198 10 20655 https://www.socictopen.socict.org/files/original/5c82fab934b2861fe7c0df5310397c16.pdf f2000ba57d4d11a975b5d5232b6663d0 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information Creator An entity primarily responsible for making the resource Zhao-Hui Zhan, Lina Jia, Yong Zhou, Liping Li, Hai-Cheng Yi Description An account of the resource The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental methods for predicting ncRNA and protein interactions, it is essential to propose an innovative and practical approach with convincing performance of prediction accuracy. In this study, based on the protein sequences from a biological perspective, we put forward an effective deep learning method, named BGFE, to predict ncRNA and protein interactions. Protein sequences are represented by bi-gram probability feature extraction method from Position Specific Scoring Matrix (PSSM), and for ncRNA sequences, k-mers sparse matrices are employed to represent them. Furthermore, to extract hidden high-level feature information, a stacked auto-encoder network is employed with the stacked ensemble integration strategy. We evaluate the performance of the proposed method by using three datasets and a five-fold cross-validation after classifying the features through the random forest classifier. The experimental results clearly demonstrate the effectiveness and the prediction accuracy of our approach. In general, the proposed method is helpful for ncRNA and protein interacting predictions and it provides some serviceable guidance in future biological research. Date A point or period of time associated with an event in the lifecycle of the resource 2019 Subject The topic of the resource ncRNA-protein interaction, bi-gram, Position specific scoring matrix, Kmers, deep learning Identifier An unambiguous reference to the resource within a given context DOI: 10.3390/ijms20040978 Source A related resource from which the described resource is derived International Journal of Molecular Sciences Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Biology (General), Chemistry Language A language of the resource EN https://www.socictopen.socict.org/files/original/52b0011dedd91e9a63ae54210a4b099f.pdf 15191f1e0d2720916f1aeb55f386fe22 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource L’énergie thermique des mers dans les Outre-mer français : un enjeu stratégique de territoire ? Creator An entity primarily responsible for making the resource Sylvain Roche Description An account of the resource Electricity production in the French overseas territories largely depends on fossil fuels. The context of the environmentalization of energy policies, joined by the current debates about blue growth, encourages the islands to present themselves as laboratory territories for new environmental technologies. It’s in this favorable context at the conjunction of various dynamics that projects of ocean thermal energy conversion (OTEC) are born, like the NEMO project in Martinique. In the field of marine energy, it’s probably the least known of the public, yet one of the most strategic. A science at the crossroads of three economic sectors, energy, industry and maritime, where France is at the forefront. Date A point or period of time associated with an event in the lifecycle of the resource 2018 Subject The topic of the resource Electricity, renewable energy, marine energy, ocean thermal energy conversion (OTEC), energy technology, natural resources Identifier An unambiguous reference to the resource within a given context DOI: 10.4000/etudescaribeennes.11971 Source A related resource from which the described resource is derived Études Caribéennes Publisher An entity responsible for making the resource available Université des Antilles Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Social Sciences, Latin America. Spanish America Language A language of the resource EN, ES, FR https://www.socictopen.socict.org/files/original/40ef0b2b4ff4a3cd3cd59477162cc4ff.pdf 2e3830847b3ad37abd12659268d9534f Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource The remarkable adaptability of syndromic surveillance to meet public health needs Creator An entity primarily responsible for making the resource Beverley J. Paterson, David N. Durrheim Description An account of the resource The goal of syndromic surveillance is the earlier detection of epidemics, allowing a timelier public health response than is possible using traditional surveillance methods. Syndromic surveillance application for public health purposes has changed over time and reflects a dynamic evolution from the collection, interpretation of data with dissemination of data to those who need to act, to a more holistic approach that incorporates response as a core component of the surveillance system. Recent infectious disease threats, such as severe acute respiratory syndrome (SARS), avian influenza (H5N1) and pandemic influenza (H1N1), have all highlighted the need for countries