Codon pair residual values for were represented on a 61 by 64 colour grid 5′, P-site codons occupy the horizontal axis and 3′, A-site codons the vertical axis

<p><b>Copyright information:</b></p><p>Taken from "tRNA properties help shape codon pair preferences in open reading frames"</p><p>Nucleic Acids Research 2006;34(3):1015-1027.</p><p>Published online 9 Feb 2006</p><p>PMCID:PMC1363775.</p><p>© The Author 2006. Published by Oxford University Press. All rights reserved</p> Each colour pixel represents a codon pair residual value. Over-represented codon pairs are represented in yellow, under-represented values in blue. Colour intensity range represents the full span of residual values. Average linkage clustering of codon pair residual values was used to group codon pairs according to their similarity, producing a dendrogram on each axis. Clustering was carried out on the P-site codons based on their similarity of pair preferences for 3′, A-site codons, and . Where groups of two A-site codons decoded by a single isoacceptor tRNA (mono-isoacceptor groups; MIGs) are clustered at the extremities of the tree (i.e. most similar to each other), they are linked by ‘U’-shaped bars (see text for details)

Dinucleotide bias at codon–codon junctions is not a dominant force shaping codon pair bias

<p><b>Copyright information:</b></p><p>Taken from "tRNA properties help shape codon pair preferences in open reading frames"</p><p>Nucleic Acids Research 2006;34(3):1015-1027.</p><p>Published online 9 Feb 2006</p><p>PMCID:PMC1363775.</p><p>© The Author 2006. Published by Oxford University Press. All rights reserved</p> Codon pair residuals in a range of genomes were grouped into 16 sets defined by the identity of the cP3-cA1 dinucleotide at the codon–codon junction. For each set, the ratio of under-represented: over-represented codon pairs was assessed and converted to an index representing the uniformity of residual value polarities for codon pairs sharing cP3-cA1 identity. The bar chart shows the average cP3-cA1 dinucleotide bias index for each genome. Error bars represent +/− 1 standard deviation ( = 16). Standard species designations were used (see )

Codon pair preference is directed by combinations of nucleotides spanning adjacent codons

<p><b>Copyright information:</b></p><p>Taken from "tRNA properties help shape codon pair preferences in open reading frames"</p><p>Nucleic Acids Research 2006;34(3):1015-1027.</p><p>Published online 9 Feb 2006</p><p>PMCID:PMC1363775.</p><p>© The Author 2006. Published by Oxford University Press. All rights reserved</p> Codon pair residuals in a range of genomes were organized and grouped according to the identities of nucleotide couples composed of one P-site nucleotide, and one A-site nucleotide (e.g. cP1-cA1 or cP2-cA3). Within each of the nine dinucleotide-organized groups, observed and expected codon pair counts were used to calculate χ values for all 16 nt pair combinations. These were summed and the significance of the ∑χ value recorded. For a range of organisms, black grid cells indicate which of the 9 nt couple frequencies differed significantly from that expected ( = 0.001)

Prokaryote and eukaryote genomes are distinguished by distinct patterns of codon pair usage

<p><b>Copyright information:</b></p><p>Taken from "tRNA properties help shape codon pair preferences in open reading frames"</p><p>Nucleic Acids Research 2006;34(3):1015-1027.</p><p>Published online 9 Feb 2006</p><p>PMCID:PMC1363775.</p><p>© The Author 2006. Published by Oxford University Press. All rights reserved</p> For all genomes tested, the codon pair residual values ( − ) were tabulated, ordered by expected frequency, and separated into 10 bins. For each bin, the mean residual value was calculated. () Mean residual values plotted for each of the 10 bins (0–10% bin is the left-most bar in each group of ten). () Residuals were further smoothed into two equal bins before averaging, one bin containing the expected 50% least abundant codon pairs (black bars), the other the expected 50% most abundant codon pairs (white bars)

