Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. log-ratio transformation-based methods

A statistical simulation model to guide the choices of analytical methods in arrayed CRISPR screen experiments

An arrayed CRISPR screen is a high-throughput functional genomic screening method, which typically uses 384 well plates and has different gene knockouts in different wells. Despite various computational workflows, there is currently no systematic way to find what is a good workflow for arrayed CRISPR screening data analysis. To guide this choice, we developed a statistical simulation model that mimics the data generating process of arrayed CRISPR screening experiments. Our model is flexible and can simulate effects on phenotypic readouts of various experimental factors, such as the effect size of gene editing, as well as biological and technical variations. With two examples, we showed that the simulation model can assist making principled choice of normalization and hit calling method for the arrayed CRISPR data analysis. This simulation model is implemented in an R package and can be downloaded from Github.

Normalization of gene counts affects principal components-based exploratory analysis of RNA-sequencing data

Normalization of gene expression count data is an essential step of in the analysis of RNA-sequencing data. Its statistical analysis has been mostly addressed in the context of differential expression analysis, that is in the univariate setting. However, relationships among genes and samples are better explored and quantified using multivariate exploratory data analysis tools like Principal Component Analysis (PCA). In this study we investigate how normalization impacts the PCA model and its interpretation, considering twelve different widely used normalization methods that were applied on simulated and experimental data. Correlation patterns in the normalized data were explored using both summary statistics and Covariance Simultaneous Component Analysis. The impact of normalization on the PCA solution was assessed by exploring the model complexity, the quality of sample clustering in the low-dimensional PCA space and gene ranking in the model fit to normalized data. PCA models upon normalization were interpreted in the context gene enrichment pathway analysis. We found that although PCA score plots are often similar independently form the normalization used, biological interpretation of the models can depend heavily on the normalization method applied.

Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results

Transcriptomics is widely used to assess the state of biological systems. There are many tools for the different steps, such as normalization, differential expression, and enrichment. While numerous studies have examined the impact of method choices on differential expression results, little attention has been paid to their effects on further downstream functional analysis, which typically provides the basis for interpretation and follow-up experiments. To address this, we introduce FLOP, a comprehensive nextflow-based workflow combining methods to perform end-to-end analyses of transcriptomics data. We illustrate FLOP on datasets ranging from end-stage heart failure patients to cancer cell lines. We discovered effects not noticeable at the gene-level, and observed that not filtering the data had the highest impact on the correlation between pipelines in the gene set space. Moreover, we performed three benchmarks to evaluate the 12 pipelines included in FLOP, and confirmed that filtering is essential in scenarios of expected moderate-to-low biological signal. Overall, our results underscore the impact of carefully evaluating the consequences of the choice of preprocessing methods on downstream enrichment analyses. We envision FLOP as a valuable tool to measure the robustness of functional analyses, ultimately leading to more reliable and conclusive biological findings.

Metatranscriptomics: A Tool for Clinical Metagenomics

In the field of bioinformatics, amplicon sequencing of 16S rRNA genes has long been used to investigate community membership and taxonomic abundance in microbiome studies. As we can observe, shotgun metagenomics has become the dominant method in this field. This is largely owing to advancements in sequencing technology, which now allow for random sequencing of the entire genetic content of a microbiome. Furthermore, this method allows profiling both genes and the microbiome's membership. Although these methods have provided extensive insights into various microbiomes, they solely assess the existence of organisms or genes, without determining their active role within the microbiome. Microbiome scholarship now includes metatranscriptomics to decipher how a community of microorganisms responds to changing environmental conditions over a period of time. Metagenomic studies identify the microbes that make up a community but metatranscriptomics explores the diversity of active genes within that community, understanding their expression profile and observing how these genes respond to changes in environmental conditions. This expert review article offers a critical examination of the computational metatranscriptomics tools for studying the transcriptomes of microbial communities. First, we unpack the reasons behind the need for community transcriptomics. Second, we explore the prospects and challenges of metatranscriptomic workflows, starting with isolation and sequencing of the RNA community, then moving on to bioinformatics approaches for quantifying RNA features, and statistical techniques for detecting differential expression in a community. Finally, we discuss strengths and shortcomings in relation to other microbiome analysis approaches, pipelines, use cases and limitations, and contextualize metatranscriptomics as a tool for clinical metagenomics.

Interpretable machine learning decodes soil microbiome's response to drought stress

BACKGROUND

Extreme weather events induced by climate change, particularly droughts, have detrimental consequences for crop yields and food security. Concurrently, these conditions provoke substantial changes in the soil bacterial microbiota and affect plant health. Early recognition of soil affected by drought enables farmers to implement appropriate agricultural management practices. In this context, interpretable machine learning holds immense potential for drought stress classification of soil based on marker taxa.

RESULTS

This study demonstrates that the 16S rRNA-based metagenomic approach of Differential Abundance Analysis methods and machine learning-based Shapley Additive Explanation values provide similar information. They exhibit their potential as complementary approaches for identifying marker taxa and investigating their enrichment or depletion under drought stress in grass lineages. Additionally, the Random Forest Classifier trained on a diverse range of relative abundance data from the soil bacterial micobiome of various plant species achieves a high accuracy of 92.3 % at the genus rank for drought stress prediction. It demonstrates its generalization capacity for the lineages tested.

CONCLUSIONS

In the detection of drought stress in soil bacterial microbiota, this study emphasizes the potential of an optimized and generalized location-based ML classifier. By identifying marker taxa, this approach holds promising implications for microbe-assisted plant breeding programs and contributes to the development of sustainable agriculture practices. These findings are crucial for preserving global food security in the face of climate change.

