The ShapiroSenapathy algorithm (S&S) is a computational method for identifying splice sites in eukaryotic genes. The algorithm employs a Position Weight Matrix (PWM) scoring formula to predict donor and acceptor splice sites in any given gene. This methodology has been used to discover splice sites and disease-causing splice site mutations in the human genome, and has become a standard tool in clinical genomics.
The S&S algorithm has been cited in thousands of clinical studies, according to Google Scholar. It has also formed the basis of widely used software, including Human Splicing Finder, SROOGLE, and Alamut, which identify splice sites and splice site mutations that cause disease. The algorithm has uncovered splicing mutations in diseases ranging from cancers to inherited disorders, and predicted the deleterious effects of these mutations including exon skipping, intron retention, and cryptic splice site activation.
The algorithm A splice site defines the boundary between a coding exon and a non-coding intron in eukaryotic genes. The S&S algorithm employs a sliding window, corresponding to the length of the splice site motif, to scan a gene sequence and detect potential splice sites. For each sliding window, the algorithm calculates a score by comparing the nucleotide sequence to a Position Weight Matrix (PWM) derived from known splice sites. This formula generates a percentile score, indicating the likelihood that a given sequence functions as a donor or acceptor splice site.
The majority of disease-causing mutations in the human genome are located in splice sites. Clinical genomics studies analyze the splice site scores generated by the S&S algorithm to predict the consequences of splice site mutations including exon skipping and intron retention. The algorithm's sensitivity to single-nucleotide changes allows it to determine mutations that may impact RNA splicing and contribute to disease.
In addition to identifying real splice sites, the S&S algorithm has been used to discover cryptic splice sites alternative splice sites activated by mutations which may disrupt normal splicing. The algorithm detects mutations that lead to the activation of cryptic splice sites, which may be located proximal to real splice sites or deep within non-coding introns. It has thus been used to determine the causes of numerous diseases that are due to cryptic splicing.
Cancer gene discovery using S&S
The S&S algorithm has been used to identify splice-site mutations in genes associated with several cancers. For example, genes causing commonly occurring cancers including breast cancer, ovarian cancer, colorectal cancer, leukemia, head and neck cancers, prostate cancer, retinoblastoma, squamous cell carcinoma, gastrointestinal cancer, melanoma, liver cancer, Lynch syndrome, skin cancer, and neurofibromatosis have been found. In addition, splicing mutations in genes causing less commonly known cancers including gastric cancer, gangliogliomas, Li-Fraumeni syndrome, Loeys–Dietz syndrome, Osteochondromas (bone tumor), Nevoid basal cell carcinoma syndrome, and Pheochromocytomas have been identified.
Specific mutations in different splice sites in various genes causing breast cancer (e.g., BRCA1, PALB2), ovarian cancer (e.g., SLC9A3R1, COL7A1, HSD17B7), colon cancer (e.g., APC, MLH1, DPYD), colorectal cancer (e.g., COL3A1, APC, HLA-A), skin cancer (e.g., COL17A1, XPA, POLH), and Fanconi anemia (e.g., FANC, FANA) have been uncovered. The mutations in the donor and acceptor splice sites in different genes causing a variety of cancers that have been identified by S&S are shown in Table 1.
