The pelagic shrimp Lucensosergia lucens is a commercially important species in Japan and Taiwan; however, a recent significant decline in L. lucens catch has been reported in Suruga Bay, Japan. In the present study, multiple molecular approaches were used to understand the population structure of L. lucens in Japanese and Taiwanese waters. Our analysis of mitochondrial cytochrome c oxidase subunit I and control region obtained by Sanger sequencing showed no evidence of different population structures, contrary to the previous study based on the control region. Genome-wide single-nucleotide polymorphism (SNP) analysis using multiplexed inter-simple sequence repeats genotyping by sequencing revealed panmixia in Japanese and Taiwanese populations. The contemporary migration rates estimated from the SNP data suggest that the Kuroshio Current plays a key role in L. lucens transportation from Taiwan to Japan. Additionally, mitogenome sequences obtained by genome skimming showed no region-specific genetic lineages in Japan or Taiwan. The results obtained by multiple molecular approaches suggested that L. lucens is widely distributed with a dispersal capacity in the Kuroshio and adjacent regions in the western North Pacific. Because apparent panmixia of L. lucens was observed in Japanese and Taiwanese waters, international cooperation is needed for the sustainable fishing of this shrimp.

RNA-seq delivers valuable insights both to transcriptional patterns and mutational landscapes for transcribed genes. However, as tumour cell lines frequently lack their matched-normal counterpart, variant calling without the paired normal sample is still challenging. In order to exclude variants of common genetic variation without a matched-normal control, filtering strategies need to be developed to identify tumour relevant variants in cell lines.

Here, variants of 29 breast cancer cell lines were called on RNA-seq data via HaplotypeCaller. Low read depth sites, RNA-edit sites, and low complexity regions in coding regions were excluded. Common variants were filtered using 1000 genomes, gnomAD, and dbSNP data. Starting from hundred thousands of single nucleotide variants and small insertions and deletions, about thousand variants remained after filtering for each sample. Extracted variants were validated against the Catalogue of Somatic Mutations in Cancer (COSMIC) for 10 cell lines included in both data sets. Approximately half of the COSMIC variants were successfully called. Importantly, missing variants could mainly be attributed to sites with low read depth. Moreover, filtered variants also included all 10 cancer gene census COSMIC variants, a condensed hallmark variant set.

Retroviruses, like other RNA viruses, mutate at very high rates and exist as genetically heterogeneous populations. The error-prone activity of viral reverse transcriptase (RT) is largely responsible for the observed variability, most notably in HIV-1. In addition, RTs are widely used in biotechnology to detect RNAs and to clone expressed genes, among many other applications. The fidelity of retroviral RTs has been traditionally analyzed using enzymatic (gel-based) or reporter-based assays. However, these methods are laborious and have important limitations. The development of next-generation sequencing (NGS) technologies opened the possibility of obtaining reverse transcription error rates from a large number of sequences, although appropriate protocols had to be developed. In this review, we summarize the developments in this field that allowed the determination of RNA-dependent DNA synthesis error rates for different RTs (viral and non-viral), including methods such as PRIMER IDs, REP-SEQ, ARC-SEQ, CIR-SEQ, SMRT-SEQ and ROLL-SEQ. Their advantages and limitations are discussed. Complementary DNA (cDNA) synthesis error rates obtained in different studies, using RTs and RNAs of diverse origins, are presented and compared. Future improvements in methodological pipelines will be needed for the precise identification of mutations in the RNA template, including modified bases.

Genomic surveillance enables the early detection of pathogen transmission in healthcare facilities and contributes to the reduction of substantial patient harm. Fast turnaround times, flexible multiplexing, and low capital requirements make Nanopore sequencing well suited for genomic surveillance purposes; the analysis of Nanopore data, however, can be challenging. We present NanoCore, a user-friendly method for Nanopore-based genomic surveillance in healthcare facilities, enabling the calculation and visualization of cgMLST-like (core-genome multilocus sequence typing) sample distances directly from unassembled Nanopore reads. NanoCore implements a mapping, variant calling, and multilevel filtering strategy and also supports the analysis of Illumina data. We validated NanoCore on two 24-isolate data sets of methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus faecium (VRE). In the Nanopore-only mode, NanoCore-based pairwise distances between closely related isolates were near-identical to Illumina-based SeqSphere+ distances, a gold standard commercial method (average differences of 0.75 and 0.81 alleles for MRSA and VRE; sd = 0.98 and 1.00), and gave an identical clustering into closely related and non-closely related isolates. In the "hybrid" mode, in which only Nanopore data are used for some isolates and only Illumina data for others, increased average pairwise isolate distance differences were observed (average differences of 3.44 and 1.95 for MRSA and VRE, respectively; sd = 2.76 and 1.34), while clustering results remained identical. NanoCore is computationally efficient (<15 hours of wall time for the analysis of a 24-isolate data set on a workstation), available as free software, and supports installation via conda. In conclusion, NanoCore enables the effective use of the Nanopore technology for bacterial pathogen surveillance in healthcare facilities.

Genomic surveillance involves sequencing the genomes and measuring the relatedness of bacteria from different patients or locations in the same healthcare facility, enabling an improved understanding of pathogen transmission pathways and the detection of "silent" outbreaks that would otherwise go undetected. It has become an indispensable tool for the detection and prevention of healthcare-associated infections and is routinely applied by many healthcare institutions. The earlier an outbreak or transmission chain is detected, the better; in this context, the Oxford Nanopore sequencing technology has important potential advantages over traditionally used short-read sequencing technologies, because it supports "real-time" data generation and the cost-effective "on demand" sequencing of small numbers of bacterial isolates. The analysis of Nanopore sequencing data, however, can be challenging. We present NanoCore, a user-friendly software for genomic surveillance that works directly based on Nanopore sequencing reads in FASTQ format, and demonstrate that its accuracy is equivalent to traditional gold standard short read-based analyses.

