Search

Background: Repetitive genome regions, such as variable number of tandem repeats (VNTR) or short tandem repeats (STR), are major constituents of the uncharted dark genome and evade conventional sequencing approaches. The protein-coding LPA kringle IV type-2 (KIV-2) VNTR (5.6 kb per unit, 1–40 units per allele) is a medically highly relevant example with a particularly intricate structure, multiple haplotypes, intragenic homologies, and an intra-VNTR STR. It is the primary regulator of plasma lipoprotein(a) [Lp(a)] concentrations, an important cardiovascular risk factor. Lp(a) concentrations vary widely between individuals and ancestries. Multiple variants and functional haplotypes in the LPA gene and especially in the KIV-2 VNTR strongly contribute to this variance. Methods: We evaluated the performance of amplicon-based nanopore sequencing with unique molecular identifiers (UMI-ONT-Seq) for SNP detection, haplotype mapping, VNTR unit consensus sequence generation, and copy number estimation via coverage-corrected haplotypes quantification in the KIV-2 VNTR. We used 15 human samples and low-level mixtures (0.5 to 5%) of KIV-2 plasmids as a validation set. We then applied UMI-ONT-Seq to extract KIV-2 VNTR haplotypes in 48 multi-ancestry 1000 Genome samples and analyzed at scale a poorly characterized STR within the KIV-2 VNTR. Results: UMI-ONT-Seq detected KIV-2 SNPs down to 1% variant level with high sensitivity, specificity, and precision (0.977 ± 0.018; 1.000 ± 0.0005; 0.993 ± 0.02) and accurately retrieved the full-length haplotype of each VNTR unit. Human variant levels were highly correlated with next-generation sequencing (R2 = 0.983) without bias across the whole variant level range. Six reads per UMI produced sequences of each KIV-2 unit with Q40 quality. The KIV-2 repeat number determined by coverage-corrected unique haplotype counting was in close agreement with droplet digital PCR (ddPCR), with 70% of the samples falling even within the narrow confidence interval of ddPCR. We then analyzed 62,679 intra-KIV-2 STR sequences and explored KIV-2 SNP haplotype patterns across five ancestries. Conclusions: UMI-ONT-Seq accurately retrieves the SNP haplotype and precisely quantifies the VNTR copy number of each repeat unit of the complex KIV-2 VNTR region across multiple ancestries. This study utilizes the KIV-2 VNTR, presenting a novel and potent tool for comprehensive characterization of medically relevant complex genome regions at scale. [ABSTRACT FROM AUTHOR]

Although Taxodium hybrid 'Zhongshanshan' 406 (Taxodium mucronatum Tenore ×  Taxodium distichum ; Taxodium 406) is an extremely flooding-tolerant woody plant, the physiological and molecular mechanisms underlying acclimation of its roots to long-term flooding remain largely unknown. Thus, we exposed saplings of Taxodium 406 to either non-flooding (control) or flooding for 2 months. Flooding resulted in reduced root biomass, which is in line with lower concentrations of citrate, α-ketoglutaric acid, fumaric acid, malic acid and adenosine triphosphate (ATP) in Taxodium 406 roots. Flooding led to elevated activities of pyruvate decarboxylase, alcohol dehydrogenase and lactate dehydrogenase, which is consistent with higher lactate concentration in the roots of Taxodium 406. Flooding brought about stimulated activities of superoxide dismutase and catalase and elevated reduced glutathione (GSH) concentration and GSH/oxidized glutathione, which is in agreement with reduced concentrations of O2− and H2O2 in Taxodium 406 roots. The levels of starch, soluble protein, indole-3-acetic acid, gibberellin A4 and jasmonate were decreased, whereas the concentrations of glucose, total non-structural carbohydrates, most amino acids and 1-aminocyclopropane-1-carboxylate (ACC) were improved in the roots of flooding-treated Taxodium 406. Underlying these changes in growth and physiological characteristics, 12,420 mRNAs and 42 miRNAs were significantly differentially expressed, and 886 miRNA–mRNA pairs were identified in the roots of flooding-exposed Taxodium 406. For instance, 1- aminocyclopropane- 1 -carboxylate synthase 8 (ACS8) was a target of Th-miR162-3p and 1 -aminocyclopropane- 1 -carboxylate oxidase 4 (ACO4) was a target of Th-miR166i, and the downregulation of Th-miR162-3p and Th-miR166i results in the upregulation of ACS8 and ACO4 , probably bringing about higher ACC content in flooding-treated roots. Overall, these results indicate that differentially expressed mRNA and miRNAs are involved in regulating tricarboxylic acid cycle, ATP production, fermentation, and metabolism of carbohydrates, amino acids and phytohormones, as well as reactive oxygen species detoxification of Taxodium 406 roots. These processes play pivotal roles in acclimation to flooding stress. These results will improve our understanding of the molecular and physiological bases underlying woody plant flooding acclimation and provide valuable insights into breeding-flooding tolerant trees. [ABSTRACT FROM AUTHOR]

