In order to test for the differential abundance of taxa that may drive the differences observed between inferred microbial communities derived from the different DNA isolation procedures, we performed DESeq2 analyses. Here we provide an example for such an analysis from human fecal specimen, examined using 16S rRNA gene profiling. This workflow relates to the article: Berith E. Knudsen, Lasse Bergmark, Patrick Munk, Oksana Lukjancenko, Anders Priemé, Frank M. Aarestrup, Sünje J. Pamp (2016) Impact of Sample Type and DNA Isolation Procedure on Genomic Inference of Microbiome Composition. mSystems Oct 2016, 1 (5) e00095-16; DOI: 10.1128/mSystems.00095-16

http://msystems.asm.org/content/1/5/e00095-16

Supplementary Table S2.5.4: ANCOM-BC analysis showing the differentially abundant predicted metabolic pathways (MetaCyc) in the amendment layer (1-2 cm beside the amendment) of planted soils without deep-banded amendment (DBA) compared to unplanted soils without DBA

The target of inference in microbiome analyses is usually relative abundance (RA) because RA in a sample (e.g., stool) can be considered as an approximation of RA in an entire ecosystem (e.g., gut). However, inference on RA suffers from the fact that RA are calculated by dividing absolute abundances (AAs) over the common denominator (CD), the summation of all AA (i.e., library size). Because of that, perturbation in one taxon will result in a change in the CD and thus cause false changes in RA of all other taxa, and those false changes could lead to false positive/negative findings. We propose a novel analysis approach (IFAA) to make robust inference on AA of an ecosystem that can circumvent the issues induced by the CD problem and compositional structure of RA. IFAA can also address the issues of overdispersion and handle zero-inflated data structures. IFAA identifies microbial taxa associated with the covariates in Phase 1 and estimates the association parameters by employing an independent reference taxon in Phase 2. Two real data applications are presented and extensive simulations show that IFAA outperforms other established existing approaches by a big margin in the presence of unbalanced library size. Supplementary materials for this article are available online.

Supplementary Material 3: Challenges evaluations

This repository contains data files exported from BugSigDB, a manually curated database of published microbial signatures.

Supplementary Material 2: Statistical validation results

Additional file 2: Tables S1-S7. Table S1- Correlation analysis between gut enterotypes and clinical factors. Table S2- Differential abundance analysis in endometriosis and control groups. Species with a prevalence > 10% and relative abundance ≥ 0.1% were compared in endometriosis and control groups using an Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC). Table S3- Differential abundance analysis in endometriosis and control groups. KEGG orthologs (KO) with a prevalence > 10% and relative abundance ≥ 0.1% were compared in endometriosis and control groups using an Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC). Table S4- Differential abundance analysis in endometriosis and control groups. EggNOG orthologs with a prevalence > 10% and relative abundance ≥ 0.1% were compared in endometriosis and control groups using an Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC). Table S5- Sensitivity differential abundance analysis in endometriosis and control groups. Species with a prevalence > 10% and relative abundance ≥ 0.1% were compared in endometriosis and control groups after excluding women with age > 50, using an Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC). Table S6- Sensitivity differential abundance analysis in endometriosis and control groups. KEGG orthologs (KO) with a prevalence > 10% and relative abundance ≥ 0.1% were compared in endometriosis and control groups after excluding women with age > 50, using an Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC). Table S7- Estrogen path enzymes with ALDEx2 analysis between endometriosis and control groups.

This repository contains data files exported from BugSigDB, a manually curated database of published microbial signatures.

