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OmicPioneer-sc is an open-source data visualization/analysis package that integrates dimensionality-reduction plots (DRPs) such as t-SNE and UMAP with Next-Generation Clustered Heat Maps (NGCHMs) and Pathway Visualization Modules (PVMs) in a seamless, highly interactive exploratory environment. It includes fluent zooming and navigation, a statistical toolkit, dozens of link-outs to external public bioinformatic resources, high-resolution graphics that meet the requirements of all major journals, and the ability to store all metadata needed to reproduce the visualizations at a later time. A user-friendly, multi-panel graphical interface enables non-informaticians to interact with the system without programming, asking and answering questions that require navigation among the three types of modules or extension from them to the Gene Ontology or information on therapies. The visual integration can be useful for detective work to identify and annotate cell-types for color-coding of the DRPs, and multiple NGCHMs can be layered on top of each other (with toggling among them) as an aid to multi-omic analysis. The tools are available in containerized form with APIs to facilitate incorporation as a plug-in to other bioinformatic environments. The capabilities of OmicPioneer-sc are illustrated here through application to a single-cell RNA-seq airway dataset pertinent to the biology of both cancer and COVID-19.[Supplemental material is available for this article.]

MotivationComplex bioinformatic data analysis workflows involving multiple scripts in different languages can be difficult to consolidate, share, and reproduce. An environment that streamlines the entire processes of data collection, analysis, visualization and reporting of such multi-language analyses is currently lacking.ResultsWe developed Script of Scripts (SoS) Notebook, a web-based notebook environment that allows the use of multiple scripting language in a single notebook, with data flowing freely within and across languages. SoS Notebook enables researchers to perform sophisticated bioinformatic analysis using the most suitable tools for different parts of the workflow, without the limitations of a particular language or complications of cross-language communications.AvailabilitySoS Notebook is hosted at http://vatlab.github.io/SoS/ and is distributed under a BSD license.Contactbpeng@mdanderson.org

Clustered heat maps are the most frequently used graphics for visualization and interpretation of genome-scale molecular profiling data in biology. Construction of a heat map generally requires the assistance of a biostatistician or bioinformatics analyst capable of working in R or a similar programming language to transform the study data, perform hierarchical clustering, and generate the heat map. Our web-based Interactive Heat Map Builder can be used by investigators with no bioinformatics experience to generate high-caliber, publication quality maps. Preparation of the data and construction of a heat map is rarely a simple linear process. Our tool allows a user to move back and forth iteratively through the various stages of map generation to try different options and approaches. Finally, the heat map the builder creates is available in several forms, including an interactive Next-Generation Clustered Heat Map that can be explored dynamically to investigate the results more fully.

Clustered heatmaps are the most frequently used graphics for visualization of molecular profiling data in biology. However, they are generally rendered as static, or only modestly interactive, images. We have now used recent advances in web technologies to produce interactive “next-generation” clustered heatmaps (NG-CHM) that enable extreme zooming and navigation without loss of resolution. NG-CHMs also provide link-outs to additional information sources and include other features that facilitate deep exploration of the biology behind the image. Here, we describe an implementation of the NG-CHM system in the Galaxy bioinformatics platform. We illustrate the algorithm and available computational tool using RNA-seq data from The Cancer Genome Atlas program's Kidney Clear Cell Carcinoma project. Cancer Res; 77(21); e23–26. ©2017 AACR.

TCGA's RNASeq data represent one of the largest collections of cancer transcriptomes ever assembled. RNASeq technology, combined with computational tools like our SpliceSeq package, provides a comprehensive, detailed view of alternative mRNA splicing. Aberrant splicing patterns in cancers have been implicated in such processes as carcinogenesis, de-differentiation and metastasis. TCGA SpliceSeq (http://bioinformatics.mdanderson.org/TCGASpliceSeq) is a web-based resource that provides a quick, user-friendly, highly visual interface for exploring the alternative splicing patterns of TCGA tumors. Percent Spliced In (PSI) values for splice events on samples from 33 different tumor types, including available adjacent normal samples, have been loaded into TCGA SpliceSeq. Investigators can interrogate genes of interest, search for the genes that show the strongest variation between or among selected tumor types, or explore splicing pattern changes between tumor and adjacent normal samples. The interface presents intuitive graphical representations of splicing patterns, read counts and various statistical summaries, including percent spliced in. Splicing data can also be downloaded for inclusion in integrative analyses. TCGA SpliceSeq is freely available for academic, government or commercial use.