Abstract Background Circulating tumor DNA (ctDNA) has potential as a specific, noninvasive, and cost‐effective new biomarker for patients with lung cancer. This study aimed to determine whether plasma ctDNA can be used to predict treatment outcomes in patients with lung cancer. Methods Pre‐ and in‐treatment blood samples were collected from 14 patients with lung cancer receiving chemotherapy. Based on next‐generation sequencing technology, we constructed a unique molecular identifier (UMI) library and performed targeted deep sequencing of 72 genes (15 000×). We used dVAF to evaluate the change level and trend of variant allele frequency (VAF). Results We identified MUC16, KMT2D, AMER1, and NTRK1 as the most‐frequently mutated genes in ctDNA associated with lung cancer. Furthermore, we showed that the change trend of dVAF in patients with lung cancer undergoing chemotherapy was closely related to the changes in both tumor volume and tumor biomarkers, including CEA, CA125, NSE, and CK (Cytokeratin). Moreover, the ctDNA analysis revealed disease progression of SCLC patients earlier than did computed tomography. Conclusions The dynamic detection of plasma ctDNA VAF has the potential value as a biomarker for evaluating the efficacy of chemotherapy in patients with SCLC and advanced NSCLC, and may predict the progression of lung cancer patients earlier than radiography.

Introduction: To optimize RNA sequencing (RNA-seq) outcomes, we investigated preanalytical variables in malignant effusions containing metastatic breast cancer. We compared 2 processing methods-Ficoll-Hypaque density gradient enrichment and CytoLyt hemolysis-focusing on their effects on RNA quality, transcript abundance, and variant detection from cytospin slides, relative to fresh-frozen samples. Additionally, we compared read-based and Unique Molecular Identifier (UMI)-based library preparation methods., Materials and Methods: Thirteen malignant effusion specimens from metastatic breast cancer were processed using both the Ficoll-Hypaque and Cytolyt methods. RNA was extracted from fresh-frozen samples stored in RNA preservative and from cytospin slides fixed in Carnoy's solution. RNA quality was evaluated using RNA integrity number (RIN) and the percentage of fragments >200 bases (DV200). Sequencing was conducted with both read- and UMI-based methods., Results: Purified RNA was more fragmented by the Cytolyt method (mean RIN: 3.56, DV200: 78.97%), compared to the Ficoll-Hypaque method (mean RIN: 6.29, DV200: 88.08%). Sequencing data had high concordance correlation coefficient (CCC) for measurements of gene expression, whether from Cytolyt or Ficoll-Hypaque treated samples, and whether using the UMI- or read-based sequencing methods (read-based mean CCC: 0.967 from Cytolyt versus 0.974 from Ficoll-Hypaque, UMI-based mean CCC: 0.972 from Cytolyt versus 0.977 from Ficoll-Hypaque)., Conclusions: Despite the increased RNA fragmentation with the Cytolyt, RNA-seq data quality was comparable across Cytolyt and Ficoll-Hypaque methods. Both clearing methods are viable for short-read RNA-seq analysis, with read and UMI-based approaches performing similarly., (Copyright © 2024 American Society of Cytopathology. Published by Elsevier Inc. All rights reserved.)

Body fluids/tissue identification (BFID) is an essential procedure in forensic practice, and RNA profiling has become one of the most important methods. Small non-coding RNAs, being expressed in high copy numbers and resistant to degradation, have great potential in BFID but have not been comprehensively characterized in common forensic stains. In this study, the miRNA, piRNA, snoRNA, and snRNA were sequenced in 30 forensic relevant samples (menstrual blood, saliva, semen, skin, venous blood, and vaginal secretion) using the BGI platform. Based on small RNA profiles, relative specific markers (RSM) and absolute specific markers (ASM) were defined, which can be used to identify a specific body fluid/tissue out of two or six, respectively. A total of 5204 small RNAs were discovered including 1394 miRNAs (including 236 novel miRNA), 3157 piRNAs, 636 snoRNAs, and 17 snRNAs. RSMs for 15 pairwise body fluid/tissue groups were discovered by differential RNA analysis. In addition, 90 ASMs that were specifically expressed in a certain type of body fluid/tissue were screened, among them, snoRNAs were reported first in forensic genetics. In brief, our study deepened the understanding of small RNA profiles in forensic stains and offered potential BFID markers that can be applied in different forensic scenarios. [ABSTRACT FROM AUTHOR]

Abstract Background Our objective was to assess whether modifications to a customized targeted RNA sequencing (RNAseq) assay to include unique molecular identifiers (UMIs) that collapse read counts to their source mRNA counts would improve quantification of transcripts from formalin-fixed paraffin-embedded (FFPE) tumor tissue samples. The assay (SET4) includes signatures that measure hormone receptor and PI3-kinase related transcriptional activity (SETER/PR and PI3Kges), and measures expression of selected activating point mutations and key breast cancer genes. Methods Modifications included steps to introduce eight nucleotides-long UMIs during reverse transcription (RT) in bulk solution, followed by polymerase chain reaction (PCR) of labeled cDNA in droplets, with optimization of the polymerase enzyme and reaction conditions. We used Lin’s concordance correlation coefficient (CCC) to measure concordance, including precision (Rho) and accuracy (Bias), and nonparametric tests (Wilcoxon, Levene’s) to compare the modified (NEW) SET4 assay to the original (OLD) SET4 assay and to whole transcriptome RNAseq using RNA from matched fresh frozen (FF) and FFPE samples from 12 primary breast cancers. Results The modified (NEW) SET4 assay measured single transcripts (p

