Portable nanopore analytics: are we there yet?

MOTIVATION: Oxford Nanopore technologies (ONT) add miniaturization and real time to high-throughput sequencing. All available software for ONT data analytics run on cloud/clusters or personal computers. Instead, a linchpin to true portability is software that works on mobile devices of internet connections. Smartphones' and tablets' chipset/memory/operating systems differ from desktop computers, but software can be recompiled. We sought to understand how portable current ONT analysis methods are. RESULTS: Several tools, from base-calling to genome assembly, were ported and benchmarked on an Android smartphone. Out of 23 programs, 11 succeeded. Recompilation failures included lack of standard headers and unsupported instruction sets. Only DSK, BCALM2 and Kraken were able to process files up to 16 GB, with linearly scaling CPU-times. However, peak CPU temperatures were high. In conclusion, the portability scenario is not favorable. Given the fast market growth, attention of developers to ARM chipsets and Android/iOS is warranted, as well as initiatives to implement mobile-specific libraries. AVAILABILITY AND IMPLEMENTATION: The source code is freely available at: https://github.com/marco-oliva/portable-nanopore-analytics.

Building a pangenome alignment index via recursive prefix-free parsing.

Pangenomics alignment offers a solution to reduce bias in biomedical research. Traditionally, short-read aligners like Bowtie and BWA indexed a single reference genome to find approximate alignments. These methods, limited by linear-memory requirements, can only index a few genomes. Emerging pangenome aligners, such as VG, Giraffe, and Moni, address this by indexing more genomes. VG and Giraffe use a variation graph, while Moni indexes sequences accounting for repetition using prefix-free parsing to build a dictionary and parse. The main challenge is the parse's size, which becomes significantly larger than the dictionary. To scale Moni, we propose removing the parse from the construction of the run-length encoded BWT (RLBWT), suffix array, and Longest Common Prefix (LCP) by applying prefix-free parsing recursively. This approach improves construction time and memory requirements, enabling efficient construction of RLBWT, suffix array, and LCP for large pangenomes, such as those from the Human Pangenome Reference Consortium.

Re2Pair: Increasing the Scalability of RePair by Decreasing Memory Usage.

The RePair compression algorithm produces a context-free grammar by iteratively substituting the most frequently occurring pair of consecutive symbols with a new symbol until all consecutive pairs of symbols appear only once in the compressed text. It is widely used in the settings of bioinformatics, machine learning, and information retrieval where random access to the original input text is needed. For example, in pangenomics, RePair is used for random access to a population of genomes. BigRePair improves the scalability of the original RePair algorithm by using Prefix-Free Parsing (PFP) to preprocess the text prior to building the RePair grammar. Despite the efficiency of PFP on repetitive text, there is a scalability issue with the size of the parse which causes a memory bottleneck in BigRePair. In this paper, we design and implement recursive RePair (denoted as Re 2 Pair ), which builds the RePair grammar using recursive PFP. Our novel algorithm faces the challenge of constructing the RePair grammar without direct access to the parse of text, relying solely on the dictionary of the text and the parse and dictionary of the parse of the text. We compare Re 2 Pair to BigRePair using SARS-CoV-2 haplotypes and haplotypes from the 1000 Genomes Project. We show that our method Re 2 Pair achieves over a 40% peak memory reduction and a speed up ranging between 12% to 79% compared to BigRePair when compressing the largest input texts in all experiments. Re 2 Pair is made publicly available under the GNU public license here: https://github.com/jkim210/Recursive-RePair.

Pfp-fm: an accelerated FM-index.

FM-indexes are crucial data structures in DNA alignment, but searching with them usually takes at least one random access per character in the query pattern. Ferragina and Fischer [1] observed in 2007 that word-based indexes often use fewer random accesses than character-based indexes, and thus support faster searches. Since DNA lacks natural word-boundaries, however, it is necessary to parse it somehow before applying word-based FM-indexing. In 2022, Deng et al. [2] proposed parsing genomic data by induced suffix sorting, and showed that the resulting word-based FM-indexes support faster counting queries than standard FM-indexes when patterns are a few thousand characters or longer. In this paper we show that using prefix-free parsing-which takes parameters that let us tune the average length of the phrases-instead of induced suffix sorting, gives a significant speedup for patterns of only a few hundred characters. We implement our method and demonstrate it is between 3 and 18 times faster than competing methods on queries to GRCh38, and is consistently faster on queries made to 25,000, 50,000 and 100,000 SARS-CoV-2 genomes. Hence, it seems our method accelerates the performance of count over all state-of-the-art methods with a moderate increase in the memory. The source code for PFP - FM is available at https://github.com/AaronHong1024/afm .

