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Over the past few decades, the rapid democratization of high-throughput sequencing and the growing emphasis on open science practices have resulted in an explosion in the amount of publicly available sequencing data. This opens new opportunities for combining datasets to achieve unprecedented sample sizes, spatial coverage, or temporal replication in population genomic studies. However, a common concern is that non-biological differences between datasets may generate batch effects that can confound real biological patterns. Despite general awareness about the risk of batch effects, few studies have examined empirically how they manifest in real datasets, and it remains unclear what factors cause batch effects and how to best detect and mitigate their impact bioinformatically. In this paper, we compare two batches of low-coverage whole genome sequencing (lcWGS) data generated from the same populations of Atlantic cod (Gadus morhua). First, we show that with a “batch-effect-naive” bioinformatic pipeline, batch effects severely biased our genetic diversity estimates, population structure inference, and selection scan. We then demonstrate that these batch effects resulted from multiple technical differences between our datasets, including the sequencing instrument model/chemistry, read type, read length, DNA degradation level, and sequencing depth, but their impact can be detected and substantially mitigated with simple bioinformatic approaches. We conclude that combining datasets remains a powerful approach as long as batch effects are explicitly accounted for. We focus on lcWGS data in this paper, which may be particularly vulnerable to certain causes of batch effects, but many of our conclusions also apply to other sequencing strategies.

Stable Intronic Sequence RNA (sisRNA) is a relatively new class of non-coding RNA. Found in many organisms, these sisRNA produced from their host genes are generally involved in regulatory roles, controlling gene expression at multiple levels through active involvement in regulatory feedback loops. Large scale identification of sisRNA via genome-wide RNA sequencing has been difficult, largely in part due to its low abundance. Done on its own, RNA sequencing often yields a large mass of information that is ironically uninformative; the potential sisRNA reads being masked by other highly abundant RNA species like ribosomal RNA and messenger RNA. In this review, we present a practical workflow for the enrichment of circular sisRNA through the use of transcriptionally quiescent systems, rRNA-depletion, and RNase R treatment prior to deep sequencing. This workflow allows circular sisRNA to be reliably detected. We also present various methods to experimentally validate the circularity and stability of the circular sisRNA identified, as well as a few methods for further functional characterisation.

Background Understanding environmental microbiomes and antibiotic resistance (AR) is hindered by over reliance on relative abundance data from next-generation sequencing. Relative data limits our ability to quantify changes in microbiomes and resistomes over space and time because sequencing depth is not considered and makes data less suitable for Quantitative Microbial Risk Assessments (QMRA), critical in quantifying environmental AR exposure and transmission risks. Results Here we combine quantitative microbiome profiling (QMP; parallelization of amplicon sequencing and 16S rRNA qPCR to estimate cell counts) and absolute resistome profiling (based on high-throughput qPCR) to quantify AR along an anthropogenically impacted river. We show QMP overcomes biases caused by relative taxa abundance data and show the benefits of using unified Hill number diversities to describe environmental microbial communities. Our approach overcomes weaknesses in previous methods and shows Hill numbers are better for QMP in diversity characterisation. Conclusions Methods here can be adapted for any microbiome and resistome research question, but especially providing more quantitative data for QMRA and other environmental applications.

Illumina sequencing platforms have been widely used for amplicon-based environmental microbiome research. Analyses of amplicon data of environmental samples, generated from Illumina MiSeq platform illustrate the reverse (R2) reads in the PE datasets to have low quality towards the 3’ end of the reads which affect the sequencing depth of samples and ultimately impact the sample size which may possibly lead to an altered outcome. This study evaluates the usefulness of single-end (SE) sequencing data in microbiome research when the Illumina MiSeq PE dataset shows significantly high number of low-quality reverse reads. In this study, the amplicon data (V1V3, V3V4, V4V5 and V6V8) from 128 environmental (soil) samples, downloaded from SRA, demonstrate the efficiency of single-end (SE) sequencing data analyses in microbiome research. The SE datasets were found to infer the core microbiome structure as comparable to the PE dataset. Conspicuously, the forward (R1) datasets inferred a higher number of taxa as compared to PE datasets for most of the amplicon regions, except V3V4. Thus, analyses of SE sequencing data, especially R1 reads, in environmental microbiome studies could ameliorate the problems arising on sample size of the study due to low quality reverse reads in the dataset. However, care must be taken while interpreting the microbiome structure as few taxa observed in the PE datasets were absent in the SE datasets. In conclusion, this study demonstrates the availability of choices in analyzing the amplicon data without having the need to remove samples with low quality reverse reads.

Protein–protein interactions (PPIs) have evolved to display binding affinities that can support their function. As such, cognate and noncognate PPIs could be highly similar structurally but exhibit huge differences in binding affinities. To understand this phenomenon, we study three homologous protease–inhibitor PPIs that span 9 orders of magnitude in binding affinity. Using state-of-the-art methodology that combines protein randomization, affinity sorting, deep sequencing, and data normalization, we report quantitative binding landscapes consisting of ΔΔGbind values for the three PPIs, gleaned from tens of thousands of single and double mutations. We show that binding landscapes of the three complexes are strikingly different and depend on the PPI evolutionary optimality. We observe different patterns of couplings between mutations for the three PPIs with negative and positive epistasis appearing most frequently at hot-spot and cold-spot positions, respectively. The evolutionary trends observed here are likely to be universal to other biological complexes in the cell.

