BackgroundHevea brasiliensis, a member of the Euphorbiaceae family, is the major commercial source of natural rubber (NR). NR is a latex polymer with high elasticity, flexibility, and resilience that has played a critical role in the world economy since 1876.ResultsHere, we report the draft genome sequence of H. brasiliensis. The assembly spans ~1.1 Gb of the estimated 2.15 Gb haploid genome. Overall, ~78% of the genome was identified as repetitive DNA. Gene prediction shows 68,955 gene models, of which 12.7% are unique to Hevea. Most of the key genes associated with rubber biosynthesis, rubberwood formation, disease resistance, and allergenicity have been identified.ConclusionsThe knowledge gained from this genome sequence will aid in the future development of high-yielding clones to keep up with the ever increasing need for natural rubber.

With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics, or shortened to just 'omics', the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing, and data archiving. The ultimate goal is towards the holistic realization of a 'systems biology' understanding of the biological question in hand. Commonly used approaches in these efforts are currently limited by the 3 i's - integration, interpretation, and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events, and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics, and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools, and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.

Rationale : Colorectal cancer (CRC) is a malignant tumor with the third highest morbidity rate among all cancers. Driven by the host's genetic makeup and environmental exposures, the gut microbiome and its metabolites have been implicated as the causes and regulators of CRC pathogenesis. We assessed human fecal samples as noninvasive and unbiased surrogates to catalog the gut microbiota and metabolome in patients with CRC. Methods : Fecal samples collected from CRC patients (CRC group, n = 50) and healthy volunteers (H group, n = 50) were subjected to microbiome (16S rRNA gene sequencing) and metabolome (gas chromatography-mass spectrometry, GC-MS) analyses. The datasets were analyzed individually and integrated for combined analysis using various bioinformatics approaches. Results : Fecal metabolomic analysis led to the identification of 164 metabolites spread across 40 metabolic pathways in both groups. In addition, there were 42 and 17 metabolites specific to the H and CRC groups, respectively. Sequencing of microbial diversity revealed 1084 operational taxonomic units (OTUs) across the two groups, and there was less species diversity in the CRC group than in the H group. Seventy-six discriminatory OTUs were identified for the microbiota of H volunteers and CRC patients. Integrated analysis correlated CRC-associated microbes with metabolites, such as polyamines (cadaverine and putrescine). Conclusions : Our results provide substantial evidence of a novel interplay between the gut microbiome and metabolome (i.e., polyamines), which is drastically perturbed in CRC. Microbe-associated metabolites can be used as diagnostic biomarkers in therapeutic explorations.

Multi-omics, variously called integrated omics, pan-omics, and trans-omics, aims to combine two or more omics data sets to aid in data analysis, visualization and interpretation to determine the mechanism of a biological process. Multi-omics efforts have taken center stage in biomedical research leading to the development of new insights into biological events and processes. However, the mushrooming of a myriad of tools, datasets, and approaches tends to inundate the literature and overwhelm researchers new to the field. The aims of this review are to provide an overview of the current state of the field, inform on available reliable resources, discuss the application of statistics and machine/deep learning in multi-omics analyses, discuss findable, accessible, interoperable, reusable (FAIR) research, and point to best practices in benchmarking. Thus, we provide guidance to interested users of the domain by addressing challenges of the underlying biology, giving an overview of the available toolset, addressing common pitfalls, and acknowledging current methods’ limitations. We conclude with practical advice and recommendations on software engineering and reproducibility practices to share a comprehensive awareness with new researchers in multi-omics for end-to-end workflow.

Foliar stomatal movements are critical for regulating plant water loss and gas exchange. Elevated carbon dioxide (CO ) levels are known to induce stomatal closure. However, the current knowledge on CO signal transduction in stomatal guard cells is limited. Here we report metabolomic responses of Brassica napus guard cells to elevated CO using three hyphenated metabolomics platforms: gas chromatography-mass spectrometry (MS); liquid chromatography (LC)-multiple reaction monitoring-MS; and ultra-high-performance LC-quadrupole time-of-flight-MS. A total of 358 metabolites from guard cells were quantified in a time-course response to elevated CO level. Most metabolites increased under elevated CO , showing the most significant differences at 10 min. In addition, reactive oxygen species production increased and stomatal aperture decreased with time. Major alterations in flavonoid, organic acid, sugar, fatty acid, phenylpropanoid and amino acid metabolic pathways indicated changes in both primary and specialized metabolic pathways in guard cells. Most interestingly, the jasmonic acid (JA) biosynthesis pathway was significantly altered in the course of elevated CO treatment. Together with results obtained from JA biosynthesis and signaling mutants as well as CO signaling mutants, we discovered that CO -induced stomatal closure is mediated by JA signaling.

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acquisition. Improved machinery in metabolomics generate increasingly complex data sets which 6 create the need for more and better processing and analysis software and in-silico approaches to 7 understand the resulting data. However, a comprehensive source of information describing the 8 utility of the most recently developed and released metabolomics resources --in the form of 9 tools, software, and databases -is currently lacking. Thus, here we provide an overview of 10 freely-available, open-source, tools, algorithms and frameworks to make both upcoming and 11 established metabolomics researchers aware of the recent developments in an attempt to advance 12 and facilitate data processing workflows in their metabolomics research. The major topics 13 include tools and researches for data processing, data annotation, and data visualization in MS 14 and NMR based metabolomics. Most in this review described tools are dedicated to untargeted 15 metabolomics workflows; however, some more specialist tools are described as well. All tools 16 and resources described including their analytical and computational platform dependencies are 17 summarized in an overview Table. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 In metabolomics, data processing and interpretation represent some of the most 2 challenging and time-consuming steps in the high throughput process, regardless of the 3 analytical platform used for data generation. The exponentially growing volume of generated 4 data has triggered the research and development of tools, software, programs, databases, and 5 applications to facilitate the robust understanding of metabolic processes of biological systems. 6For instance, natural products discovery has greatly benefited from the development of open-7 access spectral and chemical databases [1]. In addition, a large number of chemoinformatics 8 tools are finding direct application in handling metabolomics data or helping in annotation. 9Targeted metabolomics investigations obtain quantitative data on a predefined set of compounds, 10while untargeted metabolomics studies provide a broader exploration of metabolites with the 11 goal of identifying new compounds [2]. Untargeted metabolomic studies are characterized by 12simultaneous qualitative and quantitative analysis of a large number of metabolites in samples. 13Currently, untargeted metabolomics is being increasingly applied in diverse areas of research however, all these applications share the same bottlenecks: i.e., the harmonization and coverage 17of existing analytical methods, the lack of automation of spectral data processing, and the data 18 interpretation [3]. These limitations are the major driving force providing impetus for the 19 development of a huge plethora of metabolomics tools, software, programs, and databases. 20Although excellent compilations are available for M...

Background Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success. Aim of the review In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community. Key scientific concepts of review In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.