ONeSAMP 3.0: estimation of effective population size via single nucleotide polymorphism data from one population.

The genetic effective size (Ne) is arguably one of the most important characteristics of a population as it impacts the rate of loss of genetic diversity. Methods that estimate Ne are important in population and conservation genetic studies as they quantify the risk of a population being inbred or lacking genetic diversity. Yet there are very few methods that can estimate the Ne from data from a single population and without extensive information about the genetics of the population, such as a linkage map, or a reference genome of the species of interest. We present ONeSAMP 3.0, an algorithm for estimating Ne from single nucleotide polymorphism data collected from a single population sample using approximate Bayesian computation and local linear regression. We demonstrate the utility of this approach using simulated Wright-Fisher populations, and empirical data from five endangered Channel Island fox (Urocyon littoralis) populations to evaluate the performance of ONeSAMP 3.0 compared to a commonly used Ne estimator. Our results show that ONeSAMP 3.0 is broadly applicable to natural populations and is flexible enough that future versions could easily include summary statistics appropriate for a suite of biological and sampling conditions. ONeSAMP 3.0 is publicly available under the GNU General Public License at https://github.com/AaronHong1024/ONeSAMP_3.

Recursive Prefix-Free Parsing for Building Big BWTs.

Prefix-free parsing is useful for a wide variety of purposes including building the BWT, constructing the suffix array, and supporting compressed suffix tree operations. This linear-time algorithm uses a rolling hash to break an input string into substrings, where the resulting set of unique substrings has the property that none of the substrings' suffixes (of more than a certain length) is a proper prefix of any of the other substrings' suffixes. Hence, the name prefix-free parsing. This set of unique substrings is referred to as the dictionary . The parse is the ordered list of dictionary strings that defines the input string. Prior empirical results demonstrated the size of the parse is more burdensome than the size of the dictionary for large, repetitive inputs. Hence, the question arises as to how the size of the parse can scale satisfactorily with the input. Here, we describe our algorithm, recursive prefix-free parsing , which accomplishes this by computing the prefix-free parse of the parse produced by prefix-free parsing an input string. Although conceptually simple, building the BWT from the parse-of-the-parse and the dictionaries is significantly more challenging. We solve and implement this problem. Our experimental results show that recursive prefix-free parsing is extremely effective in reducing the memory needed to build the run-length encoded BWT of the input. Our implementation is open source and available at https://github.com/marco-oliva/r-pfbwt .

Recursive Prefix-Free Parsing for Building Big BWTs.

Prefix-free parsing is useful for a wide variety of purposes including building the BWT, constructing the suffix array, and supporting compressed suffix tree operations. This linear-time algorithm uses a rolling hash to break an input string into substrings, where the resulting set of unique substrings has the property that none of the substrings' suffixes (of more than a certain length) is a proper prefix of any of the other substrings' suffixes. Hence, the name prefix-free parsing. This set of unique substrings is referred to as the dictionary. The parse is the ordered list of dictionary strings that defines the input string. Prior empirical results demonstrated the size of the parse is more burdensome than the size of the dictionary for large, repetitive inputs. Hence, the question arises as to how the size of the parse can scale satisfactorily with the input. Here, we describe our algorithm, recursive prefix-free parsing, which accomplishes this by computing the prefix-free parse of the parse produced by prefix-free parsing an input string. Although conceptually simple, building the BWT from the parse-of-the-parse and the dictionaries is significantly more challenging. We solve and implement this problem. Our experimental results show that recursive prefix-free parsing is extremely effective in reducing the memory needed to build the run-length encoded BWT of the input. Our implementation is open source and available at https://github.com/marco-oliva/r-pfbwt.

PFP-FM: An Accelerated FM-index.

