Sequence Counts Research Articles

A Flexible Zero-Inflated Poisson-Gamma Model with Application to Microbiome Sequence Count Data

In microbiome studies, it is of interest to use a sample from a population of microbes, such as the gut microbiota community, to estimate the population proportion of these taxa. However, due to biases introduced in sampling and preprocessing steps, these observed taxa abundances may not reflect true taxa abundance patterns in the ecosystem. Repeated measures, including longitudinal study designs, may be potential solutions to mitigate the discrepancy between observed abundances and true underlying abundances. Yet, widely observed zero-inflation and over-dispersion issues can distort downstream statistical analyses aiming to associate taxa abundances with covariates of interest. To this end, we propose a Zero-Inflated Poisson Gamma (ZIPG) model framework to address these aforementioned challenges. From a perspective of measurement errors, we accommodate the discrepancy between observations and truths by decomposing the mean parameter in Poisson regression into a true abundance level and a multiplicative measurement of sampling variability from the microbial ecosystem. Then, we provide a flexible ZIPG model framework by connecting both the mean abundance and the variability of abundances to different covariates, and build valid statistical inference procedures for both parameter estimation and hypothesis testing. Through comprehensive simulation studies and real data applications, the proposed ZIPG method provides significant insights into distinguished differential variability and mean abundance. Supplementary materials for this article are available online.

Spatio-temporal patterns of Synechococcus oligotypes in Moroccan lagoonal environments

Synechococcus are unicellular cyanobacteria susceptible to environmental fluctuations and can be used as bioindicators of eutrophication in marine ecosystems. We examined their distribution in two Moroccan lagoons, Marchica on the Mediterranean coast and Oualidia on the Atlantic, in the summers of 2014 and 2015 using 16S rRNA amplicon oligotyping. Synechococcus representatives recruited a higher number of reads from the 16S rRNA in Marchica in comparison to Oualidia. We identified 31 Synechococcus oligotypes that clustered into 10 clades with different distribution patterns. The Synechococcus community was mainly represented by oligotype 1 (clade III) in Marchica. Cooccurring clades IV and I had an important relative abundance in Marchica in the summer of 2014, which is unusual, as these clades are widespread in cold waters. Moreover, Clades VII and subcluster “5.3” formed a sizeable percentage of the Synechococcus community in Marchica. Notably, we found low Synechococcus sequence counts in the Atlantic Lagoon. These results showed that the relative abundance of Synechococcus reads is not constant over space and time and that rare members of the Synechococcus community did not follow a consistent pattern. Further studies are required to decipher Synechococcus dynamics and the impact of environmental parameters on their spatial and temporal distributions.

NMF Clustering: Accessible NMF-based Clustering Utilizing GPU Acceleration.

Non-negative Matrix Factorization (NMF) is an algorithm that can reduce high dimensional datasets of tens of thousands of genes to a handful of metagenes which are biologically easier to interpret. Application of NMF on gene expression data has been limited by its computationally intensive nature, which hinders its use on large datasets such as single-cell RNA sequencing (scRNA-seq) count matrices. We have implemented NMF based clustering to run on high performance GPU compute nodes using CuPy, a GPU backed python library, and the Message Passing Interface (MPI). This reduces the computation time by up to three orders of magnitude and makes the NMF Clustering analysis of large RNA-Seq and scRNA-seq datasets practical. We have made the method freely available through the GenePattern gateway, which provides free public access to hundreds of tools for the analysis and visualization of multiple 'omic data types. Its web-based interface gives easy access to these tools and allows the creation of multi-step analysis pipelines on high performance computing (HPC) clusters that enable reproducible in silico research for non-programmers.

Local causal pathway discovery for single-cell RNA sequencing count data: a benchmark study

Aim: Recent developments in single-cell RNA sequencing (scRNAseq) and analysis have revealed regulatory behaviors not previously described using bulk analysis. scRNAseq features resolution at the level of the individual cell and provides opportunities for identifying cell type-specific gene regulatory networks. The technology promises to discover biomarkers and targeted treatments with enhanced effectiveness and reduced side effects. Pathway reverse engineering and causal algorithms have been validated in bulk sequencing transcriptomic data successfully for gene regulatory network reconstruction. In the current study, we evaluated the performance of local causal discovery algorithms for de novo reconstruction of local gene regulatory networks tailored to scRNAseq count data.

