Prioritising positively selected variants in whole-genome sequencing data using FineMAV

Background In population genomics, polymorphisms that are highly differentiated between geographically separated populations are often suggestive of Darwinian positive selection. Genomic scans have highlighted several such regions in African and non-African populations, but only a handful of these have functional data that clearly associates candidate variations driving the selection process. Fine-Mapping of Adaptive Variation (FineMAV) was developed to address this in a high-throughput manner using population based whole-genome sequences generated by the 1000 Genomes Project. It pinpoints positively selected genetic variants in sequencing data by prioritizing high frequency, population-specific and functional derived alleles. Results We developed a stand-alone software that implements the FineMAV statistic. To graphically visualise the FineMAV scores, it outputs the statistics as bigWig files, which is a common file format supported by many genome browsers. It is available as a command-line and graphical user interface. The software was tested by replicating the FineMAV scores obtained using 1000 Genomes Project African, European, East and South Asian populations and subsequently applied to whole-genome sequencing datasets from Singapore and China to highlight population specific variants that can be subsequently modelled. The software tool is publicly available at https://github.com/fadilla-wahyudi/finemav. Conclusions The software tool described here determines genome-wide FineMAV scores, using low or high-coverage whole-genome sequencing datasets, that can be used to prioritize a list of population specific, highly differentiated candidate variants for in vitro or in vivo functional screens. The tool displays these scores on the human genome browsers for easy visualisation, annotation and comparison between different genomic regions in worldwide human populations.

Panache: a Web Browser-Based Viewer for Linearized Pangenomes

MotivationPangenomics evolved since its first applications on bacteria, extending from the study of genes for a given population to the study of all of its sequences available. While multiple methods are being developed to construct pangenomes in eukaryotic species there is still a gap for efficient and user-friendly visualization tools. Emerging graph representations comes with their own challenges, and linearity remains a suitable option for user-friendliness.ResultsWe introduce Panache, a tool for the visualization and exploration of linear representations of gene-based and sequence-based pangenomes. It uses a layout similar to genome browsers to display presence absence variations and additional tracks along a linear axis with a pangenomics perspective.AvailabilityPanache is available at github.com/SouthGreenPlatform/panache under the MIT [email protected], [email protected]

The value of primary transcripts to the clinical and non-clinical genomics community: survey results and roadmap for improvements.

Variant interpretation is dependent on transcript annotation and remains time consuming and challenging. There are major obstacles for historical data reuse and for interpretation of new variants. First, both RefSeq and Ensembl/GENCODE produce transcript sets in common use, but there is currently no easy way to translate between the two. Second, the resources often used for variant interpretation (e.g. ClinVar, gnomAD, UniProt) do not use the same transcript set, nor default transcript or protein sequence. Ensembl ran a survey in 2018 to assay attitudes to choosing one default transcript per locus, and to gather data on reference sequences used by the scientific community. This was publicised on the Ensembl and UCSC genome browsers, by email and on social media. We had 788 respondents. Here we report our results and roadmap to create an effective default set of transcripts for resources, and for reporting interpretation of clinical variants.

About classical molecular genetics, cytogenetic and molecular cytogenetic data not considered by Genome Reference Consortium and thus not included in genome browsers like UCSC, Ensembl or NCBI

Background The Genome Reference Consortium (GRC) has according to its own statement the “mission to improve the human reference genome assembly, correcting errors and adding sequence to ensure it provides the best representation of the human genome to meet basic and clinical research needs”. Data from GRC is included in genome browsers like UCSC (University of California, Santa Cruz), Ensembl or NCBI (National Center for Biotechnology Information) and are thereby bases for scientific and diagnostically working human genetic community. Method Here long standing knowledge deriving from classical molecular genetic, cytogenetic and molecular cytogenetic data, not being considered yet by GRC was revisited. Results There were three major points identified: (1) GRC missed to including three chromosomal subbands, each, for 1q32.1, 2p21, 5q13.2, 6p22.3 and 6q21, which were defined by International System for Human Cytogenetic Nomenclature (ISCN) already back in 1980s; instead GRC included additional 6 subbands not ever recognized by ISCN. (2) GRC defined 34 chromosomal subbands of 0.1 to 0.9 Mb in size, while it is general agreement of cytogeneticists that it unlikely to detect chromosomal aberrations below 1–2 Mb in size by GTG-banding. And (3): still all sequences used in molecular cytogenetic routine diagnostics to detect heterochromatic and/ or pericentromeric satellite DNA sequences within the human genome are not included yet into human reference genome. For those sequences, localization and approximate sizes have been determined in the 1970s to 1990, and if included at least ~ 100 Mb of the human genome sequence could be added to the genome browsers. Conclusion Overall, taking into account the here mentioned points and correcting and including the data will definitely provide to the still not being completely finished mapping of the human genome.

