Amplification Bias Research Articles

Targeted DNA methylation analysis and prediction of smoking habits in blood based on massively parallel sequencing

Tobacco smoking is a frequent habit sustained by > 1.3 billion people in 2020 and the leading preventable factor for health risk and premature mortality worldwide. In the forensic context, predicting smoking habits from biological samples may allow broadening DNA phenotyping. In this study, we aimed to implement previously published smoking habit classification models based on blood DNA methylation at 13 CpGs. First, we developed a matching lab tool based on bisulfite conversion and multiplex PCR followed by amplification-free library preparation and targeted paired-end massively parallel sequencing (MPS). Analysis of six technical duplicates revealed high reproducibility of methylation measurements (Pearson correlation of 0.983). Artificially methylated standards uncovered marker-specific amplification bias, which we corrected via bi-exponential models. We then applied our MPS tool to 232 blood samples from Europeans of a wide age range, of which 90 were current, 71 former and 71 never smokers. On average, we obtained 189,000 reads/sample and 15,000 reads/CpG, without marker drop-out. Methylation distributions per smoking category roughly corresponded to previous microarray analysis, showcasing large inter-individual variation but with technology-driven bias. Methylation at 11 out of 13 smoking-CpGs correlated with daily cigarettes in current smokers, while solely one was weakly correlated with time since cessation in former smokers. Interestingly, eight smoking-CpGs correlated with age, and one displayed weak but significant sex-associated methylation differences. Using bias-uncorrected MPS data, smoking habits were relatively accurately predicted using both two- (current/non-current) and three- (never/former/current) category model, but bias correction resulted in worse prediction performance for both models. Finally, to account for technology-driven variation, we built new, joint models with inter-technology corrections, which resulted in improved prediction results for both models, with or without PCR bias correction (e.g. MPS cross-validation F1-score > 0.8; 2-categories). Overall, our novel assay takes us one step closer towards the forensic application of viable smoking habit prediction from blood traces. However, future research is needed towards forensically validating the assay, especially in terms of sensitivity. We also need to further shed light on the employed biomarkers, particularly on the mechanistics, tissue specificity and putative confounders of smoking epigenetic signatures.

Mitochondrial Heteroplasmy and PCR Amplification Bias Lead to Wrong Species Delimitation with High Confidence in the South American and Antarctic Marine Bivalve Aequiyoldia eightsii Species Complex.

The species delimitation of the marine bivalve species complex Aequiyoldia eightsii in South America and Antarctica is complicated by mitochondrial heteroplasmy and amplification bias in molecular barcoding. In this study, we compare different data sources (mitochondrial cytochrome c oxidase subunit I (COI) sequences; nuclear and mitochondrial SNPs). Whilst all the data suggest that populations on either side of the Drake Passage belong to different species, the picture is less clear within Antarctic populations, which harbor three distinct mitochondrial lineages (p-dist ≈ 6%) that coexist in populations and in a subset of individuals with heteroplasmy. Standard barcoding procedures lead to amplification bias favoring either haplotype unpredictably and thus overestimate the species richness with high confidence. However, nuclear SNPs show no differentiation akin to the trans-Drake comparison, suggesting that the Antarctic populations represent a single species. Their distinct haplotypes likely evolved during periods of temporary allopatry, whereas recombination eroded similar differentiation patterns in the nuclear genome after secondary contact. Our study highlights the importance of using multiple data sources and careful quality control measures to avoid bias and increase the accuracy of molecular species delimitation. We recommend an active search for mitochondrial heteroplasmy and haplotype-specific primers for amplification in DNA-barcoding studies.

Systemic Gender Inequities in Who Reviews Code

Code review is an essential task for modern software engineers, where the author of a code change assigns other engineers the task of providing feedback on the author's code. In this paper, we investigate the task of code review through the lens of equity, the proposition that engineers should share reviewing responsibilities fairly. Through this lens, we quantitatively examine gender inequities in code review load at Google. We found that, on average, women perform about 25% fewer reviews than men, an inequity with multiple systemic antecedents, including authors' tendency to choose men as reviewers, a recommender system's amplification of human biases, and gender differences in how reviewer credentials are assigned and earned. Although substantial work remains to close the review load gap, we show how one small change has begun to do so.