A trinomial difference autoregressive model and its applications

This paper considers the autoregressive modelling problem of ‐valued time series of counts, whose counting sequence consists of −1, 0 and 1. Most existing methods are based on the signed binomial thinning operator and its some extensions, in which the counting sequence consists of 0 and 1, that is, the cases of −1 is ignored. To fill this gap, we first construct the trinomial difference thinning operator and then propose the trinomial difference Z‐valued autoregressive (TDZAR) model and give some stochastic properties. An attractive merit of the TDZAR model is that the incorporated trinomial difference thinning operator makes the conditional maximum likelihood estimate more wieldy and easy. Second, we discuss the two‐step conditional least squares estimate and the conditional maximum likelihood estimate and establish their asymptotic properties of the estimators. Third, the performances of these estimators are compared via simulation study. Finally, we apply the proposed model to a real data set.

A characterization of graphs with regular distance-2 graphs

For non-negative integers~$k$, we consider graphs in which every vertex has exactly $k$ vertices at distance~$2$, i.e., graphs whose distance-$2$ graphs are $k$-regular. We call such graphs $k$-metamour-regular motivated by the terminology in polyamory. While constructing $k$-metamour-regular graphs is relatively easy -- we provide a generic construction for arbitrary~$k$ -- finding all such graphs is much more challenging. We show that only $k$-metamour-regular graphs with a certain property cannot be built with this construction. Moreover, we derive a complete characterization of $k$-metamour-regular graphs for each $k=0$, $k=1$ and $k=2$. In particular, a connected graph with~$n$ vertices is $2$-metamour-regular if and only if $n\ge5$ and the graph is a join of complements of cycles (equivalently every vertex has degree~$n-3$), a cycle, or one of $17$ exceptional graphs with $n\le8$. Moreover, a characterization of graphs in which every vertex has at most one metamour is acquired. Each characterization is accompanied by an investigation of the corresponding counting sequence of unlabeled graphs.

Uncovering single‐nucleus RNA velocity variations in neuropathologic Alzheimer’s disease

AbstractBackgroundDifferential single‐nucleus (snRNA‐seq) gene expression analyses in Alzheimer’s disease (AD) provide fixed snapshots of cellular alterations, failing to detect the temporal dynamics of genes at individual cells. To overcome this limitation, here we analyze single‐nucleus RNA velocities (RNA‐vel) from the prefrontal cortex to characterize dynamic genetic and cellular differences in neuropathological AD progression. RNA‐vel is the rate of change of gene expression obtained by comparing intronic and exonic sequence counts. Comparison of AD pathology associated gene expression with parallel RNA‐vel differences reveals sets of genes and molecular pathways that underlie the static and putative dynamic regimes of cell type‐specific dysregulations underlying the disease.MethodWe used snRNA‐seq data from the prefrontal cortex of 48 subjects1 with varied levels of AD pathology from ROSMAP2. We then calculated both the expression and RNA‐vel differences between low AD‐pathology and mild‐to‐severe AD‐pathology. These differences were cell‐specific across six major cell types: excitatory neurons, inhibitory neurons, astrocytes, microglia, oligodendrocytes, and oligodendrocyte progenitor cells.1. We characterized RNA‐vel differences associated with four AD neuropathology traits: neuritic plaque, neuronal neurofibrillary tangle, overall β‐amyloid load, and PHF tau tangle density.2. We demonstrated the reproducibility of the RNA‐vel differences in two independent datasets: dorsolateral prefrontal cortex (N = 24) from ROSMAP3 and superior frontal gyrus (N = 6) from GEO4.ResultOnly 10 of 843 (1.2%) genes overlap between the differential RNA expression and velocity analyses, suggesting substantial AD‐pathology related differences between these two RNA descriptors. The genes with only velocity differences relate to cell developmental and synaptic processes. Conversely, the genes with only differential expression are majorly associated with mitochondrial activity and ribosomal processes. Many genes were reproduced in the independent datasets; these overlapping genes are again associated with neural development and synaptic activities across many cell types, as well as vascular‐ and immune‐related processes in astrocytes and microglia, respectivelyConclusionWe use RNA velocity to characterize, for the first time to our knowledge, the dynamical multicellular processes underlying neuropathological AD progression. The results support the validity of the novel RNA‐vel concept for achieving a complementary molecular characterization of AD, which may be obscured by typical analysis of RNA abundance alone.

