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      Transcriptional Determinism and Stochasticity Contribute to the Complexity of Autism Associated SHANK Family Genes

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          Abstract

          Precision of transcription is critical because transcriptional dysregulation is disease causing. Traditional methods of transcriptional profiling are inadequate to elucidate the full spectrum of the transcriptome, particularly for longer and less abundant mRNAs. SHANK3 is one of the most common autism causative genes. Twenty-four Shank3 mutant animal lines have been developed for autism modeling. However, their preclinical validity has been questioned due to incomplete Shank3 transcript structure. We applied an integrative approach combining cDNA-capture and long-read sequencing to profile the SHANK3 transcriptome in human and mice. We unexpectedly discovered an extremely complex SHANK3 transcriptome. Specific SHANK3 transcripts were altered in Shank3 mutant mice and postmortem brains tissues from individuals with ASD. The enhanced SHANK3 transcriptome significantly improved the detection rate for potential deleterious variants from genomics studies of neuropsychiatric disorders. Our findings suggest the stochastic transcription of genome associated with SHANK family genes.

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          featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

          Y. Liao, G K Smyth, W Shi (2014)
          Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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            fastp: an ultra-fast all-in-one FASTQ preprocessor

            Shifu Chen, Yanqing Zhou, Yaru Chen … (2018)
            Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.
            Article 0 comments Cited 6137 times – based on 0 reviews     Review now
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              Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype

              Daehwan Kim, Joseph M Paggi, Chanhee Park … (2021)
              Rapid advances in next-generation sequencing technologies have dramatically changed our ability to perform genome-scale analyses. The human reference genome used for most genomic analyses represents only a small number of individuals, limiting its usefulness for genotyping. We designed a novel method, HISAT2, for representing and searching an expanded model of the human reference genome, in which a large catalogue of known genomic variants and haplotypes is incorporated into the data structure used for searching and alignment. This strategy for representing a population of genomes, along with a fast and memory-efficient search algorithm, enables more detailed and accurate variant analyses than previous methods. We demonstrate two initial applications of HISAT2: HLA typing, a critical need in human organ transplantation, and DNA fingerprinting, widely used in forensics. These applications are part of HISAT-genotype, with performance not only surpassing earlier computational methods, but matching or exceeding the accuracy of laboratory-based assays.
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                Author and article information

                Journal
                Journal ID (nlm-ta): bioRxiv
                Journal ID (publisher-id): BIORXIV
                Title: bioRxiv
                Publisher: Cold Spring Harbor Laboratory
                Publication date (Electronic): 19 March 2024
                Electronic Location Identifier: 2024.03.18.585480
                Affiliations
                [1 ]Department of Genetics, Yale University School of Medicine New Haven, CT, 06520 USA
                [2 ]Biomedical Informatics & Data Science, Yale University School of Medicine New Haven, CT, 06520 USA
                [3 ]Neuroscienc, Yale University School of Medicine New Haven, CT, 06520 USA
                [4 ]Pediatrics, Yale University School of Medicine New Haven, CT, 06520 USA
                [5 ]Yale Center for Genome Analysis, Yale University School of Medicine New Haven, CT, 06520 USA
                [6 ]Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, 32224 USA
                [7 ]Department of Neurology, Children’s Hospital of Fudan University, Shanghai, 201102 China
                Author notes

                Author contributions

                XL and YHJ conceived and designed the project. XL performed most of data collection and data analysis. PSM, YM, YW and AQH prepared and process human brain tissues. GW assisted the long-read sequencing production. MG and NP assist the data analysis. XL and YHJ wrote the manuscript together with all co-authors.

                Correspondence: Yong-hui Jiang, MD, PhD, Department of Genetics, Yale University School of Medicine, WWW 313, 333 Cedar Street, New Haven CT 06520, Fax:203 785 3404, yong-hui.jiang@ 123456yale.edu
                Article
                DOI: 10.1101/2024.03.18.585480
                PMC ID: 10983920
                PubMed ID: 38562714
                SO-VID: b66b748b-5d71-4b2e-862b-612668a3bb76
                License:

                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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