Nucleotide incorporation and lacZ-based forward mutation assays have been widely used to determine the accuracy of reverse transcriptases (RTs) in RNA-dependent DNA polymerization reactions. However, they involve quite complex and laborious procedures, and cannot provide accurate error rates. Recently, NGS-based methods using barcodes opened the possibility of detecting all errors introduced by the RT, although their widespread use is limited by cost, due to the large size of libraries to be sequenced. In this study, we describe a novel and relatively simple NGS assay based on single-strand consensus sequencing that provides robust results with a relatively small number of raw sequences (around 60 Mb). The method has been validated by determining the error rate of HIV-1 (BH10 strain) RT using the HIV-1 protease-coding sequence as target. HIV-1 reverse transcription error rates in standard conditions (37 °C/3 mM Mg2+) using an in vitro-transcribed RNA were around 7.3 × 10-5. In agreement with previous reports, an 8-fold increase in RT's accuracy was observed after reducing Mg2+ concentration to 0.5 mM. The fidelity of HIV-1 RT was also higher at 50 °C than at 37 °C (error rate 1.5 × 10-5). Interestingly, error rates obtained with HIV-1 RNA from infected cells as template of the reverse transcription at 3 mM Mg2+ (7.4 × 10-5) were similar to those determined with the in vitro-transcribed RNA, and were reduced to 1.8 × 10-5 in the presence of 0.5 mM Mg2+. Values obtained at low magnesium concentrations were modestly higher than the transcription error rates calculated for human cells, thereby suggesting a realistic transcriptional threshold for our NGS-based error rate determinations.

This study evaluates long-read and short-read sequencing for mitochondrial DNA (mtDNA) heteroplasmy detection. 592,315 bootstrapped datasets generated from two single-nucleotide mismatched ultra-deep sequenced mtDNA samples were used to assess basecalling error and accuracy, limit of heteroplasmy detection, and heteroplasmy detection across various coverage depths. Results showed high Phred scores of data with GC-rich sequence bias for long reads. Limit of detection of 12% heteroplasmy was identified, showing strong correlation (R2 ≥ 0.955) with expected heteroplasmy but underreporting tendency of high-level variants. Nanopore sequencing shows potential for direct applicability in mitochondrial diseases diagnostics, but stringent validation processes to ensure diagnostic result quality are required.

Cancer is a complex disease driven by genomic alterations, and tumor sequencing is becoming a mainstay of clinical care for cancer patients. The emergence of multi-institution sequencing data presents a powerful resource for learning real-world evidence to enhance precision oncology. GENIE BPC, led by American Association for Cancer Research, establishes a unique database linking genomic data with clinical information for patients treated at multiple cancer centers. However, leveraging sequencing data from multiple institutions presents significant challenges. Variability in gene panels can lead to loss of information when analyses focus on genes common across panels. Additionally, differences in sequencing techniques and patient heterogeneity across institutions add complexity. High data dimensionality, sparse gene mutation patterns, and weak signals at the individual gene level further complicate matters. Motivated by these real-world challenges, we introduce the Bridge model. It uses a quantile-matched latent variable approach to derive integrated features to preserve information beyond common genes and maximize the utilization of all available data, while leveraging information sharing to enhance both learning efficiency and the model's capacity to generalize. By extracting harmonized and noise-reduced lower-dimensional latent variables, the true mutation pattern unique to each individual is captured. We assess model's performance and parameter estimation through extensive simulation studies. The extracted latent features from the Bridge model consistently excel in predicting patient survival across six cancer types in GENIE BPC data.

We proposed a single-color fluorogenic DNA decoding sequencing method designed to improve sequencing accuracy, increase read length and throughput, as well as decrease scanning time. This method involves the incorporation of a mixture of four types of 3'-O-modified nucleotide reversible terminators into each reaction. Among them, two nucleotides are labeled with the same fluorophore, while the remaining two are unlabeled. Only one nucleotide can be extended in each reaction, and an encoding that partially defines base composition can be obtained. Through cyclic interrogation of a template twice with different nucleotide combinations, two sets of encodings are sequentially obtained, enabling the determination of the sequence. We demonstrate the feasibility of this method using established sequencing chemistry, achieving a cycle efficiency of approximately 99.5 %. Notably, this strategy exhibits remarkable efficacy in the detection and correction of sequencing errors, achieving a theoretical error rate of 0.00016 % at a sequencing depth of ×2, which is lower than Sanger sequencing. This method is theoretically compatible with the existing sequencing-by-synthesis (SBS) platforms, and the instrument is simpler, which may facilitate further reductions in sequencing costs, thereby broadening its applications in biology and medicine. Moreover, we demonstrate the capability to detect known mutation sites using information from only a single sequencing run. We validate this approach by accurately identifying a mutation site in the human mitochondrial DNA.

Cancer is a complex disease driven by genomic alterations, and tumor sequencing is becoming a mainstay of clinical care for cancer patients. The emergence of multi-institution sequencing data presents a powerful resource for learning real-world evidence to enhance precision oncology. GENIE BPC, led by American Association for Cancer Research, establishes a unique database linking genomic data with clinical information for patients treated at multiple cancer centers. However, leveraging sequencing data from multiple institutions presents significant challenges. Variability in gene panels can lead to loss of information when analyses focus on genes common across panels. Additionally, differences in sequencing techniques and patient heterogeneity across institutions add complexity. High data dimensionality, sparse gene mutation patterns, and weak signals at the individual gene level further complicate matters. Motivated by these real-world challenges, we introduce the Bridge model. It uses a quantile-matched latent variable approach to derive integrated features to preserve information beyond common genes and maximize the utilization of all available data, while leveraging information sharing to enhance both learning efficiency and the model's capacity to generalize. By extracting harmonized and noise-reduced lower-dimensional latent variables, the true mutation pattern unique to each individual is captured. We assess model's performance and parameter estimation through extensive simulation studies. The extracted latent features from the Bridge model consistently excel in predicting patient survival across six cancer types in GENIE BPC data.

