Mammalian Datasets Research Articles

Improving ncRNA family prediction using multi-modal contrastive learning of sequence and structure.

Recent advancements in high-throughput sequencing technology have significantly increased the focus on non-coding RNA (ncRNA) research within the life sciences. Despite this, the functions of many ncRNAs remain poorly understood. Research suggests that ncRNAs within the same family typically share similar functions, underlining the importance of understanding their roles. There are two primary methods for predicting ncRNA families: biological and computational. Traditional biological methods are not suitable for large-scale data prediction due to the significant human and resource requirements. Concurrently, most existing computational methods either rely solely on ncRNA sequence data or are exclusively based on the secondary structure of ncRNA molecules. These methods fail to fully utilize the rich multimodal information available from ncRNAs, thereby preventing them from learning more comprehensive and in-depth feature representations. To tackle these problems, we proposed MM-ncRNAFP, a multi-modal contrastive learning framework for ncRNA family prediction. We first used a pre-trained language model to encode the primary sequences of a large mammalian ncRNA dataset. Then, we adopted a contrastive learning framework with an attention mechanism to fuse the secondary structure information obtained by graph neural networks. The MM-ncRNAFP method can effectively fuse multi-modal information. Experimental comparisons with several competitive baselines demonstrated that MM-ncRNAFP can achieve more comprehensive representations of ncRNA features by integrating both sequence and structural information. This integration significantly enhances the performance of ncRNA family prediction. Ablation experiments and qualitative analyses were performed to verify the effectiveness of each component in our model. Moreover, since our model is pre-trained on a large amount of ncRNA data, it has the potential to bring significant improvements to other ncRNA-related tasks. MM-ncRNAFP and the datasets are available at https://github.com/xuruiting2/MM-ncRNAFP.

Genomic Diversity as a Key Conservation Criterion: Proof-of-Concept From Mammalian Whole-Genome Resequencing Data.

Many international, national, state, and local organizations prioritize the ranking of threatened and endangered species to help direct conservation efforts. For example, the International Union for Conservation of Nature (IUCN) assesses the Green Status of species and publishes the influential Red List of threatened species. Unfortunately, such conservation yardsticks do not explicitly consider genetic or genomic diversity (GD), even though GD is positively associated with contemporary evolutionary fitness, individual viability, and with future evolutionary potential. To test whether populations of genome sequences could help improve conservation assessments, we estimated GD metrics from 82 publicly available mammalian datasets and examined their statistical association with attributes related to conservation. We also considered intrinsic biological factors, including trophic level and body mass, that could impact GD and quantified their relative influences. Our results identify key population GD metrics that are both reflective and predictive of IUCN conservation categories. Specifically, our analyses revealed that Watterson's theta (the population mutation rate) and autozygosity (a product of inbreeding) are associated with the current Red List categorization, likely because demographic declines that lead to "listing" decisions also reduce levels of standing genetic variation. We argue that by virtue of this relationship, conservation organizations like IUCN could leverage emerging genome sequence data to help categorize Red List threat rankings (especially in otherwise data-deficient species) and/or enhance Green Status assessments to establish a baseline for future population monitoring. Thus, our paper (1) outlines the theoretical and empirical justification for a new GD-based assessment criterion, (2) provides a bioinformatic pipeline for estimating GD from population genomic data, and (3) suggests an analytical framework that can be used to measure baseline GD while providing quantitative GD context for consideration by conservation authorities.