One DNA sequence can code for multiple different mRNAs, and therefore many different proteins. Conversely, a variant identified at the protein or transcript level may have non-unique genomic origins. For example, EGFR:p.L747S, which mediates acquired resistance of non-small cell lung cancer to tyrosine kinase inhibitors1, can be translated from multiple genomic variants such as chr7:g.55249076_55249077delinsAG and chr7:g.55242470T>C on different isoforms defined on the human reference assembly GRCh37. One-to-many, many-to-one and many-to-many relationships among sequence variants at the genomic level and those at transcript and protein levels introduce frequent inconsistencies in current practice when vital information about the annotation process (e.g., transcript or isoform IDs) is omitted from variant identifiers. To facilitate standardization and reveal inconsistency in existing variant annotations, we have designed a novel variant annotator, TransVar, to perform three main functions supporting diverse reference genomes and transcript databases (Fig. 1a): (i) “forward annotation”, which annotates all potential effects of a genomic variant on mRNAs and proteins; (ii) “reverse annotation”, which traces an mRNA or protein variant to all potential genomic origins; and (iii) “equivalence annotation”, which, for a given protein variant, searches for alternative protein variants that have identical genomic origin but are represented based on different isoforms. Figure 1 Schematic overview of TransVar and comparison of TransVar with other tools. (a) TransVar performs forward (green arrows) and reverse annotation (pink arrows) and considers all possible mRNA transcripts or protein isoforms available in user-specified reference ... We annotated 964,132 unique single-nucleotide substitutions (SNS), 3,715 multi-nucleotide substitutions (MNS), 11,761 insertions (INS), 24,595 deletions (DEL) and 166 block substitutions (BLS) in the Catalogue of Somatic Mutations in Cancer (COSMIC v67) using TransVar, ANNOVAR2, VEP3, snpEff4, and Oncotator5, and asked whether the resulting protein identifiers (gene name, protein coordinates, and reference amino acid (AA)) match those in COSMIC. We observed comparable consistency in SNS and MNS but variable consistency in INS, DEL and BLS from different annotators (Fig. 1b, Supplementary Table 1 and Supplementary Notes). That finding can largely be attributed to a lack of standardization among variant annotations (codon or AA positions of variants) submitted to COSMIC and among conventions implemented in various annotators. Inconsistency in annotations blurred the lines of evidence for variant frequency estimation and led to inaccurate determination of variant function. TransVar revealed hidden inconsistency in these variant annotations by comprehensively outputting alternative annotations in all available transcripts in standard HGVS nomenclature, and thus resulted in greater consistency in this experiment. TransVar’s novel reverse annotation can be used to ascertain if two protein variants have identical genomic origin, thus reducing inconsistency in annotation data. It can also reveal whether or not a protein variant has non-unique genomic origins and requires caution in genetic and clinical interpretation. We reverse-annotated the protein level variants in COSMIC and found that even under the constraints imposed by the reference base or AA identity, a sizeable fraction (e.g., 11.9% of single-AA substitutions) were associated with multiple genomic variants (Supplementary Table 2), if transcripts were not specified. Among the 537 variants that were cited as clinically actionable at PersonalizedCancerTherapy.org, 78 (14.5%) (e.g., CDKN2A:p.R87P and ERBB2:p.L755_T759del) could be mapped to multiple genomic locations (Supplementary Table 3). The reverse-annotation functionality also enabled systematic genomic characterization of variants directly identified from proteomic or RNA-seq data. For example, we were able to identify in just a few minutes of compute-time the putative genomic origins of 187,464 (97.69%) protein phosphorylation sites (e.g., p.Y308/p.S473 in AKT1 and p.Y1068/p.Y1172 in EGFR) in human proteins6. Our investigation revealed frequent inconsistencies in current databases and tools and highlighted the importance of standardization. With both forward and reverse annotation enabled in TransVar, we can reveal hidden inconsistency and improve the precision of translational and clinical genomics. The source code and detailed instructions of TransVar is available at https://bitbucket.org/wanding/transvar and a web interface is at http://www.transvar.net.

