Biomedical subjects

Hanqing Xie

Publications and source records attributed to Hanqing Xie.

4 recordsLinked to original sources

PubMed2004

Over 20% of human transcripts might form sense-antisense pairs.

The major challenge to identifying natural sense- antisense (SA) transcripts from public databases is how to determine the correct orientation for an expressed sequence, especially an expressed sequence tag sequence. In this study, we established a set of very stringent criteria to identify the correct orientation of each human transcript. We used these orientation-reliable transcripts to create 26 741 transcription clusters in the human genome. Our analysis shows that 22% (5880) of the human transcription clusters form SA pairs, higher than any previous estimates. Our orientation-specific RT-PCR results along with the comparison of experimental data from previous studies confirm that our SA data set is reliable. This study not only demonstrates that our criteria for the prediction of SA transcripts are efficient, but also provides additional convincing data to support the view that antisense transcription is quite pervasive in the human genome. In-depth analyses show that SA transcripts have some significant differences compared with other types of transcripts, with regard to chromosomal distribution and Gene Ontology-annotated categories of physiological roles, functions and spatial localizations of gene products.

Base Pairing↗

PubMed2004

Bridging expressed sequence alignments through targeted cDNA sequencing.

One of the major challenges in genome research is the identification of the complete set of genes in a genome. Alignments of expressed sequences (RNA and EST) with genomic sequences have been used to characterize genes. However, the number of alignments far exceeds the likely number of genes in a genome, suggesting that, for many genes, two or more alignments can be joined through overlapping sequences to yield accurate gene structures. High-throughput EST sequencing becomes less efficient in closing those alignment gaps due to its nonselective nature. We sought to bridge these alignments through a novel approach: targeted cDNA sequencing. Human expressed sequences from GenBank version 124 were aligned with the genomic sequence from NCBI build 24 using LEADS, Compugen's EST and RNA clustering and assembly software system. Nine hundred forty-eight pairs of alignments were selected based on EST clone information and/or their homology to the same known proteins. Reverse transcriptase PCR and sequencing yielded sequences for 363 of those pairs. These sequences helped characterize over 60 novel or otherwise incomplete genes in the recent UniGene build 153, which included over 1 million additional ESTs. These results indicate that this integrated and targeted strategy, combining computational prediction and experimental cDNA sequencing, can efficiently generate the overlapping sequences and enable the full characterization of genomes. Additional information about the contig pairs, the resultant overlapping sequences, tissue sources, and tissue profiles are available in a supplemental file.

Cloning, Molecular↗

PubMed2002

Computational analysis of alternative splicing using EST tissue information.

Expressed sequence tags (ESTs) from normal and tumor tissues have been deposited in public databases. These ESTs and all mRNA sequences were aligned with the human genome sequence using LEADS, Compugen's alternative splicing modeling platform. We developed a novel computational approach to analyze tissue information of aligned ESTs in order to identify cancer-specific alternative splicing and gene segments highly expressed in particular cancers. Several genes, including one encoding a possible pre-mRNA splicing factor, displayed cancer-specific alternative splicing. In addition, multiple candidate gene segments highly expressed in colon cancers were identified.

Alternative Splicing↗

PubMed2002

Large-scale protein annotation through gene ontology.

Recent progress in genomic sequencing, computational biology, and ontology development has presented an opportunity to investigate biological systems from a unique perspective, that is, examining genomes and transcriptomes through the multiple and hierarchical structure of Gene Ontology (GO). We report here our development of GO Engine, a computational platform for GO annotation, and analysis of the resultant GO annotations of human proteins. Protein annotation was centered on sequence homology with GO-annotated proteins and protein domain analysis. Text information analysis and a multiparameter cellular localization predictive tool were also used to increase the annotation accuracy, and to predict novel annotations. The majority of proteins corresponding to full-length mRNA in GenBank, and the majority of proteins in the NR database (nonredundant database of proteins) were annotated with one or more GO nodes in each of the three GO categories. The annotations of GenBank and SWISS-PROT proteins are available to the public at the GO Consortium web site.

Animals↗