Release v1.3.0

• New signatures
• PubMed ID is the Study ID if available
• Taxonomic rank domain supported

Comparison of MetaDAVis and other popular microbiome analysis tools.

myPhyloDB is an open-source software package aimed at developing a user-friendly web-interface for accessing and analyzing all of your laboratory's microbial ecology data (currently supported project types: soil, air, water, microbial, and human-associated). The storage and handling capabilities of myPhyloDB archives users' raw sequencing files, and allows for easy selection of any combination of projects/samples from all of your projects using the built-in SQL database. The data processing capabilities of myPhyloDB are also flexible enough to allow the upload, storage, and analysis of pre-processed data or raw (454 or Illumina) data files using the built-in versions of Mothur and R. myPhyloDB is designed to run as a local web-server, which allows a single installation to be accessible to all of your laboratory members, regardless of their operating system or other hardware limitations. myPhyloDB includes an embedded copy of the popular Mothur program and uses a customizable batch file to perform sequence editing and processing. This allows myPhyloDB to leverage the flexibility of Mothur and allow for greater standardization of data processing and handling across all of your sequencing projects.

myPhyloDB also includes an embedded copy of the R software environment for a variety of statistical analyses and graphics. Currently, myPhyloDB includes analysis for factor or regression-based ANcOVA, principal coordinates analysis (PCoA), differential abundance analysis (DESeq), and sparse partial least-squares regression (sPLS).

Summary results of differential abundance analyses using various methods.

We present LTE chemical abundances for five red giants and one AGB star in the Galactic globular cluster (GC) M5 based on high-resolution spectroscopy using the Magellan Inamori Kyocera Echelle spectrograph on the Magellan 6.5m Clay telescope. Our results are based on a line-by-line differential abundance analysis relative to the well-studied red giant Arcturus.

Differential abundance analysis is a crucial task in many microbiome studies, where the central goal is to identify microbiome taxa associated with certain biological or clinical conditions. There are two different modes of microbiome differential abundance analysis: the individual-based univariate differential abundance analysis and the group-based multivariate differential abundance analysis. The univariate analysis identifies differentially abundant microbiome taxa subject to multiple correction under certain statistical error measurements such as false discovery rate, which is typically complicated by the high-dimensionality of taxa and complex correlation structure among taxa. The multivariate analysis evaluates the overall shift in the abundance of microbiome composition between two conditions, which provides useful preliminary differential information for the necessity of follow-up validation studies. In this paper, we present a novel Adaptive multivariate two-sample test for Microbiome Differential Analysis (AMDA) to examine whether the composition of a taxa-set are different between two conditions. Our simulation studies and real data applications demonstrated that the AMDA test was often more powerful than several competing methods while preserving the correct type I error rate. A free implementation of our AMDA method in R software is available at https://github.com/xyz5074/AMDA.

GSE37703: Differential analysis of HOXA1 in adult cells at isoform resolution by RNA-Seq' for quantification by Kallisto and differential abundance with Sleuth dataset used for the "Reproducible in-silico omics analyses across clouds and clusters" paper.

Additional file 7: Table S6. Dataset 2 false discovery rate (FDR) q-values for differential abundance methods when performed on filtered data. Dataset 2 FDR q-values for all differential abundance methods were aggregated into one table along with the mean relative abundance of each genus for PD patients (Case MRA) and control subjects (Control MRA) and the mean relative abundance ratio of Case MRA to Control MRA (MRAR). 323 PD patients and 184 controls were included in all analyses. Genera detected in at least 10% of samples (195 genera) were included in the analyses. If an analysis resulted in an "NA" for a result, or a result was not outputted by the method, a 1 was placed for the FDR q-value. Calculations for the number of detected DA signatures for each method are located at the bottom of the table. Calculations for the number of methods that detected or replicated a genus as differentially abundant are located to the right of the table.