Significant progress has been made in recent years in the development of techniques for Next Generation Sequencing (NGS), or Massively Parallel Sequencing (MPS), of forensically relevant short tandem repeat (STR) loci. However, as these technologies are investigated and adopted by forensic laboratories, new challenges unfold that require further scrutiny. In the analysis of DNA profiles generated using the MiSeq FGx sequencing system, we have observed noise sequences with relatively high readcounts that are challenging to distinguish from genuine alleles. These high read count noise sequences appear as allele sequences with one or a few substituted bases compared to a known allele sequence within the profile. An examination of ForenSeq DNA Signature Prep Kit STR noise sequences revealed that the substituted base of a parent allele can align to the same position on the sequence across noise sequences. This suggests that these substitution events occur at specific positions within the amplicon, resulting in multiple noise reads with substitutions at the same position. Mapping of the noise events onto the original raw read positions revealed a high number of events, or "noise spikes", occurring at specific positions within a given sequencing run. These noise spikes affected reads across the entire run, agnostic of locus or sample, while the position, occurrence, and amplitude of the spikes differed across runs. The majority of noise sequences with high read counts in a DNA profile were generated from base changes at these spike positions, and could be classified as "noise spike artefacts". In this paper we present evidence of the noise spike artefacts and their genesis during the sequencing process in the sequencing-by-synthesis (SBS) cycles, as well as the methods developed to detect them. The information and methods will assist laboratories with detecting noise spikes in MiSeq FGx sequencing runs, differentiating authentic allele sequences from noise spike artefacts, and developing protocols for analyst review and handling of MiSeq FGx data.

A critical step in the analysis of whole genome sequencing data is variant calling. Despite its importance, variant calling is prone to errors. Our study investigated the association between incorrect single nucleotide polymorphism (SNP) calls and variant quality metrics and nucleotide context. In our study, incorrect SNPs were defined in 20 Holstein-Friesian cows by comparing their SNPs genotypes identified by whole genome sequencing with the IlluminaNovaSeq6000 and the EuroGMD50K genotyping microarray. The dataset was divided into the correct SNP set (666 333 SNPs) and the incorrect SNP set (4 557 SNPs). The training dataset consisted of only the correct SNPs, while the test dataset contained a balanced mix of all the incorrectly and correctly called SNPs. An autoencoder was constructed to identify systematically incorrect SNPs that were marked as outliers by a one-class support vector machine and isolation forest algorithms. The results showed that 59.53% (±0.39%) of the incorrect SNPs had systematic patterns, with the remainder being random errors. The frequent occurrence of the CGC 3-mer was due to mislabelling a call for C. Incorrect T instead of A call was associated with the presence of T in the neighbouring downstream position. These errors may arise due to the fluorescence patterns of nucleotide labelling.

At the core of molecular biology lies the intricate interplay between sequence, structure, and function. Single-molecule techniques provide in-depth dynamic insights into structure and function, but laborious assays impede functional screening of large sequence libraries. We introduce high-throughput Single-molecule Parallel Analysis for Rapid eXploration of Sequence space (SPARXS), integrating single-molecule fluorescence with next-generation sequencing. We applied SPARXS to study the sequence-dependent kinetics of the Holliday junction, a critical intermediate in homologous recombination. By examining the dynamics of millions of Holliday junctions, covering thousands of distinct sequences, we demonstrated the ability of SPARXS to uncover sequence patterns, evaluate sequence motifs, and construct thermodynamic models. SPARXS emerges as a versatile tool for untangling the mechanisms that underlie sequence-specific processes at the molecular scale.

Next-generation sequencing (NGS), represented by Illumina platforms, has been an essential cornerstone of basic and applied research. However, the sequencing error rate of 1 per 1000 bp (10-3) represents a serious hurdle for research areas focusing on rare mutations, such as somatic mosaicism or microbe heterogeneity. By examining the high-fidelity sequencing methods developed in the past decade, we summarized three major factors underlying errors and the corresponding 12 strategies mitigating these errors. We then proposed a novel framework to classify 11 preexisting representative methods according to the corresponding combinatory strategies and identified three trends that emerged during methodological developments. We further extended this analysis to eight long-read sequencing methods, emphasizing error reduction strategies. Finally, we suggest two promising future directions that could achieve comparable or even higher accuracy with lower costs in both NGS and long-read sequencing.

Human rotaviruses exhibit limited tropism and replicate poorly in most cell lines. Attachment protein VP4 is a key rotavirus tropism determinant. Previous studies in which human rotaviruses were adapted to cultured cells identified mutations in VP4. However, most such studies were conducted using only a single human rotavirus genotype. In the current study, we serially passaged 50 human rotavirus clinical specimens representing five of the genotypes most frequently associated with severe human disease, each in triplicate, three to five times in primary monkey kidney cells then ten times in the MA104 monkey kidney cell line. From 13 of the 50 specimens, we obtained 25 rotavirus antigen-positive lineages representing all five genotypes, which tended to replicate more efficiently in MA104 cells at late versus early passage. We used Illumina next-generation sequencing and analysis to identify variants that arose during passage. In VP4, variants encoded 28 mutations that were conserved for all P[8] rotaviruses and 12 mutations that were conserved for all five genotypes. These findings suggest there may be a conserved mechanism of human rotavirus adaptation to MA104 cells. In the future, such a conserved adaptation mechanism could be exploited to study human rotavirus biology or efficiently manufacture vaccines.

Homopolymer (HP) sequencing is error-prone in next-generation sequencing (NGS) assays, and may induce false insertion/deletions and substitutions. This study aimed to evaluate the performance of dichromatic and tetrachromatic fluorogenic NGS platforms when sequencing homopolymeric regions.

A HP-containing plasmid was constructed and diluted to serial frequencies (3%, 10%, 30%, 60%) to determine the performance of an MGISEQ-2000, MGISEQ-200, and NextSeq 2000 in HP sequencing. An evident negative correlation was observed between the detected frequencies of four nucleotide HPs and the HP length. Significantly decreased rates (P < 0.01) were found in all 8-mer HPs in all three NGS systems at all four expected frequencies, except in the NextSeq 2000 at 3%. With the application of a unique molecular identifier (UMI) pipeline, there were no differences between the detected frequencies of any HPs and the expected frequencies, except for poly-G 8-mers using the MGI 200 platform. UMIs improved the performance of all three NGS platforms in HP sequencing.