An Integrated Amplification-Free Digital CRISPR/Cas-Assisted Assay for Single Molecule Detection of RNA.

Conventional nucleic acid detection technologies usually rely on amplification to improve sensitivity, which has drawbacks, such as amplification bias, complicated operation, high requirements for complex instruments, and aerosol pollution. To address these concerns, we developed an integrated assay for the enrichment and single molecule digital detection of nucleic acid based on a CRISPR/Cas13a and microwell array. In our design, magnetic beads capture and concentrate the target from a large volume of sample, which is 100 times larger than reported earlier. The target-induced CRISPR/Cas13a cutting reaction was then dispersed and limited to a million individual femtoliter-sized microwells, thereby enhancing the local signal intensity to achieve single-molecule detection. The limit of this assay for amplification-free detection of SARS-CoV-2 is 2 aM. The implementation of this study will establish a "sample-in-answer-out" single-RNA detection technology without amplification and improve the sensitivity and specificity while shortening the detection time. This research has broad prospects in clinical application.

Comparison of two molecular barcodes for the study of equine strongylid communities with amplicon sequencing.

Basic knowledge on the biology and epidemiology of equine strongylid species still needs to be improved to contribute to the design of better parasite control strategies. Nemabiome metabarcoding is a convenient tool to quantify and identify species in bulk samples that could overcome the hurdle that cyathostomin morphological identification represents. To date, this approach has relied on the internal transcribed spacer 2 (ITS-2) of the ribosomal RNA gene, with a limited investigation of its predictive performance for cyathostomin communities. Using DNA pools of single cyathostomin worms, this study aimed to provide the first elements to compare performances of the ITS-2 and a cytochrome c oxidase subunit I (COI) barcode newly developed in this study. Barcode predictive abilities were compared across various mock community compositions of two, five and 11 individuals from distinct species. The amplification bias of each barcode was estimated. Results were also compared between various types of biological samples, i.e., eggs, infective larvae or adults. Bioinformatic parameters were chosen to yield the closest representation of the cyathostomin community for each barcode, underscoring the need for communities of known composition for metabarcoding purposes. Overall, the proposed COI barcode was suboptimal relative to the ITS-2 rDNA region, because of PCR amplification biases, reduced sensitivity and higher divergence from the expected community composition. Metabarcoding yielded consistent community composition across the three sample types. However, imperfect correlations were found between relative abundances from infective larvae and other life-stages for Cylicostephanus species using the ITS-2 barcode. While the results remain limited by the considered biological material, they suggest that additional improvements are needed for both the ITS-2 and COI barcodes.

Causal Disentanglement for Implicit Recommendations with Network Information

Online user engagement is highly influenced by various machine learning models, such as recommender systems. These systems recommend new items to the user based on the user’s historical interactions. Implicit recommender systems reflect a binary setting showing whether a user interacted (e.g., clicked on) with an item or not. However, the observed clicks may be due to various causes such as user’s interest, item’s popularity, and social influence factors. Traditional recommender systems consider these causes under a unified representation, which may lead to the emergence and amplification of various biases in recommendations. However, recent work indicates that by disentangling the unified representations, one can mitigate bias (e.g., popularity bias) in recommender systems and help improve recommendation performance. Yet, prior work in causal disentanglement in recommendations does not consider a crucial factor, that is, social influence. Social theories such as homophily and social influence provide evidence that a user’s decision can be highly influenced by the user’s social relations. Thus, accounting for the social relations while disentangling leads to less biased recommendations. To this end, we identify three separate causes behind an effect (e.g., clicks): (a) user’s interest, (b) item’s popularity, and (c) user’s social influence. Our approach seeks to causally disentangle the user and item latent features to mitigate popularity bias in implicit feedback–based social recommender systems. To achieve this goal, we draw from causal inference theories and social network theories and propose a causality-aware disentanglement method that leverages both the user–item interaction network and auxiliary social network information. Experiments on real-world datasets against various state-of-the-art baselines validate the effectiveness of the proposed model for mitigating popularity bias and generating de-biased recommendations.

Bias amplification of unobserved confounding in pharmacoepidemiological studies using indication-based sampling.

