Project description:Background: International development partners and research councils are increasingly funding research management and support (RMS) capacity strengthening initiatives in sub-Saharan Africa (SSA) as part of a broader investment in strengthening national and regional research systems. However, the evidence-base to inform RMS capacity strengthening initiatives is limited at present. This research note presents a synthesis of 28 RMS capacity assessments completed in 25 universities/research institutions from across 15 SSA countries between 2014 and 2018. Methods: All 28 capacity assessments were completed following a standardised methodology consisting of semi-structured interviews conducted with research and research support staff at the respective institution as well as document reviews and observation of onsite facilities. Data were extracted from the 28 reports detailing the findings of each assessment according to a framework synthesis approach. Results: In total, 13 distinct capacity gap categories emerged from across the 28 RMS capacity assessment reports. Almost all the institutions assessed faced multiple gaps in RMS capacity within and across each of these 13 categories. The 13 categories were not independent of each other and were often closely inter-connected. Commonalities were also evident across multiple categories, the two most obvious of which were severe fiscal constraints and the often-complex bureaucracy of the institutional operating environment. Conclusions: The synthesis findings reveal multiple, commonly shared RMS capacity gaps in universities and research institutions across SSA. No single intervention type, or focus, would be sufficient to strengthen capacity across all 13 areas; rather, what is needed to facilitate a significant shift in RMS capacity within such SSA universities and research institutions is a combination of interventions, consisting of differing levels of cost and complexity, variously led (or supported) by both internal and external actors.

Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Terry Furey mailto:tsfurey@duke.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). These tracks display DNaseI hypersensitivity (HS) evidence as part of the four Open Chromatin track sets. DNaseI is an enzyme that has long been used to map general chromatin accessibility, and DNaseI "hypersensitivity" is a feature of active cis-regulatory sequences. The use of this method has led to the discovery of functional regulatory elements that include promoters, enhancers, silencers, insulators, locus control regions, and novel elements. DNaseI hypersensitivity signifies chromatin accessibility following binding of trans-acting factors in place of a canonical nucleosome. Together with FAIRE and ChIP-seq experiments, these tracks display the locations of active regulatory elements identified as open chromatin in multiple cell types (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=cellType) from the Duke, UNC-Chapel Hill, UT-Austin, and EBI ENCODE group. Within this project, open chromatin was identified using two independent and complementary methods: these DNaseI HS assays and Formaldehyde-Assisted Isolation of Regulatory Elements (FAIRE), combined with chromatin immunoprecipitation (ChIP) for select regulatory factors. DNaseI HS and FAIRE provide assay cross-validation with commonly identified regions delineating the highest confidence areas of open chromatin. ChIP assays provide functional validation and preliminary annotation of a subset of open chromatin sites. Each method employed Illumina (formerly Solexa) sequencing by synthesis as the detection platform. The Tier 1 and Tier 2 cell types were additional verified by a second platform, high-resolution 1% ENCODE tiled microarrays supplied by NimbleGen. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf

Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Terry Furey mailto:tsfurey@duke.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). These tracks display DNaseI hypersensitivity (HS) evidence as part of the four Open Chromatin track sets. DNaseI is an enzyme that has long been used to map general chromatin accessibility, and DNaseI "hypersensitivity" is a feature of active cis-regulatory sequences. The use of this method has led to the discovery of functional regulatory elements that include promoters, enhancers, silencers, insulators, locus control regions, and novel elements. DNaseI hypersensitivity signifies chromatin accessibility following binding of trans-acting factors in place of a canonical nucleosome. Together with FAIRE and ChIP-seq experiments, these tracks display the locations of active regulatory elements identified as open chromatin in multiple cell types (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=cellType) from the Duke, UNC-Chapel Hill, UT-Austin, and EBI ENCODE group. Within this project, open chromatin was identified using two independent and complementary methods: these DNaseI HS assays and Formaldehyde-Assisted Isolation of Regulatory Elements (FAIRE), combined with chromatin immunoprecipitation (ChIP) for select regulatory factors. DNaseI HS and FAIRE provide assay cross-validation with commonly identified regions delineating the highest confidence areas of open chromatin. ChIP assays provide functional validation and preliminary annotation of a subset of open chromatin sites. Each method employed Illumina (formerly Solexa) sequencing by synthesis as the detection platform. The Tier 1 and Tier 2 cell types were additional verified by a second platform, high-resolution 1% ENCODE tiled microarrays supplied by NimbleGen. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Cells were grown according to the approved ENCODE cell culture protocols (http://hgwdev.cse.ucsc.edu/ENCODE/protocols/cell). DNaseI hypersensitive sites were isolated using methods called DNase-seq or DNase-chip (Song and Crawford, 2010; Boyle et al., 2008a; Crawford et al., 2006). Briefly, cells were lysed with NP40, and intact nuclei were digested with optimal levels of DNaseI enzyme. DNaseI digested ends were captured from three different DNase concentrations, and material was sequenced using Illumina (Solexa) sequencing. DNase-seq data for Tier 1 and Tier 2 cell lines were verified by comparing multiple independent growths (replicates) and determining the reproducibility of the data. In general, cell lines were verified if 80% of the top 50,000 peaks in one replicate are detected in the top 100,000 peaks of a second replicate. For some cell types, additional verification was performed using similar material hybridized to NimbleGen Human ENCODE tiling arrays (1% of the genome) along with the input DNA as reference (DNase-chip). A more detailed protocol is available at http://hgwdev.cse.ucsc.edu/ENCODE/protocols/general/Duke_DNase_protocol.pdf. The read length for sequences from DNase-seq are 20 bases long due to a MmeI cutting step of the approximately >50kb DNA fragments extracted after DNaseI digestion. Sequences from each experiment were aligned to the genome using BWA (Li et al., 2010) for the NCBI 36 (hg19) assembly. The command used for these alignments was > bwa aln -t 8 genome.fa s_1.sequence.txt.bfq > s_1.sequence.txt.sai Where genome.fa is the whole genome sequence and s_1.sequence.txt.bfq is one lane of sequences convert into the required bfq format. Sequences from multiple lanes are combined for a single replicate using the bwa samse command, and converted in the sam/bam format using samtools. Only those that aligned to 4 or fewer locations were retained. Other sequences were also filtered based on their alignment to problematic regions (such as satellites and rRNA genes - see supplemental materials at http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeOpenChromDnase/supplemental/). The mappings of these short reads to the genome are available for download at http://hgwdev.cse.ucsc.edu/cgi-bin/hgFileUi?g=wgEncodeOpenChromDnase. The resulting digital signal was converted to a continuous wiggle track using F-Seq that employs Parzen kernel density estimation to create base pair scores (Boyle et al., 2008b). Input data has been generated for several cell lines. These are used directly to create a control/background model used for F-Seq when generating signal annotations for these cell lines. These models are meant to correct for sequencing biases, alignment artifacts, and copy number changes in these cell lines. Input data is not being generated directly for other cell lines. Instead, a general background model was derived from the available Input data sets. This should provide corrections for sequencing biases and alignment artifacts, but will not correct for cell type specific copy number changes. The exact command used for this step is > fseq -l 600 -v -f 0 -b <bff files> -p <iff files> aligments.bed where the (bff files) are the background files based on alignability, the (iff files) are the background files based on the Input experiments, and alignments.bed are a bed file of filtered sequence alignments. Discrete DNaseI HS sites (peaks) were identified from DNase-seq F-seq density signal. Significant regions were determined by fitting the data to a gamma distribution to calculate p-values. Contiguous regions where p-values were below a 0.05/0.01 threshold were considered significant. Data from the high-resolution 1% ENCODE tiled microarrays supplied by NimbleGen were normalized using the Tukey biweight normalization, and peaks were called using ChIPOTle (Buck, et al., 2005) at multiple levels of significance. Regions matched on size to these peaks that were devoid of any significant signal were also created as a null model. These data were used for additional verification of Tier 1 and Tier 2 cell lines by ROC analysis. Files containing this data can be found in the Downloads directory (http://hgwdev.cse.ucsc.edu/cgi-bin/hgFileUi?db=hg19&g=wgEncodeOpenChromDnase) labeled Validation view. Release 1 (April 2011) of this track consists of a remapping of all previously released experiments to the human reference genome GRCh37/hg19 (these data were previously mapped to NCBI36/hg18; please see the Release Notes section of the hg18 Open Chromatin track (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg18&g=wgEncodeChromatinMap) for information on the NCBI36/hg18 releases of the data). There are 21 new DNaseI experiments in this release, on 19 new cell lines. New to this release is a reconfiguration of how this track is displayed in relation to other tracks from the Duke/UNC/UT-Austin/EBI group. A synthesis of open chromatin evidence from the three assay types was compiled for Tier 1 and 2 cell lines plus NHEK will also be added in this release and can be previewed in: Open Chromatin Synthesis (http://genome-preview.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeOpenChromSynth). Enhancer and Insulator Functional assays: A subset of DNase and FAIRE regions were cloned into functional tissue culture reporter assays to test for enhancer and insulator activity. Coordinates and results from these experiments can be found at http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeOpenChromDnase/supplemental/.

Project description:BackgroundDespite considerable institutional experimentation at national and international levels in response to calls for global health security reform, there is little research on organisational models that address outbreak preparedness and response. Created in the aftermath of the 2013-16 West African Ebola epidemic, the United Kingdom's Public Health Rapid Support Team (UK-PHRST) was designed to address critical gaps in outbreak response illuminated during the epidemic, while leveraging existing UK institutional strengths. The partnership between the government agency, Public Health England, and an academic consortium, led by the London School of Hygiene and Tropical Medicine, seeks to integrate outbreak response, operational research and capacity building. We explored the design, establishment and early experiences of the UK-PHRST as one of the first bodies of its kind globally, paying particular attention to governance decisions which enabled them to address their complex mission.MethodsWe conducted a qualitative case study using 19 in-depth interviews with individuals knowledgeable about the team's design and implementation, review of organisational documents, and observations of meetings to analyse the UK-PHRST's creation, establishment and initial 2 years of operations.ResultsAccording to key informants, adopting a triple mandate (response, research and capacity building) established the team as novel in the global epidemic response architecture. Key governance decisions recognised as vital to the model included: structuring the team as a government-academic collaboration which leveraged long-term and complementary UK investments in public health and the higher education sector; adopting a more complex, dual reporting and funding structure to maintain an ethos of institutional balance between lead organisations; supporting a multidisciplinary team of experts to respond early in outbreaks for optimal impact; prioritising and funding epidemic research to influence response policy and practice; and ensuring the team's activities reinforced the existing global health architecture.ConclusionThe UK-PHRST aims to enhance global outbreak response using an innovative and integrated model that capitalises on institutional strengths of the partnership. Insights suggest that despite adding complexity, integrating operational research through the government-academic collaboration contributed significant advantages. This promising model could be adopted and adapted by countries seeking to build similar outbreak response and research capacities.

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Research management team (RMT): a model for research support services at Duke University.

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