ReviewFrontiers in immunology2013
A guide to bioinformatics for immunologists.
Review in Frontiers in immunology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Bioinformatic analysis of brucellosis and construction of a diagnostic model based on key genes.Scientific reports · 2025Article
- Molecular and Genetic Analysis of the Increased Number of Genes forPathogens (Basel, Switzerland) · 2025Article
- The Application of Exosomes From Different Sources Loaded with Natural Small-Molecule Compounds in Disease.International journal of nanomedicine · 2025Review
- Molecular similarities between the genes for Trypanosoma cruzi microtubule-associated proteins, mammalian interferons, and TRIMs.Parasitology research · 2024Article
- Tumor Cell-Derived Exosomal Hybrid Nanosystems Loaded with Rhubarbic Acid and Tanshinone IIA for Sepsis Treatment.Journal of inflammation research · 2024Article
- iLBE for Computational Identification of Linear B-cell Epitopes by Integrating Sequence and Evolutionary Features.Genomics, proteomics & bioinformatics · 2020Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bioinformatics includes a suite of methods, which are cheap, approachable, and many of which are easily accessible without any sort of specialized bioinformatic training. Yet, despite this, bioinformatic tools are under-utilized by immunologists. Herein, we review a representative set of publicly available, easy-to-use bioinformatic tools using our own research on an under-annotated human gene, SCARA3, as an example. SCARA3 shares an evolutionary relationship with the class A scavenger receptors, but preliminary research showed that it was divergent enough that its function remained unclear. In our quest for more information about this gene - did it share gene sequence similarities to other scavenger receptors? Did it contain conserved protein domains? Where was it expressed in the human body? - we discovered the power and informative potential of publicly available bioinformatic tools designed for the novice in mind, which allowed us to hypothesize on the regulation, structure, and function of this protein. We argue that these tools are largely applicable to many facets of immunology research.
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.