FM-indexes are a crucial data structure in DNA alignment, but searching with them usually takes at least one random access per character in the query pattern. Ferragina and Fischer [1] observed in 2007 that word-based indexes often use fewer random accesses than character-based indexes, and thus support faster searches. Since DNA lacks natural word-boundaries, however, it is necessary to parse it somehow before applying word-based FM-indexing. Last year, Deng et al. [2] proposed parsing genomic data by induced suffix sorting, and showed the resulting word-based FM-indexes support faster counting queries than standard FM-indexes when patterns are a few thousand characters or longer. In this paper we show that using prefix-free parsing-which takes parameters that let us tune the average length of the phrases-instead of induced suffix sorting, gives a significant speedup for patterns of only a few hundred characters. We implement our method and demonstrate it is between 3 and 18 times faster than competing methods on queries to GRCh38, and is consistently faster on queries made to 25,000, 50,000 and 100,000 SARS-CoV-2 genomes. Hence, it seems our method accelerates the performance of count over all state-of-the-art methods with a minor increase in the memory. The source code for PFP-FM is available at https://github.com/marco-oliva/afm.

Iron plaque formation and morphoanatomy of roots from species of restinga subjected to excess iron.

The restingas, a sandy coastal plain ecosystem of Brazil, have received an additional amount of iron due to the activity of mining industries. The present study aims to characterize morphoanatomically and histochemically the iron plaque formation on roots of Ipomoea pes-caprae L. and Canavalia rosea DC, cultivated in hydroponic solution with and without excess iron. The iron plaque formation as well as changes in the external morphology of the lateral roots of both species were observed after the subjection to excess iron. Changes in the nutrient uptake, and in the organization and form of the pericycle and cortex cells were observed for both species. Scanning electron microscopy showed evident iron plaques on the whole surface of the root. The iron was histolocalized in all root tissues of both species. The species of restinga studied here formed iron plaque in their roots when exposed to excess of this element, which may compromise their development in environments polluted by particulated iron.

Diagnostic and prognostic characteristics of phytotoxicity caused by fluoride on Spondias dulcis Forst. F. (Anacardiaceae).

The goal of this study was to determine the symptoms and microscopic damage caused by fluoride on Spondias dulcis, a fluoride-sensitive species. The plants were exposed to simulated fog with fluoride (0, 5, 10, 15 and 20 mg L(-1)) for 20 min daily during four consecutive days. Samples from leaflets without any apparent fluoride injury were collected to microscopic analysis. The percentage of necrosed leaf area was measured, and the level of pollutant in the dry matter from the basal and apical portions of the plant was determined. The necroses began 24 h after the first simulation mainly from the base of the leaflets. A higher level of necrosis was observed at the apical portion of the plants, a region of higher fluoride accumulation. The damage on the surface of the leaflets was characterized as plasmolysis, erosion of the epicuticular waxes and epidermal rupture. Structurally, the noticeable accumulation of granules and droplets green stained by toluidine blue in the spongy parenchima and the boundaries of ending veinlets was observed. The limb thickness reduction occurred due to plasmolysis in the mesophyll, showing an apparent correlation with the damage observed on the surface. The parameters observed in the laboratory are promising for field biomonitoring studies.

MONI: A Pangenomic Index for Finding Maximal Exact Matches.

Recently, Gagie et al. proposed a version of the FM-index, called the r-index, that can store thousands of human genomes on a commodity computer. Then Kuhnle et al. showed how to build the r-index efficiently via a technique called prefix-free parsing (PFP) and demonstrated its effectiveness for exact pattern matching. Exact pattern matching can be leveraged to support approximate pattern matching, but the r-index itself cannot support efficiently popular and important queries such as finding maximal exact matches (MEMs). To address this shortcoming, Bannai et al. introduced the concept of thresholds, and showed that storing them together with the r-index enables efficient MEM finding-but they did not say how to find those thresholds. We present a novel algorithm that applies PFP to build the r-index and find the thresholds simultaneously and in linear time and space with respect to the size of the prefix-free parse. Our implementation called MONI can rapidly find MEMs between reads and large-sequence collections of highly repetitive sequences. Compared with other read aligners-PuffAligner, Bowtie2, BWA-MEM, and CHIC- MONI used 2-11 times less memory and was 2-32 times faster for index construction. Moreover, MONI was less than one thousandth the size of competing indexes for large collections of human chromosomes. Thus, MONI represents a major advance in our ability to perform MEM finding against very large collections of related references.

Finding Maximal Exact Matches Using the r-Index.

