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      Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients

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          Abstract

          Glioma s are the most common and malignant intracranial tumors in adults. Recent studies have revealed the significance of functional genomics for glioma pathophysiological studies and treatments. However, access to comprehensive genomic data and analytical platforms is often limited. Here, we developed the Chinese Glioma Genome Atlas (CGGA), a user-friendly data portal for the storage and interactive exploration of cross-omics data, including nearly 2000 primary and recurrent glioma samples from Chinese cohort. Currently, open access is provided to whole-exome sequencing data (286 samples), mRNA sequencing (1018 samples) and microarray data (301 samples), DNA methylation microarray data (159 samples), and microRNA microarray data (198 samples), and to detailed clinical information (age, gender, chemoradiotherapy status, WHO grade, histological type, critical molecular pathological information, and survival data). In addition, we have developed several tools for users to analyze the mutation profiles, mRNA/microRNA expression, and DNA methylation profiles, and to perform survival and gene correlation analyses of specific glioma subtypes. This database removes the barriers for researchers, providing rapid and convenient access to high‐quality functional genomic data resources for biological studies and clinical applications. CGGA is available at http://www.cgga.org.cn.

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          The Sequence Alignment/Map format and SAMtools

          Heng Li, Bob Handsaker, Alec Wysoker … (2009)
          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            STAR: ultrafast universal RNA-seq aligner.

            Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)
            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              Fast and accurate short read alignment with Burrows–Wheeler transform

              Heng Li, Richard Durbin (2009)
              Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Contributors
                Zhaoshi Bao
                Tao Jiang
                Journal
                Journal ID (nlm-ta): Genomics Proteomics Bioinformatics
                Journal ID (iso-abbrev): Genomics Proteomics Bioinformatics
                Title: Genomics, Proteomics & Bioinformatics
                Publisher: Elsevier
                ISSN (Print): 1672-0229
                ISSN (Electronic): 2210-3244
                Publication date PMC-release: 02 March 2021
                Publication date (Print): February 2021
                Publication date (Electronic): 02 March 2021
                Volume: 19
                Issue: 1
                Pages: 1-12
                Affiliations
                [1 ]Beijing Neurosurgical Institute, Capital Medical University, Beijing 100070, China
                [2 ]Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, China
                [3 ]Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
                [4 ]Center of Brain Tumor, Beijing Institute for Brain Disorders, Beijing 100069, China
                [5 ]China National Clinical Research Center for Neurological Diseases, Beijing 100070, China
                Author notes
                [#]

                Equal contribution.

                Article
                Publisher Item ID: S1672-0229(21)00045-0
                DOI: 10.1016/j.gpb.2020.10.005
                PMC ID: 8498921
                PubMed ID: 33662628
                SO-VID: 81edb53b-1c23-48c4-9157-436af7858028
                Copyright © © 2021 The Authors. Published by Elsevier B.V. and Science Press on behalf of Beijing Institute of Genomics, Chinese Academy of Sciences and Genetics Society of China.
                License:

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 1 July 2019
                Date revision received : 1 October 2020
                Date accepted : 26 December 2020
                Categories
                Subject: Database

                Keywords: glioma,functional genomics,chinese glioma genome atlas,chinese cohort,database
                Data availability:
                Keywords: glioma, functional genomics, chinese glioma genome atlas, chinese cohort, database

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