Background: In recent years, the morbidity and mortality of cancer patients have continued to increase in China, and there is an urgent need to develop an effective method to monitor tumor dynamics and measure tumor burden. Derived from the cell-free fraction of blood in cancer patients, circulating tumor DNA (ctDNA) has been regarded as a promising surrogate for tumor tissue biopsies. With the development of sequencing technology, ctDNA has been recognized as a specific and highly sensitive biomarker, and it has become a hot research spot in recent years. Methods: In this paper, we investigated clonal changes before and after surgery in liver cancer patients using ctDNA. Results: First, we evaluated the accuracy and stability of the method in ctDNA detection using virtual tumor samples with known mutations. The results showed that our method detected variants with an allelic frequency of at least 0.5%. We then applied this method to 34 liver cancer patients. A total of 266 clinically relevant mutations were identified in the pretreatment plasma samples. Through the analysis of plasma DNA samples at different treatment time points, we also investigated the possibility of using ctDNA as a prognostic factor to reflect tumor dynamics and to evaluate clinical responses. Conclusions: The results demonstrated that targeted high-depth next-generation sequencing can be used in ctDNA detection. Compared to traditional biopsy, the detection of ctDNA provides more information for human liver cancer, which is essential to guide the selection of therapy and predict prognosis.

We present MultiEditR (Multiple Edit Deconvolution by Inference of Traces in R), the first algorithm specifically designed to detect and quantify RNA editing from Sanger sequencing (z.umn.edu/multieditr). Although RNA editing is routinely evaluated by measuring the heights of peaks from Sanger sequencing traces, the accuracy and precision of this approach has yet to be evaluated against gold standard next-generation sequencing methods. Through a comprehensive comparison to RNA sequencing (RNA-seq) and amplicon-based deep sequencing, we show that MultiEditR is accurate, precise, and reliable for detecting endogenous and programmable RNA editing., Graphical abstract, RNA editing allows cells to transiently rewrite the genetic code. However, detecting and quantifying RNA editing relies on RNA-seq, which is expensive and suffers from poor local resolution. Here, we develop MultiEditR (https://moriaritylab.shinyapps.io/multieditr/) to analyze RNA editing from Sanger sequencing for a fraction of the cost while maintaining a comparable fidelity to RNA-seq.

MicroRNAs are critical gene regulators at the post-transcriptional level and play essential roles in numerous developmental processes in metazoan parasites including the causative agent of cystic echinococcosis, Echinococcus granulosus. The molecular basis of different patterns of E. granulosus development in the canine definitive host and in in vitro culture systems is poorly understood. In the present study, miRNA transcriptomes of the strobilated worms derived from experimental infection in the definitive host were compared with those from diphasic culture system after 60-day protoscoleces cultivation. Total RNA was extracted from in vivo- and in vitro-derived strobilated worms. Small RNA libraries were constructed, and deep sequencing was performed. Subsequently, differential miRNA expressions and target predictions were obtained, and pathway analysis was performed by gene ontology and KEGG. Seven miRNAs were differentially expressed between the in vivo- and in vitro-derived worms. In addition, we reported 13 novel miRNA candidates and 42 conserved miRNAs. Four out of five top miRNAs with the highest read counts were shared between the in vivo and in vitro-derived worms, i.e., egr-miR-10a-5p, egr-let-7-5p, egr-bantam-3p, and egr-miR-71-5p. Target prediction of the differential miRNAs between the two systems showed significant differences in the membrane-enclosed lumen, membrane part, and an intrinsic component of the membrane. Findings of KEGG analysis indicated that differentially expressed miRNAs were involved in hippo, MAPK, and WNT signaling pathways. The study demonstrated a significant difference in miRNA transcriptomes and related signaling pathways between the two systems, suggesting the importance of host-parasite interplay in the fate of protoscoleces development in in vivo and in vitro systems.

Podophyllum hexandrum—an endangered, high-altitude medicinal herb is a vital source of the therapeutically important lignan viz. podophyllotoxin (PTOX). PTOX is widely used as a precursor for semi-synthesis of anticancer drugs etoposide, teniposide and etopophos. Although next generation sequencing has been employed to characterize the genes involved in PTOX biosynthesis; the complete biosynthetic pathway is yet to be known. miRNAs play pivotal roles in secondary metabolites biosynthesis besides plant growth and development. Till date, several novel miRNAs have been identified in different plant species, but novel miRNAs and their regulatory functions still remains unknown in P. hexandrum. Here, two small RNA libraries were prepared from control and MeJA treated cell cultures of P. hexandrum. High-throughput sequencing of these small RNA libraries yielded 59 novel miRNAs in control and 66 novel miRNAs in treated P. hexandrum apart from known miRNAs. The identified miRNAs targeted different mRNAs. Kyoto Encyclopedia of Genes and Genomes pathway analysis unveiled the role of these miRNAs in various secondary metabolites biosynthetic pathways. qRT-PCR analysis showed lower expression of a set of miRNAs identified in MeJA treated cell culture. The current study expands our knowledge of novel miRNAs mediated secondary metabolites, especially PTOX biosynthesis, in P. hexandrum.

Library preparation for high-throughput sequencing applications is a critical step in producing representative, unbiased sequencing data. The iGenomX Riptide High Throughput Rapid Library Prep Kit purports to provide high-quality sequencing data with lower costs compared to other Illumina library kits. To test these claims, we compared sequence data quality of Riptide libraries to libraries constructed with KAPA Hyper and NEBNext Ultra. Across several single-source genome samples, mapping performance and de novo assembly of Riptide libraries were similar to conventional libraries prepared with the same DNA. Poor performance of some libraries resulted in low sequencing depth. In particular, degraded DNA samples may be challenging to sequence with Riptide. There was little cross-well plate contamination with the overwhelming majority of reads belong to the proper source genomes. The sequencing of metagenome samples using different Riptide primer sets resulted in variable taxonomic assignment of reads. Increased adoption of the Riptide kit will decrease library preparation costs. However, this method might not be suitable for degraded DNA.