We first constructed an HP-containing plasmid based on an EGFR gene backbone to evaluate the performance of NGS platforms when sequencing homopolymeric regions. A highly comparable performance was observed between the MGISEQ-2000 and NextSeq 2000, and introducing UMIs is a promising approach to improve the performance of NGS platforms in sequencing homopolymeric regions.

Characterization of somatic mutations at single-cell resolution is essential to study cancer evolution, clonal mosaicism and cell plasticity. Here, we describe SComatic, an algorithm designed for the detection of somatic mutations in single-cell transcriptomic and ATAC-seq (assay for transposase-accessible chromatin sequence) data sets directly without requiring matched bulk or single-cell DNA sequencing data. SComatic distinguishes somatic mutations from polymorphisms, RNA-editing events and artefacts using filters and statistical tests parameterized on non-neoplastic samples. Using >2.6 million single cells from 688 single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) data sets spanning cancer and non-neoplastic samples, we show that SComatic detects mutations in single cells accurately, even in differentiated cells from polyclonal tissues that are not amenable to mutation detection using existing methods. Validated against matched genome sequencing and scRNA-seq data, SComatic achieves F1 scores between 0.6 and 0.7 across diverse data sets, in comparison to 0.2-0.4 for the second-best performing method. In summary, SComatic permits de novo mutational signature analysis, and the study of clonal heterogeneity and mutational burdens at single-cell resolution.

Over the past decade, the rapid development of DNA synthesis and sequencing technologies has enabled preliminary use of DNA molecules for digital data storage, overcoming the capacity and persistence bottlenecks of silicon-based storage media. DNA storage has now been fully accomplished in the laboratory through existing biotechnology, which again demonstrates the viability of carbon-based storage media. However, the high cost and latency of data reconstruction pose challenges that hinder the practical implementation of DNA storage beyond the laboratory. In this article, we review existing advanced DNA storage methods, analyze the characteristics and performance of biotechnological approaches at various stages of data writing and reading, and discuss potential factors influencing DNA storage from the perspective of data reconstruction.

In recent years, concerted efforts to map and understand epitranscriptomic modifications in mRNA have unveiled new complexities in the regulation of gene expression. These studies cumulatively point to diverse functions in mRNA metabolism, spanning pre-mRNA processing, mRNA degradation, and translation. However, this emerging landscape is not without its intricacies and sources of discrepancies. Disparities in detection methodologies, divergent interpretations of functional outcomes, and the complex nature of biological systems across different cell types pose significant challenges. With a focus of N4-acetylcytidine (ac4C), this review endeavors to unravel conflicting narratives by examining the technological, biological, and methodological factors that have contributed to discrepancies and thwarted research progress. Our goal is to mitigate detection inconsistencies and establish a unified model to elucidate the contribution of ac4C to mRNA metabolism and cellular equilibrium.

Microbial genomes are largely comprised of protein coding sequences, yet some genomes contain many pseudogenes caused by frameshifts or internal stop codons. These pseudogenes are believed to result from gene degradation during evolution but could also be technical artifacts of genome sequencing or assembly.

Using a combination of observational and experimental data, we show that many putative pseudogenes are attributable to errors that are incorporated into genomes during assembly. Within 126,564 publicly available genomes, we observed that nearly identical genomes often substantially differed in pseudogene counts. Causal inference implicated assembler, sequencing platform, and coverage as likely causative factors. Reassembly of genomes from raw reads confirmed that each variable affects the number of putative pseudogenes in an assembly. Furthermore, simulated sequencing reads corroborated our observations that the quality and quantity of raw data can significantly impact the number of pseudogenes in an assembler dependent fashion. The number of unexpected pseudogenes due to internal stops was highly correlated (R2 = 0.96) with average nucleotide identity to the ground truth genome, implying relative pseudogene counts can be used as a proxy for overall assembly correctness. Applying our method to assemblies in RefSeq resulted in rejection of 3.6% of assemblies due to significantly elevated pseudogene counts. Reassembly from real reads obtained from high coverage genomes showed considerable variability in spurious pseudogenes beyond that observed with simulated reads, reinforcing the finding that high coverage is necessary to mitigate assembly errors.

Collectively, these results demonstrate that many pseudogenes in microbial genome assemblies are actually genes. Our results suggest that high read coverage is required for correct assembly and indicate an inflated number of pseudogenes due to internal stops is indicative of poor overall assembly quality.

Continuous cell lines are important and commonly used in vitro models in breast cancer (BC) research. Selection of the appropriate model cell line is crucial and requires consideration of their molecular characteristics. To characterize BC cell line models in depth, we profiled a panel of 29 authenticated and publicly available BC cell lines by mRNA-sequencing, mutation analysis, and immunoblotting. Gene expression profiles separated BC cell lines in two major clusters that represent basal-like (mainly triple-negative BC) and luminal BC subtypes, respectively. HER2-positive cell lines were located within the luminal cluster. Mutation calling highlighted the frequent aberration of TP53 and BRCA2 in BC cell lines, which, therefore, share relevant characteristics with primary BC. Furthermore, we showed that the data can be used to find novel, potential oncogenic fusion transcripts, e.g., FGFR2::CRYBG1 and RTN4IP1::CRYBG1 in cell line MFM-223, and to elucidate the regulatory circuit of IRX genes and KLF15 as novel candidate tumor suppressor genes in BC. Our data indicated that KLF15 was activated by IRX1 and inhibited by IRX3. Moreover, KLF15 inhibited IRX1 in cell line HCC-1599. Each BC cell line carries unique molecular features. Therefore, the molecular characteristics of BC cell lines described here might serve as a valuable resource to improve the selection of appropriate models for BC research.

