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Variation in translation-elongation kinetics along a transcript’s coding sequence plays an important role in the maintenance of cellular protein homeostasis by regulating co-translational protein folding, localization, and maturation. Translation-elongation speed is influenced by molecular factors within mRNA and protein sequences. For example, the presence of proline in the ribosome’s P- or A-site slows down translation, but the effect of other pairs of amino acids, in the context of all 400 possible pairs, has not been characterized. Here, we study Saccharomyces cerevisiae using a combination of bioinformatics, mutational experiments, and evolutionary analyses, and show that many different pairs of amino acids and their associated tRNA molecules predictably and causally encode translation rate information when these pairs are present in the A- and P-sites of the ribosome independent of other factors known to influence translation speed including mRNA structure, wobble base pairing, tripeptide motifs, positively charged upstream nascent chain residues, and cognate tRNA concentration. The fast-translating pairs of amino acids that we identify are enriched four-fold relative to the slow-translating pairs across Saccharomyces cerevisiae’s proteome, while the slow-translating pairs are enriched downstream of domain boundaries. Thus, the chemical identity of amino acid pairs contributes to variability in translation rates, elongation kinetics are causally encoded in the primary structure of proteins, and signatures of evolutionary selection indicate their potential role in co-translational processes.

Context-dependent biological variation presents a unique challenge to the reproducibility of results in experimental animal research, because organisms’ responses to experimental treatments can vary with both genotype and environmental conditions. In March 2019, experts in animal biology, experimental design and statistics convened in Blonay, Switzerland, to discuss strategies addressing this challenge. In contrast to the current gold standard of rigorous standardization in experimental animal research, we recommend the use of systematic heterogenization of study samples and conditions by actively incorporating biological variation into study design through diversifying study samples and conditions. Here we provide the scientific rationale for this approach in the hope that researchers, regulators, funders and editors can embrace this paradigm shift. We also present a road map towards better practices in view of improving the reproducibility of animal research. In this Perspective, Hanno Wurbel and colleagues argue that a disregard for incorporating biological variation in study design is an important cause of poor reproducibility in animal research. They put the case for the use of systematic heterogenization of study samples and conditions in studies to improve reproducibility.

International audience; Two major goals in the study of biological systems are inference andprediction. Inference creates a mathematical model of the datageneration process to formalize our understanding or test ahypothesis about how the system behaves. Prediction aims atforecasting unobserved outcomes or future behavior, such as whethera mouse with a given phenotype will have a disease. Prediction makesit possible to identify best courses of action (e.g. treatment choice)without requiring understanding of the underlying mechanisms. In atypical research project, both inference and prediction are of value—we want to know how biological processes work and what will happennext. For example, we might want to infer which biological processesare associated with the dysregulation of a gene in a disease as well asclassify whether a subject has the disease and predict the besttherapy, such as drug intervention or invasive surgery.

Supervised learning algorithms extract general principles from observed examples guided by a specific prediction objective.

Machine learning extracts general principles from observed examples without explicit instructions.

Whereas de novo assemblies of RNA-Seq data are being published for a growing number of species across the tree of life, there are currently no broadly accepted methods for evaluating such assemblies. Here we present a detailed comparison of 99 transcriptome assemblies, generated with 6 de novo assemblers including CLC, Trinity, SOAP, Oases, ABySS and NextGENe. Controlled analyses of de novo assemblies for Arabidopsis thaliana and Oryza sativa transcriptomes provide new insights into the strengths and limitations of transcriptome assembly strategies. We find that the leading assemblers generate reassuringly accurate assemblies for the majority of transcripts. At the same time, we find a propensity for assemblers to fail to fully assemble highly expressed genes. Surprisingly, the instance of true chimeric assemblies is very low for all assemblers. Normalized libraries are reduced in highly abundant transcripts, but they also lack 1000s of low abundance transcripts. We conclude that the quality of de novo transcriptome assemblies is best assessed through consideration of a combination of metrics: 1) proportion of reads mapping to an assembly 2) recovery of conserved, widely expressed genes, 3) N50 length statistics, and 4) the total number of unigenes. We provide benchmark Illumina transcriptome data and introduce SCERNA, a broadly applicable modular protocol for de novo assembly improvement. Finally, our de novo assembly of the Arabidopsis leaf transcriptome revealed ~20 putative Arabidopsis genes lacking in the current annotation.

The bootstrap can be used to assess uncertainty of sample estimates.

