ArticleBriefings in bioinformatics2012
A toolbox for developing bioinformatics software.
Article in Briefings in bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- The Plant Phenomics and Genomics Research Data Repository: An On-Premise Approach for FAIR-Compliant Data Acquisition.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Resources and tools for rare disease variant interpretation.Frontiers in molecular biosciences · 2023Review
- Improving bioinformatics software quality through incorporation of software engineering practices.PeerJ. Computer science · 2022Article
- Simulating Illumina metagenomic data with InSilicoSeq.Bioinformatics (Oxford, England) · 2019Article
- The State of Software for Evolutionary Biology.Molecular biology and evolution · 2018Review
- GC4S: A bioinformatics-oriented Java software library of reusable graphical user interface components.PloS one · 2018Article
- Should we have blind faith in bioinformatics software? Illustrations from the SNAP web-based tool.PloS one · 2015Article
- e!DAL--a framework to store, share and publish research data.BMC bioinformatics · 2014Article
- Best practices in bioinformatics training for life scientists.Briefings in bioinformatics · 2013Article
- Pyteomics--a Python framework for exploratory data analysis and rapid software prototyping in proteomics.Journal of the American Society for Mass Spectrometry · 2013Article
- Ten simple rules for the open development of scientific software.PLoS computational biology · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Creating useful software is a major activity of many scientists, including bioinformaticians. Nevertheless, software development in an academic setting is often unsystematic, which can lead to problems associated with maintenance and long-term availibility. Unfortunately, well-documented software development methodology is difficult to adopt, and technical measures that directly improve bioinformatic programming have not been described comprehensively. We have examined 22 software projects and have identified a set of practices for software development in an academic environment. We found them useful to plan a project, support the involvement of experts (e.g. experimentalists), and to promote higher quality and maintainability of the resulting programs. This article describes 12 techniques that facilitate a quick start into software engineering. We describe 3 of the 22 projects in detail and give many examples to illustrate the usage of particular techniques. We expect this toolbox to be useful for many bioinformatics programming projects and to the training of scientific programmers.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.