Rescuing biologically relevant consensus regions across replicated samples

BACKGROUND

Protein-DNA binding sites of ChIP-seq experiments are identified where the binding affinity is significant based on a given threshold. The choice of the threshold is a trade-off between conservative region identification and discarding weak, but true binding sites.

RESULTS

We rescue weak binding sites using MSPC, which efficiently exploits replicates to lower the threshold required to identify a site while keeping a low false-positive rate, and we compare it to IDR, a widely used post-processing method for identifying highly reproducible peaks across replicates. We observe several master transcription regulators (e.g., SP1 and GATA3) and HDAC2-GATA1 regulatory networks on rescued regions in K562 cell line.

CONCLUSIONS

We argue the biological relevance of weak binding sites and the information they add when rescued by MSPC. An implementation of the proposed extended MSPC methodology and the scripts to reproduce the performed analysis are freely available at https://genometric.github.io/MSPC/ ; MSPC is distributed as a command-line application and an R package available from Bioconductor ( https://doi.org/doi:10.18129/B9.bioc.rmspc ).

Power calculator for detecting allelic imbalance using hierarchical Bayesian model

OBJECTIVE

Allelic imbalance (AI) is the differential expression of the two alleles in a diploid. AI can vary between tissues, treatments, and environments. Methods for testing AI exist, but methods are needed to estimate type I error and power for detecting AI and difference of AI between conditions. As the costs of the technology plummet, what is more important: reads or replicates?

RESULTS

We find that a minimum of 2400, 480, and 240 allele specific reads divided equally among 12, 5, and 3 replicates is needed to detect a 10, 20, and 30%, respectively, deviation from allelic balance in a condition with power > 80%. A minimum of 960 and 240 allele specific reads divided equally among 8 replicates is needed to detect a 20 or 30% difference in AI between conditions with comparable power. Higher numbers of replicates increase power more than adding coverage without affecting type I error. We provide a Python package that enables simulation of AI scenarios and enables individuals to estimate type I error and power in detecting AI and differences in AI between conditions.

Guidelines for Setting Up a mRNA Sequencing Experiment and Best Practices for Bioinformatic Data Analysis

RNA-sequencing, commonly referred to as RNA-seq, is the most recently developed method for the analysis of transcriptomes. It uses high-throughput next-generation sequencing technologies and has revolutionized our understanding of the complexity and dynamics of whole transcriptomes.In this chapter, we recall the key developments in transcriptome analysis and dissect the different steps of the general workflow that can be run by users to design and perform a mRNA-seq experiment as well as to process mRNA-seq data obtained by the Illumina technology. The chapter proposes guidelines for completing a mRNA-seq study properly and makes available recommendations for best practices based on recent literature and on the latest developments in technology and algorithms. We also remark the large number of choices available (especially for bioinformatic data analysis) in front of which the scientist may be in trouble.In the last part of the chapter we discuss the new frontiers of single-cell RNA-seq and isoform sequencing by long read technology.

Drawing statistical conclusions from experiments with multiple quantitative measurements per subject

In experiments with multiple quantitative measurements per subject, for example measurements on multiple lesions per patient, the additional measurements on the same patient provide limited additional information. Treating these measurements as independent observations will produce biased estimators for standard deviations and confidence intervals, and increases the risk of false positives in statistical tests. The problem can be remedied in a simple way by first taking the average of all observations of each specific patient, and then doing all further calculations only on the list of these patient means. A more sophisticated statistical modeling of the experiment, for example in a linear mixed model, is only required if (i) there is a large imbalance in the number of observations per patient or (ii) there is a specific interest in actually identifying the various sources of variation in the experiment.

ChiCMaxima: a robust and simple pipeline for detection and visualization of chromatin looping in Capture Hi-C

Capture Hi-C (CHi-C) is a new technique for assessing genome organization based on chromosome conformation capture coupled to oligonucleotide capture of regions of interest, such as gene promoters. Chromatin loop detection is challenging because existing Hi-C/4C-like tools, which make different assumptions about the technical biases presented, are often unsuitable. We describe a new approach, ChiCMaxima, which uses local maxima combined with limited filtering to detect DNA looping interactions, integrating information from biological replicates. ChiCMaxima shows more stringency and robustness compared to previously developed tools. The tool includes a GUI browser for flexible visualization of CHi-C profiles alongside epigenomic tracks.

Schizont transcriptome variation among clinical isolates and laboratory-adapted clones of the malaria parasite Plasmodium falciparum

BACKGROUND

Malaria parasites are genetically polymorphic and phenotypically plastic. In studying transcriptome variation among parasites from different infections, it is challenging to overcome potentially confounding technical and biological variation between samples. We investigate variation in the major human parasite Plasmodium falciparum, generating RNA-seq data on multiple independent replicate sample preparations of merozoite-containing intra-erythrocytic schizonts from a panel of clinical isolates and from long-term laboratory-adapted clones, with a goal of robustly identifying differentially expressed genes.

RESULTS

Analysis of biological sample replicates shows that increased numbers improve the true discovery rate of differentially expressed genes, and that six independent replicates of each parasite line allowed identification of most differences that could be detected with larger numbers. For highly expressed genes, focusing on the top quartile at schizont stages, there was more power to detect differences. Comparing cultured clinical isolates and laboratory-adapted clones, genes more highly expressed in the laboratory-adapted clones include those encoding an AP2 transcription factor (PF3D7_0420300), a ubiquitin-binding protein and two putative methyl transferases. In contrast, higher expression in clinical isolates was seen for the merozoite surface protein gene dblmsp2, proposed to be a marker of schizonts forming merozoites committed to sexual differentiation. Variable expression was extremely strongly, but not exclusively, associated with genes known to be targeted by Heterochromatin Protein 1. Clinical isolates show variable expression of several known merozoite invasion ligands, as well as other genes for which new RT-qPCR assays validate the quantitation and allow characterisation in samples with more limited material. Expression levels of these genes vary among schizont preparations of different clinical isolates in the first ex vivo cycle in patient erythrocytes, but mean levels are similar to those in continuously cultured clinical isolates.

CONCLUSIONS

Analysis of multiple biological sample replicates greatly improves identification of genes variably expressed between different cultured parasite lines. Clinical isolates recently established in culture show differences from long-term adapted clones in transcript levels of particular genes, and are suitable for analyses requiring biological replicates to understand parasite phenotypes and variable expression likely to be relevant in nature.