Motivation: In high-dimensional testing problems π0, the proportion of null hypotheses that are true is an important parameter. For discrete test statistics, the P values come from a discrete distribution with finite support and the null distribution may depend on an ancillary statistic such as a table margin that varies among the test statistics. Methods for estimating π0 developed for continuous test statistics, which depend on a uniform or identical null distribution of P values, may not perform well when applied to discrete testing problems. Results: This article introduces a number of π0 estimators, the regression and ‘T’ methods that perform well with discrete test statistics and also assesses how well methods developed for or adapted from continuous tests perform with discrete tests. We demonstrate the usefulness of these estimators in the analysis of high-throughput biological RNA-seq and single-nucleotide polymorphism data. Availability and implementation: implemented in R Contact: nsa1@psu.edu or naomi@psu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

© The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. The origin of novel traits is recognized as an important process underlying many major evolutionary radiations. We studied the genetic basis for the evolution of haustoria, the novel feeding organs of parasitic flowering plants, using comparative transcriptome sequencing in three species of Orobanchaceae. Around 180 genes are upregulated during haustorial development following host attachment in at least two species, and these are enriched in proteases, cell wall modifying enzymes, and extracellular secretion proteins. Additionally, about 100 shared genes are upregulated in response to haustorium inducing factors prior to host attachment. Collectively, we refer to these newly identified genes as putative "parasitism genes." Most of these parasitism genes are derived from gene duplications in a common ancestor of Orobanchaceae and Mimulus guttatus, a related nonparasitic plant. Additionally, the signature of relaxed purifying selection and/or adaptive evolution at specific sites was detected in many haustorial genes, and may play an important role in parasite evolution. Comparative analysis of gene expression patterns in parasitic and nonparasitic angiosperms suggests that parasitism genes are derived primarily from root and floral tissues, but with some genes co-opted from other tissues. Gene duplication, often taking place in a nonparasitic ancestor of Orobanchaceae, followed by regulatory neofunctionalization, was an important process in the origin of parasitic haustoria.

Background Desirable apple varieties are clonally propagated by grafting vegetative scions onto rootstocks. Rootstocks influence many phenotypic traits of the scion, including resistance to pathogens such as Erwinia amylovora, which causes fire blight, the most serious bacterial disease of apple. The purpose of the present study was to quantify rootstock-mediated differences in scion fire blight susceptibility and to identify transcripts in the scion whose expression levels correlated with this response. Results Rootstock influence on scion fire blight resistance was quantified by inoculating three-year old, orchard-grown apple trees, consisting of 'Gala' scions grafted to a range of rootstocks, with E. amylovora. Disease severity was measured by the extent of shoot necrosis over time. 'Gala' scions grafted to G.30 or MM.111 rootstocks showed the lowest rates of necrosis, while 'Gala' on M.27 and B.9 showed the highest rates of necrosis. 'Gala' scions on M.7, S.4 or M.9F56 had intermediate necrosis rates. Using an apple DNA microarray representing 55,230 unique transcripts, gene expression patterns were compared in healthy, un-inoculated, greenhouse-grown 'Gala' scions on the same series of rootstocks. We identified 690 transcripts whose steady-state expression levels correlated with the degree of fire blight susceptibility of the scion/rootstock combinations. Transcripts known to be differentially expressed during E. amylovora infection were disproportionately represented among these transcripts. A second-generation apple microarray representing 26,000 transcripts was developed and was used to test these correlations in an orchard-grown population of trees segregating for fire blight resistance. Of the 690 transcripts originally identified using the first-generation array, 39 had expression levels that correlated with fire blight resistance in the breeding population. Conclusions Rootstocks had significant effects on the fire blight susceptibility of 'Gala' scions, and rootstock-regulated gene expression patterns could be correlated with differences in susceptibility. The results suggest a relationship between rootstock-regulated fire blight susceptibility and sorbitol dehydrogenase, phenylpropanoid metabolism, protein processing in the endoplasmic reticulum, and endocytosis, among others. This study illustrates the utility of our rootstock-regulated gene expression data sets for candidate trait-associated gene data mining.

We consider dimension reduction for regression or classification in which the predictors are matrix- or array-valued. This type of predictor arises when measurements are obtained for each combination of two or more underlying variables--for example, the voltage measured at different channels and times in electroencephalography data. For these applications, it is desirable to preserve the array structure of the reduced predictor (e.g., time versus channel), but this cannot be achieved within the conventional dimension reduction formulation. In this paper, we introduce a dimension reduction method, to be called dimension folding, for matrix- and array-valued predictors that preserves the array structure. In an application of dimension folding to an electroencephalography data set, we correctly classify 97 out of 122 subjects as alcoholic or nonalcoholic based on their electroencephalography in a cross-validation sample., Published in at http://dx.doi.org/10.1214/09-AOS737 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

We develop methods to compare the positions of quantitative trait loci (QTL) with a set of genes selected by other methods, such as microarray experiments, from a sequenced genome. We apply our methods to QTL for addictive behavior in mouse, and a set of genes upregulated in a region of the brain associated with addictive behavior, the nucleus accumbens (NA). The association between the QTL and NA genes is not significantly stronger than expected by chance. However, chromosomes 2 and 16 do show strong associations suggesting that genes on these chromosomes might be associated with addictive behavior. The statistical methodology developed for this study can be applied to similar studies to assess the mutual information in microarray and QTL analyses.

