Biomedical subjects

Jill Cheng

Publications and source records attributed to Jill Cheng.

8 recordsLinked to original sources

PubMed2005

Transcriptional maps of 10 human chromosomes at 5-nucleotide resolution.

Sites of transcription of polyadenylated and nonpolyadenylated RNAs for 10 human chromosomes were mapped at 5-base pair resolution in eight cell lines. Unannotated, nonpolyadenylated transcripts comprise the major proportion of the transcriptional output of the human genome. Of all transcribed sequences, 19.4, 43.7, and 36.9% were observed to be polyadenylated, nonpolyadenylated, and bimorphic, respectively. Half of all transcribed sequences are found only in the nucleus and for the most part are unannotated. Overall, the transcribed portions of the human genome are predominantly composed of interlaced networks of both poly A+ and poly A- annotated transcripts and unannotated transcripts of unknown function. This organization has important implications for interpreting genotype-phenotype associations, regulation of gene expression, and the definition of a gene.

Cell Line↗

PubMed2005

Examples of the complex architecture of the human transcriptome revealed by RACE and high-density tiling arrays.

Recently, we mapped the sites of transcription across approximately 30% of the human genome and elucidated the structures of several hundred novel transcripts. In this report, we describe a novel combination of techniques including the rapid amplification of cDNA ends (RACE) and tiling array technologies that was used to further characterize transcripts in the human transcriptome. This technical approach allows for several important pieces of information to be gathered about each array-detected transcribed region, including strand of origin, start and termination positions, and the exonic structures of spliced and unspliced coding and noncoding RNAs. In this report, the structures of transcripts from 14 transcribed loci, representing both known genes and unannotated transcripts taken from the several hundred randomly selected unannotated transcripts described in our previous work are represented as examples of the complex organization of the human transcriptome. As a consequence of this complexity, it is not unusual that a single base pair can be part of an intricate network of multiple isoforms of overlapping sense and antisense transcripts, the majority of which are unannotated. Some of these transcripts follow the canonical splicing rules, whereas others combine the exons of different genes or represent other types of noncanonical transcripts. These results have important implications concerning the correlation of genotypes to phenotypes, the regulation of complex interlaced transcriptional patterns, and the definition of a gene.

Cell Line↗

PubMed2004

Unbiased mapping of transcription factor binding sites along human chromosomes 21 and 22 points to widespread regulation of noncoding RNAs.

Using high-density oligonucleotide arrays representing essentially all nonrepetitive sequences on human chromosomes 21 and 22, we map the binding sites in vivo for three DNA binding transcription factors, Sp1, cMyc, and p53, in an unbiased manner. This mapping reveals an unexpectedly large number of transcription factor binding site (TFBS) regions, with a minimal estimate of 12,000 for Sp1, 25,000 for cMyc, and 1600 for p53 when extrapolated to the full genome. Only 22% of these TFBS regions are located at the 5' termini of protein-coding genes while 36% lie within or immediately 3' to well-characterized genes and are significantly correlated with noncoding RNAs. A significant number of these noncoding RNAs are regulated in response to retinoic acid, and overlapping pairs of protein-coding and noncoding RNAs are often coregulated. Thus, the human genome contains roughly comparable numbers of protein-coding and noncoding genes that are bound by common transcription factors and regulated by common environmental signals.

Amino Acid Motifs↗

PubMed2004

NetAffx Gene Ontology Mining Tool: a visual approach for microarray data analysis.

SUMMARY: The NetAffx Gene Ontology (GO) Mining Tool is a web-based, interactive tool that permits traversal of the GO graph in the context of microarray data. It accepts a list of Affymetrix probe sets and renders a GO graph as a heat map colored according to significance measurements. The rendered graph is interactive, with nodes linked to public web sites and to lists of the relevant probe sets. The GO Mining Tool provides visualization combining biological annotation with expression data, encompassing thousands of genes in one interactive view. AVAILABILITY: GO Mining Tool is freely available at http://www.affymetrix.com/analysis/query/go_analysis.affx

Abstracting and Indexing↗

PubMed2004

A knowledge-based clustering algorithm driven by Gene Ontology.

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.

Algorithms↗

PubMed2004

Novel RNAs identified from an in-depth analysis of the transcriptome of human chromosomes 21 and 22.

In this report, we have achieved a richer view of the transcriptome for Chromosomes 21 and 22 by using high-density oligonucleotide arrays on cytosolic poly(A)(+) RNA. Conservatively, only 31.4% of the observed transcribed nucleotides correspond to well-annotated genes, whereas an additional 4.8% and 14.7% correspond to mRNAs and ESTs, respectively. Approximately 85% of the known exons were detected, and up to 21% of known genes have only a single isoform based on exon-skipping alternative expression. Overall, the expression of the well-characterized exons falls predominately into two categories, uniquely or ubiquitously expressed with an identifiable proportion of antisense transcripts. The remaining observed transcription (49.0%) was outside of any known annotation. These novel transcripts appear to be more cell-line-specific and have lower and less variation in expression than the well-characterized genes. Novel transcripts were further characterized based on their distance to annotations, transcript size, coding capacity, and identification as antisense to intronic sequences. By RT-PCR, 126 novel transcripts were independently verified, resulting in a 65% verification rate. These observations strongly support the argument for a re-evaluation of the total number of human genes and an alternative term for "gene" to encompass these growing, novel classes of RNA transcripts in the human genome.

Cell Line↗

PubMed2003

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.

Animals↗

PubMed2002

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.

Animals↗