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Record W2071527432 · doi:10.1101/gr.4976706

Small open reading frames: Not so small anymore

2006· letter· en· W2071527432 on OpenAlexaff
Richelle Sopko, Brenda Andrews

Bibliographic record

VenueGenome Research · 2006
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFungal and yeast genetics research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiologyGeneticsGenomeOpen reading frameGeneGene AnnotationAnnotationWhole genome sequencingComputational biology

Abstract

fetched live from OpenAlex

Today, nearly 10 years after the publication of the complete sequence of the Saccharomyces cerevisiae genome, the total number of genes in this organism is largely considered resolved and currently stands at 5782 (http://www.yeastgenome.org/cache/ genomeSnapshot.html). This gene sequence information has led to construction of numerous yeast strain and plasmid collections, including the two-hybrid (Ito et al. 2000, 2001; Uetz et al. 2000) viable haploid deletion (Winzeler et al. 1999; Giaever et al. 2002), titratable promoter allele (Mnaimneh et al. 2004), and chromosomally tagged green fluorescent protein and TAP fusion libraries (Ghaemmaghami et al. 2003; Huh et al. 2003). Because creation of these collections has relied on the accuracy of gene annotation at the time of library construction, each library has limitations. One major issue for most is the fact that initial annotation of the S. cerevisiae genome included only those regions consisting of at least 100 contiguous codons, and therefore small open reading frames (sORFs) encoding functional proteins were largely missed (Goffeau et al. 1996) and only considered later following detection of expression (Olivas et al. 1997; Velculescu et al. 1997; Kumar et al. 2002; Oshiro et al. 2002; Kessler et al. 2003). As such, the phenotypic consequences of gene disruption of more than half of these sORFs has not been assessed, and sORFs are underrepresented in genomic libraries and other collections. In this issue of Genome Research, Kastenmayer et al. (2006) provide the first systematic analysis of the prevalence of sORFs in a eukaryotic genome. Of the 299 currently recognized sORFs in the S. cerevisiae genome, they discovered that more than half (170) have been annotated since the genome was sequenced. Of these, 12% were identified by homology, and an even greater fraction (74%) was identified by combining both homology and empirical evidence such as detection of a transcribed and translated product. This is extremely significant given the variability in gene models that makes genome comparisons among species difficult and comparison between genera even more complex. The comparison of sORFs is additionally complicated given that a change in a single amino acid can create a large bias when assessing percent conservation. Also, prokaryotic genome sequence comparisons suggest that sORFs may be particularly susceptible to over-annotation (Nielsen and Krogh 2005). The significant apparent conservation of sORFs inspired Kastenmayer et al. to extend their analysis to include the entire list (299) of currently annotated sORFs. Using BLAST and analysis of the HomoloGene database, Kastenmayer et al. established that 184 S. cerevisiae sORFs have potential orthologs in other organisms and likely encode bona fide proteins with conserved functions. Like other large-scale functional genomic studies, this sORF survey affirms the importance of continued confirmation and refinement of genome annotation (for recent review, see Dolinksi and Botstein 2005). To initiate characterization of those 170 sORFs discovered since the completion of the sequencing of the S. cerevisiae genome, Kastenmayer et al. (2006) constructed 140 deletion mutants, bringing the total number of sORF deletion strains to 247 (the rest were previously constructed by the deletion consortium, Winzeler et al. 1999). Examination of these newly constructed strains in an array of conditions uncovered growth phenotypes for 22 mutants, some of which were overlapping. For example, three new sORFs are essential, six mutants had a significant growth defect, and several exhibited sensitivities to a range of cell stressors, including DNA-damaging agents and suboptimal growth conditions. What other information about sORF function might we expect to extract from experiments with the new sORF deletion collection? The standard yeast deletion mutant collection enabled a paradigm shift in yeast genetics—the collection of deletion mutants has been systematically scrutinized for a variety of phenotypes, providing rosters of genes that may contribute to a particular biological process (Scherens and Goffeau 2004). For example, the cell morphology of each mutant strain in the deletion collection has been examined by microscopy, providing the first comprehensive list of morphogenetic defects for any organism (Giaever et al. 2002; Saito et al. 2005). Also, the deletion mutants were designed with molecular “barcodes”—unique DNA sequences that identify each mutant strain—enabling parallel phenotypic analysis in pools of deletion mutants. The fitness contribution of individual strains can then be quantitatively assessed using a barcode oligonucleotide microarray readout (Giaever et al. 2002). The sORF collection has been similarly barcoded, and these mutant strains can now be included in all future functional profiling experiments. Twenty-one of the 247 sORFs (8%) for which deletion mutants exist are essential for haploid viability (including three new sORFs uncovered from the Kastenmayer study). This is much lower than the incidence of essentiality in the entire genome (22%), suggesting (1) condition-dependent essentiality for these sORFs or (2) much genetic redundancy involving sORFs. Given that the sORF collection is arrayed and compatible with methods developed for the larger mutant collection, genetic redundancy can be tackled using methods of automated yeast genetics that have been recently invented (Tong et al. 2004). In particular, the so-called Synthetic Genetic Array or SGA method can be applied to systematically construct sORF double mutants, allowing assessment of genetic background in which sORFs are required for viability. A synthetic lethal interaction network has already been constructed by screening for genetic interactions involving well over 100 mutant strains and the ∼5000 strains in the original deletion mutant collection (Tong et al. 2004). This project revealed that, on average, each gene will have about 26 synthetic lethal interactions. Using this average as a guide, we might expect sORFs to contribute 7774 (26 299) new genetic interactions to the S. cerevisiae genetic interaction network (Tong et al. 3Corresponding author. E-mail Brenda.andrews@utoronto.ca; fax (416) 946-8253. Article and publication are at http://www.genome.org/cgi/doi/10.1101/ gr.4976706. Commentary

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.527
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0040.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.138
GPT teacher head0.364
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations9
Published2006
Admission routes1
Has abstractyes

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