Estimating causal effects in observational pharmacoepidemiology is a challenging task, as it is often plagued by confounding by indication. Restricting the sample to those with an indication for drug use is a commonly performed procedure; indication-based sampling ensures that the exposed and unexposed are exchangeable on the indication-limiting the potential for confounding by indication. However, indication-based sampling has received little scrutiny, despite the hazards of exposure-related covariate control. Using simulations of varying levels of confounding and applied examples we describe bias amplification under indication-based sampling. We demonstrate that indication-based sampling in the presence of unobserved confounding can give rise to bias amplification, a self-inflicted phenomenon where one inflates pre-existing bias through inappropriate covariate control. Additionally, we show that indication-based sampling generally leads to a greater net bias than alternative approaches, such as regression adjustment. Finally, we expand on how bias amplification should be reasoned about when distinct clinically relevant effects on the outcome among those with an indication exist (effect-heterogeneity). We conclude that studies using indication-based sampling should have robust justification - and that it should by no means be considered unbiased to adopt such approaches. As such, we suggest that future observational studies stay wary of bias amplification when considering drug indications.

An MPS-Based 50plex Microhaplotype Assay for Forensic DNA Analysis.

Microhaplotypes (MHs) are widely accepted as powerful markers in forensic studies. They have the advantage of both short tandem repeats (STRs) and single nucleotide polymorphisms (SNPs), with no stutter and amplification bias, short fragments and amplicons, low mutation and recombination rates, and high polymorphisms. In this study, we constructed a panel of 50 MHs that are distributed on 21 chromosomes and analyzed them using the Multiseq multiple polymerase chain reaction (multi-PCR) targeted capture sequencing protocol based on the massively parallel sequencing (MPS) platform. The sizes of markers and amplicons ranged between 11-81 bp and 123-198 bp, respectively. The sensitivity was 0.25 ng, and the calling results were consistent with Sanger sequencing and the Integrative Genomics Viewer (IGV). It showed measurable polymorphism among sequenced 137 Southwest Chinese Han individuals. No significant deviations in the Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium (LD) were found at all MHs after Bonferroni correction. Furthermore, the specificity was 1:40 for simulated two-person mixtures, and the detection rates of highly degraded single samples and mixtures were 100% and 93-100%, respectively. Moreover, animal DNA testing was incomplete and low depth. Overall, our MPS-based 50-plex MH panel is a powerful forensic tool that provides a strong supplement and enhancement for some existing panels.

Abstract 4351: Multiplex detection of G12/G13 KRAS mutations with an electrochemiluminescent hybridization assay

Abstract Purpose: Oncogenic Kirsten rat sarcoma virus (KRAS) mutations are the most prevalent cancer mutations in all human tumors. Also, genotyping the KRAS gene has become increasingly important with the emergence of new evidence highlighting differences in downstream signaling pathways, tumor microenvironment composition, treatment responses and prognoses linked to specific single nucleotide polymorphisms (SNPs) in the G12 or G13 codons. FDA approval of G12C-specific anti-KRAS drugs highlights the importance of the development of reliable SNP assays to interrogate the mutation status in a tumor sample. Currently available genotyping assays are based on NGS, PCR, or ddPCR. These methods can be time-consuming, costly, suffer from amplification bias, or require follow-up testing or bioinformatics skills to analyze. Building on Meso Scale Discovery’s (MSD) existing technology, we aimed to develop a method for the identification of eight KRAS SNPs located on the G12-G13 codons in a single reaction. Methods: Ten-spot, 96-well N-PLEX plates were used for the study. SNP-specific upstream probes carrying unique 5′ leader sequences complementary to spot-specific captures on the N-PLEX plates were designed for eight KRAS genotypes (WT, G12R, G12C, G12S, G12A, G12D, G12V, G13D). Locus-specific downstream probes were 5’ phosphorylated for ligation and 3’ biotinylated for detection. Synthetic DNA targets were used as assay calibrators, and commercially available FFPE reference samples were tested for validation. DNA (10 ng) was PCR amplified with primers flanking the KRAS mutation sites. A multiplexed mixture of upstream and downstream probes was hybridized to the amplicons, and 30 cycles of ligation was performed using DNA ligase in a thermal cycler. Samples were subsequently hybridized to spot-specific capture oligonucleotides on the surface of the assay plates and detected with SULFO-TAG labeled streptavidin. Electrochemiluminescence readout was collected on an MSD imager. Results: Optimization of probe concentrations and ligation temperature was performed to maximize signal-to-background ratios and assay specificity. Spike recovery experiments using artificial target DNA showed successful identification of KRAS mutant genotypes at spike levels as low as 0.1% over wild-type background. Validation experiments with gDNA extracted from FFPE reference samples confirmed that the multiplex SNP assay can accurately differentiate eight KRAS genotypes in samples with >0.4% tumor burden. Conclusion: These data highlight a novel approach to simultaneously identify eight KRAS genotypes from 10 ng of DNA input in a single reaction within 4-5 hours. The assay is capable of identifying double mutants and can provide a semiquantitative assessment of tumor burden without the need for follow-up testing. Citation Format: Annamaria Szabolcs, Timothy J. Break, Isaac H. Shin, Seth B. Harkins, Jacob N. Wohlstadter. Multiplex detection of G12/G13 KRAS mutations with an electrochemiluminescent hybridization assay. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4351.

