Analysis of somatic mutations in kappa transgenes.

We have examined the nature and localization of somatic mutations in three kappa transgenes cloned from IgG-secreting hybridomas. All of the mutations identified were single base substitutions. Mutations were localized to the variable (V) region and its flanking sequences. In every case, the nuclear matrix association region, kappa enhancer, and C gene were spared. These data indicate that the rearranged kappa gene contains the necessary sequences for targeting of the mutation process, and suggest that the observed localization of mutations to the V region reflects the inherent specificity of this mutation process.

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Clinical Feature-Related Single-Base Substitution Sequence Signatures Identified with an Unsupervised Machine Learning Approach

10.21203/rs.3.rs-344127/v1 ◽

2021 ◽

Author(s):

Ji Hongchen ◽

Li Junjie ◽

Zhang Qiong ◽

Yang Jingyue ◽

Duan Juanli ◽

...

Keyword(s):

Machine Learning ◽

Clinical Features ◽

Learning Approach ◽

Single Base ◽

Base Substitutions ◽

Unsupervised Neural Network ◽

Machine Learning Approach ◽

Flanking Sequences ◽

Mutation Sequence ◽

Sequence Signatures

Abstract Background: Mutation processes leave different signatures in genes. For single-base substitutions, previous studies have suggested that mutation signatures are not only reflected in mutation bases but also in neighboring bases. However, because of the lack of a method to identify features of long sequences next to mutation bases, the understanding of how flanking sequences influence mutation signatures is limited.Methods: We constructed a long short-term memory – self organizing map (LSTM-SOM) unsupervised neural network. By extracting mutated sequence features via LSTM and clustering similar features with the SOM, single-base substitutions in The Cancer Genome Atlas database were clustered according to both their mutation site and flanking sequences. The relationship between mutation sequence signatures and clinical features was then analyzed. Finally, we clustered patients into different classes according to the composition of the mutation sequence signatures by the K-means method and then studied the differences in clinical features and survival between classes.Results: Ten classes of mutant sequence signatures (mutation blots, MBs) were obtained from 2,141,527 single-base substitutions via LSTM-SOM machine learning approach. Different features in mutation bases and flanking sequences were revealed among MBs. MBs reflect both the site and pathological features of cancers. MBs were related to clinical features, including age, gender, and cancer stage. The class of an MB in a given gene was associated with survival. Finally, patients were clustered into 7 classes according to the MB composition. Significant differences in survival and clinical features were observed among different patient classes.Conclusions: We provided a method for analyzing the characteristics of mutant sequences. Result of this study showed that flanking sequences, together with mutation bases, shape the signatures of SBSs. MBs were shown related to clinical features and survival of cancer patients. Composition of MBs is a feasible predictive factor of clinical prognosis. Further study of the mechanism of MBs related to cancer characteristics is suggested.

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Pan-cancer scale landscape of simple somatic mutations

10.1101/112367 ◽

2017 ◽

Cited By ~ 1

Author(s):

Nan Zhou ◽

Blanca Gallego ◽

Jinku Bao ◽

Guy Tsafnat

Keyword(s):

Myeloid Leukemia ◽

Somatic Mutations ◽

Whole Genome ◽

Single Base ◽

Mutational Spectra ◽

Base Substitutions ◽

Whole Exome ◽

High Prevalence ◽

Pan Cancer ◽

Acute Myeloid

AbstractGenome is the carrier of somatic mutations during the development of cancer. The catalogue of simple somatic mutations (SSM) is a subgroup of somatic mutations. It includes single base substitutions, small deletions and insertions of <= 200 bp, and multiple base substitutions of <= 200 bp. The comprehensive landscape of SSM has not been studied. After analysed 46,692,922 SSM of 10,878 samples, we proposed a pan-cancer scale landscape of SSM for 60 cancer projects in ICGC. In addition, the whole genome sequencing (WGS) and whole exome sequencing (WXS) techniques were compared according to the landscape of SSM. The result indicates numbers of SSM vary dramatically in different cancers. WGS can detect 10 times more single base substitutions and insertions than WXS. In terms of WXS, it called 10 times more deletions than insertions. Multiple base substitutions have not been well studied so they were just observed in a few cancer projects. Cancers generally show high prevalence of C > T substitutions at NpCpG trinucleotide contexts. Skin cancer showed distinct mutational spectra. Breast cancer, bladder cancer, and cervical cancer were found to have similar mutational spectra. Acute myeloid leukemia and lung cancer from South Korea, and colorectal cancer from China show high density of single base substitutions per mega base in chromosome Y. To sum up, our study and findings will be thought provoking in studying SSM in cancer.

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Somatic mutations of PIG-A in Thai patients with paroxysmal nocturnal hemoglobinuria

Blood ◽

10.1182/blood.v86.5.1736.bloodjournal8651736 ◽

1995 ◽

Vol 86 (5) ◽

pp. 1736-1739 ◽

Cited By ~ 26

Author(s):

P Pramoonjago ◽

W Wanachiwanawin ◽

S Chinprasertsak ◽

K Pattanapanayasat ◽

J Takeda ◽

...

