Computational Compensatory Mutation Discovery Approach: Predicting a PARP1 Variant Rescue Mutation

The prediction of protein mutations that affect function may be exploited for multiple uses. In the context of disease variants, the prediction of compensatory mutations that reestablish functional phenotypes could aid in the development of genetic therapies. In this work, we present an integrated approach that combines coevolutionary analysis and molecular dynamics (MD) simulations to discover functional compensatory mutations. This approach is employed to investigate possible rescue mutations of a poly(ADP-ribose) polymerase 1 (PARP1) variant, PARP1 V762A, associated with lung cancer and follicular lymphoma. MD simulations show PARP1 V762A exhibits noticeable changes in structural and dynamical behavior compared with wild type PARP1. Our integrated approach predicts A755E as a possible compensatory mutation based on coevolutionary information, and molecular simulations indicate that the PARP1 A755E/V762A double mutant exhibits similar structural and dynamical behavior to WT PARP1. Our methodology can be broadly applied to a large number of systems where SNPs have been identified as connected to disease and can shed light on the biophysical effects of such changes as well as provide a way to discover potential mutants that could restore wild type-like functionality. This can in turn be further utilized in the design of molecular therapeutics that aim to mimic such compensatory effect.

Sensitivity, specificity, and accuracy of a liquid biopsy approach utilizing molecular amplification pools

Circulating cell-free DNA (cfDNA) has the potential to be a specific biomarker for the therapeutic management of lung cancer patients. Here, a new sequencing error-reduction method based on molecular amplification pools (MAPs) was utilized to analyze cfDNA in lung cancer patients. We determined the accuracy of MAPs plasma sequencing with respect to droplet digital polymerase chain reaction assays (ddPCR), and tested whether actionable mutation discovery is improved by next-generation sequencing (NGS) in a clinical setting. This study reports data from 356 lung cancer patients receiving plasma testing as part of routine clinical management. Sequencing of cfDNA via MAPs had a sensitivity of 98.5% and specificity 98.9%. The ddPCR assay was used as the reference, since it is an established, accurate assay that can be performed contemporaneously on the same plasma sample. MAPs sequencing detected somatic variants in 261 of 356 samples (73%). Non-actionable clonal hematopoiesis-associated variants were identified via sequencing in 21% of samples. The accuracy of this cfDNA sequencing approach was similar to that of ddPCR assays in a clinical setting, down to an allele frequency of 0.1%. Due to broader coverage and high sensitivity for insertions and deletions, sequencing via MAPs afforded important detection of additional actionable mutations.

FertilityOnline, a straight pipeline for functional gene annotation and disease mutation discovery, identifies novel infertility causative mutations in SYCE1 and STAG3

Exploring the genetic basis of human infertility is currently under intensive investigation. However, only a handful of genes are validated in animal models as disease-causing genes in infertile men. Thus, to better understand the genetic basis of spermatogenesis in human and to bridge the knowledge gap between human and other animal species, we have constructed FertilityOnline database, which is a resource that integrates the functional genes reported in literature related to spermatogenesis into an existing spermatogenic database, SpermatogenesisOnline 1.0. Additional features like functional annotation and statistical analysis of genetic variants of human genes, are also incorporated into FertilityOnline. By searching this database, users can focus on the top candidate genes associated with infertility and can perform enrichment analysis to instantly refine the number of candidates in a user-friendly web interface. Clinical validation of this database is established by the identification of novel causative mutations in SYCE1 and STAG3 in azoospermia men. In conclusion, FertilityOnline is not only an integrated resource for analysis of spermatogenic genes, but also a useful tool that facilitates to study underlying genetic basis of male infertility.AvailabilityFertilityOnline can be freely accessed at http://mcg.ustc.edu.cn/bsc/spermgenes2.0/index.html.

dv-trio: a family-based variant calling pipeline using DeepVariant

Motivation In 2018, Google published an innovative variant caller, DeepVariant, which converts pileups of sequence reads into images and uses a deep neural network to identify single-nucleotide variants and small insertion/deletions from next-generation sequencing data. This approach outperforms existing state-of-the-art tools. However, DeepVariant was designed to call variants within a single sample. In disease sequencing studies, the ability to examine a family trio (father-mother-affected child) provides greater power for disease mutation discovery. Results To further improve DeepVariant’s variant calling accuracy in family-based sequencing studies, we have developed a family-based variant calling pipeline, dv-trio, which incorporates the trio information from the Mendelian genetic model into variant calling based on DeepVariant. Availability and implementation dv-trio is available via an open source BSD3 license at GitHub (https://github.com/VCCRI/dv-trio/). Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

A highly accurate platform for clone-specific mutation discovery enables the study of active mutational processes

Bulk whole genome sequencing (WGS) enables the analysis of tumor evolution but, because of depth limitations, can only identify old mutational events. The discovery of current mutational processes for predicting the tumor’s evolutionary trajectory requires dense sequencing of individual clones or single cells. Such studies, however, are inherently problematic because of the discovery of excessive false positive (FP) mutations when sequencing picogram quantities of DNA. Data pooling to increase the confidence in the discovered mutations, moves the discovery back in the past to a common ancestor. Here we report a robust WGS and analysis pipeline (DigiPico/MutLX) that virtually eliminates all F results while retaining an excellent proportion of true positives. Using our method, we identified, for the first time, a hyper-mutation (kataegis) event in a group of ∼30 cancer cells from a recurrent ovarian carcinoma. This was unidentifiable from the bulk WGS data. Overall, we propose DigiPico/MutLX method as a powerful framework for the identification of clone-specific variants at an unprecedented accuracy.