MOTIVATION: Because of the advantages of RNA sequencing (RNA-Seq) over microarrays, it is gaining widespread popularity for highly parallel gene expression analysis. For example, RNA-Seq is expected to be able to provide accurate identification and quantification of full-length splice forms. A number of informatics packages have been developed for this purpose, but short reads make it a difficult problem in principle. Sequencing error and polymorphisms add further complications. It has become necessary to perform studies to determine which algorithms perform best and which if any algorithms perform adequately. However, there is a dearth of independent and unbiased benchmarking studies. Here we take an approach using both simulated and experimental benchmark data to evaluate their accuracy. RESULTS: We conclude that most methods are inaccurate even using idealized data, and that no method is highly accurate once multiple splice forms, polymorphisms, intron signal, sequencing errors, alignment errors, annotation errors and other complicating factors are present. These results point to the pressing need for further algorithm development. AVAILABILITY AND IMPLEMENTATION: Simulated datasets and other supporting information can be found at http://bioinf.itmat.upenn.edu/BEERS/bp2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: A critical task in high-throughput sequencing is aligning millions of short reads to a reference genome. Alignment is especially complicated for RNA sequencing (RNA-Seq) because of RNA splicing. A number of RNA-Seq algorithms are available, and claim to align reads with high accuracy and efficiency while detecting splice junctions. RNA-Seq data are discrete in nature; therefore, with reasonable gene models and comparative metrics RNA-Seq data can be simulated to sufficient accuracy to enable meaningful benchmarking of alignment algorithms. The exercise to rigorously compare all viable published RNA-Seq algorithms has not been performed previously. RESULTS: We developed an RNA-Seq simulator that models the main impediments to RNA alignment, including alternative splicing, insertions, deletions, substitutions, sequencing errors and intron signal. We used this simulator to measure the accuracy and robustness of available algorithms at the base and junction levels. Additionally, we used reverse transcription-polymerase chain reaction (RT-PCR) and Sanger sequencing to validate the ability of the algorithms to detect novel transcript features such as novel exons and alternative splicing in RNA-Seq data from mouse retina. A pipeline based on BLAT was developed to explore the performance of established tools for this problem, and to compare it to the recently developed methods. This pipeline, the RNA-Seq Unified Mapper (RUM), performs comparably to the best current aligners and provides an advantageous combination of accuracy, speed and usability. AVAILABILITY: The RUM pipeline is distributed via the Amazon Cloud and for computing clusters using the Sun Grid Engine (http://cbil.upenn.edu/RUM). CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: The RNA-Seq sequence reads described in the article are deposited at GEO, accession GSE26248.

MOTIVATION: Large-scale functional genomics analysis is now feasible and presents significant challenges in data analysis, storage and querying. Data standards are required to enable the development of public data repositories and to improve data sharing. There is an established data format for microarrays (microarray gene expression markup language, MAGE-ML) and a draft standard for proteomics (PEDRo). We believe that all types of functional genomics experiments should be annotated in a consistent manner, and we hope to open up new ways of comparing multiple datasets used in functional genomics. RESULTS: We have created a functional genomics experiment object model (FGE-OM), developed from the microarray model, MAGE-OM and two models for proteomics, PEDRo and our own model (Gla-PSI-Glasgow Proposal for the Proteomics Standards Initiative). FGE-OM comprises three namespaces representing (i) the parts of the model common to all functional genomics experiments; (ii) microarray-specific components; and (iii) proteomics-specific components. We believe that FGE-OM should initiate discussion about the contents and structure of the next version of MAGE and the future of proteomics standards. A prototype database called RNA And Protein Abundance Database (RAPAD), based on FGE-OM, has been implemented and populated with data from microbial pathogenesis. AVAILABILITY: FGE-OM and the RAPAD schema are available from http://www.gusdb.org/fge.html, along with a set of more detailed diagrams. RAPAD can be accessed by registration at the site.

MOTIVATION: Gene expression array technology has become increasingly widespread among researchers who recognize its numerous promises. At the same time, bench biologists and bioinformaticians have come to appreciate increasingly the importance of establishing a collaborative dialog from the onset of a study and of collecting and exchanging detailed information on the many experimental and computational procedures using a structured mechanism. This is crucial for adequate analyses of this kind of data. RESULTS: The RNA Abundance Database (RAD; http://www.cbil.upenn.edu/RAD) provides a comprehensive MIAME-supportive infrastructure for gene expression data management and makes extensive use of ontologies. Specific details on protocols, biomaterials, study designs, etc. are collected through a user-friendly suite of web annotation forms. Software has been developed to generate MAGE-ML documents to enable easy export of studies stored in RAD to any other database accepting data in this format (e.g. ArrayExpress). RAD is part of a more general Genomics Unified Schema (http://www.gusdb.org), which includes a richly annotated gene index (http://www.allgenes.org), thus providing a platform that integrates genomic and transcriptomic data from multiple organisms. This infrastructure enables a large variety of queries that incorporate visualization and analysis tools and have been tailored to serve the specific needs of projects focusing on particular organisms or biological systems.

Abstract Motivation and Results: A relational schema is described for capturing highly parallel gene expression experiments using different technologies. This schema grew out of efforts to build a database for collaborators working on different biological systems and using different types of platforms in their gene expression experiments as well as different types of image quantification software. The tables are conceptually organized into three categories of information: Platform, Experiment (which includes image scanning and quantification), and Data. The strengths of the schema are: (i) integrating information on array elements using a gene index; (ii) describing samples using ontologies; (iii) reducing an experiment to a single RNA source for precise descriptions yet not losing the relationships between experiments done at the same time or for the same project; and (iv) maintaining both raw and processed (e.g. cleansed and normalized) data and recording how the data is processed. The result is a novel schema, which can hold both array and non-array data, is extensible for detailed experimental descriptions that are precise and consistent, and allows for meaningful comparisons of genes between experiments. Availability: The schema is available at http://www.cbil.upenn.edu/cgi-bin/RAD2/schemaBrowserRAD.pl. Contact: [email protected] * To whom correspondence should be addressed. 2 Present address: Department of Molecular Cell Biology, Weizmann Institute of Science, Israel.