command line tool.

SUMMARY: Here, we present ContEst, a tool for estimating the level of cross-individual contamination in next-generation sequencing data. We demonstrate the accuracy of ContEst across a range of contamination levels, sources and read depths using sequencing data mixed in silico at known concentrations. We applied our tool to published cancer sequencing datasets and report their estimated contamination levels. AVAILABILITY AND IMPLEMENTATION: ContEst is a GATK module, and distributed under a BSD style license at http://www.broadinstitute.org/cancer/cga/contest CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

MOTIVATION: An important step in analyzing expression profiles from microarray data is to identify genes that can discriminate between distinct classes of samples. Many statistical approaches for assigning significance values to genes have been developed. The Comparative Marker Selection suite consists of three modules that allow users to apply and compare different methods of computing significance for each marker gene, a viewer to assess the results, and a tool to create derivative datasets and marker lists based on user-defined significance criteria. AVAILABILITY: The Comparative Marker Selection application suite is freely available as a GenePattern module. The GenePattern analysis environment is freely available at http://www.broad.mit.edu/genepattern.

MOTIVATION: Predicting the metastatic potential of primary malignant tissues has direct bearing on the choice of therapy. Several microarray studies yielded gene sets whose expression profiles successfully predicted survival. Nevertheless, the overlap between these gene sets is almost zero. Such small overlaps were observed also in other complex diseases, and the variables that could account for the differences had evoked a wide interest. One of the main open questions in this context is whether the disparity can be attributed only to trivial reasons such as different technologies, different patients and different types of analyses. RESULTS: To answer this question, we concentrated on a single breast cancer dataset, and analyzed it by a single method, the one which was used by van't Veer et al. to produce a set of outcome-predictive genes. We showed that, in fact, the resulting set of genes is not unique; it is strongly influenced by the subset of patients used for gene selection. Many equally predictive lists could have been produced from the same analysis. Three main properties of the data explain this sensitivity: (1) many genes are correlated with survival; (2) the differences between these correlations are small; (3) the correlations fluctuate strongly when measured over different subsets of patients. A possible biological explanation for these properties is discussed. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: http://www.weizmann.ac.il/physics/complex/compphys/downloads/liate/

UNLABELLED: The F2CS server provides access to the software, F2CS2.00, which implements an automated prediction method of SCOP and CATH classifications of proteins, based on their FSSP Z-scores. AVAILABILITY: Free at http://www.weizmann.ac.il/physics/complex/compphys/f2cs/ SUPPLEMENTARY INFORMATION: The site contains links to additional figures and tables.

Abstract Summary: The CTWC server provides access to the software, CTWC1.00, that implements Coupled Two Way Clustering (Getz et al., 2000), a method designed to mine gene expression data Availability: Free, at http://ctwc.weizmann.ac.il Contact: [email protected] Supplementary information: The site has a link to an example which provides figures and detailed explanations * To whom correspondence should be addressed.

UNLABELLED: We present and review coupled two-way clustering, a method designed to mine gene expression data. The method identifies submatrices of the total expression matrix, whose clustering analysis reveals partitions of samples (and genes) into biologically relevant classes. We demonstrate, on data from colon and breast cancer, that we are able to identify partitions that elude standard clustering analysis. AVAILABILITY: Free, at http://ctwc.weizmann.ac.il.. SUPPLEMENTARY INFORMATION: http://www.weizmann.ac.il/physics/complex/compphys/bioinfo2/