Reduced Expression of CLEC4G in Neurons Is Associated with Alzheimer's Disease

CLEC4G, a glycan-binding receptor, has previously been demonstrated to inhibit Aβ generation, yet its brain localization and functions in Alzheimer's disease (AD) are not clear. We explored the localization, function, and regulatory network of CLEC4G via experiments and analysis of RNA-seq databases. CLEC4G transcripts and proteins were identified in brain tissues, with the highest expression observed in neurons. Notably, AD was associated with reduced levels of CLEC4G transcripts. Bioinformatic analyses revealed interactions between CLEC4G and relevant genes such as BACE1, NPC1, PILRA, TYROBP, MGAT1, and MGAT3, all displaying a negative correlation trend. We further identified the upstream transcriptional regulators NR2F6 and XRCC4 for CLEC4G and confirmed a decrease in CLEC4G expression in APP/PS1 transgenic mice. This study highlights the role of CLEC4G in protecting against AD progression and the significance of CLEC4G for AD research and management.

SIEVE: One-stop differential expression, variability, and skewness analyses using RNA-Seq data

Motivation

RNA-Seq data analysis is commonly biased towards detecting differentially expressed genes and insufficiently conveys the complexity of gene expression changes between biological conditions. This bias arises because discrete models of RNA-Seq count data cannot fully characterize the mean, variance, and skewness of gene expression distribution using independent model parameters. A unified framework that simultaneously tests for differential expression, variability, and skewness is needed to realize the full potential of RNA-Seq data analysis in a systems biology context.

Results

We present SIEVE, a statistical methodology that provides the desired unified framework. SIEVE embraces a compositional data analysis framework that transforms discrete RNA-Seq counts to a continuous form with a distribution that is well-fitted by a skew-normal distribution. Simulation results show that SIEVE controls the false discovery rate and probability of Type II error better than existing methods for differential expression analysis. Analysis of the Mayo RNA-Seq dataset for Alzheimer's disease using SIEVE reveals that a gene set with significant expression difference in mean, standard deviation and skewness between the control and the Alzheimer's disease group strongly predicts a subject's disease state. Furthermore, functional enrichment analysis shows that relying solely on differentially expressed genes detects only a segment of a much broader spectrum of biological aspects associated with Alzheimer's disease. The latter aspects can only be revealed using genes that show differential variability and skewness. Thus, SIEVE enables fresh perspectives for understanding the intricate changes in gene expression that occur in complex diseases.

Availability

The SIEVE R package and source codes are available at https://github.com/Divo-Lee/SIEVE .

Pre-challenge gut microbial signature predicts RhCMV/SIV vaccine efficacy in rhesus macaques

Background

RhCMV/SIV vaccines protect ∼59% of vaccinated rhesus macaques against repeated limiting-dose intra-rectal exposure with highly pathogenic SIVmac239M, but the exact mechanism responsible for the vaccine efficacy is not known. It is becoming evident that complex interactions exist between gut microbiota and the host immune system. Here we aimed to investigate if the rhesus gut microbiome impacts RhCMV/SIV vaccine-induced protection.

Methods

Three groups of 15 rhesus macaques naturally pre-exposed to RhCMV were vaccinated with RhCMV/SIV vaccines. Rectal swabs were collected longitudinally both before SIV challenge (after vaccination) and post challenge and were profiled using 16S rRNA based microbiome analysis.

Results

We identified ∼2,400 16S rRNA amplicon sequence variants (ASVs), representing potential bacterial species/strains. Global gut microbial profiles were strongly associated with each of the three vaccination groups, and all animals tended to maintain consistent profiles throughout the pre-challenge phase. Despite vaccination group differences, using newly developed compositional data analysis techniques we identified a common gut microbial signature predictive of vaccine protection outcome across the three vaccination groups. Part of this microbial signature persisted even after SIV challenge. We also observed a strong correlation between this microbial signature and an early signature derived from whole blood transcriptomes in the same animals.

Conclusions

Our findings indicate that changes in gut microbiomes are associated with RhCMV/SIV vaccine-induced protection and early host response to vaccination in rhesus macaques.

Metagenomic analysis of gut microbiome illuminates the mechanisms and evolution of lignocellulose degradation in mangrove herbivorous crabs

BACKGROUND

Sesarmid crabs dominate mangrove habitats as the major primary consumers, which facilitates the trophic link and nutrient recycling in the ecosystem. Therefore, the adaptations and mechanisms of sesarmid crabs to herbivory are not only crucial to terrestrialization and its evolutionary success, but also to the healthy functioning of mangrove ecosystems. Although endogenous cellulase expressions were reported in crabs, it remains unknown if endogenous enzymes alone can complete the whole lignocellulolytic pathway, or if they also depend on the contribution from the intestinal microbiome. We attempt to investigate the role of gut symbiotic microbes of mangrove-feeding sesarmid crabs in plant digestion using a comparative metagenomic approach.

RESULTS

Metagenomics analyses on 43 crab gut samples from 23 species of mangrove crabs with different dietary preferences revealed a wide coverage of 127 CAZy families and nine KOs targeting lignocellulose and their derivatives in all species analyzed, including predominantly carnivorous species, suggesting the crab gut microbiomes have lignocellulolytic capacity regardless of dietary preference. Microbial cellulase, hemicellulase and pectinase genes in herbivorous and detritivorous crabs were differentially more abundant when compared to omnivorous and carnivorous crabs, indicating the importance of gut symbionts in lignocellulose degradation and the enrichment of lignocellulolytic microbes in response to diet with higher lignocellulose content. Herbivorous and detritivorous crabs showed highly similar CAZyme composition despite dissimilarities in taxonomic profiles observed in both groups, suggesting a stronger selection force on gut microbiota by functional capacity than by taxonomy. The gut microbiota in herbivorous sesarmid crabs were also enriched with nitrogen reduction and fixation genes, implying possible roles of gut microbiota in supplementing nitrogen that is deficient in plant diet.