De novo transcriptome assembly and its utility in development and characterization of the first set of genic SSR markers in cashew

Cashew is an industrially important nut tree grown in over 30 countries. The demand for cashew is increasing due to its nutritional value and industrial applications. Despite socio-economic importance, genomic sequence and marker resources are very limited in cashew. De novo transcriptome sequencing allows the discovery of genes and molecular markers cost-effectively. In the present study, for the first time, we de novo assembled and characterized the transcriptome of cashew shoots and developed novel genic SSR markers. About 24.78 million high-quality Illumina sequences were used for de novo transcriptome assembly and it resulted in the identification of 71571 transcripts. The size of transcripts ranged from 201 to 23,274 bp with an average size of 687 bp. The N50 value of transcriptome was 1011 bp and the L50 sequence count was 13520. BUSCO analysis showed assembly completeness of 94 %. A total of 39836 transcripts had at least one BLAST hit against the Viridiplantae database. The functional annotation by KAAS analysis identified a total of 203 pathways. In silico SSR mining from assembled transcriptome identified 4271 SSRs. Tri-nucleotide (49.12 %) was the most dominant class of SSRs. The frequency of Class I SSRs (≥20 bp) was 32.1 %. Fifty-four of 80 primers synthesized successfully PCR amplified the cashew genomic DNA and 36 of them were polymorphic. Cross-species amplification rates of SSRs were 100 % in two Anacardium species whereas 61 % in Semecarpus anacardium and 87 % in S. prainii. Nineteen SSR markers were used for genetic diversity analysis in 64 accessions detected a total of 206 alleles and grouped the accessions into three major clusters. PCoA could differentiate the cashew accessions based on geographic regions to a certain extent. The population structure analysis using the Bayesian model-based clustering revealed the existence of three genetic subpopulations (K=3). The annotated transcriptome data and informative genic SSR markers developed in this study provide valuable tools for deciphering agronomically important genes and QTLs, the genetic variations in populations, and molecular breeding in cashew.

MarineMetagenomeDB: a public repository for curated and standardized metadata for marine metagenomes

BackgroundMetagenomics is an expanding field within microbial ecology, microbiology, and related disciplines. The number of metagenomes deposited in major public repositories such as Sequence Read Archive (SRA) and Metagenomic Rapid Annotations using Subsystems Technology (MG-RAST) is rising exponentially. However, data mining and interpretation can be challenging due to mis-annotated and misleading metadata entries. In this study, we describe the Marine Metagenome Metadata Database (MarineMetagenomeDB) to help researchers identify marine metagenomes of interest for re-analysis and meta-analysis. To this end, we have manually curated the associated metadata of several thousands of microbial metagenomes currently deposited at SRA and MG-RAST.ResultsIn total, 125 terms were curated according to 17 different classes (e.g., biome, material, oceanic zone, geographic feature and oceanographic phenomena). Other standardized features include sample attributes (e.g., salinity, depth), sample location (e.g., latitude, longitude), and sequencing features (e.g., sequencing platform, sequence count). MarineMetagenomeDB version 1.0 contains 11,449 marine metagenomes from SRA and MG-RAST distributed across all oceans and several seas. Most samples were sequenced using Illumina sequencing technology (84.33%). More than 55% of the samples were collected from the Pacific and the Atlantic Oceans. About 40% of the samples had their biomes assigned as ‘ocean’. The ‘Quick Search’ and ‘Advanced Search’ tabs allow users to use different filters to select samples of interest dynamically in the web app. The interactive map allows the visualization of samples based on their location on the world map. The web app is also equipped with a novel download tool (on both Windows and Linux operating systems), that allows easy download of raw sequence data of selected samples from their respective repositories. As a use case, we demonstrated how to use the MarineMetagenomeDB web app to select estuarine metagenomes for potential large-scale microbial biogeography studies.ConclusionThe MarineMetagenomeDB is a powerful resource for non-bioinformaticians to find marine metagenome samples with curated metadata and stimulate meta-studies involving marine microbiomes. Our user-friendly web app is publicly available at https://webapp.ufz.de/marmdb/.