An open‐source general purpose machine learning framework for individual animal re‐identification using few‐shot learning

Abstract Animal re‐identification remains a challenging problem due to the cost of tagging systems and the difficulty of permanently attaching a physical marker to some animals, such as sea stars. Due to these challenges, photo identification is a good fit to solve this problem whether evaluated by humans or through machine learning. Accurate machine learning methods are an improvement over manual identification as they are capable of evaluating a large number of images automatically and recent advances have reduced the need for large training datasets. This study aimed to create an accurate, robust, general purpose machine learning framework for individual animal re‐identification using images both from publicly available data as well as two groups of sea stars of different species under human care. Open‐source code was provided to accelerate work in this space. Images of two species of sea star (Asterias rubens and Anthenea australiae) were taken using a consumer‐grade smartphone camera and used as original datasets to train a machine learning model to re‐identify an individual animal using few examples. The model's performance was evaluated on these original sea star datasets which contained between 39–54 individuals and 983–1204 images, as well as using six publicly available re‐identification datasets for tigers, beef cattle noses, chimpanzee faces, zebras, giraffes and ringed seals ranging between 45–2056 individuals and 829–6770 images. Using time aware‐splits, which are a data splitting technique ensuring that the model only sees an individual's images from a previous collection event during training to avoid information leaking, the model achieved high (>99%) individual re‐identification mean average precision for the top prediction (mAP@1) for the two species of sea stars. The re‐identification mAP@1 for the mammalian datasets was more variable, ranging from 83% to >99%. However, this model outperformed published state‐of‐the‐art re‐identification results for the publicly available datasets. The reported approach for animal re‐identification is generalizable, with the same machine learning framework achieving good performance in two distinct species of sea stars with different physical attributes, as well as seven different mammalian species. This demonstrates that this methodology can be applied to nearly any species where individual re‐identification is required. This study presents a precise, practical, non‐invasive approach to animal re‐identification using only basic image collection methods.

Biochemical Property Based Positional Matrix: A New Approach Towards Genome Sequence Comparison.

The growth of the genome sequence has become one of the emerging areas in the study of bioinformatics. It has led to an excessive demand for researchers to develop advanced methodologies for evolutionary relationships among species. The alignment-free methods have been proved to be more efficient and appropriate related to time and space than existing alignment-based methods for sequence analysis. In this study, a new alignment-free genome sequence comparison technique is proposed based on the biochemical properties of nucleotides. Each genome sequence can be distributed in four parameters to represent a 21-dimensional numerical descriptor using the Positional Matrix. To substantiate the proposed method, phylogenetic trees are constructed on the viral and mammalian datasets by applying the UPGMA/NJ clustering method. Further, the results of this method are compared with the results of the Feature Frequency Profiles method, the Positional Correlation Natural Vector method, the Graph-theoretic method, the Multiple Encoding Vector method, and the Fuzzy Integral Similarity method. In most cases, it is found that the present method produces more accurate results than the prior methods. Also, in the present method, the execution time for computation is comparatively small.

Compression-Complexity Measures for Analysis and Classification of Coronaviruses

Finding a vaccine or specific antiviral treatment for a global pandemic of virus diseases (such as the ongoing COVID-19) requires rapid analysis, annotation and evaluation of metagenomic libraries to enable a quick and efficient screening of nucleotide sequences. Traditional sequence alignment methods are not suitable and there is a need for fast alignment-free techniques for sequence analysis. Information theory and data compression algorithms provide a rich set of mathematical and computational tools to capture essential patterns in biological sequences. In this study, we investigate the use of compression-complexity (Effort-to-Compress or ETC and Lempel-Ziv or LZ complexity) based distance measures for analyzing genomic sequences. The proposed distance measure is used to successfully reproduce the phylogenetic trees for a mammalian dataset consisting of eight species clusters, a set of coronaviruses belonging to group I, group II, group III, and SARS-CoV-1 coronaviruses, and a set of coronaviruses causing COVID-19 (SARS-CoV-2), and those not causing COVID-19. Having demonstrated the usefulness of these compression complexity measures, we employ them for the automatic classification of COVID-19-causing genome sequences using machine learning techniques. Two flavors of SVM (linear and quadratic) along with linear discriminant and fine K Nearest Neighbors classifer are used for classification. Using a data set comprising 1001 coronavirus sequences (causing COVID-19 and those not causing COVID-19), a classification accuracy of 98% is achieved with a sensitivity of 95% and a specificity of 99.8%. This work could be extended further to enable medical practitioners to automatically identify and characterize coronavirus strains and their rapidly growing mutants in a fast and efficient fashion.

