Mammalian Annotation Database for improved annotation and functional classification of Omics datasets from less well-annotated organisms is a dataset published in Database (2019). On theSindex it has a DataRank of 0.568, placing it in the top 47.2% of the data-sharing corpus. It has been cited 16 times, with 6 citing works in its 1-hop citation network. Its calibrated FAIR score is 46/100.
Abstract Next-generation sequencing technologies and the availability of an increasing number of mammalian and other genomes allow gene expression studies, particularly RNA sequencing, in many non-model organisms. However, incomplete genome annotation and assignments of genes to functional annotation databases can lead to a substantial loss of information in downstream data analysis. To overcome this, we developed Mammalian Annotation Database tool (MAdb, https://madb.ethz.ch) to conveniently provide homologous gene information for selected mammalian species. The assignment between species is performed in three steps: (i) matching official gene symbols, (ii) using ortholog information contained in Ensembl Compara and (iii) pairwise BLAST comparisons of all transcripts. In addition, we developed a new tool (AnnOverlappeR) for the reliable assignment of the National Center for Biotechnology Information (NCBI) and Ensembl gene IDs. The gene lists translated to gene IDs of well-annotated species such as a human can be used for improved functional annotation with relevant tools based on Gene Ontology and molecular pathway information. We tested the MAdb on a published RNA-seq data set for the pig and showed clearly improved overrepresentation analysis results based on the assigned human homologous gene identifiers. Using the MAdb revealed a similar list of human homologous genes and functional annotation results regardless of whether starting with gene IDs from NCBI or Ensembl. The MAdb database is accessible via a web interface and a Galaxy application.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
Base Score Contribution
0.425
From this paper's citation signal
Citation Network Contribution
0.143
From 4 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 75% comes from its base citations and 25% from the citation network (4 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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