Christian J. Stoeckert

Comparative genomics of the neglected human malaria parasite Plasmodium vivax.

The human malaria parasite Plasmodium vivax is responsible for 25–40% of the ∼515 million annual cases of malaria worldwide. Although seldom fatal, the parasite elicits severe and incapacitating clinical symptoms and often causes relapses months after a primary infection has cleared. Despite its importance as a major human pathogen, P. vivax is little studied because it cannot be propagated continuously in the laboratory except in non-human primates. We sequenced the genome of P. vivax to shed light on its distinctive biological features, and as a means to drive development of new drugs and vaccines. Here we describe the synteny and isochore structure of P. vivax chromosomes, and show that the parasite resembles other malaria parasites in gene content and metabolic potential, but possesses novel gene families and potential alternative invasion pathways not recognized previously. Completion of the P. vivax genome provides the scientific community with a valuable resource that can be used to advance investigation into this neglected species.

Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project

The Minimum Information for Biological and Biomedical Investigations (MIBBI) project aims to foster the coordinated development of minimum-information checklists and provide a resource for those exploring the range of extant checklists.

TriTrypDB: a functional genomic resource for the Trypanosomatidae

TriTrypDB (http://tritrypdb.org) is an integrated database providing access to genome-scale datasets for kinetoplastid parasites, and supporting a variety of complex queries driven by research and development needs. TriTrypDB is a collaborative project, utilizing the GUS/WDK computational infrastructure developed by the Eukaryotic Pathogen Bioinformatics Resource Center (EuPathDB.org) to integrate genome annotation and analyses from GeneDB and elsewhere with a wide variety of functional genomics datasets made available by members of the global research community, often pre-publication. Currently, TriTrypDB integrates datasets from Leishmania braziliensis, L. infantum, L. major, L. tarentolae, Trypanosoma brucei and T. cruzi. Users may examine individual genes or chromosomal spans in their genomic context, including syntenic alignments with other kinetoplastid organisms. Data within TriTrypDB can be interrogated utilizing a sophisticated search strategy system that enables a user to construct complex queries combining multiple data types. All search strategies are stored, allowing future access and integrated searches. ‘User Comments’ may be added to any gene page, enhancing available annotation; such comments become immediately searchable via the text search, and are forwarded to curators for incorporation into the reference annotation when appropriate.

ToxoDB: an integrated Toxoplasma gondii database resource

ToxoDB (http://ToxoDB.org) is a genome and functional genomic database for the protozoan parasite Toxoplasma gondii. It incorporates the sequence and annotation of the T. gondii ME49 strain, as well as genome sequences for the GT1, VEG and RH (Chr Ia, Chr Ib) strains. Sequence information is integrated with various other genomic-scale data, including community annotation, ESTs, gene expression and proteomics data. ToxoDB has matured significantly since its initial release. Here we outline the numerous updates with respect to the data and increased functionality available on the website.

Promoter features related to tissue specificity as measured by Shannon entropy

BackgroundThe regulatory mechanisms underlying tissue specificity are a crucial part of the development and maintenance of multicellular organisms. A genome-wide analysis of promoters in the context of gene-expression patterns in tissue surveys provides a means of identifying the general principles for these mechanisms.ResultsWe introduce a definition of tissue specificity based on Shannon entropy to rank human genes according to their overall tissue specificity and by their specificity to particular tissues. We apply our definition to microarray-based and expressed sequence tag (EST)-based expression data for human genes and use similar data for mouse genes to validate our results. We show that most genes show statistically significant tissue-dependent variations in expression level. We find that the most tissue-specific genes typically have a TATA box, no CpG island, and often code for extracellular proteins. As expected, CpG islands are found in most of the least tissue-specific genes, which often code for proteins located in the nucleus or mitochondrion. The class of genes with no CpG island or TATA box are the most common mid-specificity genes and commonly code for proteins located in a membrane. Sp1 was found to be a weak indicator of less-specific expression. YY1 binding sites, either as initiators or as downstream sites, were strongly associated with the least-specific genes.ConclusionsWe have begun to understand the components of promoters that distinguish tissue-specific from ubiquitous genes, to identify associations that can predict the broad class of gene expression from sequence data alone.