Background: Circulating tumor DNA (ctDNA) has potential as a specific, noninvasive, and cost‐effective new biomarker for patients with lung cancer. This study aimed to determine whether plasma ctDNA can be used to predict treatment outcomes in patients with lung cancer. Methods: Pre‐ and in‐treatment blood samples were collected from 14 patients with lung cancer receiving chemotherapy. Based on next‐generation sequencing technology, we constructed a unique molecular identifier (UMI) library and performed targeted deep sequencing of 72 genes (15 000×). We used dVAF to evaluate the change level and trend of variant allele frequency (VAF). Results: We identified MUC16, KMT2D, AMER1, and NTRK1 as the most‐frequently mutated genes in ctDNA associated with lung cancer. Furthermore, we showed that the change trend of dVAF in patients with lung cancer undergoing chemotherapy was closely related to the changes in both tumor volume and tumor biomarkers, including CEA, CA125, NSE, and CK (Cytokeratin). Moreover, the ctDNA analysis revealed disease progression of SCLC patients earlier than did computed tomography. Conclusions: The dynamic detection of plasma ctDNA VAF has the potential value as a biomarker for evaluating the efficacy of chemotherapy in patients with SCLC and advanced NSCLC, and may predict the progression of lung cancer patients earlier than radiography. [ABSTRACT FROM AUTHOR]

Abstract Background RNA sequencing offers advantages over other quantification methods for microRNA (miRNA), yet numerous biases make reliable quantification challenging. Previous evaluations of these biases have focused on adapter ligation bias with limited evaluation of reverse transcription bias or amplification bias. Furthermore, evaluations of the quantification of isomiRs (miRNA isoforms) or the influence of starting amount on performance have been very limited. No study had yet evaluated the quantification of isomiRs of altered length or compared the consistency of results derived from multiple moderate starting inputs. We therefore evaluated quantifications of miRNA and isomiRs using four library preparation kits, with various starting amounts, as well as quantifications following removal of duplicate reads using unique molecular identifiers (UMIs) to mitigate reverse transcription and amplification biases. Results All methods resulted in false isomiR detection; however, the adapter-free method tested was especially prone to false isomiR detection. We demonstrate that using UMIs improves accuracy and we provide a guide for input amounts to improve consistency. Conclusions Our data show differences and limitations of current methods, thus raising concerns about the validity of quantification of miRNA and isomiRs across studies. We advocate for the use of UMIs to improve accuracy and reliability of miRNA quantifications.

A recent refinement in high-throughput sequencing involves the incorporation of unique molecular identifiers (UMIs), which are random oligonucleotide barcodes, on the library preparation steps. A UMI adds a unique identity to different DNA/RNA input molecules through polymerase chain reaction (PCR) amplification, thus reducing bias of this step. Here, we propose an alignment free framework serving as a preprocessing step of fastq files, called UMIc, for deduplication and correction of reads building consensus sequences from each UMI. Our approach takes into account the frequency and the Phred quality of nucleotides and the distances between the UMIs and the actual sequences. We have tested the tool using different scenarios of UMI-tagged library data, having in mind the aspect of a wide application. UMIc is an open-source tool implemented in R and is freely available from https://github.com/BiodataAnalysisGroup/UMIc.

A recent refinement in high-throughput sequencing involves the incorporation of unique molecular identifiers (UMIs), which are random oligonucleotide barcodes, on the library preparation steps. A UMI adds a unique identity to different DNA/RNA input molecules through polymerase chain reaction (PCR) amplification, thus reducing bias of this step. Here, we propose an alignment free framework serving as a preprocessing step of fastq files, called UMIc, for deduplication and correction of reads building consensus sequences from each UMI. Our approach takes into account the frequency and the Phred quality of nucleotides and the distances between the UMIs and the actual sequences. We have tested the tool using different scenarios of UMI-tagged library data, having in mind the aspect of a wide application. UMIc is an open-source tool implemented in R and is freely available from https://github.com/BiodataAnalysisGroup/UMIc. [ABSTRACT FROM AUTHOR]

The HLA gene complex is the most important single genetic factor in susceptibility to most diseases with autoimmune or autoinflammatory origin and in transplantation matching. Most studies have focused on the vast allelic variation in these genes; only a few studies have explored differences in the expression levels of HLA alleles. In this study, we quantified mRNA expression levels of HLA class I and II genes from peripheral blood samples of 50 healthy individuals. The gene- and allele-specific mRNA expression was assessed using unique molecular identifiers, which enabled PCR bias removal and calculation of the number of original mRNA transcripts. We identified differences in mRNA expression between different HLA genes and alleles. Our results suggest that HLA alleles are differentially expressed and these differences in expression levels are quantifiable using RNA sequencing technology. Our method provides novel insights into HLA research, and it can be applied to quantify expression differences of HLA alleles in various tissues and to evaluate the role of this type of variation in transplantation matching and susceptibility to autoimmune diseases.

