DeFries-Fulker multiple regression analysis of sibship QTL data: a SAS macro

A SAS macro package for performing multipoint QTL mapping using the DeFries-Fulker multiple regression method is presented.     

Analysis of twin data using SAS

Summary .  Twin studies are essential for assessing disease inheritance. Data generated from twin studies are traditionally analyzed using specialized computational programs. For many researchers, especially those who are new to twin studies, understanding and using those specialized computational programs can be a daunting task. Given that SAS (Statistical Analysis Software) is the most popular software for statistical analysis, we suggest that the use of SAS procedures for twin data may be a helpful alternative and demonstrate that we can obtain similar results from SAS to those produced by specialized computational programs. This numerical validation is practically useful, because a natural concern with general statistical software is whether it can deal with data that are generated from special study designs such as twin studies and if it can test a particular hypothesis. We concluded through our extensive simulation that SAS procedures can be used easily as a very convenient alternative to specialized programs for twin data analysis.     

AnovArray: a set of SAS macros for the analysis of variance of gene expression data

Background  

Analysis of variance is a powerful approach to identify differentially expressed genes in a complex experimental design for microarray and macroarray data. The advantage of the anova model is the possibility to evaluate multiple sources of variation in an experiment.     

Biological assessment of robust noise models in microarray data analysis

MOTIVATION: Although several recently proposed analysis packages for microarray data can cope with heavy-tailed noise, many applications rely on Gaussian assumptions. Gaussian noise models foster computational efficiency. This comes, however, at the expense of increased sensitivity to outlying observations. Assessing potential insufficiencies of Gaussian noise in microarray data analysis is thus important and of general interest. RESULTS: We propose to this end assessing different noise models on a large number of microarray experiments. The goodness of fit of noise models is quantified by a hierarchical Bayesian analysis of variance model, which predicts normalized expression values as a mixture of a Gaussian density and t-distributions with adjustable degrees of freedom. Inference of differentially expressed genes is taken into consideration at a second mixing level. For attaining far reaching validity, our investigations cover a wide range of analysis platforms and experimental settings. As the most striking result, we find irrespective of the chosen preprocessing and normalization method in all experiments that a heavy-tailed noise model is a better fit than a simple Gaussian. Further investigations revealed that an appropriate choice of noise model has a considerable influence on biological interpretations drawn at the level of inferred genes and gene ontology terms. We conclude from our investigation that neglecting the over dispersed noise in microarray data can mislead scientific discovery and suggest that the convenience of Gaussian-based modelling should be replaced by non-parametric approaches or other methods that account for heavy-tailed noise.     

A novel and universal method for microRNA RT-qPCR data normalization

Gene expression analysis of microRNA molecules is becoming increasingly important. In this study we assess the use of the mean expression value of all expressed microRNAs in a given sample as a normalization factor for microRNA real-time quantitative PCR data and compare its performance to the currently adopted approach. We demonstrate that the mean expression value outperforms the current normalization strategy in terms of better reduction of technical variation and more accurate appreciation of biological changes.     

mRNA and microRNA quality control for RT-qPCR analysis

The importance of high quality sample material, i.e. non-degraded or fragmented RNA, for classical gene expression profiling is well documented. Hence, the analysis of RNA quality is a valuable tool in the preparation of methods like RT-qPCR and microarray analysis. For verification of RNA integrity, today the use of automated capillary electrophoresis is state of the art. Following the recently published MIQE guidelines, these pre-PCR evaluations have to be clearly documented in scientific publication to increase experimental transparency.RNA quality control may also be integrated in the routine analysis of new applications like the investigation of microRNA (miRNA) expression, as there is little known yet about factors compromising the miRNA analysis. Agilent Technologies is offering a new lab-on-chip application for the 2100 Bioanalyzer making it possible to quantify miRNA in absolute amounts [pg] and as a percentage of small RNA [%]. Recent results showed that this analysis method is strongly influenced by total RNA integrity. Ongoing RNA degradation is accompanied by the formation of small RNA fragments leading to an overestimation of miRNA amount on the chip. Total RNA integrity is known to affect the performance of RT-qPCR as well as the quantitative results in mRNA expression profiling. The actual study identified a comparable effect for miRNA gene expression profiling. Using a suitable normalization method could partly reduce the impairing effect of total RNA integrity.     

GARBAN: genomic analysis and rapid biological annotation of cDNA microarray and proteomic data

SUMMARY: Genomic Analysis and Rapid Biological ANnotation (GARBAN) is a new tool that provides an integrated framework to analyze simultaneously and compare multiple data sets derived from microarray or proteomic experiments. It carries out automated classifications of genes or proteins according to the criteria of the Gene Ontology Consortium at a level of depth defined by the user. Additionally, it performs clustering analysis of all sets based on functional categories or on differential expression levels. GARBAN also provides graphical representations of the biological pathways in which all the genes/proteins participate. AVAILABILITY: http://garban.tecnun.es.     

