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The availability of large scale epigenomic data from different cell types and conditions has provided valuable information to evaluate and learn features that predict co-binding of transcription factors (TF). However, previous attempts to develop models for predicting motif co-occurrence were not scalable for global analysis of any combination of motifs or cross-species predictions. Further, mapping co-regulatory modules (CRM) to their gene regulatory networks (GRN) is crucial in understanding the underlying function. Currently, there is no comprehensive pipeline to locate CRM and GRN on a large scale with speed and accuracy. In this study, we analyzed and evaluated different TF binding characteristics that would facilitate co-binding with biological significance to identify all possible clusters of co-binding TFs. We curated the UniBind database, which contains ChIP-Seq data from over 1983 samples and 232 TFs, and implemented two machine learning models to predict CRMs and potential regulatory networks they operate on. We narrowed our focus to study heart related regulatory motifs. Our findings highlight the importance of the NKX family of transcription factors in cardiac development and provide potential targets for further investigation in cardiac disease.

The onset of preterm labor is associated with inflammation. Previous studies suggested that this is distinct from the inflammation observed during term labor. Our previous work on 44 genes differentially expressed in myometria in term labor demonstrated a different pattern of gene expression from that observed in preterm laboring and nonlaboring myometria. We found increased expression of inflammatory genes in preterm labor associated with chorioamnionitis, but in the absence of chorioamnionitis observed no difference in gene expression in preterm myometria regardless of laboring status, suggesting that preterm labor is associated with different myometrial genes or signals originating from outside the myometrium. Given that a small subset of genes were assessed, this study aimed to use RNA sequencing and bioinformatics to assess the myometrial transcriptome during preterm labor in the presence and absence of chorioamnionitis.This study aimed to comprehensively determine protein-coding transcriptomic differences between preterm nonlaboring and preterm laboring myometria with and without chorioamnionitis.Myometria were collected at cesarean delivery from preterm patients not in labor (n=16) and preterm patients in labor with chorioamnionitis (n=8) or without chorioamnionitis (n=6). Extracted RNA from myometrial tissue was prepared and sequenced using Illumina NovaSeq. Gene expression was quantified by mapping the sequence reads to the human reference genome (hg38). Differential gene expression analysis, gene set enrichment analysis, and weighted gene coexpression network analysis were used to comprehensively interrogate transcriptomic differences and their associated biology.Differential gene expression analysis comparing preterm patients in labor with chorioamnionitis with preterm patients not in labor identified 931 differentially expressed genes, whereas comparing preterm patients in labor without chorioamnionitis with preterm patients not in labor identified no statistically significant gene expression changes. In contrast, gene set enrichment analysis and weighted gene coexpression network analysis demonstrated that preterm labor with and without chorioamnionitis was associated with enrichment of pathways involved in activation of the innate immune system and inflammation, and activation of G protein-coupled receptors. Key genes identified included chemotactic CYP4F3, CXCL8, DOCK2, and IRF1 in preterm labor with chorioamnionitis and CYP4F3, FCAR, CHUK, and IL13RA2 in preterm labor without chorioamnionitis. There was marked overlap in the pathways enriched in both preterm labor subtypes.Differential gene expression analysis demonstrated that myometria from preterm patients in labor without chorioamnionitis and preterm patients not in labor were transcriptionally similar, whereas the presence of chorioamnionitis was associated with marked gene changes. In contrast, comprehensive bioinformatic analysis indicated that preterm labor with or without chorioamnionitis was associated with innate immune activation. All causes of preterm labor were associated with activation of the innate immune system, but this was more marked in the presence of chorioamnionitis. These data suggest that anti-inflammatory therapy may be relevant in managing preterm labor of all etiologies.

Dietary nutrient composition is essential for shaping important fitness traits and behaviours. Many organisms are protein limited and for Drosophila melanogaster, this limitation manifests at the level of the single most limiting essential Amino Acid (AA) in the diet. The identity of this AA and its effects on female fecundity is readily predictable by a procedure called exome matching in which the sum of AAs encoded by a consumer’s exome is used to predict the relative proportion of AAs required in its diet. However, the exome matching calculation does not weight AA contributions to the overall profile by protein size or expression. Here we update the exome matching calculation to include these weightings. Surprisingly, although nearly half of the transcriptome is differentially expressed when comparing male and female flies, we found that creating transcriptome-weighted exome matched diets for each sex did not enhance their fecundity over that supported by exome matching alone. These data indicate that while organisms may require different amounts of dietary protein across conditions, the relative proportion of the constituent AAs remains constant. Interestingly, we also found remarkable conservation of exome matched AA profiles across taxa and that the composition of these profiles could be explained by the metabolic costs of microbial AA synthesis. Thus, it appears that bioenergetic constraints amongst autotrophs shape the relative proportion of AAs that are available across trophic levels and that that this constrains biomass composition.