The HLA gene complex is the most important single genetic factor in susceptibility to most diseases with autoimmune or autoinflammatory origin and in transplantation matching. Most studies have focused on the vast allelic variation in these genes; only a few studies have explored differences in the expression levels of HLA alleles. In this study, we quantified mRNA expression levels of HLA class I and II genes from peripheral blood samples of 50 healthy individuals. The gene- and allele-specific mRNA expression was assessed using unique molecular identifiers, which enabled PCR bias removal and calculation of the number of original mRNA transcripts. We identified differences in mRNA expression between different HLA genes and alleles. Our results suggest that HLA alleles are differentially expressed and these differences in expression levels are quantifiable using RNA sequencing technology. Our method provides novel insights into HLA research, and it can be applied to quantify expression differences of HLA alleles in various tissues and to evaluate the role of this type of variation in transplantation matching and susceptibility to autoimmune diseases. [ABSTRACT FROM AUTHOR]

Background: Our objective was to assess whether modifications to a customized targeted RNA sequencing (RNAseq) assay to include unique molecular identifiers (UMIs) that collapse read counts to their source mRNA counts would improve quantification of transcripts from formalin-fixed paraffin-embedded (FFPE) tumor tissue samples. The assay (SET4) includes signatures that measure hormone receptor and PI3-kinase related transcriptional activity (SETER/PR and PI3Kges), and measures expression of selected activating point mutations and key breast cancer genes.Methods: Modifications included steps to introduce eight nucleotides-long UMIs during reverse transcription (RT) in bulk solution, followed by polymerase chain reaction (PCR) of labeled cDNA in droplets, with optimization of the polymerase enzyme and reaction conditions. We used Lin's concordance correlation coefficient (CCC) to measure concordance, including precision (Rho) and accuracy (Bias), and nonparametric tests (Wilcoxon, Levene's) to compare the modified (NEW) SET4 assay to the original (OLD) SET4 assay and to whole transcriptome RNAseq using RNA from matched fresh frozen (FF) and FFPE samples from 12 primary breast cancers.Results: The modified (NEW) SET4 assay measured single transcripts (p< 0.001) and SETER/PR (p=0.002) more reproducibly in technical replicates from FFPE samples. The modified SET4 assay was more precise for measuring single transcripts (Rho 0.966 vs 0.888, p< 0.01) but not multigene expression signatures SETER/PR (Rho 0.985 vs 0.968) or PI3Kges (Rho 0.985 vs 0.946) in FFPE, compared to FF samples. It was also more precise than wtRNAseq of FFPE for measuring transcripts (Rho 0.986 vs 0.934, p< 0.001) and SETER/PR (Rho 0.993 vs 0.915, p=0.004), but not PI3Kges (Rho 0.988 vs 0.945, p=0.051). Accuracy (Bias) was comparable between protocols. Two samples carried a PIK3CA mutation, and measurements of transcribed mutant allele fraction was similar in FF and FFPE samples and appeared more precise with the modified SET4 assay. Amplification efficiency (reads per UMI) was consistent in FF and FFPE samples, and close to the theoretically expected value, when the library size exceeded 400,000 aligned reads.Conclusions: Modifications to the targeted RNAseq protocol for SET4 assay significantly increased the precision of UMI-based and reads-based measurements of individual transcripts, multi-gene signatures, and mutant transcript fraction, particularly with FFPE samples. [ABSTRACT FROM AUTHOR]

Abstract Loss‐of‐function screening by CRISPR/Cas9 gene knockout with pooled, lentiviral guide libraries is a widely applicable method for systematic identification of genes contributing to diverse cellular phenotypes. Here, Random Sequence Labels (RSLs) are incorporated into the guide library, which act as unique molecular identifiers (UMIs) to allow massively parallel lineage tracing and lineage dropout screening. RSLs greatly improve the reproducibility of results by increasing both the precision and the accuracy of screens. They reduce the number of cells needed to reach a set statistical power, or allow a more robust screen using the same number of cells.

