Halvade somatic: Somatic variant calling with Apache Spark

Background The accurate detection of somatic variants from sequencing data is of key importance for cancer treatment and research. Somatic variant calling requires a high sequencing depth of the tumor sample, especially when the detection of low-frequency variants is also desired. In turn, this leads to large volumes of raw sequencing data to process and hence, large computational requirements. For example, calling the somatic variants according to the GATK best practices guidelines requires days of computing time for a typical whole-genome sequencing sample. Findings We introduce Halvade Somatic, a framework for somatic variant calling from DNA sequencing data that takes advantage of multi-node and/or multi-core compute platforms to reduce runtime. It relies on Apache Spark to provide scalable I/O and to create and manage data streams that are processed on different CPU cores in parallel. Halvade Somatic contains all required steps to process the tumor and matched normal sample according to the GATK best practices recommendations: read alignment (BWA), sorting of reads, preprocessing steps such as marking duplicate reads and base quality score recalibration (GATK), and, finally, calling the somatic variants (Mutect2). Our approach reduces the runtime on a single 36-core node to 19.5 h compared to a runtime of 84.5 h for the original pipeline, a speedup of 4.3 times. Runtime can be further decreased by scaling to multiple nodes, e.g., we observe a runtime of 1.36 h using 16 nodes, an additional speedup of 14.4 times. Halvade Somatic supports variant calling from both whole-genome sequencing and whole-exome sequencing data and also supports Strelka2 as an alternative or complementary variant calling tool. We provide a Docker image to facilitate single-node deployment. Halvade Somatic can be executed on a variety of compute platforms, including Amazon EC2 and Google Cloud. Conclusions To our knowledge, Halvade Somatic is the first somatic variant calling pipeline that leverages Big Data processing platforms and provides reliable, scalable performance. Source code is freely available.

Development and validation of an expanded targeted sequencing panel for non-invasive prenatal diagnosis of sporadic skeletal dysplasia

Background Skeletal dysplasia (SD) is one of the most common inherited neonatal disorders worldwide, where the recurrent pathogenic mutations in the FGFR2, FGFR3, COL1A1, COL1A2 and COL2A1 genes are frequently reported in both non-lethal and lethal SD. The traditional prenatal diagnosis of SD using ultrasonography suffers from lower accuracy and performed at latter gestational stage. Therefore, it remains in desperate need of precise and accurate prenatal diagnosis of SD in early pregnancy. With the advancements of next-generation sequencing (NGS) technology and bioinformatics analysis, it is feasible to develop a NGS-based assay to detect genetic defects in association with SD in the early pregnancy. Methods An ampliseq-based targeted sequencing panel was designed to cover 87 recurrent hotspots reported in 11 common dominant SD and run on both Ion Proton and NextSeq550 instruments. Thirty-six cell-free and 23 genomic DNAs were used for assay developed. Spike-in DNA prepared from standard sample harboring known mutation and normal sample were also employed to validate the established SD workflow. Overall performances of coverage, uniformity, and on-target rate, and the detecting limitations on percentage of fetal fraction and read depth were evaluated. Results The established targeted-seq workflow enables a single-tube multiplex PCR for library construction and shows high amplification efficiency and robust reproducibility on both Ion Proton and NextSeq550 platforms. The workflow reaches 100% coverage and both uniformity and on-target rate are > 96%, indicating a high quality assay. Using spike-in DNA with different percentage of known FGFR3 mutation (c.1138 G > A), the targeted-seq workflow demonstrated the ability to detect low-frequency variant of 2.5% accurately. Finally, we obtained 100% sensitivity and 100% specificity in detecting target mutations using established SD panel. Conclusions An expanded panel for rapid and cost-effective genetic detection of SD has been developed. The established targeted-seq workflow shows high accuracy to detect both germline and low-frequency variants. In addition, the workflow is flexible to be conducted in the majority of the NGS instruments and ready for routine clinical application. Taken together, we believe the established panel provides a promising diagnostic or therapeutic strategy for prenatal genetic testing of SD in routine clinical practice.