Circulating tumor DNA (ctDNA) is an emerging biomarker for many cancers, but the limited sensitivity of current detection methods reduces its utility for diagnosing minimal residual disease. Here we describe phased variant enrichment and detection sequencing (PhasED-Seq), a method that uses multiple somatic mutations in individual DNA fragments to improve the sensitivity of ctDNA detection. Leveraging whole-genome sequences from 2,538 tumors, we identify phased variants and their associations with mutational signatures. We show that even without molecular barcodes, the limits of detection of PhasED-Seq outperform prior methods, including duplex barcoding, allowing ctDNA detection in the parts-per-million range in patient samples. We profiled 678 specimens from 213 patients with B-cell lymphomas, including serial cell-free DNA samples before and during therapy for diffuse large B-cell lymphoma. In patients with undetectable ctDNA by CAPP-Seq after two cycles of therapy, an additional 25% have ctDNA detectable by PhasED-Seq and have worse outcomes. Finally, we demonstrate the application of PhasED-Seq to solid tumors., Editorial summary The sensitivity of circulating tumor DNA detection is improved by identifying sequences with two or more mutations.

Background New breeding technologies (NBT) using CRISPR/Cas9-induced homology directed repair (HDR) has the potential to expedite genetic improvement in aquaculture. The long generation time in Atlantic salmon makes breeding an unattractive solution to obtain homozygous mutants and improving the rates of perfect HDR in founder (F0) fish is thus required. Genome editing can represent small DNA changes down to single nucleotide replacements (SNR). This enables edits such as premature stop codons or single amino acid changes and may be used to obtain fish with traits favorable to aquaculture, e.g. disease resistance. A method for SNR has not yet been demonstrated in salmon. Results Using CRISPR/Cas9 and asymmetrical ODNs, we were able to perform precise SNR and introduce a premature stop codon in dnd in F0 salmon. Deep sequencing demonstrated up to 59.2% efficiency in single embryos. In addition, using the same asymmetrical ODN design, we inserted a FLAG element into slc45a2 and dnd, showing high individual perfect HDR efficiencies (up to 36.7 and 32.7%, respectively). Conclusions In this work, we demonstrate that precise SNR and knock-in (KI) can be performed in F0 salmon embryos using asymmetrical oligonucleotide (ODN) donors. We suggest that HDR-induced SNR can be applied as a powerful NBT, allowing efficient introgression of favorable alleles and bypassing challenges associated with traditional selective breeding.

PurposeThe purpose of this article was to evaluate the impact of sample collection order on the diagnostic yield of metagenomic deep sequencing (MDS) for determining the causative pathogen in infectious keratitis.MethodsWe performed a cross-sectional diagnostic test evaluation among subjects with infectious keratitis at Aravind Eye Hospital in Madurai, India. All subjects underwent corneal scrapings of the affected eye to obtain potassium hydroxide smear, Gram stain, bacterial culture, and fungal culture, in this order. The order of MDS specimen collection relative to smear and culture samples was randomized and served as the primary predictor. Outcomes included the normalized copy number of pathogenic RNA detected by MDS, the proportion of MDS samples that were diagnostic, and the agreement of MDS results with cultures.ResultsMDS samples from 46 subjects with corneal ulcers were evaluated. MDS was positive in 33 subjects (76%) and had 74% overall agreement with culture results. There was no association between order of MDS sample collection and normalized copy number of genetic material detected (P = 0.62) or the likelihood of MDS positivity (P = 0.46). However, the likelihood of agreement between MDS and cultures decreased when MDS corneal swabs were collected after other diagnostic corneal scrapings (P = 0.05).ConclusionsThe overall yield of MDS for detecting the cause of infectious keratitis was not affected by sample collection order. However, diagnostic agreement between MDS and cultures decreased when MDS samples were collected after other specimens. Additional investigation is warranted to determine whether this represents increased sensitivity of MDS compared with cultures or higher susceptibility to contaminants.

Adaptive immunity is mediated by lymphocyte B and T cells, which respectively express a vast and diverse repertoire of B cell and T cell receptors and, in conjunction with peptide antigen presentation through major histocompatibility complexes (MHCs), can recognize and respond to pathogens and diseased cells. In recent years, advances in deep sequencing have led to a massive increase in the amount of adaptive immune receptor repertoire data; additionally, proteomics techniques have led to a wealth of data on peptide–MHC presentation. These large-scale data sets are now making it possible to train machine and deep learning models, which can be used to identify complex and high-dimensional patterns in immune repertoires. This article introduces adaptive immune repertoires and machine and deep learning related to biological sequence data and then summarizes the many applications in this field, which span from predicting the immunological status of a host to the antigen specificity of individual receptors and the engineering of immunotherapeutics.

