RNA-seq data science: From raw data to effective interpretation.

RNA sequencing (RNA-seq) has become an exemplary technology in modern biology and clinical science. Its immense popularity is due in large part to the continuous efforts of the bioinformatics community to develop accurate and scalable computational tools to analyze the enormous amounts of transcriptomic data that it produces. RNA-seq analysis enables genes and their corresponding transcripts to be probed for a variety of purposes, such as detecting novel exons or whole transcripts, assessing expression of genes and alternative transcripts, and studying alternative splicing structure. It can be a challenge, however, to obtain meaningful biological signals from raw RNA-seq data because of the enormous scale of the data as well as the inherent limitations of different sequencing technologies, such as amplification bias or biases of library preparation. The need to overcome these technical challenges has pushed the rapid development of novel computational tools, which have evolved and diversified in accordance with technological advancements, leading to the current myriad of RNA-seq tools. These tools, combined with the diverse computational skill sets of biomedical researchers, help to unlock the full potential of RNA-seq. The purpose of this review is to explain basic concepts in the computational analysis of RNA-seq data and define discipline-specific jargon.

Technology dictates algorithms: recent developments in read alignment.

Aligning sequencing reads onto a reference is an essential step of the majority of genomic analysis pipelines. Computational algorithms for read alignment have evolved in accordance with technological advances, leading to today's diverse array of alignment methods. We provide a systematic survey of algorithmic foundations and methodologies across 107 alignment methods, for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. We discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology.

Detection of viral gene expression in risk-stratified biopsies reveals no active HPV in cutaneous squamous cell carcinoma.

BACKGROUND: Human papillomavirus (HPV) infection is known to promote the development of mucosal squamous cell carcinoma (mSCC), including pathologically high-grade lesions, but its role in cutaneous squamous cell carcinoma (cuSCC) remains unclear, particularly in lesions that are considered high risk. OBJECTIVE: We aimed to determine whether enhanced HPV transcriptional activity can be detected in high-risk cuSCC samples compared with low-grade SCC samples or normal skin. METHODS: We performed RNA sequencing of cuSCC across 23 risk-stratified skin lesions. A subset of samples was tested for the presence of HPV DNA. High-quality, non-human reads from each sample group were used for viral analysis using Microbiome Coverage Profiler. RESULTS: None of the samples analysed had detectable expression of HPV RNA, while 64% of samples tested positive for HPV DNA. All samples were found to have expression of human endogenous retrovirus, and multiple samples showed expression of other viruses. CONCLUSIONS: Viral and prophage gene expression can be monitored in cuSCC or normal skin biopsies, yet no sample in our study showed evidence of active HPV gene expression despite evidence of HPV genome presence. This suggests HPV transcription does not play a role in differentiating high-risk cuSCCs from low-risk cuSCCs or normal skin.

Author Correction: Profiling immunoglobulin repertoires across multiple human tissues using RNA sequencing.

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

Profiling immunoglobulin repertoires across multiple human tissues using RNA sequencing.

Profiling immunoglobulin (Ig) receptor repertoires with specialized assays can be cost-ineffective and time-consuming. Here we report ImReP, a computational method for rapid and accurate profiling of the Ig repertoire, including the complementary-determining region 3 (CDR3), using regular RNA sequencing data such as those from 8,555 samples across 53 tissues types from 544 individuals in the Genotype-Tissue Expression (GTEx v6) project. Using ImReP and GTEx v6 data, we generate a collection of 3.6 million Ig sequences, termed the atlas of immunoglobulin repertoires (TAIR), across a broad range of tissue types that often do not have reported Ig repertoires information. Moreover, the flow of Ig clonotypes and inter-tissue repertoire similarities across immune-related tissues are also evaluated. In summary, TAIR is one of the largest collections of CDR3 sequences and tissue types, and should serve as an important resource for studying immunological diseases.

Telescope: an interactive tool for managing large-scale analysis from mobile devices.

BACKGROUND: In today's world of big data, computational analysis has become a key driver of biomedical research. High-performance computational facilities are capable of processing considerable volumes of data, yet often lack an easy-to-use interface to guide the user in supervising and adjusting bioinformatics analysis via a tablet or smartphone. RESULTS: To address this gap we proposed Telescope, a novel tool that interfaces with high-performance computational clusters to deliver an intuitive user interface for controlling and monitoring bioinformatics analyses in real-time. By leveraging last generation technology now ubiquitous to most researchers (such as smartphones), Telescope delivers a friendly user experience and manages conectivity and encryption under the hood. CONCLUSIONS: Telescope helps to mitigate the digital divide between wet and computational laboratories in contemporary biology. By delivering convenience and ease of use through a user experience not relying on expertise with computational clusters, Telescope can help researchers close the feedback loop between bioinformatics and experimental work with minimal impact on the performance of computational tools. Telescope is freely available at https://github.com/Mangul-Lab-USC/telescope.