The primary differentiation event during mammalian development occurs at the blastocyst stage and leads to the delineation of the inner cell mass (ICM) and the trophectoderm (TE). We provide the first global mRNA expression data from immunosurgically dissected ICM cells, TE cells, and intact human blastocysts. Using a cDNA microarray composed of 15,529 cDNAs from known and novel genes, we identify marker transcripts specific to the ICM (e.g., OCT4/POU5F1, NANOG, HMGB1, and DPPA5) and TE (e.g., CDX2, ATP1B3, SFN, and IPL), in addition to novel ICM- and TE-specific expressed sequence tags. The expression patterns suggest that the emergence of pluripotent ICM and TE cell lineages from the morula is controlled by metabolic and signaling pathways, which include inter alia, WNT, mitogen-activated protein kinase, transforming growth factor-beta, NOTCH, integrin-mediated cell adhesion, phosphatidylinositol 3-kinase, and apoptosis. These data enhance our understanding of the first step in human cellular differentiation and, hence, the derivation of both embryonic stem cells and trophoblastic stem cells from these lineages.

Large, publicly available collections of expressed sequence tags (ESTs) have been generated from Arabidopsis thaliana and rice (Oryza sativa). A potential, but relatively unexplored application of this data is in the study of plant gene expression. Other EST data, mainly from human and mouse, have been successfully used to point out genes exhibiting tissue- or disease-specific expression, as well as for identification of alternative transcripts. In this report, we go a step further in showing that computer analyses of plant EST data can be used to generate evidence of correlated expression patterns of genes across various tissues. Furthermore, tissue types and organs can be classified with respect to one another on the basis of their global gene expression patterns. As in previous studies, expression profiles are first estimated from EST counts. By clustering gene expression profiles or whole cDNA library profiles, we show that genes with similar functions, or cDNA libraries expected to share patterns of gene expression, are grouped together. Promising uses of this technique include functional genomics, in which evidence of correlated expression might complement (or substitute for) those of sequence similarity in the annotation of anonymous genes and identification of surrogate markers. The analysis presented here combines the application of a correlation-based clustering method with a graphical color map allowing intuitive visualization of patterns within a large table of expression measurements.

Human–chimpanzee comparative genome research is essential for narrowing down genetic changes involved in the acquisition of unique human features, such as highly developed cognitive functions, bipedalism or the use of complex language. Here, we report the high-quality DNA sequence of 33.3 megabases of chimpanzee chromosome 22. By comparing the whole sequence with the human counterpart, chromosome 21, we found that 1.44% of the chromosome consists of single-base substitutions in addition to nearly 68,000 insertions or deletions. These differences are sufficient to generate changes in most of the proteins. Indeed, 83% of the 231 coding sequences, including functionally important genes, show differences at the amino acid sequence level. Furthermore, we demonstrate different expansion of particular subfamilies of retrotransposons between the lineages, suggesting different impacts of retrotranspositions on human and chimpanzee evolution. The genomic changes after speciation and their biological consequences seem more complex than originally hypothesized.

The application of genomics technologies to medicine and biomedical research is increasing in popularity, made possible by new high-throughput genotyping and sequencing technologies and improved data analysis capabilities. Some of the greatest genetic diversity among humans, animals, plants, and microbiota occurs in Africa, yet genomic research outputs from the continent are limited. The Human Heredity and Health in Africa (H3Africa) initiative was established to drive the development of genomic research for human health in Africa, and through recognition of the critical role of bioinformatics in this process, spurred the establishment of H3ABioNet, a pan-African bioinformatics network for H3Africa. The limitations in bioinformatics capacity on the continent have been a major contributory factor to the lack of notable outputs in high-throughput biology research. Although pockets of high-quality bioinformatics teams have existed previously, the majority of research institutions lack experienced faculty who can train and supervise bioinformatics students. H3ABioNet aims to address this dire need, specifically in the area of human genetics and genomics, but knock-on effects are ensuring this extends to other areas of bioinformatics. Here, we describe the emergence of genomics research and the development of bioinformatics in Africa through H3ABioNet.

Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a pan-African bioinformatics network, was established to build capacity specifically to enable H3Africa (Human Heredity and Health in Africa) researchers to analyze their data in Africa. Since the inception of the H3Africa initiative, H3ABioNet's role has evolved in response to changing needs from the consortium and the African bioinformatics community. H3ABioNet set out to develop core bioinformatics infrastructure and capacity for genomics research in various aspects of data collection, transfer, storage, and analysis. Various resources have been developed to address genomic data management and analysis needs of H3Africa researchers and other scientific communities on the continent. NetMap was developed and used to build an accurate picture of network performance within Africa and between Africa and the rest of the world, and Globus Online has been rolled out to facilitate data transfer. A participant recruitment database was developed to monitor participant enrollment, and data is being harmonized through the use of ontologies and controlled vocabularies. The standardized metadata will be integrated to provide a search facility for H3Africa data and biospecimens. Because H3Africa projects are generating large-scale genomic data, facilities for analysis and interpretation are critical. H3ABioNet is implementing several data analysis platforms that provide a large range of bioinformatics tools or workflows, such as Galaxy, the Job Management System, and eBiokits. A set of reproducible, portable, and cloud-scalable pipelines to support the multiple H3Africa data types are also being developed and dockerized to enable execution on multiple computing infrastructures. In addition, new tools have been developed for analysis of the uniquely divergent African data and for downstream interpretation of prioritized variants. To provide support for these and other bioinformatics queries, an online bioinformatics helpdesk backed by broad consortium expertise has been established. Further support is provided by means of various modes of bioinformatics training. For the past 4 years, the development of infrastructure support and human capacity through H3ABioNet, have significantly contributed to the establishment of African scientific networks, data analysis facilities, and training programs. Here, we describe the infrastructure and how it has affected genomics and bioinformatics research in Africa.

Background: Leishmania (L) are intracellular protozoan parasites that are able to survive and replicate within the harsh and potentially hostile phagolysosomal environment of mammalian mononuclear phagocytes. A complex interplay then takes place between the macrophage (MΦ) striving to eliminate the pathogen and the parasite struggling for its own survival. To investigate this host-parasite conflict at the transcriptional level, in the context of monocyte-derived human MΦs (MDM) infection by L. major metacyclic promastigotes, the quantitative technique of serial analysis of gene expression (SAGE) was used. Results: After extracting mRNA from resting human MΦs, Leishmania-infected human MΦs and L. major parasites, three SAGE libraries were constructed and sequenced generating up to 28,173; 57,514 and 33,906 tags respectively (corresponding to 12,946; 23,442 and 9,530 unique tags). Using computational data analysis and direct comparison to 357,888 publicly available experimental human tags, the parasite and the host cell transcriptomes were then simultaneously characterized from the mixed cellular extract, confidently discriminating host from parasite transcripts. This procedure led us to reliably assign 3,814 tags to MΦs' and 3,666 tags to L. major parasites transcripts. We focused on these, showing significant changes in their expression that are likely to be relevant to the pathogenesis of parasite infection: (i) human MΦs genes, belonging to key immune response proteins (e.g., IFNγ pathway, S100 and chemokine families) and (ii) a group of Leishmania genes showing a preferential expression at the parasite's intra-cellular developing stage. Conclusion: Dual SAGE transcriptome analysis provided a useful, powerful and accurate approach to discriminating genes of human or parasitic origin in Leishmania-infected human MΦs. The findings presented in this work suggest that the Leishmania parasite modulates key transcripts in human MΦs that may be beneficial for its establishment and survival. Furthermore, these results provide an overview of gene expression at two developmental stages of the parasite, namely metacyclic promastigotes and intracellular amastigotes and indicate a broad difference between their transcriptomic profiles. Finally, our reported set of expressed genes will be useful in future rounds of data mining and gene annotation.