Application of the clustered regularly interspaced short palindromic repeats associated 9 (CRISPR-Cas9) technology has revolutionized biology by greatly enhancing the ability to introduce mutations into DNA for research and prospective therapeutic purposes. However, the understanding of Cas9 editing outcomes is still limited. Previously, it was considered that Cas9 introduces stochastic insertions or deletions (indels) at the target site. In the current study, we performed in vivo multiplex editing, deep sequencing, and comprehensive analysis of its editing outcomes in Bombyx mori (B. mori). A total of 31161 editing events from 9 single-guide RNA (sgRNA) sites in 16 individuals were generated and analyzed, and we found that Cas9 introduces mutations with some regularity rather than via stochastic indels. The editing efficiency varies with sgRNA sequences, individuals, and orientation. Small deletions account for the vast majority of mutated sequences, followed by a small fraction of substitutions and insertions. The most likely mutations are deletions between two microhomologous sequences or single-base deletions at the cleavage site in the absence of microhomologous pairs. Insertions are formed by diverse mechanisms, including direct acquisition of free genomic fragments, duplication of broken ends, replication of adjacent sequences, or random addition of free nucleotides. The above results indicate that the Cas9 editing spectrum is reproducible and predictable. Thus, our findings enable a deeper understanding of Cas9-mediated mutagenesis and better design of genome editing experiments, as well as elucidate the DNA double-strand break repair processes in B. mori.

Knowledge of host associations of blood-feeding vectors may afford insights into managing disease systems and protecting public health. However, the ability of methods to distinguish bloodmeal sources varies widely. We used two methods—Sanger sequencing and amplicon deep sequencing—to target a 228 bp region of the vertebrate Cytochrome b gene and determine hosts fed upon by triatomines (n = 115) collected primarily in Texas, USA. Direct Sanger sequencing of PCR amplicons was successful for 36 samples (31%). Sanger sequencing revealed 15 distinct host species, which included humans, domestic animals (Canis lupus familiaris, Ovis aries, Gallus gallus, Bos taurus, Felis catus, and Capra hircus), wildlife (Rattus rattus, Incilius nebulifer, Sciurus carolinensis, Sciurus niger, and Odocoileus virginianus), and captive animals (Panthera tigris, Colobus spp., and Chelonoidis carbonaria). Samples sequenced by the Sanger method were also subjected to Illumina MiSeq amplicon deep sequencing. The amplicon deep sequencing results (average of 302,080 usable reads per sample) replicated the host community revealed using Sanger sequencing, and detected additional hosts in five triatomines (13.9%), including two additional blood sources (Procyon lotor and Bassariscus astutus). Up to four bloodmeal sources were detected in a single triatomine (I. nebulifer, Homo sapiens, C. lupus familiaris, and S. carolinensis). Enhanced understanding of vector-host-parasite networks may allow for integrated vector management programs focusing on highly-utilized and highly-infected host species.

Next Generation Sequencing (NGS) has become an important tool in the biological sciences and has a growing number of applications across medical fields. Currently, few undergraduate programs provide training in the design and implementation of NGS applications. Here, we describe an inquiry‐based laboratory exercise for a college‐level molecular biology laboratory course that uses real‐time MinION deep sequencing and bioinformatics to investigate characteristic genetic variants found in cancer cell‐lines. The overall goal for students was to identify non‐small cell lung cancer (NSCLC) cell‐lines based on their unique genomic profiles. The units described in this laboratory highlight core principles in multiplex PCR primer design, real‐time deep sequencing, and bioinformatics analysis for genetic variants. We found that the MinION device is an appropriate, feasible tool that provides a comprehensive, hands‐on NGS experience for undergraduates. Student evaluations demonstrated increased confidence in using molecular techniques and enhanced understanding of NGS concepts. Overall, this exercise provides a pedagogical tool for incorporating NGS approaches in the teaching laboratory as way of enhancing students' comprehension of genomic sequence analysis. Further, this NGS lab module can easily be added to a variety of lab‐based courses to help undergraduate students learn current DNA sequencing methods with limited effort and cost.

T cells can be subdivided into a number of different subsets that are defined by their distinct functions. While the specialization of different T cell subsets is partly achieved by the expression of specific genes, the overall transcriptional profiles of all T cells appear very similar. Alternative mRNA splicing is a mechanism that facilitates greater transcript/protein diversity from a limited number of genes, which may contribute to the functional specialization of distinct T cell subsets. In this study we employ a combination of short-read and long-read sequencing technologies to compare alternative mRNA splicing between the CD4 and CD8 T cell lineages. While long-read technology was effective at assembling full-length alternatively spliced transcripts, the low sequencing depth did not facilitate accurate quantitation. On the other hand, short-read technology was ineffective at assembling full-length transcripts but was highly accurate for quantifying expression. We show that integrating long-read and short-read data together achieves a more complete view of transcriptomic diversity. We found that while the overall usage of transcript isoforms was very similar between the CD4 and CD8 lineages, there were numerous alternative spliced mRNA isoforms that were preferentially used by one lineage over the other. These alternative spliced isoforms included ones with different exon usage, exon exclusion or intron inclusion, all of which are expected to significantly alter the protein sequence.

Transcriptome deep sequencing (RNA-seq) has become a routine method for global gene expression profiling. However, its application to large-scale experiments remains limited by cost and labor constraints. Here we describe a massively parallel 3' end RNA-seq (MP3RNA-seq) method that introduces unique sample barcodes during reverse transcription to permit sample pooling immediately following this initial step. MP3RNA-seq allows for handling of hundreds of samples in a single experiment, at a cost of about $6 per sample for library construction and sequencing. MP3RNA-seq is effective for not only high-throughput gene expression profiling, but also genotyping. To demonstrate its utility, we applied MP3RNA-seq to 477 double haploid lines of maize. We identified 19,429 genes expressed in at least 50% of the lines and 35,836 high-quality single nucleotide polymorphisms for genotyping analysis. Armed with these data, we performed expression and agronomic trait quantitative trait locus (QTL) mapping and identified 25,797 expression QTLs for 15,335 genes and 21 QTLs for plant height, ear height, and relative ear height. We conclude that MP3RNA-seq is highly reproducible, accurate, and sensitive for high-throughput gene expression profiling and genotyping, and should be generally applicable to most eukaryotic species.

