mRNA translation plays an evolutionarily conserved role in homeostasis and when dysregulated contributes to various disorders including metabolic and neurological diseases and cancer. Notwithstanding that optimal and universally applicable methods are critical for understanding the complex role of translational control under physiological and pathological conditions, approaches to analyze translatomes are largely underdeveloped. To address this, we developed the anota2seq algorithm which outperforms current methods for statistical identification of changes in translation. Notably, in contrast to available analytical methods, anota2seq also allows specific identification of an underappreciated mode of gene expression regulation whereby translation acts as a buffering mechanism which maintains protein levels despite fluctuations in corresponding mRNA abundance (‘translational buffering’). Thus, the universal anota2seq algorithm allows efficient and hitherto unprecedented interrogation of translatomes which is anticipated to advance knowledge regarding the role of translation in homeostasis and disease.

Background: DNA microarrays provide data for genome wide patterns of expression between observation classes. Microarray studies often have small samples sizes, however, due to cost constraints or specimen availability. This can lead to poor random error estimates and inaccurate statistical tests of differential expression. We compare the performance of the standard t-test, fold change, and four small n statistical test methods designed to circumvent these problems. We report results of various normalization methods for empirical microarray data and of various random error models for simulated data.

Background: DNA microarrays are popular tools for measuring gene expression of biological samples. This ever increasing popularity is ensuring that a large number of microarray studies are conducted, many of which with data publicly available for mining by other investigators. Under most circumstances, validation of differential expression of genes is performed on a gene to gene basis. Thus, it is not possible to generalize validation results to the remaining majority of non-validated genes or to evaluate the overall quality of these studies.

DNA microarrays and RNAseq are complementary methods for studying RNA molecules. Current computational methods to determine alternative exon usage (AEU) using such data require impractical visual inspection and still yield high false-positive rates. Integrated Gene and Exon Model of Splicing (iGEMS) adapts a gene-level residuals model with a gene size adjusted false discovery rate and exon-level analysis to circumvent these limitations. iGEMS was applied to two new DNA microarray datasets, including the high coverage Human Transcriptome Arrays 2.0 and performance was validated using RT-qPCR. First, AEU was studied in adipocytes treated with (n = 9) or without (n = 8) the anti-diabetes drug, rosiglitazone. iGEMS identified 555 genes with AEU, and robust verification by RT-qPCR (∼90%). Second, in a three-way human tissue comparison (muscle, adipose and blood, n = 41) iGEMS identified 4421 genes with at least one AEU event, with excellent RT-qPCR verification (95%, n = 22). Importantly, iGEMS identified a variety of AEU events, including 3′UTR extension, as well as exon inclusion/exclusion impacting on protein kinase and extracellular matrix domains. In conclusion, iGEMS is a robust method for identification of AEU while the variety of exon usage between human tissues is 5–10 times more prevalent than reported by the Genotype-Tissue Expression consortium using RNA sequencing.

The success of high-throughput screening (HTS) strategies depends on the effectiveness of both normalization methods and study design. We report comparisons among normalization methods in two titration series experiments. We also extend the results in a third experiment with two differently designed but otherwise identical screens: compounds in replicate plates were either placed in the same well locations or were randomly assigned to different locations. Best results were obtained when randomization was combined with normalization methods that corrected for within-plate spatial bias. We conclude that potent, reliable, and accurate HTS requires replication, randomization design strategies, and more extensive normalization than is typically done and that formal statistical testing is desirable. The Statistics and dIagnostic Graphs for HTS (SIGHTS) Microsoft Excel Add-In software is available to conduct most analyses reported here.

Malaria continues to be one of mankind’s most devastating diseases despite the many and varied efforts to combat it. Indispensable for malaria elimination and eventual eradication is the development of effective vaccines. Controlled human malaria infection (CHMI) is an invaluable tool for vaccine efficacy assessment and investigation of early immunological and molecular responses against Plasmodium falciparum infection. Here, we investigated gene expression changes following CHMI using RNA-Seq. Peripheral blood samples were collected in Bagamoyo, Tanzania, from ten adults who were injected intradermally (ID) with 2.5x104 aseptic, purified, cryopreserved P. falciparum sporozoites (Sanaria® PfSPZ Challenge). A total of 2,758 genes were identified as differentially expressed following CHMI. Transcriptional changes were most pronounced on day 5 after inoculation, during the clinically silent liver phase. A secondary analysis, grouping the volunteers according to their prepatent period duration, identified 265 genes whose expression levels were linked to time of blood stage parasitemia detection. Gene modules associated with these 265 genes were linked to regulation of transcription, cell cycle, phosphatidylinositol signaling and erythrocyte development. Our study showed that in malaria pre-exposed volunteers, parasite prepatent period in each individual is linked to magnitude and timing of early gene expression changes after ID CHMI.

Systematic error is present in all high-throughput screens, lowering measurement accuracy. Because screening occurs at the early stages of research projects, measurement inaccuracy leads to following up inactive features and failing to follow up active features. Current normalization methods take advantage of the fact that most primary-screen features (e.g., compounds) within each plate are inactive, which permits robust estimates of row and column systematic-error effects. Screens that contain a majority of potentially active features pose a more difficult challenge because even the most robust normalization methods will remove at least some of the biological signal. Control plates that contain the same feature in all wells can provide a solution to this problem by providing well-by-well estimates of systematic error, which can then be removed from the treatment plates. We introduce the robust control-plate regression (CPR) method, which uses this approach. CPR's performance is compared to a high-performing primary-screen normalization method in four experiments. These data were also perturbed to simulate screens with large numbers of active features to further assess CPR's performance. CPR performs almost as well as the best performing normalization methods with primary screens and outperforms the Z-score and equivalent methods with screens containing a large proportion of active features.

Genome-wide analysis of mRNA translation (translatomics) can improve understanding of biological processes and pathological conditions including cancer. Techniques used to identify the effects of various stimuli on the translatomes such as polysome-and ribosome-profiling necessitate adjustment for changes in total mRNA levels to capture bona fide alterations in translational efficiency. In addition to changes in translational efficiency, which affect protein abundance, translation can also act as a buffering mechanism whereby protein levels remain constant despite changes in mRNA levels (translational buffering). Herein, we present anota2seq, an algorithm for analysis of translatomes, which is applicable to DNA-microarray and RNA sequencing data. In contrast to anota2seq, current methods for analysis of translational efficiency using RNA sequencing data as input identify false positives at high rates and/or fail to distinguish changes in translation efficiency from translational buffering when two conditions (e.g. stimulated and non-stimulated cells) are compared. Moreover, we identify dataacquisition specific thresholds, which are necessary during anota2seqanalysis. Finally, we employ anota2seq to show that insulin affects gene expression at multiple levels in a largely mTOR-dependent manner. Thus, the universal anota2seq algorithm allows efficient interrogation of the not peer-reviewed)