Effect of Entamoeba histolytica infection on gut microbial diversity and composition in diarrheal patients from New Delhi.

During amoebiasis, colonization of the gut by Entamoeba histolytica can lead to alterations of the host microbiota. In this study, we have compared the gut microbiota of patients of amoebiasis with healthy controls using 16S rRNA gene variable regions, (V1-V3) and (V3-V5), of the bacterial genome. From this 16S rRNA gene amplicon data, one paired-end and two single-end datasets were selected and compared by the number of OTUs obtained, sequence count, and diversity analysis. Our results showed that the V1-V3-paired-end dataset gave the maximum number of OTUs in comparison to the two single-end datasets studied. The amoebiasis samples showed a significant drop in richness in the alpha diversity measurements and lower intra group similarity compared to the healthy controls. Bacteria of genus Prevotella, Sutterella, and Collinsella were more abundant in healthy controls whereas Escherichia, Klebsiella, and Ruminococcus were more abundant in the E. histolytica-positive patients. All the healthy controls harbored bacteria belonging to Faecalibacterium, Prevotella, Ruminococcus, Subdoligranulum, and Escherichia genera while all the E. histolytica-positive patient samples contained genus Enterobacter. The compositional changes in the gut microbiome observed in our study indicated a higher prevalence of pathogenic bacteria along with a depletion of beneficial bacteria in E. histolytica-infected individuals when compared with healthy controls. These results underline the interplay between E. histolytica and the human gut microbiome, giving important inputs for future studies and treatments.

A sparse Bayesian hierarchical vector autoregressive model for microbial dynamics in a wastewater treatment plant

Proper function of a wastewater treatment plant (WWTP) relies on maintaining a delicate balance between a multitude of competing microorganisms. Gaining a detailed understanding of the complex network of interactions therein is essential to maximising not only current operational efficiencies, but also for the effective design of new treatment technologies. Metagenomics offers an insight into these dynamic systems through the analysis of the microbial DNA sequences present. Unique taxa are deduced through sequence clustering to form operational taxonomic units (OTUs), with per-taxa abundance estimates obtained from corresponding sequence counts. The data in this study comprise weekly OTU counts from an activated sludge (AS) tank of a WWTP along with corresponding measurements of chemical and environmental (CE) covariates. Directly fitting a model to the OTU data is incredibly challenging because of the high dimensionality and sparsity of the observations. The first step is therefore to aggregate the OTUs into twelve microbial communities or “bins” using a seasonal phase-based clustering approach. The mean abundances in the twelve bins are assumed to vary over time according to a multivariate linear regression on the CE covariates. Deviations from the mean are then modelled using a vector autoregressive (VAR) model of order one, which is a linear approximation to the commonly used generalised Lotka-Volterra (gLV) model. Sparsity is assumed in the interactions between microbial communities by carrying out inference in a hierarchical Bayesian framework which uses a shrinkage prior for the autoregressive coefficient matrix of the VAR model. Different shrinkage priors are explored by analysing simulated data sets before selecting the regularised horseshoe prior for the biological application. It is found that ammonia and chemical oxygen demand have a positive relationship with several bins and pH has a positive relationship with one bin. These results are supported by findings in the biological literature. Several negative interactions are also identified. These novel biological findings suggest OTUs in different bins may be competing for resources and that these relationships are complex. Although simpler than a gLV model, the VAR model is still able to offer valuable insight into the microbial dynamics of the WWTP.

2FAST2Q: a general-purpose sequence search and counting program for FASTQ files.