Abstract Background Recently we proposed efficient method to exclude undesirable primers at any stage of amplification reaction, here termed NOPE (NOnsense-mediated Primer Exclusion). According to this method, added oligonucleotide overlapping with the 3′-end of unwanted amplification primer (NOPE oligo) simultaneously provides a template for its elongation. This elongation disrupts specificity of unwanted primer, preventing its further participation in PCR. The suggested approach allows to rationally manage the course of PCR reactions in order to facilitate analysis of complex DNA mixtures as well as to perform multistage PCR bypassing intermediate purification steps. Results Here we apply NOPE method to DNA library preparation for the high-throughput sequencing (HTS) with the PCR-based introduction of unique molecular identifiers (UMI). We show that NOPE oligo efficiently neutralizes UMI-containing oligonucleotides after introduction of UMI into sample DNA molecules, thus allowing to proceed with further amplification steps without purification and associated loss of starting material. At the same time, NOPE oligo does not affect the efficiency of target PCR amplification. Conclusion We describe a simple, robust and cheap modification of UMI-labeled HTS libraries preparation procedure, that allows to bypass purification step and thus to preserve starting material which may be limited, e.g. circulating tumor DNA, circulating fetal DNA, or small amounts of isolated cells of interest. Furthermore, demonstrated simplicity and robustness of NOPE method should make it popular in various PCR protocols.

The sequencing of the transcriptome of single cells, or single-cell RNA-sequencing, has now become the dominant technology for the identification of novel cell types in heterogeneous cell populations or for the study of stochastic gene expression. In recent years, various experimental methods and computational tools for analysing single-cell RNA-sequencing data have been proposed. However, most of them are tailored to different experimental designs or biological questions, and in many cases, their performance has not been benchmarked yet, thus increasing the difficulty for a researcher to choose the optimal single-cell transcriptome sequencing (scRNA-seq) experiment and analysis workflow. In this review, we aim to provide an overview of the current available experimental and computational methods developed to handle single-cell RNA-sequencing data and, based on their peculiarities, we suggest possible analysis frameworks depending on specific experimental designs. Together, we propose an evaluation of challenges and open questions and future perspectives in the field. In particular, we go through the different steps of scRNA-seq experimental protocols such as cell isolation, messenger RNA capture, reverse transcription, amplification and use of quantitative standards such as spike-ins and Unique Molecular Identifiers (UMIs). We then analyse the current methodological challenges related to preprocessing, alignment, quantification, normalization, batch effect correction and methods to control for confounding effects. [ABSTRACT FROM AUTHOR]

Clonal hematopoiesis (CH) is defined by the presence of an expanded clonal hematopoietic cell population due to an acquired mutation conferring a selective growth advantage and is known to predispose to hematologic malignancy. In this review, we discuss sequencing methods for CH detection in bulk sequencing data and corresponding bioinformatic approaches for variant calling, filtering, and curation. We detail practical recommendations for CH calling. Finally, we discuss how improvements in CH sequencing and bioinformatic approaches will enable the characterization of CH trajectories, its impact on human health, and therapeutic approaches to mitigate its adverse effects. • Distinguishing low VAF (<2%) CH mutations from sequencing errors is difficult. • Sources of error in CH sequencing are diverse and occur at all steps of experiments. • Specialized somatic variant calling and filtering approaches improve CH detection. • Accurate low VAF mutation detection is required to study CH initiation and expansion. [ABSTRACT FROM AUTHOR]

High-throughput sequencing of immunoglobulin (Ig) repertoires (Ig-seq) is a powerful method for quantitatively interrogating B cell receptor sequence diversity. When applied to human repertoires, Ig-seq provides insight into fundamental immunological questions, and can be implemented in diagnostic and drug discovery projects. However, a major challenge in Ig-seq is ensuring accuracy, as library preparation protocols and sequencing platforms can introduce substantial errors and bias that compromise immunological interpretation. Here, we have established an approach for performing highly accurate human Ig-seq by combining synthetic standards with a comprehensive error and bias correction pipeline. First, we designed a set of 85 synthetic antibody heavy-chain standards (in vitro transcribed RNA) to assess correction workflow fidelity. Next, we adapted a library preparation protocol that incorporates unique molecular identifiers (UIDs) for error and bias correction which, when applied to the synthetic standards, resulted in highly accurate data. Finally, we performed Ig-seq on purified human circulating B cell subsets (naïve and memory), combined with a cellular replicate sampling strategy. This strategy enabled robust and reliable estimation of key repertoire features such as clonotype diversity, germline segment, and isotype subclass usage, and somatic hypermutation. We anticipate that our standards and error and bias correction pipeline will become a valuable tool for researchers to validate and improve accuracy in human Ig-seq studies, thus leading to potentially new insights and applications in human antibody repertoire profiling.

