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Scott J Emrich

Publications and source records attributed to Scott J Emrich.

6 recordsLinked to original sources

Nearly identical paralogs: implications for maize (Zea mays L.) genome evolution.

As an ancient segmental tetraploid, the maize (Zea mays L.) genome contains large numbers of paralogs that are expected to have diverged by a minimum of 10% over time. Nearly identical paralogs (NIPs) are defined as paralogous genes that exhibit > or = 98% identity. Sequence analyses of the "gene space" of the maize inbred line B73 genome, coupled with wet lab validation, have revealed that, conservatively, at least approximately 1% of maize genes have a NIP, a rate substantially higher than that in Arabidopsis. In most instances, both members of maize NIP pairs are expressed and are therefore at least potentially functional. Of evolutionary significance, members of many NIP families also exhibit differential expression. The finding that some families of maize NIPs are closely linked genetically while others are genetically unlinked is consistent with multiple modes of origin. NIPs provide a mechanism for the maize genome to circumvent the inherent limitation that diploid genomes can carry at most two "alleles" per "locus." As such, NIPs may have played important roles during the evolution and domestication of maize and may contribute to the success of long-term selection experiments in this important crop species.

Arabidopsis↗

Gene discovery and annotation using LCM-454 transcriptome sequencing.

454 DNA sequencing technology achieves significant throughput relative to traditional approaches. More than 261,000 ESTs were generated by 454 Life Sciences from cDNA isolated using laser capture microdissection (LCM) from the developmentally important shoot apical meristem (SAM) of maize (Zea mays L.). This single sequencing run annotated >25,000 maize genomic sequences and also captured approximately 400 expressed transcripts for which homologous sequences have not yet been identified in other species. Approximately 70% of the ESTs generated in this study had not been captured during a previous EST project conducted using a cDNA library constructed from hand-dissected apex tissue that is highly enriched for SAMs. In addition, at least 30% of the 454-ESTs do not align to any of the approximately 648,000 extant maize ESTs using conservative alignment criteria. These results indicate that the combination of LCM and the deep sequencing possible with 454 technology enriches for SAM transcripts not present in current EST collections. RT-PCR was used to validate the expression of 27 genes whose expression had been detected in the SAM via LCM-454 technology, but that lacked orthologs in GenBank. Significantly, transcripts from approximately 74% (20/27) of these validated SAM-expressed "orphans" were not detected in meristem-rich immature ears. We conclude that the coupling of LCM and 454 sequencing technologies facilitates the discovery of rare, possibly cell-type-specific transcripts.

Base Sequence↗

Quality assessment of maize assembled genomic islands (MAGIs) and large-scale experimental verification of predicted genes.

Recent sequencing efforts have targeted the gene-rich regions of the maize (Zea mays L.) genome. We report the release of an improved assembly of maize assembled genomic islands (MAGIs). The 114,173 resulting contigs have been subjected to computational and physical quality assessments. Comparisons to the sequences of maize bacterial artificial chromosomes suggest that at least 97% (160 of 165) of MAGIs are correctly assembled. Because the rates at which junction-testing PCR primers for genomic survey sequences (90-92%) amplify genomic DNA are not significantly different from those of control primers ( approximately 91%), we conclude that a very high percentage of genic MAGIs accurately reflect the structure of the maize genome. EST alignments, ab initio gene prediction, and sequence similarity searches of the MAGIs are available at the Iowa State University MAGI web site. This assembly contains 46,688 ab initio predicted genes. The expression of almost half (628 of 1,369) of a sample of the predicted genes that lack expression evidence was validated by RT-PCR. Our analyses suggest that the maize genome contains between approximately 33,000 and approximately 54,000 expressed genes. Approximately 5% (32 of 628) of the maize transcripts discovered do not have detectable paralogs among maize ESTs or detectable homologs from other species in the GenBank NR nucleotide/protein database. Analyses therefore suggest that this assembly of the maize genome contains approximately 350 previously uncharacterized expressed genes. We hypothesize that these "orphans" evolved quickly during maize evolution and/or domestication.

Chromosomes, Artificial, Bacterial↗

Evaluation of five ab initio gene prediction programs for the discovery of maize genes.

Five ab initio programs (FGENESH, GeneMark.hmm, GENSCAN, GlimmerR and Grail) were evaluated for their accuracy in predicting maize genes. Two of these programs, GeneMark.hmm and GENSCAN had been trained for maize; FGENESH had been trained for monocots (including maize), and the others had been trained for rice or Arabidopsis. Initial evaluations were conducted using eight maize genes (gl8a, pdc2, pdc3, rf2c, rf2d, rf2e1, rth1, and rth3) of which the sequences were not released to the public prior to conducting this evaluation. The significant advantage of this data set for this evaluation is that these genes could not have been included in the training sets of the prediction programs. FGENESH yielded the most accurate and GeneMark.hmm the second most accurate predictions. The five programs were used in conjunction with RT-PCR to identify and establish the structures of two new genes in the a1-sh2 interval of the maize genome. FGENESH, GeneMark.hmm and GENSCAN were tested on a larger data set consisting of maize assembled genomic islands (MAGIs) that had been aligned to ESTs. FGENESH, GeneMark.hmm and GENSCAN correctly predicted gene models in 773, 625, and 371 MAGIs, respectively, out of the 1353 MAGIs that comprise data set 2.

Alternative Splicing↗

A strategy for assembling the maize (Zea mays L.) genome.

UNLABELLED: Because the bulk of the maize (Zea mays L.) genome consists of repetitive sequences, sequencing efforts are being targeted to its 'gene-rich' fraction. Traditional assembly programs are inadequate for this approach because they are optimized for a uniform sampling of the genome and inherently lack the ability to differentiate highly similar paralogs. RESULTS: We report the development of bioinformatics tools for the accurate assembly of the maize genome. This software, which is based on innovative parallel algorithms to ensure scalability, assembled 730,974 genomic survey sequences fragments in 4 h using 64 Pentium III 1.26 GHz processors of a commodity cluster. Algorithmic innovations are used to reduce the number of pairwise alignments significantly without sacrificing quality. Clone pair information was used to estimate the error rate for improved differentiation of polymorphisms versus sequencing errors. The assembly was also used to evaluate the effectiveness of various filtering strategies and thereby provide information that can be used to focus subsequent sequencing efforts.

Algorithms↗

PROBEmer: A web-based software tool for selecting optimal DNA oligos.

PROBEmer (http://probemer.cs.loyola.edu) is a web-based software tool that enables a researcher to select optimal oligos for PCR applications and multiplex detection platforms including oligonucleotide microarrays and bead-based arrays. Given two groups of nucleic-acid sequences, a target group and a non-target group, the software identifies oligo sequences that occur in members of the target group, but not in the non-target group. To help predict potential cross hybridization, PROBEmer computes all near neighbors in the non-target group and displays their alignments. The software has been used to obtain genus-specific prokaryotic probes based on the 16S rRNA gene, gene-specific probes for expression analyses and PCR primers. In this paper, we describe how to use PROBEmer, the computational methods it employs, and experimental results for oligos identified by this software tool.

Algorithms↗