CONCLUSIONS

Endosymbiotic microbes play an important role in lignocellulose degradation in most crab species. Their abundance is strongly correlated with dietary preference, and they are highly enriched in herbivorous sesarmids, thus enhancing their capacity in digesting mangrove leaves. Dietary preference is a stronger driver in determining the microbial CAZyme composition and taxonomic profile in the crab microbiome, resulting in functional redundancy of endosymbiotic microbes. Our results showed that crabs implement a mixed mode of digestion utilizing both endogenous and microbial enzymes in lignocellulose degradation, as observed in most of the more advanced herbivorous invertebrates.

Engraftment of essential functions through multiple fecal microbiota transplants in chronic antibiotic-resistant pouchitis-a case study using metatranscriptomics

BACKGROUND

Ileal pouch-anal anastomosis (IPAA) is the standard of care after total proctocolectomy for ulcerative colitis (UC). Around 50% of patients will experience pouchitis, an idiopathic inflammatory condition. Antibiotics are the backbone of treatment of pouchitis; however, antibiotic-resistant pouchitis develops in 5-10% of those patients. It has been shown that fecal microbiota transplantation (FMT) is an effective treatment for UC, but results for FMT antibiotic-resistant pouchitis are inconsistent.

METHODS

To uncover which metabolic activities were transferred to the recipients during FMT and helped the remission, we performed a longitudinal case study of the gut metatranscriptomes from three patients and their donors. The patients were treated by two to three FMTs, and stool samples were analyzed for up to 140 days.

RESULTS

Reduced expression in pouchitis patients compared to healthy donors was observed for genes involved in biosynthesis of amino acids, cofactors, and B vitamins. An independent metatranscriptome dataset of UC patients showed a similar result. Other functions including biosynthesis of butyrate, metabolism of bile acids, and tryptophan were also much lower expressed in pouchitis. After FMT, these activities transiently increased, and the overall metatranscriptome profiles closely mirrored those of the respective donors with notable fluctuations during the subsequent weeks. The levels of the clinical marker fecal calprotectin were concordant with the metatranscriptome data. Faecalibacterium prausnitzii represented the most active species contributing to butyrate synthesis via the acetyl-CoA pathway. Remission occurred after the last FMT in all patients and was characterized by a microbiota activity profile distinct from donors in two of the patients.

CONCLUSIONS

Our study demonstrates the clear but short-lived activity engraftment of donor microbiota, particularly the butyrate biosynthesis after each FMT. The data suggest that FMT triggers shifts in the activity of patient microbiota towards health which need to be repeated to reach critical thresholds. As a case study, these insights warrant cautious interpretation, and validation in larger cohorts is necessary for generalized applications. In the long run, probiotics with high taxonomic diversity consisting of well characterized strains could replace FMT to avoid the costly screening of donors and the risk of transferring unwanted genetic material. Video Abstract.

Statistical normalization methods in microbiome data with application to microbiome cancer research

Mounting evidence has shown that gut microbiome is associated with various cancers, including gastrointestinal (GI) tract and non-GI tract cancers. But microbiome data have unique characteristics and pose major challenges when using standard statistical methods causing results to be invalid or misleading. Thus, to analyze microbiome data, it not only needs appropriate statistical methods, but also requires microbiome data to be normalized prior to statistical analysis. Here, we first describe the unique characteristics of microbiome data and the challenges in analyzing them (Section 2). Then, we provide an overall review on the available normalization methods of 16S rRNA and shotgun metagenomic data along with examples of their applications in microbiome cancer research (Section 3). In Section 4, we comprehensively investigate how the normalization methods of 16S rRNA and shotgun metagenomic data are evaluated. Finally, we summarize and conclude with remarks on statistical normalization methods (Section 5). Altogether, this review aims to provide a broad and comprehensive view and remarks on the promises and challenges of the statistical normalization methods in microbiome data with microbiome cancer research examples.

Dex-Benchmark: datasets and code to evaluate algorithms for transcriptomics data analysis

Many tools and algorithms are available for analyzing transcriptomics data. These include algorithms for performing sequence alignment, data normalization and imputation, clustering, identifying differentially expressed genes, and performing gene set enrichment analysis. To make the best choice about which tools to use, objective benchmarks can be developed to compare the quality of different algorithms to extract biological knowledge maximally and accurately from these data. The Dexamethasone Benchmark (Dex-Benchmark) resource aims to fill this need by providing the community with datasets and code templates for benchmarking different gene expression analysis tools and algorithms. The resource provides access to a collection of curated RNA-seq, L1000, and ChIP-seq data from dexamethasone treatment as well as genetic perturbations of its known targets. In addition, the website provides Jupyter Notebooks that use these pre-processed curated datasets to demonstrate how to benchmark the different steps in gene expression analysis. By comparing two independent data sources and data types with some expected concordance, we can assess which tools and algorithms best recover such associations. To demonstrate the usefulness of the resource for discovering novel drug targets, we applied it to optimize data processing strategies for the chemical perturbations and CRISPR single gene knockouts from the L1000 transcriptomics data from the Library of Integrated Network Cellular Signatures (LINCS) program, with a focus on understudied proteins from the Illuminating the Druggable Genome (IDG) program. Overall, the Dex-Benchmark resource can be utilized to assess the quality of transcriptomics and other related bioinformatics data analysis workflows. The resource is available from: https://maayanlab.github.io/dex-benchmark.