Competency frameworks have proved to be a powerful tool for curriculum development and assessment across many subject domains, and the field of computational biology is no exception. Efforts from the ISCB to develop and successively refine a set of competencies for bioinformatics education and various associated mapping tools have provided a framework for bringing competency-based design principles broadly to education and training of a wide range of professionals in need of some level of mastery of the principles and practice of computational biology. This document seeks to provide some basic guidance for education and training professionals in the field in how to use this framework effectively. It includes a basic background on competency-based education and the history of the ISCB competency framework specifically, leading up to the Version 3 framework considered here. It then follows with some basic principles of applying competency-based education and an illustration of how they apply to different tasks in curriculum development. Appendices and various linked documents provide further elaboration and helpful guidance on the ISCB competencies specifically and some ways in which versions of them have been used already to develop diverse forms of bioinformatics education and training experience. Our target readerships are trainers and educators working in computational biology or more broadly in the molecular life sciences, medicine, and other disciplines that use biomolecular data, including those working in academia, industry and the public sector., These guidelines are available as a google doc at https://docs.google.com/document/d/1hgBEq-C8HvmxxQ2grYiqNXt-agM1LWzW673j6uQHHmI/edit#heading=h.1x0y3vxuz6a7

Changes in cell phenotype are thought to occur through the expression of groups of co-regulated genes within topologically associated domains (TADs). In this paper, we allocate genes expressed within the myometrium of the human uterus during the onset of term labour into TADs. Transformation of the myometrial cells of the uterus into a contractile phenotype during term human labour is the result of a complex interaction of different epigenomic and genomic layers. Recent work suggests that the transcription factor (TF) RELA lies at the top of this regulatory network. Using deep RNA sequencing (RNAseq) analysis of myometrial samples (n = 16) obtained at term from women undergoing caesarean section prior to or after the onset of labour, we have identified evidence for how other gene expression regulatory elements interact with TFs in the labour phenotype transition. Gene set enrichment analysis of our RNAseq data identified three modules of enriched genes (M1, M2 and M3), which in gene ontology studies are linked to matrix degradation, smooth muscle and immune gene signatures, respectively. These genes were predominantly located within chromosomal TADs suggesting co-regulation of expression. Our transcriptomic analysis also identified significant differences in the expression of long non-coding RNAs (lncRNA), microRNAs (miRNA) and TFs that were predicted to target genes within the TADs. Additionally, network analysis revealed 15 new lncRNA (MCM3AP-AS1, TUG1, MIR29B2CHG, HCG18, LINC00963, KCNQ1OT1, NEAT1, HELLPAR, SNHG16, NUTM2B-AS1, MALAT1, PSMA3-AS1, GABPB1-AS1, NORAD and NKILA) and 4 miRNA (mir-145, mir-223, mir-let-7a and mir-132) as top gene hubs with three TFs (NFKB1, RELA and ESR1) as master regulators. Together, these factors are likely to be involved in co-regulatory networks driving a myometrial transformation to generate an estrogen-sensitive phenotype. We conclude that lncRNA and miRNA targeting the estrogen receptor 1 and nuclear factor kappa B pathways play a key role in the initiation of human labour. For the first time, we perform an integrative analysis to present a multi-level genomic signature made of mRNA, non-coding RNA and TFs in the myometrium for spontaneous term labour.

