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Key Points New high-throughput sequencing technologies have spurred explosive growth in the use of sequencing to discover mutations and structural variants in the human genome and in the number of projects to sequence and assemble new genomes. Highly efficient algorithms have been developed to align next-generation sequences to genomes, and these algorithms use a variety of strategies to place repetitive reads. Ambiguous mapping of sequences that are derived from repetitive regions makes it difficult to identify true polymorphisms and to reconstruct transcripts. Short read lengths combined with mapping ambiguities lead to false reports of single-nucleotide polymorphisms, inserts, deletions and other sequence variants. When assembling a genome de novo , repetitive sequences can lead to erroneous rearrangements, deletions, collapsed repeats and other assembly errors. Long-range linking information from paired-end reads can overcome some of the difficulties in short-read assembly. Repeat sequences in DNA remain one of the most challenging aspects of next-generation sequencing data analysis and interpretation. This Review explains the problems and current strategies for handling repeats; ignoring repeats risks missing important biological information. Repetitive DNA sequences are abundant in a broad range of species, from bacteria to mammals, and they cover nearly half of the human genome. Repeats have always presented technical challenges for sequence alignment and assembly programs. Next-generation sequencing projects, with their short read lengths and high data volumes, have made these challenges more difficult. From a computational perspective, repeats create ambiguities in alignment and assembly, which, in turn, can produce biases and errors when interpreting results. Simply ignoring repeats is not an option, as this creates problems of its own and may mean that important biological phenomena are missed. We discuss the computational problems surrounding repeats and describe strategies used by current bioinformatics systems to solve them.

Key Points The protocols used for library construction, sequencing and data pre-processing can have a great impact on the quality of an assembled transcriptome and the accuracy of gene expression quantification. Before starting an RNA sequencing (RNA-seq) experiment, one should carefully consider using protocols that are strand-specific, that remove ribosomal RNA and that do not require PCR amplification of the template. Strand-specific RNA-seq protocols are important for correctly assembling overlapping transcripts, especially for compact genomes. The reference-based, or ab initio , assembly strategy requires a reference genome and uses much fewer computing resources than the de novo strategy. However, the quality of the genome and the ability of the short-read aligner to align reads across introns will directly influence the accuracy of the assembled transcripts when using the reference-based strategy. The de novo assembly strategy does not use a reference genome but instead uses a De Bruijn graph to represent overlaps between sequences and assemble transcripts. Most de novo approaches require significant computing resources: random access memory (RAM) is the typical limitation. However, de novo assemblers can assemble trans -spliced genes and novel transcripts that are not present in the genome assembly. To take full advantage of the current assembly strategies, a combined assembly approach should be considered that leverages the strengths of reference-based and de novo assembly strategies. Most transcriptome assemblers are still being developed, and the results from these programs should be evaluated using unbiased quantitative metrics. Transcriptome assembly involves an informatics approach to solve an experimental limitation. As sequencing strategies continually improve, it may no longer be necessary in the near future to assemble transcriptomes, as the read length will be longer than any individual transcript. Advances in sequencing technologies, assembly algorithms and computing power are making it feasible to assemble the entire transcriptome from short RNA reads. The article reviews the transcriptome assembly strategies, their advantages and limitations and how to apply them effectively. Transcriptomics studies often rely on partial reference transcriptomes that fail to capture the full catalogue of transcripts and their variations. Recent advances in sequencing technologies and assembly algorithms have facilitated the reconstruction of the entire transcriptome by deep RNA sequencing (RNA-seq), even without a reference genome. However, transcriptome assembly from billions of RNA-seq reads, which are often very short, poses a significant informatics challenge. This Review summarizes the recent developments in transcriptome assembly approaches — reference-based, de novo and combined strategies — along with some perspectives on transcriptome assembly in the near future.

Current methods for comparing single-cell RNA sequencing datasets collected in multiple conditions focus on discrete regions of the transcriptional state space, such as clusters of cells. Here we quantify the effects of perturbations at the single-cell level using a continuous measure of the effect of a perturbation across the transcriptomic space. We describe this space as a manifold and develop a relative likelihood estimate of observing each cell in each of the experimental conditions using graph signal processing. This likelihood estimate can be used to identify cell populations specifically affected by a perturbation. We also develop vertex frequency clustering to extract populations of affected cells at the level of granularity that matches the perturbation response. The accuracy of our algorithm at identifying clusters of cells that are enriched or depleted in each condition is, on average, 57% higher than the next-best-performing algorithm tested. Gene signatures derived from these clusters are more accurate than those of six alternative algorithms in ground truth comparisons. Matched treatment and control single-cell RNA sequencing samples are more accurately compared at the single-cell level.