Phosphorus Fertilization Boosts Mineral-Associated Soil Organic Carbon Formation Associated with Phagotrophic Protists

Long-term fertilization affects soil organic C accumulation. A growing body of research has revealed critical roles of bacteria in soil organic C accumulation, particularly through mineral-associated organic C (MAOC) formation. Protists are essential components of soil microbiome, but the relationships between MAOC formation and protists under long-term fertilization remain unclear. Here, we used cropland soil from a long-term fertilization field trial and conducted two microcosm experiments with 13C-glucose addition to investigate the effects of N and P fertilizations on MAOC formation and the relationships with protists. The results showed that long-term fertilization (especially P fertilization) significantly (P < 0.05) increased 13C-MAOC content. Compared with P-deficient treatment, P replenishment enriched the number of protists (mainly Amoebozoa and Cercozoa) and bacteria (mainly Acidobacteriota, Bacteroidota, and Gammaproteobacteria), and significantly (P < 0.001) promoted the abundances of bacterial functional genes controlling C, N, P, and S metabolisms. The community composition of phagotrophic protists prominently (P < 0.001) correlated with the bacterial community composition, bacterial functional gene abundance, and 13C-MAOC content. Co-occurrence networks of phagotrophic protists and bacteria were more connected in soil with the N inoculum added than in soil with the NP inoculum added. P replenishment strengthened bacterial 13C assimilation (i.e., 13C-phospholipid fatty acid content), which negatively (P < 0.05) correlated with the number and relative abundance of phagotrophic Cercozoa. Together, these results suggested that P fertilization boosts MAOC formation associated with phagotrophic protists. Our study paves the way for future research to harness the potential of protists to promote belowground C accrual in agroecosystems.

clrDV: a differential variability test for RNA-Seq data based on the skew-normal distribution

Background

Pathological conditions may result in certain genes having expression variance that differs markedly from that of the control. Finding such genes from gene expression data can provide invaluable candidates for therapeutic intervention. Under the dominant paradigm for modeling RNA-Seq gene counts using the negative binomial model, tests of differential variability are challenging to develop, owing to dependence of the variance on the mean.

Methods

Here, we describe clrDV, a statistical method for detecting genes that show differential variability between two populations. We present the skew-normal distribution for modeling gene-wise null distribution of centered log-ratio transformation of compositional RNA-seq data.

Results

Simulation results show that clrDV has false discovery rate and probability of Type II error that are on par with or superior to existing methodologies. In addition, its run time is faster than its closest competitors, and remains relatively constant for increasing sample size per group. Analysis of a large neurodegenerative disease RNA-Seq dataset using clrDV successfully recovers multiple gene candidates that have been reported to be associated with Alzheimer's disease.

Strategy for RNA-Seq Experimental Design and Data Analysis

Ribonucleic acids (RNAs) are fundamental molecules that control regulation and expression of the genome and therefore the function of a cell. Robust analysis and quantification of RNA transcripts hold critical importance in understanding cell function, altered phenotypes in different biological context, for understanding and targeting diseases. The development of RNA-sequencing (RNA-Seq) now provides opportunities to analyze the expression and function of RNA molecules at an unprecedented scale. However, the strategy for RNA-Seq experimental design and data analysis can substantially differ depending on the biological application. The design choice could also have significant impact for downstream results and interpretation of data. Here we describe key critical considerations required for RNA-Seq experimental design and also describe a step-by-step bioinformatics workflow detailing the different steps required for RNA-Seq data analysis. We believe this article will be a valuable guide for designing and analyzing RNA-Seq data to address a wide range of different biological questions.

The diagnostic potential and barriers of microbiome based therapeutics

High throughput technological innovations in the past decade have accelerated research into the trillions of commensal microbes in the gut. The 'omics' technologies used for microbiome analysis are constantly evolving, and large-scale datasets are being produced. Despite of the fact that much of the research is still in its early stages, specific microbial signatures have been associated with the promotion of cancer, as well as other diseases such as inflammatory bowel disease, neurogenerative diareses etc. It has been also reported that the diversity of the gut microbiome influences the safety and efficacy of medicines. The availability and declining sequencing costs has rendered the employment of RNA-based diagnostics more common in the microbiome field necessitating improved data-analytical techniques so as to fully exploit all the resulting rich biological datasets, while accounting for their unique characteristics, such as their compositional nature as well their heterogeneity and sparsity. As a result, the gut microbiome is increasingly being demonstrating as an important component of personalised medicine since it not only plays a role in inter-individual variability in health and disease, but it also represents a potentially modifiable entity or feature that may be addressed by treatments in a personalised way. In this context, machine learning and artificial intelligence-based methods may be able to unveil new insights into biomedical analyses through the generation of models that may be used to predict category labels, and continuous values. Furthermore, diagnostic aspects will add value in the identification of the non invasive markers in the critical diseases like cancer.

