A Case Study on PPM1D and 9 Other Shared Germline Alterations in a Family.

BACKGROUND: The use of high-throughput genotyping techniques has enabled us to identify the rare germline genetic variants with different pathogenicity and penetrance, and understand their role in cancer predisposition. We report here a familial cancer case, a study from Western Indian. METHODS: NGS-WES was carried out in a lung cancer patient who has a family history of multiple cancers across generations, including tongue, lung, brain, cervical, urothelial, and esophageal cancer. The results were validated by data mining from available data bases. I-TASSER, RasMol and PyMol were used for protein structure modelling. RESULTS: The sequencing by NGS-WES revealed PPM1D c.1654C>T (p.Arg552Ter) mutation in hotspot region exon 6 leading to sudden protein truncation and loss of the C-terminal, due to the substitution of C>T. This mutation was classified as a variant of uncertain significance (VUS), due to limited data on lung cancer, The three unaffected siblings of proband did not show any pathogenic variants and comparative analysis of the four siblings indicate 9 shared genetic variants, classified as benign as per ClinVar. CONCLUSION: PPM1D constitutional genetic alterations are rare and uncommon in different ethnic populations. This gene encodes a phosphatase playing role in regulating the P53 tumor suppressor pathway and DNA damage response. Genetic alterations in the PPM1D gene maybe linked to history of gliomas, breast cancer, and ovarian cancer onset in the proband's family.
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Estimating the k-mer Coverage Frequencies in Genomic Datasets: A Comparative Assessment of the State-of-the-art.

BACKGROUND: In bioinformatics, estimation of k-mer abundance histograms or just enumerat-ing the number of unique k-mers and the number of singletons are desirable in many genome sequence analysis applications. The applications include predicting genome sizes, data pre-processing for de Bruijn graph assembly methods (tune runtime parameters for analysis tools), repeat detection, sequenc-ing coverage estimation, measuring sequencing error rates, etc. Different methods for cardinality estima-tion in sequencing data have been developed in recent years. OBJECTIVE: In this article, we present a comparative assessment of the different k-mer frequency estima-tion programs (ntCard, KmerGenie, KmerStream and Khmer (abundance-dist-single.py and unique-kmers.py) to assess their relative merits and demerits. METHODS: Principally, the miscounts/error-rates of these tools are analyzed by rigorous experimental analysis for a varied range of k. We also present experimental results on runtime, scalability for larger datasets, memory, CPU utilization as well as parallelism of k-mer frequency estimation methods. RESULTS: The results indicate that ntCard is more accurate in estimating F0, f1 and full k-mer abundance histograms compared with other methods. ntCard is the fastest but it has more memory requirements compared to KmerGenie. CONCLUSION: The results of this evaluation may serve as a roadmap to potential users and practitioners of streaming algorithms for estimating k-mer coverage frequencies, to assist them in identifying an appro-priate method. Such results analysis also help researchers to discover remaining open research ques-tions, effective combinations of existing techniques and possible avenues for future research.

A benchmark study of k-mer counting methods for high-throughput sequencing.

The rapid development of high-throughput sequencing technologies means that hundreds of gigabytes of sequencing data can be produced in a single study. Many bioinformatics tools require counts of substrings of length k in DNA/RNA sequencing reads obtained for applications such as genome and transcriptome assembly, error correction, multiple sequence alignment, and repeat detection. Recently, several techniques have been developed to count k-mers in large sequencing datasets, with a trade-off between the time and memory required to perform this function. We assessed several k-mer counting programs and evaluated their relative performance, primarily on the basis of runtime and memory usage. We also considered additional parameters such as disk usage, accuracy, parallelism, the impact of compressed input, performance in terms of counting large k values and the scalability of the application to larger datasets.We make specific recommendations for the setup of a current state-of-the-art program and suggestions for further development.