Efficiently finding maximal exact matches (MEMs) between a sequence read and a database of genomes is a key first step in read alignment. But until recently, it was unknown how to build a data structure in [Formula: see text] space that supports efficient MEM finding, where r is the number of runs in the Burrows-Wheeler Transform. In 2021, Rossi et al. showed how to build a small auxiliary data structure called thresholds in addition to the r-index in [Formula: see text] space. This addition enables efficient MEM finding using the r-index. In this article, we present the tool that implements this solution, which we call MONI. Namely, we give a high-level view of the main components of the data structure and show how the source code can be downloaded, compiled, and used to find MEMs between a set of sequence reads and a set of genomes.

CSTs for Terabyte-Sized Data.

Generating pangenomic datasets is becoming increasingly common but there are still few tools able to handle them and even fewer accessible to non-specialists. Building compressed suffix trees (CSTs) for pangenomic datasets is still a major challenge but could be enormously beneficial to the community. In this paper, we present a method, which we refer to as RePFP-CST, for building CSTs in a manner that is scalable. To accomplish this, we show how to build a CST directly from VCF files without decompressing them, and to prune from the prefix-free parse (PFP) phrase boundaries whose removal reduces the total size of the dictionary and the parse. We show that these improvements reduce the time and space required for the construction of the CST, and the memory footprint of the finished CST, enabling us to build a CST for a terabyte of DNA for the first time in the literature.

Target-enriched long-read sequencing (TELSeq) contextualizes antimicrobial resistance genes in metagenomes.

BACKGROUND: Metagenomic data can be used to profile high-importance genes within microbiomes. However, current metagenomic workflows produce data that suffer from low sensitivity and an inability to accurately reconstruct partial or full genomes, particularly those in low abundance. These limitations preclude colocalization analysis, i.e., characterizing the genomic context of genes and functions within a metagenomic sample. Genomic context is especially crucial for functions associated with horizontal gene transfer (HGT) via mobile genetic elements (MGEs), for example antimicrobial resistance (AMR). To overcome this current limitation of metagenomics, we present a method for comprehensive and accurate reconstruction of antimicrobial resistance genes (ARGs) and MGEs from metagenomic DNA, termed target-enriched long-read sequencing (TELSeq). RESULTS: Using technical replicates of diverse sample types, we compared TELSeq performance to that of non-enriched PacBio and short-read Illumina sequencing. TELSeq achieved much higher ARG recovery (>1,000-fold) and sensitivity than the other methods across diverse metagenomes, revealing an extensive resistome profile comprising many low-abundance ARGs, including some with public health importance. Using the long reads generated by TELSeq, we identified numerous MGEs and cargo genes flanking the low-abundance ARGs, indicating that these ARGs could be transferred across bacterial taxa via HGT. CONCLUSIONS: TELSeq can provide a nuanced view of the genomic context of microbial resistomes and thus has wide-ranging applications in public, animal, and human health, as well as environmental surveillance and monitoring of AMR. Thus, this technique represents a fundamental advancement for microbiome research and application. Video abstract.

AMR-meta: a k-mer and metafeature approach to classify antimicrobial resistance from high-throughput short-read metagenomics data.

BACKGROUND: Antimicrobial resistance (AMR) is a global health concern. High-throughput metagenomic sequencing of microbial samples enables profiling of AMR genes through comparison with curated AMR databases. However, the performance of current methods is often hampered by database incompleteness and the presence of homology/homoplasy with other non-AMR genes in sequenced samples. RESULTS: We present AMR-meta, a database-free and alignment-free approach, based on k-mers, which combines algebraic matrix factorization into metafeatures with regularized regression. Metafeatures capture multi-level gene diversity across the main antibiotic classes. AMR-meta takes in reads from metagenomic shotgun sequencing and outputs predictions about whether those reads contribute to resistance against specific classes of antibiotics. In addition, AMR-meta uses an augmented training strategy that joins an AMR gene database with non-AMR genes (used as negative examples). We compare AMR-meta with AMRPlusPlus, DeepARG, and Meta-MARC, further testing their ensemble via a voting system. In cross-validation, AMR-meta has a median f-score of 0.7 (interquartile range, 0.2-0.9). On semi-synthetic metagenomic data-external test-on average AMR-meta yields a 1.3-fold hit rate increase over existing methods. In terms of run-time, AMR-meta is 3 times faster than DeepARG, 30 times faster than Meta-MARC, and as fast as AMRPlusPlus. Finally, we note that differences in AMR ontologies and observed variance of all tools in classification outputs call for further development on standardization of benchmarking data and protocols. CONCLUSIONS: AMR-meta is a fast, accurate classifier that exploits non-AMR negative sets to improve sensitivity and specificity. The differences in AMR ontologies and the high variance of all tools in classification outputs call for the deployment of standard benchmarking data and protocols, to fairly compare AMR prediction tools.