Sea star wasting (SSW) disease, a massive and ongoing epidemic with unknown cause(s), has led to the rapid death and decimation of sea star populations with cascading ecological consequences. Changes in microbial community structure have been previously associated with SSW, however, it remains unknown if SSW-associated dysbiosis is a mechanism or artifact of disease progression, particularly in wild populations. Here, we compare the microbiomes of the sunflower sea star, Pycnopodia helianthoides, before (Naïve) and during (Exposed and Wasting) the initial outbreak in Southeast Alaska to identify changes and interactions in the microbial communities associated with sea star health and disease exposure. We found an increase in microbial diversity (both alpha and beta diversity) preceding signs of disease and an increase in abundance of facultative and obligate anaerobes (most notably Vibrio) in both Exposed (apparently healthy) and Wasting animals. Complementing these changes in microbial composition was the initial gain of metabolic functions upon disease exposure, and loss of function with signs of wasting. Using Bayesian network clustering, we found evidence of dysbiosis in the form of co-colonization of taxa appearing in large numbers among Exposed and Wasting individuals, in addition to the loss of communities associated with Naïve sea stars. These changes in community structure suggest a shared set of colonizing microbes that may be important in the initial stages of SSW. Together, these results provide several complementary perspectives in support of an early dysbiotic event preceding visible signs of SSW. Methods Sample collection Samples were collected in the summer of 2016 off the coast of Southeast Alaska (Fig. S1). With the aid of SCUBA, a biopsy from a single ray was collected underwater from each sampled sea star, and isolated until in a wet lab aboard a research vessel (R/V Kestrel, Alaska Department of Fish and Game). Epidermal biopsy samples were collected from sea stars at both impacted and naïve sites at depths ranging from 7 to 18 meters. Seven total sites were sampled (2 Naïve and 5 impacted) with a total of 18 Wasting, 20 Exposed, and 47 Naïve individuals sampled (total N=85). Nonlethal biopsy punches (3.5mm diameter biopsy punch, Robbins Instruments, Chatham, NJ) were taken from the body wall of each individual and preserved in RNAlater (ThermoFisher Scientific, Waltham, MA) in 2ml tubes. Only epidermal body wall tissue was sampled, even when sampling wasting individuals. For individuals displaying SSW symptoms, wasting epidermal tissue at the edge of the lesion was sampled. All biopsy tissue samples were shipped to Vermont on dry ice and stored at −80 °C until processing. RNA extraction and cDNA reverse transcription. RNA was extracted from each biopsy using a modified TRIzol protocol (TRIzol reagent ThermoFisher Scientific, Waltham, MA). After lysing tissue in 250ul TRIzol, it was homogenized for 20 minutes with a plastic pestle with 750ul additional TRIzol using a Vortex Genie2 (Scientific Industries, Bohemia, NY). To extract RNA, 200 ul chloroform (ThermoFisher Scientific, Waltham, MA) was added, inverted 15 times, incubated for 3 minutes at RT, and centrifuged at 4 °C for 15 minutes at 12,000×g. The RNA-containing supernatant was transferred to a new tube and the previous step was repeated. Adding 500 ul isopropanol (ThermoFisher Scientific, Waltham, MA) and 1 ul 5 mg/ml glycogen (Invitrogen, Carlsbad, CA), incubation for 10 minutes at room temperature, and centrifugation for 5minutes at 7500×g at 4 °C precipitated the RNA from the supernatant. After drying for 10 minutes at RT the RNA pellet was dissolved in 50 ul nuclease-free water. A NanoDrop 2000 Spectrophotometer (ThermoFisher Scientific, Waltham, MA) and Qubit 3.0 Fluorometer (Life Technologies, Carlsbad, CA) were used to measure the quality and quantity of the RNA extractions. To check for DNA contamination, we performed negative amplification PCR (16S PCR amplification parameters below). Random hexamer primers reverse transcribed cDNA with SuperScript IV (Invitrogen, Carlsbad, CA). 16S PCR amplification and sequencing. To amplify the V3 and V4 region of the 16S bacterial gene, we used the primers: forward 5′TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG and reverse 5′GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC (31). 