Despite the volume of experiments performed and data available, the complex biology of coronavirus SARS-COV-2 is not yet fully understood. Existing molecular profiling studies have focused on analysing functional omics data of a single type, which captures changes in a small subset of the molecular perturbations caused by the virus. As the logical next step, results from multiple such omics analysis may be aggregated to comprehensively interpret the molecular mechanisms of SARS-CoV-2. An alternative approach is to integrate data simultaneously in a parallel fashion to highlight the inter-relationships of disease-driving biomolecules, in contrast to comparing processed information from each omics level separately. We demonstrate that valuable information may be masked by using the former fragmented views in analysis, and biomarkers resulting from such an approach cannot provide a systematic understanding of the disease aetiology. Hence, we present a generic, reproducible and flexible open-access data harmonisation framework that can be scaled out to future multi-omics analysis to study a phenotype in a holistic manner. The pipeline source code, detailed documentation and automated version as a R package are accessible. To demonstrate the effectiveness of our pipeline, we applied it to a drug screening task. We integrated multi-omics data to find the lowest level of statistical associations between data features in two case studies. Strongly correlated features within each of these two datasets were used for drug–target analysis, resulting in a list of 84 drug–target candidates. Further computational docking and toxicity analyses revealed seven high-confidence targets, amsacrine, bosutinib, ceritinib, crizotinib, nintedanib and sunitinib as potential starting points for drug therapy and development.

Congenital Heart Disease (CHD) is characterised by a wide range of cardiac defects, from mild to life-threatening, which occur in babies worldwide. To date, there is no cure to CHD, however, progress in surgery has reduced its mortality allowing children affected by CHD to reach adulthood. In an effort to understand its genetic basis, several studies involving whole-genome sequencing (WGS) of patients with CHD have been undertaken and generated a great wealth of information. The majority of putative causative mutations identified in WGS studies fall into the non-coding part of the genome. Unfortunately, due to the lack of understanding of the function of these non-coding mutations, it is challenging to establish a causal link between the non-coding mutation and the disease. Thus, here we review the state-of-the-art approaches to interpret non-coding mutations in the context of CHD and address the following questions: What are the non-coding sequences important for cardiac function? Which technologies are used to identify them? Which resources are available to analyse them? What mutations are expected in these non-coding sequences? Learning from developmental process, what is their expected role in CHD?

MotivationLncRNAs are much more versatile and are involved in many regulatory roles inside the cell than previously believed. Existing databases lack consistencies in lncRNA annotations, and the functionality of over 95% of the known lncRNAs are yet to be established. LncRNA transcript identification involves discriminating them from their coding counterparts, which can be done with traditional experimental approaches, or via in silico methods. The later approach employs various computational algorithms, including machine learning classifiers to predict the lncRNA forming potential of a given transcript. Such approaches provide an economical and faster alternative to the experimental methods. Current in silico methods mainly use primary-sequence based features to build predictive models limiting their accuracy and robustness. Moreover, many of these tools make use of reference genome based features, in consequence making them unsuitable for non-model species. Hence, there is a need to comprehensively evaluate the efficacy of different predictive features to build computational models. Additionally, effective models will have to provide maximum prediction performance using the least number of features in a species-agnostic manner.It is popularly known in the protein world that “structure is function”. This also applies to lncRNAs as their functional mechanisms are similar to those of proteins. Generally, lncRNA function by structurally binding to its target proteins or nucleic acid forming complexes. The secondary structures of the lncRNAs are modular providing interaction sites for their interactome made of DNA, RNA, and proteins. Through these interactions, they epigenetically regulate cellular biology, thereby forming a layer of genomic programming on top of the coding genes. We demonstrate that in addition to using transcript sequence, we can provide comprehensive functional annotation by collating their interactome and secondary structure information.ResultsHere, we evaluated an exhaustive list of sequence-based, secondary-structure, interactome, and physicochemical features for their ability to predict the lncRNA potential of a transcript. Based on our analysis, we built different machine learning models using optimum feature-set. We found our model to be on par or exceeding the execution of the state-of-the-art methods with AUC values of over 0.9 for a diverse collection of species tested. Finally, we built a pipeline called linc2function that provides the information necessary to functionally annotate a lncRNA conveniently in a single window.AvailabilityThe source code is accessible use under MIT license in standalone mode, and as a webserver (https://bioinformaticslab.erc.monash.edu/linc2function).