Profiling the Site of Protein CoAlation and Coenzyme A Stabilization Interactions.

Coenzyme A (CoA) is a key cellular metabolite known for its diverse functions in metabolism and regulation of gene expression. CoA was recently shown to play an important antioxidant role under various cellular stress conditions by forming a disulfide bond with proteins, termed CoAlation. Using anti-CoA antibodies and liquid chromatography tandem mass spectrometry (LC-MS/MS) methodologies, CoAlated proteins were identified from various organisms/tissues/cell-lines under stress conditions. In this study, we integrated currently known CoAlated proteins into mammalian and bacterial datasets (CoAlomes), resulting in a total of 2093 CoAlated proteins (2862 CoAlation sites). Functional classification of these proteins showed that CoAlation is widespread among proteins involved in cellular metabolism, stress response and protein synthesis. Using 35 published CoAlated protein structures, we studied the stabilization interactions of each CoA segment (adenosine diphosphate (ADP) moiety and pantetheine tail) within the microenvironment of the modified cysteines. Alternating polar-non-polar residues, positively charged residues and hydrophobic interactions mainly stabilize the pantetheine tail, phosphate groups and the ADP moiety, respectively. A flexible nature of CoA is observed in examined structures, allowing it to adapt its conformation through interactions with residues surrounding the CoAlation site. Based on these findings, we propose three modes of CoA binding to proteins. Overall, this study summarizes currently available knowledge on CoAlated proteins, their functional distribution and CoA–protein stabilization interactions.

Mammalian eye gene expression using support vector regression to evaluate a strategy for detecting human eye disease

Background and purpose: Machine learning is a class of modern and strong tools that can solve many important problems that nowadays Humans may be faced with. Support Vector Regression (SVR) is a way to build a regression model which is an incredible member of the machine learning family. SVR has been proven to be an effective tool in real-value function estimation. As a supervised-learning approach, SVR trains using a symmetrical loss function, which equally penalizes high and low misestimates. Recently, high-dimensional datasets are the most challenging problem that may be faced. The main problems in high-dimensional data are the estimation of the coefficients and interpretation. In the high-dimension problems, classical methods are not applicable because of a large number of predictor variables. SVR is an excellent alternative method to analyze such datasets. One of the main advantages of SVR is that its computational complexity does not depend on the dimensionality of the input space. Additionally, it has excellent generalization capability, with high prediction accuracy.
 Methods: SVR is one of the best methods to analyze high-dimensional datasets. It is a really reliable and robust approach to have a good fit with high accuracy. SVR uses the same principles as the support vector machine for classification, with only a few minor differences.
 Results: The techniques for analyzing the high-dimension datasets are really important methods because we frequently face such datasets in medical science and gene expression. It is not easy to analyze the high-dimension datasets because the classic methods cannot be used to estimate and interpret them. Therefore, we have to use alternative methods to analyze them. SVR is one of the best methods that can be applied. In this research, SVR is used in a real high-dimension dataset about the gene expression in eye disease, and then it is compared with well-known methods: LASSO and Sparse least trimmed squared (sparse LTS) methods. Based on the numerical result, SVR and Sparse LTS were better than LASSO, since the real dataset contained outliers (bad observation with big residuals).
 Conclusions: SVR method was the best method to model and predict the high-dimensional mammalian eye dataset, because it was not affected by the outliers' corruptive impact, and it has minimum MSE (mean squares error), MAE (mean absolute error) and RMSE (root mean squared error) fitting criteria in comparison with the classical methods such as LASSO and sparse LTS estimations. Thus, sparse LTS was found to act better than the LASSO method. Moreover, stabilization of the data and freedom from obtaining the regularization parameter by running a complicated algorithmic program, which decreased the computational costs dramatically, were the invaluable advantages of this technique in comparison with the classical methods.