BackgroundThe increasingly widespread use of next generation sequencing protocols has brought the need for the development of user-friendly raw data processing tools. Here, we explore 2FAST2Q, a versatile and intuitive standalone program capable of extracting and counting feature occurrences in FASTQ files. Despite 2FAST2Q being previously described as part of a CRISPRi-seq analysis pipeline, in here we further elaborate on the program’s functionality, and its broader applicability and functions.Methods2FAST2Q is built in Python, with published standalone executables in Windows MS, MacOS, and Linux. It has a familiar user interface, and uses an advanced custom sequence searching algorithm.ResultsUsing published CRISPRi datasets in which Escherichia coli and Mycobacterium tuberculosis gene essentiality, as well as host-cell sensitivity towards SARS-CoV2 infectivity were tested, we demonstrate that 2FAST2Q efficiently recapitulates published output in read counts per provided feature. We further show that 2FAST2Q can be used in any experimental setup that requires feature extraction from raw reads, being able to quickly handle Hamming distance based mismatch alignments, nucleotide wise Phred score filtering, custom read trimming, and sequence searching within a single program. Moreover, we exemplify how different FASTQ read filtering parameters impact downstream analysis, and suggest a default usage protocol. 2FAST2Q is easier to use and faster than currently available tools, efficiently processing not only CRISPRi-seq / random-barcode sequencing datasets on any up-to-date laptop, but also handling the advanced extraction of de novo features from FASTQ files. We expect that 2FAST2Q will not only be useful for people working in microbiology but also for other fields in which amplicon sequencing data is generated. 2FAST2Q is available as an executable file for all current operating systems without installation and as a Python3 module on the PyPI repository (available at https://veeninglab.com/2fast2q).

Interaction networks from discrete event data by Poisson multivariate mutual information estimation and information flow with applications from gene expression data

In this work, we introduce a new methodology for inferring the interaction structure of discrete valued time series which are Poisson distributed. While most related methods are premised on continuous state stochastic processes, in fact, discrete and counting event oriented stochastic process are natural and common, so called time-point processes. An important application that we focus on here is gene expression, where it is often assumed that the data is generated from a multivariate Poisson distribution. Nonparameteric methods such as the popular k-nearest neighbors are slow converging for discrete processes, and thus data hungry. Now, with the new multi-variate Poisson estimator developed here as the core computational engine, the causation entropy (CSE) principle, together with the associated greedy search algorithm optimal CSE (oCSE) allows us to efficiently infer the true network structure for this class of stochastic processes that were previously not practical. We illustrate the power of our method, first in benchmarking with synthetic datum, and then by inferring the genetic factors network from a breast cancer micro-ribonucleic acid sequence count data set. We show the Poisson oCSE gives the best performance among the tested methods and discovers previously known interactions on the breast cancer data set.

No fingers, no SNARC? Neither the finger counting starting hand, nor its stability robustly affect the SNARC effect

The Spatial-Numerical Association of Response Codes (SNARC) effect (i.e., faster left/right sided responses to small/large magnitude numbers, respectively) is considered to be strong evidence for the link between numbers and space. Studies have shown considerable variation in this effect. Among the factors determining individual differences in the SNARC effect is the hand an individual uses to start the finger counting sequence. Left-starters show a stronger and less variable SNARC effect than right-starters. This observation has been used as an argument for the embodied nature of the SNARC effect. For this to be the case, one must assume that the finger counting sequence (especially the starting hand) is stable over time. Subsequent studies challenged the view that the SNARC differs depending on the finger counting starting hand. At the same time, it has been pointed out that the temporal stability of the finger counting starting hand should not be taken for granted. Thus, in this preregistered study, we aimed to replicate the difference in the SNARC between left- and right-starters and explore the relationship between the self-reported temporal stability of the finger counting starting hand and the SNARC effect. In line with the embodied cognition account, left-starters who declare more temporarily stable finger counting habits should reveal a stronger SNARC effect. Results of the preregistered analysis did not show the difference between left- and right-starters. However, further exploratory analysis provided weak evidence that this might be the case. Lastly, we found no evidence for the relationship between finger counting starting hand stability and the SNARC effect. Overall, these results challenge the view on the embodied nature of the SNARC effect.

Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection.