Although many pharmaceutical compounds (and their metabolites) can induce harmful impacts at the molecular, physiological and behavioural levels, their underlying mechanistic associations have remained largely unexplored. Here, we utilized RNA-Seq to build a whole brain transcriptome profile to examine the impact of a common endocrine disrupting pharmaceutical (17α-ethinyl estradiol, EE2) on reproductive behaviour in wild guppies (Poecilia reticulata). Specifically, we annotated 16,791 coding transcripts in whole brain tissue in relation to the courtship behaviour (i.e. sigmoid display) of EE2 exposed (at environmentally relevant concentration of 8 ng/L for 28-days) and unexposed guppies. Further, we obtained 10,960 assembled transcripts matching in the non-coding orthologous genomes. Behavioural responses were assessed using a standard mate choice experiment, which allowed us to disentangle chemical cues from visual cues. We found that a high proportion of the RNAseq reads aligned back to our de novo assembled transcriptome with 80.59% mapping rate. Behavioural experiments showed that when males were presented only with female visual cues, there was a significant interaction between male treatment and female treatment in the time spent in the preference zone. This is one of the first studies to show that transcriptome-wide changes are associated with the reproductive behaviour of fish: EE2 exposed male guppies that performed high levels of courtship had a gene profile that deviated the most from the other treatment groups, while both non-courting EE2 and control males had similar gene signatures. Using Gene Ontology pathway analysis, our study shows that EE2-exposed males had gene transcripts enriched for pathways associated with altered immunity, starvation, altered metabolism and spermatogenesis. Our study demonstrates that multiple gene networks orchestrate courting behaviour, emphasizing the importance of investigating impacts of pharmaceuticals on gene networks instead of single genes.

Long noncoding RNA (lncRNA) are implicated in various genetic diseases and cancer, attributed to their critical role in gene regulation. RNA sequencing is used to capture their transcripts from certain cell types or conditions. For some studies, lncRNA interactions with other biomolecules have also been captured, which can give clues to their mechanisms of action. Complementary \textit{in silico} methods have been proposed to predict non-coding nature of transcripts and to analyze available RNA interaction data. Here we provide a critical review of such methods and identify associated challenges. Broadly, these can be categorized as reference-based and reference-free or \textit{ab initio}, with the former category of methods requiring a comprehensive annotated reference. The \textit{ab initio} methods can make use of machine learning classifiers that are trained on features extracted from sequences, making them suitable to predict novel transcripts, especially in non-model species. Machine learning approaches such as Logistic Regression, Support Vector Machines, Random Forest, and Deep Learning are commonly used. Initial approaches relied on basic sequential features to train the model, whereas the use of secondary structural features appears to be a promising approach for functional annotation. However, adding secondary features will result in model complexities, thus demanding an algorithm that can handle it and furthermore, considerably increasing the utilization of computation resources. Computational strategies combining identification and functional annotation which can be easily customized are currently lacking. These can be of immense value to accelerate research in this class of RNAs.

Objective The potential for brown adipose tissue (BAT) to be targeted as a therapeutic option to combat obesity has been heightened by the discovery of a brown–like form of inducible “beige” adipose tissue in white fat which has overlapping structural and functional properties to “classical” BAT. The likelihood that both beige and brown fat are recruited functionally by neural mechanisms, taken together with the lack of a detailed understanding of the nature of changes in the nervous system when white adipose tissue (WAT) is transformed to brown, provides the impetus for this study. Here, we aim to identify whether there is a shift in the gene expression profile in neurons directly innervating inguinal white adipose tissue (iWAT) that has undergone “beiging” to a signature that is more similar to neurons projecting to BAT. Methods Two groups of rats, one housed at thermoneutrality (27 °C) and the other exposed to cold (8 °C) for 7 days, were killed, and their T13/L1 ganglia, stellate ganglion (T1/T2), or superior cervical ganglion (SCG, C2/3) removed. This approach yielded ganglia containing neurons that innervate either beiged white fat (8 °C for 7 days), inguinal WAT (27 °C for 7 days), BAT (both 27 °C and 8 °C for 7 days) or non-WAT (8 °C for 7 days), the latter included to isolate changes in gene expression that were more aligned with a response to cold exposure than the transformation of white to beige adipocytes. Bioinformatics analyses of RNA sequencing data was performed followed by Ingenuity Pathway Analysis (IPA) to determine differential gene expression and recruitment of biosynthetic pathways. Results When iWAT is “beiged” there is a significant shift in the gene expression profile of neurons in sympathetic ganglia (T13/L1) innervating this depot toward a gene neurochemical signature that is similar to the stellate ganglion projecting to BAT. Bioinformatics analyses of “beiging” related genes revealed upregulation of genes encoding neuropeptides proopiomelanocortin (POMC) and calcitonin-gene related peptide (CGRP) within ganglionic neurons. Treatment of differentiated 3T3L1 adipocytes with αMSH, one of the products cleaved from POMC, results in an elevation in lipolysis and the beiging of these cells as indicated by changes in gene expression markers of browning (Ucp1 and Ppargc1a). Conclusion These data indicate that, coincident with beiging, there is a shift toward a “brown-like” neurochemical signature of postganglionic neurons projecting to inguinal white fat, an increased expression of POMC, and, consistent with a causative role for this prohormone in beiging, an αMSH-mediated increase in beige gene markers in isolated adipocytes., Graphical abstract, Highlights • RNA Seq showed shifts in neuronal gene expression following browning of white fat. • Gene expression in ganglia projecting to white fat became brown-like with beiging. • Bioinformatics analyses revealed neuronal gene candidates associated with beiging. • Prominent gene candidates associated with beiging included POMC and CGRP. • POMC cleavage product α-MSH caused beiging of cultured fat cells.