Background: Single cell RNA sequencing (scRNA-seq) has rapidly gained popularity for profiling transcriptomes of hundreds to thousands of single cells. This technology has led to the discovery of novel cell types and revealed insights into the development of complex tissues. However, many technical challenges need to be overcome during data generation. Due to minute amounts of starting material, samples undergo extensive amplification, increasing technical variability. A solution for mitigating amplification biases is to include unique molecular identifiers (UMIs), which tag individual molecules. Transcript abundances are then estimated from the number of unique UMIs aligning to a specific gene, with PCR duplicates resulting in copies of the UMI not included in expression estimates. Methods: Here we investigate the effect of gene length bias in scRNA-Seq across a variety of datasets that differ in terms of capture technology, library preparation, cell types and species. Results: We find that scRNA-seq datasets that have been sequenced using a full-length transcript protocol exhibit gene length bias akin to bulk RNA-seq data. Specifically, shorter genes tend to have lower counts and a higher rate of dropout. In contrast, protocols that include UMIs do not exhibit gene length bias, with a mostly uniform rate of dropout across genes of varying length. Across four different scRNA-Seq datasets profiling mouse embryonic stem cells (mESCs), we found the subset of genes that are only detected in the UMI datasets tended to be shorter, while the subset of genes detected only in the full-length datasets tended to be longer. Conclusions: We find that the choice of scRNA-seq protocol influences the detection rate of genes, and that full-length datasets exhibit gene-length bias. In addition, despite clear differences between UMI and full-length transcript data, we illustrate that full-length and UMI data can be combined to reveal the underlying biology influencing expression of mESCs.

High-throughput sequencing of immunoglobulin (Ig) repertoires (Ig-seq) is a powerful method for quantitatively interrogating B cell receptor sequence diversity. When applied to human repertoires, Ig-seq provides insight into fundamental immunological questions, and can be implemented in diagnostic and drug discovery projects. However, a major challenge in Ig-seq is ensuring accuracy, as library preparation protocols and sequencing platforms can introduce substantial errors and bias that compromise immunological interpretation. Here, we have established an approach for performing highly accurate human Ig-seq by combining synthetic standards with a comprehensive error and bias correction pipeline. First, we designed a set of 85 synthetic antibody heavy-chain standards (in vitro transcribed RNA) to assess correction workflow fidelity. Next, we adapted a library preparation protocol that incorporates unique molecular identifiers (UIDs) for error and bias correction which, when applied to the synthetic standards, resulted in highly accurate data. Finally, we performed Ig-seq on purified human circulating B cell subsets (naïve and memory), combined with a cellular replicate sampling strategy. This strategy enabled robust and reliable estimation of key repertoire features such as clonotype diversity, germline segment, and isotype subclass usage, and somatic hypermutation. We anticipate that our standards and error and bias correction pipeline will become a valuable tool for researchers to validate and improve accuracy in human Ig-seq studies, thus leading to potentially new insights and applications in human antibody repertoire profiling. [ABSTRACT FROM AUTHOR]

Loss-of-function screening by CRISPR/Cas9 gene knockout with pooled, lentiviral guide libraries is a widely applicable method for systematic identification of genes contributing to diverse cellular phenotypes. Here, Random Sequence Labels (RSLs) are incorporated into the guide library, which act as unique molecular identifiers (UMIs) to allow massively parallel lineage tracing and lineage dropout screening. RSLs greatly improve the reproducibility of results by increasing both the precision and the accuracy of screens. They reduce the number of cells needed to reach a set statistical power, or allow a more robust screen using the same number of cells. [ABSTRACT FROM AUTHOR]

Background: Recently we proposed efficient method to exclude undesirable primers at any stage of amplification reaction, here termed NOPE (NOnsense-mediated Primer Exclusion). According to this method, added oligonucleotide overlapping with the 3'-end of unwanted amplification primer (NOPE oligo) simultaneously provides a template for its elongation. This elongation disrupts specificity of unwanted primer, preventing its further participation in PCR. The suggested approach allows to rationally manage the course of PCR reactions in order to facilitate analysis of complex DNA mixtures as well as to perform multistage PCR bypassing intermediate purification steps. Results: Here we apply NOPE method to DNA library preparation for the high-throughput sequencing (HTS) with the PCR-based introduction of unique molecular identifiers (UMI). We show that NOPE oligo efficiently neutralizes UMIcontaining oligonucleotides after introduction of UMI into sample DNA molecules, thus allowing to proceed with further amplification steps without purification and associated loss of starting material. At the same time, NOPE oligo does not affect the efficiency of target PCR amplification. Conclusion: We describe a simple, robust and cheap modification of UMI-labeled HTS libraries preparation procedure, that allows to bypass purification step and thus to preserve starting material which may be limited, e.g. circulating tumor DNA, circulating fetal DNA, or small amounts of isolated cells of interest. Furthermore, demonstrated simplicity and robustness of NOPE method should make it popular in various PCR protocols. [ABSTRACT FROM AUTHOR]

To overcome yearly efforts and costs for the production of seasonal influenza vaccines, new approaches for the induction of broadly protective and long-lasting immune responses have been developed in the past decade. To warrant safety and efficacy of the emerging crossreactive vaccine candidates, it is critical to understand the evolution of influenza viruses in response to these new immune pressures. Here we applied unique molecular identifiers in next generation sequencing to analyze the evolution of influenza quasispecies under in vivo antibody pressure targeting the hemagglutinin (HA) long alpha helix (LAH). Our vaccine targeting LAH of hemagglutinin elicited significant seroconversion and protection against homologous and heterologous influenza virus strains in mice. The vaccine not only significantly reduced lung viral titers, but also induced a well-known bottleneck effect by decreasing virus diversity. In contrast to the classical bottleneck effect, here we showed a significant increase in the frequency of viruses with amino acid sequences identical to that of vaccine targeting LAH domain. No escape mutant emerged after vaccination. These results not only support the potential of a universal influenza vaccine targeting the conserved LAH domains, but also clearly demonstrate that the well-established bottleneck effect on viral quasispecies evolution does not necessarily generate escape mutants.

