Comparison of structural variants detected by optical mapping with long-read next-generation sequencing.

MOTIVATION: Recent studies have shown the potential of using long-read whole-genome sequencing (WGS) approaches and optical mapping (OM) for the detection of clinically relevant structural variants (SVs) in cancer research. Three main long-read WGS platforms are currently in use: Pacific Biosciences (PacBio), Oxford Nanopore Technologies (ONT) and 10x Genomics. Recently, whole-genome OM technology (Bionano Genomics) has been introduced into human diagnostics. Questions remain about the accuracy of these long-read sequencing platforms, how comparable/interchangeable they are when searching for SVs and to what extent they can be replaced or supplemented by OM. Moreover, no tool can effectively compare SVs obtained by OM and WGS. RESULTS: This study compared optical maps of the breast cancer cell line SKBR3 with AnnotSV outputs from WGS platforms. For this purpose, a software tool with comparative and filtering features was developed. The majority of SVs up to a 50 kbp distance variance threshold found by OM were confirmed by all WGS platforms, and ∼99% of translocations and ∼80% of deletions found by OM were confirmed by both PacBio and ONT, with ∼70% being confirmed by 10x Genomics in combination with PacBio and/or ONT. Interestingly, long deletions (>100 kbp) were detected only by 10x Genomics. Regarding insertions, ∼74% was confirmed by PacBio and ONT, but none by 10x Genomics. Inversions and duplications detected by OM were not detected by WGS. Moreover, the tool enabled the confirmation of SVs that overlapped in the same gene(s) and was applied to the filtering of disease-associated SVs. AVAILABILITY AND IMPLEMENTATION: https://github.com/novosadt/om-annotsv-svc.

Standardization of Sequencing Coverage Depth in NGS: Recommendation for Detection of Clonal and Subclonal Mutations in Cancer Diagnostics.

The insufficient standardization of diagnostic next-generation sequencing (NGS) still limits its implementation in clinical practice, with the correct detection of mutations at low variant allele frequencies (VAF) facing particular challenges. We address here the standardization of sequencing coverage depth in order to minimize the probability of false positive and false negative results, the latter being underestimated in clinical NGS. There is currently no consensus on the minimum coverage depth, and so each laboratory has to set its own parameters. To assist laboratories with the determination of the minimum coverage parameters, we provide here a user-friendly coverage calculator. Using the sequencing error only, we recommend a minimum depth of coverage of 1,650 together with a threshold of at least 30 mutated reads for a targeted NGS mutation analysis of ≥3% VAF, based on the binomial probability distribution. Moreover, our calculator also allows adding assay-specific errors occurring during DNA processing and library preparation, thus calculating with an overall error of a specific NGS assay. The estimation of correct coverage depth is recommended as a starting point when assessing thresholds of NGS assay. Our study also points to the need for guidance regarding the minimum technical requirements, which based on our experience should include the limit of detection (LOD), overall NGS assay error, input, source and quality of DNA, coverage depth, number of variant supporting reads, and total number of target reads covering variant region. Further studies are needed to define the minimum technical requirements and its reporting in diagnostic NGS.

Correlation Network Analysis Reveals Relationships between MicroRNAs, Transcription Factor T-bet, and Deregulated Cytokine/Chemokine-Receptor Network in Pulmonary Sarcoidosis.

Sarcoidosis is an inflammatory granulomatous disease with unknown etiology driven by cytokines and chemokines. There is limited information regarding the regulation of cytokine/chemokine-receptor network in bronchoalveolar lavage (BAL) cells in pulmonary sarcoidosis, suggesting contribution of miRNAs and transcription factors. We therefore investigated gene expression of 25 inflammation-related miRNAs, 27 cytokines/chemokines/receptors, and a Th1-transcription factor T-bet in unseparated BAL cells obtained from 48 sarcoidosis patients and 14 control subjects using quantitative RT-PCR. We then examined both miRNA-mRNA expressions to enrich relevant relationships. This first study on miRNAs in sarcoid BAL cells detected deregulation of miR-146a, miR-150, miR-202, miR-204, and miR-222 expression comparing to controls. Subanalysis revealed higher number of miR-155, let-7c transcripts in progressing (n = 20) comparing to regressing (n = 28) disease as assessed by 2-year follow-up. Correlation network analysis revealed relationships between microRNAs, transcription factor T-bet, and deregulated cytokine/chemokine-receptor network in sarcoid BAL cells. Furthermore, T-bet showed more pronounced regulatory capability to sarcoidosis-associated cytokines/chemokines/receptors than miRNAs, which may function rather as "fine-tuners" of cytokine/chemokine expression. Our correlation network study implies contribution of both microRNAs and Th1-transcription factor T-bet to the regulation of cytokine/chemokine-receptor network in BAL cells in sarcoidosis. Functional studies are needed to confirm biological relevance of the obtained relationships.

Searching protein 3-D structures for optimal structure alignment using intelligent algorithms and data structures.

In this paper, we present a novel algorithm for measuring protein similarity based on their 3-D structure (protein tertiary structure). The algorithm used a suffix tree for discovering common parts of main chains of all proteins appearing in the current research collaboratory for structural bioinformatics protein data bank (PDB). By identifying these common parts, we build a vector model and use some classical information retrieval (IR) algorithms based on the vector model to measure the similarity between proteins--all to all protein similarity. For the calculation of protein similarity, we use term frequency × inverse document frequency ( tf × idf ) term weighing schema and cosine similarity measure. The goal of this paper is to introduce new protein similarity metric based on suffix trees and IR methods. Whole current PDB database was used to demonstrate very good time complexity of the algorithm as well as high precision. We have chosen the structural classification of proteins (SCOP) database for verification of the precision of our algorithm because it is maintained primarily by humans. The next success of this paper would be the ability to determine SCOP categories of proteins not included in the latest version of the SCOP database (v. 1.75) with nearly 100% precision.