STUDY QUESTION What are the molecular differences between mitochondrial DNA (mtDNA)-deficient and mtDNA-normal oocytes and how does mitochondrial supplementation alter these? SUMMARY ANSWER Changes to DNA methylation in a 5' cytosine-phosphate-guanine 3' (CpG) island in the mtDNA-specific replication factor (DNA polymerase gamma (POLG)) of mtDNA-deficient oocytes mediates an increase in mtDNA copy number by the 2-cell stage that positively modulates the expression of nuclear genes, which affect cellular and metabolic processes, following autologous mitochondrial supplementation. WHAT IS KNOWN ALREADY Too few copies of mtDNA in mature oocytes can lead to fertilisation failure or preimplantation embryo arrest. mtDNA-deficient oocytes that progress to blastocyst express genes associated with poor cellular and metabolic processes, transcriptional activation and mitochondrial biogenesis. STUDY DESIGN, SIZE, DURATION Using a pig oocyte model, we assessed mtDNA-deficient and mtDNA-normal oocytes during in vitro maturation for mtDNA variants and levels of DNA methylation in POLG. We supplemented mtDNA-deficient oocytes with autologous populations of mitochondria to determine if there were changes to DNA methylation in POLG that coincided with increases in mtDNA copy number. We assessed metaphase II mtDNA-deficient and mtDNA-normal oocytes by RNA sequencing to identify differentially expressed genes and compared their profiles to blastocysts derived from mtDNA-normal, mtDNA-deficient and supplemented mtDNA-deficient oocytes. PARTICIPANTS/MATERIALS, SETTING, METHODS mtDNA variant analysis (n = 24), mtDNA copy number (n = 60), POLG gene expression (n = 24), and RNA sequencing (n = 32 single; and 12 pooled cohorts of n = 5) were performed on oocytes and embryos. DNA methylation of a CpG island in POLG was determined quantitatively by pyrosequencing on oocytes to 2-cell embryos (n = 408). Bioinformatics tools were used to assess differences between mtDNA-normal and mtDNA-deficient oocytes and between mtDNA-normal and mtDNA-deficient oocytes and supplemented oocytes and their blastocyst stage equivalents. MAIN RESULTS AND THE ROLE OF CHANCE Whilst mtDNA-deficient oocytes regulated variants less stringently during maturation (P < 0.05), there were no differences in the ratio of variants in mature-stage oocytes. However, mtDNA-normal mature oocytes had significantly more molecules affected due to their higher copy number (P < 0.0001). Normal mature oocytes differently DNA methylated a CpG island in POLG compared with mtDNA-deficient oocytes (P < 0.01). Supplementation of mtDNA-deficient oocytes modulated DNA methylation at this CpG island leading to a mtDNA replication event prior to embryonic genome activation inducing significant increases in mtDNA copy number. RNA-Seq identified 57 differentially expressed genes (false discovery rate (FDR) < 0.05) between the two cohorts of oocytes with blastocyst stage gene expression altered by supplementation of mtDNA-deficient oocytes (P < 0.05) including genes associated with metabolic disorders. One key factor was branched chain amino acid transaminase 2 (BCAT2), a regulator of amino acid metabolism and associated with diabetes. LARGE SCALE DATA Sequence data are available on the NCBI Sequence Read Archive under the project number PRJNA422295. RNA sequencing data were deposited into NCBI Gene Expression Omnibus, under the accession number GSE108900. LIMITATIONS, REASONS FOR CAUTION Whilst this work was conducted in a species that is highly relevant to human reproduction, the outcomes need to be tested in human oocytes and blastocysts prior to clinical application. WIDER IMPLICATIONS OF THE FINDINGS The outcomes demonstrate a mechanism of action following mtDNA supplementation of mtDNA-deficient oocytes that results in improved gene expression at the blastocyst stage of development. STUDY FUNDING/COMPETING INTERESTS This work was funded by OvaScience Inc. OvaScience did not influence the study design, analysis of results or interpretation of the data.