The use of DNA barcodes for determining changes in genotype frequencies has been instrumental to increase the scale at which we can phenotype strain libraries by using next-generation sequencing technologies. Here, we describe the determination of strain fitness for thousands of yeast strains simultaneously in a single assay using recent innovations that increase the precision of these measurements, such as the inclusion of unique-molecular identifiers (UMIs) and purification by solid-phase reverse immobilization (SPRI) beads., (© 2022. The Author(s).)

• Unique molecular identifiers. • Molecular barcodes. • Probabilistic genotyping. • Stutter. Unique molecular identifiers (UMIs) are a promising approach to contend with errors generated during PCR and massively parallel sequencing (MPS). With UMI technology, random molecular barcodes are ligated to template DNA molecules prior to PCR, allowing PCR and sequencing error to be tracked and corrected bioinformatically. UMIs have the potential to be particularly informative for the interpretation of short tandem repeats (STRs). Traditional MPS approaches may simply lead to the observation of alleles that are consistent with the hypotheses of stutter, while with UMIs stutter products bioinformatically may be re-associated with their parental alleles and subsequently removed. Herein, a bioinformatics pipeline named strumi is described that is designed for the analysis of STRs that are tagged with UMIs. Unlike other tools, strumi is an alignment-free machine learning driven algorithm that clusters individual MPS reads into UMI families, infers consensus super-reads that represent each family and provides an estimate the resulting haplotype's accuracy. Super-reads, in turn, approximate independent measurements not of the PCR products, but of the original template molecules, both in terms of quantity and sequence identity. Provisional assessments show that naïve threshold-based approaches generate super-reads that are accurate (∼97 % haplotype accuracy, compared to ∼78 % when UMIs are not used), and the application of a more nuanced machine learning approach increases the accuracy to ∼99.5 % depending on the level of certainty desired. With these features, UMIs may greatly simplify probabilistic genotyping systems and reduce uncertainty. However, the ability to interpret alleles at trace levels also permits the interpretation, characterization and quantification of contamination as well as somatic variation (including somatic stutter), which may present newfound challenges. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is characterized by prominent genetic and phenotypic heterogeneity between patients. To facilitate high-throughput genetic testing and functional identification of tumor drivers, we developed a platform for pooled CRISPR-Cas9 screening in human colon organoids. Using transforming growth factor β (TGF-β) resistance as a paradigm to establish sensitivity and scalability in vitro, we identified optimal conditions and strict guide RNA (gRNA) requirements for screening in 3D organoids. We then screened a pan-cancer tumor suppressor gene (TSG) library in pre-malignant organoids with APC -/- ;KRAS G12D mutations, which were xenografted to study clonal advantages in context of a complex tumor microenvironment. We identified TGFBR2 as the most prevalent TSG, followed by known and previously uncharacterized mediators of CRC growth. gRNAs were validated in a secondary screen using unique molecular identifiers (UMIs) to adjust for clonal drift and to distinguish clone size and abundance. Together, these findings highlight a powerful organoid-based platform for pooled CRISPR-Cas9 screening for patient-specific functional genomics., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Precision medicine requires accurate multi-gene clinical diagnostics. In current clinical practice, the minimum confidence threshold for variant calling of targeted next-generation sequencing (NGS) on surgical specimens is set to 2%-5%. However, few studies have been conducted to identify a wide range of actionable variants using capture-based ultra-deep targeted sequencing, which has limit of detection (LOD) of 1%. The AmoyDx® Essential NGS panel for capture-based ultra-deep targeted sequencing (dual-indexed sequencing adapters with UMIs) was performed on 372 surgical specimens obtained from treatment-naive patients with primary lung adenocarcinoma, to detect actionable somatic driver mutations associated with each patient. Single-nucleotide variants, insertion/deletion events, and rearrangements were reported. Amplification-refractory mutation system (ARMS) assay and fluorescence in situ hybridization (FISH) were performed for the validation of hotspot mutations in EGFR and ALK, ROS1, and RET fusions. Potentially actionable variants were identified in 80.5% (352/437) of the nonsynonymous variants that were able to be sequenced, and were most commonly found in EGFR mutations (59.7%, 261/437), followed by KRAS mutations (5.5%, 24/437), PIK3CA mutations (3.7%, 16/437), ALK rearrangements (3.4%, 15/437), BRAF mutations (2.7%, 12/437), ERBB2 mutations (2.5%, 11/437), and RET rearrangements (2.3%, 10/437). A total of 7.2% (28/372) of the samples had multiple actionable mutations. Among the 93 triple-negative cases, which did not harbor mutations in EGFR, KRAS, or BRAF, gene fusions were detected in 26 cases (28%). Of the 328 samples, concordance of EGFR between the ARMS assay and NGS was observed in 318 samples (97.0%), and among 32 samples, concordance between ARMS/FISH test and NGS for ALK/ROS1/RET fusion genes was observed in 30 samples (93.8%). Here, we demonstrated that the capture-based ultra-deep targeted sequencing method, which has a LOD of 1% to profile a wide range of actionable variants in surgical specimens of treatment-naive lung adenocarcinoma patients, highlights the need for treatment-naive patients to undergo genomic profiling., Competing Interests: None., (IJCEP Copyright © 2020.)

