BACKGROUND: Despite the 2015 American College of Medical Genetics and Genomics (ACMG) and Association of Molecular Pathology (AMP) guideline, many variants of FBN1 gene remain inconclusive. In line with publication of the FBN1-specific variant interpretation guideline by ClinGen in 2022, we reassessed variants of uncertain significance (VUS) in FBN1 gene found in our institution.
METHODS: VUS found in the course of FBN1 sequencing between December 2015 and April 2022 were reassessed based on FBN1-specific variant interpretation guideline, review of updated literatures and additional genetic tests including family study and/or RNA study if available.
RESULTS: Out of 695 patients who underwent FBN1 sequencing, 61 VUS were found in 69 patients. Among them, 38 VUS in 43 patients (62.3%) were reclassified as pathogenic and likely pathogenic variant ((L)PV), including 20 novel (L)PV. Major causes of reclassification were: (1) gene-specific modification of ACMG/AMP criteria, (2) updated literatures and (3) additional genetic tests. The most important evidence for reclassification was clarification of critical amino acid residues.
CONCLUSIONS: After reassessing FBN1 variants according to FBN1-specific guideline and up-to-date database, a significant number of VUS was reclassified. Clinical laboratories are encouraged to perform variant reassessment at regular intervals or when there is a major change in the principle of variant interpretation.

BACKGROUND: SARS-CoV-2 related research has increased in importance worldwide since December 2019. Several new variants of SARS-CoV-2 have emerged globally, of which the most notable and concerning currently are the UK variant B.1.1.7, the South African variant B1.351 and the Brazilian variant P.1. Detecting and monitoring novel variants is essential in SARS-CoV-2 surveillance. While there are several tools for assembling virus genomes and performing lineage analyses to investigate SARS-CoV-2, each is limited to performing singular or a few functions separately.
RESULTS: Due to the lack of publicly available pipelines, which could perform fast reference-based assemblies on raw SARS-CoV-2 sequences in addition to identifying lineages to detect variants of concern, we have developed an open source bioinformatic pipeline called HAVoC (Helsinki university Analyzer for Variants of Concern). HAVoC can reference assemble raw sequence reads and assign the corresponding lineages to SARS-CoV-2 sequences.
CONCLUSIONS: HAVoC is a pipeline utilizing several bioinformatic tools to perform multiple necessary analyses for investigating genetic variance among SARS-CoV-2 samples. The pipeline is particularly useful for those who need a more accessible and fast tool to detect and monitor the spread of SARS-CoV-2 variants of concern during local outbreaks. HAVoC is currently being used in Finland for monitoring the spread of SARS-CoV-2 variants. HAVoC user manual and source code are available at https://www.helsinki.fi/en/projects/havoc and https://bitbucket.org/auto_cov_pipeline/havoc , respectively.

Single-molecule sequencing technologies produce much longer reads compared with next-generation sequencing, greatly improving the contiguity of de novo assembly of genomes. However, the relatively high error rates in long reads make it challenging to obtain high-quality assemblies. A computationally intensive consensus step is needed to resolve the discrepancies in the reads. Efficient consensus tools have emerged in the recent past, based on partial-order alignment. In this study, we discovered that the spatial relationship of alignment pileup is crucial to high-quality consensus and developed a deep learning-based consensus tool, CONNET, which outperforms the fastest tools in terms of both accuracy and speed. We tested CONNET using a 90× dataset of E. coli and a 37× human dataset. In addition to achieving high-quality consensus results, CONNET is capable of delivering phased diploid genome consensus. Diploid consensus on the above-mentioned human assembly further reduced 12% of the consensus errors made in the haploid results.

Cancer phylogenies are key to studying tumorigenesis and have clinical implications. Due to the heterogeneous nature of cancer and limitations in current sequencing technology, current cancer phylogeny inference methods identify a large solution space of plausible phylogenies. To facilitate further downstream analyses, methods that accurately summarize such a set T of cancer phylogenies are imperative. However, current summary methods are limited to a single consensus tree or graph and may miss important topological features that are present in different subsets of candidate trees.
We introduce the Multiple Consensus Tree (MCT) problem to simultaneously cluster T and infer a consensus tree for each cluster. We show that MCT is NP-hard, and present an exact algorithm based on mixed integer linear programming (MILP). In addition, we introduce a heuristic algorithm that efficiently identifies high-quality consensus trees, recovering all optimal solutions identified by the MILP in simulated data at a fraction of the time. We demonstrate the applicability of our methods on both simulated and real data, showing that our approach selects the number of clusters depending on the complexity of the solution space T.
https://github.com/elkebir-group/MCT.
Supplementary data are available at Bioinformatics online.