On taming the effect of transcript level intra-condition count variation during differential expression analysis: A story of dogs, foxes and wolves

The evolution of RNA-seq technologies has yielded datasets of scientific value that are often generated as condition associated biological replicates within expression studies. With expanding data archives opportunity arises to augment replicate numbers when conditions of interest overlap. Despite correction procedures for estimating transcript abundance, a source of ambiguity is transcript level intra-condition count variation; as indicated by disjointed results between analysis tools. We present TVscript, a tool that removes reference-based transcripts associated with intra-condition count variation above specified thresholds and we explore the effects of such variation on differential expression analysis. Initially iterative differential expression analysis involving simulated counts, where levels of intra-condition variation and sets of over represented transcripts are explicitly specified, was performed. Then counts derived from inter- and intra-study data representing brain samples of dogs, wolves and foxes (wolves vs. dogs and aggressive vs. tame foxes) were used. For simulations, the sensitivity in detecting differentially expressed transcripts increased after removing hyper-variable transcripts, although at levels of intra-condition variation above 5% detection became unreliable. For real data, prior to applying TVscript, ≈20% of the transcripts identified as being differentially expressed were associated with high levels of intra-condition variation, an over representation relative to the reference set. As transcripts harbouring such variation were removed pre-analysis, a discordance from 26 to 40% in the lists of differentially expressed transcripts is observed when compared to those obtained using the non-filtered reference. The removal of transcripts possessing intra-condition variation values within (and above) the 97^th and 95^th percentiles, for wolves vs. dogs and aggressive vs. tame foxes, maximized the sensitivity in detecting differentially expressed transcripts as a result of alterations within gene-wise dispersion estimates. Through analysis of our real data the support for seven genes with potential for being involved with selection for tameness is provided. TVscript is available at: https://sourceforge.net/projects/tvscript/.

Batch effects removal for microbiome data via conditional quantile regression

Batch effects in microbiome data arise from differential processing of specimens and can lead to spurious findings and obscure true signals. Strategies designed for genomic data to mitigate batch effects usually fail to address the zero-inflated and over-dispersed microbiome data. Most strategies tailored for microbiome data are restricted to association testing or specialized study designs, failing to allow other analytic goals or general designs. Here, we develop the Conditional Quantile Regression (ConQuR) approach to remove microbiome batch effects using a two-part quantile regression model. ConQuR is a comprehensive method that accommodates the complex distributions of microbial read counts by non-parametric modeling, and it generates batch-removed zero-inflated read counts that can be used in and benefit usual subsequent analyses. We apply ConQuR to simulated and real microbiome datasets and demonstrate its advantages in removing batch effects while preserving the signals of interest.

A transcriptome software comparison for the analyses of treatments expected to give subtle gene expression responses

BACKGROUND

In this comparative study we evaluate the performance of four software tools: DNAstar-D (DESeq2), DNAstar-E (edgeR), CLC Genomics and Partek Flow for identification of differentially expressed genes (DEGs) using a transcriptome of E. coli. The RNA-seq data are from the effect of below-background radiation 5.5 nGy total dose (0.2nGy/hr) on E. coli grown shielded from natural radiation 655 m below ground in a pre-World War II steel vault. The gene expression response to three supplemented sources of radiation designed to mimic natural background, 1952 - 5720 nGy in total dose (71-208 nGy/hr), are compared to this "radiation-deprived" treatment. In addition, RNA-seq data of Caenorhabditis elegans nematode from similar radiation treatments was analyzed by three of the software packages.

RESULTS

In E. coli, the four software programs identified one of the supplementary sources of radiation (KCl) to evoke about 5 times more transcribed genes than the minus-radiation treatment (69-114 differentially expressed genes, DEGs), and so the rest of the analyses used this KCl vs "Minus" comparison. After imposing a 30-read minimum cutoff, one of the DNAStar options shared two of the three steps (mapping, normalization, and statistic) with Partek Flow (they both used median of ratios to normalize and the DESeq2 statistical package), and these two programs identified the highest number of DEGs in common with each other (53). In contrast, when the programs used different approaches in each of the three steps, between 31 and 40 DEGs were found in common. Regarding the extent of expression differences, three of the four programs gave high fold-change results (15-178 fold), but one (DNAstar's DESeq2) resulted in more conservative fold-changes (1.5-3.5). In a parallel study comparing three qPCR commercial validation software programs, these programs also gave variable results as to which genes were significantly regulated. Similarly, the C. elegans analysis showed exaggerated fold-changes in CLC and DNAstar's edgeR while DNAstar-D was more conservative.

CONCLUSIONS

Regarding the extent of expression (fold-change), and considering the subtlety of the very low level radiation treatments, in E. coli three of the four programs gave what we consider exaggerated fold-change results (15 - 178 fold), but one (DNAstar's DESeq2) gave more realistic fold-changes (1.5-3.5). When RT-qPCR validation comparisons to transcriptome results were carried out, they supported the more conservative DNAstar-D's expression results. When another model organism's (nematode) response to these radiation differences was similarly analyzed, DNAstar-D also resulted in the most conservative expression patterns. Therefore, we would propose DESeq2 ("DNAstar-D") as an appropriate software tool for differential gene expression studies for treatments expected to give subtle transcriptome responses.

Exaggerated false positives by popular differential expression methods when analyzing human population samples

When identifying differentially expressed genes between two conditions using human population RNA-seq samples, we found a phenomenon by permutation analysis: two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates. Expanding the analysis to limma-voom, NOISeq, dearseq, and Wilcoxon rank-sum test, we found that FDR control is often failed except for the Wilcoxon rank-sum test. Particularly, the actual FDRs of DESeq2 and edgeR sometimes exceed 20% when the target FDR is 5%. Based on these results, for population-level RNA-seq studies with large sample sizes, we recommend the Wilcoxon rank-sum test.

The oral microbiome in relation to pancreatic cancer risk in African Americans

BACKGROUND

African Americans have the highest pancreatic cancer incidence of any racial/ethnic group in the United States. The oral microbiome was associated with pancreatic cancer risk in a recent study, but no such studies have been conducted in African Americans. Poor oral health, which can be a cause or effect of microbial populations, was associated with an increased risk of pancreatic cancer in a single study of African Americans.