The performance of next generation sequencing in profiling somatic mutations from plasma samples, especially in early‐stage cancer, is limited by its sensitivity due to the extremely low amount of cell‐free DNA (cfDNA). Polymerase‐chain reaction amplification of limited amounts of DNA often results in product redundancy and the amplification of contaminant DNA. Numerous methods have been developed to detect and quantify ultralow frequency mutations. In this study, the potential of tagging each DNA molecule with a unique molecular identifier (UMI) is explored to detect ultralow frequency mutations in early‐stage lung cancer patients. Paired tissue and blood samples from 32 patients with early‐stage (stage IA‐IIB) nonsmall cell lung cancer are sequenced using a panel consisting of 168 cancer‐related genes. Collectively, 93.8% of tissue samples and 59.4% of plasma samples have mutations detected. The concordance rates are acceptable for all patients except for stage IA. Notably, mutations at an alleleic frequency as low as 0.1% are detected in UMI‐tagged cfDNA samples. Moreover, patients with nonadenocarcinoma histology have a significantly higher detection rate than patients with adenocarcinoma (92.9% vs 33.3%, P = 0.002). The study demonstrates the potential of UMI in early detection of lung cancer from plasma samples. [ABSTRACT FROM AUTHOR]

Insertional mutant libraries of microorganisms can be applied in negative depletion screens to decipher gene functions. Because of underrepresentation in colonized tissue, one major bottleneck is analysis of species that colonize hosts. To overcome this, we developed insertion pool sequencing (iPool-Seq). iPool-Seq allows direct analysis of colonized tissue due to high specificity for insertional mutant cassettes. Here, we describe detailed protocols for infection as well as genomic DNA extraction to study the interaction between the corn smut fungus Ustilago maydis and its host maize. In addition, we provide protocols for library preparation and bioinformatic data analysis that are applicable to any host-microbe interaction system. © 2019 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited., (© 2019 The Authors.)

The sequencing of the transcriptome of single cells, or single-cell RNA-sequencing, has now become the dominant technology for the identification of novel cell types in heterogeneous cell populations or for the study of stochastic gene expression. In recent years, various experimental methods and computational tools for analysing single-cell RNA-sequencing data have been proposed. However, most of them are tailored to different experimental designs or biological questions, and in many cases, their performance has not been benchmarked yet, thus increasing the difficulty for a researcher to choose the optimal single-cell transcriptome sequencing (scRNA-seq) experiment and analysis workflow. In this review, we aim to provide an overview of the current available experimental and computational methods developed to handle single-cell RNA-sequencing data and, based on their peculiarities, we suggest possible analysis frameworks depending on specific experimental designs. Together, we propose an evaluation of challenges and open questions and future perspectives in the field. In particular, we go through the different steps of scRNA-seq experimental protocols such as cell isolation, messenger RNA capture, reverse transcription, amplification and use of quantitative standards such as spike-ins and Unique Molecular Identifiers (UMIs). We then analyse the current methodological challenges related to preprocessing, alignment, quantification, normalization, batch effect correction and methods to control for confounding effects., (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background Single-cell RNA-sequencing (scRNA-seq) experiments typically analyze hundreds or thousands of cells after amplification of the cDNA. The high throughput is made possible by the early introduction of sample-specific bar codes (BCs), and the amplification bias is alleviated by unique molecular identifiers (UMIs). Thus, the ideal analysis pipeline for scRNA-seq data needs to efficiently tabulate reads according to both BC and UMI. Findings zUMIs is a pipeline that can handle both known and random BCs and also efficiently collapse UMIs, either just for exon mapping reads or for both exon and intron mapping reads. If BC annotation is missing, zUMIs can accurately detect intact cells from the distribution of sequencing reads. Another unique feature of zUMIs is the adaptive downsampling function that facilitates dealing with hugely varying library sizes but also allows the user to evaluate whether the library has been sequenced to saturation. To illustrate the utility of zUMIs , we analyzed a single-nucleus RNA-seq dataset and show that more than 35% of all reads map to introns. Also, we show that these intronic reads are informative about expression levels, significantly increasing the number of detected genes and improving the cluster resolution. Conclusions zUMIs flexibility makes if possible to accommodate data generated with any of the major scRNA-seq protocols that use BCs and UMIs and is the most feature-rich, fast, and user-friendly pipeline to process such scRNA-seq data. [ABSTRACT FROM AUTHOR]