Agile software development methods promise shorter time-to-market and higher product quality, but lack the ability of long-term planning or coping with large projects. However, software companies often also want the ability of long-term planning, promised by traditional or plan-based methods. To benefit from the strengths of both approaches, software companies often use a combination of agile and plan-based methods, known as hybrid development approaches. These approaches strongly depend on the individual context and are customized. Therefore, companies have to organize their hybrid development approach individually. However, practitioners often have difficulties with the organization of hybrid approaches. The organization considers how the phases, activities, roles, and artifacts are arranged and connected. Research lacks the necessary detailed insight into how hybrid development approaches are organized to support practitioners. To gain better understanding of the organization of hybrid approaches, we conducted a systematic literature review to gather descriptions of hybrid approaches. We analyzed the found papers thoroughly and could identify three general patterns of how hybrid approaches are organized. We found that all these patterns are still based on Royce's waterfall model and use the standard software engineering activities. Our findings shall help to lead further research and help practitioners to better organize their individual development approach.
We also applied SMR16 to the same set of GWAMA studies, using the GTEx eQTL associations. We downloaded version 0.66 of the software from the SMR website, and ran it using the default parameters. We converted the GWAMA and GTEx eQTL studies to SMR input formats. In order to have SMR compute the colocalization test, for those few GWAMA studies where allele frequency was not reported, we filled in with frequencies from the 1000 Genomes Project42 as an approximation. We also used the 1000 Genomes genotype data as reference panel for SMR.
European samples had been split into ten groups during imputation to ease the computational burden on the Michigan server, so after obtaining the imputed .vcf files, we used the software PLINK43 to convert the genotype files into the PLINK binary file format and merge the ten groups of samples together, while dropping any variants not found in all sample groups. For the association analysis, we performed a logistic regression using PLINK, and following QC practices from ref. 14 we filtered out individuals with genotype missingness >0.03 and filtered out variants with minor allele frequency 0.05, out of Hardy-Weinberg equilibrium significant at 1e-6, or had imputation quality
A.N.B. contributed to S-PrediXcan software developmentl; executed S-PredixCan runs on the GWAS traits; developed framework for comparing PrediXcan and S-PrediXcan in simulated, cellular and WTCCC phenotypes; ran COLOC and SMR; developed gene2pheno.org database and web dashboard; contributed to the main text, supplement, figures, and analyses. S.P.D. performed the GTEx model training; ran the GERA GWAS; contributed to the main text. J.M.T. contributed to the main text. J.Z. contributed to the figures and predictdb.org resource. E.S.T. contributed to S-PrediXcan software. H.E.W. contributed to the main text and analysis. K.P.S. ran PrediXcan on WTCCC data and contributed to the analysis. R.B. contributed to the main text and figures. T.G. ran imputation of GERA genotypes. T.L.E. contributed to the analysis. L.M.H. contributed to the main text. E.A.S. contributed to the main text. D.L.N. contributed to the main text and analysis. N.J.C. contributed to the analysis. H.K.I. conceived the method, supervised the project, performed analysis, contributed to the main text, supplement, and figures. The GTEx consortium authors contributed in the collection, gathering, and processing of GTEx study data used for training transcriptome prediction models and running COLOC and SMR.
High-throughput "omics" based data analysis play emerging roles in life sciences and molecular diagnostics. This emphasizes the urgent need for user-friendly windows-based software interfaces that could process the diversity of large tab-delimited raw data files generated by these methods. Depending on the study, dozens to hundreds of these data tables are generated. Before the actual statistical or cluster analysis, these data tables have to be combined and merged to expression matrices (e.g., in case of gene expression analysis). Gene annotations as well as information concerning the samples analyzed may be appended, renewed or extended. Often additional data values shall be computed or certain features must be filtered out.
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While Clover claims its software tool, Clover Assistant, is aimed at helping doctors improve patient care, former employees told us that it was first and foremost a coding tool. According to one former employee:
Often, software that delights customers is sold to them, resulting in revenue for the software company. In other instances, such as with companies like Google and Facebook, the aim is to provide delightful software for free in the hopes of rapidly growing user adoption, which then forms a foundation to generate revenue.
Contrary to these market-tested approaches, Clover instead pays doctors $200 per visit to use its software, resulting in Clover paying twice the average Medicare reimbursement rate for a standard visit. [Pg. 57, Pg. 252]
Another doctor we interviewed at a large New Jersey practice that uses Clover Assistant told us doctors were frustrated with the software but were obligated to use it to generate additional revenue for the practice.
The article page (shown in Figure 4b) is the cornerstone of the TFe website, as it is where articles are accessed. Articles in TFe are organized into ten tabbed sections titled 'Summary', 'Structure', 'TFBS' (TF binding site), 'Targets', 'Protein', 'Interactions', 'Genetics', 'Expression', 'Ontologies', and 'Papers' (that is, references) (Figure 5). Above the tabs lies a standard header that displays pertinent information regarding the TF, including the TF symbol, species, classification, the date of the most recent revision, and an article completion score bar (Figure 5). Sections generally contain a mixture of author-curated and automatically populated content, typically in the form of an expert-written overview text - the author-curated portion - followed by several additional headings filled with a mixture of author-curated and automatically populated content. See Figure 6 for a comprehensive list of all automatically populated and manually curated content available in the article page. The automatically populated content represents data that we have incorporated into TFe from second and third party resources, including: BioGRID , Ensembl , Entrez Gene , Gene Ontology , MeSH , the Mouse Genome Database , Online Mendelian Inheritance in Man (OMIM) , PAZAR , RCSB Protein Data Bank , the UCSC Genome Browser  and the Allen Brain Atlas . More details on the software tools and data repositories utilized in the generation of automatically populated content found in each tab are presented in Table 1.
Like every other tab, the 'Summary' tab user interface is a content viewer and editor combined into one. When expert authors wish to implement changes to their articles, they may 'sign in' to TFe using their personalized user accounts. After this is done, they are able to see the normally hidden editing interface that allows them to upload text, figures, figure captions, references, external links, and data, depending on the tab. The editing interface supports the widely used wiki syntax to allow basic text formatting, such as bolding, italicizing, underlining, and the creation of bulleted and numbered lists. All text entered in wiki syntax is converted to HTML by a local installation of the MediaWiki software. Authors also have the option to add PubMed references anywhere in their text by using special tags that look like '(pmid:16371163)' - without the quotes. These tags are automatically converted to a proper citation (Vancouver style) by the TFe software. Figures can be uploaded in many different image formats, while figure captions are submitted as text. PubMed citations are also supported in figure caption text.
Format of the PDF article. The PDF mini summaries are composed of four pages. The first page features basic information such as the TF name, gene identifiers and classification, as well as author information. Also on the first page are the names and affiliations of the authors, an overview of the TF, an image of its active site protein structure accompanied by a brief commentary, and a featured TF binding profile selected by the author. The second and third pages contain a mixture of figures, paragraph text, and tables of genomic targets and protein as well as ligand interactors. The last page contains two brief paragraphs, a MeSH cloud, and selected references. These are the first two pages of a four-page PDF mini summary generated by the TFe system software. Our PDF creation tool, based on in-house code and the dompdf 0.5.1 open source module, is able to format a TFe article of any length and annotation depth as a standardized four-page PDF article. A fuzzy logic algorithm does all of the modifications necessary to make the conversion. These modifications may include changing the sizes of the figures, truncating excess text, reformatting the references, and calculating trade-offs between having larger figures and data tables at the expense of less text, or keeping more text at the expense of having fewer figures and smaller data tables. 2b1af7f3a8