to be rapidly aware of the spread of infectious diseases within a region and across the globe. The International Health Regulations (IHR) obligation to report public health emergencies of international concern has raised the importance of early outbreak detection and response. The emphasis in syndromic surveillance is changing from automated, early alert and detection, to situational awareness and response. Published literature on syndromic surveillance reflects the changing nature of public health threats and responses. Syndromic surveillance has demonstrated a remarkable ability to adapt to rapidly shifting public health needs. This adaptability makes it a highly relevant public health tool. Date A point or period of time associated with an event in the lifecycle of the resource 2019 Subject The topic of the resource syndromic surveillance, Outbreaks, International Health Regulations, infectious diseases, Public Health Identifier An unambiguous reference to the resource within a given context DOI: 10.1016/j.jegh.2012.12.005 Source A related resource from which the described resource is derived Journal of Epidemiology and Global Health Publisher An entity responsible for making the resource available Atlantis Press Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Public aspects of medicine Language A language of the resource EN https://www.socictopen.socict.org/files/original/6b95a26ad020f3d11a1627c90c60e319.pdf 6e231733908c9a7f38b25a00048deb6d Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparison of Serologic Assays for Middle East Respiratory Syndrome Coronavirus Creator An entity primarily responsible for making the resource Ruth Harvey, Giada Mattiuzzo, Mark Hassall, Andrea Sieberg, Marcel A. Müller, Christian Drosten, Peter Rigsby, Christopher J. Oxenford Description An account of the resource Middle East respiratory syndrome coronavirus (MERS-CoV) was detected in humans in 2012. Since then, sporadic outbreaks with primary transmission through dromedary camels to humans and outbreaks in healthcare settings have shown that MERS-CoV continues to pose a threat to human health. Several serologic assays for MERS-CoV have been developed globally. We describe a collaborative study to investigate the comparability of serologic assays for MERS-CoV and assess any benefit associated with the introduction of a standard reference reagent for MERS-CoV serology. Our study findings indicate that, when possible, laboratories should use a testing algorithm including >2 tests to ensure correct diagnosis of MERS-CoV. We also demonstrate that the use of a reference reagent greatly improves the agreement between assays, enabling more consistent and therefore more meaningful comparisons between results. Date A point or period of time associated with an event in the lifecycle of the resource 2019 Subject The topic of the resource Middle East respiratory syndrome coronavirus, MERS-CoV, Spike, Antibodies, diagnostics, serology Identifier An unambiguous reference to the resource within a given context DOI: 10.3201/eid2510.190497 Source A related resource from which the described resource is derived Emerging Infectious Diseases Publisher An entity responsible for making the resource available Centers for Disease Control and Prevention Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Infectious and parasitic diseases, Medicine Language A language of the resource EN https://www.socictopen.socict.org/files/original/617e76331c5631375ccbb0133b5d5544.pdf e33b0d6c10a9d36a436fcfc319236547 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Evaluation of the Persistence of Higher-Order Strand Symmetry in Genomic Sequences by Novel Word Symmetry Distance Analysis Creator An entity primarily responsible for making the resource Bi Huang, Lifang Huang, Shanghong Zhang Description An account of the resource For the ubiquitous phenomenon of strand symmetry, it has been shown that it may persist for higher-order oligonucleotides. However, there is no consensus about to what extent (order of oligonucleotides or length of words) strand symmetry still persists. To determine the extent of strand symmetry in genomic sequences is critically important for the further understanding of the phenomenon. Based on previous studies, we have developed an algorithm for the novel word symmetry distance analysis. We applied it to evaluate the higher-order strand symmetry for 206 archaeal genomes and 2,659 bacterial genomes. Our results show that the new approach could provide a clear-cut criterion to determine the extent of strand symmetry for a group of genomes or individual genomes. According to the new measure, strand symmetry would tend to persist for up to 8-mers in archaeal genomes, and up to 9-mers in bacterial genomes. And the persistence may vary from 6- to 9-mers in individual genomes. Moreover, higher-order strand symmetry would tend to positively correlate with GC content and mononucleotide symmetry levels of genomic sequences. The