Exploring Prediction of Antimicrobial Resistance Based on Protein Solvent Accessibility Variation.

Antimicrobial resistance (AMR) is a significant and growing public health threat. Sequencing of bacterial isolates is becoming more common, and therefore automatic identification of resistant bacterial strains is of pivotal importance for efficient, wide-spread AMR detection. To support this approach, several AMR databases and gene identification algorithms have been recently developed. A key problem in AMR detection, however, is the need for computational approaches detecting potential novel AMR genes or variants, which are not included in the reference databases. Toward this direction, here we study the relation between AMR and relative solvent accessibility (RSA) of protein variants from an in silico perspective. We show how known AMR protein variants tend to correspond to exposed residues, while on the contrary their susceptible counterparts tend to be buried. Based on these findings, we develop RSA-AMR, a novel relative solvent accessibility-based AMR scoring system. This scoring system can be applied to any protein variant to estimate its propensity of altering the relative solvent accessibility, and potentially conferring (or hindering) AMR. We show how RSA-AMR score can be integrated with existing AMR detection algorithms to expand their range of applicability into detecting potential novel AMR variants, and provide a ten-fold increase in Specificity. The two main limitations of RSA-AMR score is that it is designed on single point changes, and a limited number of variants was available for model learning.

Physiological Basis of Salt Stress Tolerance in a Landrace and a Commercial Variety of Sweet Pepper (Capsicum annuum L.).

Salt stress is one of the most impactful abiotic stresses that plants must cope with. Plants' ability to tolerate salt stress relies on multiple mechanisms, which are associated with biomass and yield reductions. Sweet pepper is a salt-sensitive crop that in Mediterranean regions can be exposed to salt build-up in the root zone due to irrigation. Understanding the physiological mechanisms that plants activate to adapt to soil salinization is essential to develop breeding programs and agricultural practices that counteract this phenomenon and ultimately minimize yield reductions. With this aim, the physiological and productive performances of Quadrato D'Asti, a common commercial sweet pepper cultivar in Italy, and Cazzone Giallo, a landrace of the Campania region (Italy), were compared under different salt stress treatments. Quadrato D'Asti had higher tolerance to salt stress when compared to Cazzone Giallo in terms of yield, which was associated with higher leaf biomass vs. fruit ratio in the former. Ion accumulation and profiling between the two genoptypes revealed that Quadrato D'Asti was more efficient at excluding chloride from green tissues, allowing the maintenance of photosystem functionality under stress. In contrast, Cazzone Giallo seemed to compartmentalize most sodium in the stem. While sodium accumulation in the stems has been shown to protect shoots from sodium toxicity, in pepper and/or in the specific experimental conditions imposed, this strategy was less efficient than chloride exclusion for salt stress tolerance.

Photosynthesis and oxidative stress in the restinga plant species Eugenia uniflora L. exposed to simulated acid rain and iron ore dust deposition: potential use in environmental risk assessment.

The Brazilian sandy coastal plain named restinga is frequently subjected to particulate and gaseous emissions from iron ore factories. These gases may come into contact with atmospheric moisture and produce acid rain. The effects of the acid rain on vegetation, combined with iron excess in the soil, can lead to the disappearance of sensitive species and decrease restinga biodiversity. The effects of iron ore dust deposition and simulated acid rain on photosynthesis and on antioxidant enzymes were investigated in Eugenia uniflora, a representative shrub species of the restinga. This study aimed to determine the possible utility of this species in environmental risk assessment. After the application of iron ore dust as iron solid particulate matter (SPM(Fe)) and simulated acid rain (pH 3.1), the 18-month old plants displayed brown spots and necrosis, typical symptoms of iron toxicity and injuries caused by acid rain, respectively. The acidity of the rain intensified leaf iron accumulation, which reached phytotoxic levels, mainly in plants exposed to iron ore dust. These plants showed the lowest values for net photosynthesis, stomatal conductance, transpiration, chlorophyll a content and electron transport rate through photosystem II (PSII). Catalase and superoxide dismutase activities were decreased by simulated acid rain. Peroxidase activity and membrane injury increased following exposure to acid rain and simultaneous SPM(Fe) application. Eugenia uniflora exhibited impaired photosynthetic and antioxidative metabolism in response to combined iron and acid rain stresses. This species could become a valuable tool in environmental risk assessment in restinga areas near iron ore pelletizing factories. Non-invasive evaluations of visual injuries, photosynthesis and chlorophyll a fluorescence, as well as invasive biochemical analysis could be used as markers.

