Evidence map›Paper›PMID 39910485›Full record

ArticleBMC genomics2025

sORFdb - a database for sORFs, small proteins, and small protein families in bacteria.

Julian M Hahnfeld, Oliver Schwengers, Lukas Jelonek, Sonja Diedrich, Franz Cemič, Alexander Goesmann

Abstract read
In one paragraph

Article in BMC genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
  2. The Ascent of Miniproteins.Biochemistry · 2026
    Review
  3. Article
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Julian M HahnfeldBioinformatics and Systems Biology, Justus Liebig University Giessen, Heinrich-Buff-Ring, Giessen, 35392, Hesse, Germany. julian.hahnfeld@computational.bio.uni-giessen.de.
Oliver SchwengersBioinformatics and Systems Biology, Justus Liebig University Giessen, Heinrich-Buff-Ring, Giessen, 35392, Hesse, Germany.
Lukas JelonekBioinformatics and Systems Biology, Justus Liebig University Giessen, Heinrich-Buff-Ring, Giessen, 35392, Hesse, Germany.
Sonja DiedrichBioinformatics and Systems Biology, Justus Liebig University Giessen, Heinrich-Buff-Ring, Giessen, 35392, Hesse, Germany.
Franz CemičDepartment of Computer Science, University of Applied Sciences Giessen, Gutfleischstrasse, Giessen, 35390, Hesse, Germany.
Alexander GoesmannBioinformatics and Systems Biology, Justus Liebig University Giessen, Heinrich-Buff-Ring, Giessen, 35392, Hesse, Germany.

Funding

Darwin Doctoral Scholarship 2020/1
6 · The paper itself

Abstract

Small proteins with fewer than 100, particularly fewer than 50, amino acids are still largely unexplored. Nonetheless, they represent an essential part of bacteria's often neglected genetic repertoire. In recent years, the development of ribosome profiling protocols has led to the detection of an increasing number of previously unknown small proteins. Despite this, they are overlooked in many cases by automated genome annotation pipelines, and often, no functional descriptions can be assigned due to a lack of known homologs. To understand and overcome these limitations, the current abundance of small proteins in existing databases was evaluated, and a new dedicated database for small proteins and their potential functions, called 'sORFdb', was created. To this end, small proteins were extracted from annotated bacterial genomes in the GenBank database. Subsequently, they were quality-filtered, compared, and complemented with proteins from Swiss-Prot, UniProt, and SmProt to ensure reliable identification and characterization of small proteins. Families of similar small proteins were created using bidirectional best BLAST hits followed by Markov clustering. Analysis of small proteins in public databases revealed that their number is still limited due to historical and technical constraints. Additionally, functional descriptions were often missing despite the presence of potential homologs. As expected, a taxonomic bias was evident in over-represented clinically relevant bacteria. This new and comprehensive database is accessible via a feature-rich website providing specialized search features for sORFs and small proteins of high quality. Additionally, small protein families with Hidden Markov Models and information on taxonomic distribution and other physicochemical properties are available. In conclusion, the novel small protein database sORFdb is a specialized, taxonomy-independent database that improves the findability and classification of sORFs, small proteins, and their functions in bacteria, thereby supporting their future detection and consistent annotation. All sORFdb data is freely accessible via https://sorfdb.computational.bio .

Indexed as

BacteriaBacterial ProteinsDatabases, ProteinMolecular Sequence AnnotationBacterial ProteinsBacteriaDatabaseProtein familiesShort open reading framesSmall proteinsSORF

Identifiers

PMID39910485
PMCPMC11796252

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Registered trials

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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.