Synonymous substitution (which is often called silent mutation before) is a kind of point mutation occurs in genes' coding region. Because the codons' degeneracy, it does not alter the Amino acid residue. As a result, for a long time, it's considered no bio function, treated as "silent".

But recent research shows "Silent Mutation Make Some Noise", and makes people focus on the area. Efforts have been made, one possible mechanism has been proposed, and synonymous mutations specifically change the splicing motifs and cause abnormal splicing.

However, there is no database focusing on the dangerous of synonymous mutations yet. Therefore, we have collected diseases related mutation to construct a Database of Deleterious Synonymous mutation (dbDSM) that would help in understanding the relationship between diseases and the synonymous mutations. Here we present the updated dbDSMv2.0 database, which newly added the tmVar data and updated ClinVar, GWAS Catalog data. In the new version of the database, we added new annotation features, including transcripts, conservatism, etc and we used the voting method to integrate the scores of the five scoring tools (SilVA, DDIG-SN, FATHMM-MKL, CADD, and TraP) which is named dbDSMScore to further assess the deleterious of the variants. Notably, variants from the original literature are marked in red.

Data source	dbDSMv1.0	dbDSMv2.0
1.Manually curated	Yes	Yes
2.GRASP	Yes	Yes
3.ClinVar	Yes	Yes
4.PolymiRTS	Yes	Yes
5.GWAS catalog	Yes	Yes
6.GWASdb	Yes	Yes
7.tmVar	No	Yes

SNPs	Number of records in dbDSMv1.0	Number of records in dbDSMv2.0
With ref ID	1219	2116
Without ref ID	116	631
Total	1335	2747

To the best of our knowledge, there is no consensus classification of molecular mechanisms by which synonymous mutations contribute to disease phenotype. Therefore, after reviewing the literature (Bali and Bebok, 2015; Gotea et al, 2015; Hunt et al, 2014; Sauna and Kimchi-Sarfaty, 2011), we used a classification system in the database as follows:

Classification	Description
Splicing regulation	Synonymous mutations may disrupt critical elements necessary for splicing, which could result in multiple outcomes, such as exon truncation, exon skipping, and intron inclusion.
mRNA structure	Synonymous mutations can alter mRNA structure, which in turn can change transcript stability and translation dynamics.
microRNA binding	Synonymous mutations can change the nucleotide sequence recognized by microRNAs, which can alter gene expression levels.
Transcription factor regulation	Synonymous mutations can add or subtract exonic transcription factor binding sites, leading to altered gene expression levels.
Protein synthesis	Protein translation and folding can also be influenced by synonymous mutations through codon usage (rare versus frequent codons), tRNA availability, mRNA shape, and ribosome structure.
n/a	If the mechanism is not available, the classification information is indicated as n/a.

References：

Bali, V., & Bebok, Z. (2015). Decoding mechanisms by which silent codon changes influence protein biogenesis and function. The International Journal of Biochemistry & Cell Biology, 64, 58-74.

Gotea, V., Gartner, J. J., Qutob, N., Elnitski, L., & Samuels, Y. (2015). The functional relevance of somatic synonymous mutations in melanoma and other cancers. Pigment Cell & Melanoma Research, 28(6), 673-684.

Hunt, R. C., Simhadri, V. L., Iandoli, M., Sauna, Z. E., & Kimchi-Sarfaty, C. (2014). Exposing synonymous mutations. Trends in Genetics, 30(7), 308-321.

Sauna, Z. E., & Kimchi-Sarfaty, C. (2011). Understanding the contribution of synonymous mutations to human disease. Nature Reviews Genetics, 12(10), 683-691.

Evidence leading to a pathogenic assertion	Strength of evidence
Significant segregation in affected family members (LOD >2)	3
Confirmed de novo inheritance in relevant disease-associated gene	3
In vivo data from mammalian model organisms suggest an impact on function	2
Case-control studies significantly associate the variant to disease	2
Nucleotide and amino acid strongly conserved in distantly related species	2
In vitro data from recombinant DNA constructs or proteins suggest an impact on function	1
Variant is present in trans with an established pathogenic variant in recessive disease	1
Variant is rare or absent in large population studies	1
Computational tools predict an impact on function and/or splicing	1

References：

Duzkale, H., Shen, J., McLaughlin, H., Alfares, A., Kelly, M. A., Pugh, T. J., ... & Lebo, M. S. (2013). A systematic approach to assessing the clinical significance of genetic variants. Clinical genetics, 84(5), 453-463.

Tool	Genomic build	Source	Threshold	Ref
SilVA	GRCh37/hg19	http://compbio.cs.toronto.edu/silva	>0.278	[1]
DDIG-SN	GRCh37/hg19	http://sparks-lab.org/ddig	>0.5	[2]
FATHMM-MKL	GRCh37/hg19	http://fathmm.biocompute.org.uk	>0.5	[3]
TraP	GRCh37/hg19	http://trap-score.org/Search?version=v2	≧0.459	[4]
CADD	GRCh37/hg19	https://myvariant.info	10-20	[5]
GERP++	GRCh37/hg19	https://myvariant.info	>0	[5]
Phylop100way	GRCh38/37	http://genome.ucsc.edu/cgi-bin/hgTables	>0	[6]
PhastCons100way	GRCh38/37	http://genome.ucsc.edu/cgi-bin/hgTables	>0.5	[6]
TransVar	GRCh38/37	http://bioinformatics.mdanderson.org/transvarweb/	-	[7]
VEP	GRCh38/37	http://www.ensembl.org/Tools/VEP	-	[8]

References：

[1] Buske, O. J., Manickaraj, A., Mital, S., Ray, P. N., & Brudno, M. (2013). Identification of deleterious synonymous variants in human genomes. Bioinformatics, 29(15), 1843-1850.

[2] Livingstone, M., Folkman, L., Yang, Y., Zhang, P., Mort, M., Cooper, D. N., ... & Zhou, Y. (2017). Investigating DNA‐, RNA‐, and protein‐based features as a means to discriminate pathogenic synonymous variants. Human mutation, 38(10), 1336-1347.

[3] Shihab, H. A., Rogers, M. F., Gough, J., Mort, M., Cooper, D. N., Day, I. N., ... & Campbell, C. (2015). An integrative approach to predicting the functional effects of non-coding and coding sequence variation. Bioinformatics, 31(10), 1536-1543.

[4] Gelfman, S., Wang, Q., McSweeney, K. M., Ren, Z., La Carpia, F., Halvorsen, M., ... & Petrovski, S. (2017). Annotating pathogenic non-coding variants in genic regions. Nature communications, 8(1), 236.

[5] Xin, J., Mark, A., Afrasiabi, C., Tsueng, G., Juchler, M., Gopal, N., ... & Torkamani, A. (2016). High-performance web services for querying gene and variant annotation. Genome biology, 17(1), 91.

[6] http://genome.ucsc.edu/cgi-bin/hgTables

[7] Zhou, W., Chen, T., Chong, Z., Rohrdanz, M. A., Melott, J. M., Wakefield, C., ... & Chen, K. (2015). TransVar: a multilevel variant annotator for precision genomics. Nature methods, 12(11), 1002.

[8] McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R., Thormann, A., ... & Cunningham, F. (2016). The ensembl variant effect predictor. Genome biology, 17(1), 122.

The browse process including “Search” and “Advanced search”.

1)Search

You can input one keyword to search the dbDSM. The search fields include disease name, gene name, GRCh38 genome position, deleterious synonymous mutation (c.DNA, protein, rsid).

2) Advanced search

You can select one or more of the four options listed in the browse area (Gene, Disease, dbDSMScore, Chromosome).