Disease typeGene symbolMutation locationOriginal sequenceMutated sequenceSplicing aberrationBreast cancerBRCA1Exon 11AAGGTGTGTAAAGTGTGTSkipping of exon 12PALB2Exon 12CAGGCAAGTCAAGCAAGTPotentially weakening the canonical donor splicing siteOvarian cancerSLC9A3R1Exon2GAGGTGATGGAGGCGATGSignificant effect in 'splicing'Colorectal CancerMLH1Exon 9TCGGTATGT TCAGTATGTSkipping of exon 8 and protein truncationMSH2Intron 8CAGGTATGCCAGGCATGCIntervening sequence, RNA processing,No amino acid changeMSH6Intron 9TTTTTAATTTTAAGGTTTTTAATTTTGAGGIntervening sequence, RNA processing,No amino acid changeSkin CancerTGFBR1Exon 5TTTTGATTCTTTAGGTTTTGATTCTTTCGGExon 5 skippingITGA6Intron 19TTATTTTCTAACAGGTTATTTTCTAACACGSkipping of the exon 20 and resulted in in-frame deletionBirt–Hogg–Dubé (BHD) syndromeFLCNExon 9GAAGTAAGCGAAGGAAGCSkipping of exon 9 and weak retention of 131 bp of intron 9Nevoid basal cell carcinomaPTCH1Intron 4CAGGTATATCAGGTGTATExon 4 Skipping MesotheliomaBAP1Exon 16AAGGTGAGGTAGGTGAGGCreates a novel 5' splice site that results in a 4 nucleotide deletion of the 3' end of exon 16
Discovery of genes causing inherited disorders using S&S Specific mutations in different splice sites in various genes that cause inherited disorders, including, for example, Type 1 diabetes (e.g., PTPN22, TCF1 (HCF-1A)), hypertension (e.g., LDL, LDLR, LPL), Marfan syndrome (e.g., FBN1, TGFBR2, FBN2), cardiac diseases (e.g., COL1A2, MYBPC3, ACTC1), eye disorders (e.g., EVC, VSX1) have been uncovered. A few example mutations in the donor and acceptor splice sites in different genes causing a variety of inherited disorders identified using S&S are shown in Table 2. Disease type Gene symbolMutation location Original sequenceMutated sequenceSplicing aberration Diabetes PTPN22Exon 18AAGGTAAAGAACGTAAAGSkipping of exon 18TCF1 Intron 4TTTGTGCCCCTCAGGTTTGTGCCCCTCGGGSkipping of exon 5Hypertension LDLIntron 10TGGGTGCGTTGGGTGCATNormolipidemic to classical heterozygous FHLDLRIntron 2GCTGTGAGTGCTGTGTGTMay cause splicing abnormalities through an in-silico analysisLPLIntron 2ACGGTAAGGACGATAAGGCryptic splice sites is activated in vivo at the sitesMarfan syndrome FBN1Intron 46CAAGTAAGACAAGTAAAAExon skipping/cryptic splice siteTGFBR2 Intron 1ATCCTGTTTTACAGAATCCTGTTTTACGGAAbnormal splicingFBN2Intron45TGGGTAAGTTGGGGAAGTSplice site alterations leading to frameshift mutations, causing a truncated proteinCardiac disease COL1A2Intron 46GCTGTAAGTGCTGCAAGTPermitted almost exclusive use of a cryptic donor site 17 nt upstream in the exonMYBPC3Intron 5CTCCATGCACACAGGCTCCATGCACACCGGAbnormal mRNA transcript with a premature stop codon will produce a truncated protein lacking the binding sites for myosin and titinACTC1Intron 1TTTTCTTCTCATAGGTTTTCTTCTTATAGGNo effect Eye disorderABCRIntron 30CAGGTACCTCAGTTACCTAutosomal recessive RP and CRDVSX1Intron 5TTTTTTTTTACAAGGTATTTTTTTACAAGGAberrant splicing
Genes causing immune system disorders More than 100 immune system disorders affect humans, including inflammatory bowel diseases, multiple sclerosis, systemic lupus erythematosus, bloom syndrome, familial cold autoinflammatory syndrome, and dyskeratosis congenita. The Shapiro–Senapathy algorithm has been used to discover genes and mutations involved in many immune disorder diseases, including Ataxia telangiectasia, B-cell defects, epidermolysis bullosa, and X-linked agammaglobulinemia.
Xeroderma pigmentosum, an autosomal recessive disorder is caused by faulty proteins formed due to new preferred splice donor site identified using S&S algorithm and resulted in defective nucleotide excision repair.