Multi-Omics Data Analyses Identify B7-H3 as a Novel Prognostic Biomarker and Predict Response to Immune Checkpoint Blockade in Head and Neck Squamous Cell Carcinoma

B7 homolog 3 (B7-H3) is a recently found superfamily B7 molecule and therefore has significant involvement in immunological regulation. However, the relationships of B7-H3 expression with the tumor microenvironment (TME), response to immunotherapy, and prognosis in head and neck squamous cell carcinoma (HNSCC) are still unknown. In the present analysis, we determined B7-H3 as a novel biomarker that predicts the prognosis and response to immunotherapy in HNSCC. B7-H3 expression is enhanced in HNSCC compared to normal sample and is stably expressed in HNSCC cell line. Besides, high B7-H3 expression is correlated with a dismal prognosis and resistance to immunotherapy and contributes to an immunosuppressive microenvironment. Moreover, single-cell RNA sequencing (scRNA-seq) analysis shows that B7-H3 is mainly expressed in the stromal as well as malignant cells. In conclusion, the study provides insight in understanding the prognostic value of B7-H3 in HNSCC and highlights its involvement in promoting the immunosuppressive microenvironment, which presents an attractive strategy for antibody-based immunotherapy.

UNMASC: tumor-only variant calling with unmatched normal controls

Despite years of progress, mutation detection in cancer samples continues to require significant manual review as a final step. Expert review is particularly challenging in cases where tumors are sequenced without matched normal control DNA. Attempts have been made to call somatic point mutations without a matched normal sample by removing well-known germline variants, utilizing unmatched normal controls, and constructing decision rules to classify sequencing errors and private germline variants. With budgetary constraints related to computational and sequencing costs, finding the appropriate number of controls is a crucial step to identifying somatic variants. Our approach utilizes public databases for canonical somatic variants as well as germline variants and leverages information gathered about nearby positions in the normal controls. Drawing from our cohort of targeted capture panel sequencing of tumor and normal samples with varying tumortypes and demographics, these served as a benchmark for our tumor-only variant calling pipeline to observe the relationship between our ability to correctly classify variants against a number of unmatched normals. With our benchmarked samples, approximately ten normal controls were needed to maintain 94% sensitivity, 99% specificity and 76% positive predictive value, far outperforming comparable methods. Our approach, called UNMASC, also serves as a supplement to traditional tumor with matched normal variant calling workflows and can potentially extend to other concerns arising from analyzing next generation sequencing data.

ContaTester: Fast cross-contamination estimation and identification for large human sequencing cohorts

BackgroundInterest in genomic medicine for human health studies and clinical applications is rapidly increasing. Clinical applications require contamination-free samples to avoid misleading results and provide a sound basis for diagnosis.ResultsHere we present ContaTester, a tool which requires only allele balance information gathered from a VCF file to detect cross-contamination in germline human DNA samples. Based on a regression model of allele balance distribution, ContaTester allows fast checking of contamination levels for single samples or large cohorts (less than two minutes per sample). We demonstrate the efficiency of ContaTester using experimental validations: ContaTester shows similar results to methods requiring alignment data but with a significantly reduced storage footprint and less computation time. Additionally, for contamination levels above 5%, ContaTester can identify contaminants across a cohort, providing important clues for troubleshooting and quality assessment.ConclusionsContaTester estimates contamination levels from VCF files generated from whole genome sequencing normal sample and provides reliable contaminant identification for cohorts or experimental batches.

The CUSUM statistic of change point under NA sequences

In this paper, we investigate the CUSUM statistic of change point under the negatively associated (NA) sequences. By establishing the consistency estimators for mean and covariance functions respectively, the limit distribution of the CUSUM statistic is proved to be a standard Brownian bridge, which extends the results obtained under the case of an independent normal sample and the moving average processes. Finally, the finite sample properties of the CUSUM statistic are given to show the efficiency of the method by simulation studies and an application on a real data analysis.