Cancers exhibit complex transcriptomes with aberrant splicing that induces isoform-level differential expression compared to non-diseased tissues. Transcriptomic profiling using short-read sequencing has utility in providing a cost-effective approach for evaluating isoform expression, although short-read assembly displays limitations in the accurate inference of full-length transcripts. Long-read RNA sequencing (Iso-Seq), using the Pacific Biosciences (PacBio) platform, can overcome such limitations by providing full-length isoform sequence resolution which requires no read assembly and represents native expressed transcripts. A constraint of the Iso-Seq protocol is due to fewer reads output per instrument run, which, as an example, can consequently affect the detection of lowly expressed transcripts. To address these deficiencies, we developed a concatenation workflow, PacBio Full-Length Isoform Concatemer Sequencing (PB_FLIC-Seq), designed to increase the number of unique, sequenced PacBio long-reads thereby improving overall detection of unique isoforms. In addition, we anticipate that the increase in read depth will help improve the detection of moderate to low-level expressed isoforms.

In sequencing a commercial reference (Spike-In RNA Variants; SIRV) with known isoform complexity we demonstrated a 3.4-fold increase in read output per run and improved SIRV recall when using the PB_FLIC-Seq method compared to the same samples processed with the Iso-Seq protocol. We applied this protocol to a translational cancer case, also demonstrating the utility of the PB_FLIC-Seq method for identifying differential full-length isoform expression in a pediatric diffuse midline glioma compared to its adjacent non-malignant tissue. Our data analysis revealed increased expression of extracellular matrix (ECM) genes within the tumor sample, including an isoform of the Secreted Protein Acidic and Cysteine Rich (SPARC) gene that was expressed 11,676-fold higher than in the adjacent non-malignant tissue. Finally, by using the PB_FLIC-Seq method, we detected several cancer-specific novel isoforms.

This work describes a concatenation-based methodology for increasing the number of sequenced full-length isoform reads on the PacBio platform, yielding improved discovery of expressed isoforms. We applied this workflow to profile the transcriptome of a pediatric diffuse midline glioma and adjacent non-malignant tissue. Our findings of cancer-specific novel isoform expression further highlight the importance of long-read sequencing for characterization of complex tumor transcriptomes.

The development of skeletal muscle in pigs might determine the quality of pork. In recent years, long non-coding RNAs (lncRNAs) have been found to play an important role in skeletal muscle growth and development. In this study, we investigated the whole transcriptome of the longissimus dorsi muscle (LDM) of Jinfen White pigs at three developmental stages (1, 90, and 180 days) and performed a comprehensive analysis of lncRNAs, mRNAs, and micro-RNAs (miRNAs), aiming to find the key regulators and interaction networks in Jinfen White pigs. A total of 2638 differentially expressed mRNAs (DE mRNAs) and 982 differentially expressed lncRNAs (DE lncRNAs) were identified. Compared with JFW_1d, there were 497 up-regulated and 698 down-regulated DE mRNAs and 212 up-regulated and 286 down-regulated DE lncRNAs in JFW_90d, respectively. In JFW_180d, there were 613 up-regulated and 895 down-regulated DE mRNAs and 184 up-regulated and 131 down-regulated DE lncRNAs compared with JFW_1d. There were 615 up-regulated and 477 down-regulated DE mRNAs and 254 up-regulated and 355 down-regulated DE lncRNAs in JFW_180d compared with JFW_90d. Compared with mRNA, lncRNA has fewer exons, fewer ORFs, and a shorter length. We performed GO and KEGG pathway functional enrichment analysis for DE mRNAs and the potential target genes of DE lncRNAs. As a result, several pathways are involved in muscle growth and development, such as the PI3K-Akt, MAPK, hedgehog, and hippo signaling pathways. These are among the pathways through which mRNA and lncRNAs function. As part of this study, bioinformatic screening was used to identify miRNAs and DE lncRNAs that could act as ceRNAs. Finally, we constructed an lncRNA-miRNA-mRNA regulation network containing 26 mRNAs, 7 miRNAs, and 17 lncRNAs; qRT-PCR was used to verify the key genes in these networks. Among these, XLOC_022984/miR-127/ENAH and XLOC_016847/miR-486/NRF1 may function as key ceRNA networks. In this study, we obtained transcriptomic profiles from the LDM of Jinfen White pigs at three developmental stages and screened out lncRNA-miRNA-mRNA regulatory networks that may provide crucial information for the further exploration of the molecular mechanisms during skeletal muscle development.

Nucleotide sequences recognized and bound by DNA-binding proteins (DBPs) are critical to controlling and maintaining gene expression, replication, chromosome segregation, cell division, and nucleoid structure in bacterial cells. Therefore, determination of the binding sequences of DBPs is important not only to study DBP recognition mechanisms but also to understand the fundamentals of cell homeostasis. While ChIP-seq analysis appears to be an effective way to determine DBP binding sites on the genome, the resolution is sometimes not sufficient to identify the sites precisely. Here we introduce a simple and effective method named Genome Footprinting with high-throughput sequencing (GeF-seq) to determine binding sites of DBPs with single base-pair resolution. GeF-seq detects binding sites of DBPs as sharp peaks and thus makes it possible to identify the recognition sequence in each "binding peak" more easily and accurately compared to the common ChIP-seq.

Amplicon sequencing of targeted genes is the predominant approach to estimate the membership and structure of microbial communities. However, accurate reconstruction of community composition is difficult due to sequencing errors, and other methodological biases and effective approaches to overcome these challenges are essential. Using a mock community of 33 phylogenetically diverse strains, this study evaluated the effect of GC content on sequencing results and tested different approaches to improve overall sequencing accuracy while characterizing the pros and cons of popular amplicon sequence data processing approaches. The sequencing results from this study can serve as a benchmarking data set for future algorithmic improvements. Furthermore, the new insights on sequencing error, chimera formation, and GC bias from this study will help enhance the quality of amplicon sequencing studies and support the development of new data analysis approaches.