Cell-based aptamer selection (Cell-SELEX) against predefined protein targets that benefits using the native form of the targets is the most promising approach to achieve aptamer probes capable of recognizing targets under both in vitro and in vivo conditions. The major disadvantages in Cell-SELEX are the imperfectness of the negative selection step and the lengthy procedure of selection. Here, we introduced the Counter-SELEX as part of our modified Cell-SELEX and implemented deep sequencing to overcome these shortcomings in developing aptamers against aspartate β-hydroxylase (ASPH) as a known tumor marker. In parallel with the conventional Cell-SELEX, five consecutive cycles of counter selection were accomplished using sequences bound to negative cells (the Counter-SELEX) to detect oligos that are not specific for ASPH. After high-throughput sequencing, the representative of each promising achieved family was subjected to further confirmatory analysis via flow cytometry, followed by the fluorescence immunostaining of histopathological sections. Implementing our innovative complementary method, annoying mis-selected sequences in Cell-SELEX enriched pools were effectively identified and removed. According to the affinity assay on the cells displaying ASPH, three aptamers, AP-Cell 1, AP-Cell 2, and AP-Cell 3, with K d values of 47.51, 39.38, and 65.23 nM, respectively, were obtained, while AP-Cell 1 and 3 could then successfully spot ASPH displayed on the tissues. Our study showed that the Counter-SELEX could be considered as a complementary method for Cell-SELEX to overcome the imperfectness of the negative selection step. Moreover, high-throughput nucleotide sequencing could help to shorten the overall process.

Small noncoding RNAs deep sequencing (sncRNA-Seq) has become a routine for sncRNA detection and quantification. However, the software packages currently available for sncRNA annotation can neither recognize sncRNA variants in the sequencing reads, nor annotate all known sncRNA simultaneously. Here, we report a novel anchor alignment-based small RNA annotation (AASRA) software package (https://github.com/biogramming/AASRA). AASRA represents an all-in-one sncRNA annotation pipeline, which allows for high-speed, simultaneous annotation of all known sncRNA species with the capability to distinguish mature from precursor microRNAs, and to identify novel sncRNA variants in the sncRNA-Seq sequencing reads.

Introduction A robust genetic test for BRCA1 and BRCA2 genes is necessary for the diagnosis, prognosis, and treatment of patients with hereditary breast and ovarian cancer. We evaluated a commercial amplicon-based massively parallel sequencing (MPS) assay, BRCA MASTR Plus on the MiSeq platform, for germline BRCA genetic testing. Methods This study was performed on 31 DNA from cell lines and proficiency testing samples to establish the accuracy of the assay. A reference cell line DNA, NA12878 was used to determine the reproducibility of the assay. Discordant MPS result was resolved orthogonally by the current gold-standard Sanger sequencing method. Results The analytical accuracy, sensitivity, and specificity for variant detection were 93.55, 92.86, and 100.00%, respectively. Both sequencing depth and variant allele frequencies were highly reproducible by comparing the NA12878 DNA tested in three separate runs. The single discordant result, later confirmed by Sanger sequencing was due to the inability of the MASTR Reporter software to identify a 40-bp deletion in BRCA1. Conclusion The BRCA MASTR Plus assay on the MiSeq platform is accurate and reproducible for germline BRCA genetic testing, making it suitable for use in a clinical diagnostic laboratory. However, Sanger sequencing may still serve as a confirmatory method to improve diagnostic capability of the MPS assay.

Motivation Ribosome Profiling (Ribo-seq) has revolutionized the study of RNA translation by providing information on ribosome positions across all translated RNAs with nucleotide-resolution. Yet several technical limitations restrict the sequencing depth of such experiments, the most common of which is the overabundance of rRNA fragments. Various strategies can be employed to tackle this issue, including the use of commercial rRNA depletion kits. However, as they are designed for more standardized RNAseq experiments, they may perform suboptimally in Ribo-seq. In order to overcome this, it is possible to use custom biotinylated oligos complementary to the most abundant rRNA fragments, however currently no computational framework exists to aid the design of optimal oligos. Results Here, we first show that a major confounding issue is that the rRNA fragments generated via Ribo-seq vary significantly with differing experimental conditions, suggesting that a ‘one-size-fits-all’ approach may be inefficient. Therefore we developed Ribo-ODDR, an oligo design pipeline integrated with a user-friendly interface that assists in oligo selection for efficient experiment-specific rRNA depletion. Ribo-ODDR uses preliminary data to identify the most abundant rRNA fragments, and calculates the rRNA depletion efficiency of potential oligos. We experimentally show that Ribo-ODDR designed oligos outperform commercially available kits and lead to a significant increase in rRNA depletion in Ribo-seq. Availability and implementation Ribo-ODDR is freely accessible at https://github.com/fallerlab/Ribo-ODDR. Supplementary information Supplementary data are available at Bioinformatics online.