When the report by the New Researchers' Committee (NRC) of the IMS was published in Statistical Science (6 163-174), readers were invited to provide additional comments. We publish here commentaries by four individuals and also two invited discussions about programs at the National Science Foundation and the National Security Agency designed to benefit new researchers (NR's). The original report by the New Researchers' Committee provides sugges- tions for NR's and covers the role of the IMS as an advocate for them. The new NRC, now chaired by Deborah Nolan, responds to these comments adding some new material. The NRC also recently has published the New Researcher's Survival Guide to help the professional development of NR's.

Background Molecular genetic studies of floral development have concentrated on several core eudicots and grasses (monocots), which have canalized floral forms. Basal eudicots possess a wider range of floral morphologies than the core eudicots and grasses and can serve as an evolutionary link between core eudicots and monocots, and provide a reference for studies of other basal angiosperms. Recent advances in genomics have enabled researchers to profile gene activities during floral development, primarily in the eudicot Arabidopsis thaliana and the monocots rice and maize. However, our understanding of floral developmental processes among the basal eudicots remains limited. Results Using a recently generated expressed sequence tag (EST) set, we have designed an oligonucleotide microarray for the basal eudicot Eschscholzia californica (California poppy). We performed microarray experiments with an interwoven-loop design in order to characterize the E. californica floral transcriptome and to identify differentially expressed genes in flower buds with pre-meiotic and meiotic cells, four floral organs at pre-anthesis stages (sepals, petals, stamens and carpels), developing fruits, and leaves. Conclusions Our results provide a foundation for comparative gene expression studies between eudicots and basal angiosperms. We identified whorl-specific gene expression patterns in E. californica and examined the floral expression of several gene families. Interestingly, most E. californica homologs of Arabidopsis genes important for flower development, except for genes encoding MADS-box transcription factors, show different expression patterns between the two species. Our comparative transcriptomics study highlights the unique evolutionary position of E. californica compared with basal angiosperms and core eudicots.

Background We have developed a simulation approach to help determine the optimal mixture of sequencing methods for most complete and cost effective transcriptome sequencing. We compared simulation results for traditional capillary sequencing with "Next Generation" (NG) ultra high-throughput technologies. The simulation model was parameterized using mappings of 130,000 cDNA sequence reads to the Arabidopsis genome (NCBI Accession SRA008180.19). We also generated 454-GS20 sequences and de novo assemblies for the basal eudicot California poppy (Eschscholzia californica) and the magnoliid avocado (Persea americana) using a variety of methods for cDNA synthesis. Results The Arabidopsis reads tagged more than 15,000 genes, including new splice variants and extended UTR regions. Of the total 134,791 reads (13.8 MB), 119,518 (88.7%) mapped exactly to known exons, while 1,117 (0.8%) mapped to introns, 11,524 (8.6%) spanned annotated intron/exon boundaries, and 3,066 (2.3%) extended beyond the end of annotated UTRs. Sequence-based inference of relative gene expression levels correlated significantly with microarray data. As expected, NG sequencing of normalized libraries tagged more genes than non-normalized libraries, although non-normalized libraries yielded more full-length cDNA sequences. The Arabidopsis data were used to simulate additional rounds of NG and traditional EST sequencing, and various combinations of each. Our simulations suggest a combination of FLX and Solexa sequencing for optimal transcriptome coverage at modest cost. We have also developed ESTcalc http://fgp.huck.psu.edu/NG_Sims/ngsim.pl, an online webtool, which allows users to explore the results of this study by specifying individualized costs and sequencing characteristics. Conclusion NG sequencing technologies are a highly flexible set of platforms that can be scaled to suit different project goals. In terms of sequence coverage alone, the NG sequencing is a dramatic advance over capillary-based sequencing, but NG sequencing also presents significant challenges in assembly and sequence accuracy due to short read lengths, method-specific sequencing errors, and the absence of physical clones. These problems may be overcome by hybrid sequencing strategies using a mixture of sequencing methodologies, by new assemblers, and by sequencing more deeply. Sequencing and microarray outcomes from multiple experiments suggest that our simulator will be useful for guiding NG transcriptome sequencing projects in a wide range of organisms.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.