Locus Reference Genomic sequences: An improved basis for describing human DNA variants
Tully, Raymond E
McLaren, William M
Vaughan, Brendan W
Taschner, Peter EM
den Dunnen, Johan T
Brookes, Anthony J
Maglott, Donna R
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Online Publication Date2010-04-15
Print Publication Date2010
Permanent link to this recordhttp://hdl.handle.net/10754/325274
MetadataShow full item record
AbstractAs our knowledge of the complexity of gene architecture grows, and we increase our understanding of the subtleties of gene expression, the process of accurately describing disease-causing gene variants has become increasingly problematic. In part, this is due to current reference DNA sequence formats that do not fully meet present needs. Here we present the Locus Reference Genomic (LRG) sequence format, which has been designed for the specifi c purpose of gene variant reporting. The format builds on the successful National Center for Biotechnology Information (NCBI) RefSeqGene project and provides a single-fi le record containing a uniquely stable reference DNA sequence along with all relevant transcript and protein sequences essential to the description of gene variants. In principle, LRGs can be created for any organism, not just human. In addition, we recognize the need to respect legacy numbering systems for exons and amino acids and the LRG format takes account of these. We hope that widespread adoption of LRGs - which will be created and maintained by the NCBI and the European Bioinformatics Institute (EBI) - along with consistent use of the Human Genome Variation Society (HGVS)- approved variant nomenclature will reduce errors in the reporting of variants in the literature and improve communication about variants aff ecting human health. Further information can be found on the LRG web site (http://www.lrg-sequence.org). 2010 Dalgleish et al.; licensee BioMed Central Ltd.
CitationDalgleish R, Flicek P, Cunningham F, Astashyn A, Tully RE, et al. (2010) Locus Reference Genomic sequences: an improved basis for describing human DNA variants. Genome Medicine 2: 24. doi:10.1186/gm145.
PubMed Central IDPMC2873802
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Data for : Poly(A) Dataset for PAS sequences and pseudo-PAS sequences Classification (fasta format)Albalawi, Fahad; Chahid, Abderrazak; Guo, Xingang; Albaradei, Somayah; Magana-Mora, Arturo; Jankovic, Boris R.; Uludag, Mahmut; Van Neste, Christophe; Essack, Magbubah; Laleg-Kirati, Taous-Meriem; Bajic, Vladimir B. (KAUST Research Repository, 2018-11-15) [Dataset]This Dataset contains DNA sequences of the human genome hg38 from GENCODE folder at EBI ftp server (ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_28/GRCh38.primary_assembly.genome.fa.gz) A-Positive set (PAS sequences) Using GENCODE annotation for poly(A) (ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_28/gencode.v28.polyAs.gff3.gz) We selected poly(A) signal annotation. Using bedtools-slop option, we found regions extended 300 bp upstream and 300 bp downstream of the poly(A) hexamer. With the bedtools-getfasta option, we extracted 606 bp fasta sequences from these regions. After eliminating duplicates, we obtained 37’516 presumed true functional poly(A) signal (PAS) sequences. Sequences from this set will be denoted as positive. B- Negative set (pseudo-PAS sequences) For the negative set, we looked for regions extended outside the region covering 1’000 bp upstream and downstream of the positive poly(A) hexamer signal using bedtools-complement. Homer tool was used to find matches for the 12 most frequent human poly(A) variants. Since the number of matches was huge, sampling was used to select 37’516 pseudo-PAS sequences. Sampling was done from each chromosome proportionally to the lengths of the chromosomes and also to the expected frequency of the poly(A) variants. Out of these predictions, for each PAS hexamer, we selected the same number of pseudo-PAS sequences as in the positive set. Training and testing sets We selected randomly from each of the positive and negative datasets 20% of sequences for the independent test data. The testing set thus consisted of 15’020 sequences. The remaining data represented the training set that consisted of 60’012 sequences. Both datasets are balanced relative to the true PAS and pseudo-PAS sequences.
Next-Generation Sequencing at High Sequencing Depth as a Tool to Study the Evolution of Metastasis Driven by Genetic Change Events of Lung Squamous Cell CarcinomaMansour, Hicham; Ouhajjou, Abdelhak; Bajic, Vladimir B.; Incitti, Roberto (Frontiers in Oncology, Frontiers Media SA, 2020-08-05) [Article]Background: The aim of this study is to report tumoral genetic mutations observed at high sequencing depth in a lung squamous cell carcinoma (SqCC) sample. We describe the findings and differences in genetic mutations that were studied by deep next-generation sequencing methods on the primary tumor and liver metastasis samples. In this report, we also discuss how these differences may be involved in determining the tumor progression leading to the metastasis stage. Methods: We followed one lung SqCC patient who underwent FDG-PET scan imaging, before and after three months of treatment. We sequenced 26 well-known cancer-related genes, at an average of ~6,000 × sequencing coverage, in two spatially distinct regions, one from a primary lung tumor metastasis and the other from a distal liver metastasis, which was present before the treatment. Results: A total of 3,922,196 read pairs were obtained across all two samples' sequenced locations. Merged mapped reads showed several variants, from which we selected 36 with high confidence call. While we found 83% of genetic concordance between the distal metastasis and primary tumor, six variants presented substantial discordance. In the liver metastasis sample, we observed three de novo genetic changes, two on the FGFR3 gene and one on the CDKN2A gene, and the frequency of one variant found on the FGFR2 gene has been increased. Two genetic variants in the HRAS gene, which were present initially in the primary tumor, have been completely lost in the liver tumor. The discordant variants have coding consequences as follows: FGFR3 (c.746C>G, p. Ser249Cys), CDKN2A (c.47_50delTGGC, p. Leu16Profs*9), and HRAS (c.182A>C, p. Gln61Pro). The pathogenicity prediction scores for the acquired variants, assessed using several databases, reported these variants as pathogenic, with a gain of function for FGFR3 and a loss of function for CDKN2A. The patient follow-up using imaging with 18F-FDG PET/CT before and after four cycles of treatment shows discordant tumor progression in metastatic liver compared to primary lung tumor. Conclusions: Our results report the occurrence of several genetic changes between primary tumor and distant liver metastasis in lung SqCC, among which non-silent mutations may be associated with tumor evolution during metastasis.