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  • 1
    In: Bioinformatics, Oxford University Press (OUP), Vol. 28, No. 5 ( 2012-03-01), p. 721-723
    Abstract: Summary: FX is an RNA-Seq analysis tool, which runs in parallel on cloud computing infrastructure, for the estimation of gene expression levels and genomic variant calling. In the mapping of short RNA-Seq reads, FX uses a transcriptome-based reference primarily, generated from ~160 000 mRNA sequences from RefSeq, UCSC and Ensembl databases. This approach reduces the misalignment of reads originating from splicing junctions. Unmapped reads not aligned on known transcripts are then mapped on the human genome reference. FX allows analysis of RNA-Seq data on cloud computing infrastructures, supporting access through a user-friendly web interface. Availability: FX is freely available on the web at (http://fx.gmi.ac.kr), and can be installed on local Hadoop clusters. Guidance for the installation and operation of FX can be found under the ‘Documentation’ menu on the website. Contact:  jeongsun@snu.ac.kr; jongil@snu.ac.kr Supplementary information:  Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2012
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 2
    Online Resource
    Online Resource
    University Library System, University of Pittsburgh ; 2014
    In:  Central Asian Journal of Global Health Vol. 3 ( 2014-12-12)
    In: Central Asian Journal of Global Health, University Library System, University of Pittsburgh, Vol. 3 ( 2014-12-12)
    Abstract: Introduction: The human genome sequence will underpin human biology and medicine in the next century, providing a single, essential reference to all genetic information. Extraordinary technological advances and decreases in the cost of DNA sequencing have made the possibility of whole genome sequencing (WGS) feasible as a highly accessible test for numerous indications. The international project “Genetic architecture of Kazakh population” is well underway to determine the complete DNA. Next generation sequencing is a powerful tool for genetic analysis, which will enable us to uncover the association of loci at specific sites in the genome associated with disease. The aim of this study was to introduce first data on WGS of 6 Kazakh individuals.Methods: This pilot study is among the first WGS performed on 6 healthy Kazakh individuals, using next generation sequencing platform HiSeq2000, Illumina by manufacturer’s protocols. All generated *.bcl files were simultaneously converted and demultiplexed using bcl2fasta application. Alignment of sequence reads performed using bwa-mem against human b19 reference genome. Sorting, removing of intermediate files, *.bam files assembling, and marking duplicates were performed using PicardTools package. GATK haplotype caller tool was used for variant calling. ClinVar, SNPedia, and Cosmic databases were processed to identify clinical genomic variants in 6 Kazakh whole genomes. Java Runtime Environment and R. Bioconductor packages were installed to perform raw data processing and run program scripts.Results: The sequence alignment and mapping procedures on reference genome hg19 of each 6 healthy Kazakh individual were completed. Between 87,308,581,400 and 107,526,741,301 total base pairs were sequenced with average coverage x29.85. Between 98.85% and 99.58% base pairs were totally mapped and on average 96.07% were properly paired. Het/Hom and Ti/Tv ratios for each whole genome ranged from 1.35 to 1.52 and from 2.07 to 2.08, respectively. We compared and analyzed each genome with on existing clinical databases ClinVar, SNPedia, Cosmic and found from 20 to 25, from 269 to 288, from 7 to 12 SNP records, respectively. The availability of a reference Kazakh genome sequences provides the basis for studying the nature of sequence variation, particularly single nucleotide polymorphisms.Conclusion: The first whole genome sequencing of Kazakhs were performed. In this pilot study, we identified SNPs associated with different conditions. Further studies of WGS on Kazakh population are needed to identify possible unique genetic variants in Kazakhs.
    Type of Medium: Online Resource
    ISSN: 2166-7403
    Language: Unknown
    Publisher: University Library System, University of Pittsburgh
    Publication Date: 2014
    detail.hit.zdb_id: 2708701-3
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