Novel perspectives for SARS-CoV-2 genome browsing

SARS-CoV-2 has spread worldwide and caused social, economic, and health turmoil. The first genome assembly of SARS-CoV-2 was produced in Wuhan, and it is widely used as a reference. Subsequently, more than a hundred additional SARS-CoV-2 genomes have been sequenced. While the genomes appear to be mostly identical, there are variations. Therefore, an alignment of all available genomes and the derived consensus sequence could be used as a reference, better serving the science community. Variations are significant, but representing them in a genome browser can become, especially if their sequences are largely identical. Here we summarize the variation in one track. Other information not currently found in genome browsers for SARS-CoV-2, such as predicted miRNAs and predicted TRS as well as secondary structure information, were also added as tracks to the consensus genome. We believe that a genome browser based on the consensus sequence is better suited when considering worldwide effects and can become a valuable resource in the combating of COVID-19. The genome browser is available at http://cov.iaba.online.

BAGET 2.0: an updated web tool for the effortless retrieval of prokaryotic gene context and sequence

Motivation The retrieval of a single gene sequence and context from completely sequenced bacterial and archaeal genomes constitutes an intimidating task for the wet bench biologist. Existing web-based genome browsers are either too complex for routine use or only provide a subset of the available prokaryotic genomes. Results We have developed BAGET 2.0 (Bacterial and Archaeal Gene Exploration Tool), an updated web service granting access in just three mouse clicks to the sequence and synteny of any gene from completely sequenced bacteria and archaea. User-provided annotated genomes can be processed as well. BAGET 2.0 relies on a local database updated on a daily basis. Availability and implementation BAGET 2.0 befits all current browsers such as Chrome, Firefox, Edge, Opera and Safari. Internet Explorer 11 is supported. BAGET 2.0 is freely accessible at https://archaea.i2bc.paris-saclay.fr/baget/

Ensembl 2021

The Ensembl project (https://www.ensembl.org) annotates genomes and disseminates genomic data for vertebrate species. We create detailed and comprehensive annotation of gene structures, regulatory elements and variants, and enable comparative genomics by inferring the evolutionary history of genes and genomes. Our integrated genomic data are made available in a variety of ways, including genome browsers, search interfaces, specialist tools such as the Ensembl Variant Effect Predictor, download files and programmatic interfaces. Here, we present recent Ensembl developments including two new website portals. Ensembl Rapid Release (http://rapid.ensembl.org) is designed to provide core tools and services for genomes as soon as possible and has been deployed to support large biodiversity sequencing projects. Our SARS-CoV-2 genome browser (https://covid-19.ensembl.org) integrates our own annotation with publicly available genomic data from numerous sources to facilitate the use of genomics in the international scientific response to the COVID-19 pandemic. We also report on other updates to our annotation resources, tools and services. All Ensembl data and software are freely available without restriction.

COVID-19 Variants Database: A repository for Human SARS-CoV-2 Polymorphism Data

ABSTRACTCOVID-19 is a newly communicable disease with a catastrophe outbreak that affects all over the world. We retrieved about 8,781 nucleotide fragments and complete genomes of SARS-CoV-2 reported from sixty-four countries. The CoV-2 reference genome was obtained from the National Genomics Data Center (NGDC), GISAID, and NCBI Genbank. All the sequences were aligned against reference genomes using Clustal Omega and variants were called using in-house built Python script. We intend to establish a user-friendly online resource to visualize the variants in the viral genome along with the Primer Infopedia. After analyzing and filtering the data globally, it was made available to the public. The detail of data available to the public includes mutations from 5688 SARS-CoV-2 sequences curated from 91 regions. This database incorporated 39920 mutations over 3990 unique positions. According to the translational impact, these mutations include 11829 synonymous mutations including 681 synonymous frameshifts and 21701 nonsynonymous mutations including 10 nonsynonymous frameshifts. Development of SARS-CoV-2 mutation genome browsers is a fundamental step obliging towards the virus surveillance, viral detection, and development of vaccine and therapeutic drugs. The SARS-COV-2 mutation browser is available at http://covid-19.dnageography.com.

VarFish: comprehensive DNA variant analysis for diagnostics and research

VarFish is a user-friendly web application for the quality control, filtering, prioritization, analysis, and user-based annotation of DNA variant data with a focus on rare disease genetics. It is capable of processing variant call files with single or multiple samples. The variants are automatically annotated with population frequencies, molecular impact, and presence in databases such as ClinVar. Further, it provides support for pathogenicity scores including CADD, MutationTaster, and phenotypic similarity scores. Users can filter variants based on these annotations and presumed inheritance pattern and sort the results by these scores. Variants passing the filter are listed with their annotations and many useful link-outs to genome browsers, other gene/variant data portals, and external tools for variant assessment. VarFish allows users to create their own annotations including support for variant assessment following ACMG-AMP guidelines. In close collaboration with medical practitioners, VarFish was designed for variant analysis and prioritization in diagnostic and research settings as described in the software's extensive manual. The user interface has been optimized for supporting these protocols. Users can install VarFish on their own in-house servers where it provides additional lab notebook features for collaborative analysis and allows re-analysis of cases, e.g. after update of genotype or phenotype databases.