25 ul PCR reactions (1X MiFi Mix (Bioline, Toronto, Canada), 200nM each primer, and 2ul cDNA) were run with the following conditions: 95 °C for 3 minutes followed by 25 cycles of 95 °C for 30 seconds, 55 °C for 30 seconds, and 72 °C for 30 seconds, with a final extension at 72 °C for 5 minutes. To clean the PCR products, AMPure XP beads (Beckman Coulter, Brea, CA) and MiSeq indexing adapters were added. The indexed PCR products were cleaned again with AMPure XP beads, following Illumina 16S metagenomic sequencing library preparation protocol. To validate band size, the cleaned, indexed PCR products were run on a 2% agarose gel. 16S rRNA Library sequencing was performed at the Cornell Biotechnology Resource Center (Ithaca, NY) using 2 × 300 base pair overlapping paired-end reads on an Illumina MiSeq platform. Sequence data processing, taxonomic assignment, and diversity metrics Sequences were demultiplexed and barcode sequences were removed by the core facility. QIIME2 (v.2-2021.8) was used for data cleaning and analysis (32). Paired end reads were imported into QIIME2 and read quality was assessed using QIIME2’s ‘qiime_demux_summarize’ function. Read quality was determined by Phred score (>30) then subsequently denoised and trimmed using DADA2 (33), which removes errors and noise from data sequenced by Illumina, and creates information about the removed data. DADA2 parameters were set at trimming forward reads at 16bp and truncating at 289bp and reverse reads were trimmed at 0bp and truncated at 257bp. Diversity metrics were run using the ‘qiime_diversity_core-metrics-phylogenetic’ function with an Amplicon Sequence Variant (ASV) sampling depth of 13,547 to ensure maximal sample depth without omitting any samples. ASV richness (alpha diversity) of Naïve, Exposed, and Wasting asteroids was calculated using the Shannon diversity index using the qiime2 plugin and the between-group diversity (beta diversity) was calculated using the weighted_unifrac_distance_matrix to take into account the relative abundance of ASVs shared between samples (34). One sample (HH02_18) was responsible for all identified outliers in our beta-diversity analysis between Naïve samples, and was removed. Variation among Naïve individuals was 22.6% outlier-inclusive, and 19.9% with outliers removed (Fig 1D). Diversity plots were made using Qiime2 outputs visualized in GraphPad Prism version 9.3.0 for windows. Taxonomic Classification Taxonomy was assigned to each ASV by using the q2-feature-classifier (Bokulich et al. 2018) then trained and mapped to known bacterial taxa classified by the Greengenes database (35). Greenegene reference sequences were trimmed to match our data with a minimum length of 100bp and a maximum of 500bp. ASVs mapped to the same taxonomic classification were collapsed into Observable Taxonomic Units (OTUs) at the lowest level of identification provided by Greengenes database using the “qiime_taxa_collapse”. It should be noted that not all taxa were identifiable to the species level. However, ASV’s assigned to the same class, family, or genus could still be identified as distinct OTUs based on phylogenetic mapping, even if Greengenes could not confidently assign a specific species identification. For example, there are two separate classifications assigned to the genus Vibrio with no further classification at the species level, yet retained distinct abundances tracked separately through the analyses. Respiratory profiles (e.g., aerobic, facultative anaerobic, and obligate anaerobic) of microbes of interest were inferred from literature, as cited, describing the genus and/or family. Differential Abundance To test for differential abundance of microbial communities associated with exposure and onset of SSW, we used Analysis of Composition of Microbiomes with Bias Correction (ANCOM-BC) (36) in R version 4.2.1 (37). ANCOM-BC differential abundance analysis uses a series of pairwise comparisons to estimate the average abundance of a given microbial species through linear regression while correcting the bias induced by differences among samples. This method provides false discovery rate (FDR) corrected p-values for each taxon and confidence intervals for differentially abundant taxa. Differentially abundant taxa were defined based on FDR < 0.05 and taxa that were not present in at least 10% of the compared samples were dropped from the analysis. The W score represents the number of times the null-hypothesis (the average abundance of a given species in a group is equal to that in the other group) was rejected for a given species. Beta value represents the effect size as a log-linear (natural log) value relative between compared groups. Fold change was calculated by taking ebeta. Violin plots were constructed using log-10 transformed values from the raw abundance of each taxa using GraphPad Prism version 9.3.0 for windows. Bipartite Clustering Analysis To identify taxonomic communities and other high-level patterns of abundance, we used Bayesian stochastic blockmodeling, a principled approach to network clustering which finds statistically significant partitions in a network. First, a bipartite network was constructed by assigning sea star samples and ASVsOTUs to separate node groups. Samples and taxa were then connected with an edge if the taxa was present, with an edge weight of log(aij )+1, where aij > 0 is the abundance of taxa j in sample i. OTU abundance was aggregated to the species level, and taxa with total abundance less than 100 across all samples were