BACKGROUND: Repeat elements are important components of most eukaryotic genomes. Most existing tools for repeat analysis rely either on high quality reference genomes or existing repeat libraries. Thus, it is still challenging to do repeat analysis for species with highly repetitive or complex genomes which often do not have good reference genomes or annotated repeat libraries. Recently we developed a computational method called REPdenovo that constructs consensus repeat sequences directly from short sequence reads, which outperforms an existing tool called RepARK. One major issue with REPdenovo is that it doesn\'t perform well for repeats with relatively high divergence rates or low copy numbers. In this paper, we present an improved approach for constructing consensus repeats directly from short reads. Comparing with the original REPdenovo, the improved approach uses more repeat-related k-mers and improves repeat assembly quality using a consensus-based k-mer processing method.
RESULTS: We compare the performance of the new method with REPdenovo and RepARK on Human, Arabidopsis thaliana and Drosophila melanogaster short sequencing data. And the new method fully constructs more repeats in Repbase than the original REPdenovo and RepARK, especially for repeats of higher divergence rates and lower copy number. We also apply our new method on Hummingbird data which doesn\'t have a known repeat library, and it constructs many repeat elements that can be validated using PacBio long reads.
CONCLUSIONS: We propose an improved method for reconstructing repeat elements directly from short sequence reads. The results show that our new method can assemble more complete repeats than REPdenovo (and also RepARK). Our new approach has been implemented as part of the REPdenovo software package, which is available for download at https://github.com/Reedwarbler/REPdenovo .

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High-throughput sequencing of B-cell immunoglobulin repertoires is increasingly being applied to gain insights into the adaptive immune response in healthy individuals and in those with a wide range of diseases. Recent applications include the study of autoimmunity, infection, allergy, cancer and aging. As sequencing technologies continue to improve, these repertoire sequencing experiments are producing ever larger datasets, with tens- to hundreds-of-millions of sequences. These data require specialized bioinformatics pipelines to be analyzed effectively. Numerous methods and tools have been developed to handle different steps of the analysis, and integrated software suites have recently been made available. However, the field has yet to converge on a standard pipeline for data processing and analysis. Common file formats for data sharing are also lacking. Here we provide a set of practical guidelines for B-cell receptor repertoire sequencing analysis, starting from raw sequencing reads and proceeding through pre-processing, determination of population structure, and analysis of repertoire properties. These include methods for unique molecular identifiers and sequencing error correction, V(D)J assignment and detection of novel alleles, clonal assignment, lineage tree construction, somatic hypermutation modeling, selection analysis, and analysis of stereotyped or convergent responses. The guidelines presented here highlight the major steps involved in the analysis of B-cell repertoire sequencing data, along with recommendations on how to avoid common pitfalls.

Currently, several different Y-chromosomal phylogenies and haplogroup nomenclatures are presented in scientific literature and at conferences demonstrating the present diversity in Y-chromosomal phylogenetic trees and Y-SNP sets used within forensic and anthropological research. This situation can be ascribed to the exponential growth of the number of Y-SNPs discovered due to mostly next-generation sequencing (NGS) studies. As Y-SNPs and their respective phylogenetic positions are important in forensics, such as for male lineage characterization and paternal bio-geographic ancestry inference, there is a need for forensic geneticists to know how to deal with these newly identified Y-SNPs and phylogenies, especially since these phylogenies are often created with other aims than to carry out forensic genetic research. Therefore, we give here an overview of four categories of currently used Y-chromosomal phylogenies and the associated Y-SNP sets in scientific research in the current NGS era. We compare these categories based on the construction method, their advantages and disadvantages, the disciplines wherein the phylogenetic tree can be used, and their specific relevance for forensic geneticists. Based on this overview, it is clear that an up-to-date reduced tree with a consensus Y-SNP set and a stable nomenclature will be the most appropriate reference resource for forensic research. Initiatives to reach such an international consensus are therefore highly recommended.

The publication of the ACMG recommendations has reignited the debate over predictive testing for adult-onset disorders in minors. Response has been polarized. With this in mind, we review and critically analyze this debate. First, we identify long-standing inconsistencies between consensus guidelines and clinical practice regarding risk assessment for adult-onset genetic disorders in children using family history and molecular analysis. Second, we discuss the disparate assumptions regarding the nature of whole genome and exome sequencing underlying arguments of both supporters and critics, and the role these assumptions play in the arguments for and against reporting. Third, we suggest that implicit differences regarding the definition of best interests of the child underlie disparate conclusions as to the best interests of children in this context. We conclude by calling for clarity and consensus concerning the central foci of this debate.

The Consensus Coding Sequence (CCDS) project (http://www.ncbi.nlm.nih.gov/CCDS/) is a collaborative effort to maintain a dataset of protein-coding regions that are identically annotated on the human and mouse reference genome assemblies by the National Center for Biotechnology Information (NCBI) and Ensembl genome annotation pipelines. Identical annotations that pass quality assurance tests are tracked with a stable identifier (CCDS ID). Members of the collaboration, who are from NCBI, the Wellcome Trust Sanger Institute and the University of California Santa Cruz, provide coordinated and continuous review of the dataset to ensure high-quality CCDS representations. We describe here the current status and recent growth in the CCDS dataset, as well as recent changes to the CCDS web and FTP sites. These changes include more explicit reporting about the NCBI and Ensembl annotation releases being compared, new search and display options, the addition of biologically descriptive information and our approach to representing genes for which support evidence is incomplete. We also present a summary of recent and future curation targets.