Data from multiple omics layers of a biological system is growing in quantity, heterogeneity and dimensionality. Simultaneous multi-omics data integration is a growing field of research as it has strong potential to unlock information on previously hidden biological relationships leading to early diagnosis, prognosis and expedited treatments. Many tools for multi-omics data integration are being developed. However, these tools are often restricted to highly specific experimental designs, and types of omics data. While some general methods do exist, they require specific data formats and experimental conditions. A major limitation in the field is a lack of a single or multi-omics pipeline which can accept data in an unrefined, information-rich form pre-integration and subsequently generate output for further investigation. There is an increasing demand for a generic multi-omics pipeline to facilitate general-purpose data exploration and analysis of heterogeneous data. Therefore, we present our R multiomics pipeline as an easy to use and flexible pipeline that takes unrefined multi-omics data as input, sample information and user-specified parameters to generate a list of output plots and data tables for quality control and downstream analysis. We have demonstrated application of the pipeline on two separate COVID-19 case studies. We enabled limited checkpointing where intermediate output is staged to allow continuation after errors or interruptions in the pipeline and generate a script for reproducing the analysis to improve reproducibility. A seamless integration with the mixOmics R package is achieved, as the R data object can be loaded and manipulated with mixOmics functions. Our pipeline can be installed as an R package or from the git repository, and is accompanied by detailed documentation with walkthroughs on two case studies. The pipeline is also available as Docker and Singularity containers.

EMBL Australia Bioinformatics Resource (EMBL-ABR) is a developing national research infrastructure, providing bioinformatics resources and support to life science and biomedical researchers in Australia. EMBL-ABR comprises 10 geographically distributed national nodes with one coordinating hub, with current funding provided through Bioplatforms Australia and the University of Melbourne for its initial 2-year development phase. The EMBL-ABR mission is to: (1) increase Australia’s capacity in bioinformatics and data sciences; (2) contribute to the development of training in bioinformatics skills; (3) showcase Australian data sets at an international level and (4) enable engagement in international programs. The activities of EMBL-ABR are focussed in six key areas, aligning with comparable international initiatives such as ELIXIR, CyVerse and NIH Commons. These key areas—Tools, Data, Standards, Platforms, Compute and Training—are described in this article.

This paper was presented at the Australian eResearch Skilled Workforce Summit on July 30, 2019Researchers in all disciplines are creating, navigating, analysing and visualising increasingly diverse and large data sets, and are seeking to up-skill in new tools and methods to help manipulate their data. However, training and support networks in the use of these tools have not always been available at scale. Skill development programs that target a diverse research community across both HASS and STEM offer significant opportunities for Graduate Researchers and ECRs to network, share experiences and learn collaboratively. However, they present a number of administrative and organisational challenges. Monash University’s Data Fluency program is a multidisciplinary collaboration led by the University Library in collaboration with the Bioinformatics Platform and eResearch, directly addressing the University's AI and Data Science focus. The program is responsible for the provision of a range of workshops that focus on developing researchers’ skills in using, exploring, interpreting and visualising data in a meaningful way and in effectively communicating research and ideas. Alongside the workshops, the program aims to build community and continue to develop skills through opportunities to engage in informal 1:1 drop-ins, seminars and networking opportunities. The workshops and drop-ins work hand in hand to provide the necessary training and continual support while learning new tools and solving problems; as well as learning through teaching and sharing expertise once the skills move beyond a foundational level. Graduate Research students, professional staff and ECRs learn from each other and share their knowledge and approaches across disciplinary areas. This paper discusses the origins of the program, its current operating model, and some suggested ways that it could interact with the sector more broadly.