Next-generation sequencing technology has been commonly applied to detect mitochondrial DNA (mtDNA) mutations, which are reported to be strongly associated with cancers. However, several key challenges still exist regarding bioinformatics analysis of mtDNA sequencing data that greatly affect the detection accuracy of mtDNA mutations. Here we comprehensively evaluated several key analysis procedures in three different sample types. We found that a trimming procedure was essential for improving mtDNA mapping performance in plasma but not tissue samples. Mapping with a revised Cambridge reference sequence and human genome 19 reference was strongly suggested for mtDNA mutation detection in plasma samples because of the extreme abundance of nuclear DNA of mitochondrial origin. Moreover, our results showed that a setting of 3 mismatches was most appropriate for mtDNA mutation calling. Importantly, we revealed the presence of a negative logarithmic relationship between mtDNA site sequencing depth and minimum detectable mutation frequency and built an innovative and efficient filtering strategy to increase the accuracy and sensitivity of mutation detection. Finally, we verified that higher sequencing depth was required for a PCR-based compared with a capture-based enrichment strategy. We established an innovative data analysis strategy that is of great significance for improving the accuracy of mtDNA mutation detection for different types of tumor samples., Graphical Abstract, An innovative data analysis strategy was established for accurate detection of NGS-based mtDNA mutations by integrating a trimming procedure, a mapping algorithm with rCRS and hg19 as reference genomes, and a novel filtering approach that is suitable for three different types of tumor samples.

BackgroundHigh-throughput sequencing (HTS) has been applied successfully for virus and viroid discovery in many agricultural crops leading to the current drive to apply this technology in routine pathogen detection. The validation of HTS-based pathogen detection is therefore paramount.MethodsPlant infections were established by graft inoculating a suite of viruses and viroids from established sources for further study. Four plants (one healthy plant and three infected) were sampled in triplicate and total RNA was extracted using two different methods (CTAB extraction protocol and the Zymo Research Quick-RNA Plant Miniprep Kit) and sent for Illumina HTS. One replicate sample of each plant for each RNA extraction method was also sent for HTS on an Ion Torrent platform. The data were evaluated for biological and technical variation focussing on RNA extraction method, platform used and bioinformatic analysis.ResultsThe study evaluated the influence of different HTS protocols on the sensitivity, specificity and repeatability of HTS as a detection tool. Both extraction methods and sequencing platforms resulted in significant differences between the data sets. Using a de novo assembly approach, complemented with read mapping, the Illumina data allowed a greater proportion of the expected pathogen scaffolds to be inferred, and an accurate virome profile was constructed. The complete virome profile was also constructed using the Ion Torrent data but analyses showed that more sequencing depth is required to be comparative to the Illumina protocol and produce consistent results. The CTAB extraction protocol lowered the proportion of viroid sequences recovered with HTS, and the Zymo Research kit resulted in more variation in the read counts obtained per pathogen sequence. The expression profiles of reference genes were also investigated to assess the suitability of these genes as internal controls to allow for the comparison between samples across different protocols.ConclusionsThis study highlights the need to measure the level of variation that can arise from the different variables of an HTS protocol, from sample preparation to data analysis. HTS is more comprehensive than any assay previously used, but with the necessary validations and standard operating procedures, the implementation of HTS as part of routine pathogen screening practices is possible.

Alternative splicing of eukaryotic precursor (messenger) RNAs in the nucleus not only increases transcriptomic complexity, but also expands proteomic and functional diversity. In addition to basic types of alternative splicing, recent transcriptome-wide analyses have also suggested other new types of non-canonical splicing, such as back splicing and recursive splicing, and their widespread expression across species Increasing lines of evidence have suggested mechanisms for back splicing, including insights from analyses of nascent RNA sequencing. In this review, we discuss our current understanding of back splicing regulation, and highlight its distinct characteristics in processing during nascent RNA synthesis by taking advantage of metabolic tagging nascent RNA sequencing. Features of recursive splicing are also discussed in the perspective of nascent RNA sequencing.

Genotyping-by-sequencing methods such as RADseq are popular for generating genomic and population-scale data sets from a diverse range of organisms. These often lack a usable reference genome, restricting users to RADseq specific software for processing. However, these come with limitations compared to generic next generation sequencing (NGS) toolkits. Here, we describe and test a simple pipeline for reference-free RADseq data processing that blends de novo elements from STACKS with the full suite of state-of-the art NGS tools. Specifically, we use the de novo RADseq assembly employed by STACKS to create a catalogue of RAD loci that serves as a reference for read mapping, variant calling and site filters. Using RADseq data from 28 zebra sequenced to ~8x depth-of-coverage we evaluate our approach by comparing the site frequency spectra (SFS) to those from alternative pipelines. Most pipelines yielded similar SFS at 8x depth, but only a genotype likelihood based pipeline performed similarly at low sequencing depth (2-4x). We compared the RADseq SFS with medium-depth (~13x) shotgun sequencing of eight overlapping samples, revealing that the RADseq SFS was persistently slightly skewed towards rare and invariant alleles. Using simulations and human data we confirm that this is expected when there is allelic dropout (AD) in the RADseq data. AD in the RADseq data caused a heterozygosity deficit of ~16%, which dropped to ~5% after filtering AD. Hence, AD was the most important source of bias in our RADseq data.