Type I Bartter syndrome (BS) is caused by mutations in the gene SLC12A1. S&S algorithm helped in disclosing the presence of two novel heterozygous mutations c.724 + 4A > G in intron 5 and c.2095delG in intron 16 leading to complete exon 5 skipping.
Mutations in the MYH gene, which is responsible for removing the oxidatively damaged DNA lesion are cancer-susceptible in the individuals. The IVS1+5C plays a causative role in the activation of a cryptic splice donor site and the alternative splicing in intron 1, S&S algorithm shows, guanine (G) at the position of IVS+5 is well conserved (at the frequency of 84%) among primates. This also supported the fact that the G/C SNP in the conserved splice junction of the MYH gene causes the alternative splicing of intron 1 of the β type transcript.
Splice site scores were calculated according to S&S to find EBV infection in X-linked lymphoproliferative disease. Identification of Familial tumoral calcinosis (FTC) is an autosomal recessive disorder characterized by ectopic calcifications and elevated serum phosphate levels and it is because of aberrant splicing.
Application of S&S in hospitals for clinical practice and research The Shapiro–Senapathy (S&S) algorithm has played a significant role in advancing the diagnosis and treatment of human diseases through its application in modern clinical genomics. With the widespread adoption of next-generation sequencing (NGS) technologies, the S&S algorithm is now routinely integrated into clinical practice by geneticists and diagnostic laboratories. It is implemented in various computational tools such as Human Splicing Finder (HSF), Splice Site Finder (SSF), and Alamut Visual, which assist in interpreting the functional impact of genetic variants on RNA splicing.
The algorithm is particularly useful in identifying pathogenic splice site mutations in cases where the clinical presentation is unclear or where conventional diagnostic methods have failed to identify a causative gene. Its utility has been demonstrated across diverse patient cohorts, including individuals from different ethnic backgrounds with various cancers and inherited genetic disorders. The following are selected examples illustrating its application in clinical research.
Cancers Cancer typePublication titleYearEthnicityNumber of patients1Hereditary Breast CancerUncovering the clinical relevance of unclassified variants in DNA repair genes: a focus on BRCA negative Tunisian cancer families2024Tunisian67 Patients2Basel Cell CarcinomaPTCH1 gene variants rs357564, rs2236405, rs2297086 and rs41313327, mRNA and tissue expression in basal cell carcinoma patients from Western Mexico2024Western Mexico250 Patients 290 Control3Non-Small Cell Lung CancerAssociations between telomere attrition, genetic variants in telomere maintenance genes, and non-small cell lung cancer risk in the Jammu and Kashmir population of North India2023India162 Patients 561 Controls4Prostate CancerSomatic and germline aberrations in homologous recombination repair genes in Chinese prostate cancer patients2023Chinese721 Patients5Colorectal CancerLynch-like syndrome is as frequent as Lynch syndrome in early-onset nonfamilial nonpolyposis colorectal cancer2023Argentina102 patients6Colorectal CancerGermline Variants of CYBA and TRPM4 Predispose to Familial Colorectal Cancer2022Poland15 Families7Hereditary Ovarian CancerThe Genetic and Molecular Analyses of RAD51C and RAD51D Identifies Rare Variants Implicated in Hereditary Ovarian Cancer from a Genetically Unique Population2022French Canadians17 Families 53 Patients8Thymic CarcinomaMutation profile and immunoscore signature in thymic carcinomas: An exploratory study and review of the literature2021Italian15 Patients9Neurofibromatosis Type 1Simultaneous Detection of NF1, SPRED1, LZTR1, and NF2 Gene Mutations by Targeted NGS in an Italian Cohort of Suspected NF1 Patients2020Italian250 Patients10Neuroendocrine Pancreatic TumorIdentification of new candidate genes and signalling pathways associated with the development of neuroendocrine pancreatic tumours based on next generation sequencing data2020Caucasian24 Patients11Oral CancerPolymorphic variants of drug-metabolizing enzymes alter the risk and survival of oral cancer patients2020Indian909 Controls