PD13-04 DEVELOPMENT OF A NOVEL GENE EXPRESSION SCORING SYSTEM FOR RECURRENCE IN NON-MUSCLE-INVASIVE BLADDER CANCER AFTER TRANSURETHRAL RESECTION OF BLADDER TUMOR

You have accessJournal of UrologyCME1 Apr 2023PD13-04 DEVELOPMENT OF A NOVEL GENE EXPRESSION SCORING SYSTEM FOR RECURRENCE IN NON-MUSCLE-INVASIVE BLADDER CANCER AFTER TRANSURETHRAL RESECTION OF BLADDER TUMOR Emina Kayama, Motohide Uemura, Yusuke Kirihana, Akifumi Onagi, Seiji Hoshi, Tomoyuki Koguchi, Tomohiko Yanagida, Fusao Murakami, and Yoshiyuki Kojima Emina KayamaEmina Kayama More articles by this author , Motohide UemuraMotohide Uemura More articles by this author , Yusuke KirihanaYusuke Kirihana More articles by this author , Akifumi OnagiAkifumi Onagi More articles by this author , Seiji HoshiSeiji Hoshi More articles by this author , Tomoyuki KoguchiTomoyuki Koguchi More articles by this author , Tomohiko YanagidaTomohiko Yanagida More articles by this author , Fusao MurakamiFusao Murakami More articles by this author , and Yoshiyuki KojimaYoshiyuki Kojima More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003260.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Non-muscle-invasive bladder cancer (NMIBC) frequently recurs after transurethral resection of bladder tumor (TURBT). However, clinical prognostic factors do not always accurately predict tumor recurrence. Therefore, we developed a new gene expression scoring system that is practical for distinguishing between high- and low-risk NMIBC that recur after TURBT. METHODS: We included 89 patients (median age 75) diagnosed with NMIBC after initial TURBT without prior or concurrent upper tract urothelial carcinoma at our institution between 2008 and 2019. This study obtained the approval of the institutional ethics board and the patient's consent. Variations in the expression signature of 14,400 genes within each patient were measured by our microarray analysis which has no mRNA amplification bias, not requiring validation by quantitative reverse transcriptase PCR. A hierarchical cluster analysis was performed to identify a set of genes that discriminates between high- and low-risk NMIBC. RESULTS: Eleven genes were extracted that predict tumor recurrence after TURBT. The cumulative equivalent of the 11 genes (microarray score) was obtained from each sample, and the optimal cutoff score was calculated to be 1.29 (area under the curve 0.83). A Kaplan-Meier curve showed the 5-year recurrence-free survival (RFS) rate was 38.2% in the “high microarray score group” (microarray score ≥1.29, n=39), which was significantly lower than in the “low microarray score group” (82.6%, p=0.002, Figure 1). The 5-year RFS rate in patients with single, Ta low grade tumor <3 cm in diameter without CIS (n=15) was 90.0%, which was higher than in the rest (55.2%); however, no significant difference was observed (p=0.063). Multivariate Cox regression analysis showed that a microarray score ≥1.29 was an independent factor for disease recurrence (hazard ratio 3.89, p=0.005), but clinical factors were not. CONCLUSIONS: Our novel gene expression scoring system proved to be more useful in predicting recurrence in patients with NMIBC after TURBT than clinical prognostic factors. This scoring system may enable individualized strategic management of high-risk NMIBC patients. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e406 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Emina Kayama More articles by this author Motohide Uemura More articles by this author Yusuke Kirihana More articles by this author Akifumi Onagi More articles by this author Seiji Hoshi More articles by this author Tomoyuki Koguchi More articles by this author Tomohiko Yanagida More articles by this author Fusao Murakami More articles by this author Yoshiyuki Kojima More articles by this author Expand All Advertisement PDF downloadLoading ...