Stellar kinematic groups are kinematical coherent groups of stars that might have a common origin. These groups are dispersed throughout the Galaxy over time by the tidal effects of both Galactic rotation and disc heating, although their chemical content remains unchanged. The aim of chemical tagging is to establish that the abundances of every element in the analysis are homogeneous among the members. We study the case of the Hyades Supercluster to compile a reliable list of members (FGK stars) based on our chemical tagging analysis. For a total of 61 stars from the Hyades Supercluster, stellar atmospheric parameters (Teff, logg, {ksi}, and [Fe/H]) are determined using our code called StePar, which is based on the sensitivity to the stellar atmospherics parameters of the iron EWs measured in the spectra. We derive the chemical abundances of 20 elements and find that their [X/Fe] ratios are consistent with Galactic abundance trends reported in previous studies. The chemical tagging method is applied with a carefully developed differential abundance analysis of each candidate member of the Hyades Supercluster, using a well-known member of the Hyades cluster as a reference (vB 153). We find that only 28 stars (26 dwarfs and 2 giants) are members, i.e. that 46% of our candidates are members based on the differential abundance analysis. This result confirms that the Hyades Supercluster cannot originate solely from the Hyades cluster.

Binary stars hosting exoplanets are a unique laboratory where chemical tagging can be performed to measure with high accuracy the elemental abundances of both stellar components, with the aim to investigate the formation of planets and their subsequent evolution. Here, we present a high-precision differential abundance analysis of the XO-2 wide stellar binary based on high resolution HARPS-N@TNG spectra. Both components are very similar K-dwarfs and host planets. Since they formed presumably within the same molecular cloud, we expect they should possess the same initial elemental abundances. We investigate if the presence of planets can cause some chemical imprints in the stellar atmospheric abundances. We measure abundances of 25 elements for both stars with a range of condensation temperature T_C_=40-1741K, achieving typical precisions of ~0.07dex. The North component shows abundances in all elements higher by +0.067+/-0.032dex on average, with a mean difference of +0.078dex for elements with T_C_>800K. The significance of the XO-2N abundance difference relative to XO-2S is at the 2{sigma} level for almost all elements. We discuss the possibility that this result could be interpreted as the signature of the ingestion of material by XO-2N or depletion in XO-2S due to locking of heavy elements by the planetary companions. We estimate a mass of several tens of M_{earth}_ in heavy elements. The difference in abundances between XO-2N and XO-2S shows a positive correlation with the condensation temperatures of the elements, with a slope of (4.7+/-0.9)x10^-5^dex/K, which could mean that both components have not formed terrestrial planets, but that first experienced the accretion of rocky core interior to the subsequent giant planets. Cone search capability for table J/A+A/583/A135/list (List of spectra) Associated data

(A) Differentially abundant microbial pathways (KEGG) predicted in stunted, wasted and undernourished children using the linear discriminant analysis (LDA) Effect Size (LEfSe) tool. (B) Differentially abundant microbial enzymes (EC terms) predicted in stunted, wasted and undernourished children using the linear discriminant analysis (LDA) Effect Size (LEfSe) tool. (C) Differentially abundant microbial MetaCyc pathways predicted in stunted, wasted and undernourished children using the linear discriminant analysis (LDA) Effect Size (LEfSe) tool. An LDA cut-off score of 2 or greater and unadjusted p-value threshold of 0.05 was used to report the significant findings. (XLSX)

Recent expansion of microbiome research has uncovered connections between resident microbial communities and blood parasite risk, establishing the potential for microbial disease treatments such as probiotics in the future. However, this field has largely focused on humans and model organisms, leaving much unknown about how microbial communities might directly or indirectly impact parasite infection in wild populations and non-mammals. To contribute to this knowledge base in wild birds, we collected fecal and blood samples from wild Eurasian tree sparrows (Passer montanus) in the United States to test for associations between blood parasite infection and the gut microbiome. We used a widespread molecular approach to test 81 samples from peripheral blood for Plasmodium and Haemoproteus, and we characterized the gut microbiome using fecal samples as a proxy. Neither alpha nor beta diversity significantly varied with detected Plasmodium infection. However, differential abundance analysis h...