Multivariate analysis of the tRNA sequence influence on codon pairing preference

<p><b>Copyright information:</b></p><p>Taken from "tRNA properties help shape codon pair preferences in open reading frames"</p><p>Nucleic Acids Research 2006;34(3):1015-1027.</p><p>Published online 9 Feb 2006</p><p>PMCID:PMC1363775.</p><p>© The Author 2006. Published by Oxford University Press. All rights reserved</p> PLS analysis was used to identify nucleotides in P and A-site tRNAs that were good predictors of codon pair residual values. Representative data (data subset in which cP3 = G) from the analysis is presented, split between panels A and B for ease of interpretation. The weights plots (A and B) shown report the quantitative relationship between the predictor variables (tRNA nucleotides) and dependent variable (codon pair residual), plotted for the two components used to model the data. On these plots, tRNA and codon nucleotides plotted close to the residual value plot position (filled square) are typically those associated with over-represented codon pairs. Conversely, those at the opposite end of a line that bisects the plot origin and residual value point are typically those associated with under-represented codon pairs. () PLS weights plot showing P- and A-site codon nucleotides together with P-site tRNA nucleotides (filled triangles) plotted versus the codon pair residual value (filled square). () A-site tRNA nucleotides (filled triangles) plotted versus the codon pair residual value (filled square). Filled circle symbols represent key A-site tRNA nucleotide positions indicated by the PLS analysis to be significant predictors of the residual value across three bacterial species tested. Influential A-site tRNA nucleotide positions (filled circles) are labelled with the standard cloverleaf model nucleotide position, and the nucleotide identity. () A cloverleaf model of the basic tRNA structure, indicating those positions on the tRNA that were identified as good predictors of the codon pair residual value. Positions identified as important for residual prediction in , and are indicated by filled circles, those important in either two or just one of the three organisms, as 2/3 and 1/3 filled circles, respectively. Sector shadings indicate positions where >40% (black) o

High-throughput transformation of <i>Saccharomyces cerevisiae</i> using liquid handling robots

<div><p><i>Saccharomyces cerevisiae</i> (budding yeast) is a powerful eukaryotic model organism ideally suited to high-throughput genetic analyses, which time and again has yielded insights that further our understanding of cell biology processes conserved in humans. Lithium Acetate (LiAc) transformation of yeast with DNA for the purposes of exogenous protein expression (e.g., plasmids) or genome mutation (e.g., gene mutation, deletion, epitope tagging) is a useful and long established method. However, a reliable and optimized high throughput transformation protocol that runs almost no risk of human error has not been described in the literature. Here, we describe such a method that is broadly transferable to most liquid handling high-throughput robotic platforms, which are now commonplace in academic and industry settings. Using our optimized method, we are able to comfortably transform approximately 1200 individual strains per day, allowing complete transformation of typical genomic yeast libraries within 6 days. In addition, use of our protocol for gene knockout purposes also provides a potentially quicker, easier and more cost-effective approach to generating collections of double mutants than the popular and elegant synthetic genetic array methodology. In summary, our methodology will be of significant use to anyone interested in high throughput molecular and/or genetic analysis of yeast.</p></div

Schematic model of yeast transformation using liquid handling robot.

<p>Step 1, Inoculate overnight culture to the deep well plate. Grow another 2 ½ -4 hours to recover cells to the mid-log phase. Step 2, Prepare plasmid and transformation mix to transformation. Normally, an OD₆₀₀ range of 0.4–1.5, ≥100ng plasmid or 4500 ng PCR product is optimal for our automated transformation method. Step 3, Heat shock of the transformants for 3–6 hours. Step 4, Transfer the transformed cells to a liquid selective media plate to grow another 2–4 days. Pin the transformed strains onto appropriate selective media to generate the new library.</p

VPS38 deletion and check primers.

<p>VPS38 deletion and check primers.</p

Strains and plasmids used in this study.

<p>Strains and plasmids used in this study.</p

Transformation efficiency is increased with extended 42°C heat shock periods.

<p>(A) BY4741 cells at OD₆₀₀ 0.5 were transformed with the indicated amount of pRB1 plasmid and incubated at 42°C for 30 minutes (min), 1, 2, 4, and 6 hours (h). Transformants were selected on –Uracil plates. (B) Transformed cells generated from transformation reactions with different 42°C incubation times (1, 2, 4 and 6 hours). Numbers indicate average stress granule (Pab1-GFP, green text) or P-body (Edc3-mCh, red text) foci per cell, and the percentage of Edc3-mCh foci co-localized with Pab1-GFP (white text). (C) Quantification of stress granule (SG) and P-body (PB) size in (B). Data is presented as mean ± standard deviation of 3 independent experiments; n.s., not significant. (D) BY4741 cells transformed with pRB1 plasmid and incubated at 42°C for different time were assessed for elevated mutation rates by development of canavanine resistance. Transformed cells were plated on canavanine media (60 mg/L). Simultaneously, 1/200 diluted amount of cells were coated on the YPD plate. Colony number was counted after 2 days incubation at 30°C. Mutation rates were normalized to the 0-hour heat shock. Data is presented as mean ± standard deviation of 3 independent experiments; n.s., not significant.</p