RNA-seq data science: From raw data to effective interpretation.

RNA sequencing (RNA-seq) has become an exemplary technology in modern biology and clinical science. Its immense popularity is due in large part to the continuous efforts of the bioinformatics community to develop accurate and scalable computational tools to analyze the enormous amounts of transcriptomic data that it produces. RNA-seq analysis enables genes and their corresponding transcripts to be probed for a variety of purposes, such as detecting novel exons or whole transcripts, assessing expression of genes and alternative transcripts, and studying alternative splicing structure. It can be a challenge, however, to obtain meaningful biological signals from raw RNA-seq data because of the enormous scale of the data as well as the inherent limitations of different sequencing technologies, such as amplification bias or biases of library preparation. The need to overcome these technical challenges has pushed the rapid development of novel computational tools, which have evolved and diversified in accordance with technological advancements, leading to the current myriad of RNA-seq tools. These tools, combined with the diverse computational skill sets of biomedical researchers, help to unlock the full potential of RNA-seq. The purpose of this review is to explain basic concepts in the computational analysis of RNA-seq data and define discipline-specific jargon.

Limitations of gene editing assessments in human preimplantation embryos

Range of DNA repair in response to double-strand breaks induced in human preimplantation embryos remains uncertain due to the complexity of analyzing single- or few-cell samples. Sequencing of such minute DNA input requires a whole genome amplification that can introduce artifacts, including coverage nonuniformity, amplification biases, and allelic dropouts at the target site. We show here that, on average, 26.6% of preexisting heterozygous loci in control single blastomere samples appear as homozygous after whole genome amplification indicative of allelic dropouts. To overcome these limitations, we validate on-target modifications seen in gene edited human embryos in embryonic stem cells. We show that, in addition to frequent indel mutations, biallelic double-strand breaks can also produce large deletions at the target site. Moreover, some embryonic stem cells show copy-neutral loss of heterozygosity at the cleavage site which is likely caused by interallelic gene conversion. However, the frequency of loss of heterozygosity in embryonic stem cells is lower than in blastomeres, suggesting that allelic dropouts is a common whole genome amplification outcome limiting genotyping accuracy in human preimplantation embryos.

Interpersonal variability of the human gut virome confounds disease signal detection in IBD

Viruses are increasingly recognised as important components of the human microbiome, fulfilling numerous ecological roles including bacterial predation, immune stimulation, genetic diversification, horizontal gene transfer, microbial interactions, and augmentation of metabolic functions. However, our current view of the human gut virome is tainted by previous sequencing requirements that necessitated the amplification of starting nucleic acids. In this study, we performed an original longitudinal analysis of 40 healthy control, 19 Crohn’s disease, and 20 ulcerative colitis viromes over three time points without an amplification bias, which revealed and highlighted the interpersonal individuality of the human gut virome. In contrast to a 16 S rRNA gene analysis of matched samples, we show that α- and β-diversity metrics of unamplified viromes are not as efficient at discerning controls from patients with inflammatory bowel disease. Additionally, we explored the intrinsic properties of unamplified gut viromes and show there is considerable interpersonal variability in viral taxa, infrequent longitudinal persistence of intrapersonal viruses, and vast fluctuations in the abundance of temporal viruses. Together, these properties of unamplified faecal viromes confound the ability to discern disease associations but significantly advance toward an unbiased and accurate representation of the human gut virome.

INAM-Based Image-Adaptive 3D LUTs for Underwater Image Enhancement.

