The non-coding landscape of head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is an aggressive disease marked by frequent recurrence and metastasis and stagnant survival rates. To enhance molecular knowledge of HNSCC and define a non-coding RNA (ncRNA) landscape of the disease, we profiled the transcriptome-wide dysregulation of long non-coding RNA (lncRNA), microRNA (miRNA), and PIWI-interacting RNA (piRNA) using RNA-sequencing data from 422 HNSCC patients in The Cancer Genome Atlas (TCGA). 307 non-coding transcripts differentially expressed in HNSCC were significantly correlated with patient survival, and associated with mutations in TP53, CDKN2A, CASP8, PRDM9, and FBXW7 and copy number variations in chromosomes 3, 5, 7, and 18. We also observed widespread ncRNA correlation to concurrent TP53 and chromosome 3p loss, a compelling predictor of poor prognosis in HNSCCs. Three selected ncRNAs were additionally associated with tumor stage, HPV status, and other clinical characteristics, and modulation of their expression in vitro reveals differential regulation of genes involved in epithelial-mesenchymal transition and apoptotic response. This comprehensive characterization of the HNSCC non-coding transcriptome introduces new layers of understanding for the disease, and nominates a novel panel of transcripts with potential utility as prognostic markers or therapeutic targets

Transcriptome Analysis of Non‐Coding RNAs in Livestock Species: Elucidating the Ambiguity

The recent remarkable development of transcriptomics technologies, especially next generation sequencing technologies, allows deeper exploration of the hidden landscapes of complex traits and creates great opportunities to improve livestock productivity and welfare. Non-coding RNAs (ncRNAs), RNA molecules that are not translated into proteins, are key transcriptional regulators of health and production traits, thus, transcriptomics analyses of ncRNAs are important for a better understanding of the regulatory architecture of livestock phenotypes. In this chapter, we present an overview of common frameworks for generating and processing RNA sequence data to obtain ncRNA transcripts. Then, we review common approaches for analyzing ncRNA transcriptome data and present current state of the art methods for identification of ncRNAs and functional inference of identified ncRNAs, with emphasis on tools for livestock species. We also discuss future challenges and perspectives for ncRNA transcriptome data analysis in livestock species

Toward reliable biomarker signatures in the age of liquid biopsies - how to standardize the small RNA-Seq workflow

Small RNA-Seq has emerged as a powerful tool in transcriptomics, gene expression profiling and biomarker discovery. Sequencing cell-free nucleic acids, particularly microRNA (miRNA), from liquid biopsies additionally provides exciting possibilities for molecular diagnostics, and might help establish disease-specific biomarker signatures. The complexity of the small RNA-Seq workflow, however, bears challenges and biases that researchers need to be aware of in order to generate high-quality data. Rigorous standardization and extensive validation are required to guarantee reliability, reproducibility and comparability of research findings. Hypotheses based on flawed experimental conditions can be inconsistent and even misleading. Comparable to the well-established MIQE guidelines for qPCR experiments, this work aims at establishing guidelines for experimental design and pre-analytical sample processing, standardization of library preparation and sequencing reactions, as well as facilitating data analysis. We highlight bottlenecks in small RNA-Seq experiments, point out the importance of stringent quality control and validation, and provide a primer for differential expression analysis and biomarker discovery. Following our recommendations will en-courage better sequencing practice, increase experimental transparency and lead to more reproducible small RNA-Seq results. This will ultimately enhance the validity of biomarker signatures, and allow reliable and robust clinical predictions

Identification of cis-regulatory sequence variations in individual genome sequences

Functional contributions of cis-regulatory sequence variations to human genetic disease are numerous. For instance, disrupting variations in a HNF4A transcription factor binding site upstream of the Factor IX gene contributes causally to hemophilia B Leyden. Although clinical genome sequence analysis currently focuses on the identification of protein-altering variation, the impact of cis-regulatory mutations can be similarly strong. New technologies are now enabling genome sequencing beyond exomes, revealing variation across the non-coding 98% of the genome responsible for developmental and physiological patterns of gene activity. The capacity to identify causal regulatory mutations is improving, but predicting functional changes in regulatory DNA sequences remains a great challenge. Here we explore the existing methods and software for prediction of functional variation situated in the cis-regulatory sequences governing gene transcription and RNA processing

Bioinformatics Tools and Genomic Resources Available in Understanding the Structure and Function of Gossypium