Custom workflows to improve joint variant calling from multiple related tumour samples: FreeBayesSomatic and Strelka2Pass

Summary This work describes two novel workflows for variant calling that extend the widely used algorithms of Strelka2 and FreeBayes to call somatic mutations from multiple related tumour samples and one matched normal sample. We show that these workflows offer higher precision and recall than their single tumour-normal pair equivalents in both simulated and clinical sequencing data. Availability and Implementation Source code freely available at the following link: https://atlassian.petermac.org.au/bitbucket/projects/DAW/repos/multisamplevariantcalling and executable through Janis (https://github.com/PMCC-BioinformaticsCore/janis) under the GPLv3 licence. Supplementary information Supplementary data are available at Bioinformatics online.

Relevant and Non-Redundant Feature Selection for Cancer Classification and Subtype Detection

Biologists seek to identify a small number of significant features that are important, non-redundant, and relevant from diverse omics data. For example, statistical methods such as LIMMA and DEseq distinguish differentially expressed genes between a case and control group from the transcript profile. Researchers also apply various column subset selection algorithms on genomics datasets for a similar purpose. Unfortunately, genes selected by such statistical or machine learning methods are often highly co-regulated, making their performance inconsistent. Here, we introduce a novel feature selection algorithm that selects highly disease-related and non-redundant features from a diverse set of omics datasets. We successfully applied this algorithm to three different biological problems: (a) disease-to-normal sample classification; (b) multiclass classification of different disease samples; and (c) disease subtypes detection. Considering the classification of ROC-AUC, false-positive, and false-negative rates, our algorithm outperformed other gene selection and differential expression (DE) methods for all six types of cancer datasets from TCGA considered here for binary and multiclass classification problems. Moreover, genes picked by our algorithm improved the disease subtyping accuracy for four different cancer types over state-of-the-art methods. Hence, we posit that our proposed feature reduction method can support the community to solve various problems, including the selection of disease-specific biomarkers, precision medicine design, and disease sub-type detection.

Cardiac Protection Activity of Ethanol Extract of White Curcumin (Curcuma Zedoaria), Against Cyclophosphamid Induced Cardiovascular Complications in Male Rat

Objective: The common side effects associated with cyclophosphamide administration are bone marrow suppression, exposure to infections, as well as cardiovascular complications, including sinus bradycardia, pericarditis, myocarditis, and type 1 heart problems. The present study was sought to investigate the effect of curcumin on modulating side effectt of cyclophosphamide and the mechanisms involved. Methods: This experimental study was carried out in the Department of Pharmacology, North Sumatra University during 2020. Thirty Wistar albino male rats, weighing 100 to 150 g (initial body weight); aged 85 to 100 days were selected for the study. After acclimatization for 14 days. Rats (n = 30) were pretreated with catechin (200 mg/kg, p.o.) alone, normal sample and different dose combination of curcumin (200, 400, 800 mg/kg, p.o.) in 10th day. The whole rats in each groups fed on 150 mg/KgBW of cyclophosphamide solution on days 11th to 15th of the trial The heart was remove blood was taken Serum creatine kinase-MB (CK-MB) level was estimated by IFCC method and Troponin T (cTnI) level by ELISA method. The blood samples were collected and complete blood count were measured by cell counter. The statistical analysis was done by one way ANOVA followed by Tukey post hoc test, with coefficient interval of 95% (α = 0,05). Results: The results revealed that the CKMB and Troponin T levels decreased along with a dosage increase in thegroup with ethanol extract of white turmeric and an improvement in homeostasis and the histopathology of the liver of the rats. The impact of white turmeric is protective against cardiac cell damage of mice.

Estimating tumor mutational burden from RNA-sequencing without a matched-normal sample

Detection of somatic point mutations using patients sequencing data has many clinical applications, including the identification of cancer driver genes, detection of mutational signatures, and estimation of tumor mutational burden (TMB). In a recent work we developed a tool for detection of somatic mutations using tumor RNA and matched-normal DNA. Here, we further extend it to detect somatic mutations from RNA sequencing data without a matched-normal sample. This is accomplished via a machine learning approach that classifies mutations as either somatic or germline based on various features. When applied to RNA-sequencing of >450 melanoma samples high precision and recall are achieved, and both mutational signatures and driver genes are correctly identified. Finally, we show that RNA-based TMB is significantly associated with patient survival, with similar or superior performance to DNA-based TMB. Our pipeline can be utilized in many future applications, analyzing novel and existing datasets where only RNA is available.