A coherent biogeographical framework for Old World Neogene and Pleistocene mammals

AbstractIn order to understand mammalian evolution and compute a wide range of biodiversity indices, we commonly use the ‘bioregion’, a spatial division adapted to ecological and evolutionary constraints. While commonly conducted by neontologists, the establishment of bioregions in palaeontology is generally a secondary analysis, shaped on subjective time scales and areas specific to the investigated questions and groups. This heterogeneity, coupled with the scale‐dependency of biodiversity indices, prevents the clear identification of macroecological and macroevolutionary trends for large taxonomic groups like extinct mammals. Here we tackle this issue by providing a coherent framework for Neogene and Pleistocene mammals of the Old World following two steps: (1) a temporal scale adapted to mammalian evolutionary history (i.e. evolutionary fauna) is defined by poly‐cohort analysis; (2) bioregions are then computed for each evolutionary fauna by clustering, ordination and intermediate approaches at multiples spatial scales (i.e. continental to regional) for Eurasia and Africa. Additionally, providing a coherent framework for a wide range of mammalian datasets, our results show: (1) the synchronous emergence and fall of five mammalian evolutionary faunas identified at chronological scales varying from the epoch to the geological stage; (2) a transition from a longitudinal to a latitudinal biogeographical structuring between the Miocene and Pliocene, especially in Europe; (3) the long‐term affinity of southern Asian with African faunas, in sharp contrast with the modern Palaearctic bioregion extension; and (4) the establishment of a vast Mediterranean bioregion from fragmented areas in the Late Miocene to its full extent in the Pleistocene.

The population frequency of human mitochondrial DNA variants is highly dependent upon mutational bias.

ABSTRACTNext-generation sequencing can quickly reveal genetic variation potentially linked to heritable disease. As databases encompassing human variation continue to expand, rare variants have been of high interest, since the frequency of a variant is expected to be low if the genetic change leads to a loss of fitness or fecundity. However, the use of variant frequency when seeking genomic changes linked to disease remains very challenging. Here, I explored the role of selection in controlling human variant frequency using the HelixMT database, which encompasses hundreds of thousands of mitochondrial DNA (mtDNA) samples. I found that a substantial number of synonymous substitutions, which have no effect on protein sequence, were never encountered in this large study, while many other synonymous changes are found at very low frequencies. Further analyses of human and mammalian mtDNA datasets indicate that the population frequency of synonymous variants is predominantly determined by mutational biases rather than by strong selection acting upon nucleotide choice. My work has important implications that extend to the interpretation of variant frequency for non-synonymous substitutions.

Prowler: a novel trimming algorithm for Oxford Nanopore sequence data.

Trimming and filtering tools are useful in DNA sequencing analysis because they increase the accuracy of sequence alignments and thus the reliability of results. Oxford nanopore technologies (ONT) trimming and filtering tools are currently rudimentary, generally only filtering reads based on whole read average quality. This results in discarding reads that contain regions of high-quality sequence. Here, we propose Prowler, a trimmer that uses a window-based approach inspired by algorithms used to trim short read data. Importantly, we retain the phase and read length information by optionally replacing trimmed sections with Ns. Prowler was applied to mammalian and bacterial datasets, to assess its effect on alignment and assembly, respectively. Compared to data filtered with Nanofilt, alignments of data trimmed with Prowler had lower error rates and more mapped reads. Assemblies of Prowler trimmed data had a lower error rate than those filtered with Nanofilt; however, this came at some cost to assembly contiguity. Prowler is implemented in Python and is available at https://github.com/ProwlerForNanopore/ProwlerTrimmer. Supplementary data are available at Bioinformatics online.

SARS-CoV-2 infects human adult donor eyes and hESC-derived ocular epithelium.

The SARS-CoV-2 pandemic has caused unparalleled disruption of global behavior and significant loss of life. To minimize SARS-CoV-2 spread, understanding the mechanisms of infection from all possible routes of entry is essential. While aerosol transmission is thought to be the primary route of spread, viral particles have been detected in ocular fluid, suggesting that the eye may be a vulnerable point of viral entry. To this end, we confirmed SARS-CoV-2 entry factor and antigen expression in post-mortem COVID-19 patient ocular surface tissue and observed productive viral replication in cadaver samples and eye organoid cultures, most notably in limbal regions. Transcriptional analysis of ex vivo infected ocular surface cells and hESC-derived eye cultures revealed robust induction of NF-κB in infected cells as well as diminished type I/III interferon signaling. Together these data suggest that the eye can be directly infected by SARS-CoV-2 and implicate limbus as a portal for viral entry.

