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I want to publish a transcriptome paper, along with interactive materials enabling readers to peruse the data behind the discussion. The R package 'plotly' enables rendering online-publishable tables that can be filtered & searched for specific entries.
I would like to publish such a table as supplementary to a paper (e.g. preprint version) however usually data repositories allow for uploading files and text. I intend to deposit the first version of my main manuscript to Bioarxiv, where they primarily publish .pdf files.
Would anyone here know an official data repository with a format such that would enable publishing online an interactive table?
A good example of such a table is given here.
Disclaimer: I have also asked this question at SE BioInformatics. Like me, they are unsure which would be the best fitting SE Community.
Journals and services where you can submit your script and manuscript and get DOI:
10% Hispanic/Latino participants), and now provides an unprecedented opportunity Asthma is a chronic inflammatory disorder of the airways characterized by episodes of reversible breathing problems due to airway narrowing and obstruction. The National Heart, Lung, and Blood Institute (NHLBI) is in the midst of a Strategic Visioning process that will help determine its future direction. Data are from PT cells freshly isolated from rat kidneys. Here we report a multi-stage procedure to identify candidate genes likely involved in the etiopathogenesis of PD. Search for NHLBI in Online Dictionary Encyclopedia. (84 FR 65169). Therefore, users who want to access one or more of the hosted controlled studies on the ecosystem must be approved for access to that study in dbGaP. J. Brief Method: kidneys from 6-week-old WT male and female mice (only female proximal tubules were included in this database) were perfused via the left ventricle with perfusion buffer (135 mM NaCl, 1 mM Na 2 HPO 4, 1. Download the catalog Proteomic and Transcriptomic Databases. Feb 10, 2021 · About the National Heart, Lung, and Blood Institute (NHLBI): NHLBI is the global leader in conducting and supporting research in heart, lung, and blood diseases and sleep disorders that advances scientific knowledge, improves public health, and saves lives. Users can perform simple and advanced searches based on annotations relating to sequence, structure and function. Webpage created by Gabrielle Gilmer, Venkatesh Deshpande, Joe Chou, Mark Knepper and Terry Dwyer in the Epithelial Systems Biology Laboratory (Mark Knepper, Chief) at the National Heart, Lung and Blood Institute as part of its Kidney Systems Biology Project. gov. Starting in 1989, and continuing through 1999, participants underwent annual extensive clinical examinations. The first five decades (1950–2000) were characterized by exponential increases in congressional funding appropriations . Individual requests for applications (RFAs) and program announcements (PA) may specify other requirements or expectations for data sharing that apply to specific projects. Jun 21, 2021 · Purpose: To identify functionally related genes associated with diabetic retinopathy (DR) risk using gene set enrichment analyses (GSEA) applied to ge… Research Assoc II-Database Specialist. The data is in a tab-delimited file with header descriptions. With the exception of CHS database programmers and IT staff, access to CHS data by Coordinating Center personnel is limited to data sets with all personal identifiers removed, replaced by a study id number. S. As the data are available for bulk download, the RePORTER system reserves the right to block IP addresses that fail to adhere to instructions in the system's robots. PHI-BLAST performs the search but limits alignments to those that match a pattern in the query. Federal Government. 05% in 49,819 participants of the UK Biobank, the majority (84. Common Fund programs are short-term, goal-driven strategic investments, with deliverables intended to catalyze research across multiple biomedical research The Office of Acquisition and Logistics Management (OALM), under the direction of Diane Frasier, OALM Director and Head of the Contracting Activity (HCA), provides leadership, guidance and oversight for acquisition and logistics at NIH. Access to TOPMed datasets on NHLBI BioData Catalyst powered by Seven Bridges is regulated by policies established by the National Institutes of Health (NIH), the National Heart, Lung, and Blood Institute (NHLBI), and the National Center for Biotechnology Information (NCBI) Database of Genotypes and Phenotypes (dbGaP). The e-mail address is not made public and will only be used if you wish to receive a new password or wish to receive certain news or The National Heart, Lung and Blood Institutue recently released Charting the Future Together: The NHLBI Strategic Vision. To find out if you're eligible for any of our services: Request Services. Research Supplements to Promote Diversity in Health-Related Research for Undergraduate Students (NHLBI) This supplement enables principal investigators with eligible NHLBI research grants to include graduate students in their projects. Users can browse available data using the BioLINCC studies page. gov/. NHLBI-FHS stands for National Heart, Lung, and Blood Institute Family Heart Study. Medical disclaimer The results in this database