Biomedical subjects

Evan Keibler

Publications and source records attributed to Evan Keibler.

4 recordsLinked to original sources

PubMed2003

Eval: a software package for analysis of genome annotations.

SUMMARY: Eval is a flexible tool for analyzing the performance of gene annotation systems. It provides summaries and graphical distributions for many descriptive statistics about any set of annotations, regardless of their source. It also compares sets of predictions to standard annotations and to one another. Input is in the standard Gene Transfer Format (GTF). Eval can be run interactively or via the command line, in which case output options include easily parsable tab-delimited files. AVAILABILITY: To obtain the module package with documentation, go to http://genes.cse.wustl.edu/ and follow links for Resources, then Software. Please contact brent@cse.wustl.edu

Computational Biology↗

PubMed2003

The DNA sequence of human chromosome 7.

Human chromosome 7 has historically received prominent attention in the human genetics community, primarily related to the search for the cystic fibrosis gene and the frequent cytogenetic changes associated with various forms of cancer. Here we present more than 153 million base pairs representing 99.4% of the euchromatic sequence of chromosome 7, the first metacentric chromosome completed so far. The sequence has excellent concordance with previously established physical and genetic maps, and it exhibits an unusual amount of segmentally duplicated sequence (8.2%), with marked differences between the two arms. Our initial analyses have identified 1,150 protein-coding genes, 605 of which have been confirmed by complementary DNA sequences, and an additional 941 pseudogenes. Of genes confirmed by transcript sequences, some are polymorphic for mutations that disrupt the reading frame.

Animals↗

PubMed2003

Comparison of mouse and human genomes followed by experimental verification yields an estimated 1,019 additional genes.

A primary motivation for sequencing the mouse genome was to accelerate the discovery of mammalian genes by using sequence conservation between mouse and human to identify coding exons. Achieving this goal proved challenging because of the large proportion of the mouse and human genomes that is apparently conserved but apparently does not code for protein. We developed a two-stage procedure that exploits the mouse and human genome sequences to produce a set of genes with a much higher rate of experimental verification than previously reported prediction methods. RT-PCR amplification and direct sequencing applied to an initial sample of mouse predictions that do not overlap previously known genes verified the regions flanking one intron in 139 predictions, with verification rates reaching 76%. On average, the confirmed predictions show more restricted expression patterns than the mouse orthologs of known human genes, and two-thirds lack homologs in fish genomes, demonstrating the sensitivity of this dual-genome approach to hard-to-find genes. We verified 112 previously unknown homologs of known proteins, including two homeobox proteins relevant to developmental biology, an aquaporin, and a homolog of dystrophin. We estimate that transcription and splicing can be verified for >1,000 gene predictions identified by this method that do not overlap known genes. This is likely to constitute a significant fraction of the previously unknown, multiexon mammalian genes.

Amino Acid Sequence↗

PubMed2003

Leveraging the mouse genome for gene prediction in human: from whole-genome shotgun reads to a global synteny map.

The availability of draft sequences for both the mouse and human genomes makes it possible, for the first time, to annotate whole mammalian genomes using comparative methods. TWINSCAN is a gene-prediction system that combines the methods of single-genome predictors like GENSCAN with information derived from genome comparison, thereby improving accuracy. Because TWINSCAN uses genomic sequence only, it is less biased toward highly and/or ubiquitously expressed genes than GENEWISE, GENOMESCAN, and other methods based on evidence derived from transcripts. We show that TWINSCAN improves gene prediction in human using intermediate products from various stages of the sequencing and analysis of the mouse genome, from low-redundancy, whole-genome shotgun reads to the draft assembly and the synteny map. TWINSCAN improves on the prior state of the art even when alignments from only 1X coverage of the mouse genome are available. Gene prediction accuracy improves steadily from 1X through 3X, more slowly from 3X to 4X, and relatively little thereafter. The assembly and the synteny map greatly speed the computations, however. Our human annotation using the mouse assembly is conservative, predicting only 25,622 genes, and appears to be one of the best de novo annotations of the human genome to date.

Animals↗