Unexpected Predictors of Antibiotic Resistance in Housekeeping Genes of Staphylococcus Aureus.

Methicillin-resistant Staphylococcus aureus (MRSA) is currently the most commonly identified antibiotic-resistant pathogen in US hospitals. Resistance to methicillin is carried by SCCmec genetic elements. Multilocus sequence typing (MLST) covers internal fragments of seven housekeeping genes of S. aureus. In conjunction with mec typing, MLST has been used to create an international nomenclature for S. aureus. MLST sequence types with a single nucleotide polymorphism (SNP) considered distinct. In this work, relationships among MLST SNPs and methicillin/oxacillin resistance or susceptibility were studied, using a public data base, by means of cross-tabulation tests, multivariable (phylogenetic) logistic regression (LR), decision trees, rule bases, and random forests (RF). Model performances were assessed through multiple cross-validation. Hierarchical clustering of SNPs was also employed to analyze mutational covariation. The number of instances with a known methicillin (oxacillin) antibiogram result was 1526 (649), where 63% (54%) was resistant to methicillin (oxacillin). In univariable analysis, several MLST SNPs were found strongly associated with antibiotic resistance/susceptibility. A RF model predicted correctly the resistance/susceptibility to methicillin and oxacillin in 75% and 63% of cases (cross-validated). Results were similar for LR. Hierarchical clustering of the aforementioned SNPs yielded a high level of covariation both within the same and different genes; this suggests strong genetic linkage between SNPs of housekeeping genes and antibiotic resistant associated genes. This finding provides a basis for rapid identification of antibiotic resistant S. arues lineages using a small number of genomic markers. The number of sites could subsequently be increased moderately to increase the sensitivity and specificity of genotypic tests for resistance that do not rely on the direct detection of the resistance marker itself.

Effects of simulated deposition of acid mist and iron ore particulate matter on photosynthesis and the generation of oxidative stress in Schinus terebinthifolius Radii and Sophora tomentosa L.

Particulate matter is a natural occurrence in the environment, but some industries, such as the iron ore sector, can raise the total amount of particles in the atmosphere. This industry is primarily a source of iron and sulfur dioxide particulates. The effects of the pollutants from the iron ore industries on representatives of restinga vegetation in a Brazilian coastal ecosystem were investigated using physiological and biochemical measures. Two species, Schinus terebinthifolius and Sophora tomentosa, were exposed to simulated deposition of acid mist and iron ore particulate matter in acrylic chambers in a greenhouse. Parameters such as gas exchange, fluorescence emission, chlorophyll content, total iron content, antioxidant enzyme activity and malondialdehyde content were assessed in order to evaluate the responses of the two species. Neither treatment was capable of inducing oxidative stress in S. terebinthifolius. Nevertheless, the deposition of iron ore particulates on this species increased chlorophyll content, the maximum quantum efficiency of photosystem II and the electron transport rate, while iron content was unaltered. On the other hand, S. tomentosa showed a greater sensitivity to the treatments. Plants of S. tomentosa that were exposed to acid mist had a decrease in photosynthesis, while the deposition of iron particulate matter led to an increase in iron content and membrane permeability of the leaves. The activities of antioxidant enzymes, such as catalases and superoxide dismutase, were enhanced by both treatments. The results suggested that the two restinga species use different strategies to overcome the stressful conditions created by the deposition of particulate matter, either solid or wet. It seems that while S. terebinthifolius avoided stress, S. tomentosa used antioxidant enzyme systems to partially neutralize oxidative stress. The findings also point to the potential use of S. tomentosa as a biomarker species under field conditions.