Selection protocols such as SELEX, where molecules are selected over multiple rounds for their ability to bind to a target of interest, are popular methods for obtaining binders for diagnostic and therapeutic purposes. We show that Restricted Boltzmann Machines (RBMs), an unsupervised two-layer neural network architecture, can successfully be trained on sequence ensembles from single rounds of SELEX experiments for thrombin aptamers. RBMs assign scores to sequences that can be directly related to their fitnesses estimated through experimental enrichment ratios. Hence, RBMs trained from sequence data at a given round can be used to predict the effects of selection at later rounds. Moreover, the parameters of the trained RBMs are interpretable and identify functional features contributing most to sequence fitness. To exploit the generative capabilities of RBMs, we introduce two different training protocols: one taking into account sequence counts, capable of identifying the few best binders, and another based on unique sequences only, generating more diverse binders. We then use RBMs model to generate novel aptamers with putative disruptive mutations or good binding properties, and validate the generated sequences with gel shift assay experiments. Finally, we compare the RBM’s performance with different supervised learning approaches that include random forests and several deep neural network architectures.

PSIII-2 The Microrna Profile of day 30 Pregnant Ipsilateral or non-Pregnant Bovine Uterus

Abstract MicroRNAs (miRNAs) have been used as biomarkers for various health issues and physiological states, and could be utilized as a tool to detect pregnancy loss. The objective of this study was to determine the variation in miRNA abundance between pregnant and non-pregnant uteri of cows at day 30 post-insemination. Uterine samples were collected from pregnant (n = 4) and non-pregnant (n = 4) cows, and snap frozen in liquid N2. Isolation of uterine RNA was performed using the mirVANA kit and RNA quantification and quality assessed. Eight libraries were generated (4 biological replicates per group) and subjected to RNAseq procedures and bioinformatic analysis by LC Sciences (Houston, TX, USA). Raw sequence data were analyzed using the ACGT101-miR program (LC Sciences, Houston, TX) removing extraneous sequence information. The remaining sequences ranging in size for 18 to 26 nucleotides were mapped to known bovine miRNA sequences (miRbase 22.1) and the bovine genome. Significant and differentially abundant miRNAs were analyzed using normalized deep sequencing counts and were subjected to ANOVA (P < 0.05). There were 55 significant (P < 0.05) differentially abundant microRNAs of which 35 were upregulated in open samples and 20 were upregulated in pregnant samples. Target prediction was performed using TargetScan for differentially abundant miRNA. Functional enrichment analysis of the miRNA target genes was conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID 2021). Gene targets were analyzed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The top 5 KEGG pathways across all significant and differentially abundant miRNAs were pathways in cancer, PI3K-Akt signaling pathway, MAPK signaling pathway, human papillomavirus infection, and metabolic pathways. These results show that there is variation in the abundance of miRNAs between pregnant and open cows at day 30 post-insemination.

Diet preferences based on sequence read count: The role of species interaction in tissue bias correction.

High-throughput sequencing and metabarcoding techniques provide a unique opportunity to study predator-prey relationships. However, in animal dietary preference studies, how to properly correct tissue bias within the sequence read count and the role of interactions between co-occurring species in metabarcoding mixtures remain largely unknown. In this study, we propose two categories of tissue bias correction indices: sequence read count number per unit tissue (SCN) and its ratio form (SCN ratio). By constructing plant mock communities with different numbers of co-occurring species in metabarcoding mixtures and conducting feeding trails on captive sika deer (Cervus nippon), we demonstrate the features of the SCN and SCN ratio, evaluate their correction effects and assess the role of species interactions during tissue bias correction. Tissue differences between species are defined as the differential ability to generate sequence counts. Our study suggests that pure tissue differences among species without a species interaction is not an optimal correction index for many biomes with limited tissue differences among species. Species interactions in mixtures may amplify tissue differences, which is beneficial for tissue bias correction. However, caution must be taken because varied species interactions among communities may increase the risk of worse correction. Correction effects based on the SCN and SCN ratio are comparable, but the SCN is less influenced by control species than the SCN ratio. Based on our study, several suggestions are provided for future animal diet studies or other high-throughput sequencing studies containing tissue bias.

Investigating differential abundance methods in microbiome data: A benchmark study.