Development of a Battery of In Silico Prediction Tools for Drug-Induced Liver Injury from the Vantage Point of Translational Safety Assessment.

Drug-induced liver injury (DILI) remains a challenge when translating knowledge from the preclinical stage to human use cases. Attempts to model human DILI directly based on the information from drug labels have had some success; however, the approach falls short of providing insights or addressing uncertainty due to the difficulty of decoupling the idiosyncratic nature of human DILI outcomes. Our approach in this comparative analysis is to leverage existing preclinical and clinical data as well as information on metabolism to better translate mammalian to human DILI. The human DILI knowledge base from the United States Food and Drug Administration (U.S. FDA) National Center for Toxicology Research contains 1036 pharmaceuticals from diverse therapeutic categories. A human DILI training set of 305 oral marketed drugs was prepared and a binary classification scheme applied. The second knowledge base consists of mammalian repeated dose toxicity with liver toxicity data from various regulatory sources. Within this knowledge base, we identified 278 pharmaceuticals containing 198 marketed or withdrawn oral drugs with data from the U.S. FDA new drug application and 98 active pharmaceutical ingredients from ToxCast. From this collection, a set of 225 oral drugs was prepared as the mammalian hepatotoxicity training set with particular end points of pathology findings in the liver and bile duct. Both human and mammalian data sets were processed using various learning algorithms, including artificial intelligence approaches. The external validations for both models were comparable to the training statistics. These data sets were also used to extract species-differentiating chemotypes that differentiate DILI effects on humans from mammals. A systematic workflow was devised to predict human DILI and provide mechanistic insights. For a given query molecule, both human and mammalian models are run. If the predictions are discordant, both metabolites and parents are investigated for quantitative structure-activity relationship and species-differentiating chemotypes. Their results are combined using the Dempster-Shafer decision theory to yield a final outcome prediction for human DILI with estimated uncertainty. Finally, these tools are implementable within an in silico platform for systematic evaluation.

Characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian

Lysine crotonylation (Kcr) is a type of protein post-translational modification (PTM), which plays important roles in a variety of cellular regulation and processes. Several methods have been proposed for the identification of crotonylation. However, most of these methods can predict efficiently only on histone or non-histone protein. Therefore, this work aims to give a more balanced performance in different species, here plant (non-histone) and mammalian (histone) are involved. SVM (support vector machine) and RF (random forest) were employed in this study. According to the results of cross-validations, the RF classifier based on EGAAC attribute achieved the best predictive performance which performs competitively good as existed methods, meanwhile more robust when dealing with imbalanced datasets. Moreover, an independent test was carried out, which compared the performance of this study and existed methods based on the same features or the same classifier. The classifiers of SVM and RF could achieve best performances with 92% sensitivity, 88% specificity, 90% accuracy, and an MCC of 0.80 in the mammalian dataset, and 77% sensitivity, 83% specificity, 70% accuracy and 0.54 MCC in a relatively small dataset of mammalian and a large-scaled plant dataset respectively. Moreover, a cross-species independent testing was also carried out in this study, which has proved the species diversity in plant and mammalian.