The method of spiking synthetic internal standard genes (ISGs) to samples for amplicon sequencing, generating sequences and converting absolute gene numbers from read counts has been used only for phylogenetic markers and has not been applied to functional markers. In this study, we developed ISGs, including gene sequences of the 16S rRNA, pmoA, encoding a subunit of particulate methane monooxygenase and amoA, encoding a subunit of ammonia monooxygenase. We added ISGs to the samples, amplified the target genes and performed amplicon sequencing. For the mock community, the copy numbers converted from read counts using ISGs were equivalent to those obtained by the quantitative real-time polymerase chain reaction (4.0 × 104 versus 4.1 × 104 and 3.0 × 103 versus 4.0 × 103 copies μL-DNA-1 for 16S rRNA and pmoA genes, respectively), but we also identified underestimation, possibly due to primer coverage (7.8 × 102 versus 3.7 × 103  μL-DNA-1 for amoA gene). We then applied this method to environmental samples and analysed phylogeny, functional diversity and absolute quantities. One Methylocystis population was most abundant in the sludge samples [16S rRNA gene (3.8 × 109 copies g-1 ) and the pmoA gene (2.3 × 109 copies g-1 )] and were potentially interrelated. This study demonstrates that ISG spiking is useful for evaluating sequencing data processing and quantifying functional markers.

Three MPS platforms are being used in forensic genetic analysis, i.e., MiSeq FGx, Ion S5 XL, and MGISEQ-2000. However, few studies compared their performance. In this study, we sequenced 83 common SNPs of 71 samples using the ForenSeq™ DNA Signature Prep Kit on MiSeq FGx, the Precision ID Identity Panel on Ion S5 XL, and the MGIEasy Signature Identification Library Prep Kit on MGISEQ-2000 and then the performance was compared. Results showed that the MiSeq FGx had the highest sequence quality but the lowest sequencing depth and allele balance. Discordant genotypes were observed at six SNPs, which may be caused by variants at primer binding regions, indel errors, or misalignments. Besides, two kinds of background noises, allele-specific miscalled reads (ASMR) and allele-nonspecific miscalled reads (ANMR), were characterized. MGISEQ-2000 showed the highest level of ASMR while Ion S5 XL had the highest level of ANMR. Site- and genotype-dependent miscalled patterns were observed at several SNPs on Ion S5 XL and MGISEQ-2000, but few on MiSeq FGx. In conclusion, the three MPS platforms perform differently with respect to sequencing quality, sequencing depth, allele balance, concordance, and background noise. These findings may be useful for data comparison, mixture deconvolution, and heteroplasmy analysis in forensic genetics.

High-throughput sequencing (HTS) has revolutionized science by enabling super-fast detection of genomic variants at base-pair resolution. Consequently, it poses the challenging problem of identification of technical artifacts, i.e. hidden non-random error patterns. Understanding the properties of sequencing artifacts holds the key in separating true variants from false positives. Here, we develop Mapinsights, a toolkit that performs quality control (QC) analysis of sequence alignment files, capable of detecting outliers based on sequencing artifacts of HTS data at a deeper resolution compared with existing methods. Mapinsights performs a cluster analysis based on novel and existing QC features derived from the sequence alignment for outlier detection. We applied Mapinsights on community standard open-source datasets and identified various quality issues including technical errors related to sequencing cycles, sequencing chemistry, sequencing libraries and across various orthogonal sequencing platforms. Mapinsights also enables identification of anomalies related to sequencing depth. A logistic regression-based model built on the features of Mapinsights shows high accuracy in detecting 'low-confidence' variant sites. Quantitative estimates and probabilistic arguments provided by Mapinsights can be utilized in identifying errors, bias and outlier samples, and also aid in improving the authenticity of variant calls.

The detection of somatic DNA variants in tumor samples with low tumor purity or sequencing depth remains a daunting challenge despite numerous attempts to address this problem. In this study, we constructed a substantially extended set of actual positive variants originating from a wide range of tumor purities and sequencing depths, as well as actual negative variants derived from sequencer-specific sequencing errors. A deep learning model named AIVariant, trained on this extended dataset, outperforms previously reported methods when tested under various tumor purities and sequencing depths, especially low tumor purity and sequencing depth.

Transposons are mobile genetic elements prevalent in the genomes of most species. The distribution of transposons within a genome reflects the actions of two opposing processes: initial insertion site selection, and selective pressure from the host. By analyzing whole-genome sequencing data from transposon-activated Drosophila melanogaster, we identified 43 316 de novo and 237 germline insertions from four long-terminal-repeat (LTR) transposons, one LINE transposon (I-element), and one DNA transposon (P-element). We found that all transposon types favored insertion into promoters de novo, but otherwise displayed distinct insertion patterns. De novo and germline P-element insertions preferred replication origins, often landing in a narrow region around transcription start sites and in regions of high chromatin accessibility. De novo LTR transposon insertions preferred regions with high H3K36me3, promoters and exons of active genes; within genes, LTR insertion frequency correlated with gene expression. De novo I-element insertion density increased with distance from the centromere. Germline I-element and LTR transposon insertions were depleted in promoters and exons, suggesting strong selective pressure to remove transposons from functional elements. Transposon movement is associated with genome evolution and disease; therefore, our results can improve our understanding of genome and disease biology.

High-throughput sequencing data enables the comprehensive study of genomes and the variation therein. Essential for the interpretation of this genomic data is a thorough understanding of the computational methods used for processing and analysis. Whereas "gold-standard" empirical datasets exist for this purpose in humans, synthetic (i.e., simulated) sequencing data can offer important insights into the capabilities and limitations of computational pipelines for any arbitrary species and/or study design-yet, the ability of read simulator software to emulate genomic characteristics of empirical datasets remains poorly understood. We here compare the performance of six popular short-read simulators-ART, DWGSIM, InSilicoSeq, Mason, NEAT, and wgsim-and discuss important considerations for selecting suitable models for benchmarking.

