Novel functional sequence variants in the promoter of a gene downregulated in preterm prom: A possible genetic basis for the predisposition to membrane rupture?

2004 ◽  
Vol 191 (6) ◽  
pp. S25
Author(s):  
Mahboob Chowdhury ◽  
Gerard Tromp ◽  
Helena Kuivaniemi ◽  
Sam Edwin ◽  
Roberto Romero
Keyword(s):  
Genetic Basis ◽  
Sequence Variants ◽  
Membrane Rupture ◽  
Functional Sequence

scholarly journals Genetic and Functional Sequence Variants of the SIRT3 Gene Promoter in Myocardial Infarction

PLoS ONE ◽  
2016 ◽  
Vol 11 (4) ◽  
pp. e0153815 ◽  
Author(s):  
Xiaoyun Yin ◽  
Shuchao Pang ◽  
Jian Huang ◽  
Yinghua Cui ◽  
Bo Yan
Keyword(s):  
Myocardial Infarction ◽  
Gene Promoter ◽  
Sequence Variants ◽  
Functional Sequence

scholarly journals Identification of novel functional sequence variants in the gene for peptidase inhibitor 3

2006 ◽  
Vol 7 (1) ◽  
Author(s):  
Mahboob A Chowdhury ◽  
Helena Kuivaniemi ◽  
Roberto Romero ◽  
Samuel Edwin ◽  
Tinnakorn Chaiworapongsa ◽  
...  
Keyword(s):  
Sequence Variants ◽  
Functional Sequence ◽  
Peptidase Inhibitor

Using Step-Wise Linear Regression to Detect “Functional” Sequence Variants: Application to Simulated Data

2001 ◽  
Vol 21 (S1) ◽  
pp. S353-S357
Author(s):  
Braxton D. Mitchell ◽  
Wen-Chi Hsueh ◽  
Jennifer L. Schneider ◽  
John Blangero
Keyword(s):  
Linear Regression ◽  
Simulated Data ◽  
Sequence Variants ◽  
Functional Sequence

scholarly journals SPDI: Data Model for Variants and Applications at NCBI

2019 ◽  
Author(s):  
J. Bradley Holmes ◽  
Eric Moyer ◽  
Lon Phan ◽  
Donna Maglott ◽  
Brandi L. Kattman
Keyword(s):  
Open Access ◽  
Data Model ◽  
Genetic Basis ◽  
Biological Function ◽  
Low Complexity ◽  
Reference Sequence ◽  
Genomic Sequences ◽  
Sequence Variants ◽  
Correction Algorithm ◽  
Protein Variants

AbstractMotivationNormalizing diverse representations of sequence variants is critical to the elucidation of the genetic basis of disease and biological function. NCBI has long wrestled with integrating data from multiple submitters to build databases such as dbSNP and ClinVar. Inconsistent representation of variants among variant callers, local databases, and tools results in discrepancies and duplications that complicate analysis. Current tools are not robust enough to manage variants in different formats and different reference sequence coordinates.ResultsThe SPDI (pronounced “speedy”) data model defines variants as a sequence of 4 operations: start at the boundary before the first position in the sequence S, advance P positions, delete D positions, then insert the sequence in the string I, giving the data model its name, SPDI. The SPDI model can thus be applied to both nucleotide and protein variants, but the services discussed here are limited to nucleotide. Current services convert representations between HGVS, VCF, and SPDI and provide two forms of normalization. The first, based on the NCBI Variant Overprecision Correction Algorithm, returns a unique, normalized representation termed the “Contextual Allele” for any input. The SPDI name, with its four operations, defines exactly the reference subsequence potentially affected by the variant, even in low complexity regions such as homopolymer and dinucleotide sequence repeats. The second level of normalization depends on an alignment dataset (ADS). SPDI services perform remapping (AKA lift-over) of variants from the input reference sequence to return a list of all equivalent Contextual Alleles based on the transcript or genomic sequences that were aligned. One of these contextual alleles is selected to represent all, usually that based on the latest genomic assembly such as GRCh38 and is designated as the unique “Canonical Allele”. ADS includes alignments between non-assembly RefSeq sequences (prefixed NM, NR, NG), as well inter- and intra-assembly-associated genomic sequences (NCs, NTs and NWs) and this allow for robust remapping and normalization of variants across sequences and assembly versions.Availability and implementationThe SPDI services are available for open access at: https://api.ncbi.nlm.nih.gov/variation/v0/[email protected]