CEPs (C-TERMINALLY ENCODED PEPTIDEs) inhibit Arabidopsis primary root growth by unknown mechanisms. We investigated how CEP3 levels control primary root growth. CEP3 peptide application decreased cell division, S-phase cell number, root meristematic cell number, and meristem zone (MZ) size in a dose- and CEP RECEPTOR1-dependent manner. Grafting showed that CEP3-dependent growth inhibition requires root and shoot CEPR1. CEP3 induced mitotic quiescence in MZ cells significantly faster than that induced by nutrient limitation alone. CEP3 also inhibited the restoration of S-phase to mitotically quiescence cells by nutrient resupply without quantitatively reducing TARGET OF RAPAMYCIN (TOR) kinase activity. In contrast, cep3-1 had an increased meristem size and S-phase cell number under nitrogen (N)-limited conditions, but not under N-sufficient conditions. Furthermore, cep3-1 meristematic cells remained in S-phase longer than wild-type cells during a sustained carbon (C) and N limitation. RNA sequencing showed that CEP3 peptide down-regulated genes involved in S-phase entry, cell wall and ribosome biogenesis, DNA replication, and meristem expansion, and up-regulated genes involved in catabolic processes and proteins and peptides that negatively control meristem expansion and root growth. Many of these genes were reciprocally regulated in cep3-1. The results suggest that raising CEP3 induces starvation-related responses that curtail primary root growth under severe nutrient limitation., Nutrient availability influences root growth in a trade-off between seedling establishment and survival. CEP3 accelerates starvation-related primary root growth inhibition under nitrogen or combined carbon and nitrogen limitation.

Long noncoding RNAs (lncRNAs) are implicated in various genetic diseases and cancer, attributed to their critical role in gene regulation. They are a divergent group of RNAs and are easily differentiated from other types with unique characteristics, functions, and mechanisms of action. In this review, we provide a list of some of the prominent data repositories containing lncRNAs, their interactome, and predicted and validated disease associations. Next, we discuss various wet-lab experiments formulated to obtain the data for these repositories. We also provide a critical review of in silico methods available for the identification purpose and suggest techniques to further improve their performance. The bulk of the methods currently focus on distinguishing lncRNA transcripts from the coding ones. Functional annotation of these transcripts still remains a grey area and more efforts are needed in that space. Finally, we provide details of current progress, discuss impediments, and illustrate a roadmap for developing a generalized computational pipeline for comprehensive annotation of lncRNAs, which is essential to accelerate research in this area.

Spontaneous coronary artery dissection (SCAD) is a clinical event, affecting female patients almost exclusively, in which intramural hematoma develops in a coronary artery and manifests as acute coronary syndrome [(1)][1]. The pathogenesis is unknown, although it is distinct from that of

MicroRNAs (miRNAs) are key players in regulation of gene expression at post-transcription level in eukaryotic cells. MiRNAs have been intensively studied in plants, animals and viruses. The investigations of bacterial miRNAs have gained less attention, except for the recent studies on miRNAs derived from Streptococcus mutans ATCC 25175 and Escherichia coli DH10B. In this study, high-throughput sequencing approach was employed to investigate the miRNA population in bacteria of the genus Thalassospira using both the miRDeep2 and CID-miRNA methods. A total of 984 putative miRNAs were identified in 9 species of the genus Thalassospira using both miRDeep and CID-miRNA analyses. Fifty seven conserved putative miRNAs were found in different species of the genus Thalassospira, and up to 6 miRNAs were found to be present at different locations in the T. alkalitolerans JCM 18968T, T. lucentensis QMT2T and T. xianhensis P-4T. None of the putative miRNAs was found to share sequence to the reported miRNAs in E. coli DH10B and S. mutans ATCC 25175. The findings provide a comprehensive list of computationally identified miRNAs in 9 bacterial species of the genus Thalassospira and addressed the existing knowledge gap on the presence of miRNAs in the Thalassospira genomes.

Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a ‘life cycle’ view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain.Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on ‘omics’ datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.

The increasing richness and diversity of biomedical data types creates major organizational and analytical impediments to rapid translational impact in the context of training and education. As biomedical data-sets increase in size, variety and complexity, they challenge conventional methods for sharing, managing and analyzing those data. In May 2017, we convened a two-day meeting between the BD2K Training Coordinating Center (TCC), ELIXIR Training/TeSS, GOBLET, H3ABioNet, EMBL-ABR, bioCADDIE and the CSIRO, in Huntington Beach, California, to compare and contrast our respective activities, and how these might be leveraged for wider impact on an international scale. Discussions focused on the role of i) training for biomedical data science; ii) the need to promote core competencies, and the ii) development of career paths. These led to specific conversations about i) the values of standardizing and sharing data science training resources; ii) challenges in encouraging adoption of training material standards; iii) strategies and best practices for the personalization and customization of learning experiences; iv) processes of identifying stakeholders and determining how they should be accommodated; and v) discussions of joint partnerships to lead the world on data science training in ways that benefit all stakeholders. Generally, international cooperation was viewed as essential for accommodating the widest possible participation in the modern bioscience enterprise, providing skills in a truly “FAIR” manner, addressing the importance of data science understanding worldwide. Several recommendations for the exchange of educational frameworks are made, along with potential sources for support, and plans for further cooperative efforts are presented.

