Next-generation sequencing: big data meets high performance computing.

Gene expression is the fundamental level at which the results of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switc...

https://www.annualreviews.org/doi/pdf/10.1146/annurev-biodatasci-072018-021255

RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis

Massively parallel sequencing techniques have revolutionized biological and medical sciences by providing unprecedented insight into the genomes of humans, animals, and microbes. Modern sequencing platforms generate enormous amounts of genomic data in the form of nucleotide sequences or reads. Aligning reads onto reference genomes enables the identification of individual-specific genetic variants and is an essential step of the majority of genomic analysis pipelines. Aligned reads are essential for answering important biological questions, such as detecting mutations driving various human diseases and complex traits as well as identifying species present in metagenomic samples. The read alignment problem is extremely challenging due to the large size of analyzed datasets and numerous technological limitations of sequencing platforms, and researchers have developed novel bioinformatics algorithms to tackle these difficulties. Importantly, computational algorithms have evolved and diversified in accordance with technological advances, leading to todays diverse array of bioinformatics tools. Our review provides a survey of algorithmic foundations and methodologies across 107 alignment methods published between 1988 and 2020, for both short and long reads. We provide rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read aligners. We separately discuss how longer read lengths produce unique advantages and limitations to read alignment techniques. We also discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology, including whole transcriptome, adaptive immune repertoire, and human microbiome studies.

Technology dictates algorithms: Recent developments in read alignment

Although different quality controls have been applied at different stages of the sample preparation and data analysis to ensure both reproducibility and reliability of RNA-seq results, there are still limitations and bias on the detectability for certain differentially expressed genes (DEGs). Whether the transcriptional dynamics of a gene can be captured accurately depends on experimental design/operation and the following data analysis processes. The workflow of subsequent data processing, such as reads alignment, transcript quantification, normalization, and statistical methods for ultimate identification of DEGs can influence the accuracy and sensitivity of DEGs analysis, producing a certain number of false-positivity or false-negativity. Machine learning (ML) is a multidisciplinary field that employs computer science, artificial intelligence, computational statistics and information theory to construct algorithms that can learn from existing data sets and to make predictions on new data set. ML–based differential network analysis has been applied to predict stress-responsive genes through learning the patterns of 32 expression characteristics of known stress-related genes. In addition, the epigenetic regulation plays critical roles in gene expression, therefore, DNA and histone methylation data has been shown to be powerful for ML-based model for prediction of gene expression in many systems, including lung cancer cells. Therefore, it is promising that ML-based methods could help to identify the DEGs that are not identified by traditional RNA-seq method. We identified the top 23 most informative features through assessing the performance of three different feature selection algorithms combined with five different classification methods on training and testing data sets. By comprehensive comparison, we found that the model based on InfoGain feature selection and Logistic Regression classification is powerful for DEGs prediction. Moreover, the power and performance of ML-based prediction was validated by the prediction on ethylene regulated gene expression and the following qRT-PCR. Our study shows that the combination of ML-based method with RNA-seq greatly improves the sensitivity of DEGs identification.

/pdf/rna-seq-assistant-machine-learning-based-methods-to-identify-1qz44raoka.pdf

RNA-seq assistant: machine learning based methods to identify more transcriptional regulated genes.

A growing number of researches demonstrate that light with a wavelength between 400 and 500nm, namely blue light (BL), has exhibited antibacterial effects on methicillin-resistant Staphylococcus aureus (MRSA) and other microbes. However, there is insufficient evidence to show that BL kills MRSA inside biofilm and the mechanisms underlying the antibacterial effects remain unclear. Here we demonstrate that BL irradiation with 460nm effectively eliminated both planktonic and biofilm MRSA in a dose-dependent manner by utilizing a planktonic MRSA or MRSA biofilm model. Furthermore, using a animal model of skin wound infections with MRSA, we found that 460nm BL showed a therapeutic effect on MRSA infected wounds in mice with significant killing of MRSA, better survival and wound healing. Moreover, RNA sequencing was used to analyze differential gene expressions in MRSA genome after BL irradiation. Our data showed that about one third of up-regulated genes were phage-related. Using phage inhibitor GS-11P, increased prophage activation in MRSA cells induced by BL irradiation was blocked and phage particles were observed. The results indicate that blue visible light irradiation with 460nm is a novel tool for eliminating both planktonic and biofilm MRSA. Prophage activation may be involved in the process. This study may provide a new perspective to understand the antibacterial mechanism by BL.

460nm visible light irradiation eradicates MRSA via inducing prophage activation.

As sequencing technologies progress, the amount of data produced grows exponentially, shifting the bottleneck of discovery towards the data analysis phase. In particular, currently available mapping solutions for RNA-seq leave room for improvement in terms of sensitivity and performance, hindering an efficient analysis of transcriptomes by massive sequencing. Here, we present an innovative approach that combines re-engineering, optimization and parallelization. This solution results in a significant increase of mapping sensitivity over a wide range of read lengths and substantial shorter runtimes when compared with current RNA-seq mapping methods available.

/pdf/highly-sensitive-and-ultrafast-read-mapping-for-rna-seq-1a3i5rodnz.pdf

J. Paschall

Papers

Highly sensitive and ultrafast read mapping for RNA-seq analysis