  Bioinformatics research depends on high-quality databases to provide accurate
results. In silico experiments, correctly performed, may prospect novel
discoveries and elucidates pathways for biological experiments through data
analysis in large scale. However, most biological databases have presented
mistakes, such as data incorrectly classified or incomplete information. Also,
sometimes, data mining algorithms cannot treat these errors, leading to serious
problems for the in silico analysis. Manual curation of data extracted from
literature is a possible solution for this problem. Systematic Literature
Review (SLR), or Systematic Review, is a method to identify, evaluate and
summarize the state-of-the-art of a specific theme. Moreover, SLR allows the
collection from databases restrictively, which allows an analysis with lower
bias than traditional reviews. The SRL approaches have been widely used for
decision-making in medical and environmental studies. However, other research
areas, such as bioinformatics, do not have a specific step-by-step to guide
researchers undertaking the procedures of an SLR. In this study, we propose a
guideline, called BiSRL, to perform SLR in bioinformatics. Our procedures cover
the most traditional guides to produce SLRs adapted to bioinformatics. To
evaluate our method, we propose a case study to detect and summarize SLRs
developed for bioinformatics data. We used two databases: PubMed and
ScienceDirect. A total of 207 papers were screened in four steps: title,
abstract, diagonal and full-text reading. Four evaluators performed the SLR
independently to reduce bias risk. A total of 8 papers was included in the SLR
case study. The case study demonstrates how to implement the methods of BiSLR
to procedure SLR for bioinformatics. BiSLR may guide bioinformaticians to
perform systematic reviews reproducible to collect accurate data for higher
quality analysis.