Motivation MicroRNA (miRNA) precursor arms give rise to multiple isoforms simultaneously called ‘isomiRs.’ IsomiRs from the same arm typically differ by a few nucleotides at either their 5′ or 3′ termini or both. In humans, the identities and abundances of isomiRs depend on a person’s sex and genetic ancestry as well as on tissue type, tissue state and disease type/subtype. Moreover, nearly half of the time the most abundant isomiR differs from the miRNA sequence found in public databases. Accurate mining of isomiRs from deep sequencing data is thus important. Results We developed isoMiRmap, a fast, standalone, user-friendly mining tool that identifies and quantifies all isomiRs by directly processing short RNA-seq datasets. IsoMiRmap is a portable ‘plug-and-play’ tool, requires minimal setup, has modest computing and storage requirements, and can process an RNA-seq dataset with 50 million reads in just a few minutes on an average laptop. IsoMiRmap deterministically and exhaustively reports all isomiRs in a given deep sequencing dataset and quantifies them accurately (no double-counting). IsoMiRmap comprehensively reports all miRNA precursor locations from which an isomiR may be transcribed, tags as ‘ambiguous’ isomiRs whose sequences exist both inside and outside of the space of known miRNA sequences and reports the public identifiers of common single-nucleotide polymorphisms and documented somatic mutations that may be present in an isomiR. IsoMiRmap also identifies isomiRs with 3’ non-templated post-transcriptional additions. Compared to similar tools, isoMiRmap is the fastest, reports more bona fide isomiRs, and provides the most comprehensive information related to an isomiR’s transcriptional origin. Availability and implementation The codes for isoMiRmap are freely available at https://cm.jefferson.edu/isoMiRmap/ and https://github.com/TJU-CMC-Org/isoMiRmap/. IsomiR profiles for the datasets of the 1000 Genomes Project, spanning five population groups, and The Cancer Genome Atlas (TCGA), spanning 33 cancer studies, are also available at https://cm.jefferson.edu/isoMiRmap/. Supplementary information Supplementary data are available at Bioinformatics online.

Digested genome sequencing (Digenome-seq) is a highly sensitive, easy-to-carry-out, cell-free method for experimentally identifying genome-wide off-target sites of programmable nucleases and deaminases (also known as base editors). Genomic DNA is digested in vitro using clustered regularly interspaced short palindromic repeats ribonucleoproteins (RNPs; plus DNA-modifying enzymes to cleave both strands of DNA at sites containing deaminated base products, in the case of base editors) and subjected to whole-genome sequencing (WGS) with a typical sequencing depth of 30×. A web-based program is available to map in vitro cleavage sites corresponding to on- and off-target sites. Chromatin DNA, in parallel with histone-free genomic DNA, can also be used to account for the effects of chromatin structure on off-target nuclease activity. Digenome-seq is more sensitive and comprehensive than cell-based methods for identifying off-target sites. Unlike other cell-free methods, Digenome-seq does not involve enrichment of DNA ends through PCR amplification. The entire process other than WGS, which takes ~1–2 weeks, including purification and preparation of RNPs, digestion of genomic DNA and bioinformatic analysis after WGS, takes about several weeks. This protocol describes a cell-free method for experimentally identifying genome-wide off-target sites of CRISPR nucleases and deaminases through in vitro digestion of genomic DNA or chromatin followed by whole-genome sequencing.

Replication initiator proteins (Reps) from the HUH-endonuclease superfamily process specific single-stranded DNA (ssDNA) sequences to initiate rolling circle/hairpin replication in viruses, such as crop ravaging geminiviruses and human disease causing parvoviruses. In biotechnology contexts, Reps are the basis for HUH-tag bioconjugation and a critical adeno-associated virus genome integration tool. We solved the first co-crystal structures of Reps complexed to ssDNA, revealing a key motif for conferring sequence specificity and for anchoring a bent DNA architecture. In combination, we developed a deep sequencing cleavage assay, termed HUH-seq, to interrogate subtleties in Rep specificity and demonstrate how differences can be exploited for multiplexed HUH-tagging. Together, our insights allowed engineering of only four amino acids in a Rep chimera to predictably alter sequence specificity. These results have important implications for modulating viral infections, developing Rep-based genomic integration tools, and enabling massively parallel HUH-tag barcoding and bioconjugation applications.

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We demonstrate that a recombinase ribozyme achieves multiple functions in the same reaction network: self-reproduction, iterative elongation and circularization of other RNAs, leading to synthesis of diverse products predicted by a kinetic model. This shows that key mechanisms can be integrated and controlled toward Darwinian evolution in RNA reaction networks., The integration of self-reproduction and diversification mechanisms in RNA reaction networks paves the way for experimental tests of prebiotic evolution.

While RNA-sequencing (RNA-seq) has emerged as a standard approach in toxicogenomics, its full potential in gaining underlying toxicological mechanisms is still not clear when only three biological replicates are used. This “three-sample” study design is common in toxicological research, particularly in animal studies during preclinical drug development. Sequencing depth (the total number of reads in an experiment) and library preparation are critical to the resolution and integrity of RNA-seq data and biological interpretation. We used aflatoxin b1 (AFB1), a model toxicant, to investigate the effect of sequencing depth and library preparation in RNA-seq on toxicological interpretation in the “three-sample” scenario. We also compared different gene profiling platforms (RNA-seq, TempO-seq, microarray, and qPCR) using identical liver samples. Well-established mechanisms of AFB1 toxicity served as ground truth for our comparative analyses. We found that a minimum of 20 million reads was sufficient to elicit key toxicity functions and pathways underlying AFB1-induced liver toxicity using three replicates and that identification of differentially expressed genes was positively associated with sequencing depth to a certain extent. Further, our results showed that RNA-seq revealed toxicological insights from pathway enrichment with overall higher statistical power and overlap ratio, compared with TempO-seq and microarray. Moreover, library preparation using the same methods was important to reproducing the toxicological interpretation.

