 provides connections to a large number of annotation resources for gene and genome information of many organisms. It can access feature data from a number of gene or genome centric packages.
Here, I want to explore some of these packages and compare the information they contain.
How many genes are there in different databases?
Because genes can have identifiers from many databases (but not necessarily from all and there is not always a one-to-one mapping between them), we compare the number of unique identifiers for Entrez and Ensembl, as well as for gene symbol.
Ensembl is a genome browser but it also includes gene annotations.
Gene symbols represent the official gene name abbreviation from HGNC.
The first package I looked at was org.Hs.eg.db, which contains genome wide annotations for the human genome.
EnsDb.Hsapiens.v79 contains annotation information from Ensembl. Here, we also find genes based on Ensembl, Entrez and HGNC IDs.
TxDb.Hsapiens.UCSC.hg38.knownGene provides annotation information from UCSC, specifically on transcripts in form of TxDb objects. Here, we only find comparable information on Entrez IDs.
When we compare the numbers of genes we can obtain from the three databases Ensembl, Entrez and HGNC via three R packages (org.Hs.eg.db, EnsDb.Hsapiens.v79 and TxDb.Hsapiens.UCSC.hg38.knownGene), we see that there are strong differences: There seem to be either around 25000 to 28000 genes or around 60000. The only database with consistent numbers is HGNC. The reason for these divergent numbers could be due to how they classify a “gene”. Everything that is around 25000 could be because they consider protein coding genes only. Will we check if that’s indeed the case in a minute.
First, I want to know how well the database information from different packages overlaps. Venn diagrams show us the number of genes overlapping between the three databases.
While there is a reasonably large overlap between packages, there are still many genes which are either in one or the other package.
For gene names this is worst, but it might be due to changing names of genes (one gene often has several symbols which can be used, the preferred one often changing with new knowledge about the gene’s function).
For Ensembl and Entrez it seems like one of the databases simply contains more gene entries than the other(s), while the majority of genes from the smaller database are included.
Gene biotype annotation tells us the general category of a gene. The biggest category is protein coding genes. They allow us to check whether the genes in different databases and package consider different categories as “genes”.
Gene biotype information is only available for genes in EnsemblDb! Thus, the classification of genes in the other packages is biased in that only genes in EnsemblDb could be considered.
## Key: Ensembl_EnsDb has 65774 unique rows without NAs.
## Key: Entrez_EnsDb has 24263 unique rows without NAs.
## Key: HGNC_EnsDb has 59074 unique rows without NAs.
## Key: Entrez_orgDb has 60136 unique rows without NAs.
## Key: Ensembl_orgDb has 28015 unique rows without NAs.
## Key: HGNC_orgDb has 60083 unique rows without NAs.
## Key: Entrez_TxDb has 25217 unique rows without NAs.
How many protein coding genes are in each database and annotation package?
Ensembl IDs from EnsDb.Hsapiens.v79 have the most protein coding genes, which is consistent with also having the highest number of annotations in general. The number of protein coding genes in the other databases/ packages is only slightly lower, however and fits well with the predicted 20000 to 25000.
colnames(genebiotypes_table_gather)[2] <- "DB"
When looking at the percentage of protein coding genes among all gene annotations we can confirm that the database/ package combinations with lower gene numbers (Ensembl org.Db, Entrez EnsDb and Entrez TxDb) are mainly comprised of protein coding genes while databases/ packages with around 60000 gene entries consider other functional units as well.
What are these other biotypes that most databases/ packages consider as genes? The second largest group following protein coding genes are processed pseudogenes, then come lincRNAs and other small RNAs.
What does this mean for genetics and genomics research?
Overall, Ensembl gene annotations from EnsDb.Hsapiens.v79 have the largest number of gene annotations.
However, this comparison shows that it can be critical for identifying genes and annotating them correctly in your analyses to consider the strengths and weaknesses of the different databases and packages you use to access the information. I believe it is well worthwhile the time and effort to consider what information a database can or cannot give you before using it on your data.
A new analysis shows that, in every state, the bill would save a family of four with an infant in full-time care a minimum on $6,000 a year.
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President Joe Biden wants to establish universal pre-K, and a massive new study shows the benefits it has on attendees can last for years afterward. Nothing would make me happier than to see FMT or anything else for that matter be shown to help with any or all autism symptoms. So I'm crossing my fingers for you and everyone with ASD.

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This research shows that games like Bingo can be used to slow down the fatal spreading of these diseases. Bingo is a fun game to play, but its intelligent healing benefits are just opening to arise.
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This reaches well past the Bingo hall, however. Insertion a black couch in a white room can also lead to the same results midst dementia patients. Increases in difference help people to well create rational divisions midst different items. But Bingo is real and even preferred because of its social sides. Bingo games bring senior citizens together and create rapports. This is roughly that simple contrasts within the home cannot replicate.
Extra result of this study showed that dementia patients will even eat