Next-generation sequencing (NGS) is present in all fields of life science, which has greatly promoted the development of basic research while being gradually applied in clinical diagnosis. However, the cost and throughput advantages of next-generation sequencing are offset by large tradeoffs with respect to read length and accuracy. Specifically, its high error rate makes it extremely difficult to detect SNPs or low-abundance mutations, limiting its clinical applications, such as pharmacogenomics studies primarily based on SNP and early clinical diagnosis primarily based on low abundance mutations. Currently, Sanger sequencing is still considered to be the gold standard due to its high accuracy, so the results of next-generation sequencing require verification by Sanger sequencing in clinical practice. In order to maintain high quality next-generation sequencing data, a variety of improvements at the levels of template preparation, sequencing strategy and data processing have been developed. This study summarized the general procedures of next-generation sequencing platforms, highlighting the improvements involved in eliminating errors at each step. Furthermore, the challenges and future development of next-generation sequencing in clinical application was discussed.

Understanding the intrahost evolution of viral populations has implications in pathogenesis, diagnosis, and treatment and has recently made impressive advances from developments in high-throughput sequencing. However, the underlying analyses are very sensitive to sources of bias, error, and artefact in the data, and it is important that these are addressed adequately if robust conclusions are to be drawn. The key factors include (1) determining the number of viral strains present in the sample analysed; (2) monitoring the extent to which the data represent these strains and assessing the quality of these data; (3) dealing with the effects of cross-contamination; and (4) ensuring that the results are reproducible. We investigated these factors by generating sequence datasets, including biological and technical replicates, directly from clinical samples obtained from a small cohort of patients who had been infected congenitally with the herpesvirus human cytomegalovirus, with the aim of developing a strategy for identifying high-confidence intrahost variants. We found that such variants were few in number and typically present in low proportions and concluded that human cytomegalovirus exhibits a very low level of intrahost variability. In addition to clarifying the situation regarding human cytomegalovirus, our strategy has wider applicability to understanding the intrahost variability of other viruses.

Detection of insertions and deletions (InDels) by short-read next-generation sequencing (NGS) technology can be challenging because of frequent misaligned reads. A systematic analysis of short InDels (1 to 30 bases) and fms-related receptor tyrosine kinase 3 (FLT3) internal tandem duplications (ITDs; 6 to 183 bases) from 46 clinical cases of solid or hematologic malignancy processed with a clinical NGS assay identified misaligned reads in every case, ranging from 3% to 100% of reads with the InDel showing mismapped bases. Mismaps also increased with InDel size. As a consequence, the clinical NGS bioinformatics pipeline undercalled the variant allele frequency by 1% to 84%, incorrectly called simultaneous single-base substitutions along with InDels, or did not report an FLT3 ITD that had been detected by capillary electrophoresis. To improve the ability of the pipeline to better detect and quantify InDels, we utilized a software program called Assembly-Based ReAligner (ABRA2) to more accurately remap reads. ABRA2 was able to correct 41% to 100% of the reads with mismapped bases and led to absolute increases in the variant allele frequency from 1% to 61% along with correction of all of the single-base substitutions except for two cases. ABRA2 could also detect multiple FLT3 ITD clones except for one 183-base ITD. Our analysis has found that ABRA2 performs well on short InDels as well as FLT3 ITDs that are <100 bases.

Tandem repeats of simple sequence motifs, also known as microsatellites, are abundant in the genome. Because their repeat structure makes replication error-prone, variant microsatellite lengths are often generated during germline and other somatic expansions. As such, microsatellite length variations can serve as markers for cancer. However, accurate error-free measurement of microsatellite lengths is difficult with current methods precisely because of this high error rate during amplification. We have solved this problem by using partial mutagenesis to disrupt enough of the repeat structure of initial templates so that their sequence lengths replicate faithfully. In this work, we use bisulfite mutagenesis to convert a C to a U, later read as T. Compared to untreated templates, we achieve three orders of magnitude reduction in the error rate per round of replication. By requiring agreement from two independent first copies of an initial template, we reach error rates below one in a million. We apply this method to a thousand microsatellite loci from the human genome, revealing microsatellite length distributions not observable without mutagenesis.

Most plastid genome sequences are assembled from short-read whole-genome sequencing data, yet the impact that sequencing coverage and the choice of assembly software can have on the accuracy of the resulting assemblies is poorly understood. In this study, we test the impact of both factors on plastid genome assembly in the threatened and rare endemic shrub Calligonum bakuense. We aim to characterize the differences across plastid genome assemblies generated by different assembly software tools and levels of sequencing coverage and to determine if these differences are large enough to affect the phylogenetic position inferred for C. bakuense compared to congeners. Four assembly software tools (FastPlast, GetOrganelle, IOGA, and NOVOPlasty) and seven levels of sequencing coverage across the plastid genome (original sequencing depth, 2,000x, 1,000x, 500x, 250x, 100x, and 50x) are compared in our analyses. The resulting assemblies are evaluated with regard to reproducibility, contig number, gene complement, inverted repeat length, and computation time; the impact of sequence differences on phylogenetic reconstruction is assessed. Our results show that software choice can have a considerable impact on the accuracy and reproducibility of plastid genome assembly and that GetOrganelle produces the most consistent assemblies for C. bakuense. Moreover, we demonstrate that a sequencing coverage between 500x and 100x can reduce both the sequence variability across assembly contigs and computation time. When comparing the most reliable plastid genome assemblies of C. bakuense, a sequence difference in only three nucleotide positions is detected, which is less than the difference potentially introduced through software choice.