To the best of our knowledge, applying adaptive three-dimensional lookup tables (3D LUTs) to underwater image enhancement is an unprecedented attempt. It can achieve excellent enhancement results compared to some other methods. However, in the image weight prediction process, the model uses the normalization method of Instance Normalization, which will significantly reduce the standard deviation of the features, thus degrading the performance of the network. To address this issue, we propose an Instance Normalization Adaptive Modulator (INAM) that amplifies the pixel bias by adaptively predicting modulation factors and introduce the INAM into the learning image-adaptive 3D LUTs for underwater image enhancement. The bias amplification strategy in INAM makes the edge information in the features more distinguishable. Therefore, the adaptive 3D LUTs with INAM can substantially improve the performance on underwater image enhancement. Extensive experiments are undertaken to demonstrate the effectiveness of the proposed method.

Optimizing insect metabarcoding using replicated mock communities.

1: Metabarcoding (high-throughput sequencing of marker gene amplicons) has emerged as a promising and cost-effective method for characterizing insect community samples. Yet, the methodology varies greatly among studies and its performance has not been systematically evaluated to date. In particular, it is unclear how accurately metabarcoding can resolve species communities in terms of presence-absence, abundances, and biomass. 2: Here we use mock community experiments and a simple probabilistic model to evaluate the effect of different DNA extraction protocols on metabarcoding performance. Specifically, we ask four questions: (Q1) How consistent are the recovered community profiles across replicate mock communities?; (Q2) How does the choice of lysis buffer affect the recovery of the original community?; (Q3) How are community estimates affected by differing lysis times and homogenization?; and (Q4) Is it possible to obtain adequate species abundance estimates through the use of biological spike-ins? 3: We show that estimates are quite variable across community replicates. In general, a mild lysis protocol is better at reconstructing species lists and approximate counts, while homogenization is better at retrieving biomass composition. Small insects are more likely to be detected in lysates, while some tough species require homogenization to be detected. Results are less consistent across biological replicates for lysates than for homogenates. Some species are associated with strong PCR amplification bias, which complicates the reconstruction of species counts. Yet, with adequate spike-in data, species abundance can be determined with roughly 40% standard error for homogenates, and with roughly 50% standard error for lysates, under ideal conditions. In the latter case, however, this often requires species-specific reference data, while spike-in data generalizes better across species for homogenates. 4: We conclude that a non-destructive, mild lysis approach shows the highest promise for presence/absence description of the community, while also allowing future morphological or molecular work on the material. However, homogenization protocols perform better for characterizing community composition, in particular in terms of biomass.

Comparative analysis of single-cell genome sequencing techniques toward the characterization of germline and somatic genomes in ciliated protists

Ciliated protists contain both germline micronucleus (MIC) and somatic macronucleus (MAC) in a single cytoplasm. Programmed genome rearrangements occur in ciliates during sexual processes, and the extent of rearrangements varies dramatically among species, which lead to significant differences in genomic architectures. However, genomic sequences remain largely unknown for most ciliates due to the difficulty in culturing and in separating the germline from the somatic genome in a single cell. Single-cell whole genome amplification (WGA) has emerged as a powerful technology to characterize the genomic heterogeneity at the single-cell level. In this study, we compared two single-cell WGA, multiple displacement amplification (MDA) and multiple annealing and looping-based amplification cycles (MALBAC) in characterizing the germline and somatic genomes in ciliates with different genomic architectures. Our results showed that: 1) MALBAC exhibits strong amplification bias towards MAC genome while MDA shows bias towards MIC genome of ciliates with extensively fragmented MAC genome; 2) both MDA and MALBAC could amplify MAC genome more efficiently in ciliates with moderately fragmented MAC genome. Moreover, we found that more sample replicates could help to obtain more genomic data. Our work provides a reference for selecting the appropriate method to characterize germline and somatic genomes of ciliates.