The widespread adoption of high-throughput next-generation sequencing (NGS) technology among the Australian life science research community is highlighting an urgent need to up-skill biologists in tools required for handling and analysing their NGS data. There is currently a shortage of cutting-edge bioinformatics training courses in Australia as a consequence of a scarcity of skilled trainers with time and funding to develop and deliver training courses. To address this, a consortium of Australian research organizations, including Bioplatforms Australia, the Commonwealth Scientific and Industrial Research Organisation and the Australian Bioinformatics Network, have been collaborating with EMBL-EBI training team. A group of Australian bioinformaticians attended the train-the-trainer workshop to improve training skills in developing and delivering bioinformatics workshop curriculum. A 2-day NGS workshop was jointly developed to provide hands-on knowledge and understanding of typical NGS data analysis workflows. The road show–style workshop was successfully delivered at five geographically distant venues in Australia using the newly established Australian NeCTAR Research Cloud. We highlight the challenges we had to overcome at different stages from design to delivery, including the establishment of an Australian bioinformatics training network and the computing infrastructure and resource development. A virtual machine image, workshop materials and scripts for configuring a machine with workshop contents have all been made available under a Creative Commons Attribution 3.0 Unported License. This means participants continue to have convenient access to an environment they had become familiar and bioinformatics trainers are able to access and reuse these resources.

Recent evidence has shown that the sperm epigenome is vulnerable to dynamic modifications arising from a variety of paternal environment exposures and that this legacy can serve as an important determinant of intergenerational inheritance. It has been postulated that such exchange is communicated to maturing spermatozoa via the transfer of small non-protein-coding RNAs (sRNAs) in a mechanism mediated by epididymosomes; small membrane bound vesicles released by the soma of the male reproductive tract (epididymis). Here we confirm that mouse epididymosomes encapsulate an impressive cargo of >350 microRNAs (miRNAs), a developmentally important sRNA class, the majority (~60%) of which are also represented by the miRNA signature of spermatozoa. This includes >50 miRNAs that were found exclusively in epididymal sperm and epididymosomes, but not in the surrounding soma. We also documented substantial changes in the epididymosome miRNA cargo, including significant fold changes in almost half of the miRNAs along the length of the epididymis. Finally, we provide the first direct evidence for the transfer of several prominent miRNA species between mouse epididymosomes and spermatozoa to afford novel insight into a mechanism of intercellular communication by which the sRNA payload of sperm can be selectively modified during their post-testicular maturation.

In recent years considerable effort has been devoted to understanding the epigenetic control of sperm development, leading to an increased appreciation of the importance of RNA interference pathways, and in particular miRNAs, as key regulators of spermatogenesis and epididymal maturation. It has also been shown that sperm are endowed with an impressive array of miRNA that have been implicated in various aspects of fertilization and embryo development. However, to date there have been no reports on whether the sperm miRNA signature is static or whether it is influenced by their prolonged maturation within the male reproductive tract. To investigate this phenomenon, we employed next-generation sequencing to systematically profile the miRNA signature of maturing mouse spermatozoa. In so doing we have provided the first evidence for the posttesticular modification of the sperm miRNA profile under normal physiological conditions. Such modifications include the apparent loss and acquisition of an impressive cohort of some 113 and 115 miRNAs, respectively, between the proximal and distal epididymal segments. Interestingly, the majority of these changes occur late in maturation and include the uptake of novel miRNA species in addition to a significant increase in many miRNAs natively expressed in immature sperm. Because sperm are not capable of de novo transcription, these findings identify the epididymis as an important site in establishing the sperm epigenome with the potential to influence the peri-conceptual environment of the female reproductive tract, contribute to the inheritance of acquired characteristics, and/or alter the developmental trajectory of the resulting offspring.