Jill Cheng

RNA Maps Reveal New RNA Classes and a Possible Function for Pervasive Transcription

Significant fractions of eukaryotic genomes give rise to RNA, much of which is unannotated and has reduced protein-coding potential. The genomic origins and the associations of human nuclear and cytosolic polyadenylated RNAs longer than 200 nucleotides (nt) and whole-cell RNAs less than 200 nt were investigated in this genome-wide study. Subcellular addresses for nucleotides present in detected RNAs were assigned, and their potential processing into short RNAs was investigated. Taken together, these observations suggest a novel role for some unannotated RNAs as primary transcripts for the production of short RNAs. Three potentially functional classes of RNAs have been identified, two of which are syntenically conserved and correlate with the expression state of protein-coding genes. These data support a highly interleaved organization of the human transcriptome.

NetAffx: Affymetrix probesets and annotations

NetAffx (http://www.affymetrix.com) details and annotates probesets on Affymetrix GeneChip microarrays. These annotations include (i) static information specific to the probeset composition; (ii) sequence annotations extracted from public databases; and (iii) protein sequence-level annotations derived from public domain programs, as well as libraries of hidden Markov models (HMMs) developed at Affymetrix. For each probeset, NetAffx lists the probe sequences, and the consensus sequence interrogated by the probes; for the larger chip sets, interactive maps display this sequence data in genomic context. Sequence annotations include Gene Ontology (GO) terms and depiction of GO graph relationships; predicted protein domains and motifs; orthologous sequences; links to relevant pathways; and links to public databases including UniGene, LocusLink, SWISS-PROT and OMIM.

Global transcription in pluripotent embryonic stem cells.

The molecular mechanisms underlying pluripotency and lineage specification from embryonic stem cells (ESCs) are largely unclear. Differentiation pathways may be determined by the targeted activation of lineage-specific genes or by selective silencing of genome regions. Here we show that the ESC genome is transcriptionally globally hyperactive and undergoes large-scale silencing as cells differentiate. Normally silent repeat regions are active in ESCs, and tissue-specific genes are sporadically expressed at low levels. Whole-genome tiling arrays demonstrate widespread transcription in coding and noncoding regions in ESCs, whereas the transcriptional landscape becomes more discrete as differentiation proceeds. The transcriptional hyperactivity in ESCs is accompanied by disproportionate expression of chromatin-remodeling genes and the general transcription machinery. We propose that global transcription is a hallmark of pluripotent ESCs, contributing to their plasticity, and that lineage specification is driven by reduction of the transcribed portion of the genome.

Biological function of unannotated transcription during the early development of Drosophila melanogaster

Many animal and plant genomes are transcribed much more extensively than current annotations predict. However, the biological function of these unannotated transcribed regions is largely unknown. Approximately 7% and 23% of the detected transcribed nucleotides during D. melanogaster embryogenesis map to unannotated intergenic and intronic regions, respectively. Based on computational analysis of coordinated transcription, we conservatively estimate that 29% of all unannotated transcribed sequences function as missed or alternative exons of well-characterized protein-coding genes. We estimate that 15.6% of intergenic transcribed regions function as missed or alternative transcription start sites (TSS) used by 11.4% of the expressed protein-coding genes. Identification of P element mutations within or near newly identified 5′ exons provides a strategy for mapping previously uncharacterized mutations to their respective genes. Collectively, these data indicate that at least 85% of the fly genome is transcribed and processed into mature transcripts representing at least 30% of the fly genome.

A Knowledge-Based Clustering Algorithm Driven by Gene Ontology

Abstract We have developed an algorithm for inferring the degree of similarity between genes by using the graph-based structure of Gene Ontology (GO). We applied this knowledge-based similarity metric to a clique-finding algorithm for detecting sets of related genes with biological classifications. We also combined it with an expression-based distance metric to produce a co-cluster analysis, which accentuates genes with both similar expression profiles and similar biological characteristics and identifies gene clusters that are more stable and biologically meaningful. These algorithms are demonstrated in the analysis of MPRO cell differentiation time series experiments.

NetAffx: affymetrix probeset annotations

One challenge in microarray experiments is assessing when the results are biologically significant. This assessment can be aided by detailed annotation of the probeset target sequences, including gene function or category, protein product, and pathway information. NetAffx compiles public and in-house annotations for all Affymetrix chip sets. Public annotations are collected from Unigene, LocusLink and Swiss-prot. In-house annotations are produced by Generalized Rapid Automated Protein Analysis (GRAPA), a high-accuracy HMM method for protein annotation. GRAPA has been used to generate novel annotations under three classification schemes: Structural Classification of Proteins (SCOP), Enzyme Commission (EC), and G protein coupled receptors (GPCR). In addition, annotations are generated by searching Pfam and BLOCKS databases. These annotation schemes have been applied to diverse genomes including human, mouse, rat, drosophila, and yeast, then mapped onto Affymetrix microarray probesets. The combination of protein-level annotations with public source annotations creates a powerful description of genes at both the genomic and protein levels. Users can collect information on a target sequence, or cluster microarray probe sets according to a given domain or functional category. NetAffx is available on the web at http://www.NetAffx.com/.

Structure-based comparison of four eukaryotic genomes.

The field of comparative genomics allows us to elucidate the molecular mechanisms necessary for the machinery of an organism by contrasting its genome against those of other organisms. In this paper, we contrast the genome of homo sapiens against C. Elegans, Drosophila melanogaster, and S. cerevisiae to gain insights on what structural domains are present in each organism. Previous work has assessed this using sequence-based homology recognition systems such as Pfam [1] and Interpro [2]. Here, we pursue a structure-based assessment, analyzing genomes according to domains in the SCOP structural domain dictionary. Compared to other eukaryotic genomes, we observe additional domains in the human genome relating to signal transduction, immune response, transport, and certain enzymes. Compared to the metazoan genomes, the yeast genome shows an absence of domains relating to immune response, cell-cell interactions, and cell signaling.