The macro-financial effects of international bank lending on emerging markets

We provide novel empirical evidence on the effects of cross-border bank lending on emerging market economies' (EMEs) macro-financial conditions. We identify causal effects by leveraging the heterogeneity in the size distribution of bilateral cross-border bank lending to construct granular instrumental variables for aggregate cross-border bank lending to 22 EMEs. Cross-border bank credit causes higher domestic activity in EMEs and looser financial conditions. Financial condition indices ease, nominal and real effective exchange rates appreciate, sovereign and corporate spreads narrow, domestic interest rates fall, and housing prices increase. Similarly, real domestic credit grows, real GDP expands, and imports rise. Using a simple model, we motivate our instrument and show how commonly used instruments that correlate with global financial conditions (e.g. the global financial cycle) can yield biased estimates. We find empirical evidence of an amplification bias, which is consistent with the prominent role of global risk perceptions and international risk spillovers.

Deconfounded recommendation via causal intervention

Traditional recommenders suffer from hidden confounding factors, leading to the spurious correlations between user/item profiles and user preference prediction, i.e., the confounding bias issue. Most works resort to only one confounding bias, which greatly block their applications on recommendations with mixture confounder, i.e., more than one bias. It is therefore of practical demand to empower the recommender with the capability of debiasing different biases from data. Moreover, the positive effect of bias is neglected in most previous works. We argue that confounding bias is actually beneficial for capturing users’ preferences in some recommendation scenarios. In this paper, we propose a novel deconfounded causal learning method called GCRec (Graph Causal Recommendetion) to debias two confounders: social network confounder and item group confounder. We employ Graph Neural Networks (GNNs) to aggregate user-user connections for social networks and user-item interactions for item groups in order to learn high-order representations that can efficiently debias these two confounders from a causal view. In the inference stage, we use symmetric Kullback–Leibler divergence to measure the user preference drift. If the divergence is large, we perform the causal intervention to alleviate the bias amplification caused by confounders on user preferences. Otherwise, we incorporate the user preferences that can potentially deliver a positive effect on favoring recommendation performance. Extensive experiments are conducted on two benchmark datasets to verify that GCRec outperforms state-of-the-art methods and achieves robust recommendations.

Noninvasive Prenatal Screening for Common Fetal Aneuploidies Using Single-Molecule Sequencing

Amplification biases caused by next-generation sequencing (NGS) for noninvasive prenatal screening (NIPS) may be reduced using single-molecule sequencing (SMS), during which PCR is omitted. Therefore, the performance of SMS-based NIPS was evaluated. We used SMS-based NIPS to screen for common fetal aneuploidies in 477 pregnant women. The sensitivity, specificity, positive predictive value, and negative predictive value were estimated. The GC-induced bias was compared between the SMS- and NGS-based NIPS methods. Notably, a sensitivity of 100% was achieved for fetal trisomy 13 (T13), trisomy 18 (T18), and trisomy 21 (T21). The positive predictive value was 46.15% for T13, 96.77% for T18, and 99.07% for T21. The overall specificity was 100% (334/334). Compared with NGS, SMS (without PCR) had less GC bias, a better distinction between T21 or T18 and euploidies, and better diagnostic performance. Overall, our results suggest that SMS improves the performance of NIPS for common fetal aneuploidies by reducing the GC bias introduced during library preparation and sequencing.

Accurate estimation of molecular counts from amplicon sequence data with unique molecular identifiers.

Amplicon sequencing is widely applied to explore heterogeneity and rare variants in genetic populations. Resolving true biological variants and quantifying their abundance is crucial for downstream analyses, but measured abundances are distorted by stochasticity and bias in amplification, plus errors during polymerase chain reaction (PCR) and sequencing. One solution attaches unique molecular identifiers (UMIs) to sample sequences before amplification. Counting UMIs instead of sequences provides unbiased estimates of abundance. While modern methods improve over naïve counting by UMI identity, most do not account for UMI reuse or collision, and they do not adequately model PCR and sequencing errors in the UMIs and sample sequences. We introduce Deduplication and Abundance estimation with UMIs (DAUMI), a probabilistic framework to detect true biological amplicon sequences and accurately estimate their deduplicated abundance. DAUMI recognizes UMI collision, even on highly similar sequences, and detects and corrects most PCR and sequencing errors in the UMI and sampled sequences. DAUMI performs better on simulated and real data compared to other UMI-aware clustering methods. Source code is available at https://github.com/DormanLab/AmpliCI. Supplementary data are available at Bioinformatics online.