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      Quality assessment of splice site annotation based on conservation across multiple species

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      Cold Spring Harbor Laboratory

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          Abstract

          Despite many improvements over the years, the annotation of the human genome remains imperfect, and even the best annotations of the human reference genome sometimes contradict one another. Hence, refinement of the human genome annotation is an important challenge. The use of evolutionarily conserved sequences provides a strategy for addressing this problem, and the rapidly growing number of genomes from other species increases the power of an evolution-driven approach. Using the latest large-scale whole genome alignment data, we found that splice sites from protein-coding genes in the high-quality MANE annotation are consistently conserved across more than 400 species. We also studied splice sites from the RefSeq, GENCODE, and CHESS databases that are not present in MANE, from both protein-coding genes and lncRNAs. We trained a logistic regression classifier to distinguish between the conservation patterns exhibited by splice sites from MANE versus sites that were flanked by the standard GT-AG dinucleotides, but that were chosen randomly from a sequence not under selection. We found that up to 70% of splice sites from annotated protein-coding transcripts outside of MANE exhibit conservation patterns closer to random sequence as opposed to highly-conserved splice sites from MANE. Our study highlights potentially erroneous splice sites that might require further scrutiny.

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          The mutational constraint spectrum quantified from variation in 141,456 humans

          Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.
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            Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation

            The RefSeq project at the National Center for Biotechnology Information (NCBI) maintains and curates a publicly available database of annotated genomic, transcript, and protein sequence records (http://www.ncbi.nlm.nih.gov/refseq/). The RefSeq project leverages the data submitted to the International Nucleotide Sequence Database Collaboration (INSDC) against a combination of computation, manual curation, and collaboration to produce a standard set of stable, non-redundant reference sequences. The RefSeq project augments these reference sequences with current knowledge including publications, functional features and informative nomenclature. The database currently represents sequences from more than 55 000 organisms (>4800 viruses, >40 000 prokaryotes and >10 000 eukaryotes; RefSeq release 71), ranging from a single record to complete genomes. This paper summarizes the current status of the viral, prokaryotic, and eukaryotic branches of the RefSeq project, reports on improvements to data access and details efforts to further expand the taxonomic representation of the collection. We also highlight diverse functional curation initiatives that support multiple uses of RefSeq data including taxonomic validation, genome annotation, comparative genomics, and clinical testing. We summarize our approach to utilizing available RNA-Seq and other data types in our manual curation process for vertebrate, plant, and other species, and describe a new direction for prokaryotic genomes and protein name management.
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              The Human Genome Browser at UCSC

              As vertebrate genome sequences near completion and research refocuses to their analysis, the issue of effective genome annotation display becomes critical. A mature web tool for rapid and reliable display of any requested portion of the genome at any scale, together with several dozen aligned annotation tracks, is provided at http://genome.ucsc.edu. This browser displays assembly contigs and gaps, mRNA and expressed sequence tag alignments, multiple gene predictions, cross-species homologies, single nucleotide polymorphisms, sequence-tagged sites, radiation hybrid data, transposon repeats, and more as a stack of coregistered tracks. Text and sequence-based searches provide quick and precise access to any region of specific interest. Secondary links from individual features lead to sequence details and supplementary off-site databases. One-half of the annotation tracks are computed at the University of California, Santa Cruz from publicly available sequence data; collaborators worldwide provide the rest. Users can stably add their own custom tracks to the browser for educational or research purposes. The conceptual and technical framework of the browser, its underlying MYSQL database, and overall use are described. The web site currently serves over 50,000 pages per day to over 3000 different users.
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                Author and article information

                Contributors
                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                02 December 2023
                : 2023.12.01.569581
                Affiliations
                Department of Biomedical Engineering, Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21211, USA
                Department of Biomedical Engineering, Center for Computational Biology, Department of Computer Science, Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21211, USA
                Author notes
                Author information
                http://orcid.org/0000-0002-0807-347X
                http://orcid.org/0000-0002-8859-7432
                Article
                10.1101/2023.12.01.569581
                10705407
                38076842
                b7e271a3-8709-4fd4-9036-2c35c15030e3

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

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