Cotton is economically and evolutionarily important crop for its fiber. In order to improve fiber quality and yield, and to exploit the natural genetic potential inherent in genotypes, understanding genome structure and function of cultivated cotton is important. In order to achieve this, a functional understanding of bioinformatics resources such as databases, software solutions, and analysis tools is required. But currently, there are very few unified reports on bioinformatics tools and even fewer repositories to access cotton genomic information. Also, resourceful developers and bioinformatics scientists actively addressing complex genomic challenges in cotton genomes are much in need. The primary goal of this chapter is to provide a review of such tools and resources for analyzing the structure and function of the cotton genome with preferential emphasis on this complex and economically important plant species. This discourse begins with a description of concurrent advances in high‐throughput genome sequencing and bioinformatics analyses and focuses on four major sections covering bioinformatics tools and resources for analysis of: (1) genomes; (2) transcriptomes; (3) small RNAs; and (4) epigenomes. In each section, recent advances in cotton have been discussed. Cotton genome sequencing and annotation efforts are outlined within these sections. This review discusses the availability of genome information of both diploid and tetraploid species that have impelled cotton genome research into the post‐genomics era, opening new avenues for exploring regulatory mechanisms associated with fine‐tuning of gene expression of fiber‐related genes. Finally, the potential impacts of these rapid advances, especially the challenges in handling and analyzing the large datasets are discussed

Discovery and annotation of novel microRNAs in the porcine genome by using a semi-supervised transductive learning approach

Despite the broad variety of available microRNA (miRNA) prediction tools, their application to the discovery and annotation of novel miRNA genes in domestic species is still limited. In this study we designed a comprehensive pipeline (eMIRNA) for miRNA identification in the yet poorly annotated porcine genome and demonstrated the usefulness of implementing a motif search positional refinement strategy for the accurate determination of precursor miRNA boundaries. The small RNA fraction from gluteus medius skeletal muscle of 48 Duroc gilts was sequenced and used for the prediction of novel miRNA loci. Additionally, we selected the human miRNA annotation for a homology-based search of porcine miRNAs with orthologous genes in the human genome. A total of 20 novel expressed miRNAs were identified in the porcine muscle transcriptome and 27 additional novel porcine miRNAs were also detected by homology-based search using the human miRNA annotation. The existence of three selected novel miRNAs (ssc-miR-483, ssc-miR484 and ssc-miR-200a) was further confirmed by reverse transcription quantitative real-time PCR analyses in the muscle and liver tissues of Göttingen minipigs. In summary, the eMIRNA pipeline presented in the current work allowed us to expand the catalogue of porcine miRNAs and showed better performance than other commonly used miRNA prediction approaches. More importantly, the flexibility of our pipeline makes possible its application in other yet poorly annotated non-model species.info:eu-repo/semantics/acceptedVersio

FUS mutant human motoneurons display altered transcriptome and microRNA pathways with implications for ALS pathogenesis

The FUS gene has been linked to amyotrophic lateral sclerosis (ALS). FUS is a ubiquitous RNA-binding protein, and the mechanisms leading to selective motoneuron loss downstream of ALS-linked mutations are largely unknown. We report the transcriptome analysis of human purified motoneurons, obtained from FUS wild-type or mutant isogenic induced pluripotent stem cells (iPSCs). Gene ontology analysis of differentially expressed genes identified significant enrichment of pathways previously associated to sporadic ALS and other neurological diseases. Several microRNAs (miRNAs) were also deregulated in FUS mutant motoneurons, including miR-375, involved in motoneuron survival. We report that relevant targets of miR-375, including the neural RNA-binding protein ELAVL4 and apoptotic factors, are aberrantly increased in FUS mutant motoneurons. Characterization of transcriptome changes in the cell type primarily affected by the disease contributes to the definition of the pathogenic mechanisms of FUS-linked ALS

Assessing the Gene Content of the Megagenome: Sugar Pine (Pinus lambertiana).

Sugar pine (Pinus lambertiana Douglas) is within the subgenus Strobus with an estimated genome size of 31 Gbp. Transcriptomic resources are of particular interest in conifers due to the challenges presented in their megagenomes for gene identification. In this study, we present the first comprehensive survey of the P. lambertiana transcriptome through deep sequencing of a variety of tissue types to generate more than 2.5 billion short reads. Third generation, long reads generated through PacBio Iso-Seq have been included for the first time in conifers to combat the challenges associated with de novo transcriptome assembly. A technology comparison is provided here to contribute to the otherwise scarce comparisons of second and third generation transcriptome sequencing approaches in plant species. In addition, the transcriptome reference was essential for gene model identification and quality assessment in the parallel project responsible for sequencing and assembly of the entire genome. In this study, the transcriptomic data were also used to address questions surrounding lineage-specific Dicer-like proteins in conifers. These proteins play a role in the control of transposable element proliferation and the related genome expansion in conifers

A survey of best practices for RNA-seq data analysis.

RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.This is the final published version. It first appeared at http://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0881-8