Modeling biomedical experimental processes with OBI

BackgroundExperimental descriptions are typically stored as free text without using standardized terminology, creating challenges in comparison, reproduction and analysis. These difficulties impose limitations on data exchange and information retrieval.ResultsThe Ontology for Biomedical Investigations (OBI), developed as a global, cross-community effort, provides a resource that represents biomedical investigations in an explicit and integrative framework. Here we detail three real-world applications of OBI, provide detailed modeling information and explain how to use OBI.ConclusionWe demonstrate how OBI can be applied to different biomedical investigations to both facilitate interpretation of the experimental process and increase the computational processing and integration within the Semantic Web. The logical definitions of the entities involved allow computers to unambiguously understand and integrate different biological experimental processes and their relevant components.AvailabilityOBI is available at http://purl.obolibrary.org/obo/obi/2009-11-02/obi.owl

Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM)

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 ggrant@pcbi.upenn.edu; epierce@mail.med.upenn.edu SUPPLEMENTARY INFORMATION The RNA-Seq sequence reads described in the article are deposited at GEO, accession GSE26248.

A molecular profile of a hematopoietic stem cell niche

The hematopoietic microenvironment provides a complex molecular milieu that regulates the self-renewal and differentiation activities of stem cells. We have characterized a stem cell supportive stromal cell line, AFT024, that was derived from murine fetal liver. Highly purified in vivo transplantable mouse stem cells are maintained in AFT024 cultures at input levels, whereas other primitive progenitors are expanded. In addition, human stem cells are very effectively supported by AFT024. We suggest that the AFT024 cell line represents a component of an in vivo stem cell niche. To determine the molecular signals elaborated in this niche, we undertook a functional genomics approach that combines extensive sequence mining of a subtracted cDNA library, high-density array hybridization and in-depth bioinformatic analyses. The data have been assembled into a biological process oriented database, and represent a molecular profile of a candidate stem cell niche.

Using OrthoMCL to assign proteins to OrthoMCL-DB groups or to cluster proteomes into new ortholog groups.

OrthoMCL is an algorithm for grouping proteins into ortholog groups based on their sequence similarity. OrthoMCL-DB is a public database that allows users to browse and view ortholog groups that were pre-computed using the OrthoMCL algorithm. Version 4 of this database contained 116,536 ortholog groups clustered from 1,270,853 proteins obtained from 88 eukaryotic genomes, 16 archaean genomes, and 34 bacterial genomes. Future versions of OrthoMCL-DB will include more proteomes as more genomes are sequenced. Here, we describe how you can group your proteins of interest into ortholog clusters using two different means provided by the OrthoMCL system. The OrthoMCL-DB Web site has a tool for uploading and grouping a set of protein sequences, typically representing a proteome. This method maps the uploaded proteins to existing groups in OrthoMCL-DB. Alternatively, if you have proteins from a set of genomes that need to be grouped, you can download, install, and run the stand-alone OrthoMCL software.

A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB

BackgroundSharing of microarray data within the research community has been greatly facilitated by the development of the disclosure and communication standards MIAME and MAGE-ML by the MGED Society. However, the complexity of the MAGE-ML format has made its use impractical for laboratories lacking dedicated bioinformatics support.ResultsWe propose a simple tab-delimited, spreadsheet-based format, MAGE-TAB, which will become a part of the MAGE microarray data standard and can be used for annotating and communicating microarray data in a MIAME compliant fashion.ConclusionMAGE-TAB will enable laboratories without bioinformatics experience or support to manage, exchange and submit well-annotated microarray data in a standard format using a spreadsheet. The MAGE-TAB format is self-contained, and does not require an understanding of MAGE-ML or XML.