variations of higher-order strand symmetry among genomes would indicate that strand symmetry itself may not be strictly relevant to biological functions, which would provide some insights into the origin and evolution of the phenomenon. Date A point or period of time associated with an event in the lifecycle of the resource 2019 Subject The topic of the resource the second parity rule, higher-order oligonucleotide, whole-genome sequences, frequency analysis, word symmetry distance (WSD) Identifier An unambiguous reference to the resource within a given context DOI: 10.3389/fgene.2019.00148 Source A related resource from which the described resource is derived Frontiers in Genetics Publisher An entity responsible for making the resource available Frontiers Media S.A. Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Genetics Language A language of the resource EN https://www.socictopen.socict.org/files/original/423933a626fb5a63bcdc3963d2d3ba57.pdf f183724380db16338dcda9a156e43771 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Use of modeling in the study of economic burden of influenza in the elderly population in Central and Eastern European countries Creator An entity primarily responsible for making the resource M. V. Leleka Description An account of the resource The economic burden of the incidence of influenza and ARI requires careful study, since annually about 15% of the world's population suffers from these diseases. In addition, the flu can cause complications that are dangerous to human life and health, especially those at risk. The elderly population belongs precisely to this group, therefore we have chosen to analyze the economic burden of this category of population. According to the international classification, this group of diseases is divided into the following types: Influenza-like illness (ILI);– Acute respiratory infection (ARI);– Severe acute respiratory infections (SARI);– Severe Acute Respiratory Syndrome (SARS).The aim of the work was to develop a model for systematization flows of patients and treatment costs in assessing the economic burden of influenza and ARI morbidity in the elderly peoples in Central and Eastern Europe. The model was developed in cooperation with the Syreon Institute, Hungary. The study involved Hungary, Poland, the Czech Republic, Romania, Ukraine and Kazakhstan.The structure of the model is based on the assumption of a flow of patients. The principle of the model was as follows: in order to assess the economic burden of the disease in a certain period of time, it is necessary to quantify (1) the number of patients, and (2) the costs of health care and treatment. The cost of treatment is different in each case, patients should be divided into relatively homogeneous groups. Since the quantity and quality of the epidemiological data on influenza and related diseases is rather heterogeneous in different countries, WHO offers certain objects of influenza research, taken as a basis for constructing such a model. According to this classification of influenza and WHO definition, the central structure of our model was formed by acute respiratory infections (ARI), representing outpatient cases and severe acute respiratory infections (SARI), representing inpatient cases.The next stage of the research was the study of the cost structure and the principles of collecting expenditure data. The economic burden of influenza in the elderly populations has been analyzed based on the type of health system. In older people, it is difficult to establish a link between influenza and complications of chronic diseases and other acute medical conditions (eg, acute coronary syndrome). This analysis does not include estimates of the economic impact of influenza on other co-morbidities and long-term chronic effects associated with complications of influenza. Since the population of 60+ or 65+ is a risk group and is a numerical group, the cost of treatment in the event of complications and hospitalization will increase cost for the patient and for the medical institution. However, in this case indirect costs is missing, such as paying a sick list. Direct medical cost were subject to analysis. The proposed methodology allows you to objectively assess the costs of treatment and calculate the economic burden, taking into account the type and health system. Date A point or period of time associated with an event in the lifecycle of the resource 2018 Subject The topic of the resource influenza and ARI, Morbidity, elderly population, Expenses, Economic burden Identifier An unambiguous reference to the resource within a given context DOI: 10.32352/0367-3057.3-4.17.04 Source A related resource from which the described resource is derived Фармацевтичний журнал Publisher An entity responsible for making the resource available The State Expert Center of the Ministry of Health of Ukraine Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Therapeutics. Pharmacology, Pharmacy and materia medica Language