Improvements in steroid receptor assays including rapid computer analysis of data

Improvements on the steroid receptor assay have been developed which eliminate tedious procedures and give faster and more accurate results. A protein assay (Waddell method) is discussed which directly measures peptide bonds in cytosolic material. It is more rapid and more sensitive than the Lowry method. Programs for the analysis of data obtained by either the dextran-coated charcoal method or the sucrose density gradient have been written. These are superior to previously published programs and can be incorporated into any laboratory with a minimum of cost. The program for the dextran-coated charcoal method converts the counts per minute from both total and nonspecific ³H-steroid binding activity into information needed for a Scatchard analysis. These data are plotted and a linear regression and determination coefficient for best fit of the data are performed. In the sucrose density gradient method program, the counts per minute from both total and nonspecific ³H-steroid binding activity is converted to disintegrations per minute and plotted versus the corresponding fraction number. The specific binding activity in femtomoles/milligram of protein in the various molecular forms can then be obtained through set programmed steps.     

Leveraging vibration of effects analysis for robust discovery in observational biomedical data science

Hypothesis generation in observational, biomedical data science often starts with computing an association or identifying the statistical relationship between a dependent and an independent variable. However, the outcome of this process depends fundamentally on modeling strategy, with differing strategies generating what can be called “vibration of effects” (VoE). VoE is defined by variation in associations that often lead to contradictory results. Here, we present a computational tool capable of modeling VoE in biomedical data by fitting millions of different models and comparing their output. We execute a VoE analysis on a series of widely reported associations (e.g., carrot intake associated with eyesight) with an extended additional focus on lifestyle exposures (e.g., physical activity) and components of the Framingham Risk Score for cardiovascular health (e.g., blood pressure). We leveraged our tool for potential confounder identification, investigating what adjusting variables are responsible for conflicting models. We propose modeling VoE as a critical step in navigating discovery in observational data, discerning robust associations, and cataloging adjusting variables that impact model output.

COVID positivity and vitamin D intake, red meat and heart disease; how can we discern when biomedical associations are reliable and when they are susceptible to our own arbitrary choices and assumptions? This study presents “quantvoe,” a software package for exploring the entirety of possible findings due to the multiverse of associations possible.     

FAST-Modelfree: a program for rapid automated analysis of solution NMR spin-relaxation data

Herein we describe the program FAST-Modelfree for the fully automated, high throughput analysis of NMR spin-relaxation data. This program interfaces with the program Modelfree 4.1 and provides an intuitive graphical user interface for configuration as well as complete standalone operation during the model selection and rotational diffusion parameter optimization processes. FAST-Modelfree is also capable of iteratively assigning models to each spin and optimizing the parameters that describe the diffusion tensor. Tests with the protein Ribonuclease A indicate that using this iterative approach even poor initial estimates of the diffusion tensor parameters will converge to the optimal value within a few iterations. In addition to improving the quality of the final fit, this represents a substantial timesaving compared to manual data analysis and minimizes the chance of human error. It is anticipated that this program will be particularly useful for the analysis and comparison of data collected under different conditions such as multiple temperatures and the presence and absence of ligands. Further, this program is intended to establish a more uniform protocol for NMR spin-relaxation data analysis, facilitating the comparison of results both between and within research laboratories. Results obtained with FAST-Modelfree are compared with previous literature results for the proteins Ribonuclease H, E. coli glutaredoxin-1 and the Ca²⁺-binding protein S100B. These proteins represent data sets collected at both single and multiple static magnetic fields and which required analysis with both isotropic and axially symmetric rotational diffusion tensors. In all cases results obtained with FAST-Modelfree compared favorably with the original literature results.     

RASP: rapid and robust backbone chemical shift assignments from protein structure

Chemical shift prediction has an unappreciated power to guide backbone resonance assignment in cases where protein structure is known. Here we describe Resonance Assignment by chemical Shift Prediction (RASP), a method that exploits this power to derive protein backbone resonance assignments from chemical shift predictions. Robust assignments can be obtained from a minimal set of only the most sensitive triple-resonance experiments, even for spectroscopically challenging proteins. Over a test set of 154 proteins RASP assigns 88 % of residues with an accuracy of 99.7 %, using only information available from HNCO and HNCA spectra. Applied to experimental data from a challenging 34 kDa protein, RASP assigns 90 % of manually assigned residues using only 40 % of the experimental data required for the manual assignment. RASP has the potential to significantly accelerate the backbone assignment process for a wide range of proteins for which structural information is available, including those for which conventional assignment strategies are not feasible.     

RT-qPCR based quantitative analysis of gene expression in single bacterial cells

Recent evidence suggests that cell-to-cell difference at the gene expression level is an order of magnitude greater than previously thought even for isogenic bacterial populations. Such gene expression heterogeneity determines the fate of individual bacterial cells in populations and could also affect the ultimate fate of populations themselves. To quantify the heterogeneity and its biological significance, quantitative methods to measure gene expression in single bacterial cells are needed. In this work, we developed two SYBR Green-based RT-qPCR methods to determine gene expression directly in single bacterial cells. The first method involves a single-tube operation that can analyze one gene from each bacterial cell. The second method is featured by a two-stage protocol that consists of RNA isolation from a single bacterial cell and cDNA synthesis in the first stage, and qPCR in the second stage, which allows determination of expression level of multiple genes simultaneously for single bacterial cells of both gram-positive and negative. We applied the methods to stress-treated (i.e. low pH and high temperature) Escherichia coli populations. The reproducible results demonstrated that the method is sensitive enough not only for measuring cellular responses at the single-cell level, but also for revealing gene expression heterogeneity among the bacterial cells. Furthermore, our results showed that the two-stage method can reproducibly measure multiple highly expressed genes from a single E. coli cell, which exhibits important foundation for future development of a high throughput and lab-on-chips whole-genome RT-qPCR methodology for single bacterial cells.