An efficient approach to finding Siraitia grosvenorii triterpene biosynthetic genes by RNA-seq and digital gene expression analysis<\/a> \u62a5\u9053\uff1a 3′ tag digital gene expression profiling of human brain and universal reference RNA using Illumina Genome Analyzer<\/a> \u62a5\u9053\uff1a Digital gene expression analysis of two life cycle stages of the human-infective parasite, Trypanosoma brucei gambiense reveals differentially expressed clusters of co-regulated genes<\/a> \u62a5\u9053\uff1a \u901a\u8fc7\u6587\u732e\u4e2d\u7684\u65b9\u6cd5\uff0c\u5206\u6790 3′ tag \u65b9\u6cd5\u7684\u51e0\u70b9\u6ce8\u610f\u4e8b\u9879\uff1a<\/p>\n 1. \u5bf9 tag \u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\u3002\u53bb\u6389\u4ee5\u4e0b\u5e8f\u5217\uff1a\u542b\u6709 adaptor \u7684 tag\uff1b \u53bb\u6389\u4f4e\u8d28\u91cf\u7684 tag\uff1b \u53bb\u6389\u4f4e\u91cd\u590d\u5ea6\u7684 tag\uff0c\u6bd4\u5982\u91cd\u590d\u6b21\u6570\u4e3a 1 \u7684 tag\u3002 \u6700\u540e\uff0c\u5f97\u5230\u7528\u4e8e\u5206\u6790\u7684 clean data\u3002<\/p>\n 2. \u63d0\u53d6\u8f6c\u5f55\u7ec4\u7684\u9176\u5207\u4f4d\u70b9\u5e8f\u5217\uff0c\u6784\u5efa\u6570\u636e\u5e93\u3002\u5982\u679c\u6709\u57fa\u56e0\u7ec4\u548c\u57fa\u56e0\u7ed3\u6784\u6ce8\u91ca\u6587\u4ef6\uff0c\u6216\u8005\u6709\u53c2\u8003\u8f6c\u5f55\u7ec4\u5e8f\u5217\uff0c\u5219\u63d0\u53d6\u51fa\u57fa\u56e0\u7684 3′ \u7aef CATG+17 \u78b1\u57fa\u7684\u5e8f\u5217\u3002\u6ce8\u610f\u7684\u662f\uff0c\u5982\u679c\u57fa\u56e0\u7ed3\u6784\u6ce8\u91ca\u6587\u4ef6\u6ca1\u6709 3′ UTR\uff0c \u5219\u53ea\u80fd\u5c06 tag \u6bd4\u5bf9\u5230\u57fa\u56e0\u7ec4\u7684 ORF \u533a\u4e86\u3002\u6b64\u5916\uff0c\u4e5f\u6709\u6587\u7ae0\u4e0d\u8fdb\u884c\u5e8f\u5217\u63d0\u53d6\uff0c\u5c31\u76f4\u63a5\u7528\u8f6c\u5f55\u7ec4\u5e8f\u5217\u4f5c\u6570\u636e\u5e93\uff0c\u6765\u8fdb\u884c tag \u7684\u6bd4\u5bf9\uff0c\u8fd9\u6837\u7684\u7ed3\u679c\u5e94\u8be5\u662f\u4e0d\u592a\u597d\u7684\u3002 3. \u4f7f\u7528\u6bd4\u5bf9\u8f6f\u4ef6\u5c06 clean data \u7684 tag \u5e8f\u5217\u6bd4\u5bf9\u5230\u6570\u636e\u5e93\u4e0a\u3002<\/p>\n 4. \u6839\u636e\u6bd4\u5bf9\u7ed3\u679c\u6765\u786e\u5b9a\u57fa\u56e0\u7684\u8868\u8fbe\u91cf\u3002\u4ee5 TPM (number of transcripts per million tags) \u6765\u8868\u793a\u3002<\/p>\n 5. \u6839\u636e\u8868\u8fbe\u91cf\u6765\u505a\u57fa\u56e0\u5dee\u5f02\u8868\u8fbe\u5206\u6790\u3002<\/p>\n","protected":false},"excerpt":{"rendered":" 1. \u6587\u732e\u62a5\u9053 An efficient approach to finding … \u7ee7\u7eed\u9605\u8bfb
\nPrior to mapping reads to the reference database, we filtered all sequences to remove adaptor sequence, low quality sequences (tags with unknown sequences ‘N’), empty tags (sequence with only adaptor sequences but no tags); low complexity, and tags with a copy number of 1 (probably sequencing error). A preprocessed database of all possible CATG+17 nucleotide tag sequences was created using our transcriptome reference database. For annotation, all tags were mapped to the reference sequences and only allowed 1 or fewer nucleotide mismatches. All the tags mapped to reference sequences from multiple genes were filtered and the remaining tags were designed as unambiguous tags. For gene expression analysis, the number of expressed tags was calculated and then normalized to TPM (number of transcripts per million tags); and the differentially expressed tags were used for mapping and annotation.<\/p>\n
\nIllumina Pipeline Software version 1.0 was used for off-instrument data processing. Images from every sequencing cycle were converted to signal intensities using Illumina Pipeline’s FireCrest v.1.9.5. Next, Bustard v.1.9.5 was run to perform base calling using the intensity values and calculate quality scores for every base. The 16-base long reads (excluding the 4-base DpnII recognition site) were aligned to DpnII tag tables generated by Stowers Institute http:\/\/research.stowers-institute.org\/microarray\/tag_tables\/index.html webcite using megaBLAST with word size of 12 and low-complexity region filtering turned off. Only reads that perfectly matched to tag tables without mis-matches and gaps were considered. From this set, reads that could be aligned to the Stowers’ repeat tag table were excluded (the repeat tag table contains any reads aligned to \u2265 2 locations, unless all locations are from the same gene). The remaining reads were aligned to the combination of canonical (exonic and splice junction tags from protein-coding transcripts), mitochondrial (tags from any mitochondrion-associated transcripts encoded by both genomic and mitochondrial DNA), and ribosomal (tags from rRNA or tRNA) tag tables. Reads mapping on genes with multiple homologous family members were excluded from our analysis. When there were multiple types of tags aligned to different locations of the same gene, the gene expression levels are represented by the summation of all. <\/p>\n
\nAll tags were mapped to the in silico generated transcriptome of T. b. brucei TREU 927[35], the most closely related fully annotated genome available to the T. b. gambiense strain, using MAQ program maq-0.6.8_x86_64-linux[65], allowing for a 2 bp mismatch between the tag and the reference transcriptome. The in silico transcriptome did not contain 5′ or 3’UTR sequences as these have not been defined in T. brucei. Tags that were generated with a poor quality sequencing score were removed from the analysis. A mapping quality score of 40, incorporating sequence quality and ability of the tag to map to one unique site in the transcriptome, was used to identify tags that align uniquely to the reference sequence. The aligned tags will be available in TritrypDB[35]. This study was limited to tags that map to open reading frames only and does not show tags that map to mRNA with long 3’UTRs. <\/p>\n2. \u65b9\u6cd5\u603b\u7ed3<\/h1>\n
\nhttp:\/\/research.stowers-institute.org\/microarray\/tag_tables\/index.html<\/a>\u7f51\u7ad9\u8c8c\u4f3c\u63d0\u4f9b perl \u811a\u672c\u6765\u63d0\u53d6 CATG \u5e8f\u5217\u3002<\/p>\n