should not necessarily deemed to be clinical applicable. Marini J. 5 mM glucose, 5 mM HEPES, pH 7. NHLBI Strategic Visioning: Setting an Agenda Together for the NHLBI of 2025 CDC WONDER Online Database, compiled from Compressed Mortality File CMF 1968–1988 Oct 23, 2020 · Gary H. This database provides estimates of NIH funding based on over 250 research categories. It is an online, continuously updated, searchable database of published scientific literature, CDC and NIH Databases considered were those acquired in ongoing and future NHLBI-funded studies and in clinical settings in which the ECG continues to provide valuable information for evaluation and treatment. He earned a Ph. The DSMB has been monitoring the study since it began in May 2011. The web-based CoPaKB portal went live in the Program’s second year and access increased steadily each year. Use of NHLBI Data Repository Datasets. 15 and March 1, early applications are encouraged. NHLBI eCONNECT. (1) This rat is important as a model organism for cardiovascular and psychological research, and has a legacy of decades of study of its physiology in academia and industry. Mar 14, 2018 · To achieve this goal in human population genomics, the NHLBI has partnered with the CDC Office of Public Health Genomics (OPHG) to launch a heart, lung, blood, and sleep disorders knowledge base in population genomics (HLBS-PopOmics). NHLBI provides leadership for a national program in the causes, diagnosis, treatment, and prevention of diseases of the heart, blood vessels, lungs, and blood, and sleep disorders and in the uses of blood and the management of blood resources. Jun 19, 2021 · Column/Element Name Description NHLBIkey. Feb 27, 2018 · It is expected that the performance of critical milestones such as expected enrollment goals will be shared on a regular basis through an NHLBI clinical dashboard database. Mar 28, 2016 · The Blood and Marrow Clinical Trials Network (BMT CTN) is supported by a U10 grant from the NHLBI and NCI, with the NHLBI as the lead institute. Jan 28, 2021 · NIH/NHLBI NIHR (UK), NHMRC (AUS), PREPARE/RECOVER (EU), CIHR (CDN), UPMC (USA), HRC (NZ), Minderoo Foundation Countries 4 2 11 Sites 58
Human cells are known to perform thousands of different biochemical functions and the central dogma of biology states that proteins that catalyze the vast majority of these functions arise from the transcription and translation of the information contained in the respective genome. The International Human Genome Sequencing Consortium reported
20,000 protein-coding genes in the human genome 1 and, surprisingly, the number of protein-coding genes does not scale with the complexity of functions of eukaryotic organisms 2 . These findings have led to the notion that the protein-coding information of the genome is substantially diversified structurally and functionally along the axis of gene expression 3 . Specific mechanisms that catalyze this diversification include alterative splicing of transcripts, posttranslational processing and modification of proteins, and the variable association of proteins in functional protein complexes. Consequently, protein-coding genes frequently give rise to multiple distinct protein species—proteoforms—which have a unique primary amino acid (AA) sequence and localized posttranslational modifications (PTMs) 4,5 and which might, in turn, partition into different protein complexes or show functional differences. Currently, it is estimated that the
20,000 coding genes generate more than a million different proteoforms 6 that can differ between individual cells, tissues, and disease phenotypes 4,7,8,9 . This increase in complexity beyond the directly translated genomic sequence information hampers genotype-based phenotype inference and highlights the importance of capturing proteome diversity to increase the mechanistic understanding of biochemical processes in basic and translational research.
Over the last decades, mass spectrometry (MS) has emerged as the key technology for proteomic analyses 10,11 . The large array of mass spectrometric techniques can be grouped into two main approaches: top-down and bottom-up proteomics. In top-down workflows, samples containing intact proteins are chromatographically separated, ionized, and analyzed in a mass spectrometer. Recorded spectra of both the intact and fragmented proteins determine the unique primary protein sequence and PTMs of individual proteoforms 12 . Recent top-down proteomic studies reported the identification of more than 3000 unique proteoforms originating from up to
1000 individual genes 13,14 . Gaining deeper proteoform coverage by top-down proteomics is challenged by the limitations of current separation techniques, the MS and tandem MS (MS/MS) analysis of large ions, and the interpretation of the resulting spectra by available analysis software 12,15 . Although top-down proteomics provides unprecedented insights into proteoform diversity and some proteoforms have successfully been annotated with molecular functions and implicated phenotypic traits 7 , the systematic assessment of proteoform-specific functions remains challenging. A shift of focus from the mere enumeration of various proteoforms detected from a cell towards establishing direct links between proteoform species and their functional significance would be a major advance in the field.