A language of the resource UK https://www.socictopen.socict.org/files/original/32422206cef26cc0b773466c49e64489.pdf 40881cf7ecabdd76511a417c1e870a4d Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource PTPD: predicting therapeutic peptides by deep learning and word2vec Creator An entity primarily responsible for making the resource Chuanyan Wu, Rui Gao, Yu-Sen Zhang, Yang De Marinis Description An account of the resource Abstract * Background In the search for therapeutic peptides for disease treatments, many efforts have been made to identify various functional peptides from large numbers of peptide sequence databases. In this paper, we propose an effective computational model that uses deep learning and word2vec to predict therapeutic peptides (PTPD). * Results Representation vectors of all k-mers were obtained through word2vec based on k-mer co-existence information. The original peptide sequences were then divided into k-mers using the windowing method. The peptide sequences were mapped to the input layer by the embedding vector obtained by word2vec. Three types of filters in the convolutional layers, as well as dropout and max-pooling operations, were applied to construct feature maps. These feature maps were concatenated into a fully connected dense layer, and rectified linear units (ReLU) and dropout operations were included to avoid over-fitting of PTPD. The classification probabilities were generated by a sigmoid function. PTPD was then validated using two datasets: an independent anticancer peptide dataset and a virulent protein dataset, on which it achieved accuracies of 96% and 94%, respectively. * Conclusions PTPD identified novel therapeutic peptides efficiently, and it is suitable for application as a useful tool in therapeutic peptide design. Date A point or period of time associated with an event in the lifecycle of the resource 2019 Subject The topic of the resource therapeutic peptide, deep learning, word2vec Identifier An unambiguous reference to the resource within a given context DOI: 10.1186/s12859-019-3006-z Source A related resource from which the described resource is derived BMC Bioinformatics Publisher An entity responsible for making the resource available BMC Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Biology (General), Computer applications to medicine. Medical informatics Language A language of the resource EN https://www.socictopen.socict.org/files/original/2d7117661515985352143f71b7a62f10.pdf 4ff7f6d08227cbee7d4e768b7105d5b4 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Emerging Coronavirus Which Gives Rise to the Disease in Humans Creator An entity primarily responsible for making the resource L. F. Stovba, V. N. Lebedev, A. A. Petrov, V. M. Ruchko, V. S. Kulish, S. V. Borisevich Description An account of the resource Coronaviruses are enveloped viruses with a single-strand “+” RNA, its genome size varying from 25 to 32 thousands of nucleotides. They cause respiratory and intestinal diseases in animals and humans. The review contains the data on human infection cases induced by a new coronavirus (NCoV), as well as the information about probable natural agent reservoirs, mechanisms of transmission, some characteristic features of the etiological agent, methods of diagnostics and identification, complete genome sequence, and NCoV relation to the established coronaviruses. Date A point or period of time associated with an event in the lifecycle of the resource 2015 Subject The topic of the resource коронавирус, резервуар возбудителя, механизм передачи инфекции, филогенетический анализ, coronavirus, agent reservoir, mechanism of transmission, Phylogenetic analysis Identifier An unambiguous reference to the resource within a given context DOI: 10.21055/0370-1069-2015-2-68-74 Source A related resource from which the described resource is derived Проблемы особо опасных инфекций Publisher An entity responsible for making the resource available Federal Government Health Institution, Russian Research Anti-Plague Institute “Microbe” Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Infectious and parasitic diseases Language A language of the resource RU https://www.socictopen.socict.org/files/original/3e4ba02223b46ef4107ff1e9ccab43b0.pdf d44713bee6a6c2337364c87cd1bdb9db Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource MiCoP: microbial community profiling method for detecting viral and fungal organisms in metagenomic samples Creator An entity primarily responsible for making the resource Nathan LaPierre, Serghei Mangul, Mohammed Alser, Igor Mandric, Nicholas C. Wu, David Koslicki, Eleazar Eskin Description An account of the resource Abstract Background High throughput sequencing has spurred the development of metagenomics, which involves the direct analysis of microbial communities in various environments such as soil, ocean water, and the human body. Many existing methods based on marker genes or k-mers have limited sensitivity or are too computationally