Pigeon foot feathering reveals conserved limb identity networks

The tetrapod limb is a stunning example of evolutionary diversity, with dramatic variation not only among distantly related species, but also between the serially homologous forelimbs (FLs) and hindlimbs (HLs) within species. Despite this variation, highly conserved genetic and developmental programs underlie limb development and identity in all tetrapods, raising the question of how limb diversification is generated from a conserved toolkit. In some breeds of domestic pigeon, shifts in the expression of two conserved limb identity transcription factors, PITX1 and TBX5, are associated with the formation of feathered HLs with partial FL identity. To determine how modulation of PITX1 and TBX5 expression affects downstream gene expression, we compared the transcriptomes of embryonic limb buds from pigeons with scaled and feathered HLs. We identified a set of differentially expressed genes enriched for genes encoding transcription factors, extracellular matrix proteins, and components of developmental signaling pathways with important roles in limb development. A subset of the genes that distinguish scaled and feathered HLs are also differentially expressed between FL and scaled HL buds in pigeons, pinpointing a set of gene expression changes downstream of PITX1 and TBX5 in the partial transformation from HL to FL identity. We extended our analyses by comparing pigeon limb bud transcriptomes to chicken, anole lizard, and mammalian datasets to identify deeply conserved PITX1- and TBX5-responsive components of the limb identity program. Our analyses reveal a suite of predominantly low-level gene expression changes that are conserved across amniotes to regulate the identity of morphologically distinct limbs.

Improved sgRNA design in bacteria via genome-wide activity profiling.

CRISPR/Cas9 is a promising tool in prokaryotic genome engineering, but its success is limited by the widely varying on-target activity of single guide RNAs (sgRNAs). Based on the association of CRISPR/Cas9-induced DNA cleavage with cellular lethality, we systematically profiled sgRNA activity by co-expressing a genome-scale library (∼70 000 sgRNAs) with Cas9 or its specificity-improved mutant in Escherichia coli. Based on this large-scale dataset, we constructed a comprehensive and high-density sgRNA activity map, which enables selecting highly active sgRNAs for any locus across the genome in this model organism. We also identified ‘resistant’ genomic loci with respect to CRISPR/Cas9 activity, notwithstanding the highly accessible DNA in bacterial cells. Moreover, we found that previous sgRNA activity prediction models that were trained on mammalian cell datasets were inadequate when coping with our results, highlighting the key limitations and biases of previous models. We hence developed an integrated algorithm to accurately predict highly effective sgRNAs, aiming to facilitate CRISPR/Cas9-based genome engineering, screenings and antimicrobials design in bacteria. We also isolated the important sgRNA features that contribute to DNA cleavage and characterized their key differences among wild type Cas9 and its mutant, shedding light on the biophysical mechanisms of the CRISPR/Cas9 system.

A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain

SummaryThe diversity of cell types and regulatory states in the brain, and how these change during aging, remains largely unknown. We present a single-cell transcriptome atlas of the entire adult Drosophila melanogaster brain sampled across its lifespan. Cell clustering identified 87 initial cell clusters that are further subclustered and validated by targeted cell-sorting. Our data show high granularity and identify a wide range of cell types. Gene network analyses using SCENIC revealed regulatory heterogeneity linked to energy consumption. During aging, RNA content declines exponentially without affecting neuronal identity in old brains. This single-cell brain atlas covers nearly all cells in the normal brain and provides the tools to study cellular diversity alongside other Drosophila and mammalian single-cell datasets in our unique single-cell analysis platform: SCope (http://scope.aertslab.org). These results, together with SCope, allow comprehensive exploration of all transcriptional states of an entire aging brain.

The evolution of CpG density and lifespan in conserved primate and mammalian promoters.

Gene promoters are evolutionarily conserved across holozoans and enriched in CpG sites, the target for DNA methylation. As animals age, the epigenetic pattern of DNA methylation degrades, with highly methylated CpG sites gradually becoming demethylated while CpG islands increase in methylation. Across vertebrates, aging is a trait that varies among species. We used this variation to determine whether promoter CpG density correlates with species’ maximum lifespan. Human promoter sequences were used to identify conserved regions in 131 mammals and a subset of 28 primate genomes. We identified approximately 1000 gene promoters (5% of the total), that significantly correlated CpG density with lifespan. The correlations were performed via the phylogenetic least squares method to account for trait similarity by common descent using phylogenetic branch lengths. Gene set enrichment analysis revealed no significantly enriched pathways or processes, consistent with the hypothesis that aging is not under positive selection. However, within both mammals and primates, 95% of the promoters showed a positive correlation between increasing CpG density and species lifespan, and two thirds were shared between the primate subset and mammalian datasets. Thus, these genes may require greater buffering capacity against age-related dysregulation of DNA methylation in longer-lived species.