Bottom-up proteomics is the more widely used technique for proteome-wide studies, because some of the technical challenges facing top-down proteomics are alleviated. Here, proteins are enzymatically digested into smaller peptide sequences, which are subsequently separated by liquid chromatography, ionized and analyzed by MS/MS. The identity and quantity of proteins in the tested sample are subsequently inferred from the peptides that are identified based on the acquired precursor and fragment ion spectra. The method is technically robust and has demonstrated the detection of translation products of the vast majority of coding genes in a number of species. However, bottom-up proteomic workflows suffer from the principal limitation that the connectivity between identified peptides and their proteins of origin is lost during the enzymatic digestion step. This necessitates an in silico inference step that maps measured peptide signals back to individual proteins. This is a challenging task in general 16 and is particularly hard for resolving different proteoforms 3 . Recent advances in instrumentation, data acquisition and data analysis, especially the development of data-independent acquisition (DIA/Sequential Window Acquisition of all Theoretical (SWATH)-MS) strategies, have enabled the measurement of large bottom-up proteomic datasets at high proteome coverage, combined with consistent and accurate quantification 17,18,19 . Based on these developments, the peptide-level bottom-up proteomic data became more reliable, both on the qualitative and quantitative level, as demonstrated in several of our previous studies 20,21 . Thus, useful information about the presence of individual modifications or sequence variants on the peptide level can be readily obtained. However, the possibilities to systematically assign and distinguish unique proteoforms from bottom-up proteomics datasets remains a mostly unexplored area to date.
Nevertheless, researchers in the early days of bottom-up proteomics already observed that peptides of the same protein might follow distinct quantitative patterns across a dataset, and that peptide co-variation analysis can be leveraged to improve proteomic analyses on different levels. The predominant focus of previous work has been to use peptide correlation analysis for the purpose of filtering out dissimilarly behaving peptides in an effort to improve protein quantification 22,23 or protein inference 24 . It has also been recognized that some of the determined “outlier” peptides could indeed contain valuable biological information, e.g., by originating from different proteoforms and previous work explored the possibility to use peptide correlation patterns for proteoform assignment 23,25,26,27 .
In this work, we present COPF, a strategy for COrrelation-based functional ProteoForm assessment in bottom-up proteomics data. COPF extends the concept of peptide correlation analysis towards establishing a generic workflow with the main purpose of systematically assigning peptides to co-varying proteoform groups (also see Glossary in Supplementary Table 1). We benchmark COPF against PeCorA, a state-of-the-art tool for proteoform identification in bottom-up proteomics data 27 , demonstrating that COPF performs better in the detection of proteoforms differing by multiple peptides. Furthermore, our data show that COPF results are based on a conservative and well-calibrated error model, and that the strategy is applicable to complex experimental designs and also the analysis of a single condition. We first demonstrate the capabilities of COPF by applying it to a dataset where cells in two cell cycle stages are compared. The dataset was generated by protein complex co-fractionation via size-exclusion chromatography (SEC) coupled to DIA/SWATH-MS 28 . The results indicate that COPF is capable to systematically detect assembly- and cell cycle-specific proteoform groups. As a second example, we apply COPF to assign functional proteoform groups in a typical bottom-up proteomic cohort study consisting of five tissue samples from the mouse BXD genetic reference panel 29 . In this dataset, COPF could determine several tissue-specific proteoform groups. The wealth of biological information that COPF provides for both the cell cycle SEC-SWATH-MS and mouse tissue datasets can be further investigated on the online platform that we provide for manual data exploration: http://proteoformviewer.ethz.ch/. The COPF algorithm is fully integrated and is available within the CCprofiler framework 21,30 . It includes specific modules to assess the biological credibility of detected proteoform groups and the unique possibility to directly integrate COPF results into protein complex analysis to determine assembly-specific proteoforms. We envision that COPF can make a significant contribution towards the systematic assessment of proteoform groups across large bottom-up proteomic datasets and for linking these groups to biological functions.