Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNA sequencing (RNA-Seq) applications have evolved in conjunction with sequence technology and bioinformatic tools advances. In most projects, bulk RNA-Seq data is used to measure gene expression patterns, isoform expression, alternative splicing and single-nucleotide polymorphisms. However, RNA-Seq holds far more hidden biological information including details of copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. Recent novel and advanced bioinformatic algorithms developed the capacity to retrieve this information from bulk RNA-Seq data, thus broadening its scope. The focus of this review is to comprehend the emerging bulk RNA-Seq-based analyses, emphasizing less familiar and underused applications. In doing so, we highlight the power of bulk RNA-Seq in providing biological insights.

Next Generation Sequencing (NGS) experiments produce millions of short sequences that, mapped to a reference genome, provide biological insights at genomic, transcriptomic and epigenomic level. Typically the amount of reads that correctly maps to the reference genome ranges between 70% and 90%, leaving in some cases a consistent fraction of unmapped sequences. This ’misalignment’ can be ascribed to low quality bases or sequence differences between the sample reads and the reference genome. Investigating the source of the unmapped reads is definitely important to better assess the quality of the whole experiment and to check for possible downstream or upstream ’contamination’ from exogenous nucleic acids. Here we propose DecontaMiner, a tool to unravel the presence of contaminating sequences among the unmapped reads. It uses a subtraction approach to identify bacteria, fungi and viruses genome contamination. DecontaMiner generates several output files to track all the processed reads, and to provide a complete report of their characteristics. The good quality matches on microorganism genomes are counted and compared among samples. DecontaMiner builds an offline HTML page containing summary statistics and plots. The latter are obtained using the state-of-the-art D3 javascript libraries. DecontaMiner has been mainly used to detect contamination in human RNA-Seq data. The software is freely available at http://www-labgtp.na.icar.cnr.it/decontaminer . DecontaMiner is a tool designed and developed to investigate the presence of contaminating sequences in unmapped NGS data. It can suggest the presence of contaminating organisms in sequenced samples, that might derive either from laboratory contamination or from their biological source, and in both cases can be considered as worthy of further investigation and experimental validation. The novelty of DecontaMiner is mainly represented by its easy integration with the standard procedures of NGS data analysis, while providing a complete, reliable, and automatic pipeline.

Long-read single-cell transcriptomics (scRNA-Seq) is revolutionizing the way we profile heterogeneity in disease. Traditional short-read scRNA-Seq methods are limited in their ability to provide complete transcript coverage, resolve isoforms, and identify novel transcripts. The scRNA-Seq protocols developed for long-read sequencing platforms overcome these limitations by enabling the characterization of full-length transcripts. Long-read scRNA-Seq techniques initially suffered from comparatively poor accuracy compared to short read scRNA-Seq. However, with improvements in accuracy, accessibility, and cost efficiency, long-reads are gaining popularity in the field of scRNA-Seq. This review details the advances in long-read scRNA-Seq, with an emphasis on library preparation protocols and downstream bioinformatics analysis tools.

Metastatic cutaneous squamous cell carcinoma (CSCC) is a highly morbid disease requiring radical surgery and adjuvant therapy, which is associated with a poor prognosis. Yet, compared to other advanced malignancies, relatively little is known of the genomic landscape of metastatic CSCC. We have previously reported the mutational signatures and mutational patterns of CCCTC-binding factor (CTCF) regions in metastatic CSCC. However, many other genomic components (indel signatures, non-coding drivers, and structural variants) of metastatic CSCC have not been reported. To this end, we performed whole genome sequencing on lymph node metastases and blood DNA from 25 CSCC patients with regional metastases of the head and neck. We designed a multifaceted computational analysis at the whole genome level to provide a more comprehensive perspective of the genomic landscape of metastatic CSCC. In the non-coding genome, 3′ untranslated region (3′UTR) regions of EVC (48% of specimens), PPP1R1A (48% of specimens), and ABCA4 (20% of specimens) along with the tumor-suppressing long non-coding RNA (lncRNA) LINC01003 (64% of specimens) were significantly functionally altered (Q-value < 0.05) and represent potential non-coding biomarkers of CSCC. Recurrent copy number loss in the tumor suppressor gene PTPRD was observed. Gene amplification was much less frequent, and few genes were recurrently amplified. Single nucleotide variants driver analyses from three tools confirmed TP53 and CDKN2A as recurrently mutated genes but also identified C9 as a potential novel driver in this disease. Furthermore, indel signature analysis highlighted the dominance of ID signature 13 (ID13) followed by ID8 and ID9. ID9 has previously been shown to have no association with skin melanoma, unlike ID13 and ID8, suggesting a novel pattern of indel variation in metastatic CSCC. The enrichment analysis of various genetically altered candidates shows enrichment of “TGF-beta regulation of extracellular matrix” and “cell cycle G1 to S check points.” These enriched terms are associated with genetic instability, cell proliferation, and migration as mechanisms of genomic drivers of metastatic CSCC.