Alternative miRNAs? Human sequences misidentified as plant miRNAs in plant studies and in human plasma

Background: A 2017 study reported that “Plant miRNAs found in human circulating system provide evidences of cross kingdom RNAi”. Analysis of two human blood plasma sequencing datasets was said to provide evidence for uptake of plant miRNAs into human plasma. The results were also purportedly inconsistent with contamination. Methods: Sequences from public datasets and miRNA databases were compared with results downloaded from the website of the reporting journal. Results: Only one putative plant miRNA (“peu-MIR2910) mapped consistently above background, and this sequence is found with 100% identity in a human rRNA. Several other rarer but consistently mapped putative plant miRNAs also have 100% or near 100% matches to human transcripts or genomic sequences, and some do not appear to map to plant genomes at all. Conclusions: Reanalysis of public data suggests that dietary plant xenomiR uptake is not supported, but instead confirms previous findings that detection of rare plant miRNAs in mammalian sequencing datasets is artifactual. Some putative plant miRNAs, including MIR2910 and MIR2911, may represent human sequence contamination or other artifacts in plant studies, emphasizing the need for rigorous controls and data filtering strategies when assessing possible xenomiRNAs.

Alternative miRNAs? Human sequences misidentified as plant miRNAs in plant studies and in human plasma.

Background: A 2017 study reported that "Plant miRNAs found in human circulating system provide evidences of cross kingdom RNAi". Analysis of two human blood plasma sequencing datasets was said to provide evidence for uptake of plant miRNAs into human plasma. The results were also purportedly inconsistent with contamination. Methods: Sequences from public datasets and miRNA databases were compared with results downloaded from the website of the reporting journal. Results: Only one putative plant miRNA ("peu-MIR2910) mapped consistently above background, and this sequence is found with 100% identity in a human rRNA. Several other rarer but consistently mapped putative plant miRNAs also have 100% or near 100% matches to human transcripts or genomic sequences, and some do not appear to map to plant genomes at all. Conclusions: Reanalysis of public data suggests that dietary plant xenomiR uptake is not supported, but instead confirms previous findings that detection of rare plant miRNAs in mammalian sequencing datasets is artifactual. Some putative plant miRNAs, including MIR2910 and MIR2911, may represent human sequence contamination or other artifacts in plant studies, emphasizing the need for rigorous controls and data filtering strategies when assessing possible xenomiRNAs.

Detecting differential DNA methylation from sequencing of bisulfite converted DNA of diverse species

DNA methylation is one of the most extensively studied epigenetic modifications of genomic DNA. In recent years, sequencing of bisulfite-converted DNA, particularly via next-generation sequencing technologies, has become a widely popular method to study DNA methylation. This method can be readily applied to a variety of species, dramatically expanding the scope of DNA methylation studies beyond the traditionally studied human and mouse systems. In parallel to the increasing wealth of genomic methylation profiles, many statistical tools have been developed to detect differentially methylated loci (DMLs) or differentially methylated regions (DMRs) between biological conditions. We discuss and summarize several key properties of currently available tools to detect DMLs and DMRs from sequencing of bisulfite-converted DNA. However, the majority of the statistical tools developed for DML/DMR analyses have been validated using only mammalian data sets, and less priority has been placed on the analyses of invertebrate or plant DNA methylation data. We demonstrate that genomic methylation profiles of non-mammalian species are often highly distinct from those of mammalian species using examples of honey bees and humans. We then discuss how such differences in data properties may affect statistical analyses. Based on these differences, we provide three specific recommendations to improve the power and accuracy of DML and DMR analyses of invertebrate data when using currently available statistical tools. These considerations should facilitate systematic and robust analyses of DNA methylation from diverse species, thus advancing our understanding of DNA methylation.