Modified nucleotides in mRNA are an essential addition to the standard genetic code of four nucleotides in animals, plants, and their viruses. The emerging field of epitranscriptomics examines nucleotide modifications in mRNA and their impact on gene expression. The low abundance of nucleotide modifications and technical limitations, however, have hampered systematic analysis of their occurrence and functions. Selective chemical and immunological identification of modified nucleotides has revealed global candidate topology maps for many modifications in mRNA, but further technical advances to increase confidence will be necessary. Single-molecule sequencing introduced by Oxford Nanopore now promises to overcome such limitations, and we summarize current progress with a particular focus on the bioinformatic challenges of this novel sequencing technology. Writers, readers, and erasers have now been discovered for many mRNA modifications.Global topographic candidate maps have been generated for many modifications, but high error rates need to be addressed by technical improvements in detection and validation using orthogonal methods that apply rigid selection criteria.Nanopore single-molecule direct RNA sequencing is progressing towards reliable detection of modified nucleotides in mRNA.

Single-cell RNA sequencing (scRNA-seq) technology is a powerful, rapidly developing tool for characterizing individual cells and elucidating biological mechanisms at the cellular level. Cardiovascular disease is one of the major causes of death worldwide and its precise pathology remains unclear. scRNA-seq has provided many novel insights into both healthy and pathological hearts. In this review, we summarize the various scRNA-seq platforms and describe the molecular mechanisms of cardiovascular development and disease revealed by scRNA-seq analysis. We then describe the latest technological advances in scRNA-seq. Finally, we discuss how to translate basic research into clinical medicine using scRNA-seq technology.

As one of the major approaches in combating the COVID-19 pandemics, the availability of specific and reliable assays for the SARS-CoV-2 viral genome and its proteins is essential to identify the infection in suspected populations, make diagnoses in symptomatic or asymptomatic individuals, and determine clearance of the virus after the infection. For these purposes, use of the quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) for detection of the viral nucleic acid remains the most valuable in terms of its specificity, fast turn-around, high-throughput capacity, and reliability. It is critical to update the sequences of primers and probes to ensure the detection of newly emerged variants. Various assays for increased levels of IgG or IgM antibodies are available for detecting ongoing or past infection, vaccination responses, and persistence and for identifying high titers of neutralizing antibodies in recovered individuals. Viral genome sequencing is increasingly used for tracing infectious sources, monitoring mutations, and subtype classification and is less valuable in diagnosis because of its capacity and high cost. Nanopore target sequencing with portable options is available for a quick process for sequencing data. Emerging CRISPR-Cas-based assays, such as SHERLOCK and AIOD-CRISPR, for viral genome detection may offer options for prompt and point-of-care detection. Moreover, aptamer-based probes may be multifaceted for developing portable and high-throughput assays with fluorescent or chemiluminescent probes for viral proteins. In conclusion, assays are available for viral genome and protein detection, and the selection of specific assays depends on the purposes of prevention, diagnosis and pandemic control, or monitoring of vaccination efficacy. During the COVID-19 pandemics, sensitive and reliable assays for SARS-CoV-2 detection are essential for screening the population, identifying asymptomatic individuals, making diagnoses, monitoring treatment responses, and determining viral clearance. This review summarizes the principles, advantages, disadvantages, and specific applications of currently available assays for detection of the viral nucleotide, genome or proteins, as well as host antibody responses, and provide overall guidelines for selection of optimal assays for specific usage.

Transcriptomics is one of the most developed fields in the post-genomic era. Transcriptome is the complete set of RNA tran- scripts in a specific cell type or tissue at a certain developmental stage and/or under a specific physiological condition, includ- ing messenger RNA, transfer RNA, ribosomal RNA, and other non-coding RNAs. Transcriptomics focuses on the gene ex- pression at the RNA level and offers the genome-wide information of gene structure and gene function in order to reveal the molecular mechanisms involved in specific biological processes. With the development of next-generation high-throughput sequencing technology, transcriptome analysis has been progressively improving our understanding of RNA-based gene regu- latory network. Here, we discuss the concept, history, and especially the recent advances in this inspiring field of study.

The application of single-cell RNA sequencing platforms has generated notable insights into the heterogeneity underlying pancreatic ductal adenocarcinoma (PDAC), encompassing both the neoplastic compartment and the tumour microenvironment. In this Comment, we discuss the most pertinent findings gleaned from both mouse models and human PDAC samples, as well as future opportunities.

Two complementary approaches, both using next-generation sequencing, have successfully tackled the scale and the complexity of mammalian transcriptomes, at once revealing unprecedented detail and allowing better quantification.

New methods for analyzing RNA-Seq data enable de novo reconstruction of the transcriptome.