METHODS

We prospectively investigated the oral microbiome in relation to pancreatic cancer risk among 122 African-American pancreatic cancer cases and 354 controls. DNA was extracted from oral wash samples for metagenomic shotgun sequencing. Alpha and beta diversity of the microbial profiles were calculated. Multivariable conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between microbes and pancreatic cancer risk.

RESULTS

No associations were observed with alpha or beta diversity, and no individual microbial taxa were differentially abundant between cases and control, after accounting for multiple comparisons. Among never smokers, there were elevated ORs for known oral pathogens: Porphyromonas gingivalis (OR = 1.69, 95% CI: 0.80-3.56), Prevotella intermedia (OR = 1.40, 95% CI: 0.69-2.85), and Tannerella forsythia (OR = 1.36, 95% CI: 0.66-2.77).

CONCLUSIONS

Previously reported associations between oral taxa and pancreatic cancer were not present in this African-American population overall.

Biodegradation of water-accommodated aromatic oil compounds in Arctic seawater at 0 °C

Oil spills in Arctic marine environments are expected to increase concurrently with the expansion of shipping routes and petroleum exploitation into previously inaccessible ice-dominated regions. Most research on oil biodegradation focusses on the bulk oil, but the fate of the water-accommodated fraction (WAF), mainly composed of toxic aromatic compounds, is largely underexplored. To evaluate the bacterial degradation capacity of such dissolved aromatics in Greenlandic seawater, microcosms consisting of 0 °C seawater polluted with WAF were investigated over a 3-month period. With a half-life (t_1/2) of 26 days, m-xylene was the fastest degraded compound, as measured by gas chromatography - mass spectrometry. Substantial slower degradation was observed for ethylbenzene, naphthalenes, phenanthrene, acenaphthylene, acenaphthene and fluorenes with t_1/2 of 40-105 days. Colwellia, identified by 16S rRNA gene sequencing, was the main potential degrader of m-xylene. This genus occupied up to 47 % of the bacterial community until day 10 in the microcosms. Cycloclasticus and Zhongshania aliphaticivorans, potentially utilizing one-to three-ringed aromatics, replaced Colwellia between day 10 and 96 and occupied up to 6 % and 23 % of the community, respectively. Although most of the WAF can ultimately be eliminated in microcosms, our results suggest that the restoration of an oil-impacted Arctic environment may be slow as most analysed compounds had t_1/2 of over 2-3 months and the detrimental effects of a spill towards the marine ecosystem likely persist during this time.

Deep ensemble learning over the microbial phylogenetic tree (DeepEn-Phy)

Successful prediction of clinical outcomes facilitates tailored diagnosis and treatment. The microbiome has been shown to be an important biomarker to predict host clinical outcomes. Further, the incorporation of microbial phylogeny, the evolutionary relationship among microbes, has been demonstrated to improve prediction accuracy. We propose a phylogeny-driven deep neural network (PhyNN) and develop an ensemble method, DeepEn-Phy, for host clinical outcome prediction. The method is designed to optimally extract features from phylogeny, thereby take full advantage of the information in phylogeny while harnessing the core principles of phylogeny (in contrast to taxonomy). We apply DeepEn-Phy to a real large microbiome data set to predict both categorical and continuous clinical outcomes. DeepEn-Phy demonstrates superior prediction performance to existing machine learning and deep learning approaches. Overall, DeepEn-Phy provides a new strategy for designing deep neural network architectures within the context of phylogeny-constrained microbiome data.

CAMPAREE: a robust and configurable RNA expression simulator

Background

The accurate interpretation of RNA-Seq data presents a moving target as scientists continue to introduce new experimental techniques and analysis algorithms. Simulated datasets are an invaluable tool to accurately assess the performance of RNA-Seq analysis methods. However, existing RNA-Seq simulators focus on modeling the technical biases and artifacts of sequencing, rather than on simulating the original RNA samples. A first step in simulating RNA-Seq is to simulate RNA.

Results

To fill this need, we developed the Configurable And Modular Program Allowing RNA Expression Emulation (CAMPAREE), a simulator using empirical data to simulate diploid RNA samples at the level of individual molecules. We demonstrated CAMPAREE’s use for generating idealized coverage plots from real data, and for adding the ability to generate allele-specific data to existing RNA-Seq simulators that do not natively support this feature.

Conclusions

Separating input sample modeling from library preparation/sequencing offers added flexibility for both users and developers to mix-and-match different sample and sequencing simulators to suit their specific needs. Furthermore, the ability to maintain sample and sequencing simulators independently provides greater agility to incorporate new biological findings about transcriptomics and new developments in sequencing technologies. Additionally, by simulating at the level of individual molecules, CAMPAREE has the potential to model molecules transcribed from the same genes as a heterogeneous population of transcripts with different states of degradation and processing (splicing, editing, etc.). CAMPAREE was developed in Python, is open source, and freely available at https://github.com/itmat/CAMPAREE.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07934-2.

Exploring the Composition and Functions of Plastic Microbiome Using Whole-Genome Sequencing

Besides the ecotoxicological consequences of microplastics and associated chemicals, the association of microbes on plastics has greater environmental implications as microplastics may select for unique microbiome participating in environmentally significant functions. Despite this, the functional potential of the microbiome associated with different types of plastics is understudied. Here, we investigate the interaction between plastic and marine biofilm-forming microorganisms through a whole-genome sequencing approach on four types of microplastics incubated in the marine environment. Taxonomic analysis suggested that the microplastic surfaces exhibit unique microbial profiles and niche partitioning among the substrates. In particular, the abundance of Vibrio alginolyticus and Vibrio campbellii suggested that microplastic pollution may pose a potential risk to the marine food chain and negatively impact aquaculture industries. Microbial genera involved in xenobiotic compound degradation, carbon cycling, and genes associated with the type IV secretion system, conjugal transfer protein TraG, plant-pathogen interaction, CusA/CzcA family heavy metal efflux transfer proteins, and TolC family proteins were significantly enriched on all the substrates, indicating the variety of processes operated by the plastic-microbiome. The present study gives a detailed characterization of the rapidly altering microbial composition and gene pools on plastics and adds new knowledge surrounding the environmental ramifications of marine plastic pollution.