demanding for many users. Additionally, most work in metagenomics has focused on bacteria and archaea, neglecting to study other key microbes such as viruses and eukaryotes. Results Here we present a method, MiCoP (Microbiome Community Profiling), that uses fast-mapping of reads to build a comprehensive reference database of full genomes from viruses and eukaryotes to achieve maximum read usage and enable the analysis of the virome and eukaryome in each sample. We demonstrate that mapping of metagenomic reads is feasible for the smaller viral and eukaryotic reference databases. We show that our method is accurate on simulated and mock community data and identifies many more viral and fungal species than previously-reported results on real data from the Human Microbiome Project. Conclusions MiCoP is a mapping-based method that proves more effective than existing methods at abundance profiling of viruses and eukaryotes in metagenomic samples. MiCoP can be used to detect the full diversity of these communities. The code, data, and documentation are publicly available on GitHub at: https://github.com/smangul1/MiCoP. Date A point or period of time associated with an event in the lifecycle of the resource 2019 Subject The topic of the resource metagenomics, virome, eukaryome, Abundance estimation, Community profiling, alignment Identifier An unambiguous reference to the resource within a given context DOI: 10.1186/s12864-019-5699-9 Source A related resource from which the described resource is derived BMC Genomics Publisher An entity responsible for making the resource available BMC Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Genetics, Biotechnology Language A language of the resource EN https://www.socictopen.socict.org/files/original/9a77ec6374d65cfb82dd7efab677af99.pdf 6d74232286e295440b076eaf9c750ac7 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource STAble: a novel approach to de novo assembly of RNA-seq data and its application in a metabolic model network based metatranscriptomic workflow Creator An entity primarily responsible for making the resource Igor Saggese, Elisa Bona, Max Conway, Francesco Favero, Marco Ladetto, Pietro Liò, Giovanni Manzini, Flavio Mignone Description An account of the resource Abstract Background De novo assembly of RNA-seq data allows the study of transcriptome in absence of a reference genome either if data is obtained from a single organism or from a mixed sample as in metatranscriptomics studies. Given the high number of sequences obtained from NGS approaches, a critical step in any analysis workflow is the assembly of reads to reconstruct transcripts thus reducing the complexity of the analysis. Despite many available tools show a good sensitivity, there is a high percentage of false positives due to the high number of assemblies considered and it is likely that the high frequency of false positive is underestimated by currently used benchmarks. The reconstruction of not existing transcripts may false the biological interpretation of results as – for example – may overestimate the identification of “novel” transcripts. Moreover, benchmarks performed are usually based on RNA-seq data from annotated genomes and assembled transcripts are compared to annotations and genomes to identify putative good and wrong reconstructions, but these tests alone may lead to accept a particular type of false positive as true, as better described below. Results Here we present a novel methodology of de novo assembly, implemented in a software named STAble (Short-reads Transcriptome Assembler). The novel concept of this assembler is that the whole reads are used to determine possible alignments instead of using smaller k-mers, with the aim of reducing the number of chimeras produced. Furthermore, we applied a new set of benchmarks based on simulated data to better define the performance of assembly method and carefully identifying true reconstructions. STAble was also used to build a prototype workflow to analyse metatranscriptomics data in connection to a steady state metabolic modelling algorithm. This algorithm was used to produce high quality metabolic interpretations of small gene expression sets obtained from already published RNA-seq data that we assembled with STAble. Conclusions The presented results, albeit preliminary, clearly suggest that with this approach is possible to identify informative reactions not directly revealed by raw transcriptomic data. Date A point or period of time associated with an event in the lifecycle of the resource 2018 Identifier An unambiguous reference to the resource within a given context DOI: 10.1186/s12859-018-2174-6 Source A related resource from which the described resource is derived BMC Bioinformatics Publisher An entity responsible for making the resource available BMC Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Biology (General), Computer applications to medicine. Medical informatics Language A language of the resource EN