Aconitum genus generally contains hypertoxic alkaloids. Poisoning incidents due to the improper ingestion of Aconitum materials frequently occur around the world. DNA barcoding is considered as a powerful tool for species identification, but complete sequences of conventional DNA barcodes are sometimes unattainable from food and highly processed products due to severe DNA degradation. Therefore, a shorter molecular marker will be more profitable for the authentication and poisoning diagnosis of Aconitum materials. In this study, 1246 psbA-trnH sequences and chloroplast genomes representing 183 taxa of Aconitum were collected, and a 23-bp nucleotide signature unique to Aconitum genus (5'-TATATGAGTCATTGAAGTTGCAG-3') was developed. The nucleotide signature was conserved and universal within Aconitum while divergent among other genera. The specific molecular signature was then successfully applied to the detection of processed Aconitum ingredients. To further evaluate the application potential of nucleotide signature in completely unknown mixture samples, boiled food mixtures, containing different ratios of Aconitum materials, were sequenced by high-throughput sequencing technology. The results showed that the nucleotide signature sequence could be directly extracted from raw sequencing data, even at a low DNA concentration of 0.2 ng/µl. Consequently, the 23-bp genus-specific nucleotide signature represents a significant step forward in the use of DNA barcoding to identify processed samples and food mixtures with degraded DNA. This study undoubtedly provides a new perspective and strong support for the identification and detection of Aconitum-containing products, which can be further introduced to the diagnosis of food poisoning.

Structural variation (SV) plays a fundamental role in genome evolution and can underlie inherited or acquired diseases such as cancer. Long-read sequencing technologies have led to improvements in the characterization of structural variants (SVs), although paired-end sequencing offers better scalability. Here, we present dysgu, which calls SVs or indels using paired-end or long reads. Dysgu detects signals from alignment gaps, discordant and supplementary mappings, and generates consensus contigs, before classifying events using machine learning. Additional SVs are identified by remapping of anomalous sequences. Dysgu outperforms existing state-of-the-art tools using paired-end or long-reads, offering high sensitivity and precision whilst being among the fastest tools to run. We find that combining low coverage paired-end and long-reads is competitive in terms of performance with long-reads at higher coverage values.

Short-read whole-genome sequencing (WGS) is a vital tool for clinical applications and basic research. Genetic divergence from the reference genome, repetitive sequences and sequencing bias reduces the performance of variant calling using short-read alignment, but the loss in recall and specificity has not been adequately characterized. To benchmark short-read variant calling, we used 36 diverse clinical Mycobacterium tuberculosis (Mtb) isolates dually sequenced with Illumina short-reads and PacBio long-reads. We systematically studied the short-read variant calling accuracy and the influence of sequence uniqueness, reference bias and GC content.

Reference-based Illumina variant calling demonstrated a maximum recall of 89.0% and minimum precision of 98.5% across parameters evaluated. The approach that maximized variant recall while still maintaining high precision (<99%) was tuning the mapping quality filtering threshold, i.e. confidence of the read mapping (recall = 85.8%, precision = 99.1%, MQ ≥ 40). Additional masking of repetitive sequence content is an alternative conservative approach to variant calling that increases precision at cost to recall (recall = 70.2%, precision = 99.6%, MQ ≥ 40). Of the genomic positions typically excluded for Mtb, 68% are accurately called using Illumina WGS including 52/168 PE/PPE genes (34.5%). From these results, we present a refined list of low confidence regions across the Mtb genome, which we found to frequently overlap with regions with structural variation, low sequence uniqueness and low sequencing coverage. Our benchmarking results have broad implications for the use of WGS in the study of Mtb biology, inference of transmission in public health surveillance systems and more generally for WGS applications in other organisms.

All relevant code is available at https://github.com/farhat-lab/mtb-illumina-wgs-evaluation.

Supplementary data are available at Bioinformatics online.

Accumulating evidence shows that microbes with their theater of activity residing within the human intestinal tract (i.e., the gut microbiome) influence host metabolism. Some of the strongest results come from recent fecal microbial transplant (FMT) studies that relate changes in intestinal microbiota to various markers of metabolism as well as the pathophysiology of insulin resistance. Despite these developments, there is still a limited understanding of the multitude of effects associated with FMT on the general physiology of the host, beyond changes in gut microbiome composition. We examined the effect of either allogenic (lean donor) or autologous FMTs on the gut microbiome, plasma metabolome, and epigenomic (DNA methylation) reprogramming in peripheral blood mononuclear cells in individuals with metabolic syndrome measured at baseline (pre-FMT) and after 6 weeks (post-FMT). Insulin sensitivity was determined with a stable isotope-based 2 step hyperinsulinemic clamp and multivariate machine learning methodology was used to uncover discriminative microbes, metabolites, and DNA methylation loci. A larger gut microbiota shift was associated with an allogenic than with autologous FMT. Furthemore, the data results of the the allogenic FMT group data indicates that the introduction of new species can potentially modulate the plasma metabolome and (as a result) the epigenome. Most notably, the introduction of Prevotella ASVs directly correlated with methylation of AFAP1, a gene involved in mitochondrial function, insulin sensitivity, and peripheral insulin resistance (Rd, rate of glucose disappearance). FMT was found to have notable effects on the gut microbiome but also on the host plasma metabolome and the epigenome of immune cells providing new avenues of inquiry in the context of metabolic syndrome treatment for the manipulation of host physiology to achieve improved insulin sensitivity.

Plant genomes contain a particularly high proportion of repeated structures of various types. This chapter proposes a guided tour of the available software that can help biologists to scan automatically for these repeats in sequence data or check hypothetical models intended to characterize their structures. Since transposable elements (TEs) are a major source of repeats in plants, many methods have been used or developed for this broad class of sequences. They are representative of the range of tools available for other classes of repeats and we have provided two sections on this topic (for the analysis of genomes or directly of sequenced reads), as well as a selection of the main existing software. It may be hard to keep up with the profusion of proposals in this dynamic field and the rest of the chapter is devoted to the foundations of an efficient search for repeats and more complex patterns. We first introduce the key concepts of the art of indexing and mapping or querying sequences. We end the chapter with the more prospective issue of building models of repeat families. We present the Machine Learning approach first, seeking to build predictors automatically for some families of ET, from a set of sequences known to belong to this family. A second approach, the linguistic (or syntactic) approach, allows biologists to describe themselves and check the validity of models of their favorite repeat family.