The development of increasingly efficient and cost-effective high throughput DNA sequencing techniques has enhanced the possibility of studying complex microbial systems. Recently, researchers have shown great interest in studying the microorganisms that characterise different ecological niches. Differential abundance analysis aims to find the differences in the abundance of each taxa between two classes of subjects or samples, assigning a significance value to each comparison. Several bioinformatic methods have been specifically developed, taking into account the challenges of microbiome data, such as sparsity, the different sequencing depth constraint between samples and compositionality. Differential abundance analysis has led to important conclusions in different fields, from health to the environment. However, the lack of a known biological truth makes it difficult to validate the results obtained. In this work we exploit metaSPARSim, a microbial sequencing count data simulator, to simulate data with differential abundance features between experimental groups. We perform a complete comparison of recently developed and established methods on a common benchmark with great effort to the reliability of both the simulated scenarios and the evaluation metrics. The performance overview includes the investigation of numerous scenarios, studying the effect on methods' results on the main covariates such as sample size, percentage of differentially abundant features, sequencing depth, feature variability, normalisation approach and ecological niches. Mainly, we find that methods show a good control of the type I error and, generally, also of the false discovery rate at high sample size, while recall seem to depend on the dataset and sample size.

Differential RNA methylation analysis for MeRIP-seq data under general experimental design.

RNA epigenetics is an emerging field to study the post-transcriptional gene regulation. The dynamics of RNA epigenetic modification have been reported to associate with many human diseases. Recently developed high-throughput technology named Methylated RNA Immunoprecipitation Sequencing (MeRIP-seq) enables the transcriptome-wide profiling of N6-methyladenosine (m6A) modification and comparison of RNA epigenetic modifications. There are a few computational methods for the comparison of mRNA modifications under different conditions but they all suffer from serious limitations. In this work, we develop a novel statistical method to detect differentially methylated mRNA regions from MeRIP-seq data. We model the sequence count data by a hierarchical negative binomial model that accounts for various sources of variations and derive parameter estimation and statistical testing procedures for flexible statistical inferences under general experimental designs. Extensive benchmark evaluations in simulation and real data analyses demonstrate that our method is more accurate, robust and flexible compared to existing methods. Our method TRESS is implemented as an R/Bioconductor package and is available at https://bioconductor.org/packages/devel/TRESS. Supplementary data are available at Bioinformatics online.

Illuminating the Artefacts of NGS Seen in PGT-A & PGT-SR

Background: Advancements in next generation sequencing (NGS) based genomics over the past 10 years have allowed the routine adoption of preimplantation genetic testing for aneuploidy (PGT-A), and/or segmental rearrangements (PGT-SR) of [Formula: see text]10Mb 1 in the assisted reproductive technology (ART) clinic. However, one challenge remains within the assessment of aligned short read sequencing data, in the ability to discriminate between embryonic profiles with genuine pathological copy number variations (CNVs) from artefacts arising from erroneous DNA amplification or library preparation. Aim: To identify the common artefacts seen in next generation sequencing (NGS) based PGT to reduce inconclusive or false positive results. Method: NGS profiles generated from biopsied human embryonic trophectoderm cells were manually analysed for common genomic artefacts. Following trophectoderm biopsy within a clinical ART cycle, cell samples underwent DNA amplification (Illumina SureplexTM DNA amplification system), sequencing (VeriSeqTM PGS kit on the MiSeq[Formula: see text] System, and bioinformatic data analysis (Bluefuse Multi v4.4, Illumina). Results: Common artefacts were identified affecting chromosomes 7, 11, and 19. Artefacts imitated small CNVs typically displaying a [Formula: see text]50% increase in centromeric sequence counts on chromosomes 7 and 11, and a global increase of sequence counts of [Formula: see text]40% across the entire chromosome 19 (except at the centromere, presenting as diploid). Interestingly a technical repeat of the library preparation and sequencing of the original DNA template somewhat normalised these artefacts. Conclusion: The study found three commonly occurring artefacts involving chromosomes 7, 11, and 19 artificially introduced during suboptimal NGS library preparation. Importantly these artefacts may be normalised through technical repeat of library preparation and sequencing. The awareness of these artefacts may reduce false positive and/or inconclusive results, increasing clinical embryonic utilisation following PGT.