Summary: PathwaysWeb is a resource-based, well-documented web system that provides publicly available information on genes, biological pathways, Gene Ontology (GO) terms, gene–gene interaction networks (importantly, with the directionality of interactions) and links to key-related PubMed documents. The PathwaysWeb API simplifies the construction of applications that need to retrieve and interrelate information across multiple, pathway-related data types from a variety of original data sources. PathwaysBrowser is a companion website that enables users to explore the same integrated pathway data. The PathwaysWeb system facilitates reproducible analyses by providing access to all versions of the integrated datasets. Although its GO subsystem includes data for mouse, PathwaysWeb currently focuses on human data. However, pathways for mouse and many other species can be inferred with a high success rate from human pathways. Availability and implementation: PathwaysWeb can be accessed via the Internet at http://bioinformatics.mdanderson.org/main/PathwaysWeb:Overview. Contact: jmmelott@mdanderson.org Supplementary information: Supplementary data are available at Bioinformatics online.

The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile., National Cancer Institute (U.S.), National Human Genome Research Institute (U.S.)

The Cancer Genome Atlas (TCGA) program is generating comprehensive molecular profiles of more than 30 clinical tumor types, the first 12 of which have been incorporated into a “Pan-Cancer12” project. One bioinformatic challenge is statistical analysis of the resulting profiles; a second is the visual detective work necessary to explore individual genes, pathways and patterns in the data. For that type of detective work, we introduced CHMs in the early 1990s for pharmacogenomic analysis (1) and later for integrated visualization of genomic, transcriptomic, proteomic, pharmacological, and functional data (2). As the ubiquitous first-order way of visualizing omic data, CHMs have appeared in many thousands of publications (3-9), including all of the major publications by the TCGA Research Network. However, a major limitation is that they have been static or only modestly interactive graphics. We have now developed “next-generation” clustered heat maps (NG-CHMs), which use a Google-maps-like tiling technology for extreme zooming and navigation without loss of resolution. NG-CHMs provide pathway and gene ontology information, re-coloring on the fly, tools for reproducibility, high-resolution graphics output, a statistical toolbox, and link-outs to public sources of information on genes, proteins, pathways and drugs. The result is a visually rich, dynamic environment for exploration of the masses of data produced by TCGA. The compendium of TCGA Pan-Cancer NG-CHMs currently includes 667 maps as an initial set, but the numbers will soon rise into the thousands as more data types, tumour types and algorithms are incorporated (at web portal http://bioinformatics.mdanderson.org/TCGA/NGCHMPortal/). As an illustrative example, NG-CHMs proved pivotal as a tool for discovering and analyzing molecular target themes common to multiple types of gynecological cancers and themes that distinguish them from each other. 1. Weinstein JN … Paull KD. Stem Cells 12; 13, 1994. 2. Weinstein JN … Paull KD. Science 275;343, 1997. 3. Myers TG … Weinstein JN. Electrophoresis 18; 467, 1997. 4. Eisen MB … Botstein D. Proc. Natl. Acad. Sci. U.S.A. 14863, 1998. 5. Golub TR … Lander ES. Science 286; 531, 1999. 6. Ross DT … Brown PA. Nature Genetics 24; 227, 2000 7. Scherf U … Weinstein JN. Nature Genetics 24; 236, 2000. 8. Zeeberg BR … Weinstein JN. BMC Bioinformatics 6; 168, 2005. 9. Weinstein JN. Science 319; 1772, 2008. Supported in part by NCI Grant No. U24CA143883, by a gift from the Mary K. Chapman Foundation, and by a grant from the Michael and Susan Dell Foundation honoring Lorraine Dell. Note: This abstract was not presented at the meeting. Citation Format: John N. Weinstein, Rehan Akbani, David W. Kane, James M. Melott, Tod D. Casasent, Rong Yao, Paul L. Roebuck, Gordon B. Mills, Michael C. Ryan, Christopher Wakefield, Bradley M. Broom. A web portal of ‘next-generation’ clustered heat maps for user-friendly, interactive exploration of patterns in TCGA data. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2979. doi:10.1158/1538-7445.AM2015-2979