Mining microbes for mental health: Determining the role of microbial metabolic pathways in human brain health and disease

There is increasing knowledge regarding the role of the microbiome in modulating the brain and behaviour. Indeed, the actions of microbial metabolites are key for appropriate gut-brain communication in humans. Among these metabolites, short-chain fatty acids, tryptophan, and bile acid metabolites/pathways show strong preclinical evidence for involvement in various aspects of brain function and behaviour. With the identification of neuroactive gut-brain modules, new predictive tools can be applied to existing datasets. We identified 278 studies relating to the human microbiota-gut-brain axis which included sequencing data. This spanned across psychiatric and neurological disorders with a small number also focused on normal behavioural development. With a consistent bioinformatics pipeline, thirty-five of these datasets were reanalysed from publicly available raw sequencing files and the remainder summarised and collated. Among the reanalysed studies, we uncovered evidence of disease-related alterations in microbial metabolic pathways in Alzheimer's Disease, schizophrenia, anxiety and depression. Amongst studies that could not be reanalysed, many sequencing and technical limitations hindered the discovery of specific biomarkers of microbes or metabolites conserved across studies. Future studies are warranted to confirm our findings. We also propose guidelines for future human microbiome analysis to increase reproducibility and consistency within the field.

Naught all zeros in sequence count data are the same

Genomic studies feature multivariate count data from high-throughput DNA sequencing experiments, which often contain many zero values. These zeros can cause artifacts for statistical analyses and multiple modeling approaches have been developed in response. Here, we apply different zero-handling models to gene-expression and microbiome datasets and show models can disagree substantially in terms of identifying the most differentially expressed sequences. Next, to rationally examine how different zero handling models behave, we developed a conceptual framework outlining four types of processes that may give rise to zero values in sequence count data. Last, we performed simulations to test how zero handling models behave in the presence of these different zero generating processes. Our simulations showed that simple count models are sufficient across multiple processes, even when the true underlying process is unknown. On the other hand, a common zero handling technique known as "zero-inflation" was only suitable under a zero generating process associated with an unlikely set of biological and experimental conditions. In concert, our work here suggests several specific guidelines for developing and choosing state-of-the-art models for analyzing sparse sequence count data.

SQMtools: automated processing and visual analysis of 'omics data with R and anvi'o

BACKGROUND

The dramatic decrease in sequencing costs over the last decade has boosted the adoption of high-throughput sequencing applications as a standard tool for the analysis of environmental microbial communities. Nowadays even small research groups can easily obtain raw sequencing data. After that, however, non-specialists are faced with the double challenge of choosing among an ever-increasing array of analysis methodologies, and navigating the vast amounts of results returned by these approaches.

RESULTS

Here we present a workflow that relies on the SqueezeMeta software for the automated processing of raw reads into annotated contigs and reconstructed genomes (bins). A set of custom scripts seamlessly integrates the output into the anvi'o analysis platform, allowing filtering and visual exploration of the results. Furthermore, we provide a software package with utility functions to expose the SqueezeMeta results to the R analysis environment.

CONCLUSIONS

Altogether, our workflow allows non-expert users to go from raw sequencing reads to custom plots with only a few powerful, flexible and well-documented commands.

Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data

BACKGROUND

The correct identification of differentially abundant microbial taxa between experimental conditions is a methodological and computational challenge. Recent work has produced methods to deal with the high sparsity and compositionality characteristic of microbiome data, but independent benchmarks comparing these to alternatives developed for RNA-seq data analysis are lacking.

RESULTS

We compare methods developed for single-cell and bulk RNA-seq, and specifically for microbiome data, in terms of suitability of distributional assumptions, ability to control false discoveries, concordance, power, and correct identification of differentially abundant genera. We benchmark these methods using 100 manually curated datasets from 16S and whole metagenome shotgun sequencing.

CONCLUSIONS

The multivariate and compositional methods developed specifically for microbiome analysis did not outperform univariate methods developed for differential expression analysis of RNA-seq data. We recommend a careful exploratory data analysis prior to application of any inferential model and we present a framework to help scientists make an informed choice of analysis methods in a dataset-specific manner.

Applications of weighted association networks applied to compositional data in biology

Next-generation sequencing technologies have generated, and continue to produce, an increasingly large corpus of biological data. The data generated are inherently compositional as they convey only relative information dependent upon the capacity of the instrument, experimental design and technical bias. There is considerable information to be gained through network analysis by studying the interactions between components within a system. Network theory methods using compositional data are powerful approaches for quantifying relationships between biological components and their relevance to phenotype, environmental conditions or other external variables. However, many of the statistical assumptions used for network analysis are not designed for compositional data and can bias downstream results. In this mini-review, we illustrate the utility of network theory in biological systems and investigate modern techniques while introducing researchers to frameworks for implementation. We overview (1) compositional data analysis, (2) data transformations and (3) network theory along with insight on a battery of network types including static-, temporal-, sample-specific- and differential-networks. The intention of this mini-review is not to provide a comprehensive overview of network methods, rather to introduce microbiology researchers to (semi)-unsupervised data-driven approaches for inferring latent structures that may give insight into biological phenomena or abstract mechanics of complex systems.