Novel and functional sequence variants within the TBX2 gene promoter in ventricular septal defects

Biochimie ◽  
2013 ◽  
Vol 95 (9) ◽  
pp. 1807-1809 ◽  
Author(s):  
Shuchao Pang ◽  
Yumei Liu ◽  
Zhongqing Zhao ◽  
Wenhui Huang ◽  
Dongfeng Chen ◽  
...  
Keyword(s):  
Gene Promoter ◽  
Sequence Variants ◽  
Ventricular Septal Defects ◽  
Functional Sequence ◽  
Septal Defects

Functional sequence variants within the SIRT1 gene promoter in indirect inguinal hernia

Gene ◽  
2014 ◽  
Vol 546 (1) ◽  
pp. 1-5 ◽  
Author(s):  
Qingluan Han ◽  
Yu Zhang ◽  
Wei Li ◽  
Hongjin Fan ◽  
Qining Xing ◽  
...  
Keyword(s):  
Inguinal Hernia ◽  
Gene Promoter ◽  
Sequence Variants ◽  
Indirect Inguinal Hernia ◽  
Functional Sequence ◽  
Sirt1 Gene

Two functional sequence variants of the GATA6 gene promoter in patients with indirect inguinal hernia

Gene ◽  
2014 ◽  
Vol 547 (1) ◽  
pp. 86-90 ◽  
Author(s):  
Yuangang Qiao ◽  
Zhiping Zhang ◽  
Wenhui Huang ◽  
Shuchao Pang ◽  
Qining Xing ◽  
...  
Keyword(s):  
Inguinal Hernia ◽  
Gene Promoter ◽  
Sequence Variants ◽  
Indirect Inguinal Hernia ◽  
Functional Sequence ◽  
Gata6 Gene

scholarly journals SPDI: data model for variants and applications at NCBI

2019 ◽  
Vol 36 (6) ◽  
pp. 1902-1907 ◽  
Author(s):  
J Bradley Holmes ◽  
Eric Moyer ◽  
Lon Phan ◽  
Donna Maglott ◽  
Brandi Kattman
Keyword(s):  
Data Model ◽  
Genetic Basis ◽  
Biological Function ◽  
Supplementary Information ◽  
Sequence Variants ◽  
Correction Algorithm ◽  
Ncbi Refseq ◽  
Protein Variants ◽  
Assembly Version ◽  
Sequence Position

Abstract Motivation Normalizing sequence variants on a reference, projecting them across congruent sequences and aggregating their diverse representations are critical to the elucidation of the genetic basis of disease and biological function. Inconsistent representation of variants among variant callers, local databases and tools result in discrepancies that complicate analysis. NCBI’s genetic variation resources, dbSNP and ClinVar, require a robust, scalable set of principles to manage asserted sequence variants. Results The SPDI data model defines variants as a sequence of four attributes: sequence, position, deletion and insertion, and can be applied to nucleotide and protein variants. NCBI web services convert representations among HGVS, VCF and SPDI and provide two functions to aggregate variants. One, based on the NCBI Variant Overprecision Correction Algorithm, returns a unique, normalized representation termed the ‘Contextual Allele’. The SPDI data model, with its four operations, defines exactly the reference subsequence affected by the variant, even in repeat regions, such as homopolymer and other sequence repeats. The second function projects variants across congruent sequences and depends on an alignment dataset of non-assembly NCBI RefSeq sequences (prefixed NM, NR and NG), as well as inter- and intra-assembly-associated genomic sequences (NCs, NTs and NWs), supporting robust projection of variants across congruent sequences and assembly versions. The variant is projected to all congruent Contextual Alleles. One of these Contextual Alleles, typically the allele based on the latest assembly version, represents the entire set, is designated the unique ‘Canonical Allele’ and is used directly to aggregate variants across congruent sequences. Availability and implementation The SPDI services are available for open access at: https://api.ncbi.nlm.nih.gov/variation/v0. Supplementary information Supplementary data are available at Bioinformatics online.

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