Keyword(s):

Blood Cells ◽

Paroxysmal Nocturnal Hemoglobinuria ◽

Somatic Mutations ◽

Surface Expression ◽

Chromosomal Gene ◽

Gpi Anchor ◽

Hematopoietic Stem ◽

Single Base ◽

Base Substitutions ◽

Cell Disorder

Paroxysmal nocturnal hemoglobinuria (PNH) is a hematopoietic stem cell disorder characterized by clonal blood cells that are deficient in the surface expression of glycosylphosphatidylinositol (GPI)-anchored proteins. In the affected cells, the X-chromosomal gene PIG-A, which participates in biosynthesis of the GPI anchor, is somatically mutated. Analyses of Japanese, British, and American patients with PNH have shown somatic mutations of PIG-A in all of them, indicating that PIG-A is responsible for PNH in most, if not all, patients in those countries. Twenty-nine of the reported somatic mutations are small, mostly involving 1 or 2 bases, except for one with a 4-kb deletion. Here we describe an analysis of PIG-A in neutrophils from 14 patients from Thailand where PNH is thought to be more common. We found small somatic PIG-A mutations in all patients. These consisted of six single base deletions, one each of 2-, 3-, 5- and 10-base deletions, two single base insertions and two base substitutions. Thus, the small somatic mutation in the PIG-A gene is also responsible for PNH in Thailand. However, base substitutions were rarer (2 of 14) than in Japan (8 of 16), and deletions of multiple bases were more common, suggesting various causes of mutation.

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Clinical feature-related single-base substitution sequence signatures identified with an unsupervised machine learning approach

BMC Medical Genomics ◽

10.1186/s12920-021-01144-1 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Hongchen Ji ◽

Junjie Li ◽

Qiong Zhang ◽

Jingyue Yang ◽

Juanli Duan ◽

...

Keyword(s):

Machine Learning ◽

Clinical Features ◽

Learning Approach ◽

Single Base ◽

Base Substitutions ◽

Unsupervised Neural Network ◽

Machine Learning Approach ◽

Flanking Sequences ◽

Mutation Sequence ◽

Sequence Signatures

Abstract Background Mutation processes leave different signatures in genes. For single-base substitutions, previous studies have suggested that mutation signatures are not only reflected in mutation bases but also in neighboring bases. However, because of the lack of a method to identify features of long sequences next to mutation bases, the understanding of how flanking sequences influence mutation signatures is limited. Methods We constructed a long short-term memory-self organizing map (LSTM-SOM) unsupervised neural network. By extracting mutated sequence features via LSTM and clustering similar features with the SOM, single-base substitutions in The Cancer Genome Atlas database were clustered according to both their mutation site and flanking sequences. The relationship between mutation sequence signatures and clinical features was then analyzed. Finally, we clustered patients into different classes according to the composition of the mutation sequence signatures by the K-means method and then studied the differences in clinical features and survival between classes. Results Ten classes of mutant sequence signatures (mutation blots, MBs) were obtained from 2,141,527 single-base substitutions via LSTM-SOM machine learning approach. Different features in mutation bases and flanking sequences were revealed among MBs. MBs reflect both the site and pathological features of cancers. MBs were related to clinical features, including age, sex, and cancer stage. The class of an MB in a given gene was associated with survival. Finally, patients were clustered into 7 classes according to the MB composition. Significant differences in survival and clinical features were observed among different patient classes. Conclusions We provided a method for analyzing the characteristics of mutant sequences. Result of this study showed that flanking sequences, together with mutation bases, shape the signatures of SBSs. MBs were shown related to clinical features and survival of cancer patients. Composition of MBs is a feasible predictive factor of clinical prognosis. Further study of the mechanism of MBs related to cancer characteristics is suggested.

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Inter- and intraspecific variation of chloroplast mini- and microsatellites DNA in the four closed related Acacia species

Indonesian Journal of Biotechnology ◽

10.22146/ijbiotech.8639 ◽

2015 ◽

Vol 19 (1) ◽

pp. 1

Author(s):

AYPBC Widyatmoko ◽

Susumu Shiraishi

Keyword(s):

Intraspecific Variation ◽

Sequence Information ◽

Single Base ◽

Coding Regions ◽

Acacia Species ◽

Base Substitutions ◽

Microsatellites Dna

Mini- and microsatellites of four Acacia species, A. aulacocarpa, A. auriculiformis, A. crassicarpa and A.mangium were investigated on four non-coding regions of cpDNA, the intron of trnL, and the intergenicspacers of trnL - trnP, trnD - trnY, and trnP – trnW. Nine single base substitutions and six informative miniandmicrosatellites were detected in the the four cpDNA non-coding regions. Based on the substitutionsand mini- and microsatellites, ten cpDNA haplotypes (A - J) could be distinguished. Acacia auriculiformispossessed fi ve haplotypes, A. aulacocarpa, four haplotypes, and A. crassicarpa, three haplotypes. All samplesof A. mangium possessed the same haplotype. Mini- and microsatellites recognized in this study can beused for species identifi cation of the four Acacia species. The ten haplotypes could divided the four speciesinto 2 groups, A. aulacocarpa-A.crassicarpa group and A. auriculiformis-A. mangium group. By developing thePCR-based markers based on the sequence information, many experiments can be carried out for the Acaciaimprovement programs.

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