Motivation The binding of T-cell receptors (TCRs) to their target peptide MHC (pMHC) ligands initializes the cell-mediated immune response. In autoimmune diseases such as multiple sclerosis, the TCR erroneously recognizes self-peptides as foreign and activates an immune response against healthy cells. Such responses can be triggered by cross-recognition of the autoreactive TCR with foreign peptides. Hence, it would be desirable to identify such foreign-antigen triggers to provide a mechanistic understanding of autoimmune diseases. However, the large sequence space of foreign antigens presents an obstacle in the identification of cross-reactive peptides. Results Here, we present an in silico modeling and scoring method which exploits the structural properties of TCR-pMHC complexes to predict the binding of cross-reactive peptides. We analyzed three mouse TCRs and one human TCR isolated from a patient with multiple sclerosis. Cross-reactive peptides for these TCRs were previously identified via yeast display coupled with deep sequencing, providing a robust dataset for evaluating our method. Modeling query peptides in their associated TCR-pMHC crystal structures, our method accurately selected the top binding peptides from sets containing more than a hundred thousand unique peptides. Availability and implementation Analyses were performed using custom Python and R scripts available at https://github.com/weng-lab/antigen-predict. Supplementary information Supplementary data are available at Bioinformatics online.

The development and innovative use of next-generation sequencing technologies have opened the doors for genetic and epigenetic research at the next level. These technologies can be used to study gene expression regulation at the transcriptional and post-transcriptional levels in both prokaryotic and eukaryotic systems. In this review, we focused on the various tools and techniques for DNA-binding proteins such as RNA polymerase, sigma factors, nucleoid-associated proteins, and transcription factors in bacteria. Descriptions on series of Chromatin ImmunoPrecipitation (ChIP) technologies, including ChIP followed by microarray hybridization (ChIP-chip), ChIP followed by deep sequencing (ChIP-seq), and ChIP with exonuclease digestion and deep sequencing (ChIP-exo) has been given. Furthermore, recent investigations on transcriptional regulatory networks of a wide range of bacteria with ChIP technologies are discussed for the model bacteria Escherichia coli followed by pathogenic and other non-pathogenic bacteria. In conclusion, ChIP technologies have proven effective and efficient to reconstruct and to delineate transcriptional regulatory network in a variety of bacteria.

Eukaryotic genomes encode thousands of small and large non-coding RNAs (ncRNAs). However, the expression, functions and evolution of these ncRNAs are still largely unknown. In this study, we have updated deepBase to version 3.0 (deepBase v3.0, http://rna.sysu.edu.cn/deepbase3/index.html), an increasingly popular and openly licensed resource that facilitates integrative and interactive display and analysis of the expression, evolution, and functions of various ncRNAs by deeply mining thousands of high-throughput sequencing data from tissue, tumor and exosome samples. We updated deepBase v3.0 to provide the most comprehensive expression atlas of small RNAs and lncRNAs by integrating ∼67 620 data from 80 normal tissues and ∼50 cancer tissues. The extracellular patterns of various ncRNAs were profiled to explore their applications for discovery of noninvasive biomarkers. Moreover, we constructed survival maps of tRNA-derived RNA Fragments (tRFs), miRNAs, snoRNAs and lncRNAs by analyzing >45 000 cancer sample data and corresponding clinical information. We also developed interactive webs to analyze the differential expression and biological functions of various ncRNAs in ∼50 types of cancers. This update is expected to provide a variety of new modules and graphic visualizations to facilitate analyses and explorations of the functions and mechanisms of various types of ncRNAs.

Plants have evolved many defense strategies for combating viral infections. One major surveillance strategy adopted by them is manipulating viral sequences to generate distinct small RNA products via Dicer-like enzymes (DCL), and thereby restricting virus multiplication through the RNA interference (RNAi) mechanism. The power of high-throughput sequencing technologies, with diverse computational tools to handle small RNA sequencing (sRNA-Seq) data, bestows unprecedented opportunities to answer fundamental questions in plant virology. Here, we present some basic concepts of virus-derived, small interfering RNA (vsiRNA) biogenesis in plants, optimization strategies, caveats, and best practices for efficient discovery and diagnosis of known as well as novel plant viruses/viroids using deep sequencing of small RNA (sRNA) pools.

Liquid biopsy, the analysis of circulating tumor DNA (ctDNA), is a promising tool in oncology, especially in personalized medicine. Although its main applications currently focus on selection and adjustment of therapy, ctDNA may also be used to monitor residual disease, establish prognosis, detect relapses, and possibly screen at-risk individuals. CtDNA represents a small and variable proportion of circulating cell-free DNA (ccfDNA) which is itself present at a low concentration in normal individuals and so analyzing ctDNA is technically challenging. Various commercial systems have recently appeared on the market, but it remains difficult for practitioners to compare their performance and to determine whether they yield comparable results. As a first step toward establishing national guidelines for ctDNA analyses, four laboratories in Switzerland joined a comparative exercise to assess ccfDNA extraction and ctDNA analysis by sequencing. Extraction was performed using six distinct methods and yielded ccfDNA of equally high quality, suitable for sequencing. Sequencing of synthetic samples containing predefined amounts of eight mutations was performed on three different systems, with similar results. In all four laboratories, mutations were easily identified down to 1% allele frequency, whereas detection at 0.1% proved challenging. Linearity was excellent in all cases and while molecular yield was superior with one system this did not impact on sensitivity. This study also led to several additional conclusions: First, national guidelines should concentrate on principles of good laboratory practice rather than recommend a particular system. Second, it is essential that laboratories thoroughly validate every aspect of extraction and sequencing, in particular with respect to initial amount of DNA and average sequencing depth. Finally, as software proved critical for mutation detection, laboratories should validate the performance of variant callers and underlying algorithms with respect to various types of mutations.