Skin Exposure to Narrow Band Ultraviolet (UVB) Light Modulates the Human Intestinal Microbiome

The recent worldwide rise in idiopathic immune and inflammatory diseases such as multiple sclerosis (MS) and inflammatory bowel diseases (IBD) has been linked to Western society-based changes in lifestyle and environment. These include decreased exposure to sunlight/UVB light and subsequent impairment in the production of vitamin D, as well as dysbiotic changes in the makeup of the gut microbiome. Despite their association, it is unclear if there are any direct links between UVB light and the gut microbiome. In this study we investigated whether exposing the skin to Narrow Band Ultraviolet B (NB-UVB) light to increase serum vitamin D levels would also modulate the makeup of the human intestinal microbiota. The effects of NB-UVB light were studied in a clinical pilot study using a healthy human female cohort (n = 21). Participants were divided into those that took vitamin D supplements throughout the winter prior to the start of the study (VDS+) and those who did not (VDS−). After three NB-UVB light exposures within the same week, the serum 25(OH)D levels of participants increased on average 7.3 nmol/L. The serum response was negatively correlated to the starting 25-hydroxy vitamin D [25(OH)D] serum concentration. Fecal microbiota composition analysis using 16S rRNA sequencing showed that exposure to NB-UVB significantly increased alpha and beta diversity in the VDS− group whereas there were no changes in the VDS+ group. Bacteria from several families were enriched in the VDS− group after the UVB exposures according to a Linear Discriminant Analysis (LDA) prediction, including Lachnospiracheae, Rikenellaceae, Desulfobacteraceae, Clostridiales vadinBB60 group, Clostridia Family XIII, Coriobacteriaceae, Marinifilaceae, and Ruminococcus. The serum 25(OH)D concentrations showed a correlation with the relative abundance of the Lachnospiraceae, specifically members of the Lachnopsira and Fusicatenibacter genera. This is the first study to show that humans with low 25(OH)D serum levels display overt changes in their intestinal microbiome in response to NB-UVB skin exposure and increases in 25(OH)D levels, suggesting the existence of a novel skin-gut axis that could be used to promote intestinal homeostasis and health.

Clinical Trial Registration:clinicaltrials.gov, NCT03962673. Registered 23 May 2019 – Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03962673?term=NCT03962673&rank=1.

A field guide for the compositional analysis of any-omics data

BACKGROUND

Next-generation sequencing (NGS) has made it possible to determine the sequence and relative abundance of all nucleotides in a biological or environmental sample. A cornerstone of NGS is the quantification of RNA or DNA presence as counts. However, these counts are not counts per se: their magnitude is determined arbitrarily by the sequencing depth, not by the input material. Consequently, counts must undergo normalization prior to use. Conventional normalization methods require a set of assumptions: they assume that the majority of features are unchanged and that all environments under study have the same carrying capacity for nucleotide synthesis. These assumptions are often untestable and may not hold when heterogeneous samples are compared.

RESULTS

Methods developed within the field of compositional data analysis offer a general solution that is assumption-free and valid for all data. Herein, we synthesize the extant literature to provide a concise guide on how to apply compositional data analysis to NGS count data.

CONCLUSIONS

In highlighting the limitations of total library size, effective library size, and spike-in normalizations, we propose the log-ratio transformation as a general solution to answer the question, "Relative to some important activity of the cell, what is changing?"

Immune and environment-driven gene expression during invasion: An eco-immunological application of RNA-Seq

Host-pathogen associations change rapidly during a biological invasion and are predicted to impose strong selection on immune function. It has been proposed that the invader may experience an abrupt reduction in pathogen-mediated selection ("enemy release"), thereby favoring decreased investment into "costly" immune responses. Across plants and animals, there is mixed support for this prediction. Pathogens are not the only form of selection imposed on invaders; differences in abiotic environmental conditions between native and introduced ranges are also expected to drive rapid evolution. Here, we use RNA-Seq to assess the expression patterns of immune and environmentally associated genes in the cane toad (Rhinella marina) across its invasive Australian range. Transcripts encoding mediators of costly immune responses (inflammation, cytotoxicity) showed a curvilinear relationship with invasion history, with highest expression in toads from oldest and newest colonized areas. This pattern is surprising given theoretical expectations of density dynamics in invasive species and may be because density influences both intraspecific competition and parasite transmission, generating conflicting effects on the strength of immune responses. Alternatively, this expression pattern may be the result of other evolutionary forces, such as spatial sorting and genetic drift, working simultaneously with natural selection. Our findings do not support predictions about immune function based on the enemy release hypothesis and suggest instead that the effects of enemy release are difficult to isolate in wild populations, especially in the absence of information regarding parasite and pathogen infection. Additionally, expression patterns of genes underlying putatively environmentally associated traits are consistent with previous genetic studies, providing further support that Australian cane toads have adapted to novel abiotic challenges.

Visualizing balances of compositional data: A new alternative to balance dendrograms

Balances have become a cornerstone of compositional data analysis. However, conceptualizing balances is difficult, especially for high-dimensional data. Most often, investigators visualize balances with the balance dendrogram, but this technique is not necessarily intuitive and does not scale well for large data. This manuscript introduces the 'balance' package for the R programming language. This package visualizes balances of compositional data using an alternative to the balance dendrogram. This alternative contains the same information coded by the balance dendrogram, but projects data on a common scale that facilitates direct comparisons and accommodates high-dimensional data. By stripping the branches from the tree, 'balance' can cleanly visualize any subset of balances without disrupting the interpretation of the remaining balances